Wi JN Hoven 95 Organizational

Organizational Learning and
Information Systems
The Case of Monitoring Information and Control
Systems in Machine Bureaucratic Organizations
Fons Wijnhoven
CIP-GEGEVENS KONINKLIJKE BIBLIOTHEEK, DEN HAAG
Wijnhoven, Alphonsus Boudewijn Jacobus Maria
Organizational learning and information systems : the case of monitoring information and
control systems in machine bureaucratic organizations / Alphonsus Boudewijn Jacobus
Maria Wijnhoven. - Enschede : Universiteit Twente, Faculteit der Technische
Bedrijfskunde
Proefschrift Universiteit Twente Enschede. - Met index, lit. opg. - Met samenvatting in het
Nederlands.
ISBN 90-365-0732-4
Trefw.: management informatiesystemen
Publ.: University of Twente, Department of Information Management

P.O. Box 217
7500 AE Enschede
The Netherlands
Copyright 1995 by A.B.J.M. Wijnhoven, Enschede, The Netherlands

ORGANIZATIONAL LEARNING AND INFORMATION SYSTEMS:
THE CASE OF MONITORING INFORMATION AND CONTROL SYSTEMS IN

MACHINE BUREAUCRATIC ORGANIZATIONS
PROEFSCHRIFT
ter verkrijging van

de graad van doctor aan de Universiteit Twente,
op gezag van de rector magnificus,
prof.dr Th.J.A. Popma,
volgens besluit van het College voor de Promoties
in het openbaar te verdedigen
op vrijdag 17 maart 1995 te 16.45 uur.
door
Alphonsus Boudewijn Jacobus Maria Wijnhoven
geboren op 29 oktober 1957
te Sint-Anthonis
Dit proefschrift is goedgekeurd door de promotoren:
Prof. dr P.A.E. van de Bunt, Faculteit der Economishe Wetenschappen en Econometrie,

Vrij Universiteit Amsterdam
Prof. R.K. Stamper, Faculteit der Technische Bedrijfskunde, Universiteit Twente.
For Carolyn
Contents vii
Preface ................................................................................................................................xiii
Chapter 1: Changes in Machine Bureaucracies and the Role of

Information Systems ......................................................................... 1
1.1 Trends in Business .............................................................................................. 1
1.2 Organizations and Organizational Learning in the Post-Industrial Society...... 5
1.3 Organizational Learning and its Problems in Machine Bureaucracies............. 6
1.4 The Role of Information Systems ....................................................................... 8
1.4.1 Management Reporting System and Innovation and Control ................. 10
1.4.2 DSS Impact on Managerial Performance ................................................ 11
1.4.3 Business Value of Computers ................................................................. 13
1.4.4 Information Systems for Organizational Learning.................................. 14
1.4.5 Monitoring Information and Control Systems (MICS) ........................... 16
1.5 Organizational Learning and MICS ................................................................. 17
Chapter 2: Investigating MICS, Machine Bureaucracies and

Organizational Learning ............................................................... 19
2.1 Approaches to Organizational Learning and MICS......................................... 19
2.1.1 Cybernetic Analysis of Organization and Business ................................ 19
2.1.2 Organization Development ..................................................................... 21
2.1.3 Semiotics and Information Management ................................................ 23
2.2 Linking Organizational Learning with Information Systems ........................... 23
2.3 Problems in Organizational Learning and Monitoring Information and
Control Systems ................................................................................................ 25
2.4 A Model of MICS and Organizational Learning in Machine Bureaucracies ... 27
Chapter 3: Methodology and Research Design ............................................ 31

3.1 Definition of the Problem ................................................................................. 31
3.2 Aims of the Research ........................................................................................ 31
3.2.1 Conceptualizing Organizational Learning .............................................. 31
3.2.2 Developing a Theory about MICS and Organizational Learning ........... 34
3.3 The Main Questions .......................................................................................... 36
3.4 Research Design and Plan ............................................................................... 37
3.4.1 Research Plan .......................................................................................... 37
3.4.2 Reliability and Validity Problems ........................................................... 39
3.5 Layout of this Book ........................................................................................... 40
Chapter 4: Concept of Organizational Learning ........................................ 43

4.1 Introduction and Working Definition ............................................................... 43
4.2 A Psychological Perspective: David Kolb's Experiential Learning ................. 44
4.3 A Classification of Organizational Perspectives to Organizational Learning. 47
4.4 The Cybernetic Perspective of Organizational Learning ................................. 50
4.4.1 The Origin of the Organizational Learning Concept .............................. 50
4.4.2 Control and Information .......................................................................... 52
4.4.3 Equipping the Learning Process.............................................................. 54
4.4.4 Problems with Organizational Learning ................................................. 57
viii Organizational Learning and Information Systems
4.4.5 Limitations of the Cybernetic Perspective...............................................58

4.5 The Organization Development Perspective of Organizational Learning .......59
4.5.1 Organization Development's Reformulation of Organizational Learning59
4.5.2 Processes of Organizational Learning and Organizational Memory .......62
4.5.3 The Problem of Creating a Learning Organization .................................64
4.5.4 Limitations of the Organization Development Perspective .....................67
4.6 The Soft Systems Perspective of Organizational Learning. ..............................68
4.6.1 Soft Systems Modelling...........................................................................68
4.6.2 Limitations of Soft Systems for Organizational Learning.......................70
4.7 The Scientific Management Perspective of Organizational Learning .............71
4.7.1 Scientific Management as Organizational Learning ...............................71
4.7.2 Time and Motion Studies ........................................................................72
4.7.3 Limitations of the Scientific Management Perspective ...........................73
4.8 Dimensions of Organizational Learning...........................................................74
4.8.1 Description of the Semantic Analysis Technique....................................74
4.8.2 Basic Terms from Different Perspectives ................................................75
4.8.3 The Semantic Chart and its Consequences ..............................................77
4.9 Operationalization of Organizational Learning ...............................................80
4.9.1 Deutero Learning: Designing Norms that Govern Single-loop and
Double-loop Learning..............................................................................80
4.9.2 Explanation of Single-Loop and Double-Loop Learning Processes. ......83
4.9.3 A Flow Diagram of Organizational Learning..........................................87
Chapter 5: Organizational Learning in Machine Bureaucracies ..........89

5.1 Why Study Organizational Learning in Machine Bureaucracies? ...................89
5.2 The Concept of Machine Bureaucracy..............................................................92
5.2.1 The Development of Machine Bureaucracies .........................................92
5.2.2 Classic and Lean Machine Bureaucracies ...............................................94
5.2.3 Manufacturing and Service Machine Bureaucracies ...............................99
5.3 Other Organizational Configurations.............................................................102
5.3.1 Environment and Coordination .............................................................102
5.3.2 Organizational Configurations ..............................................................104
5.3.3 Criticisms of the Machine Bureaucratic Configuration .........................105
5.4 Consequences of Machine Bureaucracy for Organizational Learning ..........109
5.4.1 Learning Needs for Machine Bureaucracies .........................................109
5.4.2 Machine Bureaucratic Learning Norms and Deutero Learning ............111
5.4.3 Machine Bureaucratic Single-Loop and Double-loop Learning ...........121
5.4.4 A Note on Deutero Learning .................................................................128
5.5 Summary..........................................................................................................129
Chapter 6: Role and Value of Monitoring Information and Control

Systems for Organizational Learning .....................................131
6.1 Introduction.....................................................................................................131
6.2 Information Systems: Technology and Organizational ..................................132
6.2.1 Technical and Organizational Aspects of Information Systems ...........132
6.2.2 Technological Aspects of Information Systems ....................................133
6.2.3 Organizational Aspects of Information Systems ...................................134
6.3 MICS: Technological and Organizational......................................................136
Contents ix
6.3.1 MICS: The Information Technology Dimensions ................................ 136

6.3.2 MICS: The Organizational Dimensions ................................................ 139
6.3.3 Observing MICS ................................................................................... 148
6.4 Role and Value of MICS for Organizational Learning .................................. 149
6.4.1 Role and Values .................................................................................... 149
6.4.2 Role of MICS in Double-Loop Learning .............................................. 150
6.4.3 Role of MICS in Single-Loop Learning ................................................ 152
6.4.4 A Note about MICS' Role in Deutero Learning .................................... 157
6.4.5 Organizational Learning Value of MICS .............................................. 158
6.5 Summary ......................................................................................................... 162
Chapter 7: Operationalizations and Method of Analysis ....................... 163

7.1 Purposes of this Chapter ................................................................................ 163
7.2 Statements about MICS and Organizational Learning .................................. 163
7.3 Construction of the Research Model .............................................................. 171
7.4 From Theory to Observations: Explanation of the Variables ........................ 175
7.4.1 Methodological Problems for Empirical Investigations........................ 175
7.4.2 Description of Variables........................................................................ 175
7.5 Summary and Conclusions ............................................................................ 186
Chapter 8: Case Studies...................................................................................... 189

8.1 Case 1: Cardboard Co. .................................................................................. 189
8.1.1 Introduction to this Case ....................................................................... 189
8.1.2 General Description of Cardboard Co. .................................................. 189
8.1.3 Cardboard Co.'s Learning Need ............................................................ 191
8.1.4 Cardboard Co.'s Leanness and Service-Manufacturing Nature ............ 192
8.1.5 Cardboard Co.'s Learning Norms .......................................................... 194
8.1.6 Description of MICS ............................................................................. 197
8.1.7 Role and Value of MICS ....................................................................... 200
8.1.8 Learning Problems Related to MICS and Recommendations ............... 202
8.1.9 Conclusions Regarding the Main Hypotheses ...................................... 203
8.2 Case 2: The Bank ............................................................................................ 205
8.2.2 General Description of The Bank.......................................................... 205
8.2.3 The Bank's Learning Need .................................................................... 209
8.2.4 The Bank's Leanness and Service-Manufacturing Nature .................... 211
8.2.5 The Bank's Learning Norms.................................................................. 214
8.2.7 Role and Value of MICS ....................................................................... 218
8.2.8 Learning Problems Related to MICS and Recommendations ............... 221
8.2.9 Conclusions Regarding the Main Hypotheses ...................................... 222
8.3 Case 3: Chemical Plant .................................................................................. 225
8.3.2 General Description of the Chemical Plant ........................................... 226
8.3.3 Chemical Plant's Learning Need ........................................................... 228
8.3.4 Chemical Plant's Leanness and Service-Manufacturing Nature ........... 229
8.3.5 Chemical Plant's Learning Norms ......................................................... 231
x Organizational Learning and Information Systems
8.3.7 Role and Value of MICS .......................................................................233

8.3.8 Learning Problems Related to MICS and Recommendations ...............235
8.3.9 Conclusions Regarding the Main Hypotheses .......................................236
8.4 Case 4: Health Insurance Company: Health Co. ...........................................240
8.4.1 Introduction to this Case ........................................................................240
8.4.2 General Description of Health Co. ........................................................241
8.4.3 Health Co.'s Learning Needs .................................................................241
8.4.4 Health Co.'s Leanness and Service-Manufacturing Nature ...................244
8.4.5 Health Co.'s Learning Norms ................................................................246
8.4.6 Description of MICS .............................................................................248
8.4.8 Learning Poblems Related to MICS and Recommendations ................250
8.5 Case 5: Hitec...................................................................................................253
8.5.1 Introduction to this Case ........................................................................253
8.5.2 General Description of Hitec .................................................................254
8.5.3 Hitec's Learning Need ...........................................................................255
8.5.4 Hitec's Leanness and Service-Manufacturing Nature............................256
8.5.5 Hitec's Learning Norms .........................................................................259
8.5.6 Description of MICS .............................................................................262
8.5.8 Learning Problems Related to MICS and Recommendations ...............265
Chapter 9: Conclusions and Discussion ........................................................269

9.1 Aims and Objectives of the Research ..............................................................269
9.2 What are the Basic Dimensions of Organizational Learning? .......................269
9.3 How Do Machine Bureaucratic Organizations Learn?..................................270
9.4 Do Lean and Classic Machine Bureaucracies Differ in How They Learn? ...272
9.5 What is the Influence of MICS on Organizational Learning? ........................275
9.6 How Can One Observe the Impact of MICS? .................................................278
9.7 Proposal for a Learning Audit ........................................................................282
9.7.1 Frame of Reference ...............................................................................282
9.7.2 Learning Audit.......................................................................................287
9.8 Limitations of this Study..................................................................................288
References ......................................................................................................................293
Index ................................................................................................................................307
Samenvatting (Summary in Dutch) ......................................................................315

Contents xi
xii Organizational Learning and Information Systems
Contents xiii
xiv Organizational Learning and Information Systems
Contents xv
Preface
This book is the result of a six-year part-time study on organizational learning and
information systems conducted at the School of Management Studies of the
University of Twente. The study was enabled by the research position I received in
the Department of Information Management.
Before the start of this project, I taught government organization at the School
of Public Administration of the University of Twente. Specifically I would like to
acknowledge prof. Chris L. Menting, head of the Government Organization Group
for introducing me to many aspects of organization analysis. In that group I
acquired my interest in organizational learning.
In 1987, I started work as assistant professor in information management at
the School of Management Studies. In 1988, I started this project under the
supervision of professors P.A.E. (Lisa) Van de Bunt and Ronald K. Stamper. Prof.
Van de Bunt recommended to me various papers and books about organization
studies from the large diversity of perspectives that exist in that field. To these he
also added his own practical experience. This led to a broad perspective for this
project, and a close connection of the subject with actual trends in world economy,
as discussed in chapters 1 and 2 of this book. Prof. Van de Bunt also convinced me
to focus the study on organizations with the greatest learning problems (machine
bureaucracies), and on the information systems that are most criticized from the
organizational learning perspective (monitoring information and control systems).
Arguments for this choice are explained in chapters 5 and 6. Prof. Stamper's
erudition together with an interesting and unique approach to the field of informa-
tion management formed excellent additions. One of his major ideas, semantic
analysis, is applied in chapters 4 and 7, while trying to organize the many and
confusing concepts and ideas related with organizational learning. His view on
semantics has been guiding the conceptualization and operationalization of MICS.
Prof. Stamper is also acknowledged for the considerable effort he took to manage
the EMIR (Effective Management of the Information Resource) research pro-
gramme, within which this project was carried out. I particularly appreciated his
effort in being my harshest critic.
Many other colleagues and friends should be mentioned here. I will keep the
list short as the book has become long enough already. Specifically I want to
mention dr Tinus van Drunen, Ir Cees van Slooten, drs Arjen Wassenaar with
whom I had many discussions about the concept of information system and the
importance of my study for management studies and information management. In
contrast to what is most common in computer science, I decided on basis of these
discussions, to define information systems as technical and social systems, and thus
not limited to hardware, software and data.
During this project organizational learning was becoming increasingly popular
as a theme in organization and information science. This was particularly the result
of work done by Peter Senge at MIT and A. De Geus at Shell Planning. Both
authors stressed the importance of constructing 'The Learning Organization'. I felt
very uncomfortable with the concept of 'The Learning Organization' because of the
unconditionality of the knowledge thus gained, like a carpenter who thinks that the
world exists of nails only. Therefore, I developed the concept of organizational
learning needs, to introduce a contingency element in my approach of
organizational learning. This idea is briefly expressed in chapter 4, and is described
in more detail in chapter 5, where the organizational learning of four types of
organizations is linked with the learning needs of these organizations. This idea was
also strongly supported by two of my M.Sc. students, Mark Hafkamp and Stephan
Kordelaar, who contributed substantially to this project by helping me with the data
collection in the five cases studied in chapter 8. These cases enabled me to further
work out the contingency approach to organizational learning.
I also want to thank other M.Sc. students who created important contacts with
business companies, which later on where willing to participate in this project.
Thanks to my friends dr Geerten Schrama (now at the Center of Environmen-
tal Management at the School of Public Administration) and dr Timo Saarinen from
the Helsinki School of Economics for commenting on a previous manuscript. I also
want to express my appreciation for professors B. Hedberg (Stockholm Business
School), C.L. Menting, E. Spoor (Free University Amsterdam) and O.A.M.
Fisscher (School of Management Studies, University of Twente) for acting as
members of my promotion committee and reviewing the work. Thanks also to Ms.
K. Emmett, who carefully checked my English.
Finally, I want to thank my wife Carolyn Karthaus, and my children Kim,
Jules and Armelle. They all gave me the support needed to continue.
Before starting to read this book, the reader might find the following notes
useful. Chapters 1, 2 and 3 are about the design of this project and its research
methodological basis. A reader interested solely in the concept of organizational
learning can skip these three chapters, and start reading chapters 4, 5, and 6.
Readers interested in the empirical case studies should carefully read chapters 1 to
7. Chapter 8 might be incomprehensible without reading all the preceding chapters,
because these demonstrate the basic concepts, hypotheses and goals of the case
studies. Chapter 9 is a concluding chapter, based on all insights gained in the
previous chapters. The basic conclusions might be read, however, without reading
the preceding chapters.
Hengelo, December, 1994

Changes in Machine Bureaucracies 1
Chapter 1: Changes in Machine Bureaucracies and the Role of

Information Systems
1.1 Trends in Business
Mechanized factories evolved from craft technology in the 18th century,

manufacturing cheap goods on a large scale. Features of these factories includes high
capital intensity, low labor skills, and large and expensive production installations
that are difficult to subdivide. This normally leads to specialisation of tasks,
hierarchical leadership, and many rules (formalization). Mintzberg (1979) calls this
organizational ideal type a machine bureaucracy. The production of these machine
bureaucracies was increasingly aimed at anonymous customers on a large-scale market.
Market principles were supposed to function as the means of coordination between
producers, traders and consumers, via the free functioning of demand and supply
(Smith, 1776/1975). The basic historic causes of this trend were the liberalisation of
trade, which involved a reconsideration of labor (labor as a commodity),
improvements in the technology of trade (money, banking, law etc.) and distribution
(especially transportation, decline of feudal protectionism), and innovations in
transformation technology (steam power and mechanical devices) (Stearns, 1975, pp.
77-82; also cf. Bell, 1979).
The application of steam power and mechanical devices, called mechanization,
made possible the production and distribution of large quantities of goods at low cost,
making craft technology uneconomical in many industries. To illustrate this
statement consider table 1.1.
Minutes of Effort to Assemble Late Craft Production, Mass Production, Percent Reduction
Fall 1913 Spring 1914 in Effort
Engine 594 226 62
Magneto 20 5 75
Axle 150 26.5 83
Major Components into a
Complete Vehicle 750 93 88
Source: Womack et al., 1990, p. 29, figure 2.1
Table 1.1: Craft Production in 1913 versus Mass Production in the Assembly Hall: 1913 in
1914.
The emphasis on cost reduction was the basis for investing in more machinery.
According to Zuboff (1988), at the start of this mechanization process in the 18th
2 Organizational Learning and Information Systems
and 19th centuries, it was not at all clear that manufacturing would be cheaper than
craftsmanship. According to Ashworth (1975), coal consumption and steel
production, both essential for mechanization, did not increase to a level suitable for
manufacturing before the 20th century.
The second half of the 20th century, however, led to a new era, often called the
post-industrial society (Bell, 1979), in which fabrication was replaced by the
processing and recycling of services as the dominant mode of production. The
manufacturing companies that stayed in existence had to tranform from output-
driven to market-driven organizations. The most successful machine bureaucratic
forerunners of this society are the Japanese manufacturers that capitalize on their
abilities to meet three market trends: quality, flexibility and innovation.
Trend 1. Quality demands

In the 1960s, cost issues were increasingly replaced by higher demands for quality.
Japanese car manufacturers became particularly successful in meeting these demands,
as is illustrated in table 1.2.
GM Framingham Toyota Takaoka

Gross Assembly Hours per Car 40.7 18.0
Adjusted Assembly Hours per Car 31 16
Assembly Defects per 100 Cars 130 45
Assembly Space per Car 8.1 4.8
Inventories of Parts (average) 2 weeks 2 hours
Note: Gross assembly hours per car are calculated by dividing total hours of effort in the plant by the total
number of cars produced. The researchers have adjusted this score for differences in the products, so that
the products have become comparable. Defects per car are considered a good estimate for product quality.
Assembly space (measured in square feet per vehicle per year) and inventories of parts are both important
determinants of production and product costs.
Source: Womack et al, 1990, p. 81, figure 4.1. Based on IMVP World Assembly Plant Survey
Table 1.2: General Motors Framingham Assembly Plant Versus Toyota Takaoka Assembly
Plant 1986.
Often it has been suggested that better products should be more expensive. However,
the evidence seems to contradict this statement (Womack et al., 1990, p.92 and 93),
as low quality is in fact an important cost driver (especially restoration costs).
Trend 2. Flexibility demands

In the 1970s, customers were demanding quality products, which suited their
individual preferences and were also cheap. Companies tried to solve the tension
between flexibility and cost by offering clients more influence over production processes
(co-makership) and utilizing the opportunities of large-scale economics. This was
done by developing modular products, so that specific components of products could
be produced in large series or bought from a supplier, and thus profit from the
economies of scale. The components could be assembled in a large variety of ways,
thus enabling some customization of the final products. Information technology was
also introduced for optimizing production and market demands (MRP, Aggerwal,
1985) and improving the flexibility of production devices (for instance with flexible
manufacturing systems and CAD/CAM, cf. Kerr, 1991). This situation differs
considerably from the philosophy of Henry Ford, who stated that clients could buy
any car, so long as it was a T-model and black. Nowadays the number of car types is
much greater than that available in the heyday of mass production, as is illustrated in
table 1.3.
1955 1973 1986 1989

Products on sale 30 84 117 142
Sales/Product (000s) 259 169 136 112
Share of Market Captured by 6 largest-selling products 73 43 25 24
Source: Womack et al., 1990, p. 125, figure 5.5
Table 1.3: Fragmentation of the American Auto, Van, and Light Truck Market, 1955-1989.
An important issue for product flexibility is the potential of suppliers to react to

changing demands by assemblers. Some data are presented in table 1.4.
Averages for each region Japanese in Japanese in Americans in All in Europe

Japan America America
Die Change Times (minutes) 7.9 21.4 114.3 123.7
Lead time for new dies (weeks) 11.1 19.3 34.5 40.0
No. of daily JIT1 deliveries 7.9 1.6 1.6 0.7
% of parts delivered JIT 45.0 35.4 14.8 7.9
Source: Womack et al 1990, p. 157, figure 6.1
Table 1.4: Cross Regional Comparison of Suppliers.
Trend 3. Innovation potential
1
JIT is the acronym for Just-in-Time. JIT-deliveries imply that inventories can be kept extremely low,
without the risk of being out of stock.
In the 1980s, companies also started competing on the basis of their innovation
potential. Especially in the 'high tech' line many new products were launched (cf.
Hamel and Prahalad, 1990). At that time, information technology opened the way to
new forms of competition, that especially affected information-intensive service
industries such as banking and insurance (Cash et al., 1992). Many product
innovations like Automatic Teller Machines and home banking emerged in Western
Countries. Mergers among insurance companies and banks aloowed for a broader
services portfolio, leading to synergies2, and improved client services (so-called total
financial service, to optimize the customers' added value). Also, flexibility was
improved by providing clients with a 24 hour access to services. At the end of the
1980s many I.T. competitive differentiation opportunities were exhausted and I.T.
became a necessary evil. Many financial service companies had financial troubles and
started a low price strategy to increase their share of the market (and thus reduce
overhead costs per client) (Porter, 1985). Low cost was thus introduced after quality
and innovation in the service sector. Mergers continued with the aim of reducing
production and marketing costs by the principle of economics of scale. At the same
time, many services were 'industrialized' to lower the production costs through
increased mechanization (Grönroos, 1991).
Innovation of products and the innovation potential of automobile factories
can be illustrated by measuring characteristics of the production development
process, measuring development lead times, and the number of product models that
are launched within a time period. Evidence on both issues is given in table 1.5.
Japanese American European European

Producers Producers Volume Specialist
Producers Producers
Average Engineering Hours per New Car 1.7 3.1 2.9 3.1
(Millions)
Average Development Time per New Car (in 46.2 60.4 57.3 59.9
Months)
Number of Employees in Project Team 485 903 904 904
Number of Body Types per New Car 2.3 1.7 2.7 1.3
Average Ratio of Shared Parts 18% 38% 28% 30%
Ratio of Delayed Products 1 in 6 1 in 2 1 in 3 1 in 3
Die Development Time (months) 13.8 25.0 28.0 28.0
Prototype Lead Time (months) 6.2 12.4 10.9 10.9
Time From Production Start to First Sales
(months) 1 4 2 2
Return to Normal Productivity After New
Model (months) 4 5 12 12
Return to Normal Quality After New Model
(months) 1.4 11 12 12
2
In the U.S.A. these collaborations in one business sector were not permitted under anti-trust legislation.
Source: Womack et al., 1990, p.118, figure 5.1

Table 1.5: Product Development Performance by Regional Auto Industry, mid 1980s.
Womack et al. (p.120) also gathered data about the number of models and average
age of models. Clearly, Japanese producers outperform American and European
manufacturers on this aspect of innovation potential.
1.2 Organizations and Organizational Learning in the Post-Industrial Society
The evidence of section 1 indicates that machine bureaucracies can differ

substantially in their production performance. This is obviously the result of learning
processes, which are much better in the lean organizations than in most of the
American and European manufacturers studied by Womack et al. Quite frequently,
however, organizations behave counter to their learning requirements, by using
defensive mechanisms against competitors. The traditional European car
manufacturers did not analyze their problems effectively, but:
1. Used their governments and the European Community to impose import
quotas on Japanese cars.
2. When option 1 failed, they dismissed a large proportion of their work force.
3. They saw their problems as having their roots outside the company (taxes, high
wages, increased competition, low work motivation etc.), and failed to respond
internally by improving efficiency, quality, flexibility and innovativion potential.
They found excuses not to learn, not to change their basic way of thinking. This
reaction is understandable, because changing the mind-set can be a very hard job in
large and complex organizations (Weick, 1979). In the USA, the car industry had less
government protection, and therefore had to learn more quickly. This process was
complex and painful, but unavoidable.
Countries that try to exclude themselves from the world market, such as until
recently the East European states, are an excellent demonstration of what happens
when learning is inhibited by control. When market principles dominate the
distribution of wealth, people are forced to think for themselves. When people are
only expected to follow commands, as was the case in the command economy, most
become intellectually lazy. The collapse of the command economy therefore not only
changes the system of distribution, but also demands a change of habit of people that
have been made intellectually lazy and are unused to responsibility. The frustration
that can occur in these situations can lead to the causes of problems being sought in
minority groups instead of in the lack of personal effectiveness. The solution would
be to improve learning and encourage initiative.
Third World countries are also exposed to the principles of the market for
wealth distribution. They could gain a strong competitive advantage, because the
main principle for competition in the future will be intellectual creativity. Some
Third World countries have invested heavily in their intellectual capital, by investing
in higher education for the local population (Porter, 1990, p. 466). Additionally, they
are developing their own internal markets, by making very cheap products that may
not qualify for Western standards, but suit local needs. Many Third World countries
have competitive advantages over for instance Japan: they have more space and
cheaper energy resources. They can learn from Japan that the main advantage to be
gained when use is made of both body and mind. This opportunity has never been
made use of by the colonial industry, which only sought to exert control, thus
keeping the minds of the workforce lazy as in a command economy.
Organizational learning was already an essential feature of ancient civilizations.
The Egyptians could not have constructed their pyramids without a well-developed
body of knowledge about construction and organization. For instance, the pyramid of
Cheops covers thirteen acres and contains 2,300,000 stone blocks, each weighing an
average of two-and-a-half tons. Their construction is estimated to have taken over a
hundred thousand men a period of twenty years (George, 1972, p.4). The Chinese
Empire, the Roman Empire, the Dutch colonial company (East Indies Company, a
multinational founded in 1602) all required large bureaucracies to control and
coordinate activities among the many people involved. On other words,
organizational learning in large organizations was connected with the development of
bureaucracies. Bureaucracies are often dominated principally by control, leading to
an internal command economy with the dysfunctional impacts as mentioned earlier.
The post-industrial society, also called the information age (Bell, 1979),
introduces new ways of management based on:
1. Organizational perestrojka: the introduction of organization internal market
principles and democracy (Ackoff, 1992; Peters, 1992).
2. Removal of many middle management positions whose tasks could be carried
out more efficiently by a computer or a datacommunication network (Leavitt
and Whisler, 1958).
These organizations are better learners because of their increased ability to connect
and process ideas and data, and their improved efficiency in communication (Douma
and Schreuder, 1991; Gurbaxani and Whang, 1991). Computers can make a
significant contribution to learning, because they can reduce the costs for distributing
ideas and data, and can reduce the costs and capacity for data storage and analysis.
The added value of computers is, however, not always obvious. Often there are cases
of bad use, non-use and negative impacts of computers (Jayaratna, 1990; Williams,
1991). Under what conditions then are computers useful and under what conditions
are they damaging to organizational learning? The answer to this question requires a
broader perspective of information systems than computer-based systems (Stamper,
1973), because organizational learning makes use of more ways of data processing
than that carried out by the computer (cf. Nonaka, 1988).
Let us study these questions by focusing on bureaucracies that have developed
large stores of knowledge in the past by developing good working principles. Let us
also focus on organizations that apply the simplest computer tool for management
learning, called Monitoring Information and Control Systems, systems that give
regular reports for developing insights about how things are going.
1.3 Organizational Learning and its Problems in Machine Bureaucracies
Classic machine bureaucracies developed as large, complex organizations, with a high

capital intensity, in stable environments. This market stability is essential, or else it
would not be profitable to invest so much in the accumulation of knowledge in this
form. The knowledge consists mainly of rules, procedures, a complex division of
labor, and the application of expensive machinery. This type of organization,
however, fails to deliver efficiency plus high quality, flexibility and innovation. Thus,
important organizational changes are required. Whereas machine bureaucracies
produce large quantities for low prices in simple and static environments by
exploiting order and structure, they incorporate learning limitations as well. This is
evident from general considerations: the ideal typical classic machine bureaucracy:
1. ... focusses on control, keeping the machinery going without disturbance (such as
strikes, supply problems and machine break-downs). The management and
employees react defensively to most changes;
2. ... emphasizes its own internal rationality and logic, and therefore discourages new
ideas that deviate too strongly from the existing organizational paradigm
(Hedberg, 1981). Behavior is supposed to be accountable according to the
existing organizational rules and norms. Accordingly, people behave defensively;
3. ... punishes its people when they make mistakes. Experiments are not allowed
because they risk disturbing the process;
4. ... separates decision functions (management), thinking functions (staff experts,
R & D people, technostructure) and operating functions. This makes the
relation between these functions complex, leading to ideas for products that are
almost impossible to implement and require long lead times from product
development to actual production. Here, the system has difficulty adapting;
5. ... sometimes puts a lot of effort in individual learning by sending people on
training, but nothing learned individually is put into practice because
implementing the newly learned knowledge would disturb the status quo.
However, not every machine bureaucracy fails to learn. Especially in Japan, as
mentioned at the beginning of this chapter, a new kind of manufacturing
organization, in which large-scale production is organized in large, complex,
mechanized and formal organizations, has been established since the 1950s (Womack
et al., 1990). This new style of machine bureaucracy, which is extremely effective in
organizational learning, is called lean production. Its key features can be summarized
(based on an interpretation of Womack et al., 1990) as follows:
1. Intrinsic interest in improving what one is doing (called 'kaizen' in Japanese).
2. Decentralization of management tasks, so that more people think about the

problems the organization has to face, and people are better utilized.
3. Improved organization of product design, by giving the project team and its
manager the authority to change the organization as well. These teams not only
bring in the knowledge for design, but also monitor the product through its
entire life cycle.
4. Suppliers are not in competition, but strive for a relation in which all can profit
when they learn. This requires a large exchange of information and access to
each other's knowledge.
5. Clients are not just buyers, but are included in a system for communication
between the organization and its market, so that changes in the market can be
quickly and reliabily detected.
6. Financing of the company is not based on short-term demand-and-supply
principles, but on an understanding of the organization one is investing in for a
longer term perspective.
American and European car manufacturers now seem to have learned these lessons
by introducing principles of business re-engineering, which is organizational learning
about the organization's tranformation processes (Davenport and Short, 1990;
Hammer, 1990). In applying all six of the lean machine bureaucracy principles,
information is an essential factor besides the many organizational structural and
cultural issues. A classic machine bureaucracy could use information technology to
help transform itself into a lean bureaucracy, by improving its information processing
capacities to augment its performance. In this case IT is regarded as a necessary, but
not sufficient condition to make the breakthrough to this new kind of organization
(Keen, 1991; Nolan, 1992; Peters, 1992).
One may conclude from the above evidence that, from a learning perspective,
classic machine bureaucracies are poor while lean ones are effective. We will now
investigate the role played by information technology in supporting organizational
learning.
1.4 The Role of Information Systems
Information systems are often defined as:" ...an integrated, user-machine system for
providing information to support operations, management, and decision-making functions in an
organization. The system utilizes computer hardware and software; manual procedures; models
for analysis, planning, control and decision making; and a database (Davis and Olson,
1984, p.6). Management information systems can only realize their purpose, i.e.
informing managers so they can make the best decisions, when the organization is
changed as well. Research and practice in the area of management informations has
therefore shown that it is much wiser to define an information system as a social
system consisting of models for analysis, and rules for information handling, that are
possibly served by information technology in the form of computers and data
communication (Mumford, 1983; Stamper et al., 1988). Information systems thus

include social and technical issues, and can be described in six semiotic layers. These
layers are:
1. Physics: about the computer hardware, network hardware, but also other
physical means for data storage and retrieval, like filing cabinets.
2. Empirics: about the variety and randomness of information, that could be
counted in terms of bits and bytes and a coding and signalling structure.
3. Syntactics: about the complexity of the structure of information. This is met by
rules that apply and that form a structure that corresponds in complexity to the
variety of the environment (Ashby's law on requisite variety). In information
technology this leads to complexity of the software.
4. Semantics: about the understanding of the world. Problems in this area can be
found in informal discussions and the practical use of concepts.
5. Pragmatics: about possible ambiguities over responsibilities. Solutions can be
found in re-structuring business procedures.
6. Social: about goals, values and norms that people have. Problems in this area
require changes in organizational culture and structure.
The first three issues concern the technology aspects of information. The other three
layers concern the social and organizational aspects of information systems.
Because of dramatic price and performance developments in information
technology, information systems include increasingly more computer applications.
Also, many machine bureaucracies have developed, installed or applied, IT-based
learning methods. Table 1.6 gives an overview of these in relation to major learning
issue.
Trends and Learning Issue Learning methods

Cost Cost
• How to improve cost-effectiveness? • Financial monitoring and control.
• What causes cost overrun, and what can • Operational (logistic) planning for minimizing
be done about it? costs and achieving synergies.
Quality Quality
• How to improve product and process • Quality monitoring and control (e.g. by quality
quality? audits, quality circles, market information
• Where are process quality problems to systems).
be found? • Management of norms and responsibilities.
• How do clients perceive quality?
Flexibility Flexibility
• How to improve flexibility? • Development of factory lay-out, production
• Consequences of flexibility demands for engineering.
products, processes and production • Use of CAD/CAM, production planning and
devices. manufacturing knowledge.
Innovation Innovation
• How to improve innovation. New • Getting and trying out ideas.
products and services design. • Developing and managing several core
• Launching new ideas and learning about competencies.

their feasibility. • Information and communication systems for
• Designing new processes. flexible knowledge and sharing of ideas.
Table 1.6: Trends, Learning Issues and Devices
Information technology has a prominent role in many of these learning methods, by

the application of hardware, software and procedures with a specific function in an
organization, called Management Reporting System (MRS), (Group)Decision Support
Systems (DSS) and Executive Information Systems (EIS). MRSs are developed to
create standard reports for (junior) management. DSSs are developed to make
advanced quantitative analyses of data and simulations of possible events and
consequences of decisions. EISs are developed to provide senior managers with a
flexible tool with which they can satisfy their information needs themselves
(McKeown and Leitch, 1993). Despite these many applications, it is not at all clear
that information technology for management support really pays off from the
perspective of organizational learning. Just a few studies have tried to indicate a
relationship between the use these systems and management effectiveness. These are
briefly described below.
1.4.1 Management Reporting System and Innovation and Control
Lee and Guinan (1991) tried to find an answer to the question: what are MRSs
contributions to the managerial task. More specifically they tried to measure the
influence of an MRS on the management's ability to control and innovate the
organization (two basic factors also of organizational learning) in one specific
company. In operationalizing control, they discovered two types of control that were
linked with competing theories. The first, called managerial control, is about a
manager's effectiveness in influencing organization members to behave according to
organizational directives. The second type, called self-control, is about organization
members' own ability to increase organizational effectiveness. Self-control is
particularly important when task complexity increases (Hersey and Blanchard, 1982;
Galbraith, 1973). The researchers found several variables that define scores for
control and innovation in the organization, listed in table 1.7.
Variables of control and innovation These variables were used to measure
Managerial control subjective beliefs about the relation
• Task Clarification between control, innovation and IT-use.
• Work Assignment
• Procedural Specification (like standard
The data showed that especially the
operating procedures) planning and control scores were believed
Self-control to be affected positively by the MRS.

• Ability (for instance by providing data) Contrary to the opinion of authors such
• Information Support (for instance:
analytical support tools) as Pierce and Delbecq (1977) and Hage
• Intrinsic Motivation and Aiken (1967), IT was beleived to also
• Task Feedback have a positive impact on innovation! A
• Unambiguous Procedures
• Collegial Interactions
check on the relation between the self-
control and managerial control variables
Innovation
• Information Support revealed that these measures of control
• Motivation Support correlated strongly in this case study,
• Resource Support which indicates that they are
• Specialization
• Decentralization
complementary for an effective control
• Standardization process.
Source: Lee and Guinan, 1991, pp. 241-252
Table 1.7: Control and Innovation Variables.
The study indicates that IT impacts are closely related to the organizational
norms. An identical system studied by Lee and Guinan would probably show a
different impact if the high amount of self-control that was measured in the
organization was replaced by managerial (hierarchical) control. In that case IT would
be used as an instrument to support the authority and power of managers, and thus
be mainly used for planning and control, decreasing the amount of self-control of the
employees (the Pierce-Delbecq and Hage-Aiken hypothesis). This study thus suggests
that, in that case, organizations would not profit from all the opportunities of MRS.
1.4.2 DSS Impact on Managerial Performance
A DSS is intended to be used by one or several people and contains a computer

system which processes a database and one or more models, included in a modelbase.
The data and model are supposed to form a valid representation of reality. Learning
is supposed to occur in the process of decision-making and can be measured by
comparing the quality of a sequence of decisions made by the same decision-maker or
decision-making group. It is however clearly constrained by the representation
characteristics of the DSS.
One empirical test of the impact of DSS was carried out by Sharda, Barr and
McDonnell (1988). The authors tested 5 hypotheses on DSS effectiveness, some with
clear relevance for organizational learning, in a laboratory experiment with 8 trials
and groups using a DSS constructed by the researchers. Their conclusions are
described here, with the hypotheses in italics.
1. Hypothesis 1: "DSS aided groups will show higher performance than non-DSS aided
groups (p.145)". This hypothesis was significantly statistically supported. This
could indicate some learning, when for instance the users' understanding of
reality had increased. This could be checked by looking at the number of
alternatives considered.
2. Hypothesis 2: "DSS aided groups will show less variance in profit performance than
non-DSS aided groups (p.146)". This means that there is more certainty about the
outcomes of decision processes, which is an indication of learning. "On the other
hand, less variability in performance also may not be desirable. The decision aid may be
limiting risk taking by encouraging uniform decision processes and outcomes. Given the
significant differences in net earnings between the two groups, however, more stable
performance would appear to be preferable (Sharda et. al., p. 153)".
3. Hypothesis 3: "DSS aided groups will take less time to reach a decision than non-DSS
aided groups (p.146)". This is an indication of learning and probably also
speeding-up of communication. Sharda et al.'s study however did not find
significant statistical differences among DSS users and non-DSS users regadring
time.
4. Hypothesis 4: "DSS aided groups will consider a greater number of alternatives than
non-DSS aided groups (p.146)". This is also a very substantial indication for
learning. However, even though DSS supported groups reported that they had
investigated more alternatives than the non-DSS supported groups, the
differences were not statistically significant at a .05 probability level.
5. Hypothesis 5: "DSS aided groups will report greater confidence in their decisions than
non-DSS aided groups (p.146)". This could indicate learning, i.e. improvement of
memory contents and retrieval. Sharda et al.'s study however did not show
statistically significant differences between the DSS users and the non-DSS
using group.
Van Schaik (1988) carried out a second study on the impact of DSS, in which he
assumed that a DSS does not impact on decision-making quality, but that a decision-
making strategy does. This statement is often implicit in DSS-usage and DSS
research. To test this statement he created four experimental groups, among which
he varied the use or non-use of a DSS, and the use or non-use of a decision-making
strategy. His findings are summarized in table 1.8.
No DSS difference DSS
No Strategy 4.80 (1.33) not significant 4.84 (1.42)
difference significant at p < 0.05 significant at p < 0.06
Strategy 6.55 (1.09) not significant 6.57 (1.48)
Mean scores of experimental groups (standard deviations in brackets), after 7 trials. Source: Van Schaik,
1988, exhibit 4-11, p. 121.
Table 1.8: Research Results of DSS-impacts.
Table 1.8 clearly reveales that decision-making improvements are more the result of a
well chosen decision strategy than the result of DSS usage. These findings imply that
Sharda et al.'s results could possibly be a consequence of research artefacts.
From neither study may it be deduced that a DSS contributes to organizational
learning in non-laboratory environments, because, for instance, management style,
interpersonal behavior and organizational norms were not modelled.
1.4.3 Business Value of Computers
A serious attempt to overcome the external validity problems of laboratory studies

was made by Strassmann (1985 and 1990). Strassmann stated that the main task of
management is to add value by improving the organization, thus developing new
insights and implementing them. The management value added can be computed by
removing from the net income the capital value-added (equity). The net return-on-
management is computed by subtracting management costs (salaries of managers,
plus additional support personnel and facilities) from management value-added.
Table 1.9 shows that U.S. and Japanese (lean) companies on the average substantially
differ on return-on-management.
$ Millions US Companies Japanese Companies

Sales 123,895 118,291
Purchases 28,000 72,867
Operating Costs 16,974 7,689
Management Costs 63,682 29,809
Taxes 6,566 3,785
Net Income 8,673 4,140
Capital Value-Added 9,129 2,300
Management Value-Added (456) 1,840
Return-on-Management (Management Value-
Added/Management Costs) -0.72% 6.17%
Source: Strassmann, 1990, p. 442, table 18.1. Quoted by Strassmann from Electronic Business Magazine,
April 1, 1987, p. 72. From P. Doe, U.S. versus Japan in The Year of the High Yen.
Table 1.9: Comparing Productivity for Top Electronic Companies
Strassmann related the return-on-management scores with the amount spent on

information technology for management and operations (non-management
functions). The results are shown in figure 1.1. The conclusion is that organizations
with high returns-on-management ('over-achievers') put relatively more of their
computer resources into operations, and so do not use it as overhead. Related with
the ideas stated before about lean production, management has less of an overhead
function in the lean organization because it is an integrated part of operations. The
few management jobs that are still left in these lean organizations are very well
supported by IT in the highly achieving organizations. Information technology is
used less for the reduction of the number of staff needed for production (they are
already very productive) but for making the organization as a whole more effective.
This consideration also leads to the conclusion that MRS, DSS and EIS do not
necessarily require high investments (one could even talk about lean information
sysems) to improve return-on-management. In lean organizations the close
connection between managerial and operational jobs also implies that management
support information systems should be accessible to a larger group than those who
are traditionally labelled as managers.
1.4.4 Information Systems for Organizational Learning
Markus (1984) identified and described some information systems from the
perspective of their role in organizations, as shown in table 1.10.
Information Sys- Design features Examples
tems Type
Operational Work rationalization and routinization to Letter of credit; administrative

system support and automate operational transaction processing systems,
processes. automation of production processes.
Monitoring & Norms, standards, measures, evaluation, Productivity measurement systems.
Control System feedback, reward, to control and motivate Cost and quality performance
operational processes. control systems.
Planning & Decisi- Models and data manipulation facilities, model Planning models, decision support
on System building tools, to support complex decision systems, Production and inventory
making. planning systems. Computer Aided
Design and Manufacturing.
Knowledge Based Knowledge base and inference mechanism (logic), Expert Systems, Assistant Systems.
Systems to support the storage and retrieval of knowledge
and experience.
Communication Communication procedures, standards, to Teleconferencing, office systems, e-mail,
systems support the creation and distribution of messages CSCW.
containing e.g. ideas to be reacted upon
(facilitating electronic conversations).
Inter- Procedures for interorganizational transactions Order entry systems, EDI; also
organizational and communication. interorganizational e-mail and
system conferencing.
Based on Markus, 1984
Table 1.10: A Typology of Information Systems
MICS is the focus of our study, and will be treated in the following subsection.
Operational Systems are not systems for learning. Four other types of information
systems, not investigated in this study, could support organizational learning. These
are: knowledge-based systems, planning and decision systems, communication
systems and interorganizational systems.
Knowledge-based systems are intended to routinize knowledge available in different
parts of the organization so that one could do without the expert or support the
expert on routine matters. MICS should support the development of new insights for
efficiency and control or fundamental improvements, not by experts but by managers
and employees, and thus realizing a learning loop. According to Coats (1992),
knowledge-based systems are most valuable during the period of their development
because this requires the elicitation of knowledge (a very interesting learning
exercise), and for the distribution of codified knowledge in and between
organizations.
Planning and decision systems are important as well for realizing effective
organizations (JIT etc.). They could contribute significantly to organizational learning
when they allow experimenting without disturbing reality (Senge, 1990a). They can
help to make design teams more effective by facilitating the connection of the
members' knowledge, insights and ideas: a true example of organizational learning.
Communication systems can support organizational learning and are now creeping
into organizations via E-mail, Electronic Conferencing, GDSS, CSCW.

Communication systems are, however, still mainly at the laboratory stage (Greiff et
al., 1988; Stamper et al., 1991). Executive information systems also spread among
organizations, and offer executives electronic communication opportunities to allow
for joint decision-making (Mintzberg, 1973; McAuliffe and Shamlin, 1992).
Interorganizational systems are mainly of two types: interorganizational transaction
processing systems and interorganizational communication systems. The first systems
do not impact on organizational learning, except, of course, in the systems design
process that must lead to a reconsideration of the relations with business partners.
The second type has the same impact as the previously discussed communication
systems, with the addition that they are intended to support communication between
organizations. This could improve the connection between the organization and the
market. For instance, the process of product design can be carried out more quickly
and more effectively when the design process is a joint process involving clients and
producers. This also increases the flexibility of the organization.
1.4.5 Monitoring Information and Control Systems (MICS)
The classic machine bureaucracy seems to inhibit innovation, but might be quite
good at making the existing process more efficient. Monitoring Information and Control
Systems (MICS) are very important in signalling issues for efficiency improvement. This
implies a restricted way of organizational learning, within the constraints of pre-set
targets. Lean machine bureaucracies seem to be intrinsically interested in
improvement, which suggests the possibility of using MICSs to provide incentives for
more fundamental problem solving. Because of their relatively simple character (a
database and some predefined reports), these systems are widely spread. Their
opportunities for control as well as for the fundamental analysis of problems make
them fit closely to the management style and culture of machine bureaucracies (a
further explanation for this statement is given in chapters 4 to 6). An essential feature
of MICSs is that they provide data for critical evaluation by means of data that are
compared to fixed targets. Depending on the organizational context this critical
evaluation can lead to three types of behavior:
1. Doing nothing, as when:
• people lack the means to make sense of the data.
• people lack the behavioral opportunities to act (lack of time, money,
motivation, difficulty with problem solving etc.).
• the data show that everything is on target, so there is no reason to act (of course
one should hope that the system is not misdirecting attention in this
case).
2. Giving rewards and punishments. The system then acts as an extension of the
internal competitive environment. It can easily lead to lack of learning because
people are not willing to share knowledge. If these political problems cannot be
settled by an overarching idea, power principles govern decision-making (March

and Simon, 1958). This means that organizational learning is not a relevant
issue for decision-making (individual persons and groups however can learn
how to obtain the greatest influence in decision-making processes. This political
learning however is not learning at the organization's unitary system level).
3. Using the system to understand how well or badly things are going and
pointing out opportunities for improvement. This idea is close to the essence of
organizational learning, which presumes that an organization (as a group of people)
could become smarter than its individual participants. This basic idea of
organizational synergy should be the motivation to collaborate, adding to the
synergies of the division of labor and economics of scale (Barnard, 1936), which
is also applicable to organizational learning.
It is these behavioral consequences that make a MICS a technical and social system.
One specific issue in this respect is the tension between the improvement of the
rationality of the organization as a unity (having its own goal (survival)) and the
rationality and interests of the individual participants that make up the organization
(Argyris, 1972 and 1977; Lawler and Rhode, 1978; Kirsch and Klein, 1978; Kling,
1980). This discussion goes back to the most basic discussions about rationality in
organizations and the tension between individuals and the organization as a unitary
system (Taylor, 1911; Simon, 1976), which are connected with two main streams in
organization analysis: the systems or cybernetics perspective and the parties or conflict
perspective (Lammers, 1987; Mastenbroek, 1982; Burrell and Morgan, 1979). Because
both perspectives are valid and complementary to each other, both must be studied
to gain a fuller understanding of MICS in relation to organizational learning.
1.5 Organizational Learning and MICS
From the discussion in this chapter we conclude that organizational learning is

essential if classic machine bureaucracies are to compete with the lean machine
bureaucracies, that are already effective learning organizations in many respects.
MICSs have four features that make them important for organizational learning:
1. MICSs give their feedback information to the management, who can then
check the validity of the assumptions and hypotheses. One major issue of this
research is therefore management knowledge, that can be elicited and
formalized in an explicit management theory.
2. MICSs provide data to reduce the gap between operating norms and performance, and
possibly reconsidering the assumptions of the management theory (consisting of
norms and performance measurement instruments).
3. MICSs support the interpretation of performance data and the development of
new behavior and policies. This is a social process in which people communicate
about ideas which converge to decisions.

4. MICSs can provide an electronic means for storing data and other parts of the
organizational memory. In this way they can support learning about recurring
problems, and prevent the organizations having to re-invent solutions.
MICSs, which are social as well as technical systems, can include all these four basic
features, and are available in most machine bureaucracies. This makes the choice of
this specific type of information system for this study very practical and relevant.
Previously, it was asserted that organizational learning is vital for machine
bureaucracies in order to adapt to their environment. Knowledge however is lacking
about the circumstances under which MICS leads to effective organizational learning.
We have been able to find almost no research on this issue, nor attempts to combine
knowledge from the disciplines of organization analysis and information
management on this subject. Therefore the theoretical investigation will address:
1. Organizational learning and its features (cf. chapter 4).
2. Machine bureaucracies, especially the distinction between lean and classic
(chapter 5).
3. The role and value of MICS in classic and lean machine bureaucracies in
relation to organizational learning (chapter 6).
Investigating MICS, MB and Organizational Learning 19
Chapter 2: Investigating MICS, Machine Bureaucracies and

Organizational Learning
2.1 Approaches to Organizational Learning and MICS
This study is based on three approaches for the study of organizations and
information systems. The first approach is cybernetics, that introduced the term
organizational learning (Bateson, 1972) to improve understanding of organizational
control. The second approach is organization development, that reformulated
organizational learning by emphasizing the human, interpersonal and political
aspects and preconditions for organizational learning. The third approach is
semiotics, that defines the nature of information and therefore gives insight into the
features of MICS. These approaches are used to define the major scientific problems
associated with information systems and organizational learning. A more detailed
theoretical analysis of the approaches is given in chapters 4 and 6.
2.1.1 Cybernetic Analysis of Organization and Business
The term cybernetics originates from the Ancient Greek word 'kubernetes' meaning
'steersmanship'. It was introduced into the modern language by Norbert Weiner
(1952) to describe the study of steering missiles to hit moving targets (like planes in
World War II). The problem this application of cybernetics initially wanted to solve
was that missiles are not able to catch planes without the constant input of
information about the target's movement and factors that could influence the
movement of the missile itself (like wind direction and speed). On the basis of this
information, deviations from the target are measured and corrective actions taken.
An everyday example of a cybernetic system is the house thermostat, by which the
temperature of a room can be kept at a constant (the target) despite changes in
temperature of the environment.
Garreth Morgan (1986, p. 86-87), summarizes the features of cybernetic systems in
relation to communication and learning as follows:
" First, that systems must have the capacity to sense, monitor, and scan significant aspects
of their environment. Second, that they must be able to relate this information to the
operating norms that guide system behaviour. Third, that they must be able to detect
significant deviations from these norms. And fourth, that they must be able to initiate
corrective action when discrepancies are detected."
These four features define an organizational learning process in its most elementary
form. The corrective actions initiated from learning can be reactive response,
behavioral adaptation, or organizational changes to improve the organization's
adaptation abilities. Therefore in organization analysis many authors stress the

difference between behavioral adaptation and organizing.
Behavioral adaptation aims at keeping organizational performance within a certain
required range. Data from the environment are used to make an assessment of the
situation the organization is in, from which adaptive actions are concluded (Cyert
and March, 1963). This entails two things: equilibrium and stability. If the output is
within the specified target range, then the system is deemed to be in equilibrium.
The ability of an organization to maintain its equilibrium, or regain it once it has
been lost, is defined as its stability (De Raadt, 1991, p. 31). It is evident that the
organization needs a memory about the set target and must have a short-term
memory of the data. These make up the input of the cybernetic system. Corrective
actions can be based on the memory content.
Organizing (Weick, 1979) is the adaptation of the organization's structure, culture,
style etc. to enable the organization to survive, by improving its capability of effective
behavioral responses and knowledge (Duncan, 1972; Mintzberg, 1979; Hannan and
Freeman, 1977 and 1984). The organizational memory that is required for learning is
of a long-term type, and contains two sets of knowledge: knowledge of the way people
should interact and share a culture, and knowledge about the basic identity of the
organization (mission statements).
Adaptation can lead to too much internal instability because of the loss of precious
knowledge, experience, and skills (Hannan and Freeman, 1977 and 1984). Viable
organizations therefore should not only adapt (the evolution approach) but also
select. The selection process allows organizations of certain types, which enable and
limit certain ways of adaptation, to survice. To understand this close relationship
between selection and adaptation, the understanding of organizational knowledge
and memory is essential.
An example of the working of the adaptation and selection principles is the Roman Catholic Church, an
organization that has already existed for about 2000 years. To some extent it adapts to its environment, because
it connects its activities and policies to new social problems. For instance in the 1950s the Dutch Roman
Catholic Church preached high birth rates because this was important in the social and political emancipation
process the catholic part of the population was trying to achieve against the then dominating protestant part.
Nowadays the Pope tries to influence the attitudes of catholic people against issues such as homosexuality, and
divorce, and has an ethical view on subjects such as unemployment, social security and many other politically
and socially important issues of the 1990s. The Church nevertheless does not want to adapt a number of its
more basic forms of identity, such as male priesthood, despite the fact that in The Netherlands the Church is
suffering from declining interest in celibatarian male priesthood. The philosophy behind this policy is strongly
related to ideas about celibacy and asceticism and the place and role of women in our society. It is evident that
the inertia of basic identity principles on the one hand lends strength and solidity, which are important for the
continuation of the organization, but on the other hand it also can lead to people leaving the Catholic Church
in the Netherlands (cf. Lijphart, 1968; Windmuller, 1976 for interesting further reading on Dutch politics and
the role of the Church).
The subject of adaptation and organization in organizational learning theory has

often been discussed under the headings of single-loop and double-loop learning.
Single-loop learning is about adapting to changing inputs, within the existing pre-set
norms. Double-loop learning is about the change of the pre-set norms themselves, as a
reaction of the ineffectiveness of the existing norms. In our example of the Catholic
Church a double-loop learning process could result in the removal of celibacy for
priests. There is always a tension between single-loop and double-loop learning, and
the management must decide how many single-loop or double-loop learning efforts
are required. Besides, the management must support a successful single-loop and
double-loop learning process. Both these management issues are about organizations
learning to learn, called deutero learning (Argyris and Schön, 1978). Very often
changing environments are difficult to understand, and knowledge gained soon
becomes obsolete. This means that in highly dynamic environments organizations
must be particularly good at double-loop learning. Organizations that do not allow
for this high level of double-loop learning, such as many classic machine
bureaucracies, will be selected out. This has motivated some authors incorrectly, e.g.
Swierenga and Wierdsma (1990), to equate organizational learning with double-loop
learning. Single-loop learning has also led to important competitive advantages
without inhibiting double-loop learning, as will be shown later on in the case of lean
organizations.
From the cybernetic paradigm, I have found some important issues to investigate
about organizational learning:
1. An essential characteristic of organizational learning is the reception of signals
about the organization's environment, so that organizational actions can be
initiated that make the organization head straight for its (moving) targets.
2. MICS should be developed and be available, so that (negative) feedback loops
are established that make critical evaluation and steersmanship possible.
Additionally, I think that although many traditional management information
systems were of this monitoring and control type, they were not effective because they
lacked the behavioral connections that are important in order to obtain the correct
interpretation of the monitoring data and allow the convergence of the conclusions to action
(Ansari, 1977). These behavioral issues of organizational learning are discussed in the
following section.
2.1.2 Organization Development
Mastenbroek (1982) described organization development as the management of four

types of relations (pp. 67-71), namely: instrumental relations, socio-emotional
relations, power relations, and negotiation relations.
The instrumental relations allow the people in an organization to act as each other's
means of production. Instrumental relations are technical in nature, which means
that they are about ways of achieving goals by machinery, human or other. This is
concretized in the formal structure of the organization (division of labor,
responsibility and authority), patterns of communication, procedures for decision-
making and coordination, division of space, design of production flows, and the
rational use of technical instruments. This means that organizational learning from
this perspective is of a technical, substantive kind aimed at increasing existing
technical knowledge and skills. Information systems could be useful for shortening
lead times, making problems visible and analyzable. Machine bureaucracies are good
examples of human machines, as they are constructions of rules and procedures that
should work independently of whoever applies the rules. This approach links up
perfectly with the cybernetic approach, because cybernetics also applies machines for
achieving goals. These cybernetic machines have information systems as an essential
component. In fact the bureaucracy does not differ on this issue, because it precisely
describes procedures for communication and information supply (cf. Weber, 1964).
The focus on socio-emotional relations emphasizes the fact that most organizational
learning is done by a group of people. Effective communication between the
participants is essential for increasing the group's learning potential. For effective
communication it is necessary that members of the group be loyal to each other, and
disclose their minds so that everyone profits from the group's success. According to
Argyris and Schön (1978), however, many organization members do not disclose
themselves and have egoistical attitudes that in the end are counterproductive for the
group. Much consultancy in this area is about teaching team members to disclose
themselves, accept criticism, and training people to have effective confrontation
meetings. This means that organizational learning can be inhibited when groups do
not have effective socio-emotional relations. Information systems can provide data
that give opportunities for instrumental learning, but will not be of any use when the
group is not able to discuss these data and find acceptable solutions that lead to new
behavior.
Power relations are mostly difficult to observe in organizations, because they have a
longer term perspective and demand a tacit way of operating. The aim of these
relations is to improve opportunities of influencing the behavior of other
organization members. Organizational learning can be blocked by the existing power
relations when some people have the power to not allow certain issues to be
discussed. When trying to implement new ways of working, it is necessary to find
commitment for this project among the people who have access to the required
money, time and motivation. Organizational learning, therefore, is restricted or
supported by commitments that are based on the use of power in the organization.
Negotiation relations are about decisions in organizations with respect to scarce
resources. This means that negotiation relations are much more explicit than power
relations, because it is necessary during negotiations to specify claims, to give
arguments and motivations, and respond to proposals. Organizational learning can
be restricted by negotiations because the group of bargainers must decide on what to
do and put priorities on the right actions. However, organizations can also learn to
negotiate, so that negotiations can be performed more quickly, more efficiently, and
the chance of dissatisfaction with the decisions is reduced by using certain
procedures. Information technology could be used as support for negotiations

(Jelassi, 1987; Teich, 1991).
From the organization development paradigm, I infer that organizational learning is
not only a systemic problem, but affects human emotional, power and bargaining
problems as well. It includes systemic as well as behavioral issues (Ansari, 1977),
which means that effective MICSs should be socio-technical systems. This view will be
discussed in more detail in chapter 4.
2.1.3 Semiotics and Information Management
The cybernetic as well as the organization development approach share an interest in

information as a main component for organizational learning. The cybernetic
approach views information as input signals, that could possibly be processed by
machines, which automatically generate the correct feedback signals. The
organization development approach views information as messages that influence
human behavior and attitudes. It is clear that the way both approaches define
information differs but that both perspectives are useful and complementary to each
other. When studying information it is important to recognize the multiple meanings
the term information has. Stamper (1973) therefore prefers to use the term sign
instead of the rather vague notion of information, and thus uses a semiotic approach
to the study of information. This led to the six layers of information systems that
were described in chapter 1. Each of these layers represents some aspects of the
concept of information. From the previous discussion about cybernetics and
organization development, it is clear that cybernetics only studies information as
signs at the syntactic and empirical levels. It also deals with how computers can
enable a machine-physical way of handling signs. The organization development
approach restricts its discussion about signs to the semantic and pragmatic aspects,
and aims at influencing business. The study of MICS and organizational learning
requires that both approaches be combined, as otherwise only half of the subject is
described.
2.2 Linking Organizational Learning with Information Systems
Because this study is about the contribution of information systems to organizational

learning, while information systems themselves are regarded as part of the social
system, some clarification is necessary to avoid tautological reasoning. Here Markus
and Robey's (1988, pp. 585-587) three approaches on impact research are very
illuminating. The first approach they describe is called the technological imperative
which views technology as an exogenous force determining or constraining the
behavior of individuals and organizations. The second approach, called the
organizational imperative, reverses the technological imperative by assuming almost
unlimited choice among technological options and almost unlimited control over the
consequences. The third approach, called the emergent perspective, states that the
technological and organizational imperatives are both incomplete, because it is the
interaction between the systems and the organizational features that cause certain
impacts. The latter approach emphasizes the inseparability of organizations and
systems in leading to joint impact. Impact is itself not a static moment but a learning
process in itself that could lead to inertia/reinforcements of the status quo (single-
loop learning) as well as important changes in mind (double-loop learning) or even
the development of a culture of continuously searching for improvements and
innovation (deutero learning). The best term to use as independent variable is
therefore socio-technical monitoring information and control system. This reasoning is
also consistent with the literature on MICS that emphasizes a 'broad sense' systems
definition which includes computer-based systems (Flamholtz, 1983; Lawler and
Rhode, 1976; Ansari, 1977).
It is obvious that the third approach is very similar to our concept of information
systems. The emergent perspective also does not have unrealistic expectations of
information systems and IT. It also does not say that information systems are useless
in learning activities. The emergent perspective poses the question of under what
conditions MICS-type systems can be useful for supporting organizational learning,
and states that the creation of MICS and its conditions is a learning process in itself.
The adherence of the emergent paradigm leads to a causal structure that deviates
from the one-directional relations that one would expect when using the concept of
impact, see figure 2.1.
The focus here is on the contributions of MICS to effective organizational learning

by managers at the operational and tactical business levels. The reason for this
restriction is that the study of systems for organization strategy support is very
difficult, as the researcher must venture among the many complications of power
relations that dominate at this management level. The impact of tactical and
operational systems on effective organizational learning depends not only on systems
and organizational features, but on environmental conditions that impose learning
needs as well.
Before completing this section on MICS and impact research, it is useful to mention
some alternative ways in which one could study organizational learning and
information management, namely by:
1. ... studying developments in other computer applications than MICS, that
provide managers with opportunities to understand their business much better
(e.g. Decision Support Systems, Expert Systems; cf. Huber, 1991);
2. ... redefining systems development processes as organizational learning
processes (e.g. Vennix, 1990; Business Reengineering, cf. Hammer, 1990;
Davenport, 1993);
3. ... applying systems impact studies, which study the problems in the systems
development process for learning from them, and learning from actual use
(Chew et al., 1991; Saarinen and Wijnhoven, 1994; Wijnhoven, 1992b).
The value of organizational learning as part of information management, is that it
provides an integral approach to information management and business
administration, and gives emphasis to knowledge development, the prime business
mover in post-industrial, knowledge-intensive service oriented, society (Quinn, 1992).
As a consequence, information managment must change its traditional perspective
on managing data, in managing knowledge. For information management this will
require the acquisition of new competences and a change in its organizational
structure (Burrows, 1994).
2.3 Problems in Organizational Learning and Monitoring Information and Control Systems
One of the main reasons why we know so little about the I.T.'s role in relation to
organizational learning is the obscurity of the term 'organizational learning'.
Additionally, information, information system, information technology, management
and organization should be clarified in their mutual relationship. Knowledge in these
areas is missing because of:
1. The ambiguity of the concept of organizational learning, which is caused by its many
roots in disciplines such as psychology, economics, management science, social
science, and cybernetics. This means that communication is difficult. Even
within one paradigm or research field there is much ambiguity about the
meaning of this concept. To improve this situation, a working definition of
organizational learning should be introduced. This definition is that
organizational learning is learning about organizational problems by individual
organization members via the interpretation of data about these problems in connection
with an existing frame of reference (knowledge). This happens in the organizational

context containing structures, procedures, norms, culture and information
systems (formal-informal, automated or manual when available). The result is a
change in organizationally shared knowledge. Linked with the practical
problems of production in large organizations, the subject of learning can be
reduced to the development of insights about the transformation of goods and
services. In chapter 4 this definition is further explained, and the concept is
operationalized in chapter 4 and chapter 7.
2. Ambiguity about the connection between I.T. and organizational settings (including
learning settings) is the second major problem and a subject for chapter 6.
Systems design is still mainly regarded as a definition of requisites for computer-
based systems (e.g. McKeown and Leitch, 1993; Lundeberg et al., 1981),
whereas MICSs are socio-technical systems and therefore should involve
defining the social issues as well (for instance the ways of acquiring data, ways of
communicating about data for sense-making, decision-making processes)
(Aquilar, 1967; Mumford, 1983). Often the connection between the technical
and the social aspects is neglected and thus leads to information systems that
produce voluminous reports which are not used or of which people sometimes
do not even know the existence. For developing effective MICSs it is as
important to have clear database and report definitions as to design effective
targets and management communications. Thus systems development must be
defined as political processes as well (Ansari, 1978; Dobson et al, 1994;
Hofstede, 1981; Lawler and Rhode, 1976).
3. Organizations differ enormously in learning needs and information technological
applications. This makes generalizations very difficult, which inhibits developing
knowledge in the area. A further reduction of the subject to MICSs and
machine bureaucracies therefore is absolutely essential. The choice for
monitoring information and control systems has already been argued. The
reason for choosing machine bureaucracies is based on the idea that these
organizations are notorious for their problems with learning, as has often been
repeated by authors in the organizational learning field (Argyris and Schön,
1978, and Mintzberg, 1983). This does not mean that other organization types
are better learners but that many machine bureaucracies encounter problems
with their clients and markets because they learn in a specific way. A good
example of this reasoning was (again) provided by Womack et al. (1990), while
studying the initial reactions of the American and European car industries to
the increasing Japanese competitiveness. Instead of trying to figure out why
Japanese companies were so competitive, they reacted in two classic ways
described in the beginning of chapter 1: dismissing staff to cut costs (especially
in the US), and closing the market for Japanese products by forcing the
government to impose quotas for Japanese imports (especially in Europe).
On the basis of Mintzberg's insights into organization types, the following relations
between organization types and learning problems can be defined:

Organization type Learning problem
Simple structure Acquisition of internal expertise
Machine Rigidity, meaning reinforcing of existing trends and difficulties with unlearning;
bureaucracy making sense out of data (interpretation); sharing insights among functions.
Professional Problems with introducing professional management and business science
bureaucracy innovations and the loss of managerial autonomy of professionals. Balancing
professional quality versus organizational efficiency and effectiveness.
Divisional forms Problems of knowledge sharing among strongly differentiated organizational
units.
Adhocracies Preservation of knowledge and experience; establishing continuity and efficiency.
Table 2.1: Mintzberg's Organization Configurations and Related Learning Problems
Alternatively, one can also state that the time for machine bureaucracies is over as a
result of new competitive environments. This is probably correct to some extent,
nevertheless, large organizations contain an enormous potential because so many people
contribute labor and knowledge. The problem is, as stated before, how to make optimal use of
them. When well organized and managed, especially by managing its knowledge
carefully, a big organization can profit substantially from its size.
These statements about research topics refer to some important scientific problems,
which should be solved. In this study, I will select the following question:
"Under what conditions can computer-based MICSs contribute to organizational learning in

machine bureaucracies (lean and classic)?"
This question should result in data for the construction of an explanatory theory that
might also be valuable in evaluating a MICS on its contributions to organizational
learning. Chapter 3 discusses the consequences of this approach.
2.4 A Model of MICS and Organizational Learning in Machine Bureaucracies
Organizational learning problems evolved in the evolution of organizations. Machine

Bureaucracies, for instance, invested much in rules, procedures, principles for work,
and often also in machines. This was necessary for producing large quantities
efficiently, and for optimizing the use of expensive machinery. This led to low cost
mass products (important in markets in which cost leadership is a necessary
competitive strategy). Downtime for a machine implies the loss of opportunities for
production and profit. However, the relation between profit (or turnover) and
production quantities is not a direct one, in periods of an unlimited market this
relation is almost 100%. Therefore, the basic organization principle is related to the
optimized use of expensive production capacities, which means that all materials,
supplies and people should be there in the right quantity, shapes, quality, and
moment. Formal planning is necessary to accomplish this. Also, a clear definition of
tasks, responsibilities and authority is necessary to have an effective well-organized
social system related with the technical production process.
Since the second world war, machine bureaucracies have had to face increasing
demands on quality, flexibility and innovation of their organizations. To cope with
these demands the organizations had to increase their internal and external
complexity. Competition on quality for instance demanded regulations for preserving
the quality standard required. This led to the production of quality handbooks and
certification procedures (Garvin, 1987; Evans and Lindsay, 1993). Behaving
according to these rules is supposed to be a necessary condition for realizing quality
standards. At the same time this increased the demand for bureaucratization via the
development of formal communication routines, inventory procedures, machine
maintenance rules etc.
Allowing for flexibility in the production process required in many instances the
implementation of flexible manufacturing systems. The effective use of these systems
required again many rules about the availability of data and resources at precisely the
right time (Kerr, 1991). Besides, machine bureaucracies had to think-over their
production or value-addition technology. Dependent on market characteristics
(especially specificity of customer and dynamics in demands), continuous production
(leading to stock sold) is not always optimal (stock has large opportunity costs). Many
machine bureaucracies, therefore, are series producers, but some are even batch and
discrete unit producers. This has large consequences for the managerial knowledge
needed (especially at the tactical and operational decision levels) and thus for the
learning problems as well (cf. Van Rijn, 1985; Hill, 1982). Besides, as a consequence
of modern competition, service industries are forced to optimize the relation between
output versus input, but must especially focus at optimizing their relations with
clients and markets (cf. Kotler, 1988). These issues, therefore, must be considered in
detail in the rest of the study.
Innovation requires a certain amount of de-bureaucratization, because it demands
the free flow of ideas among people (Hamel and Prahalad, 1990). The impact of the
formalization process described earlier led to a significant problem in adapting to
changing market environments. This means that many a company, in trying to be a
perfect company from an efficiency, quality and flexibility perspective, dug its own
grave at the same time (Lammers, 1985).
Another problem with bureaucratization was the reinforcement of complexity. The
larger the organization, the more complex its internal processes and the more
difficult it is to manage as a whole. Hence, in the 1980s a reconsideration of the use
of being a single organization was initiated and many organizations introduced
vertical decentralization and even introduced outsourcing of departments and
divisions (e.g. Philips Electronics; also cf. Wissema, 1987). Also, external complexity
grew enormously because many new products and new suppliers entered the market.
Liberalization of trade (GATT-discussions, European Market etc.) complicated
competition even more.
The issues relating to the growing internal and external complexity and market
dynamics changed the ways in which organizations could form a strategy. In
bureaucracies, people improved learning in a specialized way, but lost the
opportunity to create synthetic views. These increasing learning problems were met
by cognitive complexity reduction and an increase of organizational learning
capacities (cf. Galbraith, 1973). Cognitive complexity reduction techniques consist of
the creation of slack resources and the creation of independent tasks. Improvement
of organizational learning capacities can be realized by:
• ... reorganizations consisting of the development of matrix and project
organizations, and specialized intelligence groups and/or,
• .... the use of computer-based information systems for developing new ideas
(especially DSS) and analyzing existing ways of operating (MICS).
Both strategies for increasing organizational learning are dealt with in this study.
The results of these considerations are summarized in figure 2.2.
The main focusses of this study are on the dependent variable 'organizational
learning' and the independent variable 'socio-technical learning system' (MICS). In
chapters 4, 5 and 6 these theoretical constructs are described in more detail and
made accessible for empirical research. Chapter 3 describes the methodological
problems involved.
Methodology and Research Design 31
Chapter 3: Methodology and Research Design
3.1 Definition of the Problem
Chapter 1 described the growing importance of learning in modern organizations.

Especially for machine bureaucratic organizations, the implications of studying
learning are high because they are reputed to have typically poor learning habits (cf.
Swierenga and Wierdsma, 1990, p. 61-68). Machine bureaucratic organizations are
placed as number one on the research agenda, and specifically we are interested in
knowing if MICS could contribute to making the classic machine bureaucracies
leaner. This study faces two major problems. The first problem, as stated in section
2.2, is the complexity of singling out the impact of MICS on organizational learning
in machine bureaucratic contexts. This requires the generation of an explanatory and
predictive theory. The second problem is the ambiguity of the organizational learning
concept, mentioned in section 2.3, thus requiring an explicit formulation of the
concept, and much effort in operationalizing it for empirical research and MICS
evaluation purposes.
Information systems are supposed to be important instruments for helping
organizations to solve their learning problems. Nevertheless, no theory exists at the
moment that clearly explains the contributions (if any) of information systems to
organizational learning, and the organizational prerequisites to augment learning
with MICS. Studying the use of MICS is even more important when I.T. impacts are
damaging to organizational learning, e.g. by increasing the amount of formalization
and rigidity.
The basic problem, therefore, can be formulated as the absence of knowledge about the
circumstances under which monitoring information and control systems can contribute to
organizational learning in machine bureaucratic environments.
3.2 Aims of the Research
This study aims at providing clear concepts and a theory. This implies two activities:
concept formation and theory construction.
3.2.1 Conceptualizing Organizational Learning
Problems of Conceptualizing Organizational Learning
Conceptualization of organizational learning has not yet led to a consensus on basic

assumptions yet in the field. Many complex theoretical constructs are used to define
organizational learning, often exacerbating the problems of comparing insights and
research results. Some authors for instance regard organizational learning as
confirmation or rejection of organizational theory-in-use. These discoveries are then
embedded in organizational memory (Argyris and Schön, 1978). Other authors stress
the importance of creating scientifically valid action-outcome theories for
management (Duncan and Weiss, 1979), whereas others think that the coherence of
views among organization members (shared mental models) is even more important
(De Geus, 1988). One other major discussion has been the question of whether
organizational learning can be more than just the sum of the learning of the
individual organization members. Hedberg (1981) for instance states that
organizational learning indeed occurs through the individual members, but that in
some way organizations preserve certain behaviors, mental maps, norms and values so
that organizational stability is maintained despite the fact that organization members
come and go and leadership changes. Alternatively, Kim (1993) proposed a stronger
link between individual mental models or maps and organizational mental models.
He stated that despite some formal changes in an organizational mental map,
organization members could keep behaving in the old way, following their personal
mental models. But when a substantial part of the organizational membership leaves,
it is most likely that the organization's mental model will also change.
Often the ideal of a learning organization is discussed. A learning organization should
facilitate the learning of all its members and continually transform itself (Garratt,
1987, p. 77). Peter Senge uses the terms 'organizational learning' and 'the learning
organization' in a much broader sense than the cognitive psychological one that
dominated the previous definitions. He explicitly regards organizational learning as a
cognitive (theory, memory, and views) and a behavioral issue. The following quote is
illustrative of his view:
" ...in everyday use, learning has come to be synonymous with 'taking information'. (...)
Yet, taking in information is only distantly related to real learning. It would be
nonsensical to say, 'I just read a great book about bicycle riding - I've now learned that.'
Real learning gets to the heart of what it means to be human. Through learning we re-
create ourselves. Through learning we become able to do something we never were able to
do. Through learning we reperceive the world and our relationship to it. Through learning
we extend our capacity to create, to be part of the generative process of life. [...] This,
then, is the basic meaning of a 'learning organization' - an organization that is
continually expanding its capacity to create its future. For such an organization, it is not
enough to survive. 'Survival learning' or what is more often termed 'adaptive learning' is
important - indeed it is necessary. But for a learning organization, 'adaptive learning'
must be joined by 'generative learning', learning that enhances our capacity to create"
(Senge, 1990a, pp.13-14).
I shall not summarize all these definitions to a single all-inclusive definition that
satisfies all these authors. Such a definition is dangerous in that it may exclude some
important issues. Besides, an all-inclusive definition neglects the richness of

theoretical constructs that are connected with world views and ways of theorizing.
Therefore, I shall approach the problem not as a definitional problem but as a problem
of concept formation.
A Method for Conceptualizing Organizational Learning
The conceptualization of organizational learning is especially complex because of the

nature of conceptualizing phenomena for scientific purposes, which frequently
requires the use of theoretical constructs. These concepts have a role in theoretical
frameworks, to explain and predict phenomena, but are difficult to observe.
Organizational learning is a typically good example of a theoretical construct.
Observing it is not possible without further theorizing about what we mean by
organizational learning. This situation is very different from the observation of things
like 'chairs' or 'balls', that are directly and unambiguously connected with things and
therefore called concreta. This situation also slightly differs from abstracta, which
define a class of directly observable things that belong together, and exclude other
things from this class (e.g. furniture defined as tables, chairs, sofas, but excluding
beds). Abstracta can be defined by genus and differentia. In this way organizational
learning can also be defined, because organizational learning = learning (genus) by
organizations (differentia) (Stamper, 1973, p.88). The problem, however, is that
'learning' and 'organizations' are not concreta and thus cannot directly be observed.
Conceptualizing them as abstracta gives us a very incomplete understanding of what
organizational learning is about. Another type of concept often mentioned in the
research methodological literature is called illata which can be observed without
detailed theorizing, but are not observable without the use of specially constructed
instruments (e.g. microscopes for observing bacteries) (Boesjes-Hommes, 1970). Many
illata, however, are theoretical constructs that have been defined and operationalized
in a precise way so that measurement instruments can observe characteristics of these
theoretical constructs. Many examples of this kind exist in the natural sciences (e.g.
electric current, molecular motion, certain types of light). It is often said that the
natural sciences are about the physical world, and thus describe mainly concreta,
abstracta and illata, whereas the social sciences are about virtual reality (opions,
attitudes, motivations etc.) and apply concepts that have their meaning in the context
of theories (thus theoretical constructs). A closer study of the conceptualizations in
the natural sciences has been done by Thomas Kuhn (1962/1970) and shows that
this presupposition is not correct. The development of a method for conceptualizing
theoretical constructs thus could be favorable for both sciences.
The conceptualization of theoretical constructs involves the following steps:
1. Think over the purpose of the concept (explanatory and predictive value) within
the larger theoretical perspective and functions it should have. With respect to
organizational learning, the concept should be linked with the management of
modern organizations and the development of monitoring information and

control systems. This implies the identification of the causes and impacts of
organizational learning. Besides organizational learning, machine bureaucracy
and MICS are also theoretical constructs. Organizational learning will be
discussed in greater detail than the other two constructs, because it describes
the dependent variable and the main source of criteria for MICS-assessment.
Machine bureaucracy has, fortunately, already been conceptualized adequately
by Mintzberg, and the conceptualization of MICS has already led to some
consensus among information scientists (cf. Davis and Olson, 1985; Ansari,
1977).
2. Give a working definition of the concept, as a first demarcation of the object of
research. Also try to detect the basic assumptions that are implied (possibly
tacitly) in the definition.
3. Explain the different perspectives, paradigms or meta-theories that are relevant
when studying the phenomenon of organizational learning. This is necessary
for a clearer understanding of the organizational implications of the
phenomenon. It does not mean that only one perspective should be adopted,
because understanding a social phenomenon frequently requires the use of multiple
perspectives (Morgan, 1986).
4. Define the main dimensions of the concept. This means describing the most
important aspects from the perspectives defined previously. Besides, one should
also define the relations between the different dimensions of organizational
learning. This implies the investigation of demarcations between the
dimensions found and especially the study of possible relations between these
dimensions, for instance in terms of cause and effect or means and goals. This
study is important because it could assist in the systematic investigation of
phenomena. The observation of one kind could then be used for predicting
possible phenomena of another kind. In this way a procedure for investigating
organizational learning is developed, based on a theory and hypotheses that are
theoretically and empirically valid.
5. Operationalizing the dimensions into concretely observable units, for instance by
the development of questionnaires, scaling and measurement methods. This
last issue enables an empirical test of the hypotheses to be carried out.
Statements about the validity and reliability of the observations are a theory in
themselves, and therefore must be refutable as are all other types of theories (cf.
Popper, 1959; Kieser and Kubicek, 1978, part 1).
This method is further applied in chapter 4.
3.2.2 Developing a Theory about MICS and Organizational Learning
Theory construction is a major concern in the philosophy of science (Hempel, 1965;

Popper, 1959; Denzin, 1970). A summary of the different stances for the social
sciences has been made by Burrell and Morgan (1979). These authors stated that the
main discussions about the nature of social science (and therefore the development of
social theory as well) can be organized along the subjective-objective dimension.
Typically, scientists adhering to an extreme subjectivist approach emphasize the
nominal nature of 'reality', meaning that 'reality' as such does not exist outside the
mind of people and their language. They therefore believe that one cannot
objectively measure and investigate 'reality', but should try to understand it by
developing insights into the mind-set and culture of the people. On the contrary,
extreme objectivists are convinced of the fact that the world is real and factual. They
therefore think that scientific research could lead to objective and universally true
laws about the nature of society. Subjectivists state that this is not possible because
people have their own free will, which can lead to undetermined actions, and
conclude that people's behavior therefore should be explained and studied in its
idiosyncratic conditions. Both extreme positions probably have many variants. It will
suffice here to state that the subjectivist vision is typical of organization development
and the objectivist view is typical of cybernetics.
The objectivist and subjectivist approaches are mostly presented as polar extremes
between which a researcher has to choose (cf. Burrell and Morgan 1979). My
epistemological position is however pragmatic: science and theories must meet the
functions they should have for their user. Depending on the specific context,
objectivism and subjectivism are both useful. Some basic functions of social theory
are explanation, understanding, prediction, description, construction and assessment
(cf. Bernstein, 1976). Let us explain these functions, and choose what is required to
fulfill our research objectives.
• Explanation. The resulting theory must provide explanations for the success and
failure of MICS in concrete business organizations. Because no validated theory
in this area exists at the moment, it is not the generalization of the theory over a
large sample that is our concern here. What we aim at is an understanding of
MICS's influence on organizational learning in concrete business organizations
so that a theory can be generated.
• Understanding. Understanding is a type of explanation that emphasizes the
possibility of the scientist or analyst having empathy with the subject(s) under
investigation. The explanation therefore is not primarily based on
measurements, but on communication and interaction between the informant
and the investigator. Understanding is essential for creating organization
theory, especially in under-researched areas like the intersection of
organizational learning, MICS and machine bureaucracies. The reason for this
is that a language that deviates from everyday life must add considerably to
knowledge, otherwise it is a nuisance. This means that in an early stage of
theory development, concepts must be close to everyday language. This is a
major strength of grounded theory that in its substantive phase uses everyday
concepts. The value of a theory, however, increases when it has a broader
application area than just the idiosyncracies of the observed case. Therefore
generality of insights must be strived for. This is achieved via the formalization
of substantive theories, gradually introducing more academic jargon (Glaser and
Strauss, 1967).
• Prediction. According to positivism, theories must have universal validity,
meaning that the relations found are valid in the past and present (explanation)
and future (prediction) (Hempel, 1965). This statement is of course only valid
under identical (ceteris paribus) conditions and therefore hard to test
empirically (Kieser and Kubicek, 1978, part I). Prediction is also a more
ambitious target of theory construction than explanation, because many yet
unknown and maybe even non-existing facts can falsify a prediction, as is often
the case (cf. Etzioni-Halevy, 1985). It is therefore useful to distinguish between
the explanation and the prediction function. Of course many theory users
might be more interested in predictions, so that they can predict 'what would
happen if...' An example is an investigation of the impact of the lowering of a
bank discount rate on inflation in a country. Other theory users might be more
interested in explaining why certain things happen. For instance an
organization might be interested in the question why some projects were a
failure and others were a success, while developing new knowledge about
project management.
• Description. Some theories have a primarily descriptive function. This means
that they have a consistent set of issues to describe a case or phenomenon for
other purposes (explanation, understanding, prediction etc.). Often ideal types
are constructed for this purpose. Some examples of these types are Max Weber's
typology of 'Herrschaft', Jung's classification of psychological types, and
Mintzberg's organizational configurations. Description and its possible
functions are, however, closely connected, as Jung's typology can serve to
improve a therapist's understanding of his client, and Mintzberg's typology can
aid in the diagnosis and design of organizations.
• Construction and assessment. If we can describe, explain and predict phenomena,
then we also have opportunities to find out whether certain policy proposals
will work or not. This means that one can assess the effectiveness and
ineffectiveness of policies (such as using MICS for management learning) and
offer suggestions for improvement (e.g. finding leverages to augment the
effectiveness of MICS).
In this study, cases were used to obtain reliable data for the generation of an
explanatory theory, which could also be used for the evaluation of MICS. The
analysis will use principles of grounded theory construction, emphasizing the
importance of analyzing evidence from cases that, from a theoretical point of view,
are significantly different but comparable. The objective is not to find statistical
regularities, but to find new concepts and how they are related in order to reach an
explanation.
3.3 The Main Questions

The general question "Under what conditions can computer-based MICS contribute to
organizational learning in machine bureaucracies (lean and classic)?" is reformulated in the
following more concrete questions:
1. What are the basic dimensions of organizational learning, as a cognitive and
organizational process? The answer to this question gives a detailed description
of the dependent variable and solves major conceptual problems.
2. How do machine bureaucratic organizations learn? The answer to this question
gives a contextual flavor to the general description of organizational learning. It
is the basis for understanding circumstances under which specific types of
learning can occur. Machine bureaucracies are taken as a case for heuristic
purposes, because learning problems are expected to be most overt in the
classic, and mainly solved in the lean, machine bureaucracies.
3. Do lean and classic machine bureaucracies differ significantly in their way of
organizational learning? This question is most important in order to increase
the variety on the independent variable, so that we can generalize about the
research findings.
4. What is the influence of MICS on organizational learning in machine
bureaucratic contexts? The answer to this question provides a synthesis between
the general problem of organizational learning, machine bureaucratic features
and information systems features, by defining the concept of a socio-technical
learning environment. Also hypotheses will be stated about the possible impacts
of I.T. on organizational learning.
5. How can one observe the impacts of the monitoring information and control
systems in machine bureaucratic environments? This question requires the
assessment of the model which results from the theory developed, with
particular regard to its use for making important managerial observations and
inferences from a theoretical and practical perspective.
3.4 Research Design and Plan
3.4.1 Research Plan
The previous discussions emphasized the importance of studying machine

bureaucracies. Two different types of machine bureaucracies are distinguished in
chapter 1: the classic and the lean type. The first assumption is that these types of
organizations differ significantly in the way they learn and use monitoring information and
control systems. A second assumption is that organizations have learning norms (that differ
among the four machine bureaucracies) that explain the differences in how they learn. A third
assumption is that organizations have learning needs and that organizational

effectiveness depends largely on the match between learning norms and learning needs. The
fourth assumption is that MICS consists of a set of organizational learning norms that can
add to or inhibit organizational learning performance. Many supporting theoretical
arguments are given in chapters 5, 6 and 7, while chapter 8 provides empirical
evidence via comparative case studies.
The research design is a comparative study among four classes of machine
bureaucracies, distinguished by organizational leanness and organizational
transformation process (service versus manufacturing) that are supposed to explain
organizational learning performance. Chapter 6 further explores MICS as a variable,
and the consequences for the theory involved.
The study has an exploratory nature, to develop theoretical insights, because theories
in the area are lacking and conceptualization of a major variable is very ambiguous.
In these types of study, knowledge is not yet far enough developed to lead to effective
survey studies. Research methodologists then recommend comparative case studies,
and stress the importance of theoretical insights gained from observations (called
'grounded theory' by Glaser and Strauss, 1967) (Yin, 1984; Glaser and Strauss, 1967).
The cases studied are not selected randomly from the population of machine
bureaucracies, but are chosen on some indications that would make the case
appropriate for one of the classes described above3. Additionally, the individual
organization under investigation must be prepared to cooperate in the study. This is
sometimes not so easy because it demands time and effort on the part of the
organization, whereas the benefits are not all that clear in advance.
The participating organizations are briefly described in table 3.1, and selected on the
basis of insights we gained from the organization at the beginning of the case studies.
The organizations are anonymous. Cases 1, 2, 3 and 4 were selected in the beginning
of the study. Later on, High Tech Manufacturing Plant (Hitec) was selected as a case,
because it very clearly can be identified as a lean organization, whereas Chemical
Plant and Health Co were not yet lean in the strict sense of the word, as we found
out after completion of these cases studies.
Types of Commercial Leanness

Machine Bureaucracies
Classic Lean
Manu- 1. Classic Manufacturing: 2. Lean Manufacturing:
facturing A Cardboard Manufacturer, A Chemical Manufacturing
Organizational called Cardboard Co. Plant, called Chemical Plant
transformation 5. A High Tech Manufacturing
Plant, called Hitec
3
This way of selecting cases is called 'Theoretical Sampling' by Glaser and Strauss, 1967.
Service 3. Classic Service: 4. Lean Service:

A Mid-European Commercial A Health Insurance Company,
Bank, called The Bank called Health Co.
Table 3.1: Theoretical Sample of Cases
3.4.2 Reliability and Validity Problems
This cross-case comparitive study has elements of an experimental design, because it

varies the independent variable (experimental factor) machine bureaucracy type.
MICS is regarded as an intermediate variable between machine bureaucracy and
organizational learning. As an alternative hypothesis, variation in machine
bureaucracies is supposed to influence organizational learning in a direct way as well.
Data gained from interviews were used as cues to more objective data sources such as
archives and databases. Data from different data sources were checked to improve the
data reliability. When only interview data could be obtained, answers from different
respondents were compared, but we used archive material as much as possible to
increase the reliability of the data (cf. Blau and Schoenherr, 1971). Additionally, the
reliability of the data was checked by comparing interviews about the same topics
where possible. The study aimed at exploring a theory by drawing upon existing
theoretical notions in the field. This implies the need for a literature study and case
studies. The literature study aims at developing hypotheses and concepts that will be
tested in an empirical investigation. The case studies check the validity of the
hypotheses and theory developed thusfar, and aim at improving them on the basis of
empirical observations. The testing of the preliminary hypotheses thus has heuristic
purposes in this theory exploration4.
Some alternatives for testing theory were considered as well, namely experiments,
surveys, historical studies and archival analysis (Yin, 1984, p. 17). We did not choose
for controlled experiments because they require a very precise and well-formulated
theory in advance, which was not available, and because many experiments suffer
from a low external validity. A survey study was not conducted because it requires a
greater clarity about the basic concepts, which again was not available (the concept of
organizational learning in particular suffered from much ambiguity; see chapter 4). A
historical study was feasible in theory, but also would have lacked external validity
because the issues of organizational learning and information systems have gained
importance since the 1990s. Archival analysis (as done by e.g. economists) was not
considered feasible, because no databases exist about organizational learning. But
when one of the companies of our case studies has data of its own, then these were
explicitly used as additional data sources.
4
This heuristic is also called 'analytic induction' by Glaser and Strauss (1967). The reader is kindly
referred to these authors for further clarification of 'analytic induction'.
When applying the case study strategy, some important general methodological problems
must be dealt with: construct validity, internal validity, external validity and
reliability. Construct validity is about establishing correct operational measures for
the concepts being studied. Internal validity is about establishing a causal
relationship, whereby certain conditions are shown to lead to other conditions, as
distinguished from spurious relationships. External validity is about establishing the
domain to which a study's findings can be generalized. Reliability is about
demonstrating that the operations and analysis of a study (such as the data collection
procedures) can be repeated and will lead to the same results.
These requirements can be met in several ways in the case studies. Table 3.2. column
3 describes how this study treats these methodological problems.
Tests Case study tactic Research in techniques and tactic used

Construct Use multiple sources of evidence Data collection. Evidence is found by
validity: allowing interviewing key persons, study of archives and
controlled other documents.
observation
Establish chain of evidence (also Data from multiple sources are checked for
observations etc.) inconsistencies and corroborations.
Have key informants review the Drafts are send to informants for review, and
work feedback meetings are planned with each
company.
Internal validity: Compare predictions for a case Data analysis based on score card matching
allowing with empirical data (Patterns are hypothesized in chapters 5, 6,
controlled and 7.
deductions
Do explanation building Data analysis for applying the theory and
exntending the set of hypotheses.
Do time-series analysis Analyse time-series only when suitable archives
are available.
External validity: Replication in multiple-case Express theory in a research design. Test after
allowing studies. each application, modify to make the theory
generalization of more robust.
findings
Reliability: Define case study protocol Checklists and measures were used by the
allowing three members of the research team who
controlled collected the data for the five cases. Much
observation interchange of experiences occurred within the
team.
Make case study database available It is available for experts to control the quality
of the study, but is treated confidentially.
Source: Yin, p. 36, table 2.1 and Lee, 1989
Table 3.2: Case Study Tactics for Four Research Design Tests.
This means that most of the recommended tests are explicitly part of the research
strategy followed here.
3.5 Layout of this Book
Chapter 1 and 2 have explained the motivation to this study and the major problems
and questions. Chapter 3 discussed the research design and some methodological
problems involved. The following step is to describe the concept of organizational
learning, in chapter 4. Chapter 5 clarifies the term machine bureaucracy. This
concept has been studied extensively in the existing literature on organizations, and
has led to a conceptual concensus. Additionally, the way machine bureaucracies learn
is examined and some hypotheses are defined that indicate a distinction in learning
among four types of machine bureaucracies. Chapter 6 relates organizational
learning, specifically in machine bureaucracies, with the role and influence of MICS.
These theoretical chapters are then followed by empirical chapters. The theoretical
and conceptual findings first need some further elaboration to an operational
language, so that it is easier to guide the data collection and analysis of findings. This
means that the hypotheses need very precise formulations. The conceptual
ambiguities that possibly still remain must be solved. This is part of chapter 7.
Chapter 8 presents the results of the five case studies. In this chapter the results of
the first case study is taken as input to the analysis of the second etc. In this way the
analysis proceeds by accumulating insights and further formalizes the theory. Chapter
9 finally puts together all the results and discusses a further elaboration of the theory
and observation instruments.
Concept of Organizational Learning 43
Chapter 4: Concept of Organizational Learning
4.1 Introduction and Working Definition
This chapter addresses the conceptual ambiguity about organizational learning by

applying the concept formation methodology described in chapter 3, that prescribed
the following sequence of activities in conceptualizing theoretical constructs:
1. Think over the purpose of the concept. This has already been done in the
previous chapters, to illustrate the importance of research for organizational
learning and the broader theoretical perspective the concept should be part of.
It is not our intention to describe organizational learning in a psychological
way, but to show the genus and differentia; section 2 also describes a
psychological perspective that has been quoted many times in the literature on
organizational learning.
2. Write down a working definition. This definition is given later on in this
section.
3. Describe the theoretical perspectives. Sections 3 to 7 describe the perspectives
of organizational learning.
4. Describe the dimensions. The dimensions of organizational learning are
described in section 8.
5. Operationalize. An operationalization of organizational learning, which is
important for carrying out the case studies, is given in section 9.
The following working definition is used in this chapter:
Organizational learning is learning about organizational problems by organization members
via the interpretation of data about these problems in connection with an existing frame of
reference. This happens in an organizational context containing structures, procedures, norms,
culture, organizational memory and information systems.
This definition is chosen because of the following assumptions:
1. Organizational learning is about the way organizational experiences are
processed. These experiences can be about organization internal problems (e.g.
decision-making and conflict settlement) or external problems (e.g. increase of
competition). This is of course essential for staying in business.
2. Learning is done partly by individuals. Individual people must be motivated to
learn, should have the intellectual capacities to understand problems and
solutions, and must be willing to change behavior and attitudes when necessary
to improve performance.
3. Individuals require frames of reference in order to learn to understand what is
going on and what should be done. These frames of reference can be tested and
changed as well.
4. Organizational learning is done also by people together and thus is a social

process. This is because people basically learn things from each other, by face-to-
face communication, writing or other means of communication, and sharing a
frame of reference.
5. Management should facilitate the learning process by organizing, planning,
financing, tooling, controlling and improving it.
6. Organizational learning, when it leads to a change of behavior, attitudes,
organization structure and policy, can have a severe impact on organizational
relations, and therefore requires not only a cognitive capacity (accumulation,
update or removal of knowledge in organizational memory) but capacities for
organizational change as well.
4.2 A Psychological Perspective: David Kolb's Experiential Learning
Organizational learning, regarded from a psychological point of view, emphasizes an

individual's change of knowledge and behavior. The statement of the psychologist David
Kolb (1984) is particularly interesting here, because it is firmly based on insights from
major writers and philosophers in the field of learning (John Dewey, Kurt Lewin and
Jean Piaget) and has been applied to organizational learning frequently. Kolb defines
learning as (1984, p.38) "... the process whereby knowledge is created through the
transformation of experience" and calls his theory experiential learning. His approach is
relevant from an organizational learning approach, as it emphasizes the importance
of learning from experience rather than class-rooms and textbooks. This means in
many cases that organization members should be responsible for creating an adaptive
and viable organization. The experiential approach makes the following assumptions:
" First is the emphasis on the process of adaptation and learning as opposed to content or
outcomes. Second is that knowledge is a transformation process, being continuously
created and recreated, not an independent entity to be acquired or transmitted. Third,
learning transforms experience in both objective and subjective forms. Finally, to
understand learning, we must understand the nature of knowledge, and vice versa"
(Kolb, 1984, p. 38).
The definition as stated above needs clarification on some points:
1. The term of 'knowledge' in the definition is still rather ambiguous. According to
Kolb, knowledge can be two-sided: a collection of concrete experiences, or a set
of abstract conceptualizations. The concrete experiences consist of stories,
feelings, data and opinions about what someone has observed. The abstract
models consist of general theories, perhaps gained from textbooks or lectures.
Abstract models can be science, containing laws, theorems and procedures that
are accepted as being valid knowledge, or judgement, containing workable
knowledge in the form of policy rules, probabilities and heuristics (Earl, 1994,
pp. 55-59). A substitute for the term knowledge in a management context is the
term 'management theory' which is a combination of science and judgement.
Management theories thus contain goals, purposes and the way managers think
they could achieve them, possibly formulated in some hypotheses about means-
goals relationships, explanations and predictions of events. A management
theory, for instance, could state that decentralization leads to better motivated
personnel who process information from the environment more effectively,
which leads to a higher organizational performance. This theory consists of
several hypotheses that are open for refutation. The concept of management
theory looks simple; however, in practice it is difficult to observe. This is
because of the often hidden and tacit aspects of management theories. Argyris
and Schön (1978) therefore distinguished espoused theories, containing a
person's public explanation of why he does what he does, from theories-in-use,
which give a 'genuine because reason' for his behavior. The latter is often only
partially espoused for political and cognitive reasons (Schutz, 1939, for a classic
account of the methodological problems involved in observing these). Tacit
knowledge is particularly important when there is a close connection between
knowledge and action. In that case, concrete experience is a more important
motivator to action than the application of explicit abstract models. Craft
technology is based on the application of implicit knowledge because of the low
task analyzability (Perrow, 1967; Mintzberg, 1983). Sometimes a layman can ask
very fundamental questions, and initiate organizational learning by elicitating
everday used organizational knowledge (cf. Coats, 1992). Another example is
strategic planning, that often results in documents that never lead to concrete
actions. The authors of these plans often lack sufficient understanding of the
practical situation and such intangibles as socio-political involvements (Ansoff,
1988). An organization has many concrete experiences as well as abstract
conceptualizations. The learning process should be developed to connect both
and to guide the organization's energy and resources in the right proportion to
the development of prehension.
2. The concept of 'process of learning' is also vague. Kolb suggests distinguishing
between two processes: reflection and experimentation. These processes are most
interesting from a managerial point of view because they correspond with two
basic management activities: analyzing the situation and developing new ideas
(reflection), and testing ideas (experimentation) and learning from that
experience (a new reflection). Management learning, however, is not divorced
from political processes, which even sometimes take more energy than the
learning processes. For instance, Kumar (1990) found in his survey of IT-project
evaluations, that only in 18% of the cases was evaluation motivationed by: "The
use of evaluation results, as a feedback device for improving future development and
project management methods and for evaluating (and improving) the systems
development project personnel..." (Kumar, 1990, p. 210). The main motivation for
evaluation, according to Kumar's findings, was 'project closure', by
demonstrating that the objectives of the project are achieved and that appraisals
can be provided to members of the project group.
Kolb developed a learning styles inventory, that measures a person on the dimensions of
the organizational learning construct: concrete experience (apprehension), abstract
conceptualization (comprehension), active experimentation (extension) and reflective
observation (intention). This results in four dialectically opposed forms of adaptation
to the world. Mostly, people tend to specialize in a certain kind of knowledge and
learning activity, called a learning style. The convergence learning style relies
primarily on the learning abilities of abstract conceptualization and active
experimentation. The divergence learning style has the opposite learning strengths,
emphasizing concrete experience and reflective observation. The assimilation
learning style has the dominant learning abilities of abstract conceptualization and
reflective observation. Finally, the accomodation learning style emphasizes concrete
experience and active experimentation. A person's learning style is measured via two
uni-dimensional variables: AC-CE (the abstract conceptualization minus concrete
experience score) and AE-RO (active experimentation minus reflective observation
score). According to Kolb these two-dimensional variables can be reduced to uni-
dimensional variables, because of the high correlation between AC and CE, and
between AE and RO, thus possibly measuring two underlying constructs (Kolb, 1984,
p. 75). Weisner's study (1971) of a Midwestern division of a large American
industrial corporation is particularly interesting here. Weisner applied the learning
style inventory to the five major functional groups (marketing, engineering,
personnel, finance and research). About 20 managers of each group were rated. The
results are pictured in figure 4.1.
It is evident from these data that organizational learning can easily lead to
organizational differentiation, which allows for specialization, meaning the
development of specific expertise by specific people. The close connection between
organizational learning and differentiation was already mentioned by Lawrence and
Lorsch who defined differentiation as:"...the difference between cognitive and emotional
orientation among managers in different functional departments" (Lawrence and Lorsch,

1967, p.11). Many problems can occur because of differentiation and specialization.
Kolb is well aware of this fact and proposes a third learning process. In Kolb's theory,
after an initial period, people start specializing in the behavioral, symbolic, affective
or perceptual dimension of personal development, and later also learn to synthesize
these. Kolb interprets development as a process of the development of human
personality, from an infant to an adult. This interpretation is less applicable to
organizational learning, also when we conceive organizational learning as the learning
of individuals in organizations, because most organization members have already
reached the stage of adulthood. The theory is more applicable when we perceive
development as a process of initial development of experience and conceptions, the
further development by specialization (which can lead to highly specialized
knowledge, cf. Weber, 1921/1964), and integration (connecting pieces of knowledge
in the organization and the development of a shared body of knowledge (cf. Senge,
1990a).
Because organizational learning takes place via its individual members, the
possibilities and limitations of organizational learning are linked with individual
abilities to innovate and improve their understanding of reality. Increased complexity
and dynamics make important demands on the indivual's ability to absorb new ideas,
data and knowledge. Psychological limitations in this regard are important to know.
Education, socialization and culture are important influencers of these abilities, as
proposed by Berger and Luckmann (1967) and by the empirical evidence of Kolb
(1984). The reader is also referred to the work of Lessem (1991), who provides many
other interesting suggestions for research in 'organizational' learning from the
psychological perspective. The emphasis in this study is, however, on the
organizational aspects of learning in organizations. This means that the insights
generated must be added with insights from organizational perspectives in order to
achieve organizational learning.
4.3 A Classification of Organizational Perspectives to Organizational Learning
Chapter 2 stated the relevance of cybernetics and organization development as two

basic paradigms for understanding organizational learning. The first is about
structural and technical properties of social relations. The second is about socio-
emotional relations, bargaining and negotiations5. The subject of organizational
learning is about the development and use of knowledge within a social setting
(organization). The best way to organize perspectives of organizational learning
therefore is to relate them with paradigms of knowledge, and paradigms of social reality.
5
The semiotic paradigm was also described in chapter 2. This paradigm is relevant for describing MICS,
and is explained in chapter 6.
Two paradigms of knowledge were defined earlier: subjectivism and objectivism.

Subjectivism states that knowledge is connected to an individual's mind and has no
objective law-like nature. Additionally, people have a free will which cannot be
described in mechanistic terms. This perspective is also typical for the organization
development school, which developed as a reaction to scientific management and
from experience with empirical social research (cf. Daft, 1991). The Hawthorne
studies in the 1930s, conducted by Mayo and his colleagues, are particularly famous
in that they shaped the human relations movement and its later organization
development movement. Mayo et al. tried to test the influence of the amount of light
at a workplace on worker performance. What happened was that whether the
amount of light was increased or reduced, performance improved in all cases. The
researchers concluded two things:
• The fact that people were observed was a research artefact that led to unreliable
measurements.
• The human factor in the end has a much greater influence on performance
than any physical factor. This was a falsification of the scientific management
thesis that stated that work should be regarded as a technical process, and
performance is the result of engineering the technology to which people have to
adjust.
This research of Mayo was part of the so-called human relations school in industrial
sociology emphasizing the management of interpersonal relations, personal and
group motivation, and organizational culture as means for achieving effective
organization. Related to these findings, Argyris also criticized behavioral research for
its focus on superficial phenomena that can be easily measured and described in
questionnaires. To find people's genuine theory-in-use one has to seek below the
surface of what they espouse.
The cybernetic perspective demands precisely described procedures and data, so that
knowledge can be created in a mechanistic way. Research from this viewpoint
searches for objective and quantitative knowledge and scientific laws. Mathematical
analysis is also applied in order to develop insights that go beyond the notification of
facts. An example of this perspective is Cyert and March's (1963) book: 'A Behavioral
Theory of the Firm'.
The nature of social reality also has two main paradigms, one based on order and
regulation, and a second one based on conflict and radical change. These two
paradigms and their features are summarized in table 4.1.
The sociology of regulation is concerned with The sociology of radical change is concerned with
The status quo Radical change
Social order Structural conflict
Consensus Modes of domination
Social integration and cohesion Contradiction

Solidarity Emancipation
Need satisfaction Deprivation
Actuality Potentiality
Source: Burrell and Morgan, 1979, p. 18
Table 4.1: The Regulation-Radical Change Dimension.
Cybernetics is related to the sociology of regulation. Organization development has a

larger scope because it discusses the socio-emotional issues involved in organizations.
It nevertheless belongs to the sociology of regulation, because no attempt is made to
change power and material value distributions. Some authors in this area, such as
Argyris (1970 and 1971), therefore state that organization development too often is
focussing on improving management skills, without talking about the basic problems.
For instance, a manager trained in personnel motivation will not be able to solve
motivation problems when the basic reasons for the problem are not understood and
worked on. Motivation problems can have social-emotional roots, but sometimes the
problems are rooted in the power relations among the managers. To solve this last
problem sometimes requires an internal revolution.
Based on these two two-dimensional factors, four ideal typical perspectives for the
study of organizational learning exist. These are described briefly in table 4.2. The
perspectives differ on four organizational learning issues:
1. Basic definition of the concept of organizational learning (process and purpose).
2. Basic requirements for organizational learning (data, views etc.)
3. Definition of learning actors (a group or an individual, a specific elite or all
organization members).
4. Definition of the field of learning (that changes under the influence of learning).
Reality Order Conflict
Knowledge
Objectivism Cybernetic perspective. Scientific Management.
1. O.L6. is discovering objective 1. O.L. is change in conflicts and
reality and is conceived as a power relation, via the devlopment
learning process. of 'objective' knowledge.
2. Requires: data and models. 2. Requires detecting sources of
3. Individualistic developing and conflict, and latent dysfunctions.
testing of knowledge. 3. Learning is mainly done by the
4. Field of learning is the production power elite.
or transformation process. 4. Field of knowledge is technology of
domination and manipulation.
Soft Systems. Organization Development.
6
O.L. is short for Organizational Learning
Subjectivism 1. O. L. is understanding perceptions 1. O. L. is understanding dysfunctions

that motivate behavior in specific caused by routine processes and the
social contexts and is frequently problems of change.
organizational change as well. 2. Requires: open communications,
Removing unlearning problems. mutual feelings of trust and
2. Requires: feeling with 'reality', willingness to change.
possibly organized through soft 3. Social and individual: people
modeling7. interacting in a specific social setting
3. Individuals interacting with each (power relations).
other in a specific social context 4. Knowledge is about social and
(culture). political issues influencing
4. Fields of knowledge are e.g.: organizational processes and
attitudes to work, collaboration thought, and leading to the
and leadership, and development of organizational
understanding cause-effect equilibrium necessary for getting
relationships in reality. resources together.
Table 4.2: Perspectives for the Study of Organizational Learning.
The four perspectives are further described in the following sections.
4.4 The Cybernetic Perspective of Organizational Learning
4.4.1 The Origin of the Organizational Learning Concept
7
To be explained in section 4.6.
The first explicit account of organizational learning in the literature known to me,
was given by Cyert and March in their classic volume: 'A Behavioral Theory of the Firm'
in 1963. The authors described organizational learning as a part of the decision-
making process in an organization, and as consisting of four major activities:
• Quasi-resolution of conflict. Goal conflicts can be solved by constructing one
consistent set of goals (the organizational objective). Often, however, it is not
possible nor required to do so. Different and conflicting goals can coexist in
one organization by keeping them separated in discussions, and by keeping the
defenders separated locally, or by paying sequential attention to the different
goals.
• Uncertainty avoidance. Organizations often cope with uncertainty by avoiding
"...the requirement that they correctly anticipate events in the distant future by using
decision rules emphasizing short-run reaction feedback rather then anticipation of long-
run uncertainty events (p. 119)" and by avoiding:"...the requirement that they
anticipate future reactions of other parts of their environment by arranging a negotiated
environment. They impose plans, standard operating procedures, industry tradition, and
uncertainty-absorbing contracts on that environment" (p.119).
• Problemistic search. Searching for s solution is a motivated search for solving a
specific problem. It is usually based on a simple model of causality; however,
the learning process can increase its complexity. Finally, it is biased by the
experience, education and goals of the participants in the search process.
• Organizational learning. Organizational learning is: "...adaptation with respect to
three different phases of the decision process: adaptation of goals, adaptation of attention
rules, and adaptation of search rules. We assume that organizations change their goals,
shift their attention, and revise their procedures for search as a function of experience
(p.123)".
These four activities are assembled into a model of decision-making by describing the
information flows among them. This is illustrated in figure 4.2.
4.4.2 Control and Information
According to the cybernetic perspective, organizational learning is a way of processing

data to construct knowledge for effective control and decision-making. The role of a
well-designed information and communication system is essential for an effective
control system. This information system should feed back on the norms and goals
that exist in the organization. Sometimes the system feeds back on the means by
which the goals are strived for. In other cases the goals and the information and
communication system are the subject of critical evaluation. Therefore, an effective
learning system should contain:
1. Data from the environment the organization is in.
2. Processes acting on these data and receiving management input about how to
act.
3. Targets, as norms about the course an organization should aim for.
4. A comparator relating information about the results of the process with the
targets and sending information about deviations between actual performance
and targets to a meta-system called management.
5. A meta-system that decides what action demands must be communicated to the
process system.
6. Information subsystems, that process data for transactions (TPS), compare data
(MIS), aid decision-making (DSS) and allow communication between the
environment, the transformation process, and the management (ComSys).
A MICS includes the MIS (deviation

measurement) and communications
(ComSys) that are necessary for making
adjustments to actions and theory
(DSS). A transaction processing system
(TPS) can be part of the operational
system and generates data that are
further analyzed for managerial
purposes. De Raadt conceptualized a
cybernetic system in the following
figure (see figure 4.3).
Information systems are regarded as essential for reducing the uncertainty
management phases while trying to keep everything under control. The definition of
the requisite information should then be the first step in information systems
development (Weiner, 1953; Galbraith, 1973). De Raadt applied these principles to a
cybernetic study of an insurance company. The insurance company wanted to
increase the amount of premium payment by increasing the number of policies sold.
The sales are influenced by sales agency's incentives, which can be expressed in terms
of $ available for bonuses and commissions. Additionally also economic
environmental variables have an influence on this sales goal variable, which is not
under the influence of the salesmen. A statistical analysis of goal data (P), incentive
data (I) and economic variables (X), revealed the following structural equation:
P = 19 + 8.92I + 10X
A manager can optimize the amount of incentive spending by relating it to the

additional number of policies sold, divided by 30 to correct for the costs of the
policies. The relevant equation is then:
dI = -(P-P')/30
(Where: dI is the additional money for incentives; P' = target number of policies sold;
P = actual number of policies sold.)
When economic factors change, the number P might decline, requiring new
incentives to keep the system within the target range, which is important for covering
fixed costs. This means that from the previous equation a new optimal incentive
system must be found. This is typically single-loop learning: error-controlled regulation.
It might be that the impact of the new incentive system is slow, and leads to mis-
steering. This can have negative influences on the whole system. To solve this
problem, the meta-system requires information about these impacts, and has to look
for new principles that might improve the working of the single-loop. The related
double-loop is described in figure 4.48.

Many other recursion cycles can be
described by defining the meta-system 1,
2 and the operational system as an
operational system II and defining a
meta-system II (etc.). Operational system
II could be about the division life
insurance. Meta-system II is the board of
CEOs that manages the corporation
and other divisions as well to some
extent. A vital question in modeling
these systems is: what information is
required for managing the organization and its parts? Systems analysis therefore is an
essential part of the organization's facilitation of organizational learning processes (of
the single- or double-loop type). Improving systems analysis and design is, from the
cybernetic point of view, most urgent for improving learning in the organization, and
a major issue for learning to learn (deutero learning).
4.4.3 Equipping the Learning Process
The cybernetic perspective emphasizes the ability to explicitly design an organization's

learning capabilities.
Organizing for Organizational Learning
8
De Raadt does not make a distinction between double-loop and deutero learning. This distinction is
discussed in the section 4.5 (on organization development). It suffices here to state that the term double-loop
learning refers to learning about the basic assumptions of the management theory used in single-loop
learning processes. Deutero learning is about the way the organization facilitates learning by e.g. the
development of openess and creativity, encouragement of innovations, quality circles etc. The deutero
learning is specifically an organization development subject, because it is about issues like interpersonal
relations, power relations, and cultural change.
Why can people be better learners within organizations than without this social
context? This question is basic for motivating people to learn in organizations, and
very close to the subject of organization design (including the development of
structures, systems, procedures and policies). Herbert Simon (1976, pp. 102-103)
answers this question by describing five principles (premises) that transform
individual behavior to organizational behavior. These principles are: the division of
work, the establishment of standard practices and work procedures, the transmission
of decisions via systems of authority and influence, the provision of channels of
communication, and the training and indoctrination of organization members.
The division of (learning) work allows people to concentrate on specific problematic
topics, analyze them and try to find solutions for the organization. It is also possible
that people are connected to jobs that are the input for the learning process (for
instance data gathering and storage), and manage this in a very careful and
professional way. For instance, it is quite unlikely that someone can be an excellent
problem analyst, solution constructor, implementor of the solutions, and manager of
a department all at the same time (applying all relevant abstract conceptualizations).
Therefore, the division of work and allocation of learning tasks are essential for
having effective learning systems in organizations.
Because of the division of work, people need clear standard practices and procedures so
that the separate tasks in the learning process are well connected. A classic problem
here is that data providers use different meanings (semantics) for data than the
information system's end users. Multinationals, for instance, often cannot tell how
well or badly they are doing internationally, because the data are defined in different
ways in the separate countries. They therefore require expert studies to give the
CEOs a useful, consolidated, body of knowledge. Executive Information Systems
projects therefore require (re)formulation of the data definitions, so that the
interpretation of the data can be done automatically. Via this standard practise and
procedure, the delay between problem occurrence and problem identification is
shortened, and the CEOs have tools to make intelligent analyses themselves.
Transmission of knowledge and ideas occurs via systems of authority and influence.
These systems can be authoritarian, which means that the manager thinks for the
organization, and organization members only have to obey or act accordingly. In the
case of a paper mill studied by Zuboff (1988), changing the information transmission
processes implied a change in the authority and influence structure. This change was
resisted by the middle management, who feared the loss of any reason for existence
of their jobs. Nevertheless as a consequence, feedback cycles were shortened, leading
to less loss and higher performance. Also, more people were engaged in problem-
solving by adding insights from their own specialization and attention focus. For this
heterogeneous group to become effective, personal power was replaced by skills of
communication and group interaction.
Channels of communication are very important in the cybernetic paradigm for starting
learning. It is not only the computer-based information system that supports these
communication channels, but also the formal and informal communication systems
that must be made easier.
Training and indoctrination aim at the internalization of basic norms and
knowledge. The double-loop learning process challenges these internalized norms
and knowledge, which is very important for not getting an organization of
unthinking people.
A Method for Learning: Using Learning Curves
The learning curve describes the costs of a product unit through time. The
assumption of this perspective is that in doing a certain job recurrently, a learning
process is started up by which the cost
per unit product decreases. (See figure
4.5).
Knowing the precise shape of this slope
is extremely important for business,
because it improves the cost estimation
of a product considerably, and is an
indication of the price competitiveness
of a company. According to Yelle
(1979) the first decades of learning
curve research (1935-1969) were
dominated by a 'classic industrial
engineering' perspective, with as the main topics: shapes of the learning curve,
parameter estimation, industrial engineering applications such as setting time
standards and incentives, classic cost control, and purchasing and bidding functions.
Since the 1970s, topics have moved to business policy-making, and public and
service-related issues, which indicates a double-loop learning process. One of these
modern issues is e.g. the relation between the learning curve and the product life
cycle.
Argote, Beckman and Epple (1990) also posed the question: how does learning
happen through time, and how can knowledge be transferred between organizations
and departments? With respect to the first issue, the researchers found that after a
period of steady decreases, production costs start to increase. This is explained from
the fact that organizational knowledge is often not well adapted so that it depreciates.
This explanation is also consistent with results in psychology on the lack of
persistence of individual learning. Concerning knowledge transfer or distribution,
Argote, Beckman and Epple found in their empirical study on World War II US
navy ship building, that:
" The initial gain in production may have been due to learning by doing in the design and
construction of shipyards and the equipment used in them as well as to learning by doing
in the construction of ships. Once shipyards began production they did not benefit from
learning at other yards (p. 151)" (whereas the ships they built were almost
identical).
In a case study in a multinational, multi-plant electronics firm, Adler (1990) was
more successful in finding evidence for a learning curve as a result of knowledge
transfer among departments. This empirical study analyzed knowledge sharing
between the Development and Production departments of the company, knowledge
transfer from an initial location to a newly set up plant, and ongoing knowledge
transfer between different production locations. Adler concluded that from this case,
called Hi-Tech, that apparently much learning curve research has been on the wrong
track, because of the focus on capacity utilization. Adler concluded:
" It is primarily 'learning,' the accumulation of knowledge in the form of manufacturing
knowhow, rather than capacity utilization, that accounts for the rapid productivity
growth rates by Hi-Tech" (p. 939).
To create effective knowledge transfer it is important to use a communication
medium with appropriate richness (Daft and Lengel, 1986). As many organizations
are strongly differentiated, a very rich medium is required or otherwise a strong
codification of the knowledge transferred must be realized (Boisot, 1986). Some cases
illustrate the effectiveness of this last perspective (c.f. CSC Index, 1990).
4.4.4 Problems with Organizational Learning
Many problems can easily arise in learning processes according to the cybernetic view.
Eight of these are listed below.
1 Role-constrained learning. This occurs when people have discovered new insights
but are not allowed to change their behavior according to these new insights.
This means that the relation from insights to action is blocked. This is very
common in organizations after training sessions (Van der Vegt, 1973).
2 Audience learning. This happens when the coupling between individuals' actions
and the organization's actions are weak. This happens for instance when some
individuals initiate a change that is not taken over by other organization
members. This is often the case in situations where excellent ideas are
obstructed by other organization members who are afraid of losing power.
3 Superstitious learning. This occurs when individual actions are followed by
organizational actions, but for which the coupling between organizational
actions and environmental responses are ambiguous. For instance, the success
of a company can be attributed to the choice of a correct management theory,
but it could also be good luck. Also, organizational failures cannot always be
blamed solely on mismanagement.
4 Learning under ambiguity. This relates to problems with coupling environmental
responses to individual beliefs. The problem is that often there is not one single
and objective explanation of an outcome. Different people might all have a
different view about success and failure. A management theory might help in
discussing the interpretation of reality, but everyone could still in principle
question the validity of a theory, by definition. The practice in a company is

then often: "A leader or dominant coalition selects one of these interpretations and
provides legitimacy by referring to a world view that lends meaning and structure to the
situation" (Hedberg, 1981, p. 11).
5 Situational learning. This happens when individuals forget or do not codify the
learning for later use. The link between individual learning and an individual's
mental model is then severed.
6 Fragmented learning. This happens when individuals learn and also codify the
created knowledge, but the organization as a whole does not learn. The cause of
this phenomenon could be that knowledge is not disseminated, or that other
people just do not understand the created knowledge. It can easily occur in very
decentralized organizations.
7 Opportunistic learning. This happens when the shared organizational mental
models (organizational knowledge-base) are bypassed to respond quickly to
environmental needs and opportunities. The result is that an accumulation of
the shared knowledge is achieved or that the organization does not profit from
the large investments it has put into an organizational knowledge-base.
(The first four problems were described by March and Olsen, 1976, and Hedberg,
1981, and the last three were discovered by Kim, 1993).
8 An additional problem of learning is the use of obsolete memory contents. Two
subproblems can be identified in this class: one is the fact that conserved
knowledge can become out-of-date and thus misdirect action. The other
subproblem is the difficulty of removing obsolete knowledge, called unlearning.
Unlearning activity is easy when knowledge is just a piece of writing, but can be
extremely difficult when knowledge has become second nature and thus part of
our implicit understanding (Berger and Luckmann, 1967).
4.4.5 Limitations of the Cybernetic Perspective
The cybernetic perspective also has some limitations that are listed below:
1. The cybernetic perspective mainly focuses on the quantitative aspects of
information: but there is in principle no reason why information systems
should not contain qualitative and subjective information as well. Mathematics,
however, cannot be as easily applied in that case, and another epistemology
must be integrated in the approach.
2. Information systems are sometimes difficult to describe in terms of TPS, MIS,
DSS etc. Sometimes it is difficult to define and recognize the systems that
manage the organization. Informal information systems exist as well, that can have
changing collections of individuals participating in the discussions and thus
contributing their knowledge and information. It is then hard to find out
which people contributed what information in such a 'garbage can'-like
decision-making process (Cohen, March and Olson, 1972).
Cybernetics therefore requires the addition of an approach on organizations that

stresses the working of (informal) groups. The objective rational epistemology often
does not work in these cases (Van Gunsteren, 1976). Also De Raadt is well aware of
this fact, when at the end of his paper, he states:
" Thus, while the insurance company (...) responded by developing some of the necessary
meta-systemic functions, they were not accepted by the predominant culture. (...) The
outcome of this conflict led to political instability and the organisation eventually became
prey to another insurance company. The old management was lucratively rewarded with
retirement and those who composed the meta-systemic DSS were consigned to exile. It was
part of the etiquette of ancient kings to have the messengers who bore ill news executed"
(De Raadt, 1991, p. 47).
4.5 The Organization Development Perspective of Organizational Learning
4.5.1 Organization Development's Reformulation of Organizational Learning
Argyris and Schön (1978) developed hypotheses about the prehension and
transformation of knowledge in organizations, as well as about the development of
learning, which therefore neatly complements Kolb's insights9.
The prehension part is discussed in terms of theory of action, clearly linking cognition
with action. From their experiences in observing organizational learning, they stress
the importance of the distinction between theory-in-use and espoused theory. Theory-
in-use is frequently mainly tacit, and sometimes even tacit for the individual that uses
it. This type of theory is what really motivated individuals' actions. Argyris and Schön
(p.16) note that every organization member can have another theory-in-use. During
interactions people can adjust their theories-in-use, and even form shared knowledge.
Organizational knowledge can be presented in public maps, and therefore becomes
overt and espoused theory. In contrast to the theory-in-use, the espoused theories
frequently lack a clear connection to the individual's actions. Sometimes well-written
policy statements diverge strongly from actions.
Learning is described as follows by Argyris and Schön (1978, p. 18):
" When there is a mismatch of outcome to expectation (error), members may respond by
modifying their images, maps, and activities so as to bring expectations and outcomes
back into line. They detect an error in organizational theory-in-use, and they correct it.
This fundamental learning loop is one in which individuals act from organizational
theory-in-use, which leads to match or mismatch of expectations with outcome, and
thence to confirmation or disconfirmation of organizational theory-in-use."
9
Argyris and Schön did not mention Kolb's vocabulary of transformation and prehension. The connection
between the two authors is made by myself.
Argyris and Schön propose (after the cybernetician Bateson, 1971) that organizations
learn in three ways: single-loop, double-loop and deutero. These terms were already
mentioned in the discussion about the cybernetic perspective, but the differences in
use are essential.
1. Single-loop learning:
" ...members of the organization respond to changes in the internal and external
environments of the organization by detecting errors which they then correct so as to
maintain the central features of organizational theory-in-use" (p.18). It is an important
feature of organizational learning that the "...learning agent's discoveries, inventions,
and evaluations must be embedded in organizational memory. They must be encoded in
the individual images and the shared maps of organizational theory-in-use from which
individual members will subsequently act. If this encoding does not occur, individuals will
have learned but the organization will not have done so" (p.19).
So no organizational learning will happen without individual learning and change of
organizational memory!
This type of learning is consistent with the way we have defined organizational
learning in the cybernetic sense, but we have added are the involvement of human
action and a description of organizational memory that is not only formal but also
contains tacit knowledge and memory as part of an organization's culture.
2. Double-loop learning
Single-loop learning is oriented towards effectiveness, meaning: how best to achieve
existing goals and objectives, and how best to keep organizational performance
within the range specified by existing norms. In some cases, however, it is required
that the organizational norms themselves be modified. This double-loop learning
process easily leads to conflicts between parties that still support the old theory-in-use
and parties that want a fundamental change in organizational norms. A good
example is Alvin Toffler's remark on Bell Company that they should no longer strive
for their main goal "every citizen a telephone, of any color as long it is black" (theory-
in-use) but replace it by the norm: "give the client what he really wants, telephones of
different types and colors, additional telecommunication products." When Toffler
presented this idea to the Bell Company, nobody responded for three years.
Nevertheless, people were busy changing their mindset (individual memory and
theory) and after three years the consequences were well enough understood and
people knew what had to be done. Argyris and Schön defined double-loop learning
as follows:
" We will give the name 'double-loop learning' to those sorts of organizational inquiry
which resolve incompatible organizational norms by setting new priorities and weightings
of norms, or by restructuring the norms themselves together with associated strategies and
assumptions (p. 24)".
Some further remarks are necessary to compare the concepts of single-loop and
double-loop learning:
" ...it is possible to speak of organizational learning as more or less double-loop. In place of
the binary distinction we have a more continuous concept of depth of learning" (p.26).
The change of norms, targets and theories thus implies political considerations.
According to Argyris and Schön, double-loop learning therefore is more difficult
than single-loop learning. The main problems for double-loop learning are lack of
openness in communication (because of a strife for uni-lateral control) which blocks
fundamental understanding of business problems (Argyris, 1970). The true reasons
for this attitude are often concealed, and are typical for the theory-in-use in most
western organizations. Argyris and Schön call the theory-in-use that emphasizes
domination of some people over others, win-lose situations and many tricks in
communication to protect one from being hurt and evaluated negatively, 'model I'.
This set of learning norms inhibits double-loop learning and therefore leads to
organizational ineffectiveness in the longer term. Argyris and Schön proposed to
replace model I by a model II theory-in-use that emphasizes the dispersion of valid
information (also when it can be negatively evaluated), free and informed choice
instead of control by a superior authority, internal commitment to choices made, and
careful monitoring of its implementation.
This problem statement and suggested solution differs widely from the cybernetic
view, in which learning inhibitions were supposed to be rooted in a lack of data,
restricted information processing and incomplete reasoning. Here we see sharply the
differences between the subjective and objective epistemologies and the order and
conflict views on organizations.
3. Deutero learning
Deutero learning, often also called second order learning or learning to learn, is
about organizations learning to carry out single-loop or double-loop learning.
" When an organization engages in deutero learning, its members learn about
organizational learning and encode their results in images and maps. The quest for
organizational learning capacity must take the form of deutero-learning; most particularly
about the interactions between the organization's behavioral world and its ability to
learn" (p.29).
Often organizations learn how to perform single-loop learning perfectly, but without
being at all capable of carrying out double-loop learning. Hopefully for them this also
reflects on the organization's learning needs.
According to me, these definitions of single-loop, double-loop and deutero learning
are confusing, because no concrete operationalization of learning activities is given.
The use of the term 'norm' in the definition of double-loop learning leads to further
confusion, because this term can mean almost anything in organizational situations,
including norms that form 'model I' and 'model II' and the operational norms that
are applied in single-loop learning. As a consequence it is often difficult to state when
an organization is single-loop learning, double-loop learning or deutero learning. The
vagueness of the terms makes them easily applicable in situations of process

consultancy, where they can be used to help people move to a new paradigm (double-
loop learning), and even learn to do so without the continuous presence of a process
consultant (deutero learning). For research into the way organizational learning
occurs and is supported by MICS, this terminology is too vague, and must be
operationalized further (cf. sections 4.8 and 4.9 for more details). My proposal is to
use the term 'norm' in this organizational learning context exclusively for norms that
govern learning activities in a single-loop (within fixed standards and objectives) or
double-loop (creation of standards and objectives). The deutero learning process
creates the norms that govern single-loop and double-loop learning. This proposal is
further elaborated in sections 4.8 and 4.9.
4.5.2 Processes of Organizational Learning and Organizational Memory
The concept of organizational learning can be made more concrete by defining ways
in which it can be realized. Levitt and March (1988) describe four ways.
The first way of learning is called 'learning from direct experience'. With respect to
this learning type, a possible problem is the competency trap, which concerns the
situation in which organizations have invalid or incomplete knowledge which make
them believe incorrectly that they are on the right track. So, the organization might
be improving in the wrong direction. Another competency trap is that the acquired
knowledge is counterproductive to long term results.
Despite the dangers of competency traps, it is important to reflect in a systematic and
thorough way on one's experience. In Japanese management this has resulted in the
idea of Kaizen, meaning continuously thinking and improving by reflecting on
experiences. The Toyota company institutionalized this learning type by demanding
that employees think 5 times 'Why'. This means that everything that is experienced as
normal and fact, should be questioned. Answers to these questions should be
questioned as well, and so forth until what is actually happening or perceived has
been questioned to the fifth level10.
The second way of organizational learning is 'learning from the experience of others',
often also called vicarious learning (Chew et al. 1991), and was also emphasized in
Argote's study. Within one organization we could think that diffusion of knowledge is a
positive asset for the organization as a whole, because the costs of knowledge creation
are shared, and procedures, ideas and knowledge are standardized. Frequently,
however, knowledge is a strategic instrument and a source of power. The diffusion of
knowledge must then be controlled by the knowledge owner (e.g. via patents). Some
10
The problems involved are similar to the problems of learning under ambiguity, which stresses the
problematic nature of interpreting facts. It is also similar to superficial learning, which refers to the
incompleteness of knowledge.
professional organizations, such as accounting firms, however, sometimes support the

diffusion of their knowledge because by this they can gain a larger amount of
recognition and hence competitiveness.
One aspect of learning from others is the possibility of effective knowledge
transmission. The size of the audience you can serve is then dependent on the
standardization of the language used (called cosification, Boisot, 1986) and the
content of the message. This content can be descriptive, prescriptive and evaluative
(Stamper, 1973). Also, combinations of these are possible. This means that people
will evaluate, accept, or refuse a message, because it can have important normative
and political implications. The free passage of messages, therefore, is under the
influence of organizational norms and political intentions. The importance of the
normative aspect of communication is the basis for developing effective learning
systems. Quite often people who bring bad news are removed from the scene by some
powerful persons who fear to face reality. Messages therefore are transformed into an
acceptable form, and the receivers must interpret the message not only from a
technical but also from a social-political frame of reference.
The third way of learning is not about receiving information, but about the
development systems with which information can be interpreted. Data and
experience do not lead to knowledge by themselves. For this interpretation process
knowledge in the form of theories, paradigms and models must be developed. The
lack of well-articulated knowledge of this kind will lead to the use of tacit knowledge,
which is often not well validated. When this interpretation knowledge is not shared,
much confusion and misunderstanding can occur. At the same time, however,
pluriformity in interpretation knowledge can make the organization very creative and
succesful.
Creating and using organizational memory is the fourth way of organizational
learning mentioned by Levitt and March. Organizational memory has only recently
received the attention it deserves in the literature on organizational learning (Levitt
and March, 1988; Walsh and Ungson, 1991; Huber 1991). Also Simon (1976)
emphasized the possibility of organizations having, at least in theory, more knowledge
than its individual members because of the synergistic impact of organizing. Simon
here refers to the fact that organizations have cognitive structures consisting of task
definitions, goals, means, and intentions, which are not known entirely by any single
member. Organizational memories are very important but often:
" Organizations do quite frequently know less than their members. Problems in com-
munication, such as filtering, distortion, and insufficient channel capacity, make it
normal for the whole to be less than the sum of the parts" (Hedberg, 1981, p.6).
How then do organizational memories work so that their possible benefits can be
gained? According to Levitt and March (pp. 327-329), an effectively operating
organizational memory requires recording, conserving and retrieval of experience. The
knowledge inferred from the experiences can be recorded in documents, accounts,
files, standard operating procedures, organizational structures and relationships, in

standards of good professional practice, in organizational stories, and in shared
perceptions of 'the way things are done around here'. The experiences and knowledge
gained can be distributed among organization members, which makes the
organization less vulnerable to the effective functioning of a single person's memory
or the risk of losing knowledge during personnel turnover. Written rules, oral
transcriptions, and systems of formal and informal apprenticeships are vital in this
knowledge distribution process. The storage of knowledge also requires an effective
retrieval system. Retrieval costs are low when the stored knowledge is re-used
frequently (in routines), the knowledge is created recently or strongly linked to
organizational responsibilities.
The development of organizational memories enable an organization to become
smart, but also conservative, which might make it ineffective in the longer term. One
reason for the conservative nature of organizational memory is that it provides
solutions and insights to current or future problems with knowledge created in the
past. The second reason is that organizational knowledge can only be stored and
retrieved when it is closely connected to existing organizational practices and routines
(called 'organizational proximity', Levitt and March, 1988, p. 329). This means that
past routines and ways of thinking led to the selective storage of specific knowledge
(and the possibility of neglecting other knowledge elements) and that people consider
selecting specific parts of organizational memory only through routines developed in
the past. Hence, organizations should not only develop their memories but should
also be capable of changing their paradigm. The organization development
perspective to organizational learning includes this fact and considers it more
difficult to change existing organizational memory than to add to the memory.
Organizations increase their performance by balancing single-loop and double-loop
learning effort via the creation of an optimal set of organizational learning norms,
called 'the learning organization'. This optimum is not the same for all organizations,
but is dependent on the learning needs of the organization itself.
4.5.3 The Problem of Creating a Learning Organization
Deutero learning is about learning to learn. In other words it is about the creation of
conditions that optimize single-loop and double-loop processes. Argyris and Schön
constructed Model II as a blueprint for organizations that are most successful in
organizational learning. It emphasizes the following values: least defensiveness and
public testing of theories. Model II also encourages, supports and rewards learning
through the provision of a culture of openness, a management style supporting
critical thinking, and an organization structure that facilitates the easy flow of ideas
and data at all levels in the organization. It accepts and appreciates disconfirmable
statements (new insights) and double-loop learning when needed. The concept of 'the
learning organization' as proposed by e.g. Senge, is a further operationalization of
Model II. An important variable then is the set of organizational norms that enables
organizations to learn effectively. The identification and implementation of these
norms is not at all an easy process. Two authors have become reknowned for their
proposed 'solutions', Tom Peters and Peter Senge.
Peters' Solutions
To demonstrate the practical way in which deutero learning processes work from an
organization development perspective, Tom Peters provides two interesting cases:
Electronic Data Systems (EDS) and Asean Brown Boveri.
EDS is a huge 'system integrator' with about 72.000 employees, operating in about 28
countries for over 7000 clients, organized in 38 strategic business units. A most
remarkable feature of EDS is:
" Boil down any SBU and you'll find projects. In fact, EDS is one big collection of project
teams. The number of people on a project can vary greatly throughout its life. The norm
is 8 to 12 EDSers, working together for a period of 9 to 18 months" (Peters, 1992, p.
24-25).
Because the greatest emphasis is placed on the project teams, the big problem is how
to connect people in EDS with each other, and specifically how to know which
people can participate in specific projects. Therefore, EDS has to learn to leverage its
skills (mainly connected with individual persons), and must develop a base
containing information about these skills. Additionally EDS has 'Centers of Service',
that set some people free for fundamental research on a particular new skill or
subject (e.g. imaging technology). These Centers of Service are temporary
organizational units, and have to earn their income by motivating project groups to
adopt the developed knowledge. EDS has thus explicitly been designed to augment
and store knowledge and make this knowledge into a shared organizational memory
via its procedures, systems and structure.
Asean Brown Boveri is a huge company operating in power plants, power
transmission, power distribution, transportation, environmental control, financial
services, and other types of business such as metallurgy, process automation, robotics
and superchargers. In 1991 they booked $28.9 billion in revenue in 140 countries.
CEO Sune Karlsson of the $1 billion revenue Power Transformers Business Area
gives an illustration of how this giant organization is managed:
" Our most important strength is that we have 25 factories around the world, each with its
own president, design manager, marketing manager, and production manager. These
people are working on the same problems and opportunities day after day, year after year,
and learning a tremendous amount. We want to create a process of continuous expertise
transfer. If we do, that's a source of advantage none of our rivals can match" (quoted
from Harvard Business Review, in Peters, 1992, p. 51).
Additionally Karlsson tries to create internal competition by providing detailed monthly
information on the performance of all 25 units.
" But Karlsson is well aware that such competition must be constructive; he insists (...)
that the key task is creating a 'culture of trust and exchange' (p. 51).
But..
" Sharing of expertise does not happen automatically (...) People need to spend time
together, to get to know and understand each other....People must also see a payoff for
themselves (....) We have to demonstrate that sharing pays - that contributing one idea
gets you twenty-four in return" (according to Karlsson, quoted in Peters, 1992,
p.52).
Not only knowledge storage and dissemination are important for organizational
learning, but people must be motivated to participate and create open
communications as well. Both cases clearly have developed model II characteristics.
Information technology has a prominent role in the construction of these modern
organizations, by the creation of electronic highways, knowledge and skills databases.
The following motivational and leadership features are even more basic than the
construction of these (infra-)structural arrangements:
1. Internal market principles, that give people direct feedback to their
performance and provides strong incentives for high performance.
2. Decentralized organizations and reduction of bureaucracy. This empowers
people and does not frustrate initiatives and creativity.
3. Developing expertise as never before. (Something that might look contradictory
to the decentralization trend, but isn't when the organization connects smart
people together, e.g. by means of EDS's Centers of Service, or connecting with
research institutes).
4. Management's support staff must contribute to these three principles. They
must change their bureaucratic sense in which knowledge is power. Top
management must not use its support staff for control and the support staff
must not slow down the decision-making.
5. "The essence of an effective KMS11 is advertising, marketing, packaging, incentives, big
travel budgets, and the psychodynamics of knowledge management12. The crux of the
issue is not information, information technology, or knowledge per se. It's how, for
example, you get busy people in those miniature ABB units to want to contribute to the
KMS. The answer turns out to lie more with psychology and marketing (...) than with
bits and bytes" (Peters, 1992, p. 384).
Senge's 'Solutions'
Senge states, in line with Argyris and Schön, that organizational learning is not only a
11
Accronym for Knowledge Management Structures, Peters' term for learning organization.
12
Italics from Tom Peters.
cognitive activity but often requires a change in power relationships and attitudes.
Many of the norms that inhibit learning are tacit and private, and must be well
analyzed for their organizational impacts, so that true learning can occur. Senge
describes five disciplines as basic for effective organizational learning (Senge 1990a, p.
5-13):
• Systems thinking by which people learn to understand the patterns and linkages
that exist among phenomena.
• Personal mastery: Defined by Senge as: "...the discipline of continually clarifying and
deepening our personal vision, of focussing our energies, of developing patience, and of
seeing reality objectively. As such, it is an essential cornerstone of the learning
organization - the learning organization's spiritual foundation" (p. 7).
• Mental models:"....deeply ingrained assumptions, generalizations, or even pictures or
images that influence the world and how we take action" (p.8). Many of these
theories-of-action are tacit and obstruct organizational change. By
understanding these models we can change them and provide basic conditions
for innovation.
• Building shared vision. This is the discipline of translating individual visions
about a wishful future to a joint sense of destiny in an organization.
• Team learning. The strength of a team is that it can discover insights that none
of the members would have discovered alone. This can only happen, however,
when a free-flow of meaning is created among the members. This is not
something that happens by chance but must be learned and teams can learn to
become more effective by a constant process of improvement in this discipline.
It is essential, according to Senge's theory, that these five disciplines are developed as
an ensemble, which he calls 'The Fifth Discipline'. Senge presents a substantial number
of ideas to support his fifth discipline concept and is extremely normative. Senge
does not concretely explain why an organization requires these five disciplines and
does not describe how organizations in specific contexts must learn these disciplines.
The theory underlying his thought is therefore obscure and not open for academic
research. Nevertheless, it is part of the organization development tradition because of
its strong emphasis on tacit processes, personal and interpersonal development and
change. In fact Peters' ideas are more concrete because he describes concrete and
measurable organizational arrangements that should be made, given the context of
large organizations. His approach is however weaker than Senge's on the
psychological aspects of learning.
4.5.4 Limitations of the Organization Development Perspective
The strength of the organization development perspective with regard to

organizational learning is its commitment to the human consequences and social
dynamics in learning processes. The learning processes are however almost
completely reduced to the deutero process, the construction of new norms for
organizational learning. For the deutero learning process not much more knowledge
exists than what is gained from organizational change studies. Precise prescriptions of
what the organization should look like exist, but how to change organizations to
reach these ideals is a blind spot. This means that the informative content of this
perspective is very limited. Learning to learn is an interesting term, but how should it
proceed and what should be the result? Additionally, no operationalization of the
connecting thought between an organization's environment and the way an
organization should be designed to facilitate learning is made. Thus, a major issue of
the organization literature, termed contingency theory, seems not to have affected the
organization development perspective. Finally, the most dominant learning approach
in organizations seems to be single-loop according to the authors. Nevertheless, this
remark must be taken with care. There is no survey study that validates this opinion.
No attempt has yet been made to measure for instance the learning activities and
efforts in organizations on single-loop, double-loop or deutero learning via a survey.
This book will not provide the data to qualify statements about single-loop learning
effort and the learning needs. It will, however, provide some basic conceptualizations
and suggestions for measurement.
4.6 The Soft Systems Perspective of Organizational Learning.
4.6.1 Soft Systems Modelling
This perspective combines elements of systems thinking (an essential element of

cybernetics) with the subjective epistemology typical of organization development. It
tries to model reality in a subjective way by emphasizing the mental models people use.
It is a regulation approach, because it stresses the opportunities for shared mental
models.
An important representative of this perspective is (again) Peter Senge, who states:
" ...What we carry in our heads are assumptions. These mental pictures of how the world
works have a significant influence on how we perceive problems and opportunities,
identify courses of action, and make choices" (Senge, 1990b, p. 12).
Organizational learning results in these mental models. This is often difficult,
because many of our assumptions are tacit and hence difficult to test. The elicitation
of mental models is therefore an essential step in learning. A good model in Senge's
view not only explains our reactions to events, but explains it by understanding some
deeper lying systemic structure. Senge has developed so-called system archetypes,
some general ways in which systems are supposed to behave, that simplify the model
elicitation and the detection of systemic structure. One of these archetypes is
'Shifting the Burden', explained as follows:
" ...A short term 'solution' is used to

correct a problem, with seemingly happy
immediate results. As this correction is
used more and more, fundamental long-
term corrective measures are used less.
Over time, the mechanisms of the
fundamental solution may atrophy or
become disabled, leading to even greater
reliance on the symptomatic solution.
Classic examples: using corporate
human resource staff to solve local personnel problems, thereby keeping managers from
developing their own interpersonal skills" (Senge, 1990b, p. 7). See figure 4.6.
In 'The Fifth Discipline' Senge mentions 9 additional archetypes. I do however
hesitate in using them, because they can lead to too much pre-conceptualization. This
could lead to people trying to see their problems in terms of a chosen archetype, and
lacking the creativity to make a model that might better suit their situation. Hence, I
developed a software tool that supports the creation of models by means of Critical
Success Factors, called CSFmatrix. An essential assumption of CSFmatrix for
organizational learning is that models for organizational learning are used not as
individualistic models, but as shared knowledge. This implies several things:
1. Specific pieces of the model's puzzle are brought together by participants in the
model development process, for example by selecting specific topics. This can
be done by naming them in the group and a facilitator writing them down on a
whiteboard. One can also use hexagons and attach them on a (magnetic) board
(cf. Morecroft and Van der Heijden, 1992).
2. The participants must agree on a clear understanding of the meanings
(semantics) used in the expressions mentioned under number 1. Participants
must find a common vocabulary in which they can understand each other. This
also can lead to a considerable reduction of topics.
3. Participants also need to agree on the importance and validity of each other's
statements. To have an effective third step, people must pay enough attention
to the first two. The result should be a list of not more than five or seven
critical success factors (cf. Rockart, 1979).
4. The participants now connect the major issues with each other, in terms of
possible negative or positive causal relationships. They can do this in a group
discussion to reach a shared vision. The result is a filled-in n*n matrix.
5. CSFmatrix now can automatically generate a system dynamics model (with
reinforcing and balancing relationships). The facilitator presents this model to
the group which then discusses the results, and possibly comes up with some
modifications. It is my experience that participants, especially in this phase, get
a strong 'Aha Erlebnis' (suddenly seeing the light). Often they want to go back
to adjust the list of critical success factors and relations. See figure 4.7.
Although this approach stresses the

finding of shared mental models, it can
be used for analyzing conflictual
situations as well. For instance,
participants can learn to understand the
basic assumptions of the conflicts they
are in. Lee, Courtney and O'Keefe
(1992) and Acar and Heintz (1992) also
developed computer-based tools that
support the analysis of incompatibility
between models. Both publications
have found methods to solve conflicts when the models are complementary. When
the models are not complementary, a synthesis is required to construct a joint model.
A suggestion for an information system that could cope with model incompatibility is
given by Hedberg and Jönsson (1978), but hardly elaborated yet.
4.6.2 Limitations of Soft Systems for Organizational Learning
The soft systems perspective does not exclude the possibility of objectivism. For
instance, a group can find sources of ineffectiveness and the means for a more
effective control of an organization. The results of this analysis can be used as input
for the design of a MICS. In principle the soft systems perspective only provides a
tool that can be used for modeling objective (cybernetics) or subjective (OD) reality.
The technique when used in the case of order must lead to one model that relates all
important issues. In the case of the conflict view it leads to several subjective models.
For further reading in the soft systems perspective we recommend: Rosenhead et al.
(1989) and the special issue in the European Journal of Operational Research on
modelling for learning (Morecroft and Sterman eds., 1992). Both publications use a
subjective epistemology, but differ in their approach. The first places emphasis on
conceptualizing how we talk about problems. The second stresses the importance of
describing major variables and their interrelations in systems dynamics models. In my
opinion conceptualizing preceeds modeling, however, several interations between
modelling and conceptualizing will improve both. Soft systems modelling does not
always have to be followed by system dynamics modelling. Many other techniques
can be used to further define vaguely defined problems (for instance PAM, Stamper
et al., 1988; Kolkman, 1993) When reality is clearly defined, soft systems lose their
value, and hard modeling techniques are more relevant (Wijnhoven, 1992a). Also
when major conflicts about problems exists, soft modelling will not solve them. It
then depends on how the actors in the political arena want to regulate their conflict.
Precise data about possible outcomes are more valuable then and hard models are
also required (Coleman, 1972; Teich, 1991).
4.7 The Scientific Management Perspective of Organizational Learning
4.7.1 Scientific Management as Organizational Learning
This perspective introduces an objectivist epistemology, and an explicit handling of

conflict. The objectivism in this perspective is well demonstrated by the term
'scientific'. Frederick Taylor, the founder of 'scientific management' in the beginning
of this century, mentioned two ways of industrial success: the initiative of the
workmen, that can be managed by an incentive system, and the development of a
scientific approach to the design of tasks. Managers have important roles here, as
follows:
" First. They develop a science for each element of a man's work, which replaces the old
rule-of-thumb method.
Second. They scientifically select and then train, teach, and develop the workman, whereas in
the past he chose his own work and trained himself as best he could.
Third. They heartily cooperate with the men so as to insure all of the work being done in
accordance with the principles of the science which has been developed.
Fourth. There is an almost equal division of the work and the responsibility between the
management and the workmen. The management take over all work for which they are better
fitted than the workmen, while in the past almost all of the work and the greater part of the
responsibility were thrown upon the men" (Taylor, 1911, pp. 36-37).
(...)
" The development of a science, on the other hand, involves the establishment of many
rules, laws, and formulae which replace the judgment of the individual workman and
which can be effectively used only after having been systematically recorded, indexed, etc.
The practical use of scientific data also calls for a room in which to keep the books,
records, etc., and a desk for the planner to work" (Taylor, 1911, pp. 37-38).
These quotations not only demonstrate Taylor's belief in an objective epistemology
for organizational learning, but also give some interesting description of
responsibility, procedural and action norms, for achieving effective learning
processes.
In the past, Taylor's approach received a lot of criticism, because it was regarded as an
instrument for an improved exploitation of the employees, and the loss of work
content and job satisfaction. In Taylor's view, however, his approach leads to more
prosperity for the employees, because higher wages can be earned. He illustrates this
by the results at Bethlehem Steel, after three years of working under scientific
management principles (see table 4.3).
Issue of Evaluation Old Plan New Plan

Number of yard laborers 400 & 600 down to about 140
Average number of tons per man per day 16 59

Average earnings per man per day $1.15 $1.88
Average cost of handling a ton of 2240 lbs. $0.072 $0.0033
Source: Taylor, 1911, p. 71
Table 4.3: Results of Scientific Management at Bethlehem Steel after Three Years.
4.7.2 Time and Motion Studies
A more modern version of Scientific Management is labeled 'Labor, Time and

Motion Study'. Niebel (1982) explains this approach in his book 'Motion and Time
Study'. He states that the total time of operation can be broken down into a part of
work that is done ineffectively and the total work content. The ineffective part has as
causes:
1. Shortcomings of the management, including poor planning; poor material and
tool inventory control; poor scheduling; and weak supervision, instruction, and
training.
2. Shortcomings of the worker, including working at less than the normal pace,
taking excessive allowances.
Additionally, the total work content (time spent on working) consists of three parts:
1. The minimum work content of the product. This could be reduced by carrying
out methods engineering and time studies.
2. Work content added by defects in design or specification of products, including
material specification, geometry specification, tolerance and finish specification.
3. Work content added by inefficient methods of manufacturing or operation,
including processes of manufacturing, setup and tools, working conditions,
plant layout, and motion economy.
The learning process in this approach is about reducing the causes of ineffectiveness
and reducing time spent on work. Historically, these studies were regarded as not
purely technical and objective, but as instruments for improving management control
over the work force (Braverman, 1974). The result of these studies can contain some
performance norms for the labor force or departments. This knowledge must be
carefully reviewed and adjusted when necessary. In one high tech company, I met the
situation of the use of a norms system developed in the beginning of the 1960s. It is
very likely that these norms are not effective for the 1990s. It is often not in the
interest of employees to mention this problem, except when they can profit
financially from it, or when by not improving the norms the company's survival (and
hence employment) would be at stake. By applying individual incentives, the
management can try to break up the solidarity within the group of employees, so that
group knowledge about inefficiencies is communicated to the management.
Since the end of the 1980s, scientific management (including the later Time and
Motion studies movement) has been expanded with a new method, called business re-
engineering. Business re-engineering is about the redesign of business processes. This is
done first via an accurate analysis of existing processes, especially in terms of costs
and benefits, and by comparing the processes with business bench marks. After this,
a design of a new process is made. In this design transaction processing information
systems often have a vital role as business levers (Hammer, 1990; Davenport, 1993).
The business re-engineering movement therefore basically aims at reducing business
costs and optimizing the process-demands relationships in technical ways. The
authors on business re-engineering state that human involvement in the re-
engineering process is vital, but do not clarify how it should be organized (Kennedy,
1994). Business re-engineering is regarded by the latter as a necessary evil for
organizational survival to which employees have to adjust. Some authors regard
business re-engineering as a revolutionary process (Hammer and Davenport), others
regard it as an incremental process (Davenport and Short). Business re-engineering
however often implies more than changing the organization's transformation
processes. The possible changes that could be made are listed in table 4.4.
Subject From To
Work units Functional departments Process teams
Jobs Simple tasks Multi-dimensional work
People's roles Controlled Empowered
Job preparation Training Education
Focus of performance measures Activity Results
Advancement criteria Performance Ability
Value Protective Productive
Managers Supervisors Coaches
Organizational structure Hierarchical Flat
Executives Scorekeepers Leaders
Source: Personal conversation with prof. P.A.E van de Bunt, October 1993
Table 4.4: Possible Organizational Changes as a Consequence of Business Re-engineering.
4.7.3 Limitations of the Scientific Management Perspective
When order is the dominant philosophy, models of reality will not conflict. This
means that generally accepted variables are measured and accepted norms applied. A
cybernetic system thus can be constructed, with a MICS that creates accumulating
insights into reality when confusion about variables, norms and measures are
removed. However, when the conflict model is valid, then these models will be
disputed. If a MICS is implemented, its output will be object of political discussions
instead of it resolving disputes about reality. The limitations of scientific
management and the time-motion studies are therefore not that they only support
single-loop learning, because they can indeed detect major problems in working
efficient and effective. Their limitations are rather that they are disconnected from
the social requisites to make them successful. Taylor himself fought a continuous
battle against his opponents not because his method was considered incorrect, but
because the social and political consequences were not effectively disputed.
4.8 Dimensions of Organizational Learning
The preceding discussions generated a large number of concepts describing

organizational learning. This section tries to organize these concepts by placing them
in the major dimensions of organizational. These dimensions are created via
semantic analysis. In semantic analysis special attention is paid to possible synonyms,
subclassing relations (generic-specific and part-whole) and the possibility that a term
requires the logical existence of other terms (e.g. the term 'driving' requires a 'driver'
and a 'vehicle'). The result of this semantic analysis, that conceptually closely related
with Osgood, Suci and Tannenbaum's term of semantics (1957) athough it is non-
statistical, is summarized in a semantic chart (Stamper et al., 1988).
4.8.1 Description of the Semantic Analysis Technique
The semantic analysis technique was developed for disentangling the conceptual
confusion that often appear when using theoretical constructs. The technique is
described in the following products of semantic analysis (Stamper et al., 1988 and
Stamper, 1987):
• Describing generic-specific structures. For instance: the generic 'components'
can have 'parts' and 'materials' as specifics. Specifics are placed in a box with the
generic term at the top of this box. This kind of relationship can also be
represented by an arrow from the specific to the generic, as in the case of a
component of a good, which is itself a kind of good.
• Describing part-whole dependencies. For instance a good consists of several
parts. This relation is graphically represented via a line with a dot in the middle
between the part and the whole.
• Role names can be given to a line for referring to a term with a meaning that is
about the relation between the two connected terms.
• Some terms can be described in more detail. In order not to make the scheme
too complex, a number is allocated before the term, referring to the number of
the chart describes the term in more detail.
• When the connected term is a sign, this can be expressed by placing it between
brackets, or connecting it via a broken line.
• Particulars (for instance European Community, an organization name) are
represented by capital letters. Universals (for instance: organization) are written

without capital letters.
• The result of a semantic analysis is a semantic chart that gives a graphical
picture of the relations between the terms. This graphical picture starts with a
root, which could be the drafter of the graph.
• A universal name can start with a #, meaning a determiner which is something
like a measurement.
4.8.2 Basic Terms from Different Perspectives
Terms from the psychological perspective
Two major terms are explained in the psychological perspective: knowledge and
learning process.
Knowledge is regarded as the subject that is transformed via reflection and
experimentation in the learning process. Three types of knowledge are described:
science, judgement and potential knowledge. Science is public and accepted
knowledge. Judgement is uncertain, often private and not tested knowledge. Science
and judgement both constitute 'management theories' which are explanations or
means-goals theories used for action in organizations. These theories are to some
extent espoused and to some extent tacit. They can be more or less abstract when
measured in terms of Kolb's AC-CE variable. Potential knowledge is about experience
and data. This does not mean that they always speak for themselves. Often a large
amount of abstract knowledge is required to make an interpretation of these data, as
for instance in case of interpretation of macro-economic figures that requires some
knowledge of macro-economics and politics.
A learning process is a set of activities that improves or adapts knowledge. According to
Kolb this can occur basically in two ways. The first way is reflection, which is thinking
over experience for constructing models, theories and concepts. The second way is
experimentation by which people test the quality of the gained abstractions in
practical situations. Via their education, socialization and work experiences
individuals develop a learning style in favor of a certain type of knowledge (abstract
or concrete) or learning process (experimentation or reflection).
Terms from the cybernetic perspective
The cybernetic perspective distinguishes three processes of learning: single-loop, double-

loop and deutero learning. Single-loop learning is about controlling existing systems, via
the statement of its targets, measurement of its targets, comparison of targets with
data found, and the provision of necessary feedback information to correct
deviations from the norms. To accomplish this process communication and
information systems are vital. These systems are organizational (formal or informal)
and can be computer-based in some cases. The double-loop learning process

questions the targets, the attention, and the search rules. At a higher recursion level,
it also considers the assumptions of the control system and the function it has in the
quasi-resolution of conflict and uncertainty avoidance. In order to accomplish these
single-loop and double-loop learning tasks, a cybernetic system must not only analyze
data (comparison function), but also store them in organizational memory, in order
to analyze the impact of different policies during a certain period. From the learning
curve studies it is known that organizational memories must be well adapted, because
(re-)use of depreciated knowledge leads to declining effectiveness. Organizational
learning norms must be developed to govern required activities for communication,
analysis and decision-making. These organizational norms result from the deutero
learning process. Learning is not a trivial activity, because organizations can easily
draw the wrong conclusions from the knowledge acquired. How an organization
should learn depends on its learning needs, and this in turn depends on the amount
of uncertainty the organization faces. The learning needs can be decreased by quasi-
resolution of conflicts.
At the deutero level, learning norms are the field for organizational learning
processes. These norms concern responsibilities of organization members in the
learning process (responsibility norms), the actual use of data and theories stored in
the organizational memory (action norms), and the means of communication in the
learning processeses (procedural norms). Information technology is part of these
procedural norms. At a higher recursion level one can conceive norms that govern
the creation of these norms which are called learning policy norms.
Terms from the organization development perspective
The organization development perspective distinguishes also between single-loop,

double-loop and deutero learning processes, but puts most emphasis on the last two.
According to Arygis and Schön's view, organizations mostly use the model I learning
system: a set of norms that discourages double-loop learning. Effective double-loop
learning therefore requires the removal of defensiveness in organizations, and a
willingness to create shared mental models and shared vision, called model II. This
also demands a willingness to remove obsolete knowledge (unlearning). The learning
norms for effective (double-loop) learning, are not only applicable to interpersonal
relations, but also to organization design in general. Infra-structural arrangements,
introducing internal market principles and the destruction of bureaucracy are some
of the learning policy norms that could enable organizations to become much better
learners because it encourages learning motivation and increases the capability for
effective knowledge dissemination. In my view, learning policies should not be a dogma,
but should match environmental demands. Learning policies also determine the
learning effort that an organization puts into single-loop and double-loop learning.
Terms from the soft systems perspective
The soft systems perspective uses the subjective epistemology. It provides some
techniques by which people describe a theory of their perceived reality. The critical
success factor concept can be used to acquire the basic concepts. System dynamics
provide a logic in which the relations among these concepts can be defined. System
dynamics also supports the detection of deeper lying systemic structures, that explain
unexpected and dysfunctional impacts of current behavior. Soft systems techniques
are especially useful when a group of people want to define a shared mental model.
Sometimes differences of opinion become more clear and can be solved by finding
common opinions. It is, however, more problematic when the models are
incompatible, which means that they cannot be reconciled. In this last case, soft
systems cannot do more than map the different views.
Terms from the scientific management perspective
Scientific management contributes a lot to the development of scientifically correct

norms. These norms are the goals that must be pursued via labor and capital
application. This means that management theories of the technology type (means-goals)
are constructed, fine-tuned and adapted in the learning process. Business re-
engineering provides a new trend in scientific management, not looking at separate
jobs, but at the level of the business process. This means that the learning fields shift to
a more aggregate level, and that learning is often an inter-organizational process as
well.
4.8.3 The Semantic Chart and its Consequences
The generic term 'learning processes' has three specifics: single-loop, double-loop and
deutero learning. Single-loop learning consists of the following: use or re-use of
existing management theories and data, the dissemination of such knowledge, and its
storage and adaptation. The storage process is a logical antecedant for organizational
memory. Another antecedent for organizational memory is the existence of
organizational knowledge and learning norms that can be stored. Double-loop
learning also includes the removal of obsolete theories, which is a synonym for
unlearning. Deutero learning is an antecedent for learning norms. This also implies
that the concept of management theory is distinguished from the concept of learning
norms (but both are organizational knowledge). Some authors are not clear about
this distinction, but if it is not made, the distinction between double-loop learning
and deutero learning becomes irrelevant.
Organizational memory stores contain three types of knowledge: data (potential
knowledge), management theories (science and judgement), and learning norms.
Management theories are distinguished according to purpose: explanation and
technology (knowledge about means and goals). Explanations and technologies can
be tacit and/or explicit. Four types of organizational learning norms are
distinguished: learning policy (including infrastructure for learning), procedural,
action and responsibility norms. Individual learning norms are called learning style.
The generic 'learning field' has not been clearly defined so far. Learning fields are,
however, essential for knowing what learning is about and what it must achieve.
Policy norms define what are regarded as important fields of learning. Because
solving learning problems should add to organizational effectiveness, learning fields
are defined in terms of organizational effectiveness. Quinn and Rohrbaugh found
three dimensions of the organizational effectiveness:
• Structure. This dimension has two extreme values: flexibility and control.
• Focus. The extreme values here are: organizational internal and external.
• Means-goals relationships. The extremes are an emphasis on means and an
emphasis on goals.
Output quality was found to be a possible dimension on its own, but the authors are
not very clear about it. The results confirm existing distinctions among paradigms of
organizational analysis, as presented in figure 4.8.
The learning effort concept is mentioned infrequently in the literature, but should be
defined as a combination of learning fields and learning activities. This means that,
when more fields are the subject of learning, more learning activities are undertaken
and the greater the learning effort. In chapter 7, this definition is explained further.
It is also particularly interesting to note that the Human Relations and the Open
System Models emphasize differentiation, spontaneity and flexibility (Quinn and
Rohrbaugh, 1983, p. 374), which fit neatly into the Organization Development
perspective of organizational learning. The Rational Goal Model and the Internal
Process Models emphasize integration, formalization and control (Quinn and
Rohrbaugh, 1983a, p. 374), the main issues of the cybernetic perspective of

organizational learning. This leads to the following fields of organizational learning:
human resources, markets (referring to the open systems nature of organizations),
transformation processes, and products (referring to organizational goals).
Finally also learning needs and learning ability are mentioned in the literature. Both
terms are not clearly defined. The learning needs term is regarded as synonymous
with the term 'uncertainty' that has two dimensions: organizational complexity and
dynamics (Duncan, 1972). Hence, in this study will not look at specific learning
needs (e.g. knowledge to solve a specific problem in an organization) but at an index
which provides a score for the extent of the learning needs of an organization or
organizational unit. In the analysis of organizations and machine bureaucracies
(chapter 5) a further description of these terms is given. From an analysis of learning
needs and learning abilities a deutero learning process can be initiated to define new
learning norms.
These conceptual investigations result in the following semantic chart that pictures
the main terms and their meaning here. A further analysis must make these terms
operational for empirical observations. This is done in chapter 7.
4.9 Operationalization of Organizational Learning
This section operationalizes the main dimensions of organizational learning by

answering the question of how the defined learning processes should be managed. I.e. it
points out the main issues, but does not give measures yet.
4.9.1 Deutero Learning: Designing Norms that Govern Single-loop and Double-loop
Learning
Deutero learning consists of the connection of the organization's learning capabilities

with the organizational environment from a longer term perspective, via the
construction and implementation of a learning policy, responsibility, action and
procedural norms. The single-loop learning activity 'storage', stores these norms in
organizational memory and so develops continuity of existence of the norms.
1. Creation of Learning Policy Norms

The learning policy is typically a CEO responsibility. These CEO statements are
about:
• The development of an organizational learning infrastructure. This infra-structure
could consist of electronic communication highways (E-mail, Group Decision
Support Systems, Design for Manufacturing as a collective effort of marketeers,
production engineers and clients). Also the making of a more transparent
organization structure is one of these infrastructural objectives, that can be
arranged by expressing commitment to and initiating the development of a
corporate-wide database about people's engagements, experiences, knowledge
and skills. An excellent example of the creation of transparency in a company
was given by Peters in his EDS-case.
• The development of a policy about core competencies (Hamel and Prahalad,
1990). The CEO must consider what portfolio of competencies is required in
order to achieve the company's objectives and make it viable in the longer term.
Therefore, knowledge must be used as an asset. Knowledge and expertise can be
used for pilot purposes (to find out if some idea can lead to some improvement
in the future), for synergy purposes (for instance a car company that buys an
electronic engineering group, to develop electronic features for its current car
product portfolio), or for harvesting (buying a group of chemical manufacturers
that make products that fit in the existing product portfolio, and can be easily
sold via the existing marketing channel of the buyer). Core competencies can be
developed not only by taking over other companies, but also by selling them
and developing the competencies themselves by investing in people's training,
evaluation of experiences etc. An interesting example of competency
development was also given by Peters when discussing the Service Centers of
EDS.
• A learning policy must also describe the basic organizing principles in the
company that impact on the learning process. Some of these principles can be
caught under the term 'organizational perestrojka' (Ackoff, 1992), meaning the
development of an organization that is non-bureaucratic, encourages free and
open debates among its members, and encourages people to think creatively
and take initiatives. People should be empowered to do so, by being given
responsibilities and authority if necessary, and the organizational incentive
system should reward people that behave according to organizational
perestroika.
• A learning policy finally requires from CEOs that they take charge of major
projects to redesign the way business is done, and create major procedures. This
frequently requires management revolutions, that also are known under the name
of business re-engineering projects (e.g. Davenport and Short, 1990; Walker, 1992).
The learning policies must be implemented in more concrete learning norms, about
responsibilities, actions and procedures.
2. Description of Learning Responsibilities

Learning responsibilities must be well established, as otherwise learning might not
occur effectively in relation to the learning needs and policy. Taylor (1911) proposed
to organize learning responsibilities in such a way that a clear division of labor in
learning is established. We, however, think that it is often not wise to have a
concentration and specialization of learning responsibilities in such a classic
bureaucratic sense. A description of learning responsibilities might even result in a
statement that learning is everyone's responsibility (as for instance in Leonard-
Barton's learning factory case). However, by not allocating responsibilities, it is not
organized, and the organization cannot profit from the possibilities of dividing
learning work.
3. Description of Action Norms

Learning actions are not only based on people's responsibilities but also on incentives
to act on the basis of the insights found. It is one thing to know how badly or well
things are going, and another thing to put that knowledge into action.
Incentives for organizational learning are a very under-researched topic. For instance,
Argyris and Schön stress the importance of openness, removal of defensive routines
etc. But why do people behave against these basic principles of effective learning?
Why should, for instance, a software specialist of EDS disseminate his knowledge of
clients, their problems and the solutions, when he can improve his financial position
by simply not doing so? Was this also not the reason why professionals are difficult to
manage according to Mintzberg (1983)? The solution therefore is to create win-win
situations for all, by appreciating the added value of learning.
4. Description of Procedural Norms

Procedural learning norms concern the dissemination and handling of information for
organizational learning. The procedural learning norms influence the actual use of
information systems and communication media so that an organizational learning
process is established and based on available data. Not only the IT-issue in terms of
computers and telematics is important here, but also the way data are obtained, the
management of data quality, and the need for some specialists to extract information
and knowledge from the data.
The issues of deutero learning are listed in table 4.5 in the form of questions that can
be used when wanting to start up a deutero learning process.
Deutero learning Issues

norms
Learning policy Who are we as an organization? How should we think about our environment? How
important is learning to our identity and survival? What learning infra-structure is
required? What core competencies do we need? Which organizational culture do we
want? Do we want business re-engineering projects?
Responsibility Who are responsible for single-loop and double-loop learning?
norms What is the role of management in learning (facilitator or dictator)?
Action norms How should people behave towards each other in the learning process (openness vs.
defensiveness)?
When and how should knowledge adaptation decide on the removal of a
management theory and trigger a double-loop process?
How are people motivated to participate in the learning process (rewards and
punishments or the complete absence of either)?
What priority does learning have in the organization (expressed in time made
available, priority in relation to other jobs)?
What kind of education, training is required? Is knowledge from outside the
organization used (consultants, text books etc)?
Procedural norms How should performance be measured? How can signals be interpreted as incentives
for single-loop or double-loop learning?
What should be the frequency of feedback information? How should feedback
information be discussed with the people involved.
What data and what systems are required?
Table 4.5: Norms for the Deutero Learning Process
4.9.2 Explanation of Single-Loop and Double-Loop Learning Processes.
This subsection operationalizes the dimensions of single-loop and double-loop

learning.
Dimension 1. Development
This dimension is about activities by which a management theory is constructed by
scanning the internal and external environment of the organization. Important in
this step is the additional use of frames of reference, consisting of a mixture of tacit
knowledge (beliefs, norms) and explicit knowledge (obtained by internal or external
training, education, and by experience in the same or a related problem field). The
result is an explicit theory, containing goals and methods for achieving them. In this
area soft modelling is widely acknowledged as an effective method. Some computer
supported tools are also available (e.g. 'PAT', Kolkman, 1993; 'CSFmatrix',
Wijnhoven, 1993; Acar and Heintz, 1992; 'I Think' from High Performance Systems
Corp.). The output of soft modelling consists of a collection of yet imprecisely
defined terms, by which an understanding can be made of a yet under explored area.
Because of the volatility and complexity of many managerial situations, especially
those faced by the strategic apex, it is often not worth the effort to make these models
more precise. In less volatile situations, for example in the case of route planning, it
can be extremely valuable to make the model mathematically and logically precise
and to test the hypotheses that underly the models with the data available (e.g.
Wijnhoven, 1992a). So in the theory development process two sequentially related
phases can be defined: the development of a soft model and the development of a
hard model. Additionally, the models must be implemented, which requires the
explicit knowledge that was acquired, becomes integrated with tacit knowledge of the
practice field (Hedlund, 1994).
Dimension 2. Use and Re-use

Discovering the truth is one thing, making it applicable is another thing. Very often,
plans in organizations fail to become connected with actions (Ansoff, 1988). For
instance Ansoff, a well read author on strategic business planning, felt it necessary to
write a book on 'implanting strategy' after having completed his famous book
'Strategic Planning' in 1965. Plans are clearly management theories, however, not
always operationalized into specific actions; hence, hypotheses about means and goals
often end up stored away in the managers' offices and are never put into action. The
use step, therefore, is not simply the application of a piece of knowledge, but an
important learning process in itself, frequently disregarded, but essential for practical
managers. The activities in this dimension connect the theory developed in
dimension 1 to specific actions and behavior.
Dimension 3. Dissemination
It is important that the management itself believes in what it thinks, but it is also
essential that other people are convinced as to why things must be done in the way
prescribed. Effective communication in the semantic and the pragmatic sense is vital.
The first sense concerns understanding the relations among the variables in the
management theory. Large bureaucratic organizations mostly have a large amount of
differentiation among departments and organization members, which complicates
unambiguous communications, and leads to more successful intragroup learning
than learning of the organization as a whole. The semantic problems involved lead to
misunderstandings about some concepts. The resulting subcultures and professional
groups with different jargons is a well-known problem in larger and complex
organizations. By standardizing language in the organization, semantic problems can

be reduced (the result is often jargon that, however, can make communication with
outsiders more difficult) (cf. Thompson, 1967; Lawrence and Lorsch, 1967). The
second communication problem is about the motivations and appeal the theory has
on the behavior of the receivers of the messages. These pragmatic communication
problems can lead to negotiations and bargaining about what is the real problem in
the company and what should be done about it. Groups are led by their interests in
these conversations. Organizational learning in this context is especially a process of
redesigning intentions, culture and power relationships. Goal integration and
creation of commitment are important for reducing these communication problems.
Information technology can support the communication process to solve physical,
empirical and syntactical problems in information handling (Stamper, 1973) by:
1. Providing the hardware for transmitting messages in a fast and technically
reliable way, to solve the physical problems involved.
2. Distribution of messages according to some rules that can be programmed in
message distribution software like E-mail, Groupware, EDI to solve the
empirical problems involved.
3. Providing a set of clear codes that can be controlled, to improve the
effectiveness of the message distribution so that interpretations are possible to
solve the syntactical problems.
The semantic and pragmatic problems however cannot be solved by means of
information technology in the machine-like sense of the word. They require instead
the improvement of mutual understanding and agreements (cf. chapter 6 for a
further discussion of information technology).
Dimension 4. Adaptation
During the process of plan formulation, the business reality may have changed.
Hence, there is a basic need for adjustment of the management theories, and this
should be based on a test of elements of the earlier theory (e.g. the actions
prescribed). Managers could make use of a range of insights from scientific
methodologies. The adaptation can result in a refinement of the theory, but also in
the detection of the need for a complete revision. Thus a double-loop trigger could
also be the result of adaptation. From learning norms, concrete management theories
for learning processes can be derived. For instance, as part of the procedural norms,
management could decide to develop a decision support system. This information
system is a field for adaptation when its efficiency for the learning process is
evaluated. Changing the procedural norms, however, can also involve the
replacement of the decision support system by a group-decision support system to
encourage interactions of learners in a learning lab under the support of a facilitator.
The change of this norm is in fact a deutero learning process and must be evaluated
in relation to the organization's abilities to meet organizational learning needs.
Dimension 5. Storage
This issue is not well treated in the general discussion on management life cycles, but
yet is a basis for organizational learning. Knowledge can easily become forgotten or
distorted when not well documented (cf. Yelle, 1991). In order to realize a learning
loop in complex environments it is essential to write down what the operational
goals are, and the performance criteria for people involved in the project and work
process. Only then can they become effective guidelines. If they are not well
documented it is not clear how evaluations should take place, and the learning loop
is not closed (cf. Peffers and Saarinen, 1993).
Traditionally, the storage of knowledge was achieved through individuals' memories,
organizational myths and stories, symbols and many tacit norms that form the basis
part of the organization's culture. The development of administrative organizations
led to a first instrument for systematic storing management theories, especially in
terms of finance and resources. Formal planning techniques can also be used as tools
for storing organizational knowledge. Administrative organization and formal
planning techniques were instruments for management used in all great projects in
history. Nevertheless, the rise of the large machine bureaucracies (beginning of this
century) would not have been possible without the development of organizational
memory and knowledge storage procedures and techniques. Universities had an
important role in these developments, as is illustrated by the rise of business schools
at universities.
Information technology led to a larger and more powerful organizational memory, by
developments in:
1. Databases that allow for the efficient and reliable storing and retrieval of data.
2. Modelbases that allow for the management of several models represented in a
mathematical way. These models can be stored, accessed, developed, used and
improved when required.
3. Knowledge-based systems and expert systems provide opportunities for storing
qualitative knowledge, by means of the use of formal logic. This is possible
when the management theory is made explicit and represented in logical
chains. Knowledge rules can be connected to simulate a human reasoning
process.
4. Integration of these IT-opportunities (e.g. Kerr, 1991).
Dimension 6. Removal or Unlearning of Management Theories

The life cycle metaphor presupposes a start and a finish of life. This also applies to
management knowledge. Removal of knowledge is important when it becomes
obsolete and misdirects attention and actions. This aspect of the organizational
learning process is maybe the most difficult one, because:
1. People are committed to existing knowledge, because of their effort to acquire
it.
2. When well implemented, management theories become part of the tacit way of
thinking and the informal parts of an organization's culture. Therefore, removal

of management theories means a change in organizational paradigm which is
difficult to accomplish.
3. People gain their status and position from the existing management theories.
When this theory is removed people could feel that an important source of
subsistence is endangered. They will only collaborate when the new theory
would improve their position, something which is not always possible.
It is the 'management knowledge removal' process that is the start to double-loop
learning. Mostly it requires strong external forces to win from the forces obstructing
unlearning. Some organizations are good at double-loop learning, because they have
learning norms encouraging change. Machine bureaucracies have been shown to be
notoriously bad at double-loop learning, and also have a slow process of single-loop
learning. Until recently this was not a problem because the environment did not
necessitate much learning (Mintzberg, 1983 and chapter 5 for further evidence and
explanation). The need for double-loop or single-loop learning is, however,
dependent on the learning needs of the organization.
4.9.3 A Flow Diagram of Organizational Learning
The learning dimension can described in mutual relations as illustrated in the

following flow diagram (figure 4.10).
The flow diagram only describes activities that can be formally described and
managed. One should however not underestimate the importance of informal
processes in organizational learning. Brown and Duguid (1991) give an account of
how to conceive informal organizational learning. The first point they made is that in
informal learning practices, knowledge is seen as understanding practice and may
never be detached from practice. Abstract knowledge (called canonical practice by the
authors) can blind the organization to the fact that it is the practice of the
organization members that determines the success of the company. In many cases,
abstract knowledge is written down in handbooks and documentations, but is such
that when it is applied precisely, it is in fact more difficult to perform successfully!

Although these documentations try to reduce improvisation and informal working
practice, they often require many more improvisational skills to accomplish a task
successfully in terms of client demands. The informal work practice also shows that
accomplishing a job is not only a technical activity, but essentially the maintenance of
a social setting. This is very evident in the case of machine repair services. Of course
the technical problem must be solved, but doing so requires a problem-solving
process in which several actors (end users, management, technicians, experts) are
involved in a story-telling process. In the words of Brown and Duguid:
" Ultimately, these stories generated sufficient interplay among memories, tests, the
machine's responses, and the ensuing insights to lead to diagnosis and repair. (...)
Through story-telling, these separate experiences converged, leading to a shared diagnosis
of certain previously encountered but unresolved symptoms. (...) They (...) increased their
own understanding and added to their community's collective knowledge. (...) A story,
once in the possession of the community, can then be used - and further modified - in
similar diagnostic sessions" (Brown and Duiguid, 1991, p.44).
What matters in organizational learning is not the development and learning of
abstract models that are separated from practice. Additionally, learning is not
instruction or training, but becoming member of a community and behaving
effectively in that community.
" For example, learners learn to tell and appreciate community-appropriate stories,
discovering in doing so all the narrative-based resources (...). As Jordan (1989) argues in
her analysis of midwifery, 'To acquire a store of appropriate stories and, even more
importantly, to know what are appropriate occasions for telling them, is then part of
what it means to become a midwife' (p.48)."
" Learning is fostered then by fostering access to and membership of the target community-
of-practice, not by explicating abstractions of individual practice. Thus central to the
process are the recognition and legitimation of community practices (pp.49-50)."
It seems as if organizational learning is something that comes from itself. On some
occasions this is true. On many other occasions, management must give clear
guidelines and facilitate it actively (for instance via infrastructures and reward
systems). This depends on the typicalities of the environmental organizational
learning needs and the learning abilities of the organization. Learning abilities and
learning needs of the machine bureaucracies are discussed in the following chapter.
The final chapter will reflect on learning practices in five companies, that differ on
the learning needs index, so that more insight is obtained about the norms for
Chapter 5: Organizational Learning in Machine Bureaucracies
5.1 Why Study Organizational Learning in Machine Bureaucracies?
The term bureaucracy has its origin in the writings of Max Weber, who defined it as a
legal way of excersizing command and control over people. The basic categories by
which bureaucracy exists are summarized by Weber (1921/64, pp. 331-332) as
follows:
" (1) A continuous organization of official functions bound by rules.
(2) A specified sphere of competence. This involves (a) a sphere of obligations to perform
functions which has been marked off as part of a systematic division of labour. (b) The
provision of the incumbent with the necessary authority to carry out these functions. (c) That
the necessary means of compulsion are clearly defined and their use is subject to definite
conditions. (...)
(3) The organization of offices follows the principle of hierarchy; that is, each lower office is
under the control and supervision of a higher one. There is a right of appeal and of statement of
grievances from the lower to the higher (...).
(4) The rules which regulate the conduct of an office may be technical rules or norms.13 (...)
(5) In the rational type it is a matter of principle that the members of the administrative staff
should be completely separated from ownership of the means of production or
administration.(...)
(6) In the rational type case, there is also a complete absence of appropriation of his official
position by the incumbent. (...)
(7) Administrative acts, decisions, and rules are formulated and recorded in writing, even in
cases where oral discussion is the rule or is even mandatory."
The rules led to routinization and standardization and served to make output
predictable and reliable. This is important for modern government and business. It
also became a major issue in the quality movement in the 1980's (Garvin, 1988;
Evans and James, 1993).
13
Henderson and Parsons, the translaters of Weber's original German book, state (1964, p. 331): "By a
'technical rule' he probably means a prescribed course of action which is dictated primarily on grounds
touching efficiency of the performance of the immediate functions, while by 'norms' he probably means rules
which limit conduct on grounds other than those of efficiency. Of course, in one sense all rules are norms in
that they are prescriptions for conduct, conformity with which is problematical."
Organizational Learning in Machine Bureaucracies 89
Many of Weber's ideas for efficient control were taken over in business
administration, with legitimacy based more on the efficient handling of resources
than on its legality. Henry Fayol pleaded for a rational setup of management, by
separating it from the primary functions of the company (technical, commercial,
financial, security, and accounting activities). The management function is then
responsible for realizing optimal principles: division of work, authority, discipline,
unity of command, unity of direction, subordination of individual interests to the
general interest, remuneration, centralization, scalar chain (line of authority), order,
equity, stability of tenure of personnel, initiative, and esprit de corps (Fayol,
1916/1949, p. 19-20).
Researchers saw many negative effects of bureaucracy. For instance, Merton (1948)
states that bureaucratic organizations give higher priority to organizational structure
and processes than to organizational goals and their social-economic function. This
can lead to a very inflexible organization, which loses a sense of its environmental
function. Bureaucracy also leads to under-utilization of human potential, because
people become slaves of the routines that are prescribed for the work process
(Maslow, 1970; Mayo, 1945). It is particularly important for nongovernment
bureaucracies that often machinery is applied that demands the strict application of
certain rules. It therefore requires the suppression of informal organizational
processes. The importance of informal processes and informal communication
networks was however made evident by research of the so-called human relations
school of management (e.g. Mayo and Herzberg, 1938). Additionally, modern
researchers found out that the growing complexity and dynamics of organizational
environments demanded less formalized and constrained organizational behavior
(Argyris, 1970; Beer, 1981; Galbraith, 1973; Simon, 1947). The introduction of
machinery, however, often increases the need for formality in organizations. The
resulting classic machine bureaucracies therefore have large problems with
formalization, under-utilization of human resources and inflexibility in coping with
changing environmental business conditions (Woodward, 1965). This trend was
reversed in the 1950s by the development of lean machine bureaucracies in Japan.
Classic machine bureaucracies are supposed to have large problems with
organizational learning, because they have strategies, structures, cultures and
information systems as defined below.
• Classic machine bureaucracies have longer term, reactive and cost-leadership
oriented strategy. This is because of the long pay-back period of machine
investments, and a strong focus on optimization of designed processes for
realizing lowest costs. Increasing dynamics are difficult to match with these
machine bureaucratic features, because they require longer and shorter term
orientation, active and pro-active strategies (cf. Miles and Snow, 1978; Zahra
and Pierce, 1990).
• The organizational structure of classic machine bureaucracies is not developed
for organizational learning innovations (changing organizational knowledge),
but only for conserving the way of working. This is also a form of organizational
learning, but it can mismatch with learning needs in dynamic and complex
environments. Machine bureaucratic structures result in a very formal way of
collaboration, strongly institutionalized in departments, and strict authority and
responsibility allocations that govern actions. The organization usually has an
efficiency objective, but can have an expensive hierarchical coordination
mechanism (Galbraith, 1973; Gurbuxani and Whang, 1992). This is again
mainly a problem of dynamics, because the complexity can be dealt with. The
effective handling of increased dynamics often conflicts with the slow
procedures of classic machine bureaucracies. Hence, a more organic
organization type is required (Burns and Stalker, 1961).
• The organizational culture of classic machine bureaucracies is dominated by
'uni-lateral' control and defensiveness. It is the boss who thinks and decides,
and his employees act (Argyris and Schön, 1978). This goes against demands for
handling high levels of complexity, because a single manager has only one set of
brains. The thinking and innovative process therefore must be a joint effort of
many organization members.
• The increasing complexity of products, production processes, production series
and variants, requires a more rational treatment of organizational knowledge.
This is also done in classic machine bureaucracies, but mostly too slowly, as was
shown by the lean car manufacturer examples in chapter 1. The environmental
dynamics and complexity demand information systems that match perfectly
with the working flexibility required. Additionally, these systems must be easy
to adapt to frequently changing user requirements (Land, 1982).
We are not interested in the machine bureaucracies themselves, but in organizational
learning and information technology. Machine bureaucracies are used as a source of
case material to develop a substantive theory, that later can be formalized into a more
general theory (cf. chapter 3). We will avoid using the buzz word 'the learning
organization' because it suggests that there is one best type of learning organization14.
This study therefore describes several ways of learning in machine bureaucracies.
These learning types are further linked with environmental needs (learning needs)
and conditions (learning norms). A normative viewpoint is evident here stating that
effective organizations have a learning needs-learning norms match. This reasoning is
analogous to contingency statements in organizational design, which state that
organizational environment influences the effectiveness of organizational structures
and processes.
The factors in this study now are: organizational environment, organizational norms,
14
It seems as if writers on 'The Learning Organization' (like Senge, 1990; Swierenga and Wierdsma,
1990; Garratt, 1987 and Garvin, 1993) have not understood the lessons from the contingency approach that
propose the equifinality of organizational forms (cf. Mintzberg, 1983 and Dotte, Glick and Huber, 1993 for
excellent reviews).
and transformations. These are described for the organization in general and for the
organization's learning subsystem in particular in table 5.1.
Factors Machine Bureaucracy Organizational Learning
Environment Complexity and dynamics Learning needs
Norms Structure and culture learning norms (policy, action, procedural and
responsibility)
Transformation Transformation of goods Transformation of knowledge via single-loop,
and values double-loop learning, and of learning norms via
deutero learning activities
Table 5.1: Conceptual Relation Between Machine Bureaucracy and Organizational
Learning
It is important to state here that the lean-classic distinction in machine bureaucracies

is a distinction in organizational norms (structure and culture), and that the service-
manufacturing distinction is a distinction in transformation. This means that
organizational learning norms have only to be studied in terms of the lean-classic
distinction. It could however be that the organizations also differ in the way they
learn in terrms of the service-manufacturing distinction. The insights concerning
differences in learning among service and manufacturing organizations are however
very meager. Hence, this study emphasizes the lean-classic distinction in its
theoretical part (chapters 5, 6 and 7). The study will search for possible hypotheses in
terms of the service-manufacturing distinction while comparing the results of the
service and manufacturing case studies in chapters 8 and 9.
Section 2 describes machine bureaucracies in more detail, and section 3 describes
organizational environments and the related configurations. Statements about the
relations between machine bureaucracy types and organizational learning needs are
described in section 4. This leads to improvements of the theory and ideas for
measuring machine bureaucracies and organizational learning.
5.2 The Concept of Machine Bureaucracy
5.2.1 The Development of Machine Bureaucracies
Mintzberg (1983, p. 281) defines the machine bureaucracy as an organization with

much specialization, little training and indoctrination, much formalization, mostly a
functional type of grouping, large at the bottom, action planning, few liaison devices
and limited horizontal decentralization. Machine bureaucracies have existed for a
long time, but are particularly relevant since the upheaval of the industrial society.
Lammers (1984, p.362-368) identifies a traditional organization type as the
predecessor of the machine bureaucracy.
Organizations in general govern processes of cooperation that are enduring in nature.

In traditional organizations there is no segregation between the organization and the
broader social context in which it is embedded. Examples are the early English labor
organizations in which contractors acted as linking pins between entrepreneurs and
laborers, and small family owned and craftsmanship oriented organizations
(Stinchcombe, 1959). In The Netherlands many of these companies still exist,
providing 10% of the total Gross National Income in 1992 (when we take companies
of less than 100 employees as a global indicator). The entrepreneurs are personally
responsible for success, and frequently feel personally committed to the well-being of
their employees. Traditional organizations have a simple structure, with management
by direct supervision of the workers.
At the end of the Middle Ages, some organizations acquired a legal personality of
their own. This meant that the company (not a natural person) was legally
responsible for what happened, and had rights and obligations of its own. This
implied a distinction between the family and the organization. Rules for selection of
its members were formalized, internal differentiation and division of labor as well as
coordination were formalized in rules and norm systems. This type of 'modern'
organizations was a formally and rationally constructed system of norms, which
equalled the development of machines. Especially in production organizations it
required large capital investments, because of the application of expensive machinery.
The machinery had to be run by engineers who gained management positions.
Therefore, the term machine bureaucracy is applicable here.
Several types of bureaucracies have developed in the past.
1. Government administration. This type of organization is necessary to process the
large volumes of data and services the government has to process for its
increasing tasks and increasing population. Also the development of the
modern state demanded that bureaucrats should be accountable for applying
the rules, according to legal principles. The result was a growing need for rules
to control and prescribe government officials. Furthermore, government tasks
became more complex, requiring a specialization of officials, and therefore
differentiating more clearly between the tasks and jurisdictions (Weber,
1921/1964). Government bureaucracies will not be studied further in this
study about profit organizations.
• Company administrations. Especially in the larger companies, administrative
offices have developed which have been influenced by the requests for
accountability, reliability and accuracy of information handling, internally (e.g.
for budget information, order administrations and stock-keeping) as well as
externally (obligations for external reporting required by (tax) law) (Fayol,
1916/1949). These organization parts therefore developed as a social
machinery, consisting of human components and organizational assembly rules.
Because these organizations are not independent profit organizations, they are
not included in our research population.
• Manufacturing machine bureaucracies. These organizations grew from the

opportunities for producing for large (world-wide) markets in mass and large
series (e.g. cars). People on the shop floor were regarded as handlers of
machines and materials in a routine-like way, with the purpose of producing as
cheaply as possible according to specifications. Specialist expertise was
developed by production and manufacturing engineers (specialists), who
developed the machines, practices, material routs etc. These engineers, because
of their technical expertise, also gained dominant roles in the management of
the company.
• Service machine bureaucracies. Most of the commercial organizations do not
supply goods but services (Ginzberg and Vojta, 1981). Examples of these are
banks, insurance companies and telecommunications suppliers (Schmenner,
1986). Here the same organizational and functional principles apply as in the
manufacturing machine bureaucracies. The organization's technology is a
combination of professional knowledge and commercial and machine-like
handling of transactions.
In the last few decades, manufacturing organizations must deliver service in addition
to their supplies, diluting the difference between the last two groups. For instance,
some machinery manufacturers earn more money with services than with machinery
supplies. Some service organizations have also gained an increasing manufacturing
attitude (Grönroos, 1990). Nevertheless, service and manufacturing organizations
differ strongly in their transformation technology. Most importantly the
transformation technology is a means of reducing organization members' discretion in the
realization of the goods and services. The manufacturing organization has hardware
(machinery) as a source of discretion reduction. The service organization has software
(rules, formal procedures and policies) as discretion reducer. The consequence for
organizational learning is probably that opportunities for learning in manufacturing
organizations are very limited, because of the constraints set by the machinery. In
service organizations fewer machinery constraints exist, and rules, procedures and
policies are possibly more easy to change within the environment of one
organization, so that a closed learning loop can be implemented.
A recent trend that distinguishes machine bureaucracies from a learning perspective,
was initiated by Japanese companies via their concept of lean production. The
principles of leanness are concerned with the relative decrease of the number of
people that are not directly engaged in production (cf. chapter 1). This means that
huge support staffs and technostructures can be removed. Equally important are the
cultural and organization structural differences between lean and non-lean
companies (Leonard-Barton, 1992). Because of these differences it is obvious that
lean and classic machine bureaucracies have different learning norms and behave
differently in learning processes. Further reasons for this statement are given in
section 3 of this chapter.
5.2.2 Classic and Lean Machine Bureaucracies
From the study of Womack et al. (1990), lean and classic (car) manufacturers are
distinguished on the basis of 10 items described below.
• Attitude to quality
The lean organization has 'Kaizen' as a basic mind set. Kaizen is a Japanese term
meaning an intrinsic motivation to improve quality. This means that learning about
quality should come from within. In the classic bureaucracy, quality improvements
start from a need to comply to market pressures. For instance, many companies start
up quality programmes to receive a certificate, because no certificate often simply
means no business. This sometimes leads to a further formalization and
bureaucratization of the existing classic bureaucracy. In lean organizations these
quality costs are far lower than the profits.
• Level of decentralization
Lean organizations allocate many decision responsibilities to the shop floor. This is
done via vertical decentralization to improve the reaction speed when problems
occur, and via horizontal decentralization to reduce communication difficulties
among the experts and the shop floor. This means that the shop floor should be
equipped with expert knowledge, and with a motivation to take responsibilities. Staff
groups with expert knowledge are then small and mainly have coordination and
facilitation tasks. In the traditional bureaucracy, large staff groups exist that do the
thinking for the shop floor and to some extent for the management as well. People
on the shop floor then only require a low educational level, and have no managerial
responsibilities. In that case, there is a strong distinction between 'we' (the workers)
and 'them' (the management). Research in the area of machine bureaucracies has
shown that the number of people in administration and management versus people
engaged in production (so-called administration/production-ratio) has been
increasing relatively (Anderson and Warkov, 1961; Child, 1973; Indik, 1964;
Parkinson, 1958). Parkinson even believes that this trend is based on immanent laws
of machine bureaucracies, that can be described as follows: (1) an employee of a
bureaucracy aims at reproducing subordinates and tries to avoid competitors and, (2)
employees in a bureaucracy provide each other with work, mainly for coordinating
and controlling each other's jobs. The lean organization starts from the opposite
view, which could be described as: (1) regard each other as equals so that you can
profit from each other's knowledge and experience and (2) try to reduce coordination
costs by creating lateral relations instead of the slow and costly hierarchy.
• Availability of lateral structures
The classic machine bureaucracy manages its communication processes via
hierarchies of command. When a problem occurs that a person cannot handle, he
goes to his superior to ask for advice and support. When the superior cannot give
this support directly, he contacts some of his subordinates. When the problem
cannot be solved in that way, the superior goes to his superior etc. When the
problem is rather complex and needs the collaboration of several people for a certain
period, a project or task group is created, under the authority of the superior of all
the people involved. This means that a number of people are taken out of the daily
working process, to work for some time on the project. Two problems can now occur
that are solved differently in classic and lean organizations:
1. When the project work and routine work come into conflict, the lean
organization gives priority to the project work. The project leader (called 'shusa'
in Japanese) has the authority and means to enforce the collaboration of the
people involved. In the classic organization, the routine work mostly has
priority above the project work.
2. When the problems are incidental, ad hoc contacts might solve them. In the
classic bureaucracy it is very difficult to make these contacts because it requires
the approval of superiors and the internal differentiation even makes it difficult
to find out if the required expertise is in fact available within the organization.
In the lean organization the hierarchical chain is very short and people are
expected to find the required internal contacts themselves.
• Relations with suppliers
In traditional machine bureaucracies, suppliers are members in a negotiation process.
The idea dominates that what you pay for supplies increases production costs and
lowers profits, and hence you need a strong negotiating position and should apply all
kinds of negotiation tricks (Mastenbroek, 1987). In lean organizations, the supplier is
regarded as a partner in the value chain. This means that the quality of deliveries are
essential for your own success. The supplier is therefore provided with information
and expertise with which he could improve himself. In the classic bargaining
situation suppliers and buyers will seal important information off or supply
misinformation. In the mutual partnership relation co-makership is very likely. The
buyer might even take shares and thus take responsibility for the supplier's success
because he needs a continuous relationship.
• Relations with clients
In the classic organization, clients are regarded as the end of the production process.
In the lean organizations they are regarded as the starting point of everything. To
satisfy their needs is the ultimate goal of the lean organization. The classic
organization strives for maximization of profits, minimization of costs, and increase
of market share. It does not develop a clear picture of the environment, and has an
internal focus. Market research is done occasionally, but the results are not always
clearly communicated within the organization. Besides, market research mostly
describes the existing trends in the market. The lean organization tries to create the
market by having excellent contacts with buyers, constantly following them, so that
new ideas can evolve in direct contact with clients.
• Relations with employees
In classic organizations, employees are regarded as sellers of labor, and laborers and
management relations are mainly negotiations of the win-lose type. This can lead to
hostile relations. An illustration of this is in the USA where a position is mostly seen
as temporal and frequent job change is regarded as positive, because it demonstrates
the sales value of a person's human capital. In lean organizations, workers are seen as
essential for the company's success. They must be highly motivated and dedicated to
give an excellent performance. The organization invests strongly in its employees by
providing training and giving people life-long employment. In Japan the relation
between the organization and the employees even goes so far that the families of the
organization members are also regarded as part of the company (which gives these
organizations a traditional flavor).
• Financial decision-making structures
In the classic organizations, banks invest on the basis of profits and other financial
data, which are possibly compared with bench-marks from other organizations. In
lean organizations financial data are not that important. Longer term expectations
on qualitative issues such as the quality of the employee-management relations are
valued much more. A strong 'we'-feeling in the organization will possibly contribute
to the absence of strikes and the higher contributions of employees than can be
expected from the hostile relations of the classic organization. In Japan, lean
organizations are mostly part of larger conglomerates with an internal bank, called
'Keiratsu'.
• Human resource management ideas
In the classic organization an employee is attached to a specific position and may be
promoted or receive another position, although this is not common. He is also
expected to carry out uncritically whatever the management asks him to do. He must
adapt well to the existing organizational culture and setting. The lean organization
expects an open mind of its employees, and motivates people to bring in
unconventional ideas.
• People's motivation base
In the classic bureaucracy people have an extrinsic work motivation, meaning that
they work to receive a monetary retribution, and search for other jobs with better
payment. The lean organization aims at optimizing intrinsic work motivation,
meaning that people come into work because they are interested and gain great job
satisfaction from the work intself. The life-long employment, offered by many lean
organizations, also discourages looking for other jobs.
• Sources of new ideas and R&D
The classic bureaucracy suffers from a 'not-invented-here-syndrome', whereas the lean
organization actively searches for all kinds of ideas that might be interesting to
explore (Leonard-Barton, 1992).
The differences between lean and classic machine bureaucracies are thus summarized
under the 10 items mentioned. Chapter 7 describes a proposal for an index of
leanness of organizations based on these items.
Similar theorizing on lean organizations is also done by Hedlund (1994), while
defining his N-form and M-form organizations, where N stands for 'new' and
'novelty', and M stands for 'multidivisional'. His publication, however, was done after
our operationalization and case studies were completed. For completeness, we
present one of his tables that gives a summary of his conceptualizations.
N-form M-form
Technological Combination Division
interdependence
People interdependence Temporary constellations, given Permanent structures, changing pool
pool of people of people
Critical organizational level Middle Top
Communciation network Lateral Vertical
Top management role Catalyst, architect, protector Monitor, allocator
Competitive scope Focus, economies of depth, Diversification, economies of scale
combinable parts and scope, semi-independent parts
Basic organizational form Heterarchy Hierarchy
Source: Hedlund, 1994, p. 83, tabel 1.
Table 5. 1: N-form vs. M-form
Most interesting is Hedlund's statement that 'new' is not always better. Table 5.2 lists
several issues on which the traditional, M-form, organizations perform better than
the N-form organizations.
N-form weaknesses M-form strengths
Fundamental, radical innovation not achieved by Radical innovation through specialization, abstract
(re)combination and experimentation only articulation, and investment outside present
competences
Long time to acquire fundamental new knowledge Rapid infusion and diffusion of drastically new
because of restrictions on senior recruitment and prespectives through people, acquisitions, and spin-
acquisitions offs
Difficulty in coordinating very large projects because Large systems design capability through complex
of reliance on small groups articulation and tightly controlled complexity
'Competence traps' through too constrained Risk management through 'competence portfolio'
development path
Bias for internal exploitation of ideas Freedom to use most effective mode, internal or
external
Difficult to change overall vision because of internal Change of basic direction and culture through
management promotion external recruitment of top management
Strategic vulnerability through strong focus and Strategic robustness through quasi-independent
interrelationship parts
Source: Hedlund, 1994, p. 86, table 2

Table 5.2: Where the M-form is Superior
We will not further discuss which form of organization is the best, but we will
emphasize that the success of an organizational form depends on its environmental
conditions. The conditions of the most interest here are the organization's learning
needs (see further section 5.4).
5.2.3 Manufacturing and Service Machine Bureaucracies
The discussion about classic and lean organizations showed that machine
bureaucracies are moving to new, more competitive forms that match with new
environmental demands. At the same time, machine bureaucracies are changing in
their products and transformation technology. Two trends are particularly interesting
in this case. The first trend is industrialization of services (Grönroos, 1990). Services
lose their classic interpersonal nature by lowering the labor intensity and degree of
interaction (Schmenner, 1986, pp. 28-31). Cash dispensers are typical examples of
industrialized services because they enable clients to take money from their bank
accounts without direct interaction with a bank employee. The second trend
concerns manufacturing organizations that increase their supply of services (Kotler,
1988, p. 476-493). For instance, a car manufacturer may develop a car lease service as
a new business with synergy to manufacturing, and which is profitable in itself. Other
examples are: machinery manufacturing companies that also sell consultancy, and
computer manufacturers developing and selling software, supplying educational
programs and free communication services via a Value Added Network that is
otherwise used for user support and remote maintenance.
The importance of the distinction between services and manufacturing in our study
on organizational learning, is that both types of business have different types of
transformations and products that lead to different ways of organizational learning.
Some evidence for this proposition was found by Mills and Moberg (1982, reprint
from Bateson, pp. 152-153) in an overview of major research about the relation
between technology (which is treated as a synonym of transformation in their study)
and organization structure. Of the 26 studies reviewed, out of the 11 studies on
manufacturing 10 seemed to have found a clear relation between transformation and
structure. Of the 8 studies with a service sector sample, 5 showed relations between
transformation and structure. Of the 7 studies with a mixed population, only 1
showed a relation between transformation and structure. This finding is especially
important when organizational learning processes are regarded as a subtype of
organizational transformation, and when organizational learning norms are regarded
as a subtype of organizational norms. This would predict that the distinction between
service and manufacturing organizations (as a distinction in organizational

transformation) explains different ways of organizing organizational learning. Other
reasons for distinguishing service from goods manufacturing in the context of
organizational learning are provided by Quinn (1992), who stated that service
activities:"...usually rest on some special knowledge- base or intellectual skills. Increasingly,
therefore, developing and managing human intellect and skills - more than managing and
deploying physical and capital assets - will be the dominant concerns of managers in successful
companies (p. 439)".
An additional reason for studying services is that machine bureaucracies are
traditionally linked with manufacturing organizations (like car producers), but the
service sector in our economy is growing substantially and outnumbers the
manufacturing sector in many ways.
Excursion: The Service Sector

Most national accounting offices define services as all output that does not come from the four goods-
producing sectors: agriculture, mining, manufacturing and construction. The service sector embraces:
• distributive services, such as wholesales, retail trade, communications, transportation and public
utilities.
• producer services, such as accounting, legal counsel, marketing, banking, architecture, engineering and
management consulting.
• consumer services, such as restaurants, hotels, laundry.
• non-profit and government services, such as education, health care, the administration of justice and
national defence.
(Ginzberg and Vojta, 1981, p. 23-24).
In the US economy, services have increased considerably in importance in the last decades. Table 5.4 gives
Ginzberg and Vojta's data about changes in relative employment in goods-production and services:
Goods-production Services Goods-production Services

1929 45% 55% 1948 46% 54%
1948 44% 56% 1978 34% 66%
1977 32% 68% Source: Ginzberg and Vojta, 1981. Reprinted in Bateson,
1989, p. 26
Source: Ginzberg and Vojta, 1981. Reprinted Table 5.5: Percent of Gross National Product in US
in Bateson, 1989, p. 25 economy.
Table 5.4: Percent of Labor Force in US
economy.
Also in terms of gross national product, services outnumber goods-production (despite the many problems with
measuring the value, of for instance, government services). See table 5.5.
The non-profit services are excluded from our research objective. Interestingly enough, many of these services
are now becoming profit services, because of government retrenchment policies (for example, health care,
pension funds, state computer facilities, railways). Because many additional services are now priced, and their
volume can be more easily measured, the percentage of services in gross national product will rise in future
statistics.
Ginzberg and Vojta also mention that services are becoming increasingly organized in machine bureaucratic
configurations as is stated in next quote (Ginzberg and Vojta, 1981, pp. 33-34):
" Since services for consumers have to be provided where the consumers are, economists have long assumed that the
economics of scale characteristic of manufacturing could not be achieved in service enterprises. Services cannot be
produced for inventory and cannot be shipped. That, however, is not the entire story. Improvements in
communications, particularly in processing and transmitting numerical data, facilitated the growth of large service
companies in the postwar decades by linking together in single enterprises large numbers of small service
establishments. Major banks were among the first to develop worldwide systems of branches. Now multi-unit hotel
chains, automobile-rental companies and fast-food-franchise enterprises have followed the example set by the banks.
The economics of these arrangements are based on the gains that the large service company can achieve through integrated
planning, financing, accounting, marketing and similar functions. Even large producer-service firms in law and accounting
have increasingly expanded overseas through the establishment of branches, partnerships or franchises. This development
helps to explain the surprising fact that legal services have recently emerged as the largest export industry in New York City,
outranking its apparel industry."
Service and manufacturing organizations can be distinguished by their output and

transformations. Dimensions to rate the differences in output are: tangibleness of
output, discreteness of output, perception of the output value, organization's output
goal, and the role of measured output. Service output is less tangible and less discrete
than the output of manufacturing organizations. This makes it easier for
manufacturing organizations to have objective measures for output than it is for
service organizations. The output goal of manufacturing organizations is therefore
also easier to define in terms of profits and volumes, whereas service organizations
more often apply immaterial criteria such as client satisfaction. It is therefore difficult
for service organizations to use output measurement as a means for learning. The
success of service organizations is much more indirect, and output control could even
misdirect attention to the real causes of longer term success.
With respect to transformations, 8 items are proposed on the basis of Mills and
Moberg's paper, that need consideration when distinguishing service from
manufacturing. These items are briefly described in table 5.6.
M. B. Service Manufacturing
Item
Materials and Knowledge Machines, physical materials and
Equipment labour
Involvement of client Client is ego-involved Client has contact after production
in production (sales) and sometimes before
production (design and contracting)
Information High, accurate and timely Planned work
processing information from client is needed
Responsibility for Client has joint responsibility for Responsibility for success lies with the
success success producer
Description of process Input, conversion and output are Clear distinctions between input,
phases hard to distinguish conversion and output (related with
logistic stream)
Stocks and buffers Stocks are impossible. Buffers are Stocks are possible (under certain
made by selection of clients, conditions) and buffers are created by
routinization of service and rationing planning of the production stream
Systems boundaries Operating core is open system Operation and administration are
(involvement of client), both closed systems.
administration is closed system
Professionalism Can be high or low. Low (except in engineering)
Based on Mills and Moberg, 1982, pp. 154-161.
Table 5.6: Eight Items for Describing Service and Manufacturing Organizations.
On the basis of these ideal typical considerations, chapter 7 defines a scale of the
extent to which an organization can be called service or manufacturing.
5.3 Other Organizational Configurations
Besides the machine bureaucracy, Mintzberg constructed five other organizational

configurations, that differ on environmental characteristics (dynamics and
complexity) and their coordination mechanisms. This discussion is important
because of our presumption that environmental changes have different learning
needs and thus might require other learning norms. More or less deviating from
Mintzberg's argument, the presumption is used here that an increase in dynamics and
complexity does not necessarily require a new organizational configuration, because
of the benefits of machine bureaucratic configurations in terms of efficiency and
capability of producing certain goods and services. Only when these organizational
features lead to strong ineffectiveness, and a certain critical value is passed, will an
organization search for another configuration (also cf. Hage, 1965, p. 307). Let us
explore organizational environments, coordination mechanisms and configurations
further.
5.3.1 Environment and Coordination
Mintzberg found that several viable organizational configurations can be

distinguished. They are not just fads and fashion (they sometimes are) but are
effective organization types associated with certain environmental conditions. These
environmental conditions are defined by four factors (Mintzberg, 1983, pp. 136-137):
1. Dynamics. In Mintzberg's words:
" An organization's environment can range from stable to dynamic, from that of the wood
carver whose customers demand the same pine sculptures decade after decade, to that of
the detective squad that never knows what to expect next. A variety of factors can make
an environment dynamic, including unstable government, unpredictable shifts in the
economy, unexpected changes in customer demand or competitor supply, client demands
for creativity or frequent novelty as in an advertising agencies, rapidly changing
technologies as in electronics manufacturing, even weather that cannot be forecasted, as
in the case of open theater companies. Notice that dynamic here means unpredictable,
not variable; variability may be predictable, as in steady growth of demand."
2. Complexity. In Mintzberg's words:

" An organization's environment (...) can range from simple to complex, from that of the
manufacturer of folding boxes who produces his simple products with simple knowledge,
to that of the space agency that must utilize knowledge from a host of the most advanced
scientific fields to produce extremely complex outputs. Clearly, the complexity dimension
affects structure through the intermediate variable of the comprehensibility of the work to
be done. Note that rationalized knowledge, no matter how complex in principle, is here
considered simple because it has been broken down into easily comprehended parts. Thus,
automobile companies face relatively simple product environments by virtue of their
accumulated knowledge about the machines they produce."
The two other environmental factors are market diversity and hostility. Increasing
diversity may result from a broad range of clients, of products and services, or of
geographical areas in which the outputs are marketed. Diversity therefore can be
regarded as a part of the complexity factor mentioned previously. This is also
consistent with the research and theory of Duncan, which we will discuss later on.
Hostility is influenced by competition, by the organization's relations with unions,
government, and other outside groups, and by the availability of resources. Hostile
environments are typically dynamic. Mintzberg distinguishes hostility because extreme
hostility has a special effect on organization structure. For our study this is less
important, and therefore hostility is treated as part of dynamics.
Dynamics and complexity lead to the recognition of four coordination means. Stable
environments make standardization of work and skills very valuable. Complex work
can be coordinated much better when there is standardization of basic skills, and
when several experts collaborate and put the pieces of knowledge they have together.
When the problems are simple, the work process can be split up easily. In dynamic
environments with simple problems, one never knows in advance what kind of
problems must be worked on. The insights and experience of a supervisor are then
important in order to have the job done well. In dynamic environments with
complex problems, more expertise is required to figure out what precisely is the
problem, and what different ideas exist to solve it. Here, coordination is not a
supervision type, but depends on mutual adjustment. When dynamics and
complexity increase, the machine bureaucratic configuration with its standardization
of work becomes inappropriate. The research population of this study is confined to
machine bureaucracies, which often have to move away from a traditional
coordination mechanism because of increased complexity and dynamics. Figure 5.1
visualizes the research population.
The more the environment requires

market diversification, the larger the
opportunity for splitting up the
company into separate business units
and divisions. These divisions act
rather independently from each other,
and are only coordinated by output
and performance indicators. The
hostility variable influences the amount
of temporal centralization and
decentralization. In hostile environments, the top management must keep a close
grip on all activities the organization is involved in.
Because an organization is a system of norms, one should not only look at the
environmental issues of complexity and dynamics, but also at the extent to which
norms are shared among the members. When there are strong commitments relating
to goals and norms, e.g. in clans, organizations and individuals become strongly
interwoven. This situation exists in some Japanese companies (Sullivan and Nonaka,
1986). Many western organizations, influenced by Japanese management, consider
coordination via standardization, direct supervision, and mutual adjustment as too
loose, and try to make their members more committed to common goals. Mintzberg
calls this sixth coordination mechanism 'mission', which can also be applicable to all
organization types mentioned before.
5.3.2 Organizational Configurations
Organizational configurations are defined by coodination mechanisms and the

equilibrium among its interest groups. Mintzberg describes five interest groups:
1. The strategic apex (top management), tries to keep a grip on what is happening
in the organization so that it is influenced from the view the top has about the
identity and future of the organization.
2. The technostructure is involved with analyzing organizational processes, to make
them more efficient, effective, controllable, and predictable. This frequently
makes organizations more machine-like.
3. The support staff seldom has a dominant position in the machine bureaucracy. It
mainly aims at improving administrative practices, and supports
communication among members of the organization. They mostly influence the
formalization of communication and information supply. Mostly information
services (often called the MIS-department) are part of this support staff.
4. The middle line's role is to execute tactical or operational management. Its
position is often complicated, because it is in the fireline between the top and
the bottom of the organization. It seeks political power and autonomy of
handling. This can lead to balkanization of the company, divisionalization and

a source of power struggles.
5. The operating core frequently strives for autonomy and therefore wants to keep
administrators, analysts (technostructure) and managers away. It promotes
vertical and horizontal decentralization. Professionals are particularly effective
with this strategy, because they have a specific and important expertise.
Table 5.7 relates organizational configurations with their key coordination
mechanism, key groups involved and the type of decentralization.
Structural Prime coordinating Key part of Type of
configuration mechanism organization decentralization
Simple structure Direct supervision Strategic apex Vertical and horizontal
centralization
Machine bureaucracy Standardization of Technostructure Limited horizontal
work processes decentralization
Professional Standardization of skills Operating core Vertical and horizontal
bureaucracy decentralization
Divisionalized form Standardization of Middle line Limited vertical
outputs decentralization
Adhocracy Mutual adjustment Support staff/operating Selective
core decentralization
Missionary form Ideology and Top and operating core Horizontal and vertical
indoctrination decentralization
After Mintzberg, 1983, p. 153.
Table 5.7: Organizational Design Configurations.
5.3.3 Criticisms of the Machine Bureaucratic Configuration
Three major criticisms relating to machine bureaucracies can possibly affect this
study, and therefore must be commented on here. The first criticism is that, in
contrast to Mintzberg's statements, machine bureaucracies15 do not match the simple
and stable environments in an effective way. The second criticism states that machine
bureaucracies are not a relevant case to generate insights for a theory on
organizational learning. The third criticism states that machine bureaucracies are
unpractical in all cases and therefore are only the bad examples that must be removed.
Let us comments on these statements before we proceed.
Comments on Match
15
Doty, Glick and Huber (1993) make a most valuable distinction between organizational design
configuration (structure and processes) and contextual configuration (described in terms of dynamics and
complexity). Here, by the term machine bureaucracy we mean the organizational design configuration.
Doty, Glick and Huber (1993) stated that few theories have received so much
attention with such meager empirical evidence as Mintzberg's organization theory.
The authors especially reject Mintzberg's statements about the match between
organization design configuration and organization environment. Table 5.8 shows
results from a sample of 128 organizations. They found a very low correspondence
between organizational design and environment.
Contextual Configuration:
Design Configura- Simple Machine Professional Divisiona- Ad- Total for
tion  structure Bureaucrac Bureau- lized Form hocracy Design
y cracy Config.
Simple 3 1 6 2 0 12
Structure
Machine 1 5 10 21 1 38
bureaucracy
Professional 2 1 11 16 2 32
bureaucrcay
Divisionalized 0 0 0 10 1 11
form
Adhocracy 7 1 19 4 4 35
Total for 13 8 46 53 8 128
Contextual
Config.
Source Doty, Glick and Huber, 1993, p. 1217
Table 5.8: Doty, Glick and Huber's findings about Contextual and Design Configurations
This evidence would falsify the statement that design and environment should
match. Mintzberg however does not believe in a deterministic relation between
environment and organizational design. In fact, he states that more frequently
deviations of these statements exist. These findings also support our suggestions that
machine bureaucracies are often confronted with more dynamic and complex
environments than in the classic situation (low dynamics and low complexity). For
Mintzberg's theory the situation however worsens when organizations that fit
according to the theory do not perform well. The authors scored their sample on six
effectiveness criteria (derived from Quinn and Rohrbaugh, 1983), and correlated
these findings with the classification of each case in an ideal type, contingent ideal
type and contingent hybrid type16. In all these cases the correlations are very low,
16
The ideal type model is conceptualized as consistency across the relevant dimensions and is modeled as
the lack of deviation from the one type. The contingent ideal type model defines a finite number of ideal
leading to a refutation of Mintzberg's theory.
types of contexts and a single ideal type of organization structure or strategy that is appropriate for each
ideal-type context. The contingent hybrid type model allows hybridization among the initial ideal types and
defines continua of contexts. A single hybrid type should then match a specific context (Doty, Glick and
Huber, 1993, p. 1202-1203).
However, although many criticisms can be directed at the study of Doty, Glick and
Huber, for instance that they used a non-random sample of only 128 cases, the study
clearly indicates some problems with the Mintzberg theory. Because not many other
tests of Mintzberg's theory have been carried out, and the theory is based on a
volumious amount of research, summarizing most of the main stream of organization
analysis from the beginning of this century till the beginning of the 1980s, it would
be unwise to throw away this baby with the bath water. Nevertheless, important
amendments to the theory are required. In this study for example we explicitly study
lean machine bureaucracies.
Comments on Relevance
A hypothesis in this study is that many machine bureaucracies are confronted with
increasing complexity and dynamics, which would remove them from the machine
bureaucratic configuration. This is in many instances not possible, because of
typicalities of the production process in that it requires a strong technostructure that
defines standard work processes. The lean organization for instance cannot do without a
technostructure, but makes better use of knowledge and skills available in the
operating core. This leads to a closer relationship or integration between
technostructure and core. Empirically this can look like decentralization. Many
service organizations have made serious attempts to get away from the expensive
professional way of service supply, and demand more standardization in work
processes (Grönroos, 1990; Schmenner, 1986). The demand for more flexibility is
realized by the extensive use of information technology and providing service
specialists with easy access to client and product data.
The move of machine bureaucracies to simple structures is not relevant, because the
simple structure lacks the knowledge, the division of labor, and the infrastructure to
supply services and goods on a large scale for low costs. The move to divisionalized
forms often occurs when the machine bureaucracies become part of larger
organization conglomerates. This does not necessarily affects the stability and
simplicity of the environment. It could however increase the organization's
opportunities of access to knowledge and information from other business units that
are part of the divisionalized form. Research has indicated that this does not happen
often. Adhocracies are irrelevant for our research, because they are only viable in very
dynamic and complex environments, and have only limited abilities for large scale
production. Missionary forms, that use indoctrination as a major coordination
mechanism are likely for religious organizations and political parties. Some machine
bureaucracies, especially Japanese manufactures, also put much emphasis on
indoctrination. This is also very relevant for lean organizations in addition to the other
coordination mechanisms. By indoctrination, management costs can be kept low,
which keeps the organization lean. This leads to the insight that organizational
configurations are not only determined by their environmental context, but that
organizations also have the capability to adjust environments. This is especially so

when we remember that organizations are open systems, and thus that much of their
environments are more or less internal.
Comments on Practicality of Machine Bureaucracies
Some of the following criticisms of machine bureaucracies are mentioned in the

literature on organization:
• Workers are regarded as stupid machines, and human intellectual potentials are
under-utilized. This work environment leads to worker alienation, because
people do not feel committed to their task, and often want to flee from it
(McGregor, 1960).
• The machine bureaucracy is too specialized and becomes easily over-organized,
which makes the management of processes extremely complex (Morgan, 1986).
• The machine bureaucracy leads to strong internal differentiation without
enough compensating integration mechanisms (Lawrence and Lorsch, 1967).
This leads to poor services because clients are asked to go through a whole
building for just a small matter.
• The machine bureaucracy is organized hierarchically to coordinate the specialist
activities from the top (employees are not invited to think about their own
work). This leads to serious problems because important matters are sent to
people lacking the knowledge to decide about them (Galbraith, 1973).
• The bureaucratic organization requires huge lead times from a new idea to a
product on the market (for example Volkswagen took nine years to launch a
new type of car).
• The bureaucratic organization is poor at making customized products. It does
not have the flexibility to adjust standard products for specific needs.
Despite these many criticisms:
" The essence of bureaucratic organization17 is the production of standardized, predictable,
replicable performance by many different people and/or groups. It is bureaucracy that
makes every Big Mac the same, that ensures that a federal tax return filed in Chicago is
assessed the same way as one filed in Miami, and that allows you to pick up a phone,
dial a few digits, and call any other phone in North America within seconds. And in the
case of mass production, bureaucracy results in the lowest costs. (....)....efficiency is the
hallmark of the bureaucratic organization. So how do bureaucracies do this? Some of the
basic parameters are centralized control, task specialization, functioning grouping, and
internal standardization" (Bushe and Shani, 1991, pp. 5-6).
The human relations movement of organization investigated 'solutions' for the
human problems of machine bureaucracies. This was accomplished via suggestions
such as:
17
Here obviously the machine bureaucracy is impied.
• Job enlargement, and asking people to find a way of organizing this themselves.
• Flatter structures, with more decision responsibilities at lower levels (job
enrichment). This led to so-called organic organizations, that demanded a
considerable sense of responsibility of people on the shop floor, though it was
still very formal in its working methods (professional bureaucracies). Some
organic organizations also removed formal ways of working and thinking, and
are mainly designed to solve unique problems (called adhocracies).
Many of these suggestions were adopted in the conceptualization of lean
organizations (Womack et al., 1990) and 'The Learning Organization' (Senge, 1990a).
These are discussed under the headings of responsibility norms, procedural norms
and action norms.
5.4 Consequences of Machine Bureaucracy for Organizational Learning
5.4.1 Learning Needs for Machine Bureaucracies
Complexity and dynamics, are both determinants of the need for organizational
learning. Both are individual or shared perceptions of internal and external
environmental reality. After Duncan (1972), an organization's environment is split
up into 8 components, further divided into several factors (see table 5.9).
Internal Environment
1. Organizational personnel component
a. Educational and technological background and skills
b. Previous technological and managerial skill
c. Individual member's involvement and commitment to attaining systems's goals
d. Interpersonal behavior styles
2. Organizational functional and staff units component
a. Technological characteristics of organizational units
b. Interdependence of organizational units in carrying out their objectives
c. Intra-unit conflict among organizational functional and staff units
d. Inter-unit conflict among organizational functional and staff units
3. Organizational level component
a. Organizational objectives and goals
b. Integrative process integrating individuals and groups into contributing maximally to attaining
organizational goals
c. Nature of the organization's product service
External Environment
4. Customer component
a. Distributors of product and service
b. Actual users of product and service
5. Suppliers component
a. New material suppliers
b. Equipment suppliers
c. Product part suppliers
d. Labor supply
6. Competitor component
a. Competitors for suppliers
b. Competitors for customers
7. Socio-political component
a. Government regulatory control over the industry
b. Public political attitude towards industry and its particular product
c. Relationship with trade unions with jurisdiction in the organization
8. Technological component
a. Meeting new technological requirements of own industry and related industries in production of
product or service
b. Improving and developing new products by implementing new technological advances in the
industry
Source: Duncan, 1972, p. 315.
Table 5.9: Factors and Components Comprising the Organization's Internal and External
Environment.
The simple part of the simple-complex dimension deals with the degree to which their
are only a few factors of relevance in the decision unit's environment, and these
factors are similar to another in the sense that they are located in the same or a few
components. The complex part indicates that the factors and components in the
decision unit's environment are large in number (Duncan, 1972, p. 315). In complex
situations the development of knowledge (e.g. in terms of action-outcome relations) is
difficult, and needs highly specialized and educated people. Thus there is a direct
impact of the complexity dimension on the needs for knowledge development
activities, the dissemination of the relevant knowledge and the actual use of the
knowledge. Duncan proposes to measure the amount of complexity by multiplying
the number of decision factors with the square of the number of components.
The static-dynamic dimension refers to the unpredictability of the environment. It is
measured via the frequency by which the factors of the decision unit's internal and
external environment remain basically the same over time or are in a continual
process of change. This dimension contains two subdimensions:
• The degree to which the factors identified by decision unit members remain the
same over time, or are in a process of change.
• The frequency with which decision unit members take into consideration new
and different factors in the decision-making process.
This obviously indicates learning needs because high dynamics requires the ability to
update frequently and remove obsolete knowledge. Re-use of knowledge in this case
will be small because conserved knowledge depreciates easily in highly dynamic
environments, except when connected with more fundamental knowledge (double-
loop learning). In more dynamic environments 'quick fixes' (single-loop learning) are
not sufficient for an effective organization in the longer run.
Dynamics and complexity together determine the score on organizational learning
needs. Especially the dynamic dimension contributes to the learning needs score,
because complexity can be solved by developing a correct theory that is implemented
in practices and machinery, and thus considerably reduces uncertainty and the need
to learn. The dynamic factor constantly and directly increases the need for learning.
In very high dynamic environments learning is very difficult, as Hedberg (1981)
stated, and decisions must be based on incomplete knowledge. The organization in
that environment requires a very flexible, team-oriented configuration, that is closer
to an adhocracy or a professional bureaucracy than to the machine bureaucracy.
The organization literature (Buchko, 1994) has criticized this operationalization of
dynamics and complexity as a measure of environmental uncertainty. It has, however,
high construct validity as a measure of organizational learning needs because of its
emphasis on generating insights in factors and their relations (management
knowledge) (also cf. Duncan and Weiss, 1979).
5.4.2 Machine Bureaucratic Learning Norms and Deutero Learning
1. Learning Policy
The following issues for mission and policy norms were determined in chapter 4:
• The adaptation of organizational learning in mission and identity statements of
the company.
• The development of an organizational learning infrastructure.
• The development and management of core competencies.
• Basic organizing principles that support learning..
• Management motivation for business re-engineering.
As shown in sections 5.2.2 and 5.2.3, machine bureaucracies can differ significantly
on the lean-classic (organizational norms) and service-manufacturing (organizational
transformation) dimensions. Additionally it was assumed that these dimensions
strongly link to the way organizational learning occurs. Table 5.10 therefore describes
how lean and classic machine bureaucracies differ on mission and policy norms.
MB: Lean MB Classic MB

Learning policy:
Policy and mission Learning is stated in the mission, Stressing of volumes sold and
especially in terms of continual produced, Return on Investment,
improvement (Kaizen). market share, profits
Learning Lateral relations are encouraged. When No lateral relations, top-down
infra-structure cost-effective, data highways (computers communication. Use of mainframes
and networks) and information access for maintaining
control. Non-transparent organization.
Development and Top priority for high innovation People are mainly providers of labor.
management of potential Competencies are what one can do
core competencies now. Human Resource Management
and R&D have lower priority than
marketing, manufacturing and logistics.
Organizing Production teams, with high Strong departmental and functional
principles responsibility and availability of differentiation, with strong line

management information. Strong management. Large technostructures,
project teams. Organization is open with no clear influence on middle lines.
system, with close relation with Closed systems (to internal and
suppliers, banks and clients external environments)
Motivation for Emphasis on redesigning processes for Optimize existing processes from
business maximizing client satisfaction and efficiency perspective. Technostructure
re-engineering efficiency (is ultimate boss). and middle line have expertise for
Organization culture and structure optimization and dictate what happens
must adjust to process requirements. at the work floor.
Table 5.10: Differences between Lean and Classic Machine Bureaucracies with Respect to
Learning Policy Norms
The differences between both machine bureaucracy types are therefore huge on the
learning policy side. Norms and transformation technology are however not
unrelated. For instance a service organization puts much effort into an infrastructure
for supporting communication, because communication is an essential technology
for generating added value in services (Schmenner, 1986). This infrastructure is
however not necessarily used for organizational learning. Because service
organizations are mostly dependent on organization members' knowledge and skills,
they will probably place more emphasis on the development and management of core
competencies. They also require open systems, because it is the clients themselves
who are the subject of the production process. Business re-engineering is an
important issue in service organizations, because the number of services and service
varieties is exploding, and the market demands dramatic reductions of production
costs. Information technology has an important leverage potential here (Hammer,
1990). One of the most important reasons why services can more easily re-engineer
than manufacturing organizations is the fact that most of their norms are
implemented via software (in the broad sense of the term including computer
programs, organizational written or tacit rules), whereas manufacturing organizations
mostly have substantial large investments in machinery that is difficult to change or
replace (hardware constraints).
These considerations suggest that it would be easier for service organizations to
become lean than manufacturing organizations. There are however no data that
clearly support my opinion. This study wil further explore some of the statements
made about the service-manufaturing distinction in chapters 8 and 9, while
comparing service and manufacturing companies.
2. Organizational Learning Responsibility Norms

Responsibility norms are close to what is called organization structure in organization
analysis, consisting of a set of formal task descriptions, division of labor,
responsibility allocations among persons and departments, lines of authority,
hierarchical levels, and span of managers' control (Daft, 198918). Classic machine
bureaucracies are characterized by centralized authority, and some vertical
decentralization of authority to the technostructure. Because of the increase in
learning needs some interesting transformations of learning responsibilities can be
predicted.
In situations of increasing complexity, decentralization is required because
management is not able to understand and decide about all issues (Galbraith, 1973).
Increasing dynamics requires fast decision-making lines as well, which are only
realistic in situations of decentralization and delegation of authority.
In classic machine bureaucracies, division of labor is along functional lines, meaning
that people are grouped together in departments by common skills and activities.
This can lead to high expertise within separate departments and persons, but to
severe coordination problems as well, when a more dynamic environment requires a
flexible combination of skills and effective interchange of knowledge.
18
Daft also mentions formal reporting lines and systems for effective coordination as part of organization
structure. I prefer to see them as part of the organizational processes that are closely related to procedural
norms to be discussed later on.
Several solutions are discussed in the literature on organization, to make more

effective interdisciplinary collaboration. One of these solutions is the divisional
structure, in which people and departments are grouped by similar organizational
outputs. The division is created as a self-contained unit for producing a limited set of
or a single product, or serving a specific market (region) or market segment (e.g.
business and retail divisions in banks). The divisions must have all knowledge
available to realize their purpose, thus: research and development, manufacturing,
finance, marketing etc. This can easily lead to duplication of disciplines, and stronger
coordination within the division than between separate divisions.
The second solution is the matrix organization, which combines functional and
divisional chains of command simultaneously within one organization. This is an
interesting design because it has the benefits of the functional organization (with its
in-depth skills in separate departments), and the benefits of the divisional structure
(with its ability to respond flexibly and adapt to changing environmental
circumstances and demands). A disadvantage is the often complex two-bosses
problem, which demands specific interpersonal skills of its organization members. In
practice coordination is complex.
The third solution is the team approach. This concept implies strong decentralization
and the development of multidisciplinary teams that can act rather independently.
The main strength of this organization structure is that it breaks down the barriers
between departments. Therefore this organization type is flexible but less good at
solving problems that require high levels of specialist knowledge. An important
additional disadvantage is that teams can close their eyes to the rest of the
organization, leading to suboptimal solutions, and low coordination with demands
from other parts of the organization. These task oriented-teams are common in
functional, divisional and matrix organizations as well, to solve specific
interdisciplinary problems. These teams are therefore temporal, and exist only during
a specific project. I prefer the term task groups for these teams. Teams could also be a
basic element for self-management in departmental units. The team then must clearly
keep within stated targets and for the rest is free to decide how it achieves its goals.
These teams I call volvos, because these principles of self-management have been
exercised specifically within the Volvo motor company (cf. Adler and Cole, 1993).
An organization can also be split up into many temporary teams, put together
specifically to serve a client. Some organizations, such as Electronic Data Systems, as
described by Peters (1992), have temporary teams as the basic blocks. These teams
have market resources for their survival, and are very loosely coupled to the rest of
the organization. In this case the term network organization is used. As was shown in
the EDS-case, service or expertise centers (also temporary teams) were created for the
development of specialist knowledge, to be sold internally or externally. Three types
of network organization can be distinguished. One, the internal organization,
consisting of multiple persons, connecting each other on specific topics, to form
agreements and collaborative coalitions (one example is McKinsey). Second, the
organization as an ensemble of temporal task groups that serve clients independently

(e.g. EDS). Third, a collection of independent companies that collaborate on a
continuous basis, in order to realize certain purposes. One company acts as a broker
to keep the network together (e.g. Air Bus). Finally, the term virtual organization is
also gaining popularity (Hedberg, 1991). This 'organization' consists of markets that
have a joint (information) infrastructure. Some examples are the Port of Rotterdam,
a collection of companies that form an infrastructure; the international fund transfer
system (SWIFT); and the health care business in the USA.
The final and fourth solution is called parallel learning structures, and is defined as:
" ...a generic label to cover interventions where: (a) a "structure" (that is, a specific division
and coordination of labor) is created that (b) operates "parallel" (that is, in tandem or
side-by-side) with the formal hierarchy or structure and (c) has the purpose of increasing
an organization's 'learning' " (Bushe and Shani, 1991, p. 9).
" ... in its most basic form, a parallel learning structure consists of a steering committee
that provides overall direction and authority and a number of small groups with norms
and operating procedures that promote a climate conducive to innovation, learning, and
group problem solving. Members of the parallel learning structure are also members of the
formal organization. Thus within the parallel learning structure their relationships are
not limited by the formal chain of command. Some parallel learning structures are set up
on a temporary basis, while others are intended to be permanent." (Bushe and Shani,
1991, p. 10).
These organization types can be rated on their ability to handle learning needs.
Organizations' abilities to cope with increased dynamics are described below.
1. Functional organizations are often too slow in reacting to dynamics because
their members have learned to behave precisely according to very specific rules,
and therefore change is very difficult. Besides, departments involved with
developing new products and manufacturing engineering are separated from
the production department, which leads to a low interchange of ideas and
under-estimation of problems in implementing new ideas.
2. Divisional organizations are much more able to react to specific environmental
demands. Nevertheless, they are bad at joining forces with other divisions.
3. Matrix organizations combine flexibility (product orientation) with established
knowledge and skills (functional orientation). The complexity of coordination
that results, can make it a slow reactor, especially when large two-boss problems
exist, involving enduring political conflicts.
4. Volvos are very useful for reacting quickly to specific problems. Their
jurisdiction is however mostly limited to single-loop learning and quality
management. Double-loop learning often requires inter-departmental task
forces with strong top management commitment.
5. Network organization structures can be used in addition to make the many
organization types more flexible. This implies a relaxation of formal structures
and organizational borders. The network organization itself is however not
committed to learning, which is instead a strong feature of the functional as

well as the divisional organization.
6. The parallel structure enables the questioning of the unquestionable and the
proposing of the unthinkable. It is therefore particularly useful in highly
dynamic environments.
The functional and divisional organizations are typical of the classic machine
bureaucracies, whereas the matrix, teams and networks are typical of lean
organizations. The parallel learning structure can be used both in lean and classic
machine bureaucracies for initiating discussions about breakthroughs, and starting
innovations.
The following organization types can be used to cope with increasing complexity in
different ways.
1. Functional organizations organize knowledge in archives, education of
personnel, rules and procedures (Weber). There is much specialist knowledge,
but the specialists are not capable of transcending the sum of the parts of
knowledge. In this case most teams are more stupid than any individual
member of the organization!
2. Divisional organizations have very capable parts, but under-utilize the potentials
of combining the competencies developed. Management therefore should
develop an explicit policy of utilizing and developing core competencies (Hamel and
Prahalad, 1990).
3. Matrix organizations are good at solving problems with divisional organizations,
and can optimally exploit core competencies. This works best when instruments
are developed that split through hierarchies, formal rules and procedures so
that a bottom-up process of knowledge development and utilization is achieved.
4. Specifically volvos are useful for effective distribution, use and adaptation of
knowledge. The team sees the problem, discusses it, and finds a solution,
within the constraints of the volvo's targets and responsibilities. Research and
Development task groups make the breakthroughs that are required for double-
loop learning.
5. Network organizations can be very effective in responding quickly to client
needs. The problem is the application and development of major new insights
and knowledge. The use of expertise centers (that develop, concentrate and
manage mature knowledge) is a solution here. When the network does not
provide for expertise centers, it will have very low capabilities in coping with
high complexity.
6. Parallel learning structures are most valuable when the problems are complex
and multidisciplinary. The parallel learning structure then studies the problem
in detail, and generates knowledge that the existing organization would not
create.
The result of this discussion is shown in table 5.11.
high Network (independent Volvo's Network (with expertise

companies) centers)
Parallel learning structures

DYNAMICS moderate Divisional Matrix Task groups
low Functional
low moderate high
COMPLEXITY
Table 5.11: Match of Responsibility Norms with Learning Needs
The classic machine bureaucracy, with its functional organization, must move to
divisional, matrix and even to team-based organization principles in a situation of
growing learning needs. The lean organizations have already achieved a strong team-
oriented organization. The service organizations can easily be transformed to the
team-like structures, because they are often involved in highly interpersonal
technology (except with the back office) and higher market dynamics provide
stronger incentives to move in that direction. For manufacturing, the strong coupling
between activities in the production process make it more difficult to create
autonomous teams. The technostructure will have an important say in what happens.
3. Organizational Learning Action Norms

The organizational learning literature stresses the importance of learning not only as
a cognitive activity, but especially as a behavioral activity, meaning one that changes
people's behavior (Kolb, 1984; Argyris and Schön, 1978; and Fiol and Lyles, 1985).
This means that effective learning requires norms that induce behavioral change
when needed.
One of the most important, and possibly also most neglected, topics of learning
action norms is their link with reward and incentive systems. Lawler and Rhode (1976)
distiguish intrinsic and extrinsic motivation. Intrinsic work motivation is insensitive
to changes in financial reward systems, because most satisfaction is gained from
simply accomplishing the task and improving understanding of the task itself.
Extrinsic work motivation is however very sensitive to the amount of payment. This
means that reduction of payment will lead to reduced work motivation and that
people will perform precisely what is rewarded most in the organization. In practice,
intrinsic and extrinsic work motivation are both present in the motivation of a
person. Professionals and academics are mainly intrinsically motivated, whereas non-
professional workers are mainly extrinsically motivated (Hersey and Blanchard,
1982).
Punishment and reward systems are also essential for effective learning, because of
the way they motivate participation in learning. Especially when information is an
asset for power, and can be kept privately, the dissemination of information is carried
out with explicit reward expectations. For instance communication about defects and
problems is obscured when it leads to negative evaluations. In situations where
interpersonal trust is very high, these kinds of group dynamics do not work. Argyris
(1970) and Argyris and Schön (1978) however attribute most learning errors to a lack
of interpersonal trust in organizations, so that the real causes of the learning
problems of the organization are not discussed.
Knowledge and information are also often connected with the status people have.
Removing the knowledge then leads to a change in power distribution, and therefore
easily leads to resistance of change. Action norms also include statements about the
importance of learning and change in relation to existing work. Simon (1976)
therefore states that organizations do not try to achieve omniscient knowledge, but
that organizations (and people) try to achieve a satisfactory situation. Simon is right
here, however, this principle is difficult to evaluate because many different goals and
values motivate people at any one time. As a consequence, evaluation of
organizational learning needs should be based on a broader view of organizational
priorities.
Finally, action norms concern the motivation of organization members to develop
and adapt knowledge, or an organization's willingness to not do so, by buying the
knowledge and skills elsewhere. This issue is an implementation of an organization's
view of what must be regarded as its core competence in terms of concrete actions
that must be taken to acquire the knowledge.
Table 5.12 sketches how action norms relate to organizational leanness.
MB: Lean Classic

Action norms:
Incentives Intrinsic Extrinsic
Interpersonal trust Openness Defensiveness
Attitude knowledge removal Positive Negative
Learning priority Relative amount of money (budget), Idem, low.
time and authority in relation to
operational tasks.
Source of knowledge External & internal sources Internal or external sources
Self development Buying knowledge
Table 5.12: Leanness of Action Norms
4. Procedural norms
Procedural norms relate to the way information is handled in organizations.
Complexity and dynamics have a direct impact on the contents of these norms.
The traditionally stable environment of machine bureaucracies led to the
development of knowledge that was well conserved in procedures, structures and

culture, and managed by managers and the technostructure together. Increased
dynamics requires shorter communication lines between organization members. In
section 5.2.2 I stated that task groups and networks (created for specific topics) in
matrix organizations could be very useful here. In order to give direction to these
teams and networks, the management should provide organization members with a
vision and mission ('shared vision' in Senge's terms) for the future. This means that
the delegation style should be complemented with a low relationship and low task
management style, but with inspirational leadership at the top. Within these
constraints and ideas, organization members should be invited to be creative and
collaborative problem solvers. Organization members should be invited to
experiment and try to find viable solutions among the many possibilities the
organization is faced with (Hamel and Prahalad, 1991).
Feedback frequencies are one of the issues of procedural norms, and the dynamics of
the learning environment in particular. The related hypotheses are described in table
5.13.
Feedback from Environment (learning from experience)

Fast Slow
High Risk For instance in construction, For instance in aerospace, new
Decisions (= high cosmetics, movies, advertising. ventures, research and
dynamics) development, capital-intensive
Dynamics projects.
Low Risk Decisions Fashion, marketing, consumer Government, utilities, insurance,
(= low dynamics) goods, electronics19 financial services
Source: Deal and Kennedy (1982), pp. 107-127 and Daft, 1991, p. 81.
Table 5.13: Dynamics and Required Feedback Frequencies
19
Has recently changed. For instance Philips Electronics is now aiming for survival, and is taking part in a
very competitive and risky business.
Feedback frequencies are particularly important for supporting learning. The

frequency should match the dynamics of the environment. The classic machine
bureaucracy has a much lower feedback frequency than the lean machine
bureaucracy because of its slow communication and administration system.
Additionally, the service industry must respond more critically to feedback, because
its competitive position is related more strongly to the service level the organization
can achieve at the particular moment the clients demand service (cf. Grönroos, 1990,
talks of 'moments of truth' in this respect). The service level is closely related with the
client's perception of quality and slight feelings of dissatisfaction must be coped with
at once. This leads to the distribution of required feedback frequencies as shown in
table 5.14.
Lean Machine Bureaucracy Classic Machine Bureaucracy

Service High Moderately high
Manufacturing Moderately low Low
Table 5.14: Required Feedback Frequencies for Machine Bureaucractic Types.
Growing complexity in machine bureaucracies impacts on the way people

communicate about management information. In the literature on organizational
analysis, this subject is mainly treated under the term of management style. If
complexity increases, the traditional machine bureaucratic solution is to acquire
more knowledge in the technostructure that as a consequence becomes even more
differentiated from the other departments. This approach confirms the traditional
telling approach of management maintaining unequal power relations based on
coercive power, reward power, legitimate power and expert power. Stated bluntly,
employees are not considered able to think, but merely to carry out imposed tasks.
The development of super smart managers, technostructures and specialists is less
necessary nowadays, because of increasing educational levels among employees.
Nevertheless, some minimum 'critical mass' is needed to develop major
breakthroughs and effectively manage knowledge (Nonaka, 1988).
When employees develop more expertise, they will develop a greater job maturity
(Hersey and Blanchard, 1982). People low in task maturity, because of lack of ability
or training, or insecurity, need high task management (telling). Those who are highly
mature and have good abilities, skills, confidence, and willingness to work are
difficult to manage on task and require a delegating management style. A telling style
is unrealistic when no power is available for doing so and when the work is complex.
In this situation a high task and high relationship style might be more effective as a
managerial strategy. Nevertheless, an unstructured job can sometimes be redesigned
into a structured one. When people have to work together in a situation of poor
interpersonal relations, a managerial style of non-interference in a mature group
might be very ineffective, and a high relationship (low task) style is required.
Organizational learning in this case involves not only learning the job (task), but
learning to manage persons and relationships as well (Hersey and Blanchard, 1982;
see figure 5.2).
An effective leadership style must encourage organizational learning by leaders and

followers. The greater the complexity, the less a leader should be the only one to
learn. Under the influence of increasing complexity, traditional telling or selling
styles in machine bureaucracies must move to selling or participation. When
professionalism in the organization also increases (which frequently happens in
machine bureaucracies that are becoming high tech) a delegating style is required.
This new style requires a transition of management styles that is sometimes difficult
to achieve because the management style is closely linked with cultural values and
power relationships. A good example is given by Zuboff (1988) in a paper mill. She
found that plant operators demanded more autonomy and authority as a
consequence of their increased sophistication. This clashed with the traditional
power distribution between operators and management and the cultural values,
especially held by the management, which stated that management should be in
control on the basis of its knowledge, and that the management should know and the
operators should act.
As a summary, procedural norms include the temporality of data flows, data access,
number of items rated via the system, management style, feedback time, and
distribution of expertise.
5.4.3 Machine Bureaucratic Single-Loop and Double-loop Learning20
20
This study does not investigate the deutero learning process, which is about the shaping of the
organizational learning norms. It takes its results (learning norms) as an internal contingency factor for
analyzing the effectiveness of the single-loop and double-loop learning processes and the role and value of
MICS. The reason for this limition is that MICS has no role in the deutero learning process, because MICS is
part of the (procedural) learning norms itself.
This subsection explores the role of machine bureaucracies in the development and
removal of management theories, their storage, use, dissemination and adaptation.
These learning activities are labelled double-loop and single-loop learning.
1. Double-loop learning processes
Development of management theories
Stable and simple environments do not require much double-loop learning. The
development of knowledge is then often delegated to specialists in the
technostructure (frequently organized in a Research and Development department),
sometimes with the involvement of the management as well. For instance, in an
insurance company I visited21, the principle trend for managers was to delegate all
knowledge development processes to
specialists, even the most simple ones.
Development of knowledge is done
infrequently and has a low priority in
simple and stable environments.
Feedback comes slowly and irregularly,
mostly organized in market research. In
these studies potential customers
express their opinion of products and
services and their future demands when
requested. The results of these studies
lead to decisions about developing,
manufacturing and selling new products. Because of the low complexity and
dynamics the results are very precise, so that good calculations of costs and benefits
can be made. The environment is low risk, and therefore demotivates the search for
innovations.
21
Not the one that is described in chapter 8.
When the environment becomes more dynamic and complex a more active
knowledge development approach is required. Besides, it becomes difficult for
specialists alone. Often in machine bureaucracies problems are encountered in the
manufacturing of new products because members from the production department
were not involved in the product design (Hill, 1984). The risk also increases as the
environment becomes more complex. Processes of critical evaluation must be
speeded in order to gain relevant knowledge, because knowledge depreciation also
speeds up. Knowledge development must also become systematic (e.g. explicitly
asking clients about satisfaction, and actively searching for problems to be solved).
When the dynamics of the environment is very high, knowledge depreciation is faster
than the knowledge development process. In this case the value of knowledge
declines rapidly, and management is either left over to good luck or beome able to
avoid uncertainty by creating a negotiated environment (Cyert and March, 1963).
Figure 5.3 shows that the increase of the value of knowledge is directly related with
the complexity of the environment. Figure 5.4 demonstrates that the accumulation of
knowledge is effective until the environment passes a certain level of dynamics. After
that moment the depreciation of knowledge goes faster than its accumulation of
value. Speeding up the feedback process can increase the value of knowledge, as is
shown in fig. 5.5.
In case of high complexity and
dynamics, a delegating style is
appropriate, because very short
communication lines are required and
much knowledge is decentralized. The
decentralization also leads to a
restriction of the area that must be
understood, and thus simplifies the
problem. This can of course lead to
suboptimization and dysfunctional
effects in the longer run. It is typical of double-loop learning that it detects these
suboptimization problems and solves them by generating an awareness of limitations,
and the new insights that are required. Action norms (motivation to rethink the
management theories especially in a broader perspective) and procedural norms
(creating communications and activities to detect limitations and discover a wider
perspective) must be set so that double-loop learning activities emerge.
Removal of management theories
The conservatism and reification

resulting from the stability and
simplicity of the organization's
environment, makes the removal of
obsolete procedures, rules, norms etc.
especially difficult. Because an
interchange of ideas and knowledge
among departments in classic machine
bureaucracies happens infrequently, at
best only a few people can clearly assess the impacts of a knowledge shift for the
whole organization. Resistance is likely to arise from political (position power),
interpersonal and socio-technical (way of working) perspectives. Slow evaluation
cycles do not motivate the removal of old ways of working, because people do not see
what is wrong with the existing habits (everything is fine, until real problems crop up
and the process of decline can no longer be reversed). What is perceived is the risk of
losing things that are valued highly, and the fact that a major shift always bears the
risk of failure. This is called 'reorganization risk' in the literature (Hannan and
Freeman, 1977 and 1984). Perceived low risk could easily lead to neglecting the
importance of action and up-to-date knowledge. The telling style could support the
change and removal of old knowledge in an autocratic way (brute force strategies).
The result of problems with unlearning is that a growing tension arises between
knowledge needs and the available knowledge. Management then frequently becomes
mismanagement, doing precisely the wrong things. What at first seems to be an
improvement, later turns out to be a failure (dysfunctional effects). As a remedy to
this way of thinking, Senge therefore proposes system dynamics thinking,
emphasizing the analysis of (unexpected) dysfunctions of behavior.
The factors mentioned in table 5.15 thus influence the likeliness of double-loop
learning in lean and classic machine bureaucracies.
MB: Lean Classic

Removal and double-loop
trigger issues:
Source of resistance to Much emphasis on details Expert power and position power
change
Perceived urgency for High urgency, perceived as essential for Low urgency perception
double-loop learning survival and continuation of the
organization
Risk awareness on High awareness of longer term risk Low awareness of longer term
shorter and longer term risk
Learning speed Higher learning and critical evaluation High discrepancy of knowledge
processes, reducing knowledge when dynamics increase (theory
depreciation in dynamic environments vs. practice)

Table 5.15: Lean and Classic Impact on Knowledge Removal.
Definition of a Double-Loop Score
Many dimensions of double-loop learning are given in this subsection. We are

concerned mainly with two double-loop learning activities: theory development and
unlearning. In chapter 4 we found four basic fields of learning, namely: human
resources, transformation (production processes), markets (for acquisition of
resources and growth), products (as concrete field of productivity and efficiency). A
double-loop score can now be defined as the amount of theory development and
unlearning that occurs related to the four learning fields, in organizations.
2. Single-loop learning processes
Storage of knowledge
In stable environments knowledge once developed in the form of procedures, norms,

rules etc., becomes a person's second nature. This implies a separation between know-
how and know-why. I found an example of this in a Dutch bank, where a large system
of norms had been developed relating to the flow of forms and information for
processing payment services. The system became so complex that many people did
not know why certain forms were used and should be passed over to other
departments. A consultant found out that many forms were not even applicable
anymore because payment services had changed enormously in the last decades.
Sometimes only the leader or a person from the technostructure knows the
connection between know-how and know-why. This knowledge inequality is
consistent with the telling style of some managers, and is a source of expert power.
Growing complexity requires the dissemination of know-why knowledge besides
know-how, because it is difficult for the management to know everything and instruct
followers effectively. Besides, tasks often require the knowledge of several people, and
individual job execution is rare. In cases of high complexity, knowledge storage
increases the value of knowledge because problems can be explained better and
treated more effectively. The knowledge storage process however can easily lead to a
situation in which the value of knowledge decreases. This happens when an
information overload is generated. Some 'solutions' to this problem are: create
improved management theories that aid selection among valuable and invaluable
knowledge, create new learning norms that improve the use of the knowledge base,
and the removal of obsolete management theories.
When dynamics increases, the storage of knowledge is useful for creating continuity,
but can also act as a brake on innovations. Stored knowledge can be used for learning
from the past. A more interesting opportunity is that of innovation by connecting
different core competencies. This requires a matrix organization structure or task force,
because core competencies are generally not shared among departments or SBU, and
an inspirational leadership that stimulates ideas and activities in the organization for
connecting competencies that are seemingly very different and unconnected.
These considerations are generalized in table 5.16 that summarizes the use of
conserved knowledge in lean and classic machine bureaucracies.
MB: Lean Classic
Storage:
Acquisition Much knowledge and data Much knowledge and data
Retention Less, because much removal when needed Much
Closely connected with mind and motivation In archives, formal rules and procedures
Retrieval Much. Applied to problem solving Less. Connected with procedures,
indirectly linked with problems
Table 5.16: Differences in Knowledge Storage Between Lean and Classic Machine
Bureaucracies
Use of knowledge
Ideal typical machine bureaucracies use knowledge developed in the past. It is

therefore very conservative, but one could also think that change is not necessary
because of the stability of the environment. Dynamic environments do require
changes of knowledge, which is hampered by reification processes resulting from
tradition. Using stored knowledge can also lead to competency traps, as was
mentioned in chapter 4 (Kim, 1993; Levitt and March, 1988). Because the
environment is simple, much knowledge is tacit. Making it explicit is sometimes very
difficult but essential for reliable reapplicability of the knowledge.
The formal functional organization of machine bureaucracies leads to a very
specialized use of knowledge, and even to knowledge ownership. This is not only the
result of a political constellation that exists in machine bureaucracies, but also of
problems in applying knowledge created elsewhere. The problem of the applicability
of knowledge is linked with the fact that departments often lack a shared body of
knowledge.
Dissemination of knowledge
Knowledge dissemination involves three major activities, that are particularly

problematic in complex situations with low codification:

• Distribution of messages. This is the physical process.
• Mutual understanding. This is the semantic aspect of message dissimenation.
• Synchronization. This implies that people's understanding matches in time.
The first issues are standard problems mentioned in the communication literature
(Stamper, 1973; Guetzkow, 1965). The synchronization issue is less well treated, but
essential, because people must act in concert. When one organization member is still
busy selling product X while other members have already found out that selling X
only leads to losses and thus must be stopped, the organization is acting
inconsistently because of the lack of a shared body of knowledge. This
synchronization issue is well treated in the database literature (Rochfeld and Tardieu,
1986).
In stable environments, knowledge dissemination consists of regular reports and
formal data streams. Low complexity environments can more easily create
unambiguous information. Everybody receives precisely the data/information needed
for his particular job and a precise data distribution schedule exists restricting
synchronization problems. When environmental characteristics are very stable,
speeding up knowledge dissemination by automation can be very effective. When
complexity increases, standard reports no longer suffice. Dispersed knowledge must
be connected to find solutions for complex problems (task groups) increasing the risk
of asynchronous communication. Media richness should be increased to lower
ambiguity and increase understanding (Daft and Weick, 1986). High dynamics
demands faster communication channels too, and some delegation of
responsibilities.
Adaptation of knowledge
Low complexity and a rather stable environment lead to slow and incremental
changes in organizational knowledge. Actions in this situation are strongly motivated
by action plans that have undebated models containing means-goals theories as their
foundation. Not using these models can lead to actions that are not-legitimated. This
can lead to severe sanctions when with hindsight these actions seem to have been
ineffective. Adaptation of knowledge is mostly done by the person responsible for the
knowledge. In the highly differentiated structure of machine bureaucracies this
implies that knowledge adaptation is a specialist (technostructure) activity. Because
feedback about mistakes is a slow process, adaptation is a time-consuming business,
often leading to the implementation of knowledge that is already invalid.
Additionally, the low risk of the environment demotivates organization members to
start adaptation processes.
Performance control for evaluation and knowledge adaptation is often not done and
not enough attention is paid to it. For instance in the Dutch high-tech company
mentioned in chapter 4, at an assembly unit in 1992 production norms were used
that had been developed in 1968. In this organization (with fast moving dynamics
because of rapid products and process innovations, this led to norms that are not
applicable for the effective steering of the units. Strangely enough the organization
has not changed its production norms in 14 years. Now that the company has come
into a very hostile and competitive environment, it is being forced to reconsider its
norms. This study is not being carried out by its own technostructure or
management, but by one of my M.Sc. students! The rules, norms and procedures
have reified the organization in such a way that it is not capable even of realizing a
single-loop process.
The adaptation process can provide important triggers for double-loop learning. This
happens when the complexity of the environment increases, so that the management
theory cannot give a valid explanation or offer effective proposals, or when the
dynamics has increased to such an extent that a theory must be found that improves
understanding. Often uncertainty avoidance strategies are regarded as more effective
than learning. In the longer run this could be untrue, as was shown in the case of
lean production.
Definition of a Single-Loop Learning Score
The rating of learning activities can be done by noting the number of learning fields
an organization is concerned with, and the number of activities undertaken as was
done for the double-loop learning score. A further description of a scale for single-
loop learning is presented in chapter 7.
5.4.4 A Note on Deutero Learning
Lean and classic machine bureaucracies have different ways of adapting to

environmental changes. These approaches were already summarized in the previous

discussion of learning norms. From these discussions we also know that in the case of
high dynamics, double-loop learning (creation of new theories) is extremely
important to overcome the risk of working with obsolete theories. This means that
relatively more effort must be placed in double-loop learning activities. When the
dynamics is low, but complexity is high, it is important to put a greater effort into
single-loop learning, and specifically in the storage and re-use of knowledge to reduce
learning costs. Double-loop learning is less necessary in that case, because the basic
assumptions will still be valid. Learning norms govern the decisions on learning
efforts (particularly learning policies) and are themselves the result of a deutero
learning process. This is pictured in the figure 5.7.
5.5 Summary
In this chapter we presented the major factors describing the lean-classic and service-
manufacturing dimensions of machine bureaucracies. The lean-classic distinction is
defined by 10 items: attitude to quality, level of decentralization, availability of lateral
structure, relations with clients, relations with employees, financial decision-making
structures, human resources management ideas, people's motivation basis, and
sources of new ideas. The manufacturing-service distinction is described by 8 items:
material and equipment, involvement of client in production, information
processing, responsibility for success, description of process phases, stocks and
buffers, systems boundaries, and professionalism.
The term learning needs has become more concrete via the application of Duncan's
list of environmental components and factors by which the dynamics and the
complexity of organizations can be scored. In chapter 7 we will investigate how an
index of learning needs can be defined.
The term deutero learning is made more operational in this chapter. Deutero
learning can be described by its leanness and service-manufacturing nature.
Responsibility norms can be described in 8 organization structures: functional,
divisional, matrix, task groups, networks consisting of independent companies,
networks with expertise centers, volvos, and parallel learning structures. Action
norms are about incentives, interpersonal trust, attitude to knowledge removal,
learning priority, and source of knowledge. Procedural norms are about feedback
frequencies, but are not very concretely defined yet. This will be done in more detail
in chapter 6 (about MICS).
Some hypotheses were defined about the value of knowledge given a certain degree of
complexity and dynamics. One can state that increasing complexity leads to a higher
value of knowledge. The value of knowledge increases until a moderate level of
dynamics is achieved. Higher dynamics leads to a decrease of the value of knowledge.
Single-loop learning is linked with both the lean and the classic characteristics of
organizations. For storage specifically, the focus is on acquisition, retention and
retrieval of knowledge, in all cases very different in lean and classic machine
bureaucracies (table 5.16). For the dissemination of knowledge, the focus is on
distribution, mutual understanding, and synchronization of understanding. The
adaptation of knowledge seems to be different for action planning and performance
evaluation. In the following chapters we will often use the terms problem
anticipation and critical evaluation instead of action planning and performance
evaluation, to stress the importance of learning in these activities. Further arguments
for the use of these concepts are also given in section 6.3.2.
In chapters 6 and 7 the dimensions of organizational learning will be further defined.
Role and Value of MICS for Organizational Learning 133
Chapter 6: Role and Value of Monitoring Information and

Control Systems for Organizational Learning
6.1 Introduction
The organizational learning literature is full of contradictions about MICS' role and
value. In her study of Texas Instruments' planning and control information system,
Jelinek (1977) concluded that information systems contributed considerably to
(institutionalizing) organizational learning. These results have been severely criticized
by Mintzberg, because according to him the systems did not capture any knowledge
and they failed soon after her book was published. Mintzberg therefore states (1989,
p. 350):
" Texas Instruments' own fancy planning system was subsequently believed to discourage
innovation. In fact, there never was any evidence that the company's success stemmed
from anything more than a capable leader who knew how to learn and whose own energy
and enthusiasm enabled him to attract good people and to invigorate them. Good people,
of course, make for good organizations. They also design good systems, at least systems
that are good for them. But remove the good people and the systems collapse. Innovation,
it turned out, could not be institutionalized."
In another publication Mintzberg directly links MICS' abilities with machine
bureaucratic environments:
" ...in the tall administrative structure of the Machine Bureaucracy, information must pass
through many levels before it reaches the top. Losses take place at each one. (...) The fact
that the transfers are vertical - between people on different status levels of the hierarchy -
means that intentional distortions of information also occur. (...) Probably a greater
problem is the MIS's emphasis on 'hard' (quantitative), aggregated information. A good
deal of evidence suggests that it is not this kind of information top managers need to
make their strategic decisions as much as it is soft, specific information. (...) Often the
MIS data are too late as well" (Mintzberg, 1983, p.184).
Mintzberg's conclusion is that MICS contributes nothing.
Mohrman and Cummings (1989) are more optimistic about MICS use:
" High-performing organizations employ multiple systems for gathering relevant
information, making appropriate decisions, and communicating responses to specific
groups and departments. They often supplement sophisticated management information
systems with less quantitative devices to ensure that information flows in all directions.
For example, these include coordinating councils, customer focus groups, employee sensing
meetings, cascading information sessions, and weekly videos of 'messages from the
president'. These information-processing systems help organizations better scan their
environment and integrate their subparts so they respond to complex and changing
conditions cohesively" (p.9).

Other authors are less unconditional about MICS' value. For instance Argyris states:
" If we define learning as the detection and correction of error, then learning is a core
activity of any organization and any MISS22. (...) Control, in order to be effective, is
designed in many organizations to be unilateral. Along with the unilateral feature, there
tend to exist sanctions in order to make certain it has 'teeth' (1980, p.15).(...) Model I
theories-in-use are theories of top-down, unilateral control of others in order for the actors
to win, not to lose, and to control the environment in which they exist in order to be
effective. But it can be shown that Model I theories-in-use lead to effective problem-
solving, primarily for issues that do not require that the underlying assumptions of Model
I theories-in-use be questionable; that is single-loop learning Model I theories-in-use do not
make it possible for people to have problem-solving skills that question the governing
values of their theory-in-use; that is double-loop learning" (p.21).
To remove these contradictions the following questions must be asked. What can a
MICS contribute to a group of 'good' people so that they can be more than just
enthusiastic? What role could a MICS play in a group learning process? How can
MICS lead to institutionalization of learning processes? If MICS really enables
institutionalization of organizational learning, how should we evaluate this? To solve
these questions, we must first understand more clearly what we mean by MICS. An
additional question is, what is so specific about MICS in comparison to other types
of information systems? Answering this last question is important in order to
understand the domain of research. Like machine bureaucracies, MICS is used here
as a heuristic case to develop a theory about the relation between organizational
learning and information technology.
This chapter describes MICS from an organizational perspective, after having
described information systems in general. The organizational description is done in
two steps. The first step is a description of MICS' roles. The second step is a
description of MICS' roles and values for organizational learning, and describes how
MICS might be used in organizational activities.
6.2 Information Systems: Technology and Organizational
6.2.1 Technical and Organizational Aspects of Information Systems
22
Argyris uses the term MISS for monitoring information and control systems.
Many times, the term 'information system' has been connected specifically to
computer-based information systems. Although it is obvious that computer
technology has contributed substantially to the creation of modern information
systems, the major developments in information systems were not computer-based
but social and organizational. A restriction to computer-based systems does not:
1. Include the many formal and manual systems that do precisely the same work
and are often required in addition in order to achieve effective computer-based
systems.
2. Include the many informal systems and norms that are also engaged in
information processing.
Number 1 is exemplified by a case on flexible manufacturing systems. In order to
manufacture a certain steel pressed product, a small company bought a flexible
manufacturing unit costing over $200,000. This advanced system was, however,
hardly used. People continued using the traditional construction tools, because the
organization was not able to provide the required data input to manage the
manufacturing process via the computers. As a consequence, an elaborate formal
administrative organization had to be developed that was not yet in existence in this
small informal organization.
Number 2, I encountered in a large insurance company that had developed an
executive information system. The management problem was however not the lack of
accurate information, but the organizational culture, not used to top executives
having such an active approach to management and information gathering. This new
way of management by open communications between executives and directors of
the departments, meant a large change in work and culture.
An assessment of information systems thus not only requires an evaluation of their
technical operation, but also an evaluation of their social function. This means that
all six layers of information are necessary to evaluate the physics, empirics, syntactics,
semantics, pragmatics, and social aspects (Stamper, 1973; also see section 1.4). The
first three layers are about information technology, the other three layers are about
social and organizational aspects of information systems.
6.2.2 Technological Aspects of Information Systems
One of the basic reasons for starting this study, and many other IT impact studies, is
that information technology can change traditional ways of management and
organization. This statement however, does not mean that one should use a
technology deterministic view on impact, because impacts are a result of the
interaction of organizational features and information technological opportunities
(cf. chapter 2). This means that one must consider the formal and informal
information systems as a broader environment in which computer-based information
systems impact on the organizational environment. The basic computer technologies
that have fuelled these discussions are: computer components technology

(Tanenbaum, 1984), database technology (Date, 1982; McFadden and Hoffer, 1991),
data communication and telematics (Tanenbaum, 1988), and knowledge engineering
(Kerr, 1991). These technologies can be described by key terms at the physics,
empirics and syntactics levels as summarized in table 6.1.
Technology: Computer compo- Databases Telematics Knowledge engi-

Semiotics: nents neering
Physics Processors, busses Storage devices Networks Processors and

and screens storage devices
Empirics Processing capaci- Amount of bits Coding and deco- Amount of know-
ties, processing and bytes ding systems ledge rules
operation
Syntactics Virtual machines Data model, con- Communication Principles (logic)
and operating ceptual schema protocols of knowledge
systems representation
Table 6.1: Features of Information Technology Described in the Technical Layers of
Stamper's Semiotic Framework.
These technologies are based on the application of digital computers that consist of
internal and external memories, control units, registers, and logical and arithmetic
units (Tanenbaum, 1984). They serve as replacements for archives in the shape of
paper files, personal files and memory that can have exactly the same function. The
main question at the technical level therefore becomes: What is most cost effective:
computers or the traditional technology? The answer to this question depends on the
application field. In purely technological terms the answer depends on the cost of
purchase and maintenance of the physical devices, the complexity of creating the
software and procedures and technical problems with coding and storing. Each time
of use would mean some (fictional or real) payback of the investments involved. If
use is restricted to a specific period the payback is often not sufficient.
6.2.3 Organizational Aspects of Information Systems
Information systems can have several functions (roles and values) in organizations.
Several approaches are valuable in describing these functions, such as:
1. Economic principles, for instance via description of the value chain and IT's
contributions (Porter, 1985).
2. Organization design principles, by describing different coordination mechan-
isms and IT as an additional coordination mechanism (Mintzberg, 1983).
3. Management functions and IT's role. This is especially useful when trying to
define user requirements for specific management functions (Davis and Olson,
1985).
4. Organizational transformation, which closely resembles the previously
mentioned value chain, however with emphasis on the construction of products
and delivery services as a transformation of inputs to outputs (e.g. Perrow,
1967; Hill, 1983). In that case information systems can have an important role
in controlling, planning and checking production runs.
5. Planning principles by identifying stages in the development of the information
supply by constructing a hierarchy of systems (Anthony, Daerden, Bedford,
1984; Porter, 1988).
Organizational learning focuses on management in the organization's context and
therefore requires the organization design and coordination perspective. The management
approach might be interesting as well, but requires the elaboration of a different
conceptual framework.
The coordination approach states that a viable organization has an operating core, a
middle line, a strategic apex, a support staff (including administrative services), and a
technostructure. Organizations can differ tremendously in the number of people and
amount of money spent on these five groups in the organization (cf. chapter 5).
Information systems have organizational functions when supporting or replacing
these groups. These systems generally have specific names (cf. Markus, 1984; and
McKeown and Leitch, 1993), and are grouped in table 6.2.
Function group System type

Operating core Transaction or order processing systems. EDI (interorganizational).
Expert systems for professionals.
Middle line Monitoring information and control systems.
Management Information Systems or Management Reporting Systems.
Apex Executive Information Systems, or Executive Support Systems
Support Staff Office Information Systems, Computer Support for Collaborative Work, Legal
Expert Systems, knowledge-based systems
Technostructure Decision Support Systems (DSS), Group DSS, some Expert Systems, Computer
Aided Design and Computer-Aided Manufacturing.
Table 6.2: Organization Functions and Information System Types.
This study is about the MICS systems (often also called MIS or Management
Reporting Systems). At the semantic level MICS systems must have data and models
that are unambiguous for different users. This presumes a common understanding of
reality described in shared management theories. The receiver of these insights and
data is not a passive consumer of these messages, but checks its validity in relation to
other insights and data. This involves an adaptation process, which requires a specific
responsibility structure. At the pragmatic level the insights gained from new theories
and data must result in new behavior. It is therefore important that action norms are
created that induce people to use new data and insights. The receivers of these data
and insights also have opportunities for creating their own knowledge, so that the
received messages are checked for their relevance. At the social level MICS' role must
fit in a set of learning responsibilities and procedures. When for instance MICS leads
to a possible change of power (as was the case with Markus' financial information
system and Zuboff's paper mill, cf. Markus, 1983 and Zuboff, 1988) this must be
foreseen and be part of its design. Also ways of disseminating messages, ways of
communicating and decision-making can change and must be planned and
implemented when required. MICS distinction from other information systems (such
as operational systems, DSS, knowledge-based systems, and CSCW) is however only
typological, and in practice not always easy to make. It does however focus attention
on issues of importance for investigation.
An additional way of looking at IT can be mentioned: IT-architectures. IT-architectures
are configurations of systems. This proposes interconnections between systems,
which makes it sometimes difficult to talk about systems as having one function or
one technology. The architecture concept also suggests the view of separate systems
that profit from each other's data, models, and features (e.g. MICS using the database
of the order processing system that is connected with an Electronic Data Interchange
system using telematics, Earl, 1989). Because of increasing IT-integration, this is
becoming more realistic and will pose major organizational learning questions in the
near future.
Figure 6.1 gives a quick summary of the information systems typology found so far.
In the following subsections, the technical and organizational dimensions of MICS
are further described.
6.3 MICS: Technological and Organizational
6.3.1 MICS: The Information Technology Dimensions
1. Physics
MICS has undergone considerable changes since the development of computer

technology. Because of this technology it is much cheaper to apply database
technology, by which files can be managed in concert. This improves the connections
between more-or-less independent departments (such as manufacturing and
marketing). Well-designed databases not only link dispersed data, but they also
support the efficient management of data (reducing redundancies, reducing the
chance of inconsistencies and safe-guarding against unauthorized use; cf. McFadden
and Hoffer, 1991; Nijssen and Halpin, 1989). Of particular interest is the fact that
decisions can be based better on facts instead of (political) convictions and power.
Additionally, the increase of the computing power of personal computers aided the
processing of user-friendly executive information systems which, when connected to
an organization's information network, enable the executive to be much more
knowledgeable about organizational events. From a management perspective, the
organization then becomes more transparent. As a consequence, middle line
positions exclusively for the purpose of information dissemination are becoming rare.
This would solve many of the problems Mintzberg foresaw in the relation between
machine bureaucracies and organizational learning mentioned at the beginning of
this chapter. Being equipped with data about the rest of the organization via the
Executive Information System and its network, enables managerial autonomy to be
less risky and more effective than ever (Gurbaxani and Whang, 1991).
2. Empirics
From an empirics point of view, MICS's require a specific kind of information flow.
Depending on the way these data flows are organized, one can state that the
organization has a different level of
control.
A basic feature of all MICS is that data
about operations' results are created and
made available in the organization. This
first level of control, as described in
figure 6.2, leads however to
interpretation problems, because:
• Norms are unknown, which
makes it impossible to make
comparisons with plans and this
means that data about results are used as arguments for and against, depending
on the interests at hand.
• Ways of measurement are unreliable, because no systematic measurement tools
have been developed that are relevant from the managerial perspective.
Both problems are solved by developing clear standards of performance in the
problem anticipation process and by developing measurement instruments. Often
only one of these solutions is implemented leading to second degree control (cf.
figure 6.2). Option 1 is the case of feedforward control, and option 2 concerns
feedback control. Both are ineffective when not combined. The feedforward system
does not evaluate and check its assumptions. The feedback system does not know
why the operations were executed, and thus does not know the assumptions. When
both systems of feedback and feedforward are combined a closed control loop is
realized called third level control (Flamholtz, 1983; cf. figure 6.3). This closed control
loop becomes a single learning loop when an evaluation and reward component is
added, which motivates people to draw conclusions from the data (figure 6.3). In this
case the technical information system and the human motivation system are
connected, which is a basic requirement of any human learning system and the
fourth level of control. The single-learning loop becomes a double-learning loop
when the evaluation, measurement and planning subsystems become a field for
feedback. This corresponds to the fifth degree of control. IT can support closed
learning loops, because of its efficient, quick analysis, and fast dissemination of data.
Lean organization possibly profit more from IT for learning, because of their higher
awareness of data value (lower power
play and more explicit mental models).
3. Syntactics
A fully developed MICS thus consists of
six basic elements that form the MICS-
structure (Flamholtz, 1983; Ansari,
1977):
• Standards and objectives.
• Measurement instruments.
• Source of data and order of
presentation.
• Timing and frequency of data.
• Route of data flow.
• The extent of information sharing among potential users.
The cybernetic approach to organizational learning defines MICS in terms of its
information flows (empirics) and its structure (cf. the discussion of De Raadt's
cybernetic organizational learning concept in chapter 4). They do not bother about
complications with understanding a system's output and how new insights should
change behavior or organizational structures. This last concern is stressed by
organization development authors, to which we owe much of the following
description of semantics, pragmatics and the social aspects of MICS.
At the syntactic level MICS also should have some important features, as otherwise
its usableness (and success) will be low. These features are:
• Man-machine interface quality
• Flexibility of databases, providing many entrances and opportunities of
adaptation
• Quality of the administrative organization. The chance of poor data can be
reduced by introducing rigorous procedures, and methods of checking quality
of data.
• Comparability of data structures, so that the chance of effective exchange of
data is high.
6.3.2 MICS: The Organizational Dimensions
4. Semantics
Weick (1985, pp. 52-54) describes these problems as sense-making processes, with the
following elements:
1. Effectuating: "People learn about events when they prod them to see what happens. (...)
People find out what's going on by first making something happen. (...) Since action is the
major source of human perceptions and intuition, any assessment of the potential for
sense making must pay close attention to action." The involvement of the people
where the data are about in interpretation processes is therefore essential in
sense-making.
2. Triangulating: "People learn about an event when they apply several different measures
to it, each of which has a different set of flaws. (...) These various 'barometers', each of
which presents its own unique problem of measurement, begin to converge on an interpre-
tation".
3. Affiliating: "People learn about events when they compare what they see with what
someone else sees and then negotiate some mutually acceptable version of what really
happened". People not only want to have several sources of data (triangulating)
but also want to discuss with other people how they perceive reality and want to
bargain about what 'really' happened.
4. Deliberating: "People learn about events through slow and careful reasoning during
which they formulate ideas and reach conclusions". This simply means that people
need time to make up their minds about reality by means of interpreting data.
5. Consolidating: "People learn about events when they can put them in a context". The-
refore people need more than simple data, but also a view by which they can
relate data to interpretations of what happened.
Information systems, particularly MICS, are supposed to support making sense,
therefore to support the creation of information or mental models instead of information
processing capabilities (Nonaka, 1988). Weick is however not very optimistic about
the potentials of information systems to support the sense-making process:
" People using information technologies are susceptible to cosmology episodes because they
act less, compare less, socialize less, pause less, and consolidate less when they work at
terminals than when they are away from them. As a result, the incidence of senselessness
increases when they work with computer representations of events" (1985, p. 56).
In my perception the cosmology problem has its roots in a poor match of MICS with
individual and organizational interpretation systems.
Individual interpretation systems concern the individual's way of understanding the
world. Four types were identified on the basis of Kolb's prehension and
transformation dimensions in chapter 4 (figure 4.1): accommodator (concrete
experience and active experimentation), diverger (concrete experience and reflective
observation), converger (abstract conceptualization and active experimentation), and
assimilator (abstract conceptualization and reflective observation). MICS systems
match well with the 'diverger', because these systems improve the sensing process by
supporting the process of data gathering in organizations. The 'assimilation' learning
type is mainly interested in developing and applying abstract models. In fact MICS
develops models only in an incremental way. The data can improve insights in reality,
but the MICS-application is not explicitly designed for improving or generating many
alternative models. MICS contains a model of reality itself, that is difficult to change.
EIS-applications have more opportunities here. Particularly Decision Support
Systems are designed for developing models (c.f. 'I Think'; Wijnhoven, 1992b). E-
mail systems, teleconferencing and Group Decision Support Systems are particularly
designed to 'converge' insights that are available at different places and persons in the
organization.
Especially in small companies, and in situations in which managers work
independently, these psychological traits are important. If the decision-making
process is of a more collective kind, it is more useful to stress the importance of
culture and organizational structures and processes in understanding the
interpretation of data. According to Daft and Weick (1984) organizational
interpretation systems should be designed in line with the complexity of the
environment, they call it analyzability, and the extent to which organizations actively
search for interpretations themselves (learning effort in our terms). By applying both
variables dichotomously, four interpretation systems are defined, presented in table
6.3.
unanalyzable UNDIRECTED VIEWING ENACTING

Scanning Characteristics: Scanning Characteristics:
1. Data sources: external, personal 1. Data sources: external, personal.
2. Acquisition: no scanning depart- 2. Acquisition: no department, irre-
ment, irregular contacts and gular reports and feedback from
reports, casual information. environment, selective information.
Interpretation Process: Interpretation Process:

1. Much equivocality reduction 1. Some equivocality reduction
2. Few rules, many cycles 2. Moderate rules and cycles
Strategy and Decision Making: Strategy and Decision Making
1. Strategy: reactor 1. Strategy: prospector
2. Decision process: coalition building 2. Decision process: incremental trial
and error.
ASSUMPTIONS CONDITIONED VIEWING DISCOVERING
ABOUT Scanning characteristics: Scanning Characteristics:
ENVIRONMENT 1. Data sources: internal, impersonal 1. Data sources: internal, impersonal.
2. Acquisition: no department, 2. Acquisition: Separate departments,
although regular record keeping special studies and reports, extensive
and information systems, routine information.
information. Interpretation Process:
Interpretation Process: 1. Little equivocality reduction
1. Little equivocality reduction 2. Many rules, moderate cycles
2. Many rules, many cycles Strategy and Decision Making:
analyzable Strategy and Decision Making: 1. Strategy: analyzer.
1. Strategy: Defender. 2. Decision process: systems analysis,
2. Decision process: programmed, pro- computation.
blemistic search.
Passive Active
ORGANIZATIONAL INTRUSIVENESS
Daft and Weick, 1984, fig. 3, p. 291
Table 6.3: Interpretation Modes and Organizational Processes.
Computer systems could be usefully applied when the situation is analyzable and
when large amounts of data must be processed. MICS is often used in organizations
for supporting 'conditioned viewing'. This means the monitoring of processes by
means of internal and impersonal (objective) data. This implies that the information
output must lead to clear action proposals, which is only possible in closed loop
learning situations. In lean organizations, the data from monitoring processes are
however also used for a more active 'discovering' of what is at stake, and therefore a
start to learning. Executive information systems are often mentioned as tools for
supporting the 'enacting' process, which requires opportunities for processing data of
an undetermined format. 'Undirected viewing' is only possible with very informal
information systems (Hedberg and Jönsson, 1978). Computer-based information
systems will possibly not pay off in that case.
The analyzability also requires different types of control. Hofstede found four basic
questions that determine organizational control:
1. Are objectives unambiguous, or can ambiguity be resolved?
2. Are outputs measurable, or can acceptable surrogate measures be found?
3. Are the effects of management interventions known?
4. Is the activity to be managed repetitive?

When objectives are unambiguous, outputs can be measured, and if the activity to be
managed is of a repetitive type, the situation may be called analyzable, and control
systems can be used to support management effectively and efficiently when also large
data processing is required. In all other situations there is low analyzability, and the
use of computers is only useful for the generation of hypotheses (DSS-systems). Again
there must be a lot of data processing, and reliable data at the basis of the system. By
means of the following decision tree, one can detect the type of control (and thus the
type of meaning of an MICS' output) (see fig. 6.4).
Political control situations have incompatible models of reality, which means that
data can be interpreted in different ways to support conflicting interests. All other
types of control, with the exception of expert control, refer to situations in which
shared mental models are created, and therefore data lead to interpretations that
support common interests.
As a conclusion, three types of scores of MICS can be given at the semantic level:
• Learning styles of users and implicit learning styles of systems. The thesis was that
MICS mainly supports divergence and accommodation knowledge. If the
organization (or situation) requires convergence and assimilation, MICS cannot
cope.
• Analyzibility and intrusiveness of organizations and match with MICS. The thesis is
that MICS supports 'conditioned viewing' and 'discovering' (both require shared
mental models to make unambiguous interpretations), but is poor on 'enacting'
and will not support 'undirected viewing'.
• Control types and the extent of incompatibility of mental models that are the conceptual
basis for MICS. When outputs cannot be scored (also not with surrogate
measures), MICS has little use. MICS cannot support situations that are
characterized by political control. MICS will be particularly relevant at 'trial and
error' and 'routine' control, because people can learn from data about processes
that are repetitive. This can lead to a situation of knowledge saturation, thus
'expert' control.
5. Pragmatics
Pragmatics is about the question: what actions are evoked by the information made
accessible to people? Studying this question it is necessary to understand that with
MICS, managers and employees are involved in a negotiation and possible learning
process. Lawler and Rhode (1976) suggested that some of MICS' elements have a
severe impact on organization members' intrinsic and extrinsic work motivation.
Their hypotheses are summarized briefly in table 6.4.
MICS-elements Intrinsic motivation23 Extrinsic motivation24

1. Nature of A. Set by Person Being Measured A. Joint Process between Person
Standards B. Moderately Difficult and Supervisor
B. Moderate Achievement Diffi-
culty
2. Characteristics of C. Complete C. Complete
Sensor Measures D. Objective D. Objective
E. Influenceable25 E. Influenceable
3. Speed of F. Immediate F. Fast
Communication
4. Frequency of G. Close to Time Span for Job G. As Fast as allowed by time span
Communication of discretion
5. Recipients of H. Person Being Measured H. Person with Reward Power as
Communication well as Person being Measu-
red and others doing similar
work.
6. Source of I. Person Being Measured or I. Joint Process between Person
Discrimination Other Credible Source and other Trusted Person or
Persons
7. Type of Activity J. High Autonomy J. Not a Crucial Factor
K. High Task Identity
23
Based on Lawler and Rhode, 1976, p. 81, table 5-1
24
Source: Lawler and Rhode, 1976, p. 64, table 4-3.
25
Meaning that scores should reflect a person's efforts.
L. High Variety
8. Source of M Improvement in Job Capabili- K. Rewards that are important
Motivation ties
Source: Lawler and Rhode, 1976
Table 6.4: Values of MICS Elements that Produce Intrinsic and Extrinsic Work Motivation.
The MICS-elements list describes how the cybernetic approach would define a
control system. The organization development approach would however emphasize
how these information streams impact on actual behavior, motivations,
understanding and possible conflicts.
Lawler and Rhode warn for possible dangers of dysfunctional behavior that can result
from inproper application of control information systems. Dysfunctional behavior of
control information systems is defined as:
" ...employees [that behave] in ways that look good in terms of the control system measures
but that are dysfunctional as far as the generally agreed upon goals of the organization
are concerned" (p. 83).
This has been researched in the sociology of organizations since the 1930s (Michels,
1925/66; Merton, 1940; and Gouldner, 1950), and now has received considerable
attention by Senge under the heading of 'systems thinking', meaning a way of
conceiving the indirect and non-obvious impact of decisions in the longer run. A
classic example is described by March and Simon (1958) in figure 6.5.
Lawler and Rhode describe four types of dysfunctional behavior in relation to MICS,
namely:
• Rigid bureaucratic behavior. Figure 6.5 illustrates this type of dysfunctional
behavior. The demand for greater reliability leads to an increase of de-

fensiveness of behavior, thus to an increase of rigid bureaucratic behavior by a
more strict application of rules. This can lead to dissatisfied clients, which in
turn increases defensiveness and behavior led solely by rules.
• Strategic behavior. This type of behavior is defined as: "...altering behaviors for a
period of time to make the control system measures look acceptable" (p. 86). A classic
example from the Sovjet command economy is the Kolomensk Machinery
Works in Moscow County in 1940: "...In the first ten days of every month it
produced 5 to 7 per cent of the month's output, in the second ten days, 10 to 15 per cent,
and in the third ten days, 75 to 80 per cent" (Berliner, 1956, pp. 87-88, quoted in
Lawler and Rhode, 1976, p. 87). This way of production probably led to serious
under-utilization of people and capacities in the first periods, and over-
utilization and exhaustion of production factors in the last period. These
problems are also common to machine bureaucracies in western economies
with insufficient market feedback.
• Invalid data reporting. Two types of invalid data reporting can be recognized.
One, feeding invalid information about what has happened into the control
system. This is obviously lying and can thus be detected, leading to punishment.
This tactic is clearly very risky. The second way of invalid reporting is more
common and difficult to detect, and concerns the process of underestimating
revenues and overstating costs in budgetting processes (Wildavsky, 1974). It
makes a lot of sense for managers to create slack in budgets in this way, because
not staying within the budget could lead to punishment.
• Resistance. Many authors have stressed the possible negative impact in terms of
resistance to control systems (Markus, 1983; Pettigrew, 1973; Argyris, 1971).
The main explanations for resistance are as follows: (1) control systems can
automate the expertise of managerial jobs, (2) control systems can create new
experts and give them power (Pettigrew, 1973), (3) control systems can measure
individual performance more accurately and completely (this can have positive
results for a group whose performance was under-estimated, but at the same
time can enforce a feeling of 'big brother is watching you'), (4) control systems
can change the social structure of an organization (e.g. pay-incentive systems
that turn colleagues into competitors instead of friends), and (5) control systems
can reduce opportunities for intrinsic need satisfaction when extrinsic output
measures are used for appraisal.
Of course MICS does not have only negative consequences. Scientific management
in particular emphasizes the importance of the accumulation of knowledge about
work processes. It is however often not clear what role MICS has in this. Learning
processes have many informal characteristics guided by informal and tacit norms that
do not apply to MICS. To my knowledge, no study has empirically found any
convincing evidence about the impact of MICS-usage on performance. It could be
that the effect was not found because the intermediating conditions influencing the
relation between MICS-usage and performance, i.e. organizational norms, were not
included in the research design (cf. our discussion of the study of Lee and Guinan in
chapter 1).
6. Social
According to Mintzberg one should distinguish between two types of systems for
monitoring and control:
• Performance control systems:
" The purpose of performance control is to regulate the overall results of a given unit.
Objectives, budgets, operating plans, and various other kinds of general standards are
established for the unit, and its performance is later measured in terms of these standards
and the results fed back up the hierarchy by the MIS" (Mintzberg, 1983, p.75).
This means that performance control influences decision-making and action only
indirectly by establishing the targets the decision-maker must achieve. Performance
control information systems can contribute to understanding the company's activities
when the organizational units rated are more or less independent. When units
influence each other's performance a separate measurement system per unit could
bias the results because some of the performance is not the result of the unit's own
operation. The choice then can be to disentangle the units, or to develop a market-
like pricing principle for inter-unit transactions, or to develop one performance
measurement instrument for all the units together!
• Action planning systems:
" Action planning emerges as the means by which the nonroutine decisions and actions of
an entire organization, typically structured on a functional basis, can be designed as an
integrated system. All this is accomplished in advance, on the drawing board so to speak.
Behavior formalization designs the organization as an integrated system too, but only for
its routine activities. Action planning is its counterpart for the nonroutine activities, for
the changes. It specifies who will do what, when, and where, so that the change will take
place as desired" (p. 78).
Whereas performance control systems cannot really cope with the interdependences
of functional units, action planning is typically used to solve this problem.
In relation with Mintzberg's organization types, planning and control information
systems have different roles. This finding is summarized in table 6.5.
Organization Simple Machine Bu- Professional Divisionalized Adhocracy

Configuration: structure reaucracy bureaucracy form
Design
parameters:
Key coor- Direct Standardizati- Standardizati- Standardizati- Mutual adjust-
dinating supervision on of work on skills on of output ment
mechanism
Environment Simple and Simple and Complex and Relatively Complex and
dynamic. stable stable simple and dynamic;

Sometimes stable; diversi- sometimes dis-
hostile fied markets parate (in
(esp. products admin.
and services) adhoc.)
Planning and Little Action planning Little planning Much perfor- Limited action
control system planning and control mance control planning (esp.
and control admin.
adhoc.)
Based on Mintzberg, 1983, p. 280, table 12-1.
Table 6.5: Planning and Control Information Systems Related to Some of Mintzberg's
Organizational Configurations.
In the social context of organizational learning, action planning is a search for

problems to anticipate, and the design of activities to avoid these problems. During the
course of this process, organizations create and apply predictive models. In this
context performance control becomes a process of critical evaluation of what has
happened. The intention is find explanations for problems. These explanations can
then be used to improve problem anticipation. To realize problem anticipation and
critical evaluation, all other levels of the semiotic framework must be dealt with
effectively, so that the right data come to the right person(s), on time, and are
interpreted correctly so that effective actions can follow.
As this study is about machine bureaucracies, we must take note of an interesting
hint from Mintzberg, i.e. classic machine bureaucracies do not use critical evaluation
control systems, and only use problem anticipation systems! This can be explained
from the low learning needs of machine bureaucracies. When stability declines, the
knowledge that is stored in action planning systems depreciates more quickly and
becomes an uncertain source for planning. Therefore, evaluations of plans are
required to adjust for possible errors. Complexity also demands a stronger emphasis
on rationalizing the planning process. More information and knowledge must be
processed to come to effective plans. Thus, in situations of increasing dynamics and
increasing complexity a combination of problem anticipation and critical evaluation
systems is required. According to my understanding of lean organizations, these
organizations do indeed combine these roles of MICS, and in this they differ sharply
from classic organizations.
6.3.3 Observing MICS
Table 6.6 summarizes the items that can be observed in MICS in empirical research.
Leaness: Lean Classic
MICS:
Technical
Physics • Coupling of systems, via net- • Functional systems. Islands of automation
work and databases
Empirics • On-line systems • Offline systems, with period reports
Syntactics • High quality user interfaces • Hard copy reports
(easy understandable • Change of database on request and when
structure of software) feasible
• Flexibility of databases • Inconsistent data
• High quality administrative • Incompatible data structures
organization
• Compatible data structures
Organization
Semantics • Consistency with possible • Inconsistency with control type
control type • Mental models are diverse and
• Shared mental models incompatible. They reflect stake holders'
positions
Pragmatics • Decisions are implemented in • Many complications in translating decisions
high speed and trust to actions
• Action based on theoretical • Action based on past experience (routine)
understanding of practical or command
problems
Social • Social networks of problem • Separation of problem anticipation and
anticipation and critical critical evaluation networks
evaluation are closely • MICS service problem anticipation, or
connected punish-reward
• MICS serves problem
anticipation and critical
evaluation
Table 6.6: Differences of MICS in Lean and Classic MB's.
The following section discusses in more detail the roles of MICS for organizational
learning activities (single-loop and double-loop), and what this implies for the value
of MICS in learning processes.
6.4 Role and Value of MICS for Organizational Learning
6.4.1 Role and Values
In the preceding chapters a distinction was made between the role and values of
MICS. The role of MICS concerns the way MICS aids and changes learning
processes. The value of MICS in organizational learning terms is related to the
contributions of MICS in single-loop, double-loop and deutero learning processes.
6.4.2 Role of MICS in Double-Loop Learning

Theory development in this context is a process of learning in which ideas and

information are combined to form plans and principles about the way business
should be conducted. Some of the results of this process are action plans for the
coming month, a new way of handling material, guidelines for personnel allocation
to projects and principles for deciding on make or buy. Some ways of theory
development in which information is explicitly used and created are:
• The development of industry standards for performance, as cornerstones for
developing internal standards. This approach is complex because it is difficult
to interpret general data for the specific needs and situations of one company.
It is more effective to use the standards for a first discussion about what could
be possible (cf. Camp, 1989; Chew et. al., 1991; Walker, 1992).
• The development of engineered standards. This requires the use of methods for time
and motion studies as discussed in chapter 4 (cf. Niebel, 1982). These standards
require a lot of adaptation because they can easily become obsolete. Because
standards also easily lead to performing to minimum expectations, and thus
discourage higher ambitions and initiative, effective theory development could
often do without any performance standards.
• Work simulation. This technique involves the definition of an ideal work
situation, to support finding the gap between performance and ideal. It requires
setting up a laboratory environment, or the development of a computer-based
model of the ideal situation. The method also could be supported by decision
support systems to predict the impacts of alternatives.
• Analysis of work and business processes, also called business re-engineering (e.g.
Davenport and Short, 1990; Hammer, 1990). This contains an analysis of the
tasks and activities that are needed for the organization's survival, and a clear
description of how the activities should be conducted. An excellent idea is to
have an outsider carry out this analysis, so that seemingly trivial questions can
be asked that encourage the discussion of the basic assumptions of the business.
CASE26-tools exist that can help the analyst to be more consistent and more
productive in reporting. Business process analysis also could profit from CASE-
tools containing some artificial intelligence so that the analysis can be much
more profound.
26
CASE-tool is short for computer-assisted systems analysis, and commonly used in the business analysis
phase of information systems analysis and design. One widely spread CASE-tool is System Development
Workbench, a trademark of CAP Gemini Pandata.
• Comparative analysis. This is a theory development method which requires data

about different machines, departments, persons etc, so that explanations can be
found for variations in performance. This can lead to rethinking existing ways
of working. This method is interesting, but it is difficult to compare
departments and people as they each have their own idiosyncrasies.
• Complaints files. These are absolute necessities for organizations wanting to
improve their service quality. The information must be interpreted as symptoms
of deficiencies of the theory that governs the processes and therefore are most
valuable from an organizational learning point of view.
• Waste analysis. Some waste is clearly visible to the management: garbage. The
amount of garbage is not always related to business performance. This could be
done by measuring the amount of garbage in terms of costs, and in terms of its
impact on longer term image and goodwill. Additional types of wastes are:
defective products and scrap, rework (anything not done right the first time),
amount of inventory costs and costs of work-in-process, time (downtime, setup
time, delay time), and motion (efforts required to move materials and people).
Some sources of waste are difficult to measure such as employees that lack the
right tools and information, people without the skills to use the tools properly,
people with skills that are under-utilized, and managers spending time on trivial
matters. MICS could be used to gather and make available data on many of
these subjects (Kaydos, 1991).
• Market analysis. Market analysis is a method to analyse a company's sales and
customer satisfaction. This can be done by interviewing clients and prospects,
but also by analyzing sales figures, by comparing sales with expected sales and
the importance of several products in several submarkets and market segments.
A useful technique for doing so is the Boston Consultancy Group grid analysis
(Kotler, 1988).
• Strategy analysis. In strategy analysis the main aim is to compare a company with
other companies. Bench marks can be used for several competitive issues (e.g.
quality, costs, lead times, variety of products, client satisfaction, image). A quite
famous method for this purpose was developed by Dow Chemicals in the 1960s
and is now under the management of the Strategic Planning Institute (SPI)
under the name of PIMS (Buzzell and Gale, 1985). The essence of PIMS is that
companies deliver periodic data to the SPI. The SPI analyzes the data by means
of an econometric model so that possible impacts of current strategies can be
predicted. SPI advises the subscribers of PIMS (of course anonymously) about
possible strategies.
These techniques support double-loop learning in several learning fields. Engineered
and industry standards support learning about human resources by developing
motivating and realistic performance criteria for employees. Work simulation,
analysis of work and processes create knowledge about processes. Comparative
analysis is a technique for developing human resources and processes. Complaint
files, waste analysis and variance analysis are techniques to augment process and
product quality. Additionally, complaint files are sources for the improvement of
market performance. Market analysis results in insights into markets and their
developments. Finally, the PIMS analysis results in insights into product, process and
market innovations. These methods all require performance and planning data that
can be provided by a MICS. Table 6.7 gives a summarizing list of the roles of MICS
in the double-loop learning process.
Double-loop MICS' role

field
Human resource Data about required personnel skills and knowledge to meet new business standards.
development Data about investments in human resources in relation to payroll
Market Data about market and market segments, about profitability, short and long term
development scenarios
Product Data about product portfolio and profitability, short and long term scenarios
development Complaints data and analysis.
Scenario analysis about consequences of new products for existing resources
Process Data about process and activity structures specifically about costs and quality
development compared to some preset standards or performance of competitors and other
reference companies
Waste data, linked with processes.
Table 6.7: A List of Some MICS' Double-Loop (theory development) Learning Roles
Table 6.7 only provides insight into roles for theory development. Unlearning or
knowledge removal is a social process that cannot be supported in a direct sense by
MICS. MICS can only show what would happen if no changes were made in all these
fields.
6.4.3 Role of MICS in Single-Loop Learning
MICS in Adaptation
Single-loop learning consists of changes of the theory while keeping the basic norms
unaffected. Adaptation of knowledge is about the assessment of existing theories and
management principles, so that its working is improved. It is basically an application
of the empiric cycle, which states that hypotheses should be tested and changed on
the basis of empirical evidence. The adaptation process leads to minor changes in the
theory, but also could initiate a request for a double-loop learning process.
The basic idea of the single-loop learning process is that predefined norms are used
to control existing behavior. Many means for control exist that correspond to this
idea:
• Training. People are taught to behave according to some principles in a specific
situation. This training can be achieved by an external educational institution

or in-house via an internal school and on-the-job training. Information systems
are more often used for these educational practices, because they can provide
students with standard responses to the questions and give quantitative
feedback or hints about what to study. Games can be used during the
instruction to help the students understand the mutual interdependence of
activities and processes in the organization. This type of system is knowledge-
based or DSS and is not further considered here.
• Indoctrination. Via indoctrination, people replace their own theories by
someone else's. Here computers are not very effective, because they usually lack
effective means to communicate the inducements that are required to overcome
possible resistance. A higher media richness therefore is required (Daft and
Weick, 1984; Kiesler, 1988).
• Problem anticipation and critical evaluation.
Problem anticipation systems are useful for control as part of coordinative policies. These
policies are about the way people are expected to collaborate. This can result in task
distributions and agreements about what people should contribute to each other.
MICS can play a role in scheduling tasks and monitoring the progress of activities.
Information about deviations from previously agreed targets can lead to a
rescheduling of other activities. This type of problem anticipation system can be a
module of a logistics tracking and scheduling system. The rescheduling and adjust-
ments are typical adaptations of management theories that are operationalized in
plans. The basic norms of the plan and the way of production are not disputed. In
machine bureaucracies specialists of the technostructure develop the insights necessa-
ry to develop the problem anticipation system. This is a deutero learning process, that
changes the existing way of scheduling and monitoring and thus affects the
procedural norms. It is not the output of these systems that triggers double-loop
learning, but the idea of having a system.
The learning process in the problem anticipation case is ex ante (before the activities
take place) and in the critical evaluation case ex post. When applying Kolb's learning
cycle, the problem anticipation uses abstract conceptualizations in practical
situations. The learning cycles focus on the quality and applicability (active experi-
mentation) of the concepts and abstractions. The critical evaluation systems reflect
on concrete experience and behavior. It might easily lead to just giving feedback
without clear additions of knowledge (abstractions) and therefore have a political
nature. In Kolb's approach, problem anticipation and critical evaluation have
different roles in the learning cycle. These roles can be termed 'constructing abstract
models and experimenting with them' and 'concrete experience and reflecting about
the experience'. Combined, they support a closed learning loop, the objective of MICS.
MICS primarly supports single-loop learning, and provides triggers for the
performance of double-loop learning activities. Cf. table 6.8.
Learning activity Single-Loop Learning Double-Loop Trigger

MICS
Problem anticipation system (e.g. Improving schedules Reflecting about organizational
scheduling systems) sense of existing planning pro-
cedures and norms
Critical evaluation system (e.g. Use of information for feed- Developing new policies and
complaint files, variance analysis) back ideas
Table 6.8: Monitoring Information and Control System and Single-Loop and Double-Loop
Triggers.
The problem anticipation and critical evaluation control systems also have different
owners, the technostructure and middle line respectively. This leads to two different
social networks for learning. In machine bureaucracies these networks are socially
separated and meet infrequently. The lean organization has procedural norms that
connect them, thus enabling a closed learning loop.
MICS in Storage
Chapter 5 described storage as storing knowledge and enabling its re-use. More
precisely, three organizational knowledge storage processes were described:
acquisition, retention and retrieval (after Walsh and Ungson, 1991). These processes
can be assisted by MICS.
• Acquisition of knowledge and MICS. By acquisition we mean the way knowledge
(data, experience, judgement and science) enters into the knowledge storage
activity. Frames of reference play an important role here because issues that
cannot be given a place within them are kept out of the attention focus, and are
thus regarded as irrelevant for storage. A double-loop trigger would recognize
the inadequacy of the existing frame of reference, thus requiring its redesign by
theory development. The sensory parts of MICS can play an important role in
storing data that are relevant for creating science and judgement. It is important
to note that the data themselves do not contain knowledge, but that a clear
classification scheme related with a frame of reference can lead to easy
interpretations that do result in knowledge.
• Retention of knowledge and MICS. Many media can be used for knowledge
retention: individual memory and files, cultural elements (language, symbols,
sagas etc.), rules and principles of transformation processes, organization
structures, organizational physical layout and external archives. MICS can be a
particularly good instrument for organizational knowledge retention, when the
acquisition facility is well-designed. The problem is often however that much
knowledge is based on heuristics that are difficult to elicit (Kerr, 1991), or that
are not repetitively used and therefore not worth storing. This is not so with
operational information for scheduling and controling processes. Particularly

useful data are those relating to costs, sales, quality and efficiency, that can
often be collected as a by-product of manufacturing and services.
• Information retrieval and MICS. From information management we know the
importance of retrieval related to storage (McFadden and Hoffer, 1991). This
issue is very important because a poor retrieval system will make storage efforts
futile. When effective, the frame of reference implemented in some concrete
guidelines for gathering, storing and retrieving information solves this problem.
This means that a frame of reference is not only a management theory, but also
leads to rules for connecting formal information systems to organizational
understanding. These rules can be described in software code, by which the
automated search and retrieval of data is made possible. In principle MICS only
has a standard reporting ability. A more flexible usage is often termed
'Executive Information System', especially when data sources can be flexibly
accessed. In most organizations, executive information systems are not used at
the operational-tactical management levels because of the high expenses
involved and the still dominant idea that these levels do not need flexible tools.
MICS in Knowledge Dissemination
Knowledge dissemination concerns the distribution of knowledge, the development

of a mutual understanding and making possible the access of knowledge sources to a
range of people (cf. chapter 5).
MICS can play a role in knowledge distribution, by its opportunities to give people access
to data via terminals or personal computers in networks or the interchange of
databases on floppy disks. To create effective knowledge dissemination (at the
syntactic level) the organization needs a common mode of interpretion. Effective use
of MICS in knowledge dissemination therefore requires that a single set of data
definitions be used, or that people are able to make translations. Because people in
different places will probably view the data from a different body of knowledge and
interests, they can come to different conclusions. It is however not true that
misunderstandings always result from differences in definitions, because differences
in the theories used might also explain the misunderstandings.
Knowledge dissemination from a semantic point of view would mean that mutual
understanding in the organization is augmented. Projects of MICS development
require some elicitation of the different kinds of frames of reference that are used in
the organization. Usually only the systems analysts know precisely what these
differences are. The best way to create mutual understanding is not via these
professionals, but by creating direct interactions among the intended users of MICS,
so that they learn to understand each other's way of thinking.
An additional role MICS could play is in the synchronization of understanding. By
combining shared knowledge (which offers information about management theories)
with shared data (which gives information about a specific moment in time),
synchronization of understanding can occur. MICS can provide data that are more
timely than the traditional manual bureaucracy. Automatic sensors could even lead
to on-line information. The question is whether on-line information is required. This
of course depends on the number of changes in the processes, and the consequences
(risks) of reacting too late. In the process industry, where for instance chemical
processes are controlled by computers, a late reaction can lead to a catastrophe,
whereas in the banking industry late reactions to performance decline can lead to
mismanagement but do not necessarily lead directly to a disaster.
MICS in Knowledge (re-)Use
Knowledge use has two big problems: relevance and applicability. Relevance is about
the potential of knowledge for solving problems. Applicability is about the problems
related to using the knowledge, for instance its complexity in relation to the organiz-
ation members' ability to handle it. MICS makes knowledge easily available but is
poor in the distribution of complex knowledge.
Knowledge re-use suffers particularly from two problems: reapplicability and validity.
Reapplicability concerns the opportunities of re-using knowledge for solving
recurring problems. The main problem is that problems do not always recur
frequently enough to make knowledge storage cost-effective (cf. Hofstede, 1981).
Another problem with reapplicability is that situations and problems sometimes
might look the same but are not. Regarding one problem as being the same as a
previous one might lead to a bias in perception that could lead to serious mistakes.
The validity problem concerns the fact that theories and insights are only valid for a
specific period. For instance, economic theory of the 17th century is probably invalid
for understanding business in the post-industrial society. The solution to this validity
problem is a constant validity checking of theories in the adaptation process.
Connected with MICS, theories are mostly the basis for the definition of the norms
and the measurements that are part of a MICS. This means that each time MICS is
used, the underlying management theory is reapplied. MICS should be audited on
the understanding of its basic principles by its users, and the validity of its underlying
theory.
Although MICS' power in storing and retrieving data and experience has not yet
been explored, it is important here to state some of the limitations of MICS as a
computer-based information system. The following quotation of Kim defines these
limitations:
" ... the mental models in individuals' heads are where a vast majority of an organization's
knowledge (both know-how and know-why) lies. Imagine an organization in which all the
physical records disintegrate overnight. Suddenly, there are no reports, no computer files,
no employee records, no reporting manuals, no calenders - all that remain are the people,
buildings, capital equipment, raw materials, and inventory. Now imagine an
organization where all the people simply quit showing up for work. New people, who are
similar in many ways to the former workers but who have no familiarity with that
particular organization, come to work instead. Which of these two organizations will be
easier to rebuild to its former status?" (Kim, 1993, p. 44).
Kim's answer is the first. This answer provides an important insight into the relative
importance of the social and technical side of the learning system. The question is
however not what is most important, because hopefully the situation as described by
Kim will never happen. What is important is what MICS can contribute to the
broader social system, and what the social system cannot do without it!
Table 6.9 summarizes MICS' roles in single-loop learning.
Single-loop learning activity MICS' role

Adaptation Problem anticipation
Critical evaluation
Storage Acquisition
Retention
Retrieval
Dissemination Distribution of knowledge
Creating mutual understanding
Synchronization of knowledge
Use Applicability of knowledge
Reapplicability of knowledge
Table 6.9: A List of Possible MICS-roles in Single-Loop Learning Activities
6.4.4 A Note about MICS' Role in Deutero Learning
Deutero learning is about the description of norms that govern learning processes.
These norms are about learning policy/identity, responsibilities, and procedures of
communication and message handling. MICS can play a role in deutero learning,
because it is an implementation of some of the procedural norms. For instance, when
an organization wants to change its informal way of learning to a formal one, MICS
might play an important role because MICS requires clearly defined responsibilities
(output expectations). MICS also makes it possible to measure and control people by
defining measurable performance. This can lead to an organization type that
emphasizes single-loop learning. The possible negative effects of this kind of control
have been discussed earlier, and must be well considered while formulating policy
norms. The impact of MICS on procedural norms can be described in technical and
social terms:
• The technical parts of MICS are interesting from the deutero learning
perspective because they demand clear definitions of data, which is sometimes

very new for an organization and a major improvement in communication
could emerge. Also, questions about what should be stored, how it should be
stored and made accessible to whom are interesting questions that, when
treated seriously, lead to major changes in an organization's learning abilities by
providing a learning infrastructure.
• From the social perspective MICS plays a role in making information and
knowledge (specifically aggregated information) accessible and can lead to a
correct view of reality. The critical evaluation mechanisms that are enabled by
MICS can motivate people to single-loop and/or double-loop learning. Socially
MICS can tear down walls between departments, and combine activities and
results from diverse sources.
These remarks all are about MICS' influence on learning norms. I will, however, not
further investigate MICS' role in the deutero learning process.
6.4.5 Organizational Learning Value of MICS
The model of organizational learning used so far, is a descriptive model. On the basis
of this model alone it is not possible to state how good or bad MICS is from the
organizational learning point of view. If we want to make such statements, it is
necessary to define the values of MICS in learning processes. Table 6.10 describes
MICS' values in general.
Learning Process Possible Values

Deutero Changes of learning norms (policies, action norms, responsibilities and procedures)
to improve their match with learning needs.
Double-loop Innovation in human resources, processes, markets and products.
Single-loop Improvements in adaptation, use, storage, and dissemination of knowledge.
Table 6.10: Learning Processes and Business Values
As deutero learning is not studied further, only double-loop and single-loop learning
values are further defined here.
MICS' Value in Double-loop Learning Processes
Double-loop learning is rated by adding up the scores (-1, 0 or +1) on each cell
intersecting learning fields (human resources, processes, markets and products) and
learning activities (development and unlearning). MICS can add value to the eight
thus recognizable learning incidents as follows.
• MICS' additions to human resources. MICS can provide data about human
resources, e.g. about skills, motivation, strategic knowledge, costs and

productivity. These data can be the subject for further analysis when connected
with knowledge about strategic directions, market developments, forecasts etc.
This can lead to a reformulation of human resource policies and action plans
(training, change projects, changes in function remuneration etc.).
• MICS' additions to products. MICS can supply answers to questions about
products such as: how many new products and product series have been
launched within a period of time, how do competitors perform on this subject,
what profit do the products generate, what trends are most likely in what
markets, what are the costs of developing new products? These data have to be
connected with less formal and precise insights, to make strategic decisions
about product lines, series and trends. These result in new policies for research
and development, and parameters for the longer term success of investments.
• MICS' additions to market insights and developments. MICS could provide
data to answer questions such as: how many markets and market segments are
served, what strategies are required for effective sales, how do competitors
perform on important markets, is it profitable to diversify, to penetrate or to
quit certain markets? MICS provides data that basically describe past trends.
For strategic decisions, insights into possible future scenarios are important.
Hence, intelligence information is often required (less formal and frequently
tacit) added to scenarios about possible futures, e.g. constructed and
understood by use of decision support tools (Galer, 1993).
• MICS' additions to process insights. Much organizational learning concerns
changing the way products and services are established, so that the organization
is able to adjust more easily to specific demands. For instance shorter delivery
periods, more flexibility in colors and product features. A lot of learning is also
related to redesigning processes to meet competition by decreasing the costs of
transactions and coordination. This is not just a marginal change but a
revolution in thinking and working to accomplish the same job. Before starting
these redesigning projects it is often wise to do some bench-marking and have
an accurate quantitative understanding of the existing processes (Davenport
and Short, 1990).
Table 6.11 provides a list of possible items for the theory development process.
Learning field MICS' double-loop learning value
Human resource Insights into knowledge for developing HR-policies.
development Insights into cost-benefit relations of HR-investments.
Market development Strategic insights and forecasts.
Product development Informed decisions about product lines and series.
Parameters for investments.
R & D policies and investments
Process development Bench-marks.
Production norms.
Insights into causes and effects of inefficiencies.

Insights into process limitations and capabilities.
Remark: Only theory development issues are listed. Unlearning is removal of theories.
Table 6.11: List of Possible MICS' Values in Double-Loop Learning.
MICS' role and value in double-loop learning is mainly as a quick data supplier. Lean
organizations have many and well-organized information sources to make the many
connections between relevant databases. These databases may be spread among
several organizational units, such as marketing, product engineering, manufacturing
engineering and strategic business units. This means that classic organizations will
have many problems putting the data together at the physical (access to databases),
empirical (connections between databases and systems), syntactic (consistent data
definitions) and semantic (creating interpretations from data between separate groups
in the company) levels. The value of MICS therefore differs significantly between
lean and classic machine bureaucracies. The classic machine bureaucracy will regard
MICS as overhead and is not clearly aware of MICS' contribution. Lean
organizations have lean information systems that are directly connected with the
generation of value. Systems or system parts that are regarded as non-contributing are
removed (cf. Van Nievelt, 1992). The problem of increasing information overloads
that happen in complex environments with large databases and information
processing (Ackoff, 1968), is managed in lean organizations by developing an explicit
view of the business problem and information systems that are directly connected
with these problems (Nonaka, 1988). This means that the chance of irrelevant and
uninterpretable data is much smaller than in the case of classic machine
bureaucracies. Additionally, the lean learning norms enable a higher learning speed
because of their many lateral structures and decentralized learning procedures that
relieve communication channels in the organization. The resulting value patterns of
lean and classic machine bureaucracies in dynamic and complex environments are
drawn in figure 6.7.
Most remarkably, industrialized service

organizations have some advantages for
learning with MICS, because their
processes are mainly data-processing.
This means that relatively few
additional investments are required to
generate MICS-systems on top of the
transaction processing systems
(McKeown and Leitch, 1993). It is the transaction processing systems that require
large investments. These systems form a substantial part of the core of the service
organizations. MICS requires only limited investments when it is an addition to the

transaction processing systems, but it generates a substantial knowledge value for the
management (cf. Earl, 1994). This is pictured in figure 6.8.
MICS' Value in Single-loop Learning Processes
The single-loop learning processes

should result in improvements of
products, processes, markets and
human resources via storage,
adaptation, dissemination and/or (re-
)use of knowledge (data and
management theories). Possible roles of
MICS in the single-loop learning
activities were discussed in section
6.4.3. MICS' values are mainly in the
area of cost reductions and efficiency improvement, but are sometimes also negative.
For instance, the storage of data requires an administrative organization, which
means an increase in costs. These costs can be regarded as marginal in service
organizations, which already have well-developed administrative branches. In
manufacturing organizations this could require a substantial increase in overhead
costs. The costs for manufacturing organizations are therefore more visible than in
the service types. This leads to a more explicit demand for cost savings resulting from
investments for single-loop learning in manufacturing organizations. Lean and classic
manufacturing organizations differ sharply in their view about savings and benefits of
single-loop activities. The lean organization is convinced of these savings, though it
will also apply lean principles to MICS. The classic manufacturing organization
requires a classic cost-benefit calculation. The result is that if the likeliness of a MICS
for single-loop learning is rated from 4 (most) to 1 (least), lean-service is rated 4, lean-
manufacturing 3, classic-service 2 and classic-manufacturing 1. The relation between
administration costs and investments in MICS for single-loop learning is described in
figure 6.9.
Finally, table 6.12 provides examples of MICS' values for learning activities. The
examples are such that they apply for all learning fields.
Single-learning activity MICS' value

Adaptation Decrease of error costs
Increase of theory applicability
Increase of learning costs
Accumulation of knowledge
Storage Increased administration costs
Reduction theory development costs
Reduction of quality costs
Steeper learning curve

Dissemination Reduced coordination costs
Reduced development costs
(Re)use Reduced development costs
Reduced costs of buying expertise externally
Faster and improved problem solving.
Table 6.12: List of Possible MICS' Values in Single-loop Learning
6.5 Summary
The concept of MICS is described in this chapter by applying Stamper's layers of

semiotics. For all these layers MICS has different features in the lean and in the
classic machine bureaucracy. The role of MICS is analyzed in relation to the single-
loop and double-loop learning activities. The results for double-loop learning are
summarized in table 6.7. The insights for single-loop learning are summarized in
table 6.9. The value of MICS differs in lean and classic organizations. Classic learning
norms lead to information overload in cases of increased complexity, and to learning
speed problems when dynamics increase. Lean norms are able to solve these
problems to a large extent. As a consequence the value of MICS is higher when
learning norms that are close to those of lean organizations are adopted. Service
organizations profit more from MICS than manufacturing organizations, because
MICS-information can be a by-product of their operations.
Chapter 7: Operationalizations and Method of Analysis
7.1 Purposes of this Chapter
This chapter first presents the key statements in the theory about organizational
learning, MICS and machine bureaucracies that must be operationalized and tested
for explorative purposes. Secondly, the theoretical concepts calling for observation
are extracted from these statements and then it is shown how they are
operationalized, a step which requires further elaboration of the theory.
7.2 Statements about MICS and Organizational Learning
The theoretical model summarized here consists of statements and conclusions. With
a statement we mean a proposition. Some statements are hypotheses and thus are
subject to empirical confirmation or falsification. To reduce the number of
statements to be investigated, most statements are assembled via a syllogism to a
conclusion. Some of these conclusions are hypotheses for the empirical research. In
this chapter an 'S#' is placed before a statement, and an 'Con#' in front of a
conclusion. For reasons of parsimony (Leege and Francis, 1974, p. 35), the number
of statements and variables is reduced to a minimum. This is done by removing

statements that include redundant information in the theoretical framework, and
keeping the number of concepts needed to a minimum.
Two of the statements that formed the basis for this study from the start, can be
formulated as follows:
S1: Machine Bureaucracies have strong controls.

S2: Controls inhibit innovation.
Evidence for both statements was presented in sections 1.3 and 5.1., where it was
shown that most (classical) machine bureaucracies are very slow in adapting to
environmental changes. The following conclusion can be drawn from both:
Con 1: Machine Bureaucracies are poor in innovation.
This study does not enable a test of S1, because only machine bureaucracies are
studied here. We can however make observations of control mechanisms in machine
bureaucracies and see how they affect innovation and inertia, but we can make no
comparison with other types of organizations. We also link this statement with
organizational learning by assuming (on basis of Argyris and Schön 1978 and De
Raadt, 1992) that:
• the degree of control achieved results only from single-loop learning, and
• the degree of innovation achieved results from double-loop learning.
The literature is however very vague about the relation between single-loop and
double-loop learning and how these activities relate to the learning norms in
organizations. Two definitions of a degree of organizational learning are optional
here, with considerable consequences for the theory as a whole. These are:
1. Organizational learning is a single variable, where double-loop learning (DLL) is
a deeper form of learning than single-loop learning (SLL) (e.g. Senge, 1990;
Hammer, 1990). In this hierarchical model of OL, the presence of DLL scores
higher (say 2) than SLL (say 1). It might be argued in justification that, although
SLL is possible without DLL the contrary is impossible. This does not seem
entirely reasonable because DLL is not always more important or more
complicated than SLL. For instance in any heavily capitalized manufacturing
process only the incremental changes brought about by SLL are possible until
investment in a new expensive plant is possible, so most of the time DLL is very
limited while SLL is highly sophisticated. So we prefer to leave this hierarchical
model aside.
2. Organizational learning is composed of a mixture of SLL and DLL which
probably co-vary in ways that depend on the learning norms but can mostly be
treated as independent. We could try to rate the effort devoted to each kind of
organizational learning and treat them as a vector measurement of organizational
Operationalizations and Method of Analysis 165
learning or perhaps attempt to justify some way of combining them (by simple
addition, possibly). It may be appropriate to introduce a limit to the total
learning effort so that more SLL leads to less DLL and vice versa. Learning norms
however also could be such that the total amount of organizational learning
increases.
The vector model of the organizational learning concept is chosen in this study and
implies the following statement:
S3: Organizations have learning norms

that determine the effort an
organization puts into single-loop
and double-loop learning.
Evidence for this statement was

presented in sections 4.9.1, 5.4.3
and 5.4.2. and in figure 4.10 that is
reprinted in reduced format in figure
7.1. This statement is heuristically
interesting, but empirically incomplete because the type of influence (inhibitor or
reinforcer) of the learning norms is not defined. Some indications for the type of
influence can be inferred from the hypothesis that learning norms differ per type of
machine bureaucracy. The following classification of machine bureaucracies was
proposed: Classic-Manufacturing, Lean-Manufacturing, Classic-Service, and Lean-
Service. This leads to the following set of statements (see table 7.1).
Classic Lean Statements

Manufacturing S3.1 and 3.2
Service S3.3 and 3.4
Statements S3.5 and 3.6 S3.7 and 3.8
Table 7.1: Statements about Classic-Lean and Manufacturing-Service Differences
Concerning Single-loop and Double-loop Learning Effort.
The following statements can be applied to the corresponding learning norms:
S3.1: Classic manufacturing MBs put less effort into double-loop learning than lean
manufacturing MBs.
S3.2 Classic and lean manufacturing MBs do not differ regarding the amount of
learning effort put into single-loop learning.
S3.3 Classic service MBs put less effort into double-loop learning than lean service MBs.
S3.4 Classic and lean service MBs do not differ regarding the amount of learning effort
put into single-loop learning.

S3.5: Classic manufacturing MBs put less effort into double-loop learning than classic
service MBs.
S3.6: Classic manufacturing MBs put less effort into single-loop learning than classic service
MBs.
S3.7 Lean manufacturing MBs put less effort into double-loop learning than lean service
MBs.
S3.8 Lean manufacturing MBs put less effort into single-loop learning than lean service
MBs.
Statements S3.1 to 3.8 are based on our previous treatment of machine bureaucracies
(chapter 5). The main conclusion drawn about the classic-lean distinction was that
both machine bureaucracy types are organized for cheap production by applying
many control mechanisms (single-loop learning), but that the lean MB was not only
cheap but also much more innovative than the classic case. Additionally, all MBs are
expected to invest substantially in single-loop learning via the construction of control
mechanisms, rules and procedures. Service organizations are expected to invest more
in these SLL-activities than manufacturing organizations, because of their shorter
learning cycles and more direct contact with clients who can provide the organization
with feedback signals. At the same time, however, it is expected that service
organizations have more problems with constructing systems for SLL, because as their
output is intangible it is more difficult to measure their performance. Therefore,
service MBs may be expected to have more double-loop learning activities. According
to these assumptions, the following patterns of values are predicted among the four
types of MBs (see fig. 7.2).
Learning norms are supposed to influence the single-loop and double-loop learning
processes. Learning norms themselves are however influenced by environmental
circumstances (complexity and dynamics). This is expressed in the following two

statements.
S4: MBs face increased environmental dynamics.

S5: MBs face increased environmental complexity.
The validity of statements S4 and S5 is widely accepted among academics and

managers from most industries today, particularly in Europe since the 1990s, and has
been treated extensively in chapter 1 (particularly in sections 1.1 and 1.2). Both
statements therefore can be taken as axioms that will not be researched in our
empirical investigation. It is more important for the empirical investigation in this
project to explain the impact of dynamics and complexity on learning norms, because
both form the organizational learning needs that must be coped with by a specific set
of learning norms (cf. section 5.4, and Con2, S12.1, S12.2, S13 and Con3 later on).
S6: Single-loop learning efforts counteract low environmental complexity and dynamics.
This statement is based on theoretical and empirical research about error correction,
which has been treated in section 5.4.2. A most important feature is that errors are
detected via a performance measurement tool (Juran, 1964) and that the existing
management theory can explain and handle the existing variety in simple and stable
situations. The single-loop learning process is, however, restricted to solving problems
that can be managed within the management theory and its related tools. Probems of
huge complexity however often require the development of new management
theories by specific research of a (interdisciplinary) project team, the R&D group or
some outside consultants, that are typical of double-loop learning.
S7: Double-loop learning efforts counteract high environmental complexity and dynamics.
Evidence for this statement was presented in sections 5.4.1 and 5.4.2. This statement
seems to be intuitively correct, and has been propagated widely by organizational
development authors. A highly dynamic environment requires frequent major
changes in the business; high complexity calls for high levels of knowledge, and the
combination of high dynamics and high complexity multiplies the efforts required.
An excellent example is the situation in which a television set producer is confonted
by situations of increasing competition (dynamics), that demand reduced costs,
improved quality and a greater variety of client's choices at the same time (increasing
complexity). In that case the production process does not require simple
improvements in existing procedures and technology, but also reconsiderations of
what should be produced and what kinds of technologies are needed. If, however,
these innovations are carried out in low dynamic and simple environments, the
resulting learning process may lead to many inefficiencies by creating too many
changes.
S8: Double-loop learning involves reorganization and so entails higher risks than single-loop
learning.
Hannan and Freeman (1976 and 1984) suggested that double-loop learning also
carries 'Reorganization Risks', which lowers the survival chances of an organization.
This is obvious because it creates instability and the chance of dysfunctioning, which
can lead to a loss of assets, loss of clients and markets, quality problems etc. This
problem can be solved or reduced by developing procedures and norms that
accompany organizational double-loop learning and that can guide the choice for
single-loop or double-loop learning activities (Garratt, 1987). Many organization
analysts and consultants therefore propose methodologies of change for reducing the
risks of reorganization (c.f. Bushe and Shani, 1989 for an excellent example relating
to machine bureaucracies). Also, project management methods have been developed
in many cases for this purpose (c.f. Rogers, 1964, Franke, 1987). Evidence for S8 has
also been presented in sections 4.9.1, 4.9.2 and 5.4.3.
S9: Organizations create learning policies to reduce the risks of reorganization.
These policies consist of change plans, prescriptions of reporting and communication

lines (procedural norms), instructing people and motivating them to achieve the final
objective (action norms), and describing people's responsibilities in the change
process (responsibility norms) (Chew et al, 1991). Some evidence for this statement
has been presented in section 4.4.3.
S10: Reorganization risks increase organizational complexity and dynamics.
Complexity increases when new methods, rules, tools, and knowledge must be
applied in addition to the existing ones. Dynamics increase as a result of
reorganization until new practices have settled in. Change reduces certainty about
how to work and collaborate. Evidence for these statements has been treated in
sections 5.4.1 and 5.4.3.
An organizational learning paradox can be formulated as follows: learning is required
for survival, but learning may reduce survival chances as well. This paradox must be
solved by the development of learning norms that match learning needs and are
effectively implemented in learning activities to improve learning abilities.
S11: The more environmental complexity and dynamics, the lower the survival chance of an
organization which is unable to learn and adapt.
S12: The lower the survival chance of an organization, the greater its need for organizational
learning.
Evidence for both statements was presented in sections 1.1, 1.2 and 1.3. Because of
S11 and S12, organizations must be careful not to embark too quickly on double-
loop learning strategies, because these could reinforce the trend to lower survival
chances. At the same time the choice for SLL or DLL or deutero learning must be
taken rationally, as far as the environment can be analyzed rationally. This requires
the creation and explanation of management theories. If not done so, only political
forces determine what will happen in the organization.
Con 2: A combination of complexity and dynamics determines the amount of learning

need.
This conclusion results from S11 and S12, and has also been discussed in sections
4.8.3 and 5.4.1. The following two statements can be derived from this conclusion:
S12.1: Lowest learning needs exist in cases where low complexity and low dynamics exist
simultaneously.
S12.2: Highest learning needs exist when high complexity and high dynamics exist
simultaneously.
In these statements the situations of high complexity with low dynamics and low
complexity with high dynamics are not explained. Therefore we need a statement
about the relation between dynamics, complexity and learning needs.
S13: Dynamics contributes more to learning needs than complexity.
Complexity generates learning needs, because it requires knowledge to solve

problems which must be mastered. An effective knowledge storage medium could
lower the learning needs in complex situations. However, learning need will continu
to be high in dynamic environments, even if the environment is simple. Section 5.4.3
specifically gives further evidence for this argument by stating that in dynamic
environments especially the value of knowledge depreciates quickly. This means that
the organization must increase its learning effort to stay in pace with environmental
changes.
Now two conclusions can be drawn.
Con 3: Lowest learning needs exist in cases of low complexity and low dynamics.
Moderately low learning needs exist in cases of low dynamics and high complexity.
Moderately high learning needs exist in cases of high dynamics and low complexity.
High learning needs exist in cases of high dynamics and high complexity.
This conclusion is based on S12.1, S12.2 and S13.
Con 4: Learning needs determine the learning norms required for survival.
This conclusion is based on statements S3, S10, S11 and S12.

In previous sections and chapters, MICS was regarded as a part of the organizational
learning norms, more specifically the procedural norms (section 5.4.2). This leads to
some clarity problems when the impact of MICS is studied for single-loop and
double-loop learning. It is then specifically problematic to separate the impact of
MICS from the impact of other learning norms. As finding the specific impact of
MICS is essential for the research problem stated previously, in the following MICS
is treated separately from the learning norms.
SLL and DLL are significantly different ways of organizational learning. Studying
MICS' impact on organizational learning therefore requires distinct statements about
SLL and DLL. The impact of MICS on organizational learning was described in
section 6.4 in terms of MICS' role and MICS' values in relation to learning activities
and learning fields. In order to observe MICS' role we will only look at the problem
anticipation and critical evaluation roles of MICS, because they are indications of
SLL and DLL roles (setion 6.4.3 table 6.8) and are also indicative of lean and classic
norms. These considerations lead to the following statements about MICS' roles and
values:
S14: Lean learning norms emphasize the critical evaluation and problem anticipation roles of
MICS, whereas classic learning norms emphasize the problem anticipation and
accounting roles of MICS.
S15: MICS contributes considerably to SLL-effort.
S16: MICS inhibits DLL-effort.
Evidence for the validity of S15 was given by Argyris (1980) and De Raadt (1992),
who both state that MICS is a useful tool for detecting errors and for error
correction. S16 is based on the research and findings of Argyris (1980), Hannan and
Freeman's inertia theory, Markus (1983), and classic theory about the impact of
control systems on motivation in organizations (Merton et al. as described in Lawler
and Rhode, 1976), described in sections 6.1, 6.3.2 and 6.4.5. The argument
essentially is that MICS includes a management theory that emphasizes uni-lateral
control in organizations, meaning the increase of power of certain people at the
expense of other people's power. This reduces the chance that new ideas will be
supported when they come bottom-up in the organization.
In order to score these contributions, we follow chapter 4, which proposes a score for
single-loop and double-loop learning effort in section 4.8.3, and section 6.4.5, which
states that the value of MICS should be assessed by looking at the intersections of the
learning field and learning activities dimension of organizational learning. Thus the
following assumption is made:

A score for the contribution of MICS to organizational learning consists of points gained on the
intersections of the dimensions of learning: learning fields and learning activities (see table 7.2).
A MICS can score -1, 0, or +1 when it contributes negatively, indifferently, or positively
respectively on that cell. The total of MICS contributions is then obtained by adding the scores.
The scores range from -16 to +16 for single-loop learning, and from -8 to +8 for double-loop
learning.
Learning fields: Human Resources Product Transformation Markets
Learning
activities:
Double-loop learning
Theory
development
Unlearning
Single-loop learning
Adaptation
Storage
Dissemination
(re-)use
Values can be -1, 0, or +1. Scores for DLL and SLL are obtained by adding the scores of the cells, and thus
can range between -8 and +8, and -16 and +16 for DLL and SLL respectively.
Table 7.2: Table Describing Cells on Which to Score MICS's value.
7.3 Construction of the Research Model
The previous considerations identified many possible variables and hypotheses

(Statements and Conclusions). These are further connected in a causal diagram to
make the coherence between them clearer (see fig. 7.3).
The variables 'Reorganization Risks' and 'Organizational Survival Chance' are

excluded from the empirical research. The others will be operationalized in section
7.4. We decided earlier on only to study the direct relation between
complexity/dynamics and learning needs, and between learning processes and
complexity. Because of conclusion 2 (a combination of complexity and dynamics
determines the amount of learning need), removing the organization survival chance
variable does not lead to any complications. This is not true for the reorganization
risks variable. Removing this last variable would make the theory logically
inconsistent, because then organizational learning processes (SLL and DLL) would
reduce complexity and dynamics (S6 and S7) and increase them at the same time (the
indirect effect of SLL and DLL via reorganization risks stated in S10). The solution to
this consistency problem is to connect learning norms directly with complexity and
dynamics via a new conclusion (5) based on S9 and S10 that is formulated as follows:
Con 5: Learning norms decrease dynamics and complexity.
Because this study is about the impact of MICS on organizational learning and, more
specifically, MICS' role and value for effective organizational learning, we will not
further elaborate the direct causal line between learning norms, complexity and
dynamics. Therefore, conclusion 5 is excluded as part of this investigation.
Two additional conclusions (con 6 and 7) can now be stated as well.
Con 6: MICS contributes to single-loop learning effort and inhibits double-loop learning
effort.
.
This conclusion is a conjunction of statements S15 en S16.
Con 7: Depending on the Learning Norms, MICS contributes to or decreases complexity

and dynamics.
This last conclusion is based on S6, S7, S10, S15 and S16.
As a summary to this section, first all major statements and conclusions are listed,
and figure 7.4 shows their connections with the conclusions. The figure also shows
which of these hypotheses are the subject of empirical research.
The major statements are:
S1: Machine Bureaucracies have strong controls.
S2: Controls inhibit innovation.
S3: Organizations have learning norms that determine the effort an organization puts into single-loop and double-loop
learning.
S4: MBs face increased environmental dynamics.
S5: MBs face increased environmental complexity.
S6: Single-loop learning efforts counteract low environmental complexity and dynamics.
S7: Double-loop learning efforts counteract high environmental complexity and dynamics.
S8: Double-loop learning involves reorganization and so entails higher risks than single-loop learning.
S9: Organizations create learning policies to reduce the risks of reorganization.
S10: Reorganization risks increase organizational complexity and dynamics.
S11: The more environmental complexity and dynamics, the lower the survival chance of an organization which is unable
to learn and adapt.
S12: The lower the survival chance of an organization, the greater its need for organizational learning.
S13: Dynamics contributes more to learning needs than complexity.
S14: Lean learning norms emphasize the critical evaluation and problem anticipation roles of MICS, whereas classic
learning norms emphasize the problem anticipation and accounting roles of MICS.
S15: MICS contributes considerably to SLL-effort.
S16: MICS inhibits DLL-effort.
The Conclusions are:
Con 1: Machine Bureaucracies are poor in innovation.
Con 2: A combination of complexity and dynamics determines the amount of learning need.
Con 3: Lowest learning needs exist in cases of low complexity and low dynamics.
Moderately low learning needs exist in cases of low dynamics and high complexity.
Moderately high learning needs exist in cases of high dynamics and low complexity.
High learning needs exist in cases of high dynamics and high complexity.
Con 4: Learning needs determine the learning norms required for survival.
Con 5: Learning norms decrease dynamics and complexity.
Con 6: MICS contributes to single-loop learning effort and inhibits double-loop learning effort.
Con 7: Depending on the Learning Norms, MICS contributes to or decreases complexity and dynamics.
Figure 7.4 summarizes the links between the Statements and the Conclusions.
Tabel 7.3 now lists the concepts of the statements and conclusions that are the
subject of empirical investigation. These concepts are operationalized in the following
sections.
Concept Variable Location

Organizational Learning Need Var. 1: Organizational Learning Needs Index Table 7.4
Machine Bureaucracy Var. 2: Machine Bureaucracy Type Table 7.5
Var. 2, factor 1: Lean-classic nature of MB Table 7.6
Var. 2, factor 2: Transformation nature of MB Table 7.7
Learning Norms Var. 3.1: Learning policy and identity norms Table 7.8
Var. 3.2: Learning responsibility norms Table 7.9
Var. 3.3: Learning action norms Table 7.10
Var. 3.4: Learning procedural norms Table 7.11
MICS Var. 4: MICS-description Table 7.12
Single-loop learning effort Var. 5: SLL-effort index Table 7.2
Double-loop learning effort Var. 6: DLL-effort index Table 7.2
MICS'-role Var. 7: MICS'-role No table
MICS'-value Var. 8.1: MICS'-value on SLL Table 7.13
Var. 8.2: MICS'-value on DLL Table 7.13
Table 7.3: List of Concepts, Variables, and Location of Operationalization.
On the basis of these reflections, the previously described model can now be
reformulated and simplified to the following research model (see figure 7.5).
7.4 From Theory to Observations: Explanation of the Variables
7.4.1 Methodological Problems for Empirical Investigations
When going from theory to observations the following problems appear according to
the research methodology literature (Lee, 1989; Yin, 1984):
1. The theory must be described clearly and unambiguously in operational terms.
2. Measurement instruments must be correct operationalizations of the constructs
to be observed.
3. Often multiple items form one index to find a score for a case on a theoretical
construct. This requires a sound theory that combines the observations in one
score.
4. Observations among cases must be comparable.
The operationalization of the theory is partially realized. This is because of the
elicitation of the statements and conclusions. We must now add a correct
operationalization of basic theoretical constructs. The statements and conclusions
contain variables that must be observed, and by which empirical testing and further
exploration of theoretical notions are made possible. Some parts of the theoretical
model can be tested whereas others can only be described (e.g. Conclusion 4). In this
section some proposals are given for scaling variables that require observations of
multiple items. The validity of these scales will only be tested via reasoning about
observations made. Statistical testing of the reliability and validity is not possible,
because we lack the data to do so (for further insights into these methodological
problems see Kerlinger, 1986). Also, a test on the uni-dimensionality of the scales is
not possible here because the factor analysis technique that is required for this
purpose requires a large amount of statistical data. The reader must be well aware of
these limitations. The most important issue for empirical research, its meaning in
practical settings, is however closely guided. This is done via the application of case
studies and a research design that takes the two basic explaining variables as main
factors (lean-classic and service-manufacturing distinction).
Comparability of the cases is in principle difficult, because each case is in some sense
unique (even a statistical research approach cannot avoid this fact). Comparability is
achieved by constructing precise and standarized instruments by which we want to
observe the cases. Hence, much effort has been put into constructing the scores that
are described in the following subsections.
7.4.2 Description of Variables
This subsection provides a further operationalization of the variables described in the

theoretical framework.
Variable 1: Organizational Learning Needs Index.
Conclusion 2 stated: "A combination of complexity and dynamics determines the amount of
learning need". What is required therefore are scores of environmental dynamics and
complexity with which a learning needs score can be assigned. The measures for
complexity and dynamics are based on a classic study of Duncan (1972) and Duncan
and Weiss (1978). Complexity was defined as the number of factors of relevance for
decision-making, and the number of components to which these factors refer. The
factors and components are issues that define an organization's internal and external
environment (cf. chapter 5, table 5.7). Dynamics was defined as the degree to which
relevant factors for decision-making remain the same over time or are changing, and
the frequency with which new factors are relevant (cf. section 5.4.1).
Duncan and Weiss' measures were developed for analyzing decision-making, and
were not applied to organizational learning. This study therefore applies the list of
factors and components to understand complexity and dynamics in the case studies
we will conduct. The observations will however not be made at the individual's
decision-making level, because this will make the data-acquisition and analysis too
laborious for our purposes. In the case studies we will ask interviewees for data
sources about the factors and components of the environment, and will try to
qualitatively assess the dynamics and complexity on the basis of these data. It will be
difficult to obtain data on some components and factors, which also means that not
all factors and components will be treated in detail. Only the most significant ones,
from our theoretical perspective, will be considered.
Conclusion 3 provides a starting point for constructing an index of learning needs.
Con 3 states: "Lowest learning needs exist in cases of low complexity and low dynamics.
Moderately low learning needs exist in cases of low dynamics and high complexity. Moderately
high learning needs exist in cases of high dynamics and low complexity. High learning needs
exist in cases of high dynamics and high complexity". Thus four scores of the organizational
learning needs index are defined. See table 7.4.
Variable 1: Organizational Simple Complex

Learning Needs Index
Static Low learning needs Moderately low learning needs
Score 1 Score 2
Dynamic Moderately high learning needs High learning needs
Score 3 Score 4
Based on Duncan, 1972, p. 320, and Duncan and Weiss, 1979.
Table 7.4: A Four-Point Index of Organizational Learning Needs.
Because this study does not make learning need scores independent from complexity
and dynamics, Con 2 and Con3 are assumptions for measurement.
Variable 2: MB-types.
Four types of Machine Bureaucracies were distinguished from the beginning of the
book, by two main dimensions: classic-lean and manufacturing-service. The classic-
lean distinction concerns the organizational norms (policy, structure, culture,
motivations) that govern the organization. The service-manufacturing distinction is
about transformation. Variable 2 thus has four values described in table 7.5.
Variable 2: Norms
MB-types
Classic Lean
Transfor- Manufacturing Classic-Manufacturing Lean-Manufacturing
mation
Service Classic-Service Lean-Service
Table 7.5: MB-types
Rating an organization on its lean-classic (Norms) dimension is based on a score

using items found in Womack et al.'s study on lean production. These items were
also considered to be relevant for lean organizational learning according to many
other researchers in this area (Nonaka, 1988; Walker, 1991; Leonard-Barton, 1992;
Adler and Cole, 1993). Table 7.6 lists items for this first factor of MB-types.
Variable 2, Lean Classic

Factor 1: Indicators for Lean-Classic
Quality attitude Yes=1 No=0
Decentralization Yes=1 No=0
Lateral structures Yes=1 No=0

Emphasis on relation with supplier Yes=1 No=0
Emphasis on relation with client Yes=1 No=0
Emphasis on positive management- Yes=1 No=0
employee relationship
Financial decision-making structure Within consortium with low Outside consortium
interest rate=1 with banks=0
Human resource management Career paths and emphasis on No career path and local
mobility=1 position=0
Motivation Intrinsic=1 Extrinsic=0
Source of new ideas Internal and external=1 Internal or external=0
Scale minimum of leanness = 0; Maximum of leanness = 10; Lean organization = 6....10; Classic
organization = 0.....5.
Table 7.6: Criteria for Scoring Organizational Leanness.
Items for the process factor are found in the previously discussed paper of Mills and
Moberg (1982). The distinction we draw here is of course a simplification of reality as
for instance Schmenner has shown (1986). In fact many types of services and
manufacturing organizations will score on both values at the same time. Schmenner,
and also other writers on services like Grönroos (1991), however do not dispute the
relevance of the eight items listed in table 7.7.
Var. 2, factor 2, Indicators Service Manufacturing

for transformation
Materials and Equipment Knowledge Machines, physical materials and
labor
Involvement of client in Client is ego-involved because he Client has contact after production
production must participate in the service (sales) and sometimes before
process production (design and contracting)
Information processing High, accurate and timely Planned work
information from client is needed
Responsibility for success Client shares responsibility of Responsibility of success lies with
success the producer
Description of process Input, conversion and output are Clear distinctions between input,
phases hard to distinguish conversion and output (logistic
stream)
Stocks and buffers Stocks are impossible. Buffers are Stocks are possible (under certain
made by selection of clients, conditions) and buffers are created
routinization of service and by planning of the production
rationing (delays and interrupts) stream
Systems boundaries Operating core is open system Operation and administration are
(involvement of client), both closed systems.

administration is closed system.
Professionalism Can be high or low. Low (except in engineering)
An organization scores 1 when the left column value is assigned and 0 when a right column value applies.
Minimum score = 0, maximum score = 8. Service organizations have score of 5...8. Manufacturing
organizations score 0...4.
Table 7.7: Scoring Organizations on Transformation.
Variable 3: Learning Norms
Learning norms are operationalized in four dimensions: policy and identity norms,
responsibility norms, action norms and procedural norms (cf. chapter 4 section 9).
The scores on the four related variables (3.1, 3.2, 3.3 and 3.4) can have two extremes
that are related to the lean and classic nature of organizations. MICS is treated
separately from procedural norms, because it is the independent variable in this
study.
Var 3, factor 1: Policy and Identity Norms

Policy and identity norms can be typically lean or classic (cf. chapter 5). Therefore,
the score of the index of variable 3.1 can be either 'work smarter' (typical of lean
organizations), and 'work harder' (typical of classic organizations). Table 7.8 lists the
items for describing learning policy and identity norms, and describes indicative
values for both extremes of the index.
Var 3: Learning norms; factor Extremes

1: Policy and identity norms
Work Smarter Work Harder
Policy and mission Learning is mentioned in the Stressing of volumes sold and
mission, especially in terms of produced, Return on
continual improvement (Kaizen). Investment, market share, profits
Learning infra-structure Lateral relations are encouraged. No lateral relations, top-down
When cost-effective, data communication. Use of
highways (computers and mainframes and information
networks) access and maintaining control.
Non-transparent organization.
Development and management Top priority for high innovation People are mainly providers of
of core competencies potential labor. Competencies are what
one can do now. Human
Resource Management and
R&D have lower priority than
marketing, manufacturing and
logistics.
Organizing principles Production teams, with high Strong departmental and
responsibility and availability of functional differentiation, with
management information. Strong strong line management. Large

project teams. Organization is technostructures, with unclear
open systems, with close relation influence on middle lines.
with suppliers, banks and clients Closed systems (to internal and
external environments)
Motivation for business re- Emphasis on excellent processes, Optimize processes from
engineering to maximize client satisfaction efficiency perspective.
(ultimate boss). Organization Technostructure and middle line
culture and structure must adjust have expertise for optimization
to process requirements. and dictate what happens on the
shop floor.
Maximum score of leanness is five when all left alternatives apply. Minimum score is zero when none of
the alternatives apply.
Table 7.8: Scoring the Organization on its Leanness Using Policy and Identity Learning
Norms.
Var 3, factor 2: Responsibility norms

Responsibility norms can be typically lean or classic. The first is competence-based,
and the second is power-based. The related index is given in table 7.9.
Var 3, factor 2: Learning Extremes
Responsibilities
Competence-based Power-based
Functional No, because the functional Yes, because the functional
division is regarded as too slow, division in an organization is a
and has limited information- means for hierarchical control
processing opportunities.
Divisional No, because it is almost as Yes, because now the work
bureaucratic as the functional division concerns the products
organization. or the markets, but the same
hierarchical control dominates.
Matrix Yes, and the organization has the No, because it leads to a power
skills to handle this complex struggle in which the functional
organizational arrangement, or divisional branch dominates.
which supports specialization as
well as market/client orientation
Network with joint (expertise) Yes, because expertise is treated No, because expertise is regarded
centra as a strategic asset that should be as owned by a department or
available to all in the person.
organization.
Volvo teams Yes, because it is important to No, because laborers are under-
have smart laborers. valued in their cognitive abilities.
Task groups with much Yes, so that double-loop learning No, because improvements can
authority can be effective and quick when be done individually, in a
required. department, and should not
upset the existing organization.
Maximum score is nine, when the left column values apply. Minimum score is one, when none of the left
values apply.
Table 7.9: Scoring the Organization's Leanness Using Responsibility Norms.
Chapter 5 also mentions three other organization structures that were considered as
part of this index, namely: Network with independent companies, Parallel learning
structures, and Project groups. We have however no arguments why these three
structures would behave differently in the lean or the classic cases. Therefore they
would make no contribution to the index.
Var 3, factor 3: Action norms
The main items describing action norms are given in table 7.10. This table gives an
index of action norms, with extremes related to lean and classic organizations. The
lean extreme emphasizes the team, the client, self-realization and fast reactions as the
main motivators for organizational learning. The classic extreme emphasizes money,
pain avoidance, and slow reactions as motivators for learning.
Var. 3, factor 3: Action norms: Extremes

Team and fast Money and slow
Incentives Intrinsic Extrinsic
Interpersonal trust Openness Defensiveness
Attitude knowledge removal Positive Negative
Learning priority Relative amount of money Idem, low.
(budget), time and authority in
relation to operational tasks.
Source of knowledge External & internal sources Internal or external sources
Self development Buying knowledge
When left score 1, when right score 0. Sum scores. Lean = sum of scores 3...5; Classic = sum of scores 0...2
Table 7.10: Scoring the Organization for Leanness Using Action Norms.
Var 3, factor 4: Procedural norms
Table 7.11 describes the procedural norms index. The lean extreme emphasizes free
and continuous flow of data and information. The classic extreme emphasizes
discrete and constrained flows of data and information.
Variable 3, factor 4: Procedural Extremes

norms
Free-continuous Discrete-constrained
Temporality of data flows Continuous Discrete

Data access Free Constraints by authority limits
Number of Parameters measured Everything potentially important Specific targets
for overall performance
Management style Participation and selling Telling
Feedback time Fast Slow
Distribution of expertise Technostructure and workgroup Technostructure and
management
Maximum score of leanness is six, when all left column values apply. Minimum score is zero when none of
the values apply.
Table 7.11: Scoring the Organization for Leanness Using Procedural Norms.
Variable 4: MICS-description
This is a study of the impact of MICS on organizational learning in four types of

organizations. MICS might however be very different in each case. Before assessing its
role and value, a description of the technical and social aspects of MICS is valuable
for a first orientation. This is done via Stamper's list of semiotic layers. This list is
given in table 7.12 for the lean and classic extremes.
Var 4: MICS Extremes

Lean MICS Classic MICS
Technical
Physics Coupling of systems, via network and Functional systems. Islands of
databases. automation.
Empirics On-line systems. Off-line systems, with period reports.
Syntactics High quality user interfaces (easily Hard copy reports.
understandable structure of software). Change of database on request and
Flexibility of databases. when feasible.
High quality administrative organization. Inconsistent data.
Compatible data structures. Incompatible data structures.
Organization
Semantics Consistency with possible control type. Inconsistency with control type. Mental
Shared mental models. models are diverse and incompatible and
reflect stake holders' positions.
Pragmatics Decisions are implemented at high speed Many complications in translating
and trust. decisions to actions.
Action based on theoretical understanding Action based on past experience
of practical problems. (routine) or command.
Social Social networks of problem anticipation Separation of problem anticipation and
and critical evaluation are closely critical evaluation networks.
connected. MICS service problem anticipation, or

MICS serves problem anticipation and punish-reward.
critical evaluation.
Table 7.11: Describing Leanness of MICS.
Variable 5: Single-Loop Learning Effort
Single-Loop Learning is rated in terms of efforts that organizations allocate to any of

the four learning activities (adaptation, storage, dissemination and (re)-use) and
learning fields. The researcher must document his observations and conclusions
when assigning a value (0 or 1) to each field and activity per case. The minimum
value for SLL efforts is 0 and the maximum is 16. This scoring principle thus is the
same as the one proposed for MICS' value on SLL-effort, with the exception that
SLL-effort cannot be negative. For a description of SLL-effort the reader therefore is
referred to table 7.2.
Variable 6: Double-Loop Learning Effort
The double-loop learning process concerns the creation of goals for the learning
fields and the removal of goal definitions that are out of date. This process of double-
loop learning is therefore often called 'innovation'. Double-loop learning can be rated
via the application of the following questions:
• Human resources. How much money is spent on training? Is this training only
for developing skills to accomplish routine tasks, or is it also for learning to
develop new insights? Are newly acquired insights implemented in new
practices? Are people encouraged to think and create innovations?
• Tranformations. Much innovation is about changing the way products and
services are made, so that the organization is able to adjust more easily to
specific demands. For instance shorter delivery periods, more flexibility in
colors and product features. Much innovation is also about redesigning
processes to meet competition by decreasing costs of transactions and
coordination. This is not just marginal change but a revolution in thinking and
working to accomplish the same job.
• Markets. How many markets and market segments are served? It is important to
mention that markets for a company do not merely exist, but must be created
by improving communications to potential clients, developing a strategy and
plan to create a new profitable market, and adjusting products and services to
the specific needs and demands of these new markets or market segments.
• Products. How many new products and product series have been launched
within a period of time? Especially the series is an important observational unit,
because many organizations only produce a small number of products. For
instance car manufactures produce cars, and maybe also lorries, fork-lift trucks
and motor bikes.
A score for double-loop learning effort is obtained by finding evidence of learning
within the four fields of learning. A score of 0 is assigned when no learning and no
unlearning happens in the four fields. A score of 8 is attached when the organization
learns and unlearns on all four fields. The scoring principle is thus exactly the same
as the score that was defined for MICS' DLL-value in table 7.2, with the exception
that DLL-effort cannot be negative.
Variable 7: MICS' role
Section 6.4.3 detected two roles of MICS, namely: Problem Anticipation and Critical
Evaluation. MICS' role in one case can vary depending on the learning field and
learning activity it supports. Thus MICS can have the critical evaluation role at the
human resources field, and the problems anticipation role in the process
development field. The researcher can easily detect the role, when asking about the
purpose of MICS-usage.
Variable 6: MICS' value
Measuring MICS' value is important for investigating the most basic conclusions of
this study: "MICS contributes to single-loop learning effort and inhibits double-loop learning
effort" (Con 6). MICS' value is assessed by assigning a value (-1 for negative influence,
inhibit, 0 for no influence and +1 for positive influence of MICS) to the cells that
intersect learning fields and learning activities in table 7.2. This is done seperately for
single-loop and double-loop learning (var 6.1 and 6.2 respectively), because they were
regarded as specifically different. The minimum score for single-loop learning is then
-16 and the maximum score is +16. The minimum score for double-loop learning is -8
and the maximum is +8. The researcher can apply table 7.13, which gives indicators
for DLL- and SLL-values. This table is not intented to be complete. The learning
activities for DLL-values are omitted, because these activities are binary (development
versus removal). The researcher can easily ask himslef the question if the activity was
development or removal (or both). The learning fields for SLL-values are omitted in
the table, because they are easy to detect by asking about the application field.
Examples of Single-Loop Value Indicators
Learning activity MICS' single-loop learning value

Adaptation Decrease of error costs
Increase of theory applicability
Increase of learning costs
Accumulation of knowledge
Storage Increased administration costs; Reduction of theory development

costs; Reduction of quality costs; Steeper learning curve
Dissemination Reduced coodination costs
Reduced development costs
(Re-)use Reduced development costs
Reduced costs of buying expertise externally
Faster and improved problem solving
Examples of Double-Loop Value Indicators.
Learning field MICS' double-loop learning value

Human resource development Insights into knowledge for developing HR-policies.
Insights into cost-benefit relations of HR-investments.
Market development Strategic insights and forecasts.
Product development Informed decisions about product lines and series.
Parameters for investments.
R & D policies and investments
Process development Bench-marks.
Production norms.
Insights in causes and effects of inefficiencies.
Insights in process limitations and capabilities.
MICS double-loop value score (var 6.1) is determined by scores (-1, 0, +1) on the intersections of learning
fields and learning activities that are touched by MICS. The index score is created by adding these cell
scores, and can reach a maximum of +8 and a minimum of -8 for DLL, and +16 and a minimum of -16 for
SLL (confer tabel 7.2).
Table 7.13: Example of Indicators for MICS' Learning Values.
These scores are of course tentative and experiences with them in the separate case
studies must be documented. The final chapter must conclude about possible
adjustments and consequences for the information audit instrument.
Con 6 also requires the rating of Single-Loop Learning and Double-Loop Learning
effort.
7.5 Summary and Conclusions

This chapter has summarized the main statements about organizational learning,
machine bureaucracies, and information systems. These statements are related to
each other to show the coherence (a theory), to reduce the amount of redundancy,
and to identify the main variables of this study. In total 8 variables are defined
(learning needs, machine bureaucracy type, learning norms, description of MICS,
role of MICS, value of MICS, single-loop learning effort, and double-loop learning
effort) and operationalized. The case studies provide observations on these variables
in order to draw conclusions about the hypotheses (five conclusions and one
statement) listed in table 7.15. The comparative analysis of data from the cases must
result in conclusions about the validity of the Conclusions, and about possibilities to
further develop our understanding of organizational learning and information
systems (for instance by constructing a normative theory).
The following steps are undertaken in the separate case studies.
1. General description of the case in terms of contextual variables such as size, age,
organization chart, and learning fields.
2. Description of the organization in terms of learning needs, by scoring its internal and
external dynamics and complexity.
3. Description of the organization in terms of its lean-classic and service-manufacturing
nature. This for the classification of the case according to the main independent
variable machine bureaucracy type. Note that we do not assign a value in terms
of better or worse to the MB-type found.
4. Description of the organization in terms of learning norms. The description of
organizational learning starts with learning norms because this is similar to the
description made earlier of organization configurations, and sets the norms for
the two learning processes to be described additionally. At the same moment
the match between the machine bureaucracy type mentioned earlier and the
values on the learning dimensions is tested. Deviations between the theory and
the observations are of particular interest.
5. Description of MICS
6. Description of learning (single-loop and double-loop learning processes and
learning fields). Description of each learning step (development, storage, (re-
)use, dissemination, adaptation, removal/double loop trigger) and the role and
value of MICS therein.
7. Explanation of possible problems in learning steps from the role and value of MICS and
recommendations.
8. Conclusion regarding the validity of the main hypotheses.
Score sheets are used to summarize the findings. See tables 7.14 and 7.15.
Var 2: M.B.-type: 1: Classic- 2: Classic- 3: Lean- 4: Lean-Service
Org. Learning Machine Service Manufacturing
variables:
Var 1: Learning need 1 or 2 2 or 3 2 or 3 3 or 4
Var 3.1: learning Work harder Work harder Work smarter Work smarter
polcy & identity
Var 3.2: Res-ponsibil- Power-based and Power-based and Competence- Competence-
ity norms functional functional based based
Var 3.3: Action Money and slow Money and slow Team and fast Team and fast
norms
Var 3.4: Procedural Discrete and Discrete and Continuous and Continuous and
norms constraint constraint free free

Var 4: Description of Classic Classic Lean Lean
MICS
Var 5: SLL- effort 4 8 12 16
(score 0..16)
Var 6: DLL-effort 0 or 1 2 or 3 4, 5 or 6 6, 7 or 8
(score 0..8)
Var 7: MICS' role Problem Problem Poblem Poblem
Anticipation Anticipation anticipation and anticipation and
Critical evaluation Critical
evaluation
Var 8.1: MICS' value 0..4 4..8 8..12 12..16
on SLL-effort (score
16..+16)
Var 8.2: MICS' value -8..-4 -4..0 0..4 4..8
on DLL-effort (score
8..+8)
Table 7.14: Score Card for Each Case Based on the Descriptions of the Organizational
Learning Variables and MB-type Classification.
Hypothesis case 1 case 2 case 3 case 4 case 5

Con 4: Learning needs determine the learning norms
required for survival.
S14: Lean norms emphasize critical evaluation and
problem anticipation roles of MICS, whereas classic
norms emphasize problem anticipation and accounting
roles of MICS
Con 6: MICS contributes to single-loop learning effort
and inhibits double-loop learning effort.
Con 7 Depending on the Learning Norms, MICS
contributes to or reduces complexity and dynamics.
Table 7.15: Evaluation Table for Cross-Comparative Assessment
Score sheet 7.14 contains a summary of expected values. Each case is related with
these values, so that a test of the predictive quality of the theory per case is made.
Because the purpose is to make a comparison between MB-types, the cases are
presented in order of their expected leanness. Thus first a classic manufacturer is
described, second a classic service company, third a manufacturer that is on the move
towards leanness, but, as was noted later, not lean yet, fourth a service company that
is at about the same stage of leanness, and finally a fully lean producer that also
places a high emphasis on service. The results of all the cases are compared,
specifically to find any patterns among MB-types and the organizational learning
variables. In the most optimistic sense, the discovered patterns will be interpreted
normatively, so that something can be said about the values the learning variables
should have, given a certain context. Score sheet 7.15 brings together the results
about the validity, or invalidity of certain Conclusions and Statements. This is input
for a further elaboration of the theory. The results from sheets 7.14 and 7.15 are
used to further elaborate on a theory on the role and value of information systems in
Case Studies 189
Chapter 8 : Case Studies
8.1 Case 1: Cardboard Co.27
8.1.1 Introduction to this Case
This case is about Cardboard Co., which previously consisted of three independent small
manufacturers. At the end of the 1980s they merged under the heading of a packaging and paper
division of a multinational operating in the office supplies business. Since 1989 these three
companies still have separate production locations, but they share one management team that takes
responsibility for commercial and strategic policies and planning. The management team is located
at a separate town from the three production locations. The production locations are in Western
Europe and in towns, approximately 40 miles from each other. As suggested by the management,
two of these locations have significantly different organization cultures. This would explain
differences in success of an information system dedicated to the management of adhesive paper,
which is a relatively precious production component for finishing cardboard products. This fitted
well in our research objective of detecting learning norms and the interaction of these norms with
the learning process and MICS' use and value.
8.1.2 General Description of Cardboard Co.
Cardboard Co. is established as part of the paper and cardboard division of the multinational
mentioned above and officially exists since 1989. One location of this company was already
established in the 1920s. The merger of the companies was intended to create more benefits from
large-scale economies. The three companies were acquired to reduce the company's dependence on
suppliers, to reduce oscillation of prices in the industry and to optimize mutual deliveries.
The organization has three production plants, of which two are studied in detail. These are
numbered 1 and 2 in table 8.1. The main differences between the plants are the product volumes,
products and processes, organization culture, and effectiveness of their MICS. MICS consists of an
adhesive paper management system (APMS) and a logistics management system (LMS). This case
study mainly concerns the APMS part of MICS, which provides information to the management
and employees about adhesive paper management in the (recent) past.
27
I am grateful to Mr St. Kordelaar, who contributed substantially in the data collection for this case.
Location Product Transformation Employees

1 Packaging cardboard Large volume series 150
2 Packaging cardboard Short series 193
3 Luxurious and fine cardboard Small volume Not known
Table 8.1: Differences between Locations
A production location has an organization chart that looks like fig. 8.1.
The organization produces about 250.000 tons of solid cardboard a year. This
cardboard is a semi-manufactured product for the packaging industry. The Plant
mainly makes use of recycled paper, and applies several types of adhesive paper on
the cardboard. By changing the type of adhesive paper the cardboard acquires
different characteristics that are important for the particular packaging purpose. For
instance, the demands of the fruit industry with respect to waterproofing differ from
those of the toy industry. Adhesive paper is the main factor influencing the potential
variations Cardboard Co. can manufacture. The product variation is currently about
350 types. Adhesive paper is also the most costly raw material applied. For an average
type of cardboard, the adhesive paper makes up about 30% of the manufacturing
costs. The additional costs are: paper (10%), glue (1%) and fixed costs (personnel,
machinery, buildings etc.) (60%). Included in these cost estimations are maintenance
costs, technical support and overhead. Cardboard for tomato packaging, however,
requires a better quality of adhesive paper, leading to 64% of production costs.
Managing adhesive paper carefully is therefore a critical success factor.
Cardboard Co. delivers about 80 % of its products to other plants of the Cardboard
and Paper Division. Additionally it has about 250 external clients. Production is
Case Studies 191
mainly based on order processing principles, although plans have been made to
produce more on stock in the near future. As cardboard is a typical commodity, it is
difficult to follow a differentiation or focus strategy in this industry. The strategy is
therefore 'cost leadership' with some constraints on quality demands, depending on
the specific processing demands of the clients. The clients and their quality demands
are well known to Cardboard Co.
Obviously the efficiency of the production process is a major learning field. The
cardboard market is typically led by cost leaders. Human resource issues are
becoming increasingly important learning fields and training has been emphasized as
important for the company's success in several interviews. There is not much
innovation in products and most of the customers are known.
8.1.3 Cardboard Co.'s Learning Need
The Static-Dynamic Dimension

The internal environment of both locations is very stable because no major innovations in
transformation occur. The company seals the production locations off from possible disturbing
influences from the environment by placing marketing and planning functions at the central office
of Cardboard Co. In the last four years authorities for logistics and commerce also moved to the
central office.
The external environment is stable as well. Some cyclic events might influence stocks and orders.
For instance, during our investigation, the poor summer led to a poor fruit harvest resulting in a
small demand for fruit packaging. The division and top executives strategically try to minimize
turbulence. The acquisitions of the three locations were explicitly intended to lower turbulence and
dependence on unpredictable market forces. Yearly delivery contracts with its customers led to
further stabilization. Only very infrequently are high urgency orders processed. During our study,
the divisional Headquarters negotiated with a major competitor to create a merger. This was
realized in the second half of the year. The delivery of raw material, used paper and adhesive paper
are very stable because there is an oversupply on the market. Used paper can be obtained for very
low prices. On the other hand, adhesive paper is very expensive, difficult to obtain on time, and
frequently quality problems occur because the paper is vulnerable to mistreatment during
transportation. It is important for the company therefore to have firm negotiating power against the
suppliers of adhesive paper.
The Simple-Complex Dimension

Locations 1 and 2 differ slightly on complexity. The internal complexity of location 1 (high
volume, long batches) is very low. The production process is almost continuous, in which used
paper is preprocessed to a basis material, and then processed to cardboard. Finally, the adhesive
paper is glued to the cardboard. Depending on the features of the product, different types of
adhesive paper are used. During the production process, visual inspection and sensors are used to
monitor product quality. The manufacturing at location 1 is carried out by 72 persons.
Additionally, Technical Services (25 employees) maintains the machinery and electronics
involved. Human Resource Management regularly organizes training to improve operators'
abilities of machine handling and trouble-shooting. More recently, courses have been developed to
increase knowledge about production control, automation, administrative procedures and quality
awareness. The Logistics and Procurement department manages the stocks, and develops detailed
production schedules on the basis of the orders that are received from the Central Logistics and
Planning department.
The external complexity of location 2 is slightly greater because the production runs are smaller
and quality demands are higher than in location 1. This leads to a greater complexity in scheduling,
logistics and materials handling. The result is a much higher spoilage of adhesive paper. Besides
the quality of monitoring adhesive paper usage, cultural differences also explain part of the higher
spoilage at location 2. When MICS was introduced, spoilage of adhesive paper at location 1 was
16% and at location 2 this was 30%!
Conclusion: the complexity and dynamics of CBM are very low. This means that the learning
needs score is the lowest possible (score 1), which confirms insights into classic manufacturing
machine bureaucracies.
8.1.4 Cardboard Co.'s Leanness and Service-Manufacturing Nature
Leanness of Cardboard Co.

Cardboard Co. does not have a detailed and dominant quality attitude, because its strategy is based
on cost leadership and differentiation on possible use for clients. The division has developed a plan
called 'Cost Reductions', which is the dominant action plan at the moment. It aims at reducing the
costs of adhesive paper, production disturbances, machine stand-still, and use of personnel. Ideas
about differentiation are restricted to making variants in size, thickness and strength.
Vertical decentralization is reduced to a minimum. Since the merger of the three Plants, the
director of Cardboard Co. attracts his own staff who take care of many managerial responsibilities
that in the past were the responsibility of separate locations. The commercial function is
completely centralized and the location director is confronted with a large list of authority limits.
The logistics manager at Headquarters plans procurement and Plant loading among the locations.
The locations are only responsible for the detailed scheduling of the runs and the internal storage
and distribution of the supplies. Only the human resource function is decentralized. The task of this
function is restricted to training, mainly technical training and on-the-job courses. For the adhesive
paper management problem the local administration, logistics, human resource and production
functions are important. The tasks of these groups are listed in table 8.2.
Function Adhesive Paper Management Task
Logistics Check quality of delivery
Analyze source of spoilage and propose action
Negotiate with suppliers about quality of delivery
Production Key-in (in MICS) problems with adhesive paper in production process
Aim at minimal spoilage
Administration Check data of MICS (control data from production for reliability)
Make statistics about adhesive paper spoilage
Human Resource Provide training to minimize costs of spoilage (e.g. by use of simulations)
Detect needs for new courses
Detect needs for external training
Table 8.2: Localized Authorities Related to Adhesive Paper Management
The lateral structures are not strong because of the geographical distance and local
cultural differences. Additionally, the production processes are not easily comparable
because of some important differences in the processes. Despite these differences, the
large difference in spoilage of adhesive paper (16% at location 1 and 30 % at location
2) became apparent by MICS-data and led to an active search at location 2 for causes
and opportunities for improvement.
Because poor deliveries increased production costs, one location started to
systematically evaluate deliveries using MICS. Systematic problems were
Case Studies 193
communicated to suppliers leading to a joint search for improvement. The relations

with the clients are particularly strong, mainly because this is part of the larger
consortium policy of decreasing instabilities in the supply market for their packaging
manufacturers. The importance of clients is not as strongly felt at the locations.
There is no systematic client feedback system. Relations with clients are bureaucratic
and based on yearly plans created by the divisional top management.
The local Plants were purchased by a huge consortium for strategic reasons. Low
internal interest rates to encourage innovation and competitiveness (as in Japanese
lean production) are less important than direct financial success. Cardboard Co. also
differs in its career patterns from a typical lean organization. A production assistant,
for instance, can become a paper maker or maybe shift leader. Because of their
limited education and the decrease of management numbers as a result of several
mergers, no further career progression is likely for the employees. Relations between
the local management and the employees are informal and friendly. At the same time
however, management does not explicitly allow employees to participate in
managerial affairs. The work motivation is extrinsic. MICS introduced a new element
in motivation, namely the opportunity to measure a shift's performance in adhesive
paper management. This does not always lead to positive behavior. Shifts sometimes
try to pass on problems to the subsequent shift. This behavior obstructs the effective
use of MICS for improving intrinsic motivation.
New ideas on logistics frequently originate at the local logistics department. Most
other innovations are thought out at the divisional level and implemented via local
training.
Conclusion: the company only scores lean on its emphasis for positive management-
employee relations, and to a lesser extent on its seeking mutually beneficial relations
with clients and suppliers. As part of a larger division it deviates from the pure classic
type through its more secure financial situation.
Service-Manufacturing
Cardboard Co. is obviously not a service company. Its output is tangible and discrete,
thus easily measured in tons of each type of cardboard. Profits and (gross) costs are
detected easily as well. Only a few objective reference points are needed to determine
the performance of the Plant. This means that there are relatively many unambiguous
reference points.
The organization's output goals are defined in yearly plans, in terms of tons and
profits. Although this company is a clear case of a manufacturing machine
bureaucracy, its position in a consortium with no internal market feedback
mechanisms complicates learning from its output.
Cardboard Co.'s clients are only involved at the contracting stage and for the
definition of requirements. This is done at the general and divisional management
level. Work is a planned process, disturbed by only a few urgent orders. The final
success of the cardboard is partly dependent on what the clients (of the packaging
Plants) do with it. Phases in production can be clearly demarcated. Stock production
is avoided because stand-still costs less than stocking this relatively cheap but
voluminous product, and because stocks are risky as clients require specific types of
cardboard. The operation and administration systems are closed to the clients. The
informal communication in the Plant seems to be effective. Professionalism, also at
the managerial level at locations, is rather low. The merger introduced a trend for
more advanced management.
Conclusion: Cardboard Co. is a typical example of a Classic-Manufacturing machine
bureaucracy.
8.1.5 Cardboard Co.'s Learning Norms
Learning policy
Cardboard Co.'s management aims at reducing costs, increasing volumes produced and sold, and
increasing returns-on-investments. Learning is not specifically an objective in this company. One
director for instance stated that Cardboard Co. is a company of do-ers rather than of theorists.
Cardboard Co. has no policy to explictly encourage communication and has no infrastructure for
organizational learning. Nevertheless, it places much emphasis on improving skills via training.
The training manager communicates with the quality manager about systematic errors, and
sometimes develops a course to improve the situation. Most production assistants also learn the job
of paper and cardboard making by a combination of working on the shop floor and studying at
school. Cardboard Co., because of its small size, has many possibilities for informal lateral
contacts. In this, Cardboard Co. clearly differs from the 'work harder' extreme. Nevertheless, self-
management of production groups is very limited. The planning department, logistics and
production management clearly decide about what has to be done on the shop floor. Business re-
engineering is a non-existing word at Cardboard Co. The management is however very keen on
opportunities to reduce costs. Motivation for business re-engineering therefore exist, at least in
principle.
Conclusion: this organization in most senses has 'work harder' learning policy norms. It has a
limited informality because of its small size.
Responsibility norms
Two learning processes can be detected in Cardboard Co. which I call project learning and
adhesive paper management learning.
Project learning concerns engineering, developing and discussing investment projects to improve
cardboard production. The initiative comes mostly from the project manager, who uses people
from Technical Services and Mechanics to find out the best solutions. No market orientation is
carried out, and responsibilities are clearly associated with the project manager and one or a few
additional support departments. The frequency of projects is quite limited (no process innovations
were mentioned during our visits).
Adhesive paper management learning can occur when shifts receive data about their performance
and receive possible instructions to improve. Particularly important here are:
• The Administration who makes statistical analyses via MICS and feeds these insights back
to the shifts in concrete suggestions. The difference between locations 1 and 2 in adhesive
paper management is frequently explained by location 2's lessor experience with MICS and
the fact that the administration there lacks understanding of the shop floor because none of
the administrators have ever worked there.
• The logistics manager, who analyses delivery data from APMS, and feeds these insights
back to the suppliers.
Case Studies 195
The second learning process has been especially successful in the past three years. Location 1 has
reduced its spoilage from 16 to 8%. Location 2 has just started to improve, now that reliable data
are available. They reduced spoilage in the last two years from 25 to 17%. One percent decrease of
adhesive spoilage leads to a cost reduction of about $175,000 a year!
Because the organization is in a stable and simple environment no type of active search via
networks exists. The volvos (shop floor groups, shifts) have some responsibilities to lower the
adhesive paper costs. They are supported in this by the management and the technostructure
(logistics manager and administration in particular and human resources for specific training).
Project groups do exist sometimes, but are restricted to solving technical problems. Task forces
with clear strategic intent are absent.
Conclusion: this case verifies the opinion that learning in classic machine bureaucracies is
basically power-based and functionally organized. The learning process is organized around the
technical production process. No divisional and matrix structures exist.
Action norms
Although we did not have the opportunity to interview workers, motivation is probably extrinsic
because the routine and often dirty nature of the work does not give much instrinsic satisfaction.
Extrinsic work motivations reduce the chance for creative learning and kaizen to null. People on
the shop floor have a defensive attitude to innovations because they do not have the training or
ability to get another job. Knowledge removal, when linked to the loss of jobs or requiring
additional training to keep a job are not favored. Our informants (project manager MICS, trainer,
location director) could not give exact data about training budgets, because these data were not
seperately registered in the organization. The locations do have their own full-time training
consultant. Some younger people worked in the organization as well, engaged in external training
to become professional 'paper makers'.
Conclusion: Cardboard Co. has 'money and slow' action norms. Its small size however enables
quick implementation of new operational insights.
Procedural norms
Feedback frequencies for a location as a whole (from divisional headquarters) are mostly once a
year, when the yearly plans are reconsidered and budgets are allocated to the locations. This is
slow, even for a classic manufacturer.
Data flows are discrete. For APMS, data are keyed in at the shop floor. Next day the
Administration checks the data and corrects errors. Additionally, Administration analyses the data.
Finally the shift can have its feedback and the logistics manager can take action if required. A big
problem in this long procedure is that the shifts are not present when the data become available.
Sometimes they stay away for the weekend, and sometimes even for a week. This makes
communication about what happened and what the sources of the spoilage are quite difficult.
Data access is not free. There are precise authority limits and defined information needs. It is not
clear how this hurts the company, but it certainly does not contribute to business awareness.
The number of issues measured is very limited. The main system, APMS, only measures adhesive
paper spoilage. Additionally a logistics information system includes data about materials, people
and machine hours. At Cardboard Co.-Headquarters, commercial data are also available. These
data sources are not connected in an integrated system that could give people at all levels access to
information. No-one seems to think that the dissemination of data could be valuable and people are
not interested in data other than those of direct relevance to their task.
The management style is a uni-directional communication from Cardboard Co.-Headquarters to the
location directors. The operators and paper makers only receive instructions to produce cardboard
in certain volumes and with certain characteristics.
Feedback is slow, because the information supply is slow. The environment does not require this to
be speeded up, although technically it would not be difficult to arrange. The impact of this on a
further reduction of adhesive paper spoilage would probably be less at location 1. This learning
cycle has already reduced spoilage to 8%; further reduction depends on production planning and
the commercial process (order acquisition), because the major spoilage source is 'loss caused by
width' (LCW). LCW is caused by the fact that each location has machines of different width. In the
commercial process, orders are made concerning the delivery of cardboard with a certain volume,
features and width. For instance, a client asks for a width of e.g. 2.00m. This order may be planned
on a machine of width 2.34m. The loss is .34m of production. The solution for this problem could
be:
• Install more machines of different or varying widths.
• Increase the price to compensate for losses, or refuse the order.
Because Cardboard Co. has a very stable environment the first option is realistic. The second
option is commercially unattractive and not considered by Cardboard Co.
Expertise is concentrated in departments. There are no interdisciplinary groups. Only occasionally
an 'interdisciplinary' project group is created, mostly consisting of the project manager and some
people from Technical Services.
Conclusion: the procedural norms are 'discrete and constrained', though more discrete than
constrained, because shifts have access to data about other shifts' performance.
8.1.6 Description of MICS
MICS is a functional system. Data from the APMS branch can be transferred to its LMS-branch
(logistics management system), so that the quality of the data can be compared with registrations
from logistics and purchasing. It is however not yet completely integrated in the Logistics
Management System.
At the moment MICS is off-line. When it becomes part of a Manufacturing Resource Planning
system, which completely integrates APMS and LMS, it could become on-line as well, and the
learning process could speed up considerably. This is especially the case at location 2, where the
administrative process is much more complex because of the shorter product orders, larger variety,
and availability of two machines (instead of one at location 1). The use of an on-line system for
performance evaluation at the end of each day, could enable much learning in both locations.
Until now, however, data are often inconsistent between APMS and LMS, and interpretation is
done via hard copy reports. Changes in data supply must go through a bureaucratic request for
proposal, thus constraining flexibility in use.
There is some inconsistency with the control type that is possible in this situation. For instance
shifts could be set spoilage targets, and MICS could give data about performance. It is important
that a correct interpretation is made of the data, based on a concrete management theory. This
theory is not described explicitly, but easy to describe in a causal diagram (see fig. 8.2).
Case Studies 197
The theory visualized in diagram 8.2 is made concrete on the subject of adhesive paper
management in the causal diagram of figure 8.3.

A conflict of aims is apparent in the logistics choice between higher order processing targets and
decreased spoilage. The larger the batch, the lower the spoilage. Predictability of processes could
decrease width spoilage, but possibly decrease flexibility. This problem must be carefully
considered by Headquarters when making the yearly plans with the other Plants in the division.
Because decisions are about operational issues they can be implemented easily. This is the
advantage of being a rather small company. The typical examples of ineffective implementation of
decisions in bureaucracies are mostly based on large, government bureaucracies, which require
many translations before a policy is put into practice (Van Gunsteren, 1976). This mostly results in
many communication and agency problems (Douma and Schreuder, 1991) and people losing a
critical attitude to their work.
The problem anticipation network is centralized at Headquarters. Some further detailed scheduling
of activities is carried out at the locations. The performance control network consists of
administrators. At location 1 there was also a close connection between the local logistics manager
and the administrators. This was because the head of Administration at location 1 had a strong
affinity with production, and the administration and logistics people also work in the same room.
At location 2 this was both not the case, which hindered the understanding of APMS data!
MICS is thus used for logistics problem anticipation (LMS) or critical evaluation of performance
(APMS). These are however not yet integrated.
Conclusion: procedural norms are 'discrete and constrained', which is typical of classic machine
bureaucracies. Some deviations are important to note:
• Location 1 is clearly less bureaucratic than location 2 at the social level. This might explain
its better performance in adhesive paper management, because more complexity is involved
there.
• Absence of conflicts in management theories.
• Absence of problems of inconsistent data structures. The small size management
information system makes the problem of data-structure inconsistency easily manageable.
• High speed of decision-making.
8.1.7 Role and Value of MICS
Adapation of management theory

Supply and actual use of adhesive paper is administered and evaluated precisely. The adapation
process is carried out by the Administration Department and local logistics managers. The results
were substantial (from 30 to 16% spoilage at location 2 and from 16 to 8% at location 1 in a few
years). This impact is so great because for the first time APMS enabled the availability of accurate
and timely data for generating knowledge about adhesive paper management. Previously, adhesive
paper management was simply not an issue because its consequences were not known. At the
moment location 2 seems to be improving considerably more than location 1. Only more
fundamental solutions could improve location 1. The management theory is particularly well
developed for the following variables:
• Amount of spoilage per shift. This introduced a competition element among the shifts.
• Reduction of damage. The impact of poor deliveries became evident, and suppliers were
threatened by claims if they did not improve their delivery quality. Internal damage was also
measured, but it seems difficult to influence poor quality in this respect.
• Improving care of working and techniques applied. This was done via various suggestions
from the logistics manager, and by the creation and execution of training schemes.
The basic question of introducing new, more flexible production techniques and machinery, and
improving the fit between market demand and production capabilities is a difficult double-loop
learning issue which we will discuss later on.
The role of MICS is in performance measurement and evaluation via APMS, and problem
anticipation via the logistics planning and scheduling system LMS. MICS aids the adaptation of
knowledge, although learning is restricted to the transformation field and human resource issues
(performance measurement of shifts and feedback).
Storage
Storage of knowledge is realized via:
• Training and handbooks that are used for that purpose.
• The management of delivery is carried out by Administration and the creation of delivery
performance files. The logistics manager has a precise historic overview per supplier which
is used for improving deliveries.
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• MICS historical data about performance, and LMS models.

This means that knowledge is aquired, retained and retrieved via information systems and some
additional organizational memories. APMS and LMS parts of MICS both contribute to this
activity, though learning is restricted to the fields of transformation an human resources.
Dissemination
Dissemination of knowledge occurs by supplying data from MICS to the locations 1 and 2. The
locations therefore have bench-marks, that act as de facto standards against which to assess
improvements. MICS' role in this learning activity is obvious, and also has a positive value on it.
Dissemination is thus restricted to the transformation field and the field of human resources
(providing shifts with performance data).
(Re)-use
Planning algorithms have been developed to distribute orders to locations. This knowledge is part
of LMS and adapted and (re-)used by Cardboard Co.'s Headquarter logistics manager. APMS
generates historic overviews that are reused for performance control and motivation of personnel.
Here APMS' role is performance control. APMS' value is not restricted to process (efficiency)
improvement, but also for developing the human resource. APMS motivates people and also
clarifies some problems, which could be tackled by training.
Conclusion: MICS' role in Cardboard Co. is in the critical evaluation and problem anticipation
restricted to the learning fields of transformation and human resources. Cardboard Co. seems to be
learning in all the four areas of SLL. It scores on all cells intersecting SLL-effort activities and the
fields of transformation and human resources, leading to an SLL-effort score of 8. During our visit
no systematic SLL learning procedures about the other learning fields were detected. The reason
for this are the so-called 'authority limits', responsibility norms that restrict the opportunity for
learning in other fields. No negative impacts of APMS were found in the organization. In fact,
MICS contributes to all cells of SLL-learning in which Cardboard Co. is active. MICS' SLL-value
is thus +8. The only reason why it did not work in location 2 was because the administrative
procedures and evaluation experiences were absent.
Double-loop learning is completely absent at the locations but is a specific task for Headquarters.
The tomato project is one example in which some product development happened to capture a
larger share of the fruit packaging industry. This project was a joint project of Cardboard Co.
together with some other parts of the division. Cardboard is a mature product making market
development difficult. New markets are created by taking over competitors or by fundamental
process innovations that could strongly decrease production costs. The main production factors are:
adhesive paper, used paper, personnel and (inflexible) machines. Personnel costs could be lowered
by further production automation. The costs of these innovations are however also substantial.
Automation of production could however be combined with flexibility improvement (introducing
Flexible Manufacturing Systems). Cardboard Co. is not seriously considering this.
Conclusion: Cardboard Co. has a DLL learning effort score of 0. Information systems are therefore
not used for double-loop learning. MICS reinforces the single-loop attention focus. This could
predict negative impact of MICS on DLL-value. The cause for this restriction to DLL learning is
however not MICS itself, but the existing learning norms (specifically the learning
responsibilities). Hence, the impact of these systems on the DLL-value is scored zero and not
negative.
8.1.8 Learning Problems Related to MICS and Recommendations
Many learning problems were detected, namely:

• Location 2 has a decision-making network that seperates problem anticipation from critical
evaluation.
• Cardboard Co. locations (the Plants) are physically and mentally separated from their
Headquarters.
• Cardboard Co. has no systematic insight into other single-loop learning issues than the
adhesive paper management.
• Cardboard Co. has a strong functional separation of responsibilities, decreasing the capacity
for interdisciplinary thinking.
• Double-loop learning is not supported in Cardboard Co. (even not at Headquarters).
My advice is to develop a new MICS, and some new management structures, as follows.
MICS:
• The new APMS could easily be made part of an integarted MICS. It should not only prompt
suggestions for improved planning, but also systematic reviews of performance indicators.
The organization should develop a set of performance indicators and implement these as part
of the new LMS.
• The new LMS should be developed on the basis of an explicit management theory, which is
recognizable and understandable by all organization members.
• Organization members should be invited to participate in thinking about Cardboard Co.'s
success by combining data from LMS with the managerial theory.
Management structures:
• Authority limits should be less detailed and used more informally. To decrease the
communication problems, Cardboard Co. Headquarters should be closer to the locations.
• Cardboard Co.'s commercial department should consider innovations in product, markets
and transformations. The existing transformational technology is getting out of date, and
causes spoilage of expensive materials. It is quite certain that some competitor will introduce
solutions for the spoilage problem within a few years.
• Possibilities for reducing spoilage of adhesive paper with the existing machinery are now
exhausted. It is now time to consider new flexible manufacturing technology. Cardboard
Co's 'width loss' is substantially larger than its 'cut loss' (3.1 versus .4 in location 1). Width
loss can only be reduced by: not accepting non-standard orders, improving planning
processes by using an advanced planning module of LMS, or introducing flexibility in
machine width.
8.1.9 Conclusions Regarding the Main Hypotheses
For Cardboard Co. the score sheet is as follows.
Org. Learning Variables: Var 2 (MB-type) is 1: Classic-Machine

Var 1: Learning needs 1 (low complexity and low dynamics)
Var 3.1: Policy and Identity norms work harder
Var 3.2: Responsibilities Power-based, functional
Var 3.3: Action norms Money and slow, but quick implementation of operational
insights. The small size of the organization for an MB-type
might explain this deviation from the theory.
Var 3.4: Procedural More discrete than constraint, because shifts have access to data
from other shifts for motivational purposes.
Var 4: Description of MICS Classic (functional, some parts off-line, no intergated database)
Var 5: SLL effort (rate 0..16) 8, four points higher than expected maximum
Var 6: DLL effort (rate 0..8) 0
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Var 7: MICS' role Problem anticipation and Critical evaluation.
Var 8.1: MICS' value on SLL (rate -16..+16) +8, two points higher than expected maximum
Var 8.2: MICS' value on DLL (rate -8..+8) 0, not negative
Unexpected values are italicized in the table.
Table 8.3: Score Card for Cardboard Co.
Some deviations from the expectations about learning norms and MICS are
commented on here:
• Cardboard Co. also uses project groups, though not frequently, to solve
problems. This improves the single-loop learning process. MICS contributes
sometimes as a system that signals problems.
• Cardboard Co. also learns by disseminating the experiences of volvos and
disseminating experience among the locations. Improvement projects often
involve the collaboration of logistics, volvos and training and human resource
specialists.
• New operational insights are quickly implemented. Because of Cardboard Co.'s
small size, management can easily check the effectiveness of the implementation
by just walking into the factory.
• The MICS provides data for budgeting and problem anticipation, but also for
critical evaluation.
• The most important reason for the high SLL-score is that MICS contributes to
two fields of learning, and does this on all SLL-activities.
The differerences on the learning efforts score are not very significant. It is more
significant that the negative impact (inhibiting) of MICS on double-loop learning
seems not to exist in this case. As far as DLL is constrained in Cardboard Co., the
causes are based on (responsibility) learning norms (called authority limits).
Con 4 states that learning needs determine the learning norms required. This
statement is a truism, but leaves open the important question of how learning needs
influence learning norms. The learning needs score for this case is 1 and this
correlates with the following learning norms:
• Absence of a learning identity and policy definition. Learning is not a major
issue, and therefore has not led management to define a learning policy.
• Because learning needs are low and the problems are simple, it is mostly not
necessary to make responsibility norms involving an interdisciplinary group.
• Action norms concern the implementation of concrete instructions and
suggestions for improvement (mostly found out as a result of an improvement
project, or as an idea of a manager or expert of the technostructure). The
organization likes concrete action suggestions, and disapproves of complex
theorizing.
• Procedural norms exist to disseminate information about performance between
locations 1 and 2, and between shifts.
Statement 14 says that classic learning norms only lead to problem anticipation
MICS roles, whereas lean learning norms lead to MICS that also supports critical
evaluation roles. This classic case however shows that statement 14 is not true
because APMS and LMS support both roles.
The validity of Con 6 in case 1 is not yet clear. MICS indeed increases SLL-effort. It
has however no impact on DLL-efforts. This means that Con 6 is invalid. The
suggestion therefore is to confirm Statement 15, and to modify Statement 16 to:
MICS has a negative or no influence on DLL-efforts.
Con 7 proves to be right in this case. The learning norms support SLL-effort that can
only reduce complexity and dynamics when they are already low. MICS contributes
to this SLL process by providing data useful for this process (Statement 6). In this
case, the critical evaluation role of MICS does not lead to theory development and
unlearning (DLL-efforts), but only leads to theory adaptation, because the
organization perceives neither high complexity nor high dynamics.
8.2 Case 2: The Bank28
This case is about a major European Commercial Bank located in most European countries and
other continents. The case studied was its Branch in one European country. It has been particularly
successful in the business market segment, and now is entering into the less cyclically sensitive
private sector (especially the richer subsegment). The Bank aims at delivering full financial
services in both market segments. We stressed the functioning of the Branches in this case, because
these are the sites at which the 'moments of truth' occur when interaction with clients take place
(cf. chapter 5). A bank was chosen as a typical example of a classic machine bureaucracy. The
organization in this case is however larger than in the others (about 3000 employees). The Bank is
also interesting because of attempts by its senior management to improve organizational learning
and reduce its bureaucratic nature.
8.2.2 General Description of The Bank
The bank originated from mergers among small banks under the leadership of a huge European
bank. In the country studied it has about 3000 employees. There is pressure to cut staff as the
payoff of information technology, and to meet price competition among banks in Europe. Data
about employment in this bank are given in table 8.4.
28
I am grateful to Mr M. Hafkamp, who contributed substantially in the data gathering for this case.
Case Studies 203
Year Number of Employees Full time equivalents**

1990 3637 3450
1991 3417 3261
1992 3235 3070
1996* 2800 2650
* According to business plan 1992-1996
** FTE data are estimated by multiplication of number with a factor .95
Table 8.4: The Bank's Employment Figures
The Bank has a business plan for 1992-1996 listing these critical success factors:
• Actively approaching the target groups (upper 40% of the private market and
business companies).
• Stabilizing its market size in the state of operation.
• Full service to optimize relationships with clients.
• Reduction of costs (mainly personnel) by means of business process redesign
and information technology.
• Improving internal communication for improving core activities for the
business market, private market, and support and facilitation of these markets.
The influence of Headquarters should consequently decrease and some
structural organization changes are planned.
• Improvement of its image via new products and communication programmes.
• Improvement of the international character of the bank, mainly through the
development of an international educational center for its personnel and
managers.
• Improving performance stability by penetrating the less cyclical private market.
The bank has an explicit management theory stating that its success depends directly
on its public image, operational effectiveness and efficiency, and the stabilization of
cyclical performance. Figure 8.4 gives a causal diagram of this theory and some
indirectly related factors.
Striving for full service and the elite target group are not consistent with a cost
leadership strategy. The Bank is well aware of this fact and does not have a cost leader
but client satisfaction strategy.
The Bank is divided into two types of Directorates: Commercial Directorates (Stocks,
Corporate & International Affairs, Product Management & Marketing, Branch
Management and Liquidities) and Support Directorates (Credits, Payments, Human
Resources, Information & Organization, and Management & Planning). Most
employees work in the Commercial Directorates (about 2000). About 1600 of these
people work for the Branch Management Department, which consists of the
Branches that directly serve The Bank's clients. In the Branch Management
Department about 200 people have non-managerial jobs. Figure 8.5 gives a general
organization chart of The Bank.
A further detailed study was made of the
Branches. One reason for this choice was
that the Branches are the main locations
where The Bank can learn about its
performance. The other reason is that The
Bank is explicitly trying to make the
Branches more self-managerial. The
Department of Branch Management is
divided geographically into six regions,
managed by Regional Directors. In total
the Department consists of 78 Branches,
with 4 big Branches (containing more than 50 employees), 38 small Branches (7
employees) and 36 middle-sized Branches (between 7 and 50 employees).
Coordination between the Branches is hierarchical via the Regional Directors and
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the Departments at Headquarters. Headquarters prescribes the main rules in the

organization by: setting return-on-investments, making the strategic market plan,
choosing the service type (full service in The Bank's case), setting acceptable risk
limits, developing new products, stating human resource policies, setting rules and
norms for the administrative organization, developing information technological
plans and related investments and projects, and developing financial control systems
for cost accounting and cost allocation.
The Department of Branch
Management has an organization chart
as illustrated in fig. 8.6. MICS-use and
organizational learning were studied in
ten Branches. Two Branches were
studied in detail, one medium-sized and
one small Branch. We found, in a first
interview round, that the way of
management, learning and MICS use,
does not differ much among these
Branches, although they all had very different client groups. To obtain a correct and
complete picture of organizational learning and MICS, we received information from
the Regional Directorate and Headquarters as well. This means that this study is a
typical embedded case study, with multiple units of analysis (Yin, 1984, pp. 44-46).
The emphasis is however on the functioning of the Branches.
The following functions are most important in each Branch: commercial functions,
administrative functions and managerial functions. Commercial functions include:
market orientation, action planning, acquisition planning, acquisition, credit loan,
credit consulting, additional banking services, management of credit relations,
general account management and documentation. Administrative functions include:
counter-activities, cash management, payments, book-keeping, deposits handling,
database management, administration and sending of cheques, handling of PBX,
telexing and faxing, postage, mail and archives, input of data in The Bank's total
management reporting system, and some additional administration activities.
The management tasks are listed below.
• To lead the commercial activities of the Branch, by developing commercial
policies.
• To make budget proposals for the Branches and discuss these with the regional
director.
• To report periodically to the regional director about goal attainment and
reasons for ineffectiveness.
• To select and acquire a qualitatively and quantitatively excellent work force. To
make proposals for changes to the required work force.
• Responsibility for several management reports.
• To lead coordination and discussions between chiefs of administration and
account management.
• To control and inspect the ways administrative procedures are performed.
• To assign tasks to Administration for performing internal controls according to
rules set by the regional director and the Internal Accounting & Control
Group.
• To authorize contracts and transactions within a certain authorization limit.
• Outside representation of the Branch.
• To contact important outside stakeholders such as solicitors and local
government.
These management tasks are to some extent shared with other sections of The Bank
(such as departments of Headquarters).
If we look at the organization charts, the four learning fields all are clearly allocated
to departments, as is shown in table 8.5.
Learning field Department

Process Systems and Facilities, Administration, Financial Planning and Control
Human resource Organization Development, Human Resources
Market Product Management and Marketing, Regional Director and Branches
Product Product Management and Marketing
Table 8.5: Learning Fields and Departments Responsible
8.2.3 The Bank's Learning Need

The banking business is extremely vulnerable to macro-economic trends. This is particularly so
with banks that have their roots mainly in business services, like The Bank. At the time of our
investigation (1992-1993) a world-wide recession was leading to increased problems. At the same
time interest rates, currencies and stock exchange indexes were strongly fluctuating, making
business unpredictable. Additionally, a trend of further deregulation and liberalization of the
economy (Common Market and GATT discussions) influenced the European banking business.
National market boundaries were being removed and national banks were confronted with
increased competition from abroad. Banks were also looking for new markets to enter (e.g.
insurance, travel, real estate). To survive in this environment, banks chose one or more of the
following options:
• Strong emphasis on cost reduction, by use of information technology.
• Active and even aggressive marketing of services and products.
• Many new products and services.
• Mergers between banks to stabilize their position and increase power over a market.
All these issues are relevant for The Bank. The first three have been shortly touched on in our
model of the management theory. The latter issue is relevant for The Bank as well, because The
Bank resulted from mergers of small banks under the leadership of a multinational. A fifth option
was also chosen by The Bank: optimization of client-bank relationships as a strategy to enter a
mature bank market.
Uncertainty is a dominant factor and The Bank was trying to decrease it by putting more emphasis
Case Studies 207
on the private market (which up to that time had been contributing about 20% to The Bank's
revenues).
After the mergers that led to the existence of The Bank, the CEOs centralized authority and
introduced bureaucratic mechanisms to reduce internal instability. This period is now over and The
Bank is following a philosophy of decentralization and encourages self-management. However,
this increases internal instability. The instability differs with respect to administration and
commercial functions.
Because administrative tasks are clearly defined and easily measured, performance and planning
data for this part of Branch work are easily determined. The commercial functions however are
much harder to measure in terms of amounts and predictability of work-loads. As a consequence
only data about commercial performance are available with indirect relations to work-loads.
The bank is well aware of the risks involved in the commercial activities. Therefore it has
developed a specific approach to treat prospects, to estimate a relation and to develop a cost-
effective sales strategy. Nevertheless, work-load as well as changes in profitability are extremely
difficult to determine in advance, and risk is an inherent issue of the commercial services.

In the sixties, banks offered just a few standardized products. Since then, many new services have
been developed to differentiate from competitors. Since the 1980s, this differentiation trend has
been reversed, in the sense that banks merged to provide clients with a total service. The Bank is an
example of such a 'full service' bank. This means that complexity has increased considerably.
Clients also demand more added value by a better customer fit, which requires more expertise and
specialization. Simultaneously, banks have been offering services that were recently not considered
as bank services (insurance and travel services). Information technology enables new products
such as electronic banking, salary processing, and management information for its clients. These
trends are all applicable to the situation of The Bank now.
The external complexity is clearly reflected in the specialization which is an indicator of internal
complexity. Additionally, banks are also supposed to act according to some general rules and laws
that garantee their trustworthiness and credibility. These are implemented in many rules for
internal control and inspection. In studying the functions, tasks and handlings in a Branch, we
found some interesting indicators of The Banks internal complexity (see table 8.6).
Function Management Administration Commerce

Tasks 24 12 10
Activities (including control) 76 94 12
Control activities 24 30 3
The cells show the number of tasks, activities and control activities that are prescribed in The Bank's handbook for
performing managerial, administratitive and commercial functions.
Table 8.6: The Bank's Branch Tasks and Activities Indicating Internal Complexity
What is remarkable is not only the amount of specialization but also of control to
check for possible mistakes, fraud and performance (the latter was only performed by
the management). Of the 24 managerial control activities only 2 were for
performance control. The managerial (76) activities were the task of the location
director (13), Chief of Administration (25) and several other managers at local,
regional and Headquarters levels.
Many procedures are described in administrative handbooks, overview lists for
authorizations, circulars, internal instructions from the Chief of Administration and

Headquarters, waivers, warning lists in case of calamities, etc.
Conclusion: The Bank has a dynamic and complex environment, and therefore has
the maximum learning needs score of 4, a much higher score than expected.
8.2.4 The Bank's Leanness and Service-Manufacturing Nature
Leanness of The Bank

The Bank has not developed a particular quality policy. The required control procedures and
checks are compulsory by law and market demands. High quality of service is a minimal
requirement for staying in this service business. Important mistakes lead to a low image. The bank
tries to augment its image by an explicit public relations campaign and through creation of new
products. Some of these products are particularly interesting for the new (private) market it wants
to enter.
After some years of centralization, Branches are being given more autonomy and responsibilities.
They have to develop their own business plans and are held increasingly responsible for their own
performance. The task and activity descriptions are explicitly not written in terms of how people
should execute the job, but explicitly in terms of what they should have achieved.
Lateral structures, i.e. interactions among Branches, are not found in this case. In general the
Branches work independently, and the regional director coordinates when necessary. Meetings at
the regional office are sometimes organized to have interactions at the lower management level.
This is not an explicit policy. Mostly the problems and policies of the Branches all go up to the
regional office and from there to Headquarters. The added value of the regional director seems to
be as a post-box.
A main supplier is the Central Bank which prescribes credits and loan policies. The Central Bank
is a superior authority, and has legal means to enforce its policy. The Bank is part of a huge
financial consortium that can provide cheap money to help The Bank survive in case of threatening
situations. The Bank is also used by its multinational parent to have a foot-in-the-door in the
national market.
Because of the strong competition in the banking business, the client has a very strong position and
The Bank does everything to optimize the client-bank relationship. Additionally The Bank has a
relatively expensive workforce, because the general age, experience and knowledge level in its
workforce is higher than those of its competitors. This makes a cost leadership strategy not yet
feasible. Only high IT-investments could make cost leadership achievable. The Bank's strategy is
primarily to show clients its added value despite its higher price.
Management-employee relationships are hierarchical. The small Branches (7 or less employees)
have very friendly and constructive interpersonal relationships. Nevertheless the tasks and
responsibilities are clearly demarcated. There are now specific programmes to raise worker
participation in management, however, human resource management is ambiguous because the
management and the lower level personnel are treated very differently. Management positions are
mobile and have an international orientation. Lower level management and clerks have a clearly
local position and mobility is unlikely. Motivation is extrinsic. No specific programmes exist to
create a more professional work attitude (possibly leading to lower incomes, but more interesting
jobs).
New ideas are taken from where possible. The Headquarter departments develop new products, do
assessments of performance, think over information systems and facilities, and consult on possible
improvements. They have highly educated personnel (mostly university Master's Degrees) and
apply the new insights where possible.
Conclusion: the human resource management, quality attitude and financial decision-making
structure looks lean, but also classic at the same time. Decentralization is low, although, The Bank
Case Studies 209
has recently started moving to a more decentralized structure. The Bank could therefore better be
qualified as moving towards lean, but it is still fundamentally a classic machine bureaucracy. The
score on the 10 point leanness scale is 3.
Service-Manufacturing Nature of the The Bank

Although the services The Bank provides are intangible, they are not difficult to measure when an
output has been delivered. The amount and volume of loans are easy to measure. This is also true
for stocks delivered and traded, and liabilities paid. The output is less easily measurable when it is
professional advice. In fact the bank is not paid for advice only, but gains its revenues from
transactions that result from advice. Even more important than the transaction volume is the client's
perception of the output. Objective reference points could be constructed by counting the payoff of
a service for a client. The Bank, however, prefers to monitor its contact with clients closely and
frequently. Administrative services are much more easily quantifiable in this respect than
commercial services. The output goal of the bank is defined in terms of client-service relationship.
This is monitored in terms of profit per client/relation and the associated costs.
Commercial services are difficult to measure, but a further problem is that the time lag between
activities and success can be quite long. For instance it may take a client more than two years of
communication and negotiations with account managers before he decides on some huge and (for
The Bank) very profitable transactions. For administrative services the relation between activities
and success is almost direct, because the costs and prices of the administrative activities are known
in advance.
The main resource for the commercial function is knowledge about products that support the
services, and knowledge about clients and the local market. The Bank has explicitly chosen a
decentralized Branch policy in recent years because of the specific characteristics of local markets.
One Branch might for instance earn a substantial amount with one 'big' client, whereas others earn
their money from many small (private) clients. The Branches sources of income vary so much that
The Bank's CEOs have removed centrally established performance targets. Planning and
budgetting is therefore a top-down and bottom-up communication process. As well as a good
understanding of The Bank's products, 'close touch' between account managers and
clients/prospects is a critical success factor for the commercial function. The administrative
function is very different. Although the establishment of effective and efficient procedures is a
knowledge-intensive activity, administrative processes are increasingly carried out by programmed
machines possibly in a network environment.
Clients are ego-involved in the transformation process, because it is otherwise difficult to deliver
the right services to a client. This is however less so for the machine-like transaction processing of
administrative services. The Bank's focus on business and the top 40% of the private market makes
most of The Bank's activities less mechanistic and they clearly require specific attention to the
clients' needs.
Some routine information processing is carried out (e.g. salary accounts and salary services). This
makes the information processing a planned process. The same applies for much of the
administrative function. Our inventory of administrative activities showed that there were few
deviations from the plans in this area.
The administration function distinguishes clearly between input, transformation and output. The
commercial function is also more procedurally organized. The Bank developed an information
system that supports commercial functions by means of a process view of commercial activities.
Buffers are only available in the administrative process, but in general this is quite limited and
clients demand timely service. Some buffering in the commercial function is possible via selection
of clients and making agreements about the delivery of the services that are acceptable to the client.
A well-functioning agenda/calendering system is required to ensure that a complete service is
delivered on time.
The commercial function is open for prospects (potential clients) and clients. This is true also of
some parts of the administration (front office). Electronic banking and cash dispensers make the
administrative function more directly accessible to clients. Dual cores (see chapter 5) are however
still available. This can also be seen in the organization structure of the support and commercial
departments and the split between the Branches and the Headquarter departments. The Bank is
now in a process of reorganization and wants to reduce the barriers between the cores, and aims at
complete integration.
Professionalism is high in the commercial and management positions. Also at Headquarters,
professionals are organizing administrative functions. Professionalism is low in most
administrative positions.
Conclusion: The Bank is a service company, but there are still many manufacturing features,
particularly within the administrative function. The Bank is also a classic MB. Hence, The Bank
belongs to the classic-service MB type.
8.2.5 The Bank's Learning Norms
Learning policy norms

The Bank does not have an explicit learning philosophy. At the Branches there is no process of
continual improvement. They regularly receive feedback and instructions from Headquarters, but it
is not clear how they should improve on the basis of experiences in the market. There are regular
learning meetings to update and adapt management knowledge at Headquarters. The Branch
Management department communicates the findings to the Branches.
Communication is via the hierarchical command and reporting structures. The organization is
non-transparent as it is sometimes difficult to find out who should be contacted to solve certain
problems. This is of course closely related to the size and geographical separation of the company's
parts, but certainly a problem when wanting to create learning processes.
The development of new products and training people to improve The Bank's performance are
recognized as basic for The Bank's survival. In 1992 an average of about US$950 and 1% of the
total working time was spent per employee on training. This amount represents a strong increase in
relation to previous years. Recently, a special training center has been established. There is also a
trend to shift the emphasis of courses from internal knowledge and product skills to skills for
effective management. This training is also closely related to the internal change processes.
Headquarters has a large technostructure, thinking about processes and products. The decisions are
implemented via the Branch Management Department. Because of the integration of back and front
offices, branches can gain a better overview of the performance of all their activities.
Business re-engineering is an important issue. Many ways of improving performance and
efficiency are being considered. A major project now underway is the network project, which is
trying to implement data highways to improve internal communication in the bank, and to smooth
interactions between the front and (virtual) back offices. These data highways could enable very
lean communication processes. The network project has however not yet been realized.
The commercial function is pure people's business and demands the development of human
resources and core competences. At the same time it makes administrative and back office services
more industrialized. This requires new skills and expertise. The account managers function more
and more in teams, so that they can support each other and have access to information about
clients. The Branches however only have some ad hoc collaborations initiated by the Regional
Director.
Motivation for business re-engineering is high. People are professional and aware of the need for
modernization. Also, employees think that The Bank is still able to grow in the market.
Conclusion: The Bank has many initiatives for improving learning via changing the existing
learning policy norms. These policies in many ways preceed behavioral and organizational
changes. The Bank therefore is in an organizational turnaround. Also, the relation between the
Branch Management Department and the other commercial and support departments is being
reconsidered. The 'work harder' extreme is dominant. The 'work smarter' extreme is exemplified by
the implementation of networks, the way account managers work, and the motivation for business
Case Studies 211
re-engineering. This is possibly also the result of the service transformation, which e.g. requires
less additional investments to create a network (cf. chapter 5).
The Bank is a prototype of a classic machine bureaucracy with a classic distribution of learning
responsibilities. Learning occurs along hierarchical lines with support from the technostructure at
Headquarters.
The Bank has a functional learning structure, thus dominated by the hierarchy (management and
technostructure). No use is made of organizational networks. The volvo principle is to some extent
available in the Branches to adjust work processes and communicate specific issues of interest.
Task groups have not been formed despite the fact that The Bank is starting a major organizational
change process. The change process is strongly supported by the organizational apex, but the
changes are implemented top-down with a major role played by the Branch Management
Department's Organizational Development group. Project groups are frequently started up to
accomplish technical and product changes. These are mostly initiated by Headquarter departments.
Conclusion: the competence-based structure in The Bank is restricted to operational working in the
Branches. The learning structure follows the principle of the command chain, and thus is of the
type 'power-based and functional'.
Action norms
Work motivation in the bank is primarily extrinsic and oriented towards personal success and
income. There is however no policy to pay people for success. This is also almost impossible in the
commercial function, because of the time lag between action and result, and the ambiguity about
this relation. The success of the administrative group is easier to measure, but again it is difficult to
assign efficiency gains solely to the efforts of the administrative group alone. The Department of
Information & Organization, regional director, account managers etc. all contribute in some sense
to the success of the administration. Despite this motivation, no indications of defensiveness were
found that would obstruct learning processes.
Learning priority is very high in the bank, although learning was not a basic part of its identity
definition. The reason for this conclusion is the large number of employees (443, making 15,6% of
the total work-force) in technostructure positions, which includes those responsible for the
standardization of work processes, personnel analysis, recruitment and training, analysts for
planning and control, budget analysts and accounting.
Knowledge sources for learning are internal as well as external. Many insights are gained from
professional education, and many are gained from experience and analysis of the business The
Bank is in.
Conclusion: despite The Bank's action norms falling mainly in the 'money & slow' extreme, it
invests substantially in learning (high learning priority).
Procedural norms
The operational feedback, client-relation interaction, must be very quick. A poor response of the
Branches to complaints could seriously harm The Bank. However, it is bureaucratic and reacts
slowly when problems about products are discovered. The problems are then communicated from
the bottom (account managers) to the Branch Director, who in turn communicates with the regional
director. The latter then communicates them to the director of Branch Management, who in turn
communicates with the Department of Product Management and Marketing. Recently, however,
the Branch Management Department and Product Management and Marketing moved into the
same building. Because both departments are rather small, communication at this level is quick and
effective. In our interviews with Branch employees and Headquarters nobody complained about
the existing communication procedures, which might indicate that feedback frequencies are in line
with demands. Larger banks would probably suffer more from this problem. The future
information network could be used as a lateral structure to support quick communication about
products.
In most cases the procedural norms can be qualified as 'discrete and constrained', which is typical
of classic machine bureaucracies. The information flow is discrete and not on-line. Many reports
are produced and disseminated on a periodic basis, e.g. once a week, once a month, once a year etc.
Data access is clearly connected to authorities and responsibilities. The management style is telling
and the technostructure and management are important knowledge creators in the organization.
Deviations from the classic case are the feedback time and the number of issues measured. The
feedback time differs strongly between operational and tactical-strategic feedback. Operational
feedback is sometimes rapid for administrative services, which have a very precise and limited
period. A service failure provokes quick feedback requiring direct action, because competing banks
can supply most administrative services as well. For commercial services the feedback period can
be long, because the impact of these services manifest themselves no sooner than after a few years.
The tactical and strategic feedbacks take a long time as well. Many communication nodes in the
hierarchy must be passed before problems from the Branches finally reach the attention of
Headquarters.
A most remarkable feature of The Bank's MICS is the large number of indicators measured. For
instance a Branch director receives the following reports, generated from several information
systems, all called MICS here:
• Monthly Overviews showing the profits of the bank related to several products, the costs
involved and the costs and profits per employee. Also, a comparison with the budgets is
made.
• Monthly Budget Comparison. About 193 items of cost and profit data are generated by
comparing the results to the budget, cumulative budget data from the beginning of the year
are given, and data about budget target realization are given.
• Status Interests Analysis, providing data on six interest products with respect to the capital
available, the interest margins and the interest margins related to the amount of capital
involved. It also compares these data with data from the last two years.
• Balance and Profit/Loss Accounts per product, generated monthly, quarterly and yearly.
• Human Resource Costs, on the implications of personnel changes on personnel costs.
• Absentee Overviews, on absenteeism through illness at all Branches in one region, for
commercial and administrative personnel.
• Acquisition Plan, which presents data about the money gained by account managers in
relation to periods in the past. The data only concern the six interest-generating products of
the bank. Data about acquired provisions (e.g. insurances sold) are registered elsewhere.
• Transaction Monitor, showing the average number of transactions (monthly, quarterly,
yearly), the work-force allocated to the Branch and the number of people required
(according to a general norm) to process these transactions. In total 196 transaction types are
identified in the reports!
• Overview of Transaction Bills. For each account manager and for each account and relation
a monthly report is made of the amount of money billed to a client.
• Total Overview of Added Value of an Account Manager. This is a report about the interest
margins, provisions received, amount billed, and costs of transactions per account manager,
quarterly and cumulative per year. This report is also made per relation per account
manager. As The Bank now has 6 interest-generating products and 8 provision-generating
products, and account managers have an average of 250 relations, reports can have a length
of about 3584 items!
• Number of Relations (accounts) in Relation to Commercial Work-Force. Per region and
Branch an overview is generated of the number of private and business relations, split up
into debit and credit relations, the size of the commercial work-force and number of
Case Studies 213
relations per commercial employee.

• Audit Reports of the Internal Accountants Office, about the quality and reliability of
procedures and how these are applied.
• Overview Prospecting, describing per account manager the prospects and some main data on
prospects, such as Branch involved, size, status of the prospecting process, and dates for
further activities.
• Credit Overview, describing per account manager the accounts' credit limits and terms of
payback.
• Conversation Notes. Many notes about the communications between account managers and
prospects and clients are made that are used for further communication and for other account
managers to take over the conversations when required.
• Calendering System. This system combines data from Credit Overview, Conversation Notes
and Overview Prospecting, so that in weekly meetings between account managers and the
Branch directors operational actions can be planned.
• Overview of Relations and Accounts, containing an overview of relations (private, business
and its legal shape) to their account status (positive, negative, no account).
Conclusion: this MICS is not lean because no explict connections are made with the mental models
of its users. The system will change from hard copy to interactive output. The problems of
accomplishing this are however huge. At the moment therefore, almost all organization members
receive the same package of paper output, with many irrelevant details and difficult to analyze!
Single-loop Learning
Adaptation
When initiated by Branches, adaptation is sometimes a slow process. Problems with products are
first communicated to regional directors and the director of Branch Management. Secondly, they
are communicated to Product Management. Product Management also initiates adaptation itself.
This is done by asking account managers about previous experiences.
The Department of Management and Planning also initiates adaptation. Depending on past
performance and The Bank's policy and aims, this department develops new ways for internal
budgeting, tariffs and pricing. It develops new products, processes and markets, which identifies
this activity as adaptation.
Adaptation is also an activity of the Branch Management Department, mainly by suggesting new
parameters for budgeting. This statement explicitly uses the term 'suggesting' because The Bank
has changed its internal policy from a command (top-down) structure to a more participatory
approach that gives the Branches more freedom. The reason for this policy change was not only the
maturing of the organization (the initial years after the merger were completed and the internal
situation has become more stable), but also in order to widen the information processing
capabilities at the local levels, which was required because of the large variety of locations.
Finally, the Department of Information & Organization also has a task, in evaluating and
suggesting changes for administrative procedures and internal control, but also through changing
information supply and systems.
The role of MICS in adaptation is problem anticipation (scheduling and longer term) and critical
evaluation (of products, operations, costs etc). MICS' value is limited to the fields of processes,
products and markets. No specific human resource planning and performance measurement system
exists.
Storage
Administrative procedures are well stored in handbooks, courses and people's experience and
education. The Bank also has experience with storing the knowledge of the account managers. The
following systems were specifically developed for this purpose: Overview Prospecting, which
supports the account managers via expert knowledge about the prospecting process, Conversation
Notes, which document conversations and make them accessible to other account managers, and
the Calendering System that combines information from the two previously mentioned systems, for
making and archiving schedules of activities. The many other management reporting systems
mostly provide data in relation to past periods. This enables interesting learning from the past, but
it is not clear how it could be used for more than extrapolating the past. MICS' roles are thus
scheduling and monitoring progress. Its value in these activities is restricted to the process field.
Dissemination
The coordination between Headquarters and the Branches could be improved considerably via the
IT-network. The Bank has a project to develop this. At the moment the periodic reports are a basic
mechanism of dissemination. The Branch Management Department also organizes regular
meetings at one Branch, or region, in which interpretations are given of the data and the
conclusions are discussed. The Branch Management Department then serves as a facilitator in the
discussions. More operationally, the systems for the account managers also have an important role
in disseminating experience and knowledge.
MICS' roles are on the process, market and product fields here. MICS' value is positively related to
these three learning fields.
(Re-)use
The systems do not specifically lead to improved skills. It is also not clear how they contribute to
augmented knowledge. Nevertheless, the dissemination of data when accompanied by a discussion
facilitator seems a good way to update knowledge and improve behavior.
MICS' roles are problem anticipation and the critical evaluation of the fields of process, products
and markets. MICS' values are positively related to these three learning fields.
Conclusion: The Bank is actively engaged in single-loop learning in all four learning fields, and
scores 1 for all cells. Its single-loop learning effort score is therefore 16. MICS contributes to most
cells. It helps to adapt, store and disseminate knowledge in all fields, but it does not contribute
much to using this knowledge, because of the complexity in interpreting, combining and searching
through the many reports. No inhibitors in SLL were found because of MICS. This means that its
SLL-value is +12. Statement 15 (MICS' positive impact on SLL-efforts) is confirmed. The human
factor is however essential (Con 7). MICS' role is in problem anticipation as well as critical
evaluation.
There is a lot of training and indoctrination at The Bank. Training amounts to about 1% of its total
budget. It is, however, not explicitly aimed at innovation. Double-loop learning efforts in the
human resource field is therefore zero, and MICS has no specific role here.
The network project is an important issue for technological innovation, but it is not clear how the
change of technical infrastructure is related to new ways of providing The Bank's services. The
network project has already been going on for some years and implementation of this new
philosophy is slow. At the Branch no systematic re-engineering is taking place, despite the
feedback data available. Only the Department of Information & Organization seems to initiate
some process innovation. There is however no systematic business re-engineering process and
philosophy. DLL thus seems not to occur in the transformation field, and MICS neither contributes
towards, nor inhibits double-loop learning in this field.
The Bank is in a typical growth situation. Its percentage of the mature national market is still quite
low and they are working to get more. At Headquarters much energy is spent on how to achieve it.
This is why The Bank entered the private market, a segment in which they were not very
experienced. They approach the private market with the idea that customer relationships are more
Case Studies 215
important than short-term profit, so that longer term success can be achieved. This means that The
Bank displays double-loop learning on the market development field. MICS contributes to this
field by providing precise market information. It is however not clear how it unlearns. The director
for Organization Development mentioned this also as a typical problem in The Bank.
New products are being developed especially for the private market. At the same time, The Bank
wants to provide a full financial service. This requires the development of relationships with
suppliers (e.g. insurance) to sell their products. The Bank is very active in this product
development double-loop learning field. MICS contributes by providing accurate data about the
performance of products. Unlearning was not found.
Conclusion: double-loop learning occurs at the Headquarters level, where the experts are. Effective
double-loop learning however also requires bottom-up participation, involving the later producers
of services. The Bank seems to have no systematic process innovation. The Department of
Information & Organization, in collaboration with the Branch Management Department, should
consider developing a process innovation philosophy, policy and methodology. Most remarkable in
this case is the positive impact of MICS on The Bank's double-loop learning processes.
Headquarters seems to be particularly keen on MICS data for designing new products, and
initiating marketing strategies and process innovations. This is contrary to Statement 15!
In total, DLL-learning efforts are counted for one activity and two fields leading to a score of 2.
MICS contributes positively to both fields and the activity, and does not inhibit DLL learning.
MICS' DLL-value is therefore +2. MICS' role is in critical evaluation and problem anticipation.
As a conclusion we would state that The Bank must improve its procedural norms considerably.
This can be done more concretely at the technical and organizational levels as follows.
• The Bank measures its operations, but under-utilizes this potential, because not everyone has
direct access to the data. The system is batch and information dissemination is selective. An
electronic and on-line management information system seems most needed.
• The Bank could profit considerably from a clear managerial theory, so that the most
important issues to measure are identified. Specifically, management theories for Branch
Management are under-developed, leading to reactive behavior.
• Try to improve learning capabilities at the Branches, so that action and theory are more
closely connected. The coordination problem that this could generate should be solved by
quick network communications between the Branches and Headquarters. The long and slow
communication lines increase the chance of mutual misunderstanding. Solution: flatten the
organization. Develop organizational procedures that increase the learning speed in the
organization according to network principles that can be easily realized in The Bank (the
technical infrastructure is already available). The value of the regional director is also
obscure, and possibly superfluous.
• Knowledge creation is a centralized activity in The Bank. This leads to under-utilization of
intellectual capabilities at the Branches and increases the tension between theory and action.
Solution: empower Branches to think and make lateral structures that can act as
electronically supported project groups and task forces.
The Bank's score card
Org. Learning Variables: Var 2: M.B.-type: Classic-Service

Var 1: Learning needs 4
Var 3.1: Identity and Policy norms Work harder
Var 3.2: Responsibilities norms Power-based and functional.

Var 3.3: Action norms Money and slow (implementation of new theories). Quick
implementation of operational insights.
Var 3.4: Procedural norms Discrete and constraint (hierarchical chains), but free within
group in the Branches
Var 4: Description of MICS Classic (functional, most parts off-line and hard copy, no
integrated database)
Var 5: SLL effort (rate 0..16) 16, extremely high for classic service, but corresponds with
learning needs
Var 7: MICS' role Problem anticipation, accounting and critical evaluation
Var 8.1: MICS' value on SLL (rate -16..+16) +12, expectation was between 4 and 8!
Var 8.2: MICS' value on DLL (rate -8..+8) +2
Unexpected values are italiced in the table.
Table 8.7: Score Card for The Bank.
Some interesting deviations from the theory must be mentioned here:

• The Bank has a high learning need, whereas its structure is still classic. The
organization is clearly moving in a lean direction. Headquarters take the lead in
this, and use an incremental change philosophy. Therefore its score for DLL is
higher than would be expected in a classic MB.
• Some activities in the commercial part are difficult to measure and control
precisely, because they have more professional features and therefore are not
precise examples of MB-types.
• Task forces do not exist in The Bank. The change process is steered from the
top.
• Action norms are according to the expectations for classic machine
bureaucracies. Implementation of new theories requires many discussions in
many management layers. Operational improvements are discovered on a day-
by-day or week-by-week basis in the Branches. Because the implications of
operational measures can be easily understood, implementation goes quickly.
• MICS helps to produce critical evaluation information, however in a batch-like
way and in standard reports.
• Most remarkable are the very high scores for single-loop learning effort and
SLL-value of MICS. This can be explained by the decentralization and
professionalism in the Branches. MICS is detailed and well developed,
combining many systems. MICS scores high on its SLL-value, despite the fact
that it is user-unfriendly (mostly periodic hard copies) and not well integrated.
Conclusions and evaluation

Con 4 states that learning needs determine the learning norms required for survival.
As in the previous case, Con 4 bothers us again. The high learning needs of The
Bank corresponds with high SLL-effort, but not with the corresponding extent of
DLL-effort. The Bank is improving this DLL-process by the construction of new
Case Studies 217
learning norms. The senior management for instance is experimenting with self-
management and empowerment (delivering data to the shop floor so that they can
carry out their own analysis, facilitated by some central expertise). After case 1, I
stated that Con 4 is a truism and requires defining learning norm profiles in order to
become informative. The following learning profile is found for The Bank.
Learning Learning norms The Bank Ideal situation

Need
4 No identity or learning policy norms Learning policy and identity
4 Learning in functional groups and Everybody learning with good ideas and
management time
4 Dissemination of performance data for Quick and on-line dissemination of data
detecting problems by management and and knowledge. Electronic learning
business analysts highways
4 Quick implementation of concrete action Close connection between theory and
suggestions, instructions, and many action. Motivating people to act and think.
initiatives by management for new theories.
Table 8.8: Linking Learn Need Score 4 with Learning Norms for Case 2
The point we detected was that management indeed recognized the importance of
single-loop and double-loop learning, but that its learning norms are such that they
prevent employees, other than managers and business analysts at Headquarters, to be
involved. The bureaucratic hierarchical structure thus is imposed on the organization
of the learning processes. The 'determination' of these organization norms therefore
is a clearly political process of distributing learning responsibilities. Information
technology can enable a flatter organization structure with many lateral structures, a
high learning environment, to be efficient.
Statement 14 tells us that lean norms lead to MICS with critical evaluation and
problem anticipation roles, whereas classic norms lead to MICS with problem
anticipation and accounting roles. This classic MB however has a MICS with both
critical evaluation and problem anticipation roles. This means that statement 14 is
not true. It is interesting to note that the critical evaluation role of MICS in this case
is often ineffective, because MICS is too complex to handle and the learning norms
(especially procedural norms) limit the effectiveness of critical evaluation.
Con 6: "MICS contributes to single-loop

effort and inhibits double-loop learning effort".
MICS indeed influences SLL in a
positive way, as in case 1. However, it
also has a positive impact on DLL. This
means that Con 6 is invalid. The
suggestion therefore is to confirm
Statement 15. Statement 16 not only
should include opportunities of negative
or no influence on DLL (case 1) but also
positive impact. This makes Statement 16 completely uninformative. The conclusion
to be drawn is that DLL is probably more influenced by learning norms than by
information technology. MICS only has a mediating (reinforcing when well organized
and inhibiting when bad) impact on organizational learning norms and double-loop
learning relationships. This would also explain the usefulness of the cybernetic
paradigm for SLL and the usefulness of the group dynamics paradigm for DLL. A
possible revised model is given in figure 8.7. The mediating relations are still
undetermined. We will wait until the other cases have been analyzed for a further
consideration of these relations. Meanwhile it suffices to state that the value of these
relations depends on the effectiveness of the MICS and Learning Norms variables.
This effectiveness can be described in terms of technical quality for MICS (in lean
terms) and organizational quality of Learning Norms (also in lean terms).
Con 7 states: Depending on the Learning Norms, MICS can increase or decrease complexity
and dynamics. Con 7 is proved to be correct, but its formulation is such that it would
be difficult to prove the contrary. The following cases will be analyzed to come to
more insights and better formulations of the subject at stake here. The learning
norms are in this case supportive for SLL and DLL. MICS contributes to both
processes by providing data.
8.3 Case 3: Chemical Plant29
In 1991, one of my M.Sc. students integrated the information systems that were available at that
time in this Chemical Plant. These systems contained data for logistics, production planning, and
performance control. We investigated whether the organizational conditions were available to use
this management information system effectively. We concluded that many organizational changes
would be required. For instance, the existing cultural differentiation between the management and
the employees was regarded as a huge obstacle to effective communications and management.
After one year, we were invited to observe the changes. Meanwhile, the fibers industry was going
29
I am grateful to Mr St. Kordelaar, for contributing substantially to the data collection for this case.
Case Studies 219
through a crisis on the world scale and Western European producers suffered from fierce
competition from low wage countries. During our stay (November-February 1992-1993), over 100
employees out of the initial 240 were dismissed. Just after the completion of our data collection we
heard that the company had been sold to a competitor, and is now in a turnaround phase. All the
information we present here refers of course to the situation before the take-over.
8.3.2 General Description of the Chemical Plant
The Chemical Plant is part of an international chemical concern and has over 200 employees. The
Plant produces chemical fibers used mainly in the carpet industry. It is particularly good at
producing a large variety of fibers, also for small orders and in a short time span. By a combination
of input materials and sequences of transformations about 40 different production streams exist. At
the top of the Plant's organization structure is the Plant manager. Organization charts are given in
figures 8.8 and 8.9.
The production is based on orders that are

received from carpet manufacturers. Production
planning is a difficult task because it is difficult
to predict when orders will be received. The
fibers produced are of an extremely high quality
(specific for the so-called industrial project
market) and client specific (e.g. strength, color,
width).
The Plant produces finished fibers for the carpet
industry in a sequential process of flushing,
winding of fibers onto a cone, and twining. The
production is scheduled so that clients can
receive their products at an agreed date. Several problems can occur when orders are changed (for
instance because of changes in delivery time or volume), when disturbances in production occur
(because of machine break-down or personnel illness), and when there is a high amount of
disapproved quality. In principle there is a planned schedule, but important clients have special
priorities.
Three types of policy problems exist, closely connected to specific organizational functions,
summarized in table 8.10.
Stakeholder Problem
Sales Satisfying important clients
Logistics Achieving planned delivery time
Production Achieving cost minimization
Table 8.10: Problems and Stakeholders at The Chemical Plant
The reasons for these separate and sometimes conflicting issues, is the existing
performance measurement system, which evaluates these departments on their
handling of these problems. This results in very different management theories,
elicited on the basis of our interviews (see figures 8.10 and 8.11). We will restrict the
discussion to production and logistics, because sales is not actually part of the case
studied, but an external actor.
Case Studies 221
The so-called pre-stream (controlling, planning, and unit management) at this

moment boosts the production norms. Production finds this dangerous for the Plant,
because it will lead to less intensive machinery maintenance and workplace cleaning,
at the end lead to declined productivity.
The delivery and costs problems found are further described in table 8.10, which also
shows the relation of these problems with departments and responsibilities.
Delivery Problems Cost Problems

• Adjustments between Logistics and Sales are Personnel Cost
problematic. The groups communicate • Too many employees in relation to orders.
ineffectively because of the spatial distance • Low productivity per employee.
between both. • Sequential production process (some
• Time between planning and order production competitors already work in parallel).
start can lead to serious problems. Production Means
• Duration of through-put must be well • High out-turn and disapprobation.
controlled (according to expectations). • Some overproduction to avoid re-start.
Table 8.10: Analysis of Main Problems at the Chemical Plant
The Chemical Plant is only learning in a single field: production process

(transformation).
8.3.3 Chemical Plant's Learning Need

Although the orders are unpredictable, the company masters the technological aspects of the
production very well. However, no new technologies are tried and no systematic research for
product and process innovation are carried out. The company has a particular advantage in being
able to make a large variety of fibers in about one million color combinations. At the same time it
uses relatively obsolete transformational technology, because it makes the products in sequences,
whereas some competitors use parallel production modes. Because of the fierce price competition
in the market these process innovations are absolutely necessary. Substantial organizational
learning is therefore needed in order to stay in the market.

Because of the many production routs and assembling combinations, the process looks complex.
Nevertheless, much experience has been gained in this area, thus leading to perceived low
complexity. More complex is the need to combine demands from sales, production and logistics.
These are competing and difficult to manage.
The innovations required and the integration abilities give rise to new learning needs. These are
especially important because of the strong competition from low wage countries, which require the
company to decrease its costs, or to further differentiate in quality. Quality improvement means
process innovation (decreasing waste, increasing client satisfaction by improving delivery
performance).
Conclusion: The Chemical Plant has a simple but dynamic environment. This means that its
learning needs are 3 on our scale with a maximum of 4.
8.3.4 Chemical Plant's Leanness and Service-Manufacturing Nature
Lean-Classic
The company is seriously involved in quality programmes and has had an ISO 9000 certificate for
some years. Effective quality control demands decentralization of responsibilities. This has been
tried by the management, but is very ineffective because people at lower levels lack the skills and
authority to make independent decisions.
The Plant is organized via its sequential production process, and only aims at producing certain
amounts of fibers of certain types. For this no direct communication with clients is needed. The
sales people therefore disconnect the Plant from its clients. On the shop floor shifts divide jobs, and
some vague evaluation of the performance is made. At the Plant level, the managers form a
management team, which meets every three months. It is not clear what kind of communication
occurs in this way. Lateral structures will therefore be mainly informal.
The Plant is part of a larger division that in turn is part of a large multinational chemical concern.
Decisions about investments in the Plant are basically motivated by strategic concerns (product
portfolio and possible synergies), rather than the actual costs of investments compared to financial
interest rates or revenues. New ideas are to be gained from the consortium's research and
development organization. A small lab only supports quality control and inspection. The sources of
innovative ideas from inside the Plant are limited.
Career paths for lower level management (chiefs) and workers are very restricted. Unit and Plant
managers, however, are academically trained and have taken (or are taking) part in a management
trainee programme and have an international career path. As a consequence the relation between
the management and the employees is problematic because of the cultural segregation of both
groups (high versus low education, cosmopolitan versus local). The Plant manager emphasizes
improving these relations but this is still wishful thinking. The motivation of the workers and the
management is mainly extrinsic: keep the job, keep the salary or even increase it if possible, with a
minimum of effort.
The organization is a classic MB. Only its emphasis on quality and its being part of a larger
consortium could give it lean features. These observations lead to some discrepancies, because the
rather high learning needs require a more lean organization.
Service-Manufacturing
As the company produces fibers, the output is tangible and measurable in tons. Because of the lack
Case Studies 223
of direct contact with the clients it does not receive a clear and useful market feedback. The fibers
produced are high quality and custom made (order production). Clients only participate in the
design and planning of the production, which is Sales' final responsibility. The Plant's production
process is frequently affected by the specific demands of big and important clients, who require
delivery in a short period. This obstructs the scheduling of production. As a compensation, the
Plant hopes to acquire rebuy from these major clients.
The following points describe the production process:
• Machines, material, labor, and know-how (chemical knowledge, production organization
knowledge and experience) are the main production factors.
• Clients are not ego-involved and the sales group shut the Chemical Plant off from the
market.
• Production scheduling determines much of the Plant's efficiency and must be done very
accurately. This requires a high amount of information processing.
• If the Plant makes a poor product this is clearly visible and is the Plant's own responsibility.
• The production process has three clearly distinguished phases.
• Stocks are possible, but small because the production process is order oriented.
• Operation and administration (including management) are spatially and mentally separated,
but the production personnel is responsible for part of the data input to the MICS.
• Professionalism is low, except in engineering and management.
Conclusion: except for information processing, this case fits perfectly in the manufacturing class.
The Chemical Plant therefore scores classic-manufacturing on the MB-type variable.
8.3.5 Chemical Plant's Learning Norms

Quality improvement has been stated as a high priority by the divisional senior management, and
formally announced as such. In fact however this is not much more than a letter of intent. The ISO
9000 certificate has been awarded, but volumes, costs and return-on-investment are the main
drivers. This makes the learning norms variable ambiguous. Therefore we could consider adding
one more activity to the previous two (theory development and unlearning), namely:
implementation.
The organization is a classic functional organized system, and computers are not used to support
lateral communications (for instance E-mail is not used). Nevertheless, performance data about the
shifts and groups are openly available. The production teams are well equipped with data about
performance. Project teams clearly have a minor position in relation to the standing organization.
The technostructure is very small, and since a few years ago is integrated in the line. The Chemical
Plant is trying to move to these lean principles, but the culture lags behind.
As well as data, also skills and theoretical knowledge are important. For this purpose, some
recognition of core competences exists that must be managed and is part of the manager's
responsibilities. The basic learning driver is achieving cost cuts and business re-engineering is
motivated as such. At the same time there are some trends to change the culture to achieve a
stronger internal commitment. The Plant manager talks in this respect about the creation of a 'we-
feeling' that enables an open and supportive attitude for the detection of problems and creation of
solutions.
Conclusion: The management is interested in achieving the 'work-smarter' learning norms but
cannot yet implement them. It also has short term (survival) priorities that dominate all other
activities. In crises this is a typical management attitude that could bring the organization into a
negative vicious circle.
The following responsbilities distribution exists:
1. Two unit managers are responsible for evaluating the materials used and the personnel.
2. Chiefs-of-shifts evaluate personnel and material, mainly on a daily basis. This does not
however lead to an evaluation of methods and techniques.
3. The Department of Control checks the targets. When it finds structural deviations, it first
tries to investigate possibilities for improving work methods. When this is not possible it
changes the target (single-loop and double-loop learning).
The chiefs-of-shifts and shift chiefs are responsible for implementing structural changes that are
designed by logistics, production management and unit management. They however lack the time
and the skills to implement incompatible 'solutions'. They are in a difficult position, because they
have several bosses and must maintain good contacts with the management as well as with the
workers. Line management is the most important coordination instrument. The shifts are volvo
teams and have a large autonomy of task allocation. The shifts however do not explicitly document
what they are doing to improve processes. They restrict themselves to ad hoc problem solving.
Projects do not fill in this gap, because after an initial enthusiasm, implementation problems are
usually experienced, leading to the termination of a project. The organization thus fits into the
'power-based and functional' extreme of learning responsibilities.
Action norms
The Chemical Plant has the following action norms:
1. Management is not directly involved in the production process, and expects workers to solve
problems themselves, whereas they in their turn lack the skills and motivation to do so.
2. The shop floor regards problems as incidents and applies only ad hoc solutions.
3. The shop floor feels that problems that recur frequently originate elsewhere (such as
production planning, logistics and sales).
It is important to note that the possible sale of the Chemical Plant is being prepared by a
managerial task group at divisional headquarters. This may possibly result in some further work
displacement at the Chemical Plant. Hence employees on the shop floor are more defensive and
less cooperative with the management. The Chemical Plant's action norms are thus according to the
'money and slow' extreme.
Procedural norms
The PLATO system contains all basic data to support the learning processes:
• Performance indicators and defined targets.
• Operational planning (scheduling) data.
• On-line access to data about the production process.
The system provides excellent capabilities for the on-line adjustment of processes, and gives access
to management as well as to operators. However, it lacks aggregated data, trends and overviews for
management (according to the Plant manager).
To generate management information via PLATO, the following additional responsibility norms
exist:
• A production assistant makes aggregated overviews.
• Input of weekly schedules and monitoring of realizations is carried out by the programme
office of Logistics.
• Input of raw data is the task of production personnel.
The frequency of the feedback is not a problem, but rather the organization that must connect the
data for effective use in the right culture and structure. At this moment, nobody takes the
responsibility for anything done with the data, and the production assistant is too junior to
influence what happens.
Except for data access and the number of issues measured, the procedural norms are according to
the 'continuous and free' extreme. The restriction of the number of issues measured to specific
targets is not always an indicator of a classic MB. It could be that these specific targets are the
result of an excellent understanding of the business, and that these are well monitored for their
relevance. At The Chemical Plant this is not the case, because mental models are incomplete and
Case Studies 225
conflicting.
Two reasons might explain this unexpected data pattern:
• Management is forced to create a participatory management style, because the shifts are
well-organized groups and form a counter-force against the management.
• The existing MICS is technically very sound, so data flows continuously and freely. There
are no technical restrictions to fast data flows.
• Learning is strongly horizontally decentralized. The procedural norms variable does not
measure the vertical decentralization of learning activities (this is done by the responsibility
variable). Looking at vertical decentralization of learning, the volvos are not well equipped
to participate.
MICS consists of PLATO and several additional computer-based and non-computer-based

information sources. These additional sources are:
• Target/performance indicators that are very explicitly defined and used in performance
control and evaluation.
• Action plans are described for departments and specific agents.
• Internal audits and quality audits.
• Structurally planned meetings and conferences, such as unit-management team meetings
every three months. These meetings seem to use the three previously mentioned data sources
extensively.
The MICS is from a technological point of view quite modern and lean, because of its couplings
and integration of data and on-line access. Nevertheless, from an organizational point of view it is
poorly connected with the learning processes in the organization (Most important are the problems
with the management theory that is essential for making sense out of MICS' data (the so-called
semantic issue). Another complication is how to start action from well-defined insights).
Sales at the divisional Headquarters emphasizes the importance of meeting delivery demands. The
CEOs at the business unit stress the importance of reducing costs because of increased competition
from low wage countries. Both are issues for single-loop and double-loop learning in the Plant.
Adaptation
The adaptation activities are dominated by cost reduction and quality improvement needs. The
logistics system and a performance control system have been established to support adaptation.
The logistics system is used for the problem anticipation role, by supplying a tool for scheduling
and rescheduling. The critical evaluation system consists of a set of standards for departments and
shifts. This critical evaluation system was only recently introduced and was regarded as a major
innovation. The intention is to monitor performance indicators and to adjust them on the basis of
evaluations of output in management employee communications.
Despite these learning needs and information systems, the organization has some important
problems with learning:
• Complexity of understanding the impact of actions that are interdependent and taken at the
same time. A solution could be the formulation of a coherent management theory, upon
which actions can be based that will be tested later for their impact.
• Urgency of orders (external dynamics) that disturbs the discovery of regularities and
theories.
The role of MICS here is therefore in critical evaluation and problem anticipation. MICS' value is
positive, as it contributes to learning processes of this kind. Nevertheless the effectiveness of the
learning processes is questionable. The effectiveness seems to depend on the availability of a
closed learning loop (c.f. our discussion of Hofstede in section 6.3.2).
Storage
The Chemical Plant stores knowledge as follows:
• Weekly predicted costs and output figures are stored and compared with results, based on
PLATO data.
• Frequently reports are made about garbage and out-turn.
• Monthly reports are made about aggregated data of weekly reports.
• Each year the Plant makes extensive reports about its activities, performance and plans.
• Audit checklists are made and are ready for re-use in case new audits are required.
MICS' role and value here is considerable.
Dissemination
Two main and incompatible management theories (the logistics management theory with its
emphasis on delivery times, and the production cost management theory emphasizing phased
production and the ability to produce buffers and stocks) exist that have been 'sold' by their
creators to the chiefs-of-shifts for implementation. This has resulted in a rather ineffective use of
both theories, because the chiefs-of-shifts are unable to connect them. Because of this conflict over
theories, MICS (PLATO) data cannot be interpreted unambiguously, and MICS data were not used
to test the validity of either of these theories.
Re-use
The logistics system has a logistics model that is re-used in scheduling processes. The performance
control system has standards and measures that are re-used in performance appraisal.
Conclusion: single-loop learning occurs for all four activities, but only in the process field. The
single-loop learning effort score is therefore 4. The role of MICS is substantial: problem
anticipation and critical evaluation. MICS contributes to all activities, and has no negative impact
on single-loop learning. Its value is therefore +4. The question about the effectiveness of the
learning process is related to responsibility, action and procedural norms. Given the existing
situation the norms negatively influence single-loop learning effectiveness. This is also strongly
influenced by the external instability (possible sale of the Plant, work displacement, world market
competition and production over-capacity)! A model about organizational learning should
therefore distinguish between learning effort and learning effectiveness.
Double-loop Learning
At the moment the management is vaguely considering investing in modern parallel transformation
technology. The chance that this will really happen is nil, because the implementation of a new
management theory is a slow and resistance-evoking process (according to several lower and
higher managers). At the same time all large investments have been frozen because of a possible
take-over of the company by a US-multinational. The double-loop learning score for The Chemical
Plant therefore is 0.
MICS has no positive or negative influence on this double-loop learning score, thus also leading to
a MICS' value of 0. It has only a critical evaluation control role in this area, for detecting the major
cost factors and providing internal data on which investments can be based.
The PLATO system seems to be technically and theoretically very sound. Also some
responsibilities have been created to extract knowledge out of the PLATO data. The major
Case Studies 227
problems in learning with MICS are caused by the unsuccessful integration and connection of the
existing management theories. In principle there is nothing against theoretical pluralism in a
company, but now the chiefs-of-shifts have the task to combine the theories, while they lack the
skills and authority to do so.
The following solutions are suggested:
• Raise the educational level of the chiefs-of-shifts so that they can carry out the theoretical
combinations, and give them the required authority (decentralization).
• Get unit management closer to the shop floor, so that they make decisions that are based on
concrete observations and experience of the shop floor. This suggestion could be combined
with the first one, and result in another type of unit manager and a career path from shift
chief to unit manager.
• Additionally, a much stronger integration of sales, logistics and production is required. The
geographical distance between Sales and the two other parts of the Plant inhibits this.
Discussions between logistics and production should also be based on the development of a
common management theory, and joint responsibilities for the whole.
• Regular meetings in a management team (sales, logistics and production) should be held
with the production assistant and logistics office to discuss the evaluation figures in order to
learn systematically from the Plant's performance. Of course the Plant's policy should also
be evaluated and adjusted on the basis of these figures.
The Chemical Plant's score card

Org. Learning Variables: Var 2: M.B-type: Classic-Manufacturing: Chemical Plant
Var 3.1: Identity and policy norms Work harder
Var 3.2: Responsibility norms Power-based and functional, however divisional, R & D department,
some ineffective project groups
Var 3.3: Action norms Money and slow
Var 3.4: Procedural norms Continuous and free
Var 4: Description of MICS Technically lean, but critical evaluation and problem anticipation are
not well connected
Var 5: SLL effort (rate 0..16) 4
Var 7: MICS' role Problem anticipation and Critical evaluation
Var 8.1: MICS' value for SLL (score range - +4
16..+16)
Var 8.2: MICS' value for DLL (score range - 0, not negative
8..+8)
Table 8.12: Score Card for The Chemical Plant.
Some interesting deviations from predictions should be mentioned here:

• Although this case concerns a classic manufacturer, it is in great need of
learning (costs must be reduced, delivery quality improved and the way
management works must also be reconsidered). The organization structure, and
the fact that it is part of a larger consortium might negatively influence

opportunities to increase learning speed and depth.
• The learning need is high, but not enough effort is made for effective single-
loop learning because the work-load prevents people from reflecting on their
work and developing and testing new ways of working.
• Double-loop learning is not done at all at the Plant locations. Some learning
identity definition is made by the senior management at the top of the division.
This definition is something like 'we are a quality company'. Concrete
suggestions, except the procedures required to obtain the ISO 9000 certificate,
are not found anywhere in the Plant.
• Learning responsibilities are only allocated to some junior staff members of
logistics and production. Task forces and project groups were not found at the
moment of this investigation (or they are more-or-less secret). They seem to be
very ineffective in implementation.
• Information is distributed within and between groups. MICS is in principle a
critical evaluation and problem anticipation mechanism. Both these remarks
indicate that the Plant is very lean with regard to its information handling. It is
however very classic with respect to its learning norms and processes.
Theoretically this is significant because an effective MICS cannot be forced on
an organization.
Conclusions evaluation
Hypothesis Case 3
Con 4: Learning needs determine the learning norms required for survival. True
Statement 14: Lean norms emphasize the critical evaluation and problem anticipation roles of False
MICS, whereas classic norms emphasize the problem anticipation and accounting roles of
MICS
Con 6: MICS contributes to single-loop learning effort and inhibits double-loop learning False
effort.
Con 7 Depending on the Learning Norms, MICS contributes to or decreases complexity and True
dynamics.
Table 8.12: Evaluation Table for Cross-Comparative Assessment.
Comments
This case showed that this chemical manufacturing Plant, able to manufacture a large
variety of products, can handle its complexity very well. The big problem is handling
situations that require change, which are often induced from 'outside' (market
changes and divisional policies). This supports conclusion 3, that dynamics
contributes more to learning needs than complexity.
Concerning Con 4: after case 1, I stated that Con 4 is a truism and requires the
Case Studies 229
definition of learning norm profiles in order to become informative. The Plant's

profile is described in table 8.13.
Learning Learning norms The Chemical Plant Ideal situation

needs
3 Identity or learning policy norms Learning policy and identity
described
3 Learning in many groups, horizontally Committed learning: decentralized learning
decentralized where possible
3 Dissemination of performance data for Dissemination of data and improvement of
discussing performance with shifts communication between management and
shifts, and training of shifts in management
skills
3 Slow implementation of concrete Quick implementation required through
instructions, and initiatives by senior effective communication and understanding.
management for new theories. Motivate workers to be creative and to think.
Table 8.13: Linking Learning Need Score 3 with Learning Norms after Case 3.
Concerning Statement 14: MICS has problem anticipation and critical evaluation
roles in this case, and is very advanced in lean terms. The other learning norms are
however very classic, which means that the potentials of MICS are under-utilized
from a learning perspective.
Concerning Con 6: MICS does not have much impact when the learning norms are
not appropriate. MICS only contributes to facilitate learning by providing data.
These data can only be interpreted via mental models. When mental models clash,
the learning situation can become political and disruptive. These insights
considerably influence our understanding of the relation between MICS and Single-
loop and Double-Loop Learning. Let us write this in a set of additional statements.
S17: MICS is a provider of data. The better MICS is, the more relevant are the data at any
time and place.
S18: The type of data that are available from a MICS depend on a mental model. This model
has semantic implications which are incorporated into the structure of the information
system.
S19: The availability of data and a mental model are prerequisites for an interpretation of
reality.
S20: In organizations, people or groups of people can share models, but also can have
unconnected and even incompatible mental models.
S21: When, in interpretation processes (the cognitive part of the learning processes),
incompatible mental models are applied (as in the Chemical Plant case), the result is an
increased double-loop learning effort if people are not deliberateley avoiding the problem.
S22: When, in interpretation processes, shared (non-conflicting) mental models are applied, the
interpretation process will be followed by actions (the behavioral part of organizational

learning) if people are motivated (action norms) to take these actions.
This leads to a considerable revision of our insights about MICS, SLL and DLL. This
insights are summarized in figure 8.12.

Concerning Con 7: Although the Chemical Plant's learning needs are high (3), its
learning norms only enable it to handle low complexity and low dynamics. Dynamics
in particular are totally beyond the Plant's control. In the longer term this will
require reorganizations (the Plant was sold to a competitor just after the completion
of our study).
8.4 Case 4: Health Insurance Company: Health Co.30
30
I am grateful to Mr M. Hafkamp, who contributed substantially in the data collection for this case.
Case Studies 231
As a result of a students visit, the author received a lot of information about the innovation efforts
of this company, which was supposed to be lean in many respects. We contacted the company for
further information, which they were very willing to provide. The information showed that the
company had a large and complex MICS, but it was not clear how it was used. After an initial
discussion it became clear that the company was still heavily engaged in moving towards a more
innovative organization. It was clear that the MICS was not functioning properly and that the
organization was still not lean in all respects. The purpose of our study therefore was to find out
how lean this service organization was and what the problems were with using MICS for
8.4.2 General Description of Health Co.
Health Co. is a health insurance company, founded in the 1930s, and recently confronted with
major problems because of its old-fashioned management style. As a result the management was
replaced and fundamental organizational restructuring took place, with innovative information
technology as one of its basic changes. The company could easily cope with the changes, because
most of its initial employees had retired and young employees had been appointed. In 1990 Health
Co. had about 324 employees. This number was 240 at the time we obtained our data (February
1993). In terms of its market share in the health insurance business, it belongs to the biggest in
Western Europe. It acts in the private sector as well as the business market.
The senior management had discovered two basic problems for the company:
1. The management had not enough control over business processes, which led to many
inefficiencies (cost problem) and long lead times (quality problem). Measuring quality and
operationalizing it was regarded to be of strategic importance.
2. Some tension between cost and quality problems also existed.
At the beginning of 1991, the senior management (assisted by an external consultancy firm)
developed a management theory consisting of five basic concepts, namely:
1. Planning and management on the basis of explicit targets in terms of volumes handled and
time required for handling the activities.
2. Development of an organization structure that supports high speed and quality: the so-called
volvo teams.
3. The development of an Incentive Bonus Scheme to motivate departments and units to
improve and find improvements, by rewarding them with a percentage of the profits the
solution provides.
4. Developing a training programme to improve individual and group performance.
Organization members should be rated on several indicators to find out what their training
needs are.
5. Use of information technology to boost individual productivity (like E-mail and image
processing).
The major learning fields therefore are process (efficiency, quality and IT) and human resources.
Also, some activities for market development exist in the construction of specific products for
specific market groups.
8.4.3 Health Co.'s Learning Needs

Internal dynamics are in all senses high. This is not what one would expect of an insurance
company, because stability was a typical feature of this business just a few years ago.
Educational and technological backgrounds and skills had changed recently, mainly as a result of
the introduction of new information technologies in the company. The introduction of electronic
networking, E(lectronic)mail, CD-ROM and WORM memory technology, changed the
organization and its way of handling documents dramatically. Additionally, many new products
and product innovations are required to keep pace with competitors all working in a business with
declining profit margins. As a result the organization also puts a systematic effort into education
and training. In 1992 it spent an average of about 470 US dollars on personnel training per
employee. In general this led to an optimization at the individual task level. Interdepartmental
coordination is not well managed. The impact of the target setting explains this situation.
Interpersonal behavior style is informal but there is also a keen control on meeting standards.
The technological characteristics of the organizational units are very bureaucratic, meaning that
work is regulated by many formally written rules. The organization consists of six business units
that carry out the direct administrative interaction with a client and 12 service departments that
support the business units with specific knowledge and activities. The business units are:
• Corporate Business Division: responsible for the collective insurances of the employees of a
company.
• Individual Business Unit: services individual subscribers, professionals, and small business
entrepreneurs.
• Special Business Unit: responsible for special client groups for which specific rules apply.
• Personal Health Division: service the market of more wealthy client groups, not insured
according to minimal legal requirements.
• Health Saver Scheme: insurance for special medical treatments and supplies.
• International: to promote the company abroad, for traveling and world wide-coverages.
The twelve service departments support the business units with medical-technical knowledge,
information technology, finance (payments, accounting etc), marketing, information services for
dissemination of internal messages, human resources management, quality improvement, support
for brokers, telemarketing, client service teams for sales to large companies, contract storage and
maintenance, and a company secretariat for mainly legal issues. This organization structure is not
very stable because market developments and government policies (which influence the business
severely) often demand changes.
There is a considerable exchange of information between the service departments and the business
units, but not much between the business units and between the service departments themselves.
The Business Management Group (consisting of the general managers of the business units and the
managers of the service departments) meets only once a month with a standard agenda. This
discussion platform was started recently and is not yet well established. Problems are solved
informally when they occur. There is much more integration within the business units, where the
general manager, supervisors and volvo members (lowest shop floor level) meet each week for 1½
hour to discuss issues. The low frequency of interactions and the rather stable organization
structure indicate low internal dynamics.
Intra- and inter-unit conflict is low. When it arises, senior management interferes. The
competitiveness of the environment seems to strengthen the senior management's control over the
organization.
The objective and goal of the company is mainly to survive. There is no clear formal description of
the goals, but by employee training and the supervision of new managers, basic values (quality and
efficiency) are taught. New managers have an experienced manager as supervisor and mentor
during the first period. When the new manager is regarded as competent enough he is allowed to
'fly solo', i.e. act without supervison. With the training schemes the employees learn a lot about
activities that are carried out elsewhere in the company. This is done to achieve high personnel
flexibility (data about flexibility are documented on the flexibility chart that is part of MICS) and
mutual understanding.
The service is increasingly differentiated to meet specific client needs and to gain a larger market
share. The business units described earlier demonstrate the importance of trying to attract special
market segments.
Many new distribution channels are being actively developed, such as tele-selling and direct
Case Studies 233
marketing. In general, however, the networks that are made with brokers and existing client groups
are still very essential, making the business less dynamic.
New material for health insurance could be reinterpreted in terms of suppliers of health care.
Developments in medical science are important here, making the business more dynamic. The
suppliers of information technology, by which the effective and efficient handling of the huge
amount of documents is improved, are important. Labor supply changes all the time mainly
because younger and higher educated people are applying for jobs. High unemployment makes it
possible to recruit bright young people for administrative jobs. These people apply their greater
abilities to work that was previously thought to be merely routine. This means that many
improvements are made in these jobs.
Government policy has a huge influence on the insurance industry in Western Europe. It decides
about what treatments should be provided. Government also sets production capacities for health
care, and in some countries the governments have nationalized or nearly nationalized health care.
This makes the industry dynamically dependent on medical science developments that lead to
increasing health service costs and declining financial possibilities of governments. The public
attitude is that a minimum of health care should be accessible for all citizens. This puts a lot of
pressure on prices.
Conclusion: the dynamics is increasing in the business. Health Co.'s management is actively
engaged in making the situation more stable.
The simple-complex dimension

Higher educated people are required for handling the complexities of modern transformation
technology and understanding the increasing complexity in services. Training therefore is not only
a matter of learning to master routine tasks, but also to increase business awareness (knowledge
about the branch and understanding of working of other departments).
The technological characteristics of organizational units are very bureaucratic, meaning that the
work is regulated by many formally written rules. To demonstrate the amount of regulation we
found that the Corporate Business Division had 13 main tasks, consisting of 89 activities, which in
their turn consisted of 468 detailed handlings. These handlings are not precisely described in a
handbook, but are learned via training.
The company has not developed strong lateral structures, and the hierarchical command structure
dominates. MICS measures the performance of individual organization members and in great
detail. It has no measures to rate the effectiveness of inter-business unit collaboration.
Complexity increases because insurance products are becoming more complex and client groups
are less homogeneous. This means that different ways of treating clients must be developed, and
hence an increased variety in distribution channels created. Information technology enables
efficient management of this increased complexity.
Competitors can make business more complex, because many try to create new services to attract
clients. The management keeps a keen eye on these developments and is forced to increase the
quality and variety of service. The government demands a decrease in health care costs, without
reducing coverages.
Information technology creates a new complexity that is treated by sophisticated specialists in the
IT-field. Keeping pace with innovative technology is a strategic asset in the insurance industry.
Advances in R & D and in product and process development are required. Product and market
development go hand-in-hand with commercial activities. The technology applied at this moment
is well known and easy to learn by practice and training courses. The complexity in this company
is thus not high, though it is increasing especially in the area of the application of information
technology.
Conclusion: the score for learning need is 3 (dynamic but simple).
8.4.4 Health Co.'s Leanness and Service-Manufacturing Nature

Leanness of Health Co.

The company is in some senses typically lean, but in others typically classic. This ambiguity can be
explained from the fact that:
1. Its learning needs are not extremely high, because its complexity is moderate.
2. Survival seems to dominate what happens in the organization. The company is not part of a
huge financial consortium that could give it security. This has resulted in a type of crisis in
which the management has a strong and dominating position (therefore low
decentralization). Lateral structures exist at the lowest work-groups, but between the
Business Units these structures are just at the first stage of development and many of the
discussions of the Business Management Group seem to have the character of window
dressing.
3. Motivation in the company is primarily extrinsic, people want to keep their jobs and are
strongly valued by means of their output (measured via MICS). Output is measured on
'reasonable expectancies'. For each business unit a Major Volume Indicator is established
that describes a unit's activities in a measurable form. For the Corporate Business Division
this indicator was related to the number of subscribers to insurances that are managed by this
unit.
Health Co. scores 6 on our 10 point scale of leanness and thus is slightly lean.
Service-Manufacturing Nature of Health Co.

Although the case is about an insurance company, obviously servicing in nature, many deviations
from the theory are signalled.
1. Outputs of employees, business units and departments are carefully measured for their
separate contributions.
2. Because of the many measures, it is possible to compare performances between units and
over time.
3. Because of these output features, it is likely that performance control systems can be
effectively used. It is however important to know what issues must be measured, because
they have large impact on behavior and outcomes (Ansari, 1977; Lawler and Rhode, 1976).
4. The insurance business not only uses knowledge but makes especially use of information
technology as equipment.
5. The organization is in a competitive environment, and thus must take a large amount of
responsibility for the success of the service measured in client satisfaction terms.
6. Many objective (intersubjective) reference points are created, to systematically evaluate
performance and give feedback.
All these issues are different from most ideas about service companies, because professionalism in
this case is low. Professional services (e.g. legal, educational and medical) demand high
responsibility from the client because the professional applies knowledge and skills, but does not
have a routine process. When services are routine, the organization can analyze how to improve
them. It is obvious that professional and machine bureaucracies are very different (compare the
The Bank that has a routine, administrative, and a professional, commercial, service branch).
Health Co. scores on three service items of our service scale, but also on four manufacturing items
of this five point scale. According to the definition it is therefore service, but also could be called
industrialized service to express its manufacturing-like transformation process.
Conclusion: Health Co. is a service and slightly lean machine bureaucracy on transition to
increased leanness.
8.4.5 Health Co.'s Learning Norms

Case Studies 235
Although the organization borders on leanness, it does not behave in this way according to its
identity and policy norms. There are several reasons for this:
1. The organization has changed dramatically since 1988 and is still in a process of further
development of its lateral structures. This is done via the previously mentioned Business
Management Group, which was established just three months before our study started. The
Quality Improvement Team is best comparable to a technostructure and therefore supports
management in decision-making. The RTA processes (for MICS-maintenance) hardly work.
In general the learning in the company is carried out via traditional line management
structures. At the same time, informal communication (e.g. via E-mail) is well developed for
operational problems. Electronic data highways however are not implemented to support
information logistics. They are only used for operational processes and not for managerial
and learning purposes yet.
2. Health Co. lacks a learning policy and identity formulation. This could also be explained
because of its low administration/production ratio, by which management has not enough
time to formulate policy-identity goals. Health Co.'s management does not see the use of a
policy formulation because it wants to keep the management as flexible as possible.
Systematic investments in people are made, and performance control information systems
are used explicitly to detect training needs, which is important for increasing the operational
effectiveness of the organization.
3. The organization is not very decentralized. The influence of the hierarchical lines is strong,
which can be explained by the fact that the company is still busy redesigning itself and by
the strong competitiveness in the industry. Business re-engineering is an important issue in
the organization now, to improve service quality and decrease costs simultaneously.
Information technology applications are considered to realize these demands.
Because of these data, Health Co.'s learning identity and policy norms therefore fall in the 'work
harder' extreme.
The company combines the traditional classic functional and divisional coordination mechanisms
in a matrix structure. There is a weakly developed technostructure (Quality Improvement Team)
and small work-groups on the shop floor manage client problems at an operational level, but do not
have authority for self-management. Project groups are sometimes created (for instance for MICS
maintenance) but their authority is weak in relation to the authorities in the standing organization.
Service companies were expected to work with loosely coupled groups (chapter 5). This is not so
within the business units, although coordination among the business units is weak. The routine-like
nature of this service work bears a strong resemblance to manufacturing organizations.
Although Health Co. is seriously trying to change, its responsibility norms are still according to the
'power-based' extreme.
Action norms
The organization members' involvement and commitment to attaining the set goals were high.
Management tries to motivate people by means of the clear definition of targets. People are
measured daily for their time performance, and managers can thus carefully control the output.
Senior management holds the management accountable for performance and is very keen on
monitoring them. Managers who underperform are dismissed. Management explicitly tries to
create intrinsic motivation and openness. This is done via:
1. The strong emphasis on training that would make employees more connected with the fate
of the business and triggers interests in developing the whole.
2. Openness via the business unit is exercised in weekly meetings of 1½ hours with all levels in
the business unit together.
3. People are willing to remove knowledge when it improves practice. This is contrary to our
previous statement that MICS would make unlearning more difficult. The deutero norms
(specifically the action norms) make it otherwise. For the future, the organization can
achieve an important competitive advantage when MICS can have the same role for
innovation support, and simultaneously be used for efficiency control.
4. Learning priorities are high in the organization, which is indicated by a high emphasis on
training, and the institutionalization of improvement and innovation procedures. For instance
the procedures for MICS maintenance (called RTA procedures) are well documented. Also
other innovation projects are well guided by project support (so called Situation Review
Procedure).
5. Sources of knowledge are internal and external. An organization consultancy firm provided
the basic knowledge and insights for transforming the 1988 organization to what it is now.
Contacts with universities have been established leading to frequent joint projects.
This evidence places Health Co.'s action norms in the 'team and fast' extreme, which is typical of
lean organizations.
Procedural norms
Nevertheless, competition is fierce and clients feel no obligation to stay with the company. Thus
client dissatisfaction must be reacted to at once. This also counts for reacting to changes in
government policies and new medical services that are covered by competitors. Health Co.'s
management follows these trends intensively.
The existing MICS is still a periodic standard report. This makes the flexible use of data difficult,
and thus inhibits opportunities to learn from the large amount of data gathered. Improving people's
awareness of the organization's performance can be very motivating and increases people's
willingness to participate in creating improvements and innovation. This is excluded when the
management style remains of the telling form. This also explains the deviations from 'off-line
systems', 'standard reports', 'inflexible databases' and the fact that actions are based more on
tradition and command than business understanding, described in the following description of
MICS.
Health Co. has many features of a lean learner, but is not yet complete. This is clearly seen in the
following:
• Learning processes are not connected with the development of theoretical knowledge. The
so-called Situation Review Procedures are seldom effective and the RTA procedures are not
applied.
• Performance control is ineffective, because people lack the time to review and analyze the
huge amount of data that is created. Computer support for MICS could solve this problem,
but does not exist.
We had the impression that no explicit connection between problem anticipation and critical
evaluation was made for developing business understanding. This also indicates that Health Co.'s
MICS is according to the 'classic' extreme. To test this opinion we made a more detailed study of
single-loop and double-loop learning and MICS' role, as described in the following section.
We made some quantification of the role of MICS in developing, disseminating, and storing
efficiency knowledge consisting of production standard concepts. We compared these concepts
with alternative media like memos, vision statements (regular letters of management to its
Case Studies 237
personnel) and output measurements. Some of the following conclusions are based on this
investigation.
Adaptation
MICS use has led to an improvement in insights into the business for the management, particularly
on how to match work volumes and work capacities. According to the financial director this
resulted in a system's payback period of only 9 months, and has resulted in a total performance
improvement of 30%. There are however no indications that MICS increases people's feeling of
responsibility. Also no attempts were observed for increased self-management, except the use of
the term volvo and a not yet successfully working Business Management Group. The system
therefore has the role of a support of top-down management communications. The theories about
these types of systems (Lawler and Rhode, 1976) suggest that it can easily lead to dysfunctional
behavior. We have no evidence for this, but at this moment it is also unlikely to happen because the
standards are not too difficult to achieve and the company has the opportunity of dismissing less
motivated people under the influence of its survival crisis. No double-loop triggers were observed.
Storage
MICS is primarily used for reporting about operational tasks. It does not develop a major
management theory in the form of planning models that later on are tested for validity. This is
because this service industry has much difficulty with planning its processes, as the work-volumes
depend on unpredictable moments when clients approach the company with a request for service.
Particularly its quality policy, which demands speedy handling of claims and other services,
inhibits a longer term planning and the use of buffers and stocks. Additionally the activities consist
of many small handlings, whereas manufacturing environments have often large orders and project,
with detailed planning, pre-budgeting and post-evaluations. This will make the development of a
management theory much more complicated, but not impossible and very rewarding.
Dissemination
Data are quite easily accessible, and when used in connection with business awareness
programmes, might motivate doing things better than before. The business management group is
not yet working properly. The business units hold weekly meetings that could lead to important
dissemination of knowledge. How these meetings precisely work is not clear (possibly very top-
down again). E-mail and informal gatherings are often used to solve problems and disseminate
suggestions.
(Re-)use
Understanding of cost and quality sources seems to have increased and the developed knowledge
was explicitly used in departmental budgetting, which leads to a further reduction of the required
labor force (as mentioned under adaptation). Strong management and positive attitudes to the
company's survival, linked with the young age of the employees, enable the quick implementation
of progressive ideas.
Health Co.'s single-loop learning efforts are in all the four activities, but restricted to the
transformation and human resource fields. This means that Health Co. has a SLL effort score of 8.
MICS' role is in problem anticipation and critical evaluation. MICS contributes to (re-)use, storage
and dissemination of knowledge gained about transformations and human resources. It does not
contribute to adaptation, because the adaptation of norms that are used is a very underdeveloped
process by now. MICS has no negative consequences as far as can be mentioned. Its contribution is
thus +6.
Double-loop learning is typically an activity carried out by senior management, that does not use
much MICS data at the moment. MICS' value could increase at this level if:
1. Excellent user interfaces and interactive systems are made.
2. It is used to provide data about markets and products.
3. Decision proposals are well grounded on correct data and well discussed before they lead to
decesions. The E-mail system could have an important function here, because it can work as
a lateral structure when the people can react in an anonymous way to concept proposals.
At this moment the company seems strongly internally focused. This can be explained from the
fact that it is hard to become a market leader in this business and that much of the health insurance
business is regulated by government policies. Therefore the learning fields are transformation and
human resources, and not much product and market innovation is carried out. The double-loop
learning effort is development as well as unlearning. It is not clear how systematic and theory-
based these DLL-activities are. Implementation is quick in this organization, although DLL is a
centralized activity. This results in a DLL-effort score of 4 (2 activity and 2 fields). MICS role and
value in DLL is however not present yet, but also not negative, leading to a MICS value of 0.
8.4.8 Learning Poblems Related to MICS and Recommendations
MICS does not seem to lead to learning problems, however:

1. At this moment it is rather expensive, because data gathering is done via filling in many
forms. It is recommended that data be collected as by-products of the activities of the
company.
2. MICS is not yet very actively used in adaptation processes, because these organizational
processes are not yet well established in this case company.
3. MICS does not yet contribute anything to market and product development, but is restricted
to process improvement. This attitude can be explained from the restrictions that are set on
market and product development. At the same time it is not clear what additional market and
product information is needed for learning.
4. There are no unlearning problems connected with MICS, because of the progressive attitude
of employees and management. However, this should be followed carefully to ensure that
these problems will not happen in the future.
The following concrete list of recommendations were given to Health Co.:
1. Develop an on-line MICS.
2. Collect data as by-product.
3. Develop norms such that people consistently check MICS data for possible issues of
management and policy. This can be done by dissemination of MICS data or broad access to
an on-line MICS system, and/or making this activity part of an internal intelligence office.
4. Specific issues detected should be followed by the creation of a task force or parallel
learning structure (Bushe and Shani, 1991), which can advise on improvements and test
solutions.
Health Co.'s score card
Learning Variables Var 2: MB-type is a moving service (value 4)

Var 3.1: Policy norms Work harder
Var 3.2: Responsibilities Power-based in transition
Case Studies 239
Var 3.3: Action norms Team and fast

Var 3.4: Procedural norms Discrete and constraint
Var 4: Description of MICS Classic and not computer-based.
Var 5: SLL effort (0..16) 8
Var 6: DLL effort (0..8) 4
Var 8.1: MICS' SLL value (16..+16) +6
Var 8.2: MICS' DLL value (8..+8) 0, not negative
Unexpected values are italiced in the table.
Table 8.13: Score Card for Health Co.
Comments:
• Because the learning needs score is 3 (low complexity, high dynamics), it is
understandable that management tries to centralize the learning process.
Essential in this situation are action norms that realize quick implementations.
This was indeed the case at Health Co.
• Double-loop learning efforts are however lower than would be expected for a
lean service company. This is caused by Health Co.'s internal focus on quality
and efficiency and its lack of opportunities for product and market
development. The small size of Health Co. (about 250 employees) contributes
to the fact that Health Co. cannot create enough managerial knowledge and
skills for improving the double-loop learning process. This 'small-size' fact also
contributes to a management style that is strongly telling and centralized, and
without a clearly defined management policy.
Conclusions evaluation
Hypothesis Case 4
Con 4: Learning needs determine the learning norms required for survival. True and false
Statement 14: Lean learning norms emphasize problem anticipation and the critical True
evaluation roles of MICS, whereas classic norms emphasize the problem anticipation
and accounting role of MICS
Con 6: MICS contributes to single-loop learning effort and inhibits double-loop False
learning effort.
Con 7 Depending on the Learning Norms, MICS contributes to or decreases Learning True
Needs.
Table 8.15: Evaluation Table for Cross-Comparative Assessment
Comments
Concerning Con 4: learning needs are important, but the way an organization reacts
to learning needs also depends on issues such as internal power relations (compare
The Bank), being part of larger consortia (to have shared learning resources in e.g.
divisional R&D departments) and, for this case specifically, its size that prescribes the
effectiveness of centralized learning. After case 1, I stated that Con 4 is a truism and
requires the definition of learning norm profiles in order to become informative.
Table 8.16 presents the learning profile for Health Co.
Learning Learning norms Health Co Ideal situation

needs
3 No identity or learning policy norms Learning policy and identity
described
3 Centralized learning by management Committed learning: decentralized learning
where possible
3 Dissemination of performance data for Dissemination of data and improving
punishment and reward communication between management and
shifts, and training shifts in management skills
3 Quick implementation of concrete Quick implementation required through
instructions, and initiatives by senior effective communication and understanding.
management for new theories. Motivate Motivate workers to be creative and think.
employees to obey.
Table 8.16: Linking Learning Needs Score 3 with Learning Norms after Case 4.
About Statement 14. In this moving-to-lean case MICS has critical evaluation and
problem anticipation roles. The effectiveness of the critical evaluation role is however
still low, because the organization lacks the appropriate technical tools to make the
use of MICS cost-effective. Additionally, the reponsibility norms are not yet clearly
enough defined so that all data can be usefully exploited to improve SLL and DLL.
Critical evaluation and problem anticipation groups are not integrated.
Concerning Con 6: MICS has little impact when the learning norms and technology
are not appropriate. When mental models are shared, as in this case, the following
observations are important:
• Action norms are no limitation to implementation. Theories are quickly
transformed to action because their non-execution is clearly visible to the
powerful people who constructed the theory.
• Model incompatibility therefore has a strong impact on action norms as
supporters or inhibitors of theory implementation.
An additional statement therefore is required:
S23: The greater the model incompatibility, the more action norms exist inhibiting behavioral
learning (SLL and DLL). The more compatible the mental models, the more action
norms lead to quick implementation of new theories and insights.
Case Studies 241
1.5 Case 5: Hitec
This case concerns a high tech manufacturing Plant, named Hitec, located in Western Europe and
part of a division of a US multinational. It produces electronic apparatus for industry and the
military. In the beginning of the 1980s it had about 700 employees, gradually reduced to 200 in
1993 with a constant output of 75 million US dollars and the introduction of some new products. In
the mid 1980's the company faced possible closure. A new management team (consisting of
Europeans) was formed with a survival strategy, based on increasing worker commitment to the
organization's success, aiming at top quality products, just-in-time (JIT) delivery and excellent
internal communication. This resulted in an ISO certificate (1986), many other quality awards, an
MRPA+ certificate and increased labor productivity. It is now one of the best performing Plants of
the company. The Plant has one product line of its own and others manufactured under license
from Headquarters in the USA. Their main markets are: USA (21.4%), Germany (16.1%), France
(15.3%), Britain (10.8%), Italy (9.4%), Scandinavia (5.8%), and Japan (1.7%). Despite the large
budget cuts in the military in the recent years, sales have been constant and there is a large order
volume at the time of this study (second half of 1993).
8.5.2 General Description of Hitec
The parent multinational was founded in the first half of this century. The Hitec Plant was started
in 1961. Because of personnel reductions some hierarchical lines (middle management) have been
removed. The local Plant is headed by an the operations manager who is directly supported by a
Human Resources Department, a Public Relations Department, a Controller, a Quality Assurance
Department and the manufacturing operations manager. The manufacturing operations manager is
responsible for the work done in seven departments, named: Value Engineering, Manufacturing
Technical Support, Instrument Manufacturing, Order Processing, Purchasing, Manufacturing
Planning, and Warehouse, Customs and Shipping. This study will focus on the Instrument
Manufacturing Department, of which an organization chart is given in fig. 8.13.
The span of control of supervisors in the production organization is large: 8 for the 'Insertion and
Board Build' group (responsible for the final assembly and th production of electronic boards) and
30 to 40 for the two other groups. This is realized by the creation of semi-autonomous groups
(called work cells), which have many joint responsibilities with respect to:
• quality
• output (delivery time and volume)
• work coordination and distribution
• detailed planning and materials supply
• administration of issues directly related to production (such as failures of delivery and
quality problems)
• coordination with support staff (e.g. with quality and planning)
The work cells have a very mature way of collaborating, not requiring much supervision. The
'Insert and Board Build' cell has regular meetings on Monday mornings chaired by each of its
members in rotation. The supervisor is only present when special problems occur. Work cells and
the existing organization charts were introduced in the second half of the 1980s, after the
introduction of a new management philosophy called 'Manufacturing Excellence' based on four
main issues: Total Quality Commitment, People Involvement, Manufacturing Resource Planning
and Just-In-Time production. The major learning fields therefore are 'process' and 'human
resources'.
8.5.3 Hitec's Learning Need

The internal environment was stable because of low personnel turnover and product lines that have
not changed much. Since 1985 the Total Quality philosophy, however, changed the way of
working considerably, finally leading to a completely new way of thinking. The organization
became more democratic, open communications have developed, procedures have been precisely
measured and evaluated, and a new organizational culture was introduced. Things have begun
changing rapidly. New product lines and transformations were introduced. For instance material
supply is changing from traditional kits to Kanban systems. Automation will have a large impact
on the production process in the near future. Labor productivity for some product lines
(instruments) increased by more than 250% in the past three years.
The external environment is only indirectly perceived by the Plant, via the marketing department
(located at headquarters in the USA) and the sales and service offices. These offices report on
delivery problems and problems of clients that could be related with the production process. At the
same time, countries in the Far East are severe competitors for European manufacturers, not only
because of the low price they offer, but especially because of their superior quality. According to
the manufacturing manager however, wages are a decreasing part of production costs. Material
handling and lead times are important, because of the high material costs and costs of work-in-
process. Supplier instability is decreasing gradually because of the Plant's programme to define
supplier requirements precisely. Process innovations are particularly important in this saturated and
slightly declining market (military budget cuts and the world economic recession).

The internal environment is increasing in complexity, mainly because of the demands for higher
quality, shorter lead times (high work-in-process (WIP) costs), cost reduction, and improved client
delivery services. The Plant has reacted by developing an organization that is extremely flexible
by:
• reducing 31 specializations to three skills levels, so that employees have become more
flexible;
• removing departmental barriers. Work cells are flexible task groups that take responsibility
for specific client orders. Because of this responsibility they have the authority to deal with
Case Studies 243
specialists, staff members etc. when required in order to carry out their tasks. Supervisors
can assist in this process, but in general are only facilitators and intervene when problems in
coordination are structural and absolutely require senior involvement;
• JIT philosophy. In planning and production the emphasis is on delivery targets, and not a
just-in-time approach to all parts of the logistic stream. The production system is certainly
not push, but pull and then some push when possible (cf. Aggerwal, 1985).
The external environment is increasing in complexity because customers demand more product
innovation. From 10 years the product life cycle was reduced to just a few years. At the same time
innovating the manufacturing process is important for producing the new products and keeping
pace with the cost reductions that are demanded by the industry. This means that management is
still faced with many uncertainties. Many projects are underway to develop new knowledge about
how quality can be improved and how innovations can be carried out.
Conclusion: complexity and dynamics are high and still increasing. The learning needs score
therefore is 4.
8.5.4 Hitec's Leanness and Service-Manufacturing Nature
The Lean-Classic dimension

The quality attitude is accepted, especially in the production function. The support departments are
also finding out what the Total Quality Management philosophy implies for them. Many quality
awards have been gained. The company audits its quality systematically according to ISO 9001,
9002 and 9003 standards, but also follows a continuous improvement philosophy (ISO 9004). For
this last purpose it applies the European Malcolm Baldridge Award criteria31. The quality
assurance manager is a leading member of a European national quality managers society.
The work cells have the responsibilities and authority for self-management. This autonomy allows
them to engage in (lateral) relations with other departments, without asking permission from
supervisors. Another aspect is the availability of data about their performance. This, according to
my informants (a cell member, a cell coordinator, and the director of manufacturing) increases a
cell's motivation to achieve targets and initiate actions to find causes of problems and opportunities
for improvement.
31
See Evans and James, 1993 for further information about ISO norms and Malcolm Baldridge Awards.
The relation with suppliers is regarded as one of mutual benefit. Hitec helps suppliers to improve
their quality by its Supplier Performance programme. Suppliers are precisely monitored on their
performance (delivery time, defects etc.) via a special information system. Problems are fed back
and suggestions for improvements are given.
Although the client-plant relation is indirect (mediated via sales, marketing and services), the Plant
appreciates systematic feedback from clients via service and sales reports which are analyzed by
the quality assurance department for possible structural sources of problems (in procedures,
equipment used, and skills of personnel).
Because the Plant is part of a larger division, essential financial decisions are made in the USA.
Hitec's management team does not have much influence on this decision-making process. The idea
of low interest rates in large consortia therefore is not true, but depends on top management's
perception of the Plant's profits in relation to investments. Therefore the Plant has an uncertain
future. The managerial skills and the quality of the organization however are unique in the larger
organization, possibly safeguarding it from tough decisions.
One of the main pillars of the new management philosophy is to increase the involvement and
skills of the employees. The Plant therefore has invested considerably in human resources by
technical training for job enlargement, and interpersonal and management training for realizing
autonomous work cells and establishing a new culture. The total training effort is about 5% of
annual wage costs. As a consequence, job rotation is now easy though with a negative reaction
from the more highly educated employees who feel they are given tasks not matching their
qualifications. However, this job mobility is confined to this Plant. The Hitec managers have not
been invited to become members of the American or international management team. But as a
benificial side-effect, Hitec's management gets on extremely well with local employees.
Major new ideas are not infused via labor force composition, but via the process of learning in the
quality programme and some external training and consultants.
Motivation is intrinsic and extrinsic. The intrinsic part is institutionalized via the quality audits and
diverse other learning norms (work cells, performance data monitoring by managers and
employees etc.). The US management has also instituted awards for extremely good performance
which some managers think are appreciated although interviews on the shop floor suggest these
awards and quarterly management 'appreciation' meetings are typically American and do not
belong to their culture. They prefer monetary awards.
New ideas are consistently searched for via the quality improvement processes. At the same time
management is very open for new ideas that emerge from academic research. This was one of their
motivations for offering me the opportunity to interview many people and study their organization.
In general they appreciated discussions about their management theory to preserve them from
possible business blindness.
Conclusion: the company is lean for all criteria of our scale of leanness.
The Service-Manufacturing Nature

The output is tangible: instruments produced. Units can be distinguished and measured in terms of
quantities of specific types and variants.
The value of the output is precisely known in monetary terms, because the production is almost
100% order production with a preset price. After completion of production one therefore already
knows the profit contribution (if the client pays, no large currency fluctuations happen and no
unexpected shipment and disbursement costs occur).
The organization is keen to satisfy clients, and communicates precisely what problems clients have
with the products. This is of course increases its quality reputation and enhances its image and
maintains client loyality.
Output control is important. As a total quality Plant Hitec tries to increase process quality.
Therefore, errors are detected as soon as possible by inspectors. Measurements of 'first part yield'
have been developed to find out how frequently one product is made in one go without expensive
and time-consuming reiterations. Much effort is put in finding the causes of quality problems.
Case Studies 245
Regular audits are carried out on each department.

As opposed to the situation of many manufacturing bureaucracies, machine costs are only a small
part of the total production costs. The materials and components are the most costly. Employees
are extensively trained, and their technical as well as managerial skills are regarded as an essential
production resource. The knowledge about processes and quality are regarded as major issues for
achieving the quality ambitions of Hitec. Work is done on basis of weekly schedules, that are in
principle flexible. Of great importance is accurate information about what must be produced
(which product, what variants, what bill of materials applies, which cell can do the job, what
quantities are demanded and what delivery date has been agreed). This is typical of unit order
production.
A clear distinction between input, semi-finished and finished products can be made. The work cell
structure makes handling these subprocesses more flexible, because tasks are often rotated and task
integration is implemented.
Stocks are technically possible, but economically unfeasible. This is because the costs of WIP and
materials are high and the order volumes for specific types are unpredictable.
The service, sales and purchase departments seal the production system off from the market
environment. Clients are not ego-involved. Responsibility for product success lies mainly with the
Plant, and to some extent with services. Services and the Plant have regular meetings to exchange
information. The Total Quality philosophy is now also being implemented with the services.
Within the Plant however there is much openness between production, quality and planning. The
administration (including data processing) however has very poor relations with other departments,
and is called a 'factory within a factory'.
Professionalism is high among managers, who are continuously trying to improve. The shop floor
has a mature (Hersey and Blanchard, 1982) work force, capable of self-management and
committed to quality for the longer term survival of the company. Most members of the cells have
a lower level technical education plus some additional on-the-job training. They have extensive
experience on the job (most are over 40 years of age and joined Hitec for their first job). Payment
is good in relation to other companies. These facts explain a strong commitment to the company.
Conclusion: Hitec has both characteristics of a manufacturing and of a service machine
bureaucracy. This combination of characteristics is typical of lean manufacturers.
8.5.5 Hitec's Learning Norms

Hitec emphasizes its TQM policy. At the same time however, divisional management sets the
targets about output volumes and costs. Plant management does not have much influence on this
target-setting process.
Lateral relations are strongly encouraged. Initiatives to start lateral contacts can be prompted by
signals from the MRP or other information systems. The organization is however not equipped
with electronic communications. When problems occur, face-to-face or telephone communications
are used.
Much effort is put into skill development. The organization invests about 5% of its wage costs in
training. Simultaneously much effort is placed in improvement projects and quality assurance
activities under the support of a full-time project manager. Of the 200 people employed, 10 work
within the quality assurance department, and much operational quality assurance work is done on
the shop floor, mainly in the weekly (sometimes twice weekly) work meetings of a work cell.
Production teams are responsible for their performance, and have access to relevant management
information systems. Project teams are strong, because senior department members participate in
them. This is likely to happen in this rather small organization and makes problems about project
team responsibility and authority less difficult to solve than for instance in the car industry. Hitec is
seperated from its environment via the purchase and sales departments. The supplier improvement
programme tries to solve the problems that could occur as a consequence. The Quality Assurance
department is now busy with a quality assurance programme for services, and makes systematic
assessment of field failures.
Business re-engineering is an enduring activity and motivated from quality improvement and
possible technological changes (in products and processes).
Conclusion: Hitec has 'work smarter' learning policy norms. Policy and mission norms are indeed
oriented towards the 'work smarter' extreme, but the 'work harder' norms (expressed in production
volumes) are also applicable to Hitec. Remarkably, Hitec has a large administration/production
ratio (123/7732). This could indicate a non-lean organization. The interpretation of the a/p-ratio is
however very complex. In situations where production automation is increasing (as at Hitec), it is
likely that the a/p-ratio will rise. Furthermore, a head count is not a good indicator of leanness
when personnel costs are only a minor part of production costs.
At Hitec, learning is an organizational responsibility, in which departments, management and work
cells participate. Work cells, responsible for and committed to quality, search for information to
support continual improvement. When the problems are interdepartmental, contacts with these
departments are made. A quality engineer may also be involved.
At Hitec the subdivision between a functionally and divisionally (market) oriented work force is
becoming irrelevant. Only the Insert and Board Build group is a functional 'work cell'. The other
two work cells are related to product and market lines. The work cells produce as a group from the
beginning to the end. Volvo teams are the basic organization principle in the production
department. Further automation in production and technical developments will decrease the
number of people involved and require closer connections between production and support groups.
This could lead to the development of one large work cell.
Project groups are created frequently to research possible errors, improve quality and search for
renewal. Professional project management exists here. The error detection and improvement
projects often have the same collection of participants and a routine-like procedure. The renewal
projects (that introduce a basically new way of working, e.g. Kanban), often use a completely
different way of working and other participants.
The Plant constructs partnerships with suppliers and service/sales companies, also to learn from
each other. The Plant also has a leading position in a national quality association, and via this
distributes ideas and picks up new interesting ideas as well. At this moment the Plant is regarded as
a production unit within the division. Divisional Headquarters centralizes design and engineering,
however some decentralization would be feasible because the expertise and technology is
available.
32
Only 77 of the 200 people employed are working in the manufacturing department (Instrument
Manufacturing Department).
Case Studies 247
Hitec combines functional specialism and market orientation in a matrix-like learning structure.
Learning is embedded to some extent in the volvos (work cells) that have substantially large
responsibilities and discretion in analyzing and solving problems. This resulted in a large span of
control for two production cells (about 30 to 40 employees per supervisor) and a very passive
management attitude of the senior coordinator of the Insert and Board Build cell. The importance
of task groups in this company is also interesting and indicative of its greater appreciation of
learning. The task groups also indicate interest in double-loop learning (major changes that
fundamentally influence the way of working and thinking. (Some examples were the task force
SMD33 and Kanban). These double-loop learning activities are restricted to transformational
innovations. The Plant is not allowed by headquarters to think strategically about product and
market development! The Plant's management team and divisional CEO's only discuss targets and
output performance (typically the divisional control type as described by Mintzberg). Double-loop
learning about products and markets therefore is a top-down activity.
Conclusion: distribution of learning responsibilities is competence-based, however restricted to
single-loop learning and the process field of double-loop learning! Major double-loop learning for
this Plant is done by divisional Headquarters, and results are communicated top-down, leaving
only implementation activities at the Plant level. Some double-loop learning occurs as a result of
the quality monitoring.
Action norms
The Plant and company emphasize the importance of work satisfaction created through a 'we-
feeling' and the use of internal awards to individuals and groups. In this West European case this
system is possibly not effective. Employees stated that they were very committed to the company's
success because this is essential for maintaining the relatively well-paid jobs. The intrinsic
motivation to help the company succeed and excel is therefore closely related to extrinsic
motivations. People prefer financial rewards.
No indications of defensiveness were found. According to a tester interviewed, people on the shop
floor were eager to improve their work when they received comments about failures. Additionally,
the quality assurance group communicates clearly about what they will audit and when they will do
so. Findings lead to improvements, some through simple changes of work, sometimes through
training, sometimes through projects.
New findings and technologies are easily introduced and adopted. At the same time it is likely that
people on the shop floor will resist these changes, because many involve labor-displacing
automation. For some workers several years of training effort also can become worthless because
the new technologies do not require the new skills.
Conclusion: Hitec has action norms that are according to the 'team-fast' extreme. At Hitec we also
see the close relation between theory (the managerial philosophy of excellence) and action.
Implementation of insights is carried out persistently and carefully during the course of many
years.
33
SMD is a new soldering technique, completely automated and extremely quick.
Procedural norms
The feedback frequencies are much higher than expected from a classic manufacturer but still are
not on-line (most are weekly or monthly reports). The high frequencies result from the Plant's
desire for excellence, through detecting errors and the source of the mistake as soon as possible, so
that people will connect it more easily with what they have done. For these purposes testers give
feedback to the shop floor, and the quality assurance group analyzes outgoing quality data, and
communicates problems when found.
In all cases the existing information norms seem to fit with the lean type. The only deviation is the
fact that data from the main MRP system cannot be analyzed and accessed on-line. The MRP
system now is being replaced by one that has this on-line ability.
Conclusion: Hitec's procedural norms belong to the 'free and continuous' extreme, that was
regarded as typical of lean organizations.
It is particularly interesting to mention the main issues and systems with which the organization
assesses its performance, because this gives an operational description of the management theory
used and the procedure for communicating about it. The official management theory emphasizes:
Total quality commitment, People involvement, Manufacturing resource planning and logistics,
and Just-in-time production34.
From these major issues a number of indicators are derived:
• Total quality commitment indicators: customer service level, outgoing quality, reliability of
deliveries, error registration in delivery and production, vendor analysis and performance
rating, and the Malcom Baldridge assessments35.
• People involvement with absenteeism indicators.
• Manufacturing resource planning and logistics indicators: inventory as percentage of net
sales, work-in-progress volume costs also specified by components and parts and,
manufacturing costs of sales.
• Just-in-time production, or OPT indicators are: manufacturing velocity, inventory turns, and
manufacturing service level.
Some additional reports are present as well:
• Efficiency measures: net sales per employee (NOE); administration expenses as percentage
of net sales (these measures are extracted from the accounting system).
• Several financial performance indicators.
34
In fact, Hitec has not yet achieved JIT in all its processes, but it has OPT which means just-in-time
delivery and adjusting processes to accomplish this with the least effort (cf. Aggerwal, 1985).
35
See Evans and Lindsay (1993) for a detailed treatment of the Malcom Baldridge assessment.
Case Studies 249
• First part yield.

At this moment various systems are being developed by separate departments (e.g. error
registration system is developed and maintained by the Quality Assurance department). These
systems can in all cases be coupled to the MRP (corporate) information system. This situation can
still easily lead to data islands, lack of data compatibility and applications that do not communicate
easily. Therefore the situation is not optimal for creating flexible reports and leading to incomplete
(suboptimal) pictures of reality. The high quality of the data administration therefore refers only to
the MRP system.
The solution to these problems must be found in applying a consistent, shared mental model, from
which the information needs are derived, and secondly by solving possible problems of duplication
(redundancy) and inconsistency. A first attempt to make this shared model explicit is found in the
manufacturing excellence policy description. The organization is very willing to monitor
performance and analyze the data for prescribing new actions. Therefore, the data-action gap is
small. The existing data are also closely related to what is required for manufacturing excellence.
Because the organization has an open culture and is increasingly small, problem anticipation (done
by production planning and logistics) and critical evaluation (done by quality assurance and
controlling) are closer together. In the production meetings and quality meetings (each week, in a
group consisting of the supervisors, senior coordinator, quality assurance, production planning
manager and some additional managers), the social networks of problem anticipation and critical
evaluation are integrated.
Conclusion: at the social level the MICS is lean. The technical level however does not yet fully
correspond to the leanness demands:
• The main (MRP) information systems is still off-line and requires hard copy reports that are
less user-friendly for analysis and learning. The new MRP system will solve part of this
problem.
• There are many information systems that provide information for learning, but they are not
integrated within the management information system. Links are made with the MRP
system.
• Because systems are more or less separate, the chance for incompatible data and data-
structures is huge.
• The Plant has no internal electronic mail.
Adaptation
Adaptation of knowledge at the Hitec Plant consists of testing the quality of the way people are
working, and thus could lead to adjustments of theories by critical evaluation and training. MICS
has several functions here in detecting problems. Several people are involved in analyzing these
data and suggesting improvements. Data about outgoing quality and delivery are available.
Adaptation of knowledge about planning, ways of budgeting, ways of organizing (work cell
construction etc.) is done on a permanent basis through open communications. According to some
managers the distance between management and employees is rather small, therefore leading to
effective feedback. It is not clear if this is just wishful thinking. The distance between the
management team and employees seems to be too large. The quarterly meetings are ineffective for
bottom-up communication (they are clearly top-down).
Storage
Storage of knowledge is done whenever new procedures are discovered that could improve
effectiveness. Knowledge is written down in handbooks and the modular construction of the
product concepts, makes it easy to build some variants on a product line. New product lines lead to
more severe changes. This recently happened. Training then can become obsolete, which is quite
frustrating for people who have passed difficult exams. The stored knowledge is also to a large
extent available by electronic means. It was quite easy to obtain data about performance in recent
years, so that improvements could be related to managerial practices.
Dissemination
Much of the conceptual dissemination occurs through training in the basic issues of manufacturing
excellence. These trainings are followed by all members (top and shop floor). This training is about
social communicative aspects of successful team membership, management and motivation, and
quality concepts. The role of MICS in the dissemination of concepts is implicit. The information
systems supply data that are interpreted via the concepts. At the same time, concepts used for data
gathering and understanding were disseminated during the information systems user training and
systems development processes. Because concepts and information are mentally and
organizationally closely connected, interpretation problems are not problems about the syntactics
of the data, but problems about the structure of reality. The data can support alternative points of
view (Hegelian principles according to Kirsch and Klein, 1978). Nevertheless there is one clear
organizational philosophy, which decreases the chance of conflict about interpretations.
(Re-)use
(Re-)use is not problematic, because knowledge and data are closely connected and knowledge and
action are closely connected via training and the fact that learning processes are an inherent part of
everyday life in the Plant (work cell meeting, Tuesday's quality meetings, and Thursday's
production meetings).
Conclusion: MICS is absolutely crucial for the adaptation process. It only has a limited role in
storage. Of course set targets are part of the monitoring process, but even more essential is how
people understand these targets and norms. For this purpose extensive training and many meetings
and handbooks are essential. The dissemination process is supported by the quick way reports can
be generated and distributed by hard copy and by data on the terminal. Dissemination of concepts
(targets, norms and theory) goes through trainings and meetings. (Re-)use happens through the
interpretation of data that are the output of the various information systems. The MRP-system
seems to be particularly effective here because no additional informal information circuit exits in
the organization that could lead to conflicting interpretations. This means that the system must be
always right.
Hitec's SLL-learning effort therefore concerns all four activities and two fields (transformation and
human resources). All the SLL-activities for the two fields are performed, thus its SLL-effort score
is 8. MICS contributes to all these values and no negative impacts were found, so MICS' value for
SLL-effort is +8. MICS' role is in problem anticipation and critical evaluation.
Double-loop Learning
At Hitec, a production Plant, the opportunities for market and product developed are almost
restricted to zero. This type of double-loop learning is the task of the marketing department located
at Headquarters in the USA. The Plant has no opportunities to seriously influence what happens
there. One could describe the larger division best as a classic divisionalized form, with a command
and report relation between Headquarters and divisional units (Plants). This could seriously
threaten the Plant's longer term survival despite its excellence in quality.
Much effort however is placed in improvements of transformation. Some of the major innovations
in this area were not developed and chosen by the Plant itself. Again divisional Headquarters
decides what investments are made in transformations. Project management is an important
organizational information system to support the introduction of these innovations. MICS is often
used to discover required innovations.
Conclusion: Hitec has a very restricted double-loop learning process. Most double-loop learning
Case Studies 251
occurs in the USA with no participation of Hitec. Hitec is a successful implementor of ideas
developed by Headquarters. Here we touch the delicate question of the effectiveness of excellence
for longer term survival (cf. replications of Peters and Waterman, as described in Lammers, 1986).
Because Hitec is a successful implementor, its unlearning capacity is high. It has done this in the
past through innovation in transformation and products, which were externally developed. This
means a learning score of 2 (1 activity and 2 fields). MICS' value is very limited for double-loop
learning. Market data and strategic product data are not processed at Hitec. This means that MICS'
value for DLL is 0.
MICS could accelerate the single-loop learning process when technical features are improved
(compatible data structures, integration of systems, on-line high quality user interface). The social
system is ready to profit from these investments. The double-loop learning process is inhibited by
responsibility norms that cannot be changed by information technology. When changes occur,
MICS could be an important support instrument when market and commercial data are made
accessible to Hitec's management, and when the design and product engineering group of Hitec is
given more responsibilities.
8.5.9 Conclusions Regadring the Main Hypotheses
Hitec's score card
Org. Learning variables Var 2: MB-type, score for Hitec is Lean

Manufacturing
Var 3.1: Policy norms Work smarter and work harder
Var 3.2: Responsibility norms Competence-based
Var 3.3: Action norms Team and fast
Var 3.4: Procedural norms Free and continuous
Var 4: Description of MICS Lean. Integration via MRP, no common model
Var5: SLL effort (0..16) 8
Var 6: DLL effort (0..8) 2
Var 8.1: MICS' SLL-value (16..+16) +8
Var 8.2: MICS' DLL- value (8..+8) 0
Table 8.17: Score Card for Hitec.
Some deviations from the stated hypotheses are interesting to mention:

• The Hitec situation is complex (complex product and process) and dynamic
(many competitors and changes in markets and technologies). Especially the
complexity makes it deviate from the predictions. The complexity is high

because of its high-tech production. It is however not a professional
bureaucracy. The highest entrance knowledge is a bachelors degree and these
positions are rare. Most education is on-the-job training provided by Hitec
itself, so that the external knowledge gained by Hitec employees is low.
• Double-loop learning effort is much lower than expected. Hitec only double-
loop learns about the 'transformation' field (score of 2 on DLL efforts). This is
because the larger organization does not permit the Plant to engage in more
than production and some service (learning responsibilities are inhibitors here).
Most remarkably MICS does not contribute to DLL at all.
• Single-loop learning effort is also below what is expected. MICS does contribute
to all the items for SLL.
• Generally one could say that the lean structure developed does not lead to the
expected learning abilities, because of the existing internal power relationships
and authority limits.
Conclusion evaluation
Hypothesis Case 5
Con 4: Learning needs determine the learning norms required for survival. True and false
Statement 14: Lean norms emphasize problem anticipation and the critical True
evaluation roles of MICS, whereas classic norms emphasize the problem anticipation
and accounting role of MICS.
Con 6: MICS improves single-loop learning effort and inhibits double-loop learning False
effort.
Con 7 Depending on the Learning Norms, MICS contributes to or decreases True
complexity and dynamics.
Table 8.18: Evaluation Table for Cross-Comparative Assessment.
Comments
Concerning Con 4: learning needs are important, but the way an organization reacts
to learning needs also depends on issues like internal power relations (compare The
Bank), and its being part of a larger consortium (to have shared learning resources in
a centralized divisional R&D department). After case 1, I stated that Con 4 is a
truism and requires the definition of learning norm profiles in order to become
informative. Table 8.19 presents the learning profile for Hitec.
Learning Learning norms Hitec Ideal situation

score
4 Identity and learning policy norms Learning policy and identity
Case Studies 253
described (mainly in TQM terms)

4 Decentralized quality and operational Committed learning: decentralized learning
learning, and divisional strategic learning where possible
4 Dissemination of performance data for Dissemination of data and improving
improving and some rewarding communication between management and
volvos, and training volvos in management
skills
4 Quick implementation of concrete Quick implementation required through
instructions, initiatives for DLL by senior effective communication and understanding.
management, and quick implementation Motivate workers to be creative and think.
of new theories.
Table 8.19: Linking Learning Needs Score 4 with Learning Norms for Hitec.
Concerning statement 14: Hitec is a lean organization that indeed emphasizes

problem anticipation and critical evaluation roles of MICS. It also has a philosophy
that integrates the problem anticipation and critical evaluation groups in the
organization.
Concerning Con 6: MICS does not have much impact on double-loop learning
because the responsibility norms do not require Hitec to participate in this process.
This statement is typically true for the cognitive part of the learning process because
in the implementation part (behavioral aspect of learning) the action norms
determine whether the uninvolved people are motivated to participate in the
implementation of the theory (this is of course essential). Hitec seems to be an
effective implementor of management theories. This is typical of lean organizations.
Classic organizations are (mentally) less integrated wholes and therefore organization
departments and units can resist implementation. Responsibility norms therefore
largely determine the extent to which mental models will be shared or are
incompatible. In the case of Hitec improved participation of Hitec management and
design group in divisional decision-making could decrease the chance of resistance.
Lean organizations have however a stronger sense of commitment to the whole of the
organization. The lower integration in classic MB-s improve their chance of
resistance. These ideas can be formulated in the following additional statements:
S24: Lean machine bureaucracies have a stronger consensus on management theories than
classic machine bureaucracies.
S25: Effective implementation of management theories is improved by having the
implementors involved in the development and adaptation processes of those theories.
S26: Lean machine bureaucracies create commitment to management theories via the
development of consensual mental models. Classic machine bureaucracies do so by
bargaining.
S27: Effective MICS for single-loop and double-loop learning depends heavily on effective
responsibility norms.
Conclusion 8 (based on S24, S25, S26 and S27):
Lean machine bureaucracies have very effective theory implementation processes without
having implementors involved in the theory development and adaptation. Classic MBs require
responsibility norms that have implementors involved in the development and adaptation
process.
Conclusions and Discussion 255
Chapter 9 : Conclusions and Discussion
8.6 Aims and Objectives of the Research
The objective of this study was to understand and explain under what conditions computer-based
MICS contribute to organizational learning in lean and classic machine bureaucracies.
Management information systems were studied because of their pretention of creating smarter
organizations. Because of their particular problems with organizational learning, the study
focussed on machine bureaucracies (MBs) and monitoring information and control systems
(MICS). In seeking to answer this question, we were first faced with the lack of clear concepts and
theory in the joint area of organizational learning and information systems. Here we set ourselves
the following tasks:
1. Review the theory of organizational learning. This resulted in definitions and
operationalizations of organizational learning, and a basic assumption that states that
learning effort should be based on the learning need, and that learning can be facilitated by
learning norms and information systems.
2. Study the specific organizational and technical features of monitoring information and
control systems, and especially the way these features are combined with socio-technical
learning systems in machine bureaucracies.
3. Generate a theory to explain MICS' impact on organizational learning.
Tasks 1 and 2 should result in concrete hypotheses and variables for the construction of a theory.
On the basis of these tasks the following questions were formulated:
1. What are the basic dimensions of organizational learning?
2. How do lean and classic machine bureaucratic organizations learn?
3. Do lean and classic machine bureaucracies differ significantly in their way of using MICS
for organizational learning?
4. What is the influence of MICS on organizational learning in machine bureaucratic contexts?
5. How can one observe the impacts of MICS in machine bureaucratic environments?
These five questions are evaluated in this chapter, some comments are made about the results, and
issues for further research are mentioned.
8.7 What are the Basic Dimensions of Organizational Learning?
Organizational learning consists of three processes, each with several activities (see table 9.1),
which are governed by organizational learning norms. This preliminary answer was based on
insights from four approaches to organizational analysis: cybernetics, organization development,
scientific management, and soft systems analysis.
Learning Process Learning Activity

Single-Loop Learning (SLL) Knowledge Adaptation
(getting better at handling the
theory) Dissemination
Storage
(Re-)use
Double-Loop Learning (DLL) Theory development (incl. implementation)
(fi di th i )
Unlearning
Deutero Learning Learning identity and policy norms creation
(learning about learning)
Learning responsibility norms creation
Learning action norms creation
Creation of procedural learning norms (included MICS)
Table 9.1: Process Dimensions of Organizational Learning
Learning may take place in several relatively independent fields (transformation,

human resources, products and markets), which are relatively easy to observe. The
learning needs are the responses to the complexity and dynamics of the business
environment. The learning norms, which determine the manner of learning, relate to
four main areas: corporate identity and policy, responsibilities, procedures and
actions. To observe learning in action we can see how new management theories are
adopted, these theories being models of how domain variables influence one another
(e.g. fig. 9.1). In the hierarchy of learning processes, deutero learning is learning
about learning and its norms govern changes to the single-loop and double-loop
learning norms.
8.8 How Do Machine Bureaucratic Organizations Learn?
MBs, simple structures, professional bureaucracies, divisionalized forms and adhocracies have
different learning needs and learning norms. As the complexity and dynamics of the business
environment give rise to the learning needs, complexity is higher in MBs than in simple structures
but lower than in the other types (Mintzberg, 1983). Machine bureaucracies have a less dynamics
environment than all other types of organizations (Mintzberg, 1983). Nevertheless, environmental
pressures show that in all MB-cases studied, the learning needs score was increasing. As a result,
four profiles of effective (ideal) MB-learning combinations of norms and needs are described.
These profiles are summarized in table 9.2.
Learning needs 1: low complexity 2 high complexity and 3: High dynamics and 4: High dynamics and
score: and low dynamics low dynamics low complexity high complexity
Ideal learning
norms
Identity & policy None, or 'work Emphasizing core Learning policy and Learning policy and
norms harder' competencies and identity norms identity norms
managing them to 'work described as 'work described as 'work
harder' smarter' smarter'. Learning
infra-structures are
designed and core
competencies are
developed.
Responsi-bility Learning in Learning in specialist Committed learning: decentralized learning
norms functional groups, groups (mostly where possible, and competence-based.
and power-based technostructure), which
have power
Action norms Money and pain Money and pain Quick implementation of insights is required, and
avoidance avoidance. Quick enabled via effective and fast teamwork. Workers
motivation. Quick implementation because are creative members of the team.
implementation of of expert power and
commands hierarchy.
Procedural norms Discrete and Discrete and Dissemination of data Dissemination of data
constrained constrained is free and continuous, and knowledge is free
dissemination of dissemination of improves and continuous,
performance data. performance data communication improves
(among specialists and between management communication
managers only). and shifts, and supports between
shifts' self-management management, experts
and employees
Example Low tech The Roman Catholic Classic MBs that are High tech companies,
manufacturers, like Church. No case like moving to lean, like like Hitec
Cardboard Co. this was found in our Health Co. and
study. Chemical Plant
Table 9.2: Learning Profiles in Machine Bureaucracies.
The differences between the 4 profiles are:

• Profiles 1 and 2 differ because profile 2 requires more internal expertise, which
means that competencies are treated as an asset in the second profile. The first
profile pays no specific attention to competencies.
• Profiles 2 and 3 differ because profile 3 has a much higher dynamics score,
which makes the precise prescription of work and required knowledge less
effective. More emphasis is then given to developing knowledge in Learning
Policies and Identity norms. Profile 2 (like 1) does not need such an explicit
policy statement.
• Profile 4 differs from profile 3 in that it has a higher complexity to treat. This
calls attention to core competencies (as in profile 2), but now higher dynamics
requires all other organization members besides the specialists to participate in
knowledge development and adaptation. It is important to develop a learning
infrastructure to speed up the learning process and to store and adapt the
knowledge gained. Procedural and responsibility norms must enable this
decentralized learning.
Because learning needs are obviously increasing today, it is important to correctly rate
learning needs and design learning norms.
8.9 Do Lean and Classic Machine Bureaucracies Differ in How They Learn?
This research question was valuable for detecting major insights into MICS' roles in organizational
learning. We compared several cases, some clearly classic and some clearly lean, and some that
were moderately lean or moving from classic to lean (table 9.3).
1: Classic- 2: Classic-Service: 3: Classic-manufact. 4: Moving to lean 5: Lean Manufacturer

M.B.-type Manufacturer The Bank Chemical Plant service Hitec
Cardboard Co. Health Co.

(var.2)
1 4 3 3 4
Learning need
(var. 1)
work harder work harder work harder work harder work harder and work
Policy norms smarter
(var. 3.1)
Power-based and functional Power-based and Power-based and Competence-based
Responsibility functional, but also R functional but
norms & D department and moving to
(ineffective) project competence-based
(var. 3.2) groups
Money (financially motivated) and slow to Money and slow Team and fast. Team and fast.
Action norms adopt new theories, but quick implementation of
(var. 3.3) operational insights.
Discrete but less Discrete and Continuous and free Discrete and Free and continuous
Procedural constrained because constrained. Free constrained via
norms shifts have access within branch, hierarchy
to mutual constrained via
(var. 3.4) performance data. hierarchical lines
Classic Classic, but very large Technically lean, but Classic (however Lean. Integration via
Description of socially separated large) but not MRP, no common
MICS problem anticipation computer-based. model
and critical evaluation
(var. 4)
8, four points 16, extremely high for 4 8 8
SLL-effort higher than classic service
(0..16) expected
(var. 5)
0 2 0 4 2
DLL-effort
(0..8)
(var. 6)
Problem Accounting, problem Problem anticipation Problem anticipation Problem anticipation
MICS' role anticipation and anticipation and and Critical and Critical and critical evaluation
(var. 7) Critical evaluation critical evaluation evaluation evaluation
+8, four points +12, expectation was +4 +6 +8

MICS' SLL- higher than between 4 and 8!
value expected
(16..+16)
(var. 8.1)
0, not negative +2 0, not negative 0, not negative 0
MICS' DLL-
value (8..+8)
(var. 8.2)
Table 9.3: Score Card for all Cases.
The Lean-Classic distinction and MICS Use

Lean and Classic MB-organizations differ significantly in their Action Norms. Lean
organizations are effective and quick implementors of new management theories, also
when these theories have not been developed by the implementors themselves. In the
classic cases the implementation of new theories was problematic (slow, incomplete,
provoking resistance).
For all the other organizational learning variables, no systematic patterns along the
lean-classic line were found. This was probably not the result of research artefacts,
because the rating of lean and classic characteristics was done with care.
We expected MICS in classic cases to only be an appendix to accounting, problem
anticipation or critical evaluation systems. This was not true, because in our
observations no differences existed about MICS' roles. More interesting was the
question whether critical evaluation and problem anticipation communications were
done by separate groups or among groups that formed a closely knit network. The
lean case (Hitec) indeed showed that planners (performing problem anticipation)
and performance evaluators (performing critical evaluation) exchanged insights and
this demonstrated a closed learning loop at the organizational level. This type of
loop was absent in all other (classic) MB-cases, where problem anticipation and
critical evaluation were performed by strongly separated groups.
The Manufacturing-Service distinction

An alternative hypothesis was introduced in chapter 1, and explained in chapters 5
and 7, stating that the differences between machine bureaucracies were not only the
result of learning norms, but also of transformations (Manufacturing-Service). The
following were observed:
• Double-loop learning was stronger in the service cases (scores 2 and 4) than in the
manufacturing cases (scores 0 and 2). A possible explanation (Adler and Cole,
1993) is the cycle time of the work done: very short cycle times restrict attention
to detailed process improvements because there is not enough time to
experience and learn about the wider process involved. This also restricts
double-loop learning. In service industries detailed monitoring is more difficult
as people are assigned a budget and a longer time period (week or longer) to
accomplish the task without detailed control. This encourages double-loop
learning, thinking about the interconnections of the task with the broader
process involved.
• Responsibility norms differ: manufacturing cases used project groups whereas the
service cases learned via functional and managerial lines.
• Procedural norms: the role of management in learning was significant in the
service cases and less so in manufacturing.
• The expected differences in MICS-use (MICS used for problem anticipation in
manufacturing and for critical evaluation in service cases) seem not to hold. In
all cases MICS was used for problem anticipation and critical evaluation.
Nevertheless MICS differed in social as well as technical features.
Many of these results are possibly a consequence of the idiosyncracies of the small
sample of cases. Only a survey could test this. The result on Double-loop learning is
however also theoretically significant and unexpected. It could be that product and
market development are more important issues for service than for manufacturing
organizations. Manufacturing may also be more technically constrained with respect

to introducing new product markets (manufacturing organizations must invest
heavily in machinery, whereas service organizations only have to invest in people's
skills and knowledge). This last statement was however not investigated here.
Most interesting were the differences between two organizations with respect to the
role of MICS (Cardboard Co. and Hitec, the former is classic and the latter is lean).
In both cases MICS contributed considerably to single-loop organizational learning
activities. In the classic case MICS' contributions were achieved by the fact that
someone took responsibility for creating knowledge with MICS (responsibility
norms) and also had the power to implement their insights (action norms). In the
Hitec case responsibilities were clear in the organization, but what was more
important was that MICS was considered an essential element in the company's
TQM philosophy. This is a verification of our statements that learning norms should
be linked with learning needs. The Cardboard Co. case had very low learning needs,
thus no explicit policy norms were defined, and MICS existed to perform
administrative routines. In the Hitec-case the high learning needs demanded an
explicit learning policy and procudural norms of which MICS was an essential
ingredient. This means that the design of effective MICS requires, in analogy to the
principles of socio-technical design (Mumford, 1983), the assessment of learning
needs and the corresponding learning norms, and also the assessment of learning
processes and the procedural learning norms (included MICS) to support them.
8.10 What is the Influence of MICS on Organizational Learning?
Organizational learning includes three very different processes with different impacts of MICS:
single-loop, double-loop and deutero learning, the third not the subject of this but of a new study36.
To assess the value of MICS for organizational learning, we need to understand these two learning
processes (Con 2, 3 and 4). Finally, we assumed that the effectiveness of learning can be not only
the result of MICS, but also of the learning norms (Con 7).
36
This study is titled 'Information Systems to Enable Organizational Learning' and is being conducted by
Marc C.P. Hafkamp.
Hypothesis Case 1: Case 2: Case 3: Case 4: Case 5:

Cardboard Bank Chemical Health Co Hitec
1. Learning needs determine the True but what True, but requiring True. Reactions to needs are
learning norms required for survival learning learning profile dependent on power
(Con 4). needs require relations, sharing learning
what learning resources, and organizational
norms? size
2. Lean norms emphasize the critical False, MICS supports both roles in this True, but no True, and
evaluation and problem anticipation classic case. Chemical also has a technically social inte- social inte-
roles of MICS, whereas classic norms lean MICS. gration of gration of
emphasize the problem anticipation both roles. both roles.
and accounting roles of MICS (S 14)
3. MICS improves single-loop and True for SLL, True for False, False, False,
inhibits double loop learning efforts false for DLL SLL, false MICS' incompat- responsi-
(Con 6). (no impact on for DLL impact is ible mental bility norms
DLL) (MICS has mediated models can inhibit
positive via mental inhibit DLL DLL (S24-
impact on model actions 27; Con 8)
DLL) (S17-S22) (S23)
4. Depending on the Learning Norms, True True True True True
MICS increases or decreases
complexity and dynamics (Con 7).
Table 9.4: Table for Cross-Comparative Evaluation of Conclusions.
Our opinion about Con 4 evolved during the project. After case 1, we felt that the
conclusion was not concrete enough, because no answer could be given about the
question which learning norms are required given a certain extent of learning needs.
Therefore we started by describing learning profiles for each case. This resulted in
table 9.2, which prescribes certain learning norms for each learning need level. Table
9.2 only offers hypotheses. We were however not able to test the quality of these
ideas yet, because no scores for 'survival chance' were defined. As a next step in this
subject, the researcher should also check the influence of interpersonal relations,
abilities of sharing learning resources, and the size of the organization on the
required learning norms profile.
Statement 14 is correct in the two lean cases, but in the classic cases MICS also had
critical evaluation and problem anticipation roles. The most important difference
between lean and classic MICS is therefore not so much in their roles, but in how
the MICS (as formalized procedural norms) are linked with the other organizational
learning norms. Especially important here are the policy norms (that state the
importance of MICS explicitly and create an infrastructure), the action norms (how is
behavior changed as a consequence of conclusions drawn from data-analysis), the
responsibility norms (who takes responsibility for the quality of data and its analysis)
and other procedural norms (the way the insights are communicated and discussed).
More specifically we found that lean organizations have a more developed social
aspect of MICS. In particular we found that these organizations have an intergrated
network of problem anticipation and critical evaluation, which is not available in the
classic organizations.
In the first two cases we found that Con 6 was correct with respect to SLL. The
impact of MICS on DLL was not clear (in one case positive, and in another no
impact was found at all). I concluded that MICS does not have much impact when
the learning norms are not appropriate (cf. Markus' interaction paradigm, 1983).
MICS facilitates learning by providing data, but these can only be interpreted via
mental models. When organization members have incompatible mental models, the
learning situation can become political and disruptive. Information systems can then
only be supportive when they aid in discussion and negotiation. This type of system is
called semi-confusing (Hedberg and Jönsson, 1978). These insights considerably
influence our understanding of the relation between MICS and single-loop and
double-loop learning, and are summarized in the following paragraph.
MICS is a provider of data. The better MICS is, the more relevant are data at any
time and place (S17). The type of data that are available from a MICS depend on a
mental model. This model has semantic implications which are incorporated into the
structure of the information system (S18). The availability of data and (a) mental
model(s) are a prerequisite for an interpretation of reality (S19). In organizations
people or groups of people can share models, but also can have unconnected and
even incompatible mental models (S20). When, in interpretation processes (the
cognitive part of the learning processes), incompatible mental models are applied (as
in the Cardboard case), the result is an increased double-loop learning effort, if
people are not trying to avoid the problems involved (S21). This leads to the folowing
new statement: When shared mental models are used, this leads to more effective single-loop
learning processes (even with the same amount of SLL-effort) (S28). When, in interpretation
processes, shared (compatible) mental models are applied, the interpretation process
will be followed by actions (the behavioral part of organizational learning), if people
are motivated (action norms) to take these actions (S22). The greater the model
incompatibility, the more action norms exist inhibiting behavioral learning. The
more compatible the mental models, the better the action norms lead to quick
implementation of new theories and insights (S23).
Conclusion 8 now can be drawn, based on S24, S25, S26 and S27,:
Lean organizations have very effective theory implementation processes without having
implementors involved in the theory development and adaptation. Classic MBs require
responsibility norms that have implementors involved in the development and adaptation
process.
This results in model 9.1.

The insights of model 9.1 enable us to predict MICS-impact in different settings, and
use this knowledge to design an effective MICS as a socio-technical accomplishment.
The model is formed on the basis of insights gained in MBs, but is applicable to all
organizations, because the variables involved can be observed in all other
organizations. This means, in Glaser and Strauss' terms, that the 'substantive' theory
has become 'formal' (cf. chapter 3.2.2 and 3.5). Subsequent research should find out
the validity of this formal theory, by testing it with evidence from other organization
types. The theory is however limited to MICS systems.
8.11 How Can One Observe the Impact of MICS?
The diagnosticum in the cases, if effective, may in the future be applied to practical business
problems. The scales used thus far could be improved by evaluating the construct validity and
reliability. The ratings as used thusfar are briefly evaluated here.
The learning needs score (var 1):

• This score was originally based on Duncan and Weiss' measures. These were however too
elaborate to use here. Mostly, the cases were subjectively rated. Duncan's factors and
dimensions are useful, but scoring via this scale was found to be very laborious and
complex. It is therefore important to develop a shorter checklist for learning needs and to
decide about how the observations can be combined into a single score.
• The statement that dynamics contributes more to learning needs than complexity (an
essential assumption for the learning needs scale) seems to be true.
MB-type (var. 2)
The score of this variable was based on an index of leanness and a transformation. The ratings of
leanness and transformation were in some cases ambiguous, because most cases did not score
neatly along the extremes of the scales, and in some cases the organizations scored on both
extremes for the same item. For instance Health Co., although scoring high on the service extremes
of the transformation scale, also scored high on the manufacturing extremes of this same scale,
because it was an industrialized service company. A solution to this 'problem' might be to regard
these scales as multi-dimensional. It would be useful to find out if a factor analysis also finds
evidence for correlations among the dimensions. This is however useless without a rather large
statistical sample. If these correlations are strong, the items could also be reformulated.
Learning Norms (var. 3)

Factor 1: Identity and Policy norms.
The items mentioned gave a quite complete picture of the identity and policy norms. This factor
however has some ambiguity because it is sometimes not clear whether the items refer to ideas and
wishes or whether the items must refer to about actual organizational behavior. This problem is
typically relevant for the 'policy and mission' item. For instance the Chemical Plant has an explicit
statement of its learning intentions, formulated in its charter. In practice however not much
learning was going on there. On the other hand, Health Co.'s management stated that it did not like
to make statements about organizational learning, though in practice it did quite a lot to improve
the organization's learning. When improving the ratings this problem should be sorted out.
Factor 2: Learning responsibilities.
The distinction between standing and change organization is important. Some problems are:
• If an organization scores on functional and divisional responsibilities it does not score
automatically on matrix. To score on 'matrix', it is also required to assess the organizations
abilities in handling the complexities of the matrix structure.
• We found that task groups were difficult to observe in classic organizations, where they
were more or less secret groups formed by a few top members. In lean organizations they
are better known and overt, making them more easily observable. This was very clearly the
case in The Bank and Cardboard Co. where much strategic thinking was done at the top,
without people at the Branch and Plant knowing what kind of strategic projects were under
way. In Hitec however, people on the shop floor were knowledgeable about strategic plans.
For instance they knew that the top management was considering whether to close the Plant,
sell it, or invest in new technology.
Factor 3: Action norms.
To observe this factor, people should be interviewed using a quite long questionnaire that
unfortunately is also quite complex. Therefore our ratings were reduced and based on some
indications of incentives, interpersonal trust, attitude about knowledge removal, and source of
knowledge.
Factor 4: Procedural norms.
No specific comments.
MICS (var. 4).

The semiotic approach in describing MICS was extremely useful. In many cases the technical and
organizational issues could be traced as causes of problems in the learning process. This approach
is also consistent with our view of MICS as a socio-technical system. This information audit
method also requires contributions from the area of other information systems, which are important
from an organizational learning perspective. Of special interest are Executive Information Systems
because of the role of vertical communications in learning (Adelman, 1992; McAuliffe and
Shamlin, 1992; Boone, 1991), Computer Support for Collaborative Work because of the role of
lateral communications (and possibly inter-organizational communications as well) (Greiff et al.,
1988; Kraemer and Kling, 1988; Stamper et al., 1991) and knowledge-based systems because of
elicitation of tacit knowledge, storage and re-use of knowledge (Coats, 1991; Venugopal and
Baets, 1994). Most interesting are non-computer-based information systems (i.e. informal
communications, defined in procedural and other learning norms) because these contribute at least
as much to organizational learning than computer-based information systems do. This was clearly
seen in the Cardboard Co. case, where a specific computer system, successful in one location,
failed in another location because of these learning norms. The very broad systems of The Bank
were only of limited learning value because of the lack of well-designed procedural and action
norms. Learning in that case was almost completely based on the informal system. The Hitec case
showed that the advanced information systems for quality control were successful because this
system was well connected to the company's philosophy (identity and policy norms). The analysis
of informal systems is however still at an early stage. Some consultancy companies are now trying
to develop this as a service under the headings of communication audit. This is a valuable
approach. Stamper's MEASUR methods might be particularly interesting to apply here (Stamper et
al., 1994).
Single-loop learning effort (var. 5).

SLL-effort was rated by counting the scores (1 or 0) on the intersections of learning activities and
learning fields. This means that no scoring was made of the amount on SLL-effort in more precise
terms terms such as the amount of money invested, hours that are spent, or the number of people
involved in learning. The reason for not doing this was that it would have taken an enormous
amount of research effort, and that the resulting data would not have been clearly interpretable. For
instance what would it mean if an organizations states that it spends 1 million dollars and 1000
hours a year on learning? The only way to make these data interpretable is to relate them to other
data, via the definition of ratios, that can be compared to the ratios of other companies or to what is
theoretically a maximum score. This will be explored in a further study.
Double-loop learning effort. (var. 6)

The reflections about SLL-effort scoring also are applicable to the DLL-scale. Some additional
problems with the DLL-scale are further discussed here. In the theory, only two activities of DLL
were mentioned (development and unlearning). But the cases revealed more activities. In some, for
instance, we found that well-formulated learning norms existed, but that in practice no concrete
activities resulted. This theory-action problem (or cognition-behavior problem) was very clear in
The Bank case, were management was trying hard to develop a leaner and learning organization,
but were still in a turnaround situation. The same was true in the Health Co., where management
preached leanness and total quality management, but where, according to our observations, still a
lot had to be done to make Health Co. comparable to the ideal lean situation. Both cases indicate
that theory development and unlearning are not the only activities of DLL. The implementation of
new insights is also most important. One could argue that implementation is part of unlearning
(removing resistance to change), but it is also a separate activity (training people in new ways of
thinking, advocating and boadcasting new ideas etc.).
MICS' role (var. 7)

In this study, the scoring of MICS' role in the learning activities of the learning fields concerned
only knowledge adaptation in the related learning fields. This led to a simple variable with two
values: problem anticipation role versus critical evaluation role. All the cases showed MICS in
both roles. Additionally we observed whether the problem anticipation and critical evaluation
activities were explicitly connected (for instance, by invoking the same people, or by explicit
procedural or action norms), because these links create closed learning loops which are important
for single-loop learning and double-loop learning. MICS' role can be summarized as follows:
• MICS has no explicit roles in DLL besides providing data, which can be operationalized by
asking if MICS' data provide incentives to DLL.
• When MICS has problem anticipation and critical evaluation roles, it automatically supports
the (re-)use activities. This is because problem anticipation requires the application of
existing models and data, and critical evaluation is only effective when reference data and
standards (developed in the past) can be used.
• In order to realize its problem anticipation, accounting, and critical evaluation roles, MICS
must store data and parts of a management theory, because critical evaluation and probem
anticipation are based on these.
• Data dissemination must be solved less as a technical problem (but see Health Co.), and
more as a problem of procedural and responsibility norms.
For these arguments, the two-value variable we applied is an invalid operationalization of MICS'
roles. MICS' roles are better operationalized in three values: problem anticipation, critical
evaluation and procedural links between problem anticipation and critical evaluation. In MICS
design processes, it might be more useful to look at all SLL- and DLL-activities separately,
because MICS then is expected to support different learning activities.
MICS' Value (var. 8)

MICS' value was scored by counting the scores on the intersections of the learning activities and
learning fields with SLL and DLL. The only values that were allowed on the intersecting cells
were +1 (supporting learning), 0 (no impact) and -1 (inhibiting learning). This is because as yet we
lack more detailed measures. Another limitation of the variable is the operationalization of the
DLL-value. We have not scored 'implementation' separately, because DLL was not operationalized
on this activity. The MICS' value for SLL seems to be useful and practical and does not need
additional comments.
8.12 Proposal for a Learning Audit
A learning audit requires a frame of reference for selecting issues to investigate and instruments
for observation. Additionally, the auditor might adopt a prescribed method. This study's theory can
be used as a frame of reference with the operationalized variables. This section deals with a
possible audit method.
8.12.1 Frame of Reference
Learning audits should reveal significant problems and make recommendations about
improvements. The theory is important as a guide to observation. It also gives criteria for what is
going right or wrong.
This study emphasized contingency factors (called learning needs) for explaining effective
organizational learning and information systems' effectiveness. Deutero learning is the process by
which the information about these contingencies is used in developing skills, structures, policies
and instruments for SLL and DLL. So by defining the organizational learning needs we can detect
a mismatch between needs and learning abilities (norms, efforts and MICS) and suggest
improvements. If this discrepancy is very large, an organization must go through several stages to
evolve the required abilities. Additionally, if stages of learning can be defined, organizations can
also be prescribed how to develop learning abilities.
In this study we have found four learning profiles, consisting of sets of learning norms that match
with certain levels of learning needs (defined as an index of complexity and dynamics). In this
section we will describe these profiles, and add the learning efforts to enable a complete evaluation
of SLL, DLL and their learning norms. This leads to four stages of organizational learning.
Consistent with organizational growth theories (e.g. Greiner, 1972; Quinn and Cameron, 1983) an
additional stage is added in which learning is still at a beginning (birth) stage.
Stage 1: Fighting the moras. This first stage as yet has no procedures or policy concern for
organization learning, and we find no allocation of organizational resources to learning. This can
happen in at least two types of organizations:
• Organizations in their infancy, when it is too early to have developed procedures for
organizational learning. They are preoccupied mainly with survival. If this period lasts too
long, they will be beaten by organizations that are learning to acquire more efficiency and
competitiveness.
• Organizations in a hyper-dynamic and complex environment. In this case anything learned
will be obsolete before it can be applied (Hedberg, 1981). At this stage an organization
cannot even define the knowledge needed, and when it is acquired, applying it would
endanger service and products. This is typically the case in organizations which must deliver
unique services rapidly (e.g. police and hospitals).
Stage 2: Bureaucratic learning. At this stage learning needs are recognized and met by learning
norms consisting of procedures and rules. This may be done provided that the environment is
analyzable and not too dynamic, the typical environment of the classic machine bureaucracy. This
situation can lead to efficient learning, but not without danger:
• The shop floor can easily feel alienated from a formal and centralized learning process
(organized in Research & Development, Management, Technostructure etc.) as they are
trained uncritically to obey commands.
• This easily leads to an under-utilization of human potential, which after a while is not able to
do more than obey.
• Although a lot of learning can best be done on the shop floor, where a close connection
between thinking and doing can be established, it will however not happen because these
organizations have no learning responsibility for shop floor people.
• Creating major changes is extremely difficult because of a lack of understanding on the shop
floor. At the same time, problems are incompletely comprehended at the top because no
knowledge goes bottom-up. A lot of senior management courage and effort are required to
achieve changes, a process which may take many years.
The organization in this stage corresponds with the profile mentioned in the simple-stable situation
and is typical of the classic MB. It only learns in the transformation field. When looking at the
priorities among learning activities, most priority will be given to knowledge storage (archiving).
The second priority is the adaptation of existing rules and the dissemination of these adaptations
via formal letters. Hopefully this knowledge is also (re-)used. The lowest priorities are given to
theory development and unlearning, thus resulting in very inert organizations.
Stage 3: Expert learning. At this stage management regards knowledge as a strategic asset. It
allocates budgets to specialist groups to develop the very complex new knowledge required.
Organizational learning therefore is institutionalized learning. Research & Development
departments and project groups are created. After the development activities most emphasis is
placed on storing and adapting the findings, considered to be of strategic value and kept secret or
patented. This is appropriate in environments of high complexity and low dynamics, but also can
lead to serious problems. The dissemination of the findings is often a neglected topic, leading to
under-utilizations of knowledge (Hamel and Prahalad, 1990). A critical success factor in these
cases is not only the creation of knowledge and skills, but especially the communication of
findings and the speed at which these findings can be implemented as new products, production
processes and services. Almost no emphasis is placed on the implementation of the new ideas in
the area of human resources and marketing, which is the major reason for the failures of the great
new ideas (Hamel and Prahalad, 1990).
Stage 4: Dispersed learning. To solve the many communication problems in organizational
learning typical of stage 3, some organizations allow strong decentralization (vertical and
horizontal) to encourage learning in all corners of the organization. The guiding principles for
learning are in this case:
• Support creativity and critical thinking.
• Allow experimentation.
• Encourage democratic relations among organization members.
• Create strong commitments to the organization via indoctrination and reward systems.
• Give priority to innovation in relation to costs.
An excellent example of such an organization is documented by Leonard-Barton (1992) in her
study of Chaparral Steel. The strong point of this learning configuration is the enormous amount of
energy that is freed from organization members, encouraging learning everywhere in the
organization. The most detailed problems are taken up by someone for analysis, improvement,
and/or innovation (people are allowed to create a group or project to think about possible new
products, even when in the initial stage the ideas might seem stupid).
Dispersed organizational learning is very strong in the adaptation of existing work procedures,
especially because the improvements are created by the people who must use them. The findings
are disseminated well in the organization, and picked up by people who have the knowledge and
skills to develop more profound innovations. A relatively weak point in this organization type is
the way how improvements and insights are stored. The human factor (individuals' memories,
culture, stories, myths and beliefs) are vital organizational memories here. On the other hand,
however, unlearning is accepted more than sticking to old experiences and insights. This type of
organization learns on the fields of transformation (especially quality), and human resources (by
appealing to people's interest in growth and personal curiosity).
The organization corresponding to this stage has high dynamics and low complexity, and is
sometimes called a 'learning lab' (Leonard-Barton, 1992). The basic problem with this type of
organizational learning is the allocation of limited learning resources and stopping projects. The
learning activities can become so popular that they could go out of control. Another problem is that
the amount of communication required, though possibly less than in the case where learning is
organized via hierarchical procedures, will generate problems of too much decentralization (high
coordination costs and agency problems. (Galbraith, 1973; Douma and Schreuder 1991).
Stage 5: Middle-Up-Down Management. This is a stage in which dispersed learning is guided by
business ideas, which are made more concrete by middle management functions that facilitate the
learning process. The following statements are typical of this stage of learning:
• Management gives direction about learning priorities via its strategic view of core
competencies.
• Management becomes receptive to ideas flowing upward, connecting them with their own
priorities and insights.
• Middle management makes general ideas about learning concrete by formulating budgets,
organizing work (especially the relation between on-going work and change work) and other
resources such as learning infrastructures (computers, archives, communication networks
etc.).
• Top management lays down a policy about the importance of learning and how learning
should be supported.
• A major issue is that learning does not occur within the confines of a separate organizational
unit, but is close to the everyday work process and includes awareness of its effects on other
organizational departments and work groups.
In this organization type, top managers that have brilliant and appealing visions (theory
development) are important, but even more important are the middle managers that can bring these
visions to ground level (adaptation). This also requires strong communication and indoctrination
processes as a coordination instrument (Mintzberg's 'missionary form'), so that learning is better
organized than in the dispersed type. As a result of this communication process people must feel
committed to using and re-using the gained insights, although more emphasis is given to the first
than the second because of the high demands of innovation. This learning type (originating from
Ikujiro Nonaka Honda Company experiences, 1988) serves an environment of high learning needs
(high dynamics and high complexity).
The stages mentioned describe levels of learning norms, and must themselves be learned. It is not
likely that an organization that has not yet reached stage 2 (the bureaucratic procedures that are
minimally required to stay in business) could be ready for stage 3 (expert learning), because expert
learning requires an administration that can be counted on. Stage 4 is mostly a reaction to the
limitation of the expert learning experiences, when organizational dynamics increase. At stage 5,
management realizes the essential importance of managing the learning process, not only for
keeping the process within the confines of budgets, but also for linking the efforts with strategic
demands.
The single-loop and double-loop learning efforts have costs involved that are valued differently in
the different learning stages. The first stage sees learning as not relevant yet and therefore
evaluates learning efforts as costs that should be omitted. The second stage sees learning as an
improvement of administration and operational management. At the third stage, learning is
initiated by the management, and thus management costs. The fourth stage has learning
implemented in its on-going operational processes. The costs thus are embedded in operations, and
impossible to administer separately from these. The fifth stage regards learning as vital for the
organization's mission, because it is initiated by top management and requires the involvement of
all organization members.
When a limited budget exists for learning, priorities will be shared out differently at the different
stages. These priorities are summarized in table 9.3.
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5

Cost view Must be Administrative & Management Embedded in Mission critical
omitted operational costs operations
management costs
Theory 6 2 6 3 5
Development
Unlearning/ 1 1 1 2 2
Removal
Adaptation 2 5 4 6 6
Dissemination 4 4 3 4 4
Storage 5 6 5 1 1
Re-use 3 3 2 5 3
Total spending 21 21 21 21 21
points on activities
Learning fied none Transformation Trans- Transformation Transformation
formation Product Product
Product Human resource Human resource
Market Market
Table 9.5: Stages of Organizational Learning and Ranking of Priorities for Learning
Activities and Learning Fields
8.12.2 Learning Audit
The audit could follow the steps used in the cases, starting with a general focus and then narrowing
to SLL, DLL and finally MICS' role and value.
The aim however should not only be to gain quick and valid scores but also reliable insights to
help the client understand his problems, and find a course of action. Well-designed presentation
tools can help support discussions and the development of an opinion. Our empirical work suggests
that the existing tables about the learning norms (table 9.2 and the tables in chapter 7) are well
designed for presenting major distinctions. The tables about learning effort and the learning value
of MICS could however better be split into a table for SLL and a table for DLL, to make clear that
they are very different types of learning (see tables 9.6 and 7.7).
SLL-fields: Human Resource Process Market Product

SLL-activities
Adaptation
Storage
Dissemination
(Re-)use
Table 9.6: SLL-Assessment Sheet
DLL-fields: Human Resources Process Market Product

DLL-activities
Development
Unlearning
Table 9.7: DLL-Assessement Sheet
Each cell can be discussed separately to aid the systematic discovery of opportunities
to improve organizational learning activities and to detect and solve problems with
MICS, or other information systems. Finding which combinations of activities and
fields appear in the organization can reveal learning limitations. The application of
these tables however must be done in the broader context of the organization's
learning needs and the related learning norms and learning efforts.
8.13 Limitations of this Study
This study has a number of limitations. As with every project with a deadline, choices had to be
made. For instance I decided to operationalize single-loop and double-loop learning processes but
not deutero learning processes. Statements were formulated about the link between learning needs
and learning norms, but no description is made of how organizations can organize the deutero
learning process themselves, and thus assess learning needs and norms.
Another limitation is related to the implicit rationalist approach to reality that is adopted here,
which implies that people, by understanding organizations and their problems, will find better
(transformation) technologies. The technology involved should imply a scientifically correct
relation (management theory) between the goals and the most effective and efficient means. This
optimistic view has been challenged by writers such as H.A. Simon (1976) emphasizing people's
bounded rationality. Organizations have many ways of coping with these human limitations, and
accomplish very complex tasks by such devices as division of labor, use of standard operating
procedures, use of decision premisses (task goals and constraints), which can be effective when the
organizational environment can be analyzed. When the environment is difficult to analyze, it is
difficult to find an optimal organization or information systems design. In that case the rationalist
paradigm reveals its limitations. Consequently, Cohen, March and Olsen (1972) developed a so-
called 'garbage-can' model, that states that decisions are not made via a rational selection from
several alternatives, but emerge from the interactions among more or less independent streams: a
stream of problems (with dates of first appearance of the problems, energy required for finding
solutions, and a list of decisions), a stream of choices (containing decision moments, agendas, and
participants), a stream of decisions, and a stream of participation energy per participant. This
model explains that solutions for problems in organizations depend on the moment the problems
were recognized, the energy participants put into solving them, and group structure and task
division. In this unanalyzable situation knowledge about the right solutions is not a critical issue. In
principle, someone with the least knowledge about a subject could become the most important
decision-maker, when he has the power gained via his position, or by putting more energy into the
decision-making process than the others. Information systems could help to identify problems
more quickly, after which the priority on the decision-making agenda becomes a problem and a
part of the garbage-can. This model gives an insight into the limitations of organizational
rationality and learning. It is however only valid in cases of unanalyzable environments. When the
environments are analyzable, the best learning organization will be most effective. It seems as if
lean production organizations have solved many limitations of learning. Competitors then have no
other choice than to improve their learning ability as well.
A third limitation of this study is its restriction to the organizational level of analysis. Many
improvements in organizational rationality imply the adoption of new production technologies,
especially automation of manufacturing and services. This process of impovement is not limited to
within the gates of the factory, but increasingly involves improvement along the external value
chains as well. Some examples are the demands for better supplies of materials by applying ISO
9000 standards (cf. case of Cardboard Co. and its adhesive paper supply). Another example is the
trend to outsource the activities of a company (e.g. its administration) that are considered not to
belong to its core business. This outsourcing process is not limited to national borders, but often
involves the search for optimal physical and socio-political environments for production and
service anywhere in the world. Famous examples are the outsourcing of computer programming by
European software houses to India, where many skillful programmers are available for low wages.
Other examples are the establishment of West European manufacturing plants in developing
countries. The problems of coordination that limited these opportunities in the past are nowadays
solved via telecommunications means. The consequences of these trends are large, because
(western) countries must now rethink their economic function in the world.
A fourth limitation concerns about the conceptualization of learning needs. It was decided to score
learning needs in terms of organizational uncertainty (complexity and dynamics) following Duncan
and Weiss (1979). This scale does not rate individual learning needs nor did it rate specific
problems or problem classes and their urgency of solution. Individual learning needs were
excluded because this study is about organizational learning. Organizational learning can also be
approached by studying problem classes and how organizations could manage them (Etheredge,
1980 and Mason and Mitroff, 1973). The only reason why this was not done is that learning about
problem classes, for an unknown reason, is not a main issue in the tradition of organizational
learning and information systems.
This study also clarifies the need for further research in many areas. Some of these needs are listed
below.
• Chapter 6 proposed that dynamics and complexity have a different impact on the value of
knowledge. This proposition needs further empirical research, because it could shed light on
the value of organizational knowledge and organizational memory stores. In such a study
also criteria for investments in different organizational memories could be developed.
• The scales and indexes we developed are only a beginning for further research, which
should be more codified, to support surveys on the issues of organizational learning. It
would be particularly interesting to develop a measure for organizational learning
effectiveness. This requires the development of a scale that measures learning effort and
learning needs. The match between needs and effort could be an indicator of learning
effectiveness. In a survey, measures could be tested for their validity and reliability, and it
could clarify the quality of organizational learning in the research population.
• Deutero learning was not studied. Rather then describing learning processes (many
researchers have done this already under the headings of quality research, organizational
change, and innovation) one could study the effectiveness of techniques being marketed at
this moment (such as computer-aided systems engineering, micro-worlds, executive
information systems, knowledge-based systems, case-based reasoning, learning labs).
• Research also might be fruitful in the area of novel types of information systems such as
group decision support systems, electronic highways, knowledge-based systems. What are
the opportunities and limitations of these systems for organizational learning? What kinds of
changes in organizations and people are required to make them profitable from an
organizational learning perspective?
• Research on the method of information systems development could profit from an
organizational learning perspective in two ways. First, information systems development is a
learning activity, which means that the knowledge created should be stored, removed, (re-
)used, maintained and disseminated. How are these activities linked with the practice of
systems development, and how should this be done? Secondly, system development has
often suffered from a technological determinism, for example in business re-engineering
which usually fails to consider how to make business re-engineering a social activity,
involving intelligent people each with their own experience and knowledge. Business re-
engineering practice could thus profit considerably from applying the organization
development and soft systems perspectives, by which it creates a more complete and closed
learning loop, rather than by limiting itself to the scientific management and cybernetic
approach.
• A final research project could concern problem classes and the types of organizational
norms that are most suited to cope with these problem classes (Etheredge, 1980). Strangely
enough, as far as I know, it has not led to any larger research project until now.
Researchers interested in organizational learning are very much encouraged to participate in this
fascinating field.
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Blau 39
Index Boesjes-Hommes .................................................33
Boisot 57, 62
"garbadge can"-model ........................................ 289 Boone 280
"I Think" 140 bounded rationality ..........................................288
Acar 70, 83 Braverman ...........................................................72
Ackoff 6, 81, 160 broad sense systems definition ...........................24
acquisition of knowledge................. 126, 141, 154 Brown 87, 88
acquisition of knowledge and MICS ............... 154 building shared vision ........................................67
action norms 71, 82, 178, 180, 196, 216, bureaucratic learning
232, 247, 253, 261, 273,........................................
280 283
action planning ................................................. 127 Burns 91
action planning systems.................................... 153 Burrell 17, 34, 49
action-outcome theories ..................................... 32 Burrows 25
adaptation of knowledge. . . . . 127, 200, 219, Bushe 108, 115, 167, 251
234, 248, 263 business awareness ............................................244
Adelman 280 business re-engineering .................. 8, 73, 113, 150
Adler 56, 114, 177, 274 business value of computers ...............................13
administration/production ratio ..................... 260 Buzzell 151
administrative organization ................................ 86 CAD/CAM ........................................................... 3
affiliating 139 Camp 150
Aggerwal 3, 256 canonical practice ...............................................87
Aiken 11 Cardboard Co ...................................................189
analytic induction ............................................... 39 Cash 3
Anderson95 causal structure ...................................................24
Ansari 21, 23, 24, 34, 138, 245 Centers of Service ...............................................65
Ansoff 45, 84 channels of communication ...............................55
Anthony 135 Chaparral Steel .................................................284
APMS 189, 193 Chemical Plant .................................................225
Aquilar 26 Chew 25, 62, 150, 168
Argote 56 Child 95
Argyris 17, 21, 26, 32, 49, 59, 60, 64, 82,90, 91, 117,classic machine
132, 146, 163, bureaucracy
170 ...............................94
Asean Brown Boveri ........................................... 65 closed control loop ...........................................138
Ashby 9 closed learning loop ................................. 153, 234
Ashworth 1 closed loop learning situations .........................141
assimilation ....................................................... 140 co-makership .......................................................96
attitude to quality ...................................... 95, 129 Coats 45
audience learning ............................................... 57 codification of knowledge ..................................57
Automatic Teller Machine ................................... 3 Cohen 289
Bateson 19, 59 Cole 114, 177, 274
Beckman 56 Coleman 71
Bedford 135 colonial industry ................................................... 5
Beer 90 command economy .............................................. 5
behavioral adaptation ......................................... 20 Common Market ..............................................209
Bell 1, 2, 6 communication systems......................................16
Bell Company ..................................................... 60 company administration.....................................93
Berger 58 comparative analysis .........................................150
Berliner 145 competency trap ..................................................62
Bernstein 35 complexity ................... 79, 90, 102, 166, 169, 173
Blanchard ................................... 10, 117, 120, 259 concreta 33
References 285
conditioned viewing ......................................... 141 dispersed learning ............................................ 284

consolidating.....................................................139 dissemination of knowledge 84, 126, 201, 220, 234, 2
contingency theory ............................................. 68 divergence ........................................................ 140
control 10, 52 division of learning work ....................................55
converge 140 divisional structure .......................................... 113
coordination costs .............................................. 95 Dobson 26
coordination mechanisms .................................. 91 Doty 105
core competence ................................ 81, 118, 125 double-loop learning 21, 60, 122, 138, 167, 201, 220, 2
Courtney 70 double-loop score ............................................. 124
craft technology ....................................... 1, 14, 45 double-loop trigger ..............................................85
creation of information .................................... 139 Douma 6, 199, 285
critical evaluation.............................................. 129 Dow Chemicals ................................................ 151
critical success factors ......................................... 69 Duguid 87, 88
criticisms of Machine Bureaucratic Duncan 20, 79, 109, 110, 176, 278, 289
configurations ................................... 105 dynamics 79, 90, 102, 166, 169
CSFmatrix ........................................................... 69 dysfunctional behavior .................................... 144
Cummings ........................................................ 131 Earl 44, 136, 161
customers .............................................................. 2 effectuating ....................................................... 139
cybernetic analysis............................................... 19 Electronic Data Systems ............................ 65, 114
cybernetic approach 17, 48, 50, 58, 76, electronic
138 high ways ............................................66
cycle time274 emergent perspective ..........................................23
Cyert 20, 48, 50, 123 empirics 137
Daerden 135 enacting 141
Daft 57, 113, 119, 127, 140, 153 engineered standards ....................................... 150
database technology .......................................... 137 Epple 56
databases 86 error-controlled regulation .................................53
data flows .......................................................... 137 espoused theory ..................................................59
Date 133 Etzioni-Halevy .....................................................36
Davenport .................................. 8, 25, 73, 81, 159 European Malcolm Baldridge Award.............. 256
Davis 8, 34, 134 evaluation table ................................................ 188
De Geus 32 Evans 28, 89
De Raadt 20, 53, 58, 59, 138, 163 executive information system .......................... 137
Deal 119 experience ...........................................................63
decentralization................................................... 95 experiential learning ...........................................44
decision support systems ........................... 11, 140 experimentation ..................................................45
decision-making .................................................. 50 expert learning ................................................. 284
Delbecq 11 expert systems......................................................86
deliberating ....................................................... 139 extrinsic work motivation .......................... 97, 117
Denzin 34 Fayol 90, 93
depreciation of knowledge ................................. 56 feedback control .............................................. 138
depth of learning ................................................ 60 feedback frequencies ........................................ 119
determiner .......................................................... 75 feedforward control ......................................... 138
deutero learning.......... 21, 61, 111, 128, 270, 288 Feigenbaum ...................................................... 280
development of management theories 83, fighting
122 the morass .......................................... 283
dialectical process ............................................... 70 financial retribution systems ........................... 117
differentiation ..................................................... 47 Fiol 117
diffusion of knowledge ....................................... 62 Flamholtz.................................................... 24, 138
discovering ........................................................ 141 flexible manufacturing systems...........................28
Ford 3 fragmented learning ............................................57
frame of reference ...................................... 43, 282 human relations school of management............90

Francis 163 implementation ............................... 231, 261, 273
Franke 167 Indik 95
Freeman 20, 124, 167, 170 indoctrination ...................................................107
full service bank ................................................ 210 industrial engineering .........................................56
functional lines ................................................. 113 industrialization of services ................................99
functions of theory ............................................. 35 industry standards.............................................150
Galbraith 10, 29, 53, 90, 91, 108, 113, 285 inertia 24, 163
Gale 151 informal processes ..............................................90
Galer 159 information .........................................................52
Garratt 32, 167 information retrieval and MICS ......................155
Garvin 28, 89 information systems technological aspects ......133
GATT 29, 209 information technology 3, 8, 9, 23, 25, 66, 76, 209, 224
generic-specific structures ................................... 74 inhibit of double-loop learning ........................221
Ginzberg 94, 100 innovation ........................................ 3, 10, 28, 163
Glaser 36, 38 institutionalizing organizational learning ........131
Glick 105 instrumental relations ........................................21
Gouldner145 internal competition ...........................................65
government administration................................ 93 interorganizational systems.................................16
Greiff 16, 280 interpersonal trust ............................................118
Grönroos 4, 94, 99, 107, 119, 178 intrinsic work motivation .......................... 97, 117
grounded theory ................................................. 35 Invalid data reporting .......................................145
Guetzkow .......................................................... 126 ISO 9000 229, 231, 237
Guinan 10, 146 IT-architectures .................................................136
Gurbaxani .............................................. 6, 91, 137 IT-payoff 8
Hafkamp 205 James 89
Hage 11, 102 Japan 5
Halpin 137 Jayaratna 6
Hamel 3, 28, 81, 116, 119, 284 Jelassi 22
Hammer 8, 25, 73, 113, 164 Jelinek 131
Hannan 20, 124, 167, 170 JIT philosophy ..................................................256
Health Co ......................................................... 240 Jönsson 70, 141, 277
Hedberg 7, 32, 57, 58, 63, 70, 111, 115,141, 277, 283Jung 36
Hedlund 97 Juran 167
Heintz 70, 83 just-in-time.........................................................262
Hempel 34, 36 kaizen 62, 95
Hersey 10, 117, 120, 259 Karlsson 65
Herzberg 90 Kaydos 151
hierarchical model ............................................ 164 Keen 8
High Performance Systems Corp ....................... 83 keiratsu 97
Hitec 253 Kennedy 119
Hill 28, 122, 135 Kerlinger 175
Hoffer 133, 137, 155 Kerr 3, 28, 86, 134, 154
Hofstede 26, 141, 156 Kieser 34
Honda Motor Company .................................. 286 Kiesler 153
hostility 103 Kim 32, 58, 126, 156
Huber 25, 63, 105 Kirsch 17, 264
human capital ..................................................... 96
Klein 17, 264 Kling 17, 280
References 287
knowledge .....................................................44, 75 legitimacy.............................................................89

knowledge distribution.............................. 56, 155 Leitch 10, 135, 161
knowledge storage ............................................... 66 Lengel 57
knowledge transfer.............................................. 56 Leonard-Barton ............................ 94, 97, 177, 284
knowledge-based systems.........................15, 86 level of control ................................................. 137
Kolb 44, 75, 117, 140 leverage potential ............................................. 113
Kolkman 70, 83 Levitt 62-64, 126
Kordelaar189 Lijphart 20
Kotler 28, 99, 151 Lindsay 28
Kraemer 280 logistic management theory ............................. 235
Kubicek 34 Lorsch 84, 108
Kuhn 33 Luckmann ...........................................................58
Kumar 45 Lyles 117
labor, time and motion study............................. 72 machine bureaucracy ...............1, 27, 92, 163, 270
Lammers 17, 28, 92, 265 management costs ............................................ 107
Land 91 management styles ........................................... 120
lateral structures.................................................. 95 management theory 17, 45, 75, 135, 168, 198, 206, 2
Lawler 17, 24, 26, 117, 143, 170, 245 managerial control ..............................................10
Lawrence 84, 108 manufacturing machine bureaucracy .......... 93, 99
lean machine bureaucracies .......................... 94 manufacturing-service distinction ................... 274
lean production .................................................... 7 March 17, 20, 48, 50, 58, 62-64, 123, 126,145, 289
lean-classic distinction ...................................... 273 market diversity ................................................ 103
leanness 192, 211, 229, 244, 256 market research ...................................................96
learning ability .................................................... 79 Markus 15, 135, 136, 146, 170, 277
learning activities .............................................. 170 Maslow 90
learning curves .................................................... 56 Mastenbroek .......................................... 17, 21, 96
learning effectiveness ........................................ 290 matrix organization .......................................... 114
learning effort ........................ 68, 77, 78, 164, 182 Mayo 90
learning fields .................. 49, 76-78, 79, 158, 170, MB-type variable .............................................. 231
182, 183, 191, 209, 281 MB-types 177
learning infrastructure ........................................ 80 McAuliffe ................................................... 16, 280
learning need score ........................................... 238 McFadden ........................................ 133, 137, 155
learning needs 79, 87, 99, 102, 109, 111, McGregor
168, 169,.........................................................
173, 175, 191, 228, 241, 255, 108
271, 278
learning profiles ....................... 223, 252, 267, 271 McKeown ........................................... 10, 135, 161
learning norms 62, 99, 102, 111, 164, 169, McKinsey
172, 178,114214, 231, 238, 246, 259, 271, 279
learning policy norms 80, 111, 194, 214, medium
231, 246,richness
259, 279 .................................................57
learning problems ...................... 26, 202, 221, 235 memory store ......................................................78
learning process ......................................... 75, 270 mental maps ........................................................32
learning responsibility norms ....... 71, 81,113, 279 mental models ...................................... 67, 68, 218
learning speed ................................................... 160 Merton 90, 145, 170
learning styles inventory ..................................... 46 method of information audit .................. 282, 287
Leavitt 6 methodological problems ................................ 175
Lee 10, 70, 146, 175 Michels 145
Leege 163 MICS 16, 53, 136, 169, 172, 197, 217, 221,233, 235, 248, 250
legality 89 MICS in knowledge storage ............................ 154
MICS in knowledge (re-)use ............................. 156 MICS-description ............................................. 181
MICS in knowledge dissemination .................. 155 MICS-use274
MICS' role ....................... 149, 150, 152, 183, 187 middle-up-down management ................. 285, 286
MICS' value ............................................. 170, 183 Miles 90
Mills 99, 177 outsourcing ................................................ 28, 289

Mintzberg 1, 16, 20, 26, 36, 82, 87, 92, 102, 105, of
paradigms 131, 134, 146,
knowledge 148, 261, 270
..................................... 48
misinformation ................................................... 96 parallel learning structures ...............................115
Moberg 99, 178 Parkinson ............................................................95
model antagonism .............................................. 70 part-whole dependencies ....................................74
model bases ......................................................... 86 particulars............................................................75
model incompatibility ............. 199, 232, 253, 277 parties or conflict approach ................................17
Mohrman .......................................................... 131 Peffers 85
Morecroft ......................................................69, 70 performance control .........................................127
Morgan 17, 19, 34, 49, 108 performance control systems ............................146
motivation base................................................... 97 Perrow 135
MRPA+ 253 personal development.........................................47
Mumford 8, 26, 275 personal mastery .................................................67
mutual understanding ...................................... 155 Peters 6, 8, 65, 81, 114, 265
negotiation relations ........................................... 22 Pettigrew 146
network 274 Philips Electronics ............................................119
network of planning and control ..................... 263 physics 137
network organization ........................................ 114 Pierce 11, 90
Niebel 72, 150 PIMS 151
Nijssen 137 planning and decision systems ...........................15
Nolan 8 policy and identity norms ........................ 178, 179
Nonaka 6, 104, 120, 139, 160, 177, 286 Popper 34
norms for lean-classic ....................................... 177 Porter 4, 5, 134, 135
not-invented-here-syndrome ............................... 97 post-industrial society ........................................... 5
O'Keefe 70 power 17
objectivism .......................................................... 70 power relations....................................................22
objectivists ........................................................... 35 practicality .........................................................108
obsolete memory contents.................................. 58 practices and procedures ....................................55
Olsen 58, 289 pragmatics .........................................................143
Olson 8, 34, 134 Prahalad 3, 28, 81, 116, 119, 284
opportunistic learning ........................................ 58 prehension ........................................... 45, 59, 140
organization development 21, 49, 59, 76, 138
problem anticipation ........................................129
organizational configurations ........................... 102 problemistic search .............................................51
organizational control ...................................... 142 procedural learning norms 71, 82, 118, 178, 181, 196, 216
organizational learning .............................. 51, 269 procedural norms ...................................... 71, 274
organizational learning capacity ...................29, 61 process of learning ..............................................45
organizational learning paradox ....................... 168 product life cycle .................................................56
organizational memory ........................ 60, 63, 290 production cost management theory ...............235
organizational norms .......................................... 64 project groups ...................................................284
organizational perestrojka .............................. 6, 81 public maps .........................................................59
organizational proximity..................................... 64 quality 2
organizational survival chance.......................... 171 quasi-resolution of conflict .................................51
organizing............................................................ 20 Quinn 25, 78, 106
Osgood 74 reapplicability ....................................................126
output 101
reflection 45 relations with suppliers .......................................96
reinforcement of complexity .............................. 28 reliability of research...........................................39
relations with clients........................................... 96 reorganization risks .......................... 124, 167, 171
relations with employees .................................... 96 requisite information ..........................................53
References 289
research & development departments ...... 91, 284 Sharda 11

research model .................................................. 171 shared mental models .........................................32
research plan ....................................................... 37 shifting the burden .............................................68
research problems ............................................... 31 Short 8, 73, 81, 159
research purposes................................................ 31 Simon 17, 54, 63, 90, 118, 145, 288
research questions .............................................. 36 simple-complex dimension 110, 210, 229, 244, 255
resistance 146 single-learning loop .......................................... 138
responsibility norms 178, 179, 195, 215, 231, 246, 260
single-loop learning 20, 53, 59, 125, 128, 166, 200, 2
retention 126, 154 situational learning .............................................57
retention of knowledge and MICS .................. 154 skills databases ....................................................66
retrieval 126, 154 Smith 1, 3, 6, 14, 15, 17, 24, 31, 32, 34
retrieval costs ...................................................... 64 Snow 90
return-on-management ....................................... 14 social aspect of semiotics ................................. 146
Rhode 17, 117, 143, 170, 245 social dynamics ...................................................67
rigid bureaucratic behavior .............................. 145 socio-emotional relations ....................................22
Rochfeld 127 socio-technical systems ........................................23
Rockart 69 soft systems modelling ........................................68
Rogers 167 specialization .......................................................47
Rohrbaugh ................................................. 78, 106 Stalker 91
role names ........................................................... 75 Stamper 6, 8, 16, 23, 63, 70, 74, 85, 126, 133,162, 182, 280
role-constrained learning .................................... 57 static-dynamic dimension ........111, 228, 241, 255
Roman Catholic church ..................................... 20 status 118
Rosenhead .......................................................... 70 Stearns 1
Saarinen 25, 85 Sterman 70
Schmenner ...........................94, 99, 107, 112, 178 Stinchcombe .......................................................93
Schoenherr .......................................................... 39 storage of knowledge ................85, 125, 200, 219,
Schön 21, 26, 32, 59, 60, 64, 82, 91, 117, 163 234, 249, 264
Schreuder ............................................. 6, 199, 285 Strassmann ..........................................................13
scientific management ........................................ 71 strategic behavior ............................................. 145
score card .......................................................... 187 Strategic Planning Institute ............................. 151
second order learning ......................................... 61 Strauss 36, 38
selection processes .............................................. 20 subjectivism ............................................ 35, 48, 68
self control .......................................................... 10 Suci 74
semantic analysis ................................................. 74 Sullivan 104
semantic chart ..................................................... 74 superstitious learning ..........................................57
semantics 233 Swierenga ..................................................... 21, 31
semiotics 23 synchronization of understanding .................. 155
Senge 32, 64, 68, 69, 109, 164, 285 syntactics 138, 264
service machine bureaucracies ................94, 99 system archetypes ................................................68
service sector ..................................................... 100 systemic structure ................................................68
service-manufacturing 192, 194, 212, 230, systems
245, 258thinking..................................................66
Shamlin 280 tacit knowledge ...................................................45
Shani 108, 115, 167, 251 Tanenbaum ...................................................... 133
Tannenbaum ...................................................... 74 technological imperative .....................................23
Tardieu 127 technology ....................... 28, 50, 78, 94, 253, 288
task groups ........................................................ 114 Teich 22, 71
Taylor 17, 71, 81 The Bank . . . . . . . . . . . . . . . . . . . . . . . . . . .205
team approach .................................................. 114 the fifth discipline ...............................................67
team learning ...................................................... 67 the learning organization ....................... 32, 64, 91
theoretical constructs ......................................... 33 Weber 22, 36, 89, 93, 116

theoretical model .............................................. 163 Weick 5, 20, 127, 139, 140, 153
theoretical sampling ........................................... 38 Weiner 19, 53
theory of action................................................... 59 Weisner 46
theory-in-use ..................................................32, 59 Weiss 176, 289
model I .......................................................... 61 Whang 6, 137
model II ............................................ 61, 64, 66 Whisler 6
Third World countries ......................................... 5 Wijnhoven ..................................... 25, 70, 83, 140
Thompson........................................................... 84 Wildavsky ..........................................................146
Toffler 60 Williams 6
total quality management ................................. 256 Windmuller ........................................................20
Toyota Company ................................................ 62 Wissema 28
transaction processing systems ......................... 161 Womack 2, 4, 7, 26, 94, 109, 177
transformation 38, 44, 50, 59, 73, 91, 92, 99, 101, 140,
Woodward 177, 226, 246, 274, 279
.......................................................... 90
transformation technology 1, 94, 99, 112, 132,simulation
work 154, 167, 203, 229, 235, 244
................................................ 150
transmission of knowledge and ideas ................ 55 work-in-process (WIP) .......................................255
triangulating...................................................... 139 working definition of organizational learning .......
types of technology ............................................. 99 25, 43
uncertainty .......................................................... 79 Yelle 56, 85
uncertainty avoidance......................................... 51 Yin 38, 175, 208
undirected viewing ........................................... 141 Zahra 90
Ungson 63, 154 Zuboff 1, 55, 121, 136
uni-lateral control .........................................61, 91
universals75
unlearning .....................................................58, 86
use and reuse of knowledge ............. 84, 126, 201,
220, 235, 249, 264
validity of research results .................................. 39
value of MICS .................................................. 149
Van de Bunt ....................................................... 73
Van der Heijden ................................................. 69
Van der Vegt ....................................................... 57
Van Gunsteren .......................................... 58, 199
Van Nievelt ....................................................... 160
Van Rijn 28
Van Schaik .......................................................... 12
variables 175
vector measurement of organizational learning164
Vennix 25
vertical decentralization ...................................... 28
virtual organization ........................................... 115
Vojta 94, 100
volvo's .......................................................... 114
Walker 81, 150, 177
Walsh 63, 154
Wang 91
Warkov 95
waste analysis .................................................... 151
Waterman ......................................................... 265
Samenvatting 291
Samenvatting (Summary in Dutch)
Vooronderstellingen bij deze studie
De afgelopen jaren is een discussie gevoerd over het belang van informatiesystemen
voor ondernemingen (Earl, 1989; Strassmann, 1990). Hierbij stond meestal de vraag
centraal wat informatietechnologische investeringen financieel opleveren. Deze vraag
veroorzaakt verwarring, ten eerste omdat informatie-technologie een zeer ruim begrip
is en ten tweede omdat voor sommige toepassingen van informatietechnologie
kosten-baten berekeningen bijna onuitvoerbaar zijn. Het tweede punt is in dit
onderzoek nader bestudeerd voor de zogenaamde management-informatiesystemen,
in het bijzonder het type management-rapportagesystemen. Dit soort systemen is
reeds veelvuldig ingevoerd in bedrijven, met vaak teleurstellende resultaten. Zij laten
zich moeilijk beoordelen op kosten en baten aangezien zij niet primair de
verandering van kostbare productieprocessen dienen, maar ten dienste staan van de
verandering van intellectuele vermogens van managers. Dit laatst is moeilijk direct te
relateren aan verbetering van kosten-baten verhoudingen van informatiesystemen,
maar is wel essentieel voor de effectiviteit van organisaties. Vanuit deze
probleemformulering is gekozen om management-informatiesystemen vanuit een
organisatorisch leerperspectief te evalueren.
Vervolgens is na gegaan hoe deze probleemstelling geconcretiseerd kan worden.
Gekozen is voor een eerste beperking van het onderzoek tot het gebied van Prestatie-
Evaluerende en Controlerende Systemen (een type management-rapportagesysteem dat in
het Engels Monitoring Information and Control System heet, en hier verder MICS
wordt genoemd). De reden hiervoor is dat met name deze systemen in het verleden
veel kritiek hebben gekregen van organisatie-deskundigen op grond van mogelijk
negatieve effecten van deze systemen op organisatie-leerprocessen. Deze argumenten
zijn echter gebaseerd op slechts summier geformuleerde theorie en empirisch
nauwelijks onderzocht. Voor zover studies op dit gebied bekend zijn (Jelinek, 1979 en
Lee and Guinan, 1991) wijzen de resultaten juist op een positieve bijdrage van MICS
op organisatorisch leren. Deze resultaten worden echter bestreden door enkele
vooraanstaande organisatiedeskundigen (o.a. Mintzberg, 1989 en Argyris, 1971).
Volgens Markus (1983) en Markus en Robey (1988) spelen organisatiecondities een
essentiële rol bij het totstandkomen van positieve of negatieve impact van
informatietechnologie. Het methodologische probleem dat zich dan voordoet is dat de
grens tussen informatiesysteem en organisatie diffuus wordt, waardoor geen
uitspraken meer mogelijk worden over de impact van MICS op organisaties en vice
versa. De gekozen oplossing is om het begrip MICS op te vatten als een set van
formele normen volgens welke informatie zou moeten worden opgeslagen, verspreid en
veredeld. Daarnaast kennen organisaties ook informele normen waarin het MICS is
Samenvatting 293
ingebed, en die behalve bepalend zijn voor de effectiviteit van het MICS, tevens de
leerprocessen in een organisatie vormgeven en sturen.
De organisatieliteratuur levert door de onderkenning van organisatietypen, logische
combinaties van organisatienormen. Door een vergelijking van organisatietypen kan
per organisatietype de invloed van MICS op leren vastgesteld worden. Bij de definitie
van deze organisatietypen is gebruik gemaakt van de configuraties zoals door Mintzberg
beschreven op basis van zijn synopsis van de organisatie-literatuur tot eind jaren
zeventig. Ter vereenvoudiging van het onderzoeksontwerp is alleen gekozen voor de
bestudering van de machine bureaucratie, een configuratie met een simpele en stabiele
omgeving. Hiermee is de tweede beperking van het onderzoeksdomein gegeven. De
keuze voor deze configuratie is ingegeven op grond van de volgende redenen. Ten
eerste wordt de machine bureaucratie door velen beschreven als het prototype van
een slecht lerende organisatie (Argyris, 1971; Senge, 1990). Bestudering van dit
organisatietype moet daarom veel informatie opleveren over leerproblemen in
organisaties. Ten tweede is dit organisatie-type gekozen omdat de omgevingsdynamiek
en -complexiteit van deze organisaties de laatste decennia aanzienlijk is toegenomen,
wat vraagt om verbetering van het organisatorische leervermogen. Hierbij kon ook
aangesluiting worden gevonden bij studies over 'lean'-productie, een machine
bureaucratie-type dat onder toenemende dynamiek en complexiteit is
getransformeerd tot een zeer efficiënte, flexibele en hoge kwaliteit genererende
organisatie (Womack e.a., 1990). De derde reden om machine bureaucratieën te
bestuderen, is gelegen in het feit dat deze organisaties te groot zijn om alleen een
informeel MICS te hebben, waardoor duidelijker de invloed van MICS kan worden
waargenomen.
Dit onderzoek concentreert zich aldus om drie hoofdvariabelen: Machine
Bureaucratieën, MICS en Organisatorisch leren. De eerste twee variabelen zijn reeds
duidelijk beschreven in de bestaande organisatiekundige en informatiekundige
literatuur. De derde variabele is echter bijzonder onduidelijk. Het was daarom
noodzakelijk een uitgebreide literatuurstudie uit te voeren naar het begrip
organisatorisch leren, alvorens criteria voor evaluatie van MICS te formuleren. De
literatuur over organisatorisch leren heeft de gedachte van Argyris en Schön dat
organisatorisch leren uit drie hoofdprocessen aanvaard. Het eerste proces noemen
Argyris en Schön 'single-loop'-leren: het creëren en evalueren van feedback-
informatie, met als doel om bestaande transformatieprocessen te verbeteren. Hierbij
worden de basisdoelen van dat proces niet ter discussie gesteld. Het tweede leerproces
wordt 'double-loop'-leren genoemd: activiteiten met als doel om de basisdoelen of
assumpties van organisatorische processen te evalueren, te vervangen en te
vernieuwen. Het derde leerproces, 'deutero leren', heeft als doel om de organisatorie
(cultuur, managementstijl, informatiesystemen etc.) dusdanig te verbeteren dat haar
leervermogen wordt vergroot. In deze studie is deze terminologie geaccepteerd. De
onderliggende cybernetische gedachte is hiermee tevens aangenomen, met de
aantekening dat organisatorisch leren plaatsvindt in een politieke en sociale context
die bepalend is voor het feitelijke leergedrag en de acceptatie van haar resultaten. Tot
slot is een nadere operationalisering van de begrippen gemaakt, door concrete
activiteiten te benoemen. Deze zijn weergegeven in de onderstaande tabel.
Leerproces Leeractiviteit
Deutero Leren Creatie van identiteits- en leerbeleidsnorm

Bepaling van leerverantwoordelijkheden
Bepaling van actienormen
Bepaling van procedurele normen en MICS
Double-Loop Leren Theorie-ontwikkeling (incl. implementatie)
Afleren
Single-Loop Leren Kennisaanpassing
Kennisverspreiding
Kennisopslag
Gebruik en hergebruik van kennis
Dimensies van Organisatorisch Leren
Overeenkomstig de gedachte dat leren niet alleen een proces is maar ook betrekking
heeft op onderwerpen waarover geleerd moet worden (Kolb, 1984), zijn de volgende
leervelden op basis van Quinn en Rohrbaugh (1983) geïndentificeerd:
transformatieprocessen, mensen, producten en markten.
Het Deutero leerproces is niet bestudeerd in deze studie. Wel is het belang
onderkend van de bepaling van organisatorische leernormen, die bepalend zijn voor de
wijze waarop Single-loop en Double-loop leerprocessen plaatsvinden. Deze
leernormen dienen afgestemd te zijn op organisatorische leerbehoeften, welke
beschreven worden in termen van een combinatie van organisatorische complexiteit
en dynamiek.
Doel en aanpak van het onderzoek
Het doel van het onderzoek is het leveren van een manier waarop MICS-systemen
kunnen worden beoordeeld naar hun waarde voor organisatorisch leren. Hiervoor is
het noodzakelijk te beschikken over een referentiekader waarmee bepaald kan
worden welke variabelen geobserveerd moeten worden, en hoe uit observaties
conclusies getrokken kunnen worden. Een gevalideerd referentiekader, bestaande uit
een theorie over de relatie tussen MICS, organisatorisch leren, en machine
bureaucratieën, ontbreekt echter in zijn geheel in de literatuur. De onderzoeker heeft
daarom zelf zo'n theorie geconstrueerd in twee stappen, namelijk: (1) Een
Samenvatting 295
literatuurstudie, en (2) een empirisch onderzoek gericht op het testen en verder

ontwikkelen van de bevindingen uit de literatuurstudie.
De literatuurstudie leidt ondermeer tot het inzicht dat organisatorische normen en
organisatorische leerbehoeften bepalend zijn voor de wijze van organisatorisch leren
en de rol en waarde die MICS hierin heeft. Machine bureaucratieën hebben tevens
een grote diversiteit aan leerbehoeften en -normen. Met name is dit verschil groot
tussen 'lean'- en 'klassieke' machine bureaucratieën. Daarnaast suggereert de literatuur
over service-industrieën, dat de kosten-baten verhouding van MICS in leerprocessen
bij service-industrieën positiever is dan in productie-organisaties. Aldus moeten
hypothesen over de relaties tussen MICS, organisatorisch leren en machine
bureaucratieën, worden genuanceerd naar de variabelen 'slankheid' (leanness) en
voortbrengingsproces (organizational tranformation). De inzichten uit de
literatuurstudie zijn vastgelegd in Stellingen en Conclusies die gebruikt zijn om het
onderzoek richting te geven. Een meer uitvoerige beschrijving van de stellingen en
conclusies is gegeven in hoofdstuk 7.
Vervolgens zijn de variabelen geïdentificeerd en geoperationaliseerd. Deze variabelen
zijn: Leerbehoefte van een organisatie, Organisatietype, Leernormen (beleid,
verantwoordelijkheden, actienormen en procedurele normen), Beschrijving van
MICS, MICS' role, MICS' waarde (voor single-loop en double-loop leren afzonderlijk)
en de Leerinspanningen van een organisatie (wederom voor single-loop en double-
loop apart). Deze set van variabelen vormen het waarnemingsinstrument dat is
toegepast in het veldonderzoek.
In het veldonderzoek is de eerder genoemde diversiteit aan machine-bureaucratieën
bestudeerd. Deze vier ideal-typische machine bureaucratieën zijn met elkaar
vergeleken. Door de variëteit in organisaties kan tevens het MICS-effect van het
organisatie-effect op organisatorisch leren worden afgezonderd. Tevens is een
beperking opgelegd. Overheidsorganisaties en 'not-for-profit' organisaties (Hofstede,
1981) zijn buiten de beschouwing gelaten. Het onderzoeksontwerp lijkt aldus het
meeste op een semi-experimentele veldstudie, aangezien slankheid en transformatieproces
worden gezien als determinanten voor leerbehoeften, leernormen en MICS-gebruik
en -waarde (vgl. Glaser en Strauss, 1967, analytical induction).
Beschrijving van de gevalstudies
In het aanvankelijke onderzoeksontwerp werd gepleit voor een onderzoekspopulatie

bestaande uit vier machine bureaucratieën: (1) een klassiek productiebedrijf, (2) een
klassiek dienstverlenend bedrijf, (3) een slank productiebedrijf, en (4) een slank
dienstverlenend bedrijf. Vier bedrijven zijn benaderd die op grond van een eerste
inschatting leken te voldoen aan de gestelde eisen. Gedurende de studie bleek echter
dat de vermeende slanke bedrijven niet aan de eisen van een slank bedrijf voldeden.
In feite was de onderzoeker misleid door uitspraken van de eerste contactpersonen
(mensen van de bedrijfsleiding), die slankheid als een ideaal beschreven dat de
onderneming al zou hebben bereikt. Deze verwarring tussen ideaal en actuele
toestand werd helaas pas later ontdekt. Wel bleken beide gevallen achteraf toch zeer
waardevol te zijn, aangezien hierdoor stellingen te formuleren zijn over de overgang
van klassiek naar slank, een leerproces in zichzelf. De onderzoeker was aldus
genoodzaakt een extra gevalstudie (5) uit te voeren naar een bedrijf waarvan met meer
zekerheid gezegd kon worden dat het aan de eisen van slankheid voldeed.
De resultaten per geval worden hieronder kort gegeven.
Geval 1: Een kartonfabricant (Cardboard Co. genoemd) in een stabiele en simpele
omgeving (met een lage leerbehoefte).
Een plakpapierregistratiesysteem (APMS) werd in deze organisatie gebruikt in twee
van haar locaties. In de ene locatie was het succes groot en in de andere locatie
gering. De oorzaak van dit feit lag niet in het systeem (dat was in beide gevallen
identiek), maar in het vermogen van operationele managers om de gegevens uit het
systeem te interpreteren, en op grond daarvan gerichte en cumulatieve verbeteringen
voor te stellen en in te voeren. Essentieel voor het vormen van voorstellen voor
verbetering was kennelijk een goed begrip van hoe in de practijk met het plakpapier
wordt omgegaan. In locatie 1 (het succesgeval) was de operationele manager zelf
jarenlang werkzaam geweest op de werkvloer en kende de (informele)
managementtheorie daardoor van binnenuit. In locatie 2 was de operationele
manager administratief geschoold, en had geen ervaring in het productieproces.
Het succes van het single-loop leerproces in locatie 1 is daardoor meer te verklaren
uit de wijzen waarop gegevens en kennis in actie worden omgezet (actienormen) dan
uit het informatiesysteem en andere leernormen. De bijdrage van het MICS (APMS)
was zeer groot bij het single-loop leren. Locatie 1 bijvoorbeeld, verminderde in twee
jaar tijd het plakpapierverlies van 16% naar 8%, wat een kosten besparing van 1,4
miljoen US dollars per jaar met zich mee heeft gebracht.
Geval 2: Een commerciële Europese bank (The Bank).
Deze bank begeeft zich in een complexere omgeving dan geval 1 (veel producten en
veel verschillende klanten), en de dynamiek vertoont een stijgende lijn (met name
door de toename van concurrentie en verdere liberalisering van de markt). Hierdoor
heeft de bank, tegen onze aanvankelijke verwachting in, een hoge leerbehoefte (score
4 op een schaal van 1 tot en met 4). De bank heeft een zeer diverse en omvangrijke
verzameling van MICS-systemen. Deze systemen zijn echter onsamenhangend,
gebruiksonvriendelijk en hun gegevens krijgen een geringe aandacht bij het
management. Het leerproces vindt daarom minder direct via de systemen plaats, en is
sterk gecentraliseerd bij het hoofdkantoor. Leren vanuit de decentrale kantoren vindt
zelden plaats, en is een moeizame aangelegenheid vanwege de ingewikkelde
communicatiewegen die daarvoor afgelegd dienen te worden. De ineffectiviteit van
deze situatie wordt door het management onderkend, en men is nu bezig met het
veranderen van de procedurele en verantwoordelijksnormen. Tegelijkertijd is een
herautomatiseringsproject gestart dat moet leiden tot een betere samenhang in
Samenvatting 297
systemen, grotere gebruiksvriendelijkheid en de ondersteuning van laterale

communicatieprocessen. Een datanetwerk is ontwikkeld om hiervoor een deel van de
infrastructuur te bieden.
Deze case toont dat de leerbeleids- en identiteitsnormen reeds in overeenstemming
zijn met de toegenomen leerbehoefte van de organisatie. Het belangrijkste probleem
is nu het implementeren van de noodzakelijke nieuwe normen en
informatiesystemen.
Geval 3: Chemisch Productiebedrijf (Chemical Plant).
Dit bedrijf kent een chemisch productieproces dat in eerste instantie complex lijkt
vanwege de grote hoeveelheid productvariaties. Er bestaat echter veel routinekennis
over dit proces, waardoor zich weinig onverklaarbare problemen voordoen. De markt
voor deze producten is wel sterk in beweging, m.n. door de verhoogde concurrentie
uit lage-lonen-landen, die ook steeds betere kwaliteit leveren, en door diverse proces-
innovaties. De hoge leerbehoefte (score 3 op de 4-puntschaal) die hieruit resulteert,
wordt niet opgevangen met specifieke leervermogens. De divisie, waar dit bedrijf een
onderdeel van is, blijft op hoger managementniveau bepalen wat en hoeveel er wordt
geproduceerd. Het bedrijf voert geen eigen beleid en wordt van
omgevingsturbulenties afgeschermd. Het bestaande MICS is integraal, maar levert
weinig zinvolle informatie op. Voorzover problemen worden gesignaleerd heeft de
organisatie weinig middelen om deze op te lossen vanwege de conflictueuse aard van
de diverse managementtheorieën die door verschillende belanghebbenden (m.n.
verkoop versus logistiek en productie) worden aangehangen (dit verschijnsel wordt
model-incompatibiliteit genoemd). De ploegenchefs worden geconfronteerd met deze
tegenstrijdigheden, maar hebben niet het vermogen om ze te overbruggen op het
operationele niveau.
Concluderend kan men stellen dat het rendement van MICS nihil is, en alleen kan
worden vergroot door de ontwikkeling van een nieuwe integrale managementtheorie
die door alle betrokkenen kan worden gedeeld. Dit double-loop leerproces moet nog
aanvangen.
Geval 4: Zorgverzekeraar Health Co.
De Europese zorgverzekeringsindustrie is sterk in ontwikkeling als gevolg van
bezuinigingen in de gezondheidssector, vernieuwingen in de
gezondheidszorgindustrie (nieuwe specialismen en nieuwe producten), vergroting van
het internationale karakter van gezondheidszorg en verzekeringen, en
procesvernieuwingen (o.a. door herontwerp van bedrijfsprocessen en door
toepassingen van informatietechnologie). Health Co heeft een leerscore van 3. Het
bedrijf bestaat sinds de dertiger jaren, en heeft eind jaren tachtig een grondige
verjonging ondergaan. De managementtheorie is sinds die tijd veranderd door dat de
focus is verschoven van routine-afhandeling naar differentiatie in diensten en
kostenreductie. Een van de elementen in de nieuwe managementtheorie is het MICS
dat bij Health Co de prestaties van medewerkers tot op detail meet. De hoeveelheid
gegevens die zo verkregen wordt, is bijzonder groot en Health Co komt er niet aan
toe om ze goed te analyseren. Essentieel is daarom een vergroting van de

informatieverwerkingscapaciteit van het MICS. De huidige handmatige werkwijze is
te kostbaar en biedt onvoldoende meerwaarde voor het leerproces.
Geval 5: Een slanke electronische apparaten-producent (Hitec).
Hitec is een electronische apparaten-producent, en een onderdeel van een divisie van
een Amerikaanse multinational. De markt waarin dit bedrijf opereert wordt
gekenmerkt door korte levenscycli van producten, en bijzonder belangrijke
procesinnovaties (o.a. invoering van robots, nieuwe soldeertechnieken en flexibele
productiesystemen). De leerbehoefte van dit bedrijf is reeds vanaf het begin van de
jaren tachtig zeer groot. Midden jaren tachtig werd het bedrijf met sluiting bedreigd.
Het topmanagement van de divisie heeft vervolgens het bedrijf de kans gegeven om
in afgeslankte vorm verder te gaan, en een nieuwe managementtheorie in te voeren,
gebaseerd op principes van Integrale Kwaliteitszorg en kenmerken van slanke
organisaties. De nieuwe leernormen zijn met succes ingevoerd. Het MICS heeft een
belangrijke rol in het overzien van de kwaliteit in het productieproces, en het
genereren van kwantitatieve en kwalitatieve feedback aan werknemers. Eveneens
wordt feedback van klanten systematisch verzameld en geanalyseerd. Ook hierin heeft
MICS een belangrijke rol. Het single-loop leerproces kan daarom geschetst worden
als tot-in-de-puntjes georganiseerd en uiterst perfect. Het double-loop leerpoces is
echter sterk beperkt vanwege de geringe bevoegdheden die het locale management
hierin heeft. Het divisionele management is ook niet van plan in deze verdeling van
leer-verantwoordelijkheden verandering aan te brengen.
Onderzoeksvragen en antwoorden
1. Wat zijn de basisdimensies van organisatorisch leren? Deze vraag eist

verheldering van het begrip organisatorisch leren. Hierbij is expliciet niet een
psychologische invalshoek gekozen, maar is gekozen voor benaderingen uit de
organisatie-analyse, namelijk: Cybernetica, Organisatie-Ontwikkeling, 'Soft
Systems' Analyse, en Wetenschappelijk Management. Deze benaderingen vullen
elkaar aan op epistemologisch en ontologisch vlak. Het resultaat bestaat uit zes
leeractiviteiten voor Single-loop en Double-loop leren, en de beschrijving van
vier leernormen die deze leerprocessen aansturen en het resultaat zijn van
Deutero-leren.
2. Hoe leren machine bureaucratieën? De resultaten van de veldstudies tonen dat
machine bureaucratieën zeer verschillend leren afhankelijk van hun
leernormen.
3. Leren slanke en klassieke machine bureaucratieën significant verschillend? Deze
vraag poogt te doorgronden of de organisatorische leernormen waarin beide
organisatietypen verschillen, bepalend zijn voor de aard en wijze van gebruik
van MICS in leerprocessen. Hoewel slechts één sterk slanke organisatie is
Samenvatting 299
onderzocht, bleek dat deze toch heel anders leert dan de klassieke machine
bureaucratie. De slanke organisatie heeft een totaal-leervermogen ontwikkeld
(middle-up-down). De drijvende kracht achter het leren in zo'n organisatie zijn
de policy en identity norms. In de klassieke organisaties wordt leren meestal
gedelegeerd aan specialisten en staat vaak los van alledaagse werkprocessen.
Enkele organisaties toonden een overgangstrend, waardoor een aantal leerstadia
konden worden beschreven. De volgende leerstadia kan een organisatie
ondergaan in het leersproces: Gevecht tegen de Chaos, Bureaucratisch Leren,
Expert Leren, Verspreid Leren en tot slot Totaal Leren. Deze indeling wordt
geschouwd als een aantal fasen in een deutero leerproces. Bij iedere fase
behoren andere leernormen.
4. Wat is de invloed van MICS op organisatorisch leren in de machine
bureaucratische context? De invloed van MICS op organisatorisch leren staat
sterk onder invloed van de managementtheorie die wordt gebruikt en de
bestaande leernomen. De managementtheorie vormt enerzijds een kader
waaruit de objecten voor gegevensverzamelingen en -verwerkingen zijn afgeleid.
Anderzijds zijn de managementtheorieën een referentiekader waardoor de
gegevens betekenis kunnen krijgen. In de meeste gevallen vormen de objecten
voor MICS slechts een beperkte afbeelding van de totale (impliciete)
managementtheorie. De niet afgebeelde elementen (tacit knowledge genoemd)
zijn evenwel net zo belangrijk voor de interpretatie van andere
informatiebronnen (Hedlund, 1994). Belangrijker is echter de bevinding dat
managementtheorieën niet altijd door iedereen gedeeld worden, en dat
managementtheorieën een incompatibele relatie tot elkaar kunnen hebben. Dit
doet zich met name voor in de klassieke machine bureaucratieën, waar
verschillende afdelingen verschillende theorieën hanteren, en elkaar soms niet
begrijpen en zodoende niet tot een synthese kunnen komen. MICS wordt dan
alleen gebruikt voor single-loop leerprocessen binnen de normen en het kader
van de afzonderlijke afdeling (suboptimalisatie), of als een wapen voor de
politieke strijd met andere organisatie-onderdelen (dialectisch gebruik). Binnen
slanke organisaties wordt minder nadruk gelegd op
afdelingsverantwoordelijkheden, en bestaat er een duidelijke bedrijfsfilosofie,
die door iedereen wordt geaccepteerd (shared mental model). Hierdoor heeft
het MICS de mogelijkheid om fundamentele organisatieproblemen te
detecteren en als communicatiemiddel te fungeren tussen afdelingen. Indien
belangentegenstellingen zich voordoen, wordt binnen de filosofie naar een
gezamenlijke oplossing gezocht. De resultaten van deze tweede benadering
komen niet alleen op het cognitieve vlak tot uitdrukking, maar ook in een
betere acceptatie en implementatie van nieuwe inzichten, waardoor de kloof
tussen theorie en practijk aanzienlijk kleiner is.
5. Hoe kan men de impact van MICS in de machine bureaucratische omgeving
bepalen? Hiervoor zijn twee meetinstrumenten ontwikkeld, één voor Single-
loop leren en één voor Double-loop leren, waarbij de scores worden bepaald
door scores op de snijpunten van de dimensies leeractiviteiten en leervelden.
Indien leren plaatsvindt op een cel wordt een 1 gescoord, anders 0. Voor het
instrument voor meting Single-loop-leer-inspanningen zijn vier leeractiviteiten
onderscheiden (aanpassing, opslag, verspreiding en (her-)gebruik) en vier
leervelden gemeten (mensen, producten, processen en markten). De waarde van
MICS op Single-loop leren wordt bepaald door per cel aan te geven of de
bijdrage van MICS positief (+1), neutraal (0), of negatief (-1) (belemmering van
leren) is. De maximumscore is aldus +16, en de minimumscore is -16. Voor
Double-loop leren zijn slechts twee activiteiten gemeten (theorie-ontwikkeling
en theorie-verwijdering). Aangezien dezelfde leervelden van toepassing zijn als
bij Single-loop leren, kan hierdoor een maximale score van +8 en een minimale
score van -8 worden gemeten. In dit onderzoek is nergens een geval van een
negatieve score gevonden, waardoor de frekwent geuitte stelling dat MICS een
belemmering is voor het Double-loop leren (stelling S16) moet worden
verworpen. Opmerkelijk was ook dat in de lean case, de scores op Single-loop
leren zeer hoog waren, maar de scores op Double-loop leren niet hoger waren
dan bij de klassieke organisaties. De verklaring hiervoor moet worden gezocht
in de beperkte leerverantwoordelijkheden die de bestudeerde 'lean' organisatie
van het moederbedrijf had gekregen (stelling S27).
Samenvatting 301
About the Author
A. (Fons) B.J.M. Wijnhoven is born in The Netherlands in 1957. He received a

bachelors (candidatum) and a masters degree in political science (major political
research methodology) from the Catholic University of Nijmegen in The
Netherlands. He taught government organization at the School of Public
Administration at the University of Twente. He followed several courses on
information management and read extensively in business administration. He
became assistant professor in information management at the School of Management
Studies of the same university in 1987. He published several papers in English about
information technology impact on organization and management, evaluation of
information systems, organizational learning and knowledge management. He joined
the IFIP 8.2 WG programme committee (Ann Harbor 1994), and reviewed papers
for several international conferences and journals. Besides his work and study, Fons
co-founded a specialized center for epileptic health care, is member of the board of a
local primary school, and takes managerial responsibilities at the University of
Twente. His current research subjects are knowledge management, designing
organizational memory, and he participates in the research management of the
Department. He teaches on information management (overview course) and impact
of information technology. He has a study book about Impact of information technology
in press. He is married to Carolyn Karthaus. Carolyn and Fons have three children,
Kim, Jules and Armelle, of age 11, 6 and 2 respectively.

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Organizational Learning and

Wijnhoven, Alphonsus Boudewijn Jacobus Maria

Publ.: University of Twente, Department of Information Management

Copyright 1995 by A.B.J.M. Wijnhoven, Enschede, The Netherlands

THE CASE OF MONITORING INFORMATION AND CONTROL SYSTEMS IN

ter verkrijging van

Prof. dr P.A.E. van de Bunt, Faculteit der Economishe Wetenschappen en Econometrie,

Chapter 1: Changes in Machine Bureaucracies and the Role of

Chapter 2: Investigating MICS, Machine Bureaucracies and

Chapter 3: Methodology and Research Design ............................................ 31

Chapter 4: Concept of Organizational Learning ........................................ 43

4.4.5 Limitations of the Cybernetic Perspective...............................................58

Chapter 5: Organizational Learning in Machine Bureaucracies ..........89

Chapter 6: Role and Value of Monitoring Information and Control

6.3.1 MICS: The Information Technology Dimensions ................................ 136

Chapter 7: Operationalizations and Method of Analysis ....................... 163

Chapter 8: Case Studies...................................................................................... 189

8.3.7 Role and Value of MICS .......................................................................233

Chapter 9: Conclusions and Discussion ........................................................269

Samenvatting (Summary in Dutch) ......................................................................315

Hengelo, December, 1994

Chapter 1: Changes in Machine Bureaucracies and the Role of

1.1 Trends in Business

Mechanized factories evolved from craft technology in the 18th century,

Trend 1. Quality demands

GM Framingham Toyota Takaoka

Trend 2. Flexibility demands

1955 1973 1986 1989

An important issue for product flexibility is the potential of suppliers to react to

Averages for each region Japanese in Japanese in Americans in All in Europe

Trend 3. Innovation potential

Japanese American European European

Source: Womack et al., 1990, p.118, figure 5.1

1.2 Organizations and Organizational Learning in the Post-Industrial Society

The evidence of section 1 indicates that machine bureaucracies can differ

1.3 Organizational Learning and its Problems in Machine Bureaucracies

Classic machine bureaucracies developed as large, complex organizations, with a high

2. Decentralization of management tasks, so that more people think about the

1.4 The Role of Information Systems

communication (Mumford, 1983; Stamper et al., 1988). Information systems thus

Trends and Learning Issue Learning methods

• Launching new ideas and learning about competencies.

Information technology has a prominent role in many of these learning methods, by

1.4.1 Management Reporting System and Innovation and Control

Self-control to be affected positively by the MRS.

Table 1.7: Control and Innovation Variables.

1.4.2 DSS Impact on Managerial Performance

A DSS is intended to be used by one or several people and contains a computer

1.4.3 Business Value of Computers

A serious attempt to overcome the external validity problems of laboratory studies

$ Millions US Companies Japanese Companies

Strassmann related the return-on-management scores with the amount spent on

1.4.4 Information Systems for Organizational Learning

Operational Work rationalization and routinization to Letter of credit; administrative

into organizations via E-mail, Electronic Conferencing, GDSS, CSCW.

1.4.5 Monitoring Information and Control Systems (MICS)

settled by an overarching idea, power principles govern decision-making (March

1.5 Organizational Learning and MICS

From the discussion in this chapter we conclude that organizational learning is