Structuring Collective Behavior in a Global Information System:
Reaching Toward a New Paradigm of Rationality1
Wita Wojtkowski
Boise State University, USA
Networking, Operations and Information System
wwojtkow@boisestate.edu
Jacek Unold
Wroclaw University of Economics, Poland
Faculty of Management and Computer Science
unold@han.ae.wroc.pl
W. Gregory Wojtkowski
Boise State University, USA
Networking, Operations and Information Systems
gwojtkow@boisestate.edu
Geoffrey Black
Boise State University, USA
Department of Economics
gblack@boisestate.edu
Abstract : This research was supported by a Marie Curie International Fellowship within the 6th European
Community Framework Program.
The research topic we propose to present concerns the issue of collective behavior within a Global
Information System, and a relating question of human rationality. The first issue is still new and unexplored. The
interests in the area of information systems have concentrated mainly on technological aspects. If the human
component was at all taken into account, it has been analyzed from the level of an individual. So have all new
concepts of rationality. However, globalization and virtualization of human activity denote the growth of
dispersed collectivities, the nature of which has yet to be fully comprehended.
The detailed project objectives are included in three basic groups: theoretical, methodological and
empirical, and arise from the following research questions:
1. Is collective behavior a phenomenon of exclusively qualitative nature, impossible to structure?
2. Does irrationality and unpredictability of individual actions determine irrationality and unpredictability
of the whole social system?
3. Is there a research method allowing to identify and analyze a phenomenon of “social rationality”, from
its theoretical to methodological to empirical dimension?
The research method consists of the following stages: identification-analysis-synthesis-exemplificationverification-interpretation-discussion.
Most generally, the method is based on the identification and analysis of the determinants of an IS
(information system) dynamics, and relating this phenomenon to the behavior of a social subsystem of a given
IS, thus to the issue of collective behavior. The point will be to identify a quantitative dimension of this,
otherwise known as purely qualitative, phenomenon.
In the introductory analysis we have found that this social subsystem reveals all four attributes of a
nonlinear, complex adaptive system. The classification of a social subsystem among complex adaptive systems
allows for the application of the most recent achievements of Complexity Theory and Chaos Theory.
A model exemplification of a Global Information System is a modern, electronic stock exchange, because
the performance of capital markets is a typical example of crowd reactions.
The idea of self-organization and emergence can be used to identify and explain the dynamics of individual and
collective behavior. Thousands of independent and difficult to observe transactions, carried out by individual
participants of the market, generate an emergence of specific and predictable patterns of collective behavior.
These phenomena can only be identified on the higher - collective, not individual - level of social organization.
S. Kauffman’s famous phrase “order for free” describes that process of “crystallization”, also known as the
emergence of complexity in complex adaptive systems. The fundamental challenge of this project is finding a
quantitative measure of that emergence.
The project is based in Systems Science, with many multidisciplinary and intersectorial references. Our
intent is to initiate fruitful discussion and possible collaboration with the attendants of the Congress. We propose
that the anticipated research findings may help formulate a new paradigm of rationality that might be important
to ponder in the context of the integration and globalization trends of many modern societies. We express hope
that paying attention to structuring collaborative behavior will help us overcome the social barriers arising from
the historical and cultural differences among nations.
1
This research was supported by a Marie Curie International Fellowship within the 6th European Community
Framework Program.
1. INTRODUCTION: THE RESEARCH TOPIC
One of the main issues both in the theory and practice of Social and Economic Sciences is the
question of integration, i.e., how actions and interactions of individuals lead to the emergence
of phenomena which characterize social entireties. This topic acquires particular importance
in the light of the dynamic integration processes of societies, e.g., the next stage of E.U.
enlargement. The integration processes, aided with the most recent achievements in
information technology, harmonize with globalization and virtualization of human activity,
from social to political to business one.
The outlined issue is a background of two basic research threads proposed in the project’s
title, Structuring Collective Behavior in a Global Information System: Reaching Toward a
New Paradigm of Rationality. The first part refers to the question of human behavior within
an information system (IS). A fully integrated society of the future will make a fundamental,
subjective element of a Global Information System. That Global IS will be either a ubiquitous
and wireless Internet or some totally different, unknown yet, technological platform. And it is
crucial that so far, the interests in the area of IS have concentrated mainly on technological
aspects. The issue of human behavior within an IS has been generally omitted, as one
belonging to other disciplines. Admittedly, since the mid-1990s we have observed some
growth of interest in the domain of social aspects of the IS development2, but those interests
have concentrated on the specificity of individual behavior. However, the nature of global
phenomena and the features of dispersed collectivities denote a necessity of a new perspective
on the society and organization. No longer can we perceive the human component of an IS as
independent individuals. The users of local, regional and global telecommunications networks
create a specific form of a “virtual crowd”, accessing the same sources of information and
reacting to the same sets of stimuli. A pressing need for analyzing social information
processes and discovering the mechanisms of collective behavior has then emerged.
And here we find the other research thread of the proposed title: the issue of human
rationality. The Western organizational culture is still based on the three main determinants:
individualism, competition, and a mechanistic-reductionist perspective. As a result, the
essential body of scientific achievements in the area of human behavior concerns individuals3,
and this is reflected in the paradigm of rationality. This depiction, known as Rational Choice
Theory4, assumes that individuals are perfectly rational, with clearly defined preferences, and
optimizing their behavior at all levels of a decision-making process. Reductionism, which is
related to it, postulates that collective behavior is composed of the sum of rational behavior of
all individuals. Since this “sum” is purely theoretical and abstract, it is generally accepted that
all phenomena concerning a collectivity are exclusively qualitative and cannot be structured.
The deficiencies of the traditional, idealistic approach to rationality have been known and
discussed for a long time. Admittedly, since Simon’s idea of “bounded rationality” it has been
allowed that human actions can be more “satisficing” than “optimizing”, but all new concepts
of rationality still refer only to individual behavior5. At the same time, it has been emphasized
that there is a need for such a formulation of the rationality principle so that it can take into
account the specificity of collective behavior, so different from the individual one.
2
Avison D., Fitzgerald G. (2003): Information Systems Development: Methodologies, Techniques and Tools.
Mc-Graw Hill, New York.
3
Nelson D. L., Quick J. C. (2000): Organizational Behavior: Foundations, Realities, and Challenges. South
Western College Publishing, Cincinnati.
4
Halpern J., Stern R. (1998): Debating Rationality: Nonrational Aspects of Organizational Decision Making.
Cornell University Press, London.
5
Halpern J., Stern R. (1998): Debating Rationality: Nonrational Aspects of Organizational Decision Making.
Cornell University Press, London.
The research project takes up this issue, aiming to model information processes of
collectivity and to structure this phenomenon through the identification of its quantitative
dimension. These findings will help formulate a new, wider approach to rationality, which
could respond to the integration and globalization trends of modern societies.
2. PROJECT OBJECTIVES
An information system (IS) is a set of interacting components: people, data/information,
procedures, hardware, software, and communications6. In another exemplary approach, an IS
is a system which assembles, stores, processes and delivers information relevant to an
organization or to society7. The authors of this definition stress that an IS is a human activity
(social) system which may or may not involve the use of computers. It is evident that,
regardless of an approach, a social subsystem (people) makes a basic, subjective element of
every IS.
The scope of application divides IS into micro- and macroeconomic categories, and the
dynamic growth of the Internet and Internet-based information systems initiated the birth and
development of a Global Information System. Owing to the interest of a social subsystem, a
model exemplification of a future Global IS will be in this project an IS of a present-day
electronic stock exchange, because the performance of financial markets is a typical example
of group (crowd) reactions8.
The issue of human behavior within an IS is the most recent research trend in the
discussed area. In 1994, A. Morita, the founder of Sony Corp., pointed to the constantly
growing gap between the world of business, and generally a society, and the new world of
information technology (“IT/business gap”)9. It was the first human aspect within the area of
IS that was detected so clearly.
The problem identified by A. Morita a decade ago concerned individual attitudes and
actions. This proposal takes up a new and unrecognized issue of collective behavior within an
IS. The importance of this topic results from the fact that collective information processes and
collective behavior compose the basic determinants of an IS dynamics, and this phenomenon
plays a key role in the development and functioning of the Global Information Society.
According to C. Eden and J.C. Spender10, the dynamics of an organization represents changes
in the various types of knowledge, in the learning and unlearning processes. At the same time,
the collective knowledge cannot be understood without paying attention to the communication
processes going on among the group’s members11. It follows that the basic determinants of the
IS dynamics are: knowledge, learning and unlearning, and these phenomena relate to the
information processes of collectivity. The operation of collective information processes
determines organizational learning, which is a useful metaphor describing the way an
organization, also a virtual and global one, adapts to its environment. Obviously, the
individualistic and optimizing approach to rationality has become insufficient in that new,
virtual environment, and one of the biggest challenges in the discussed area is an attempt to
adapt the traditional paradigm of rationality to the new reality.
This research project proposes an innovative approach to the analysis and modeling of
collective information processes and the mechanisms of collective behavior. This is the main
6
Benson S., Standing C. (2002): Information Systems: A Business Approach. John Wiley & Sons, Milton.
Avison D., Fitzgerald G. (2003): Information Systems Development: Methodologies, Techniques and Tools.
Mc-Graw Hill, New York.
8
Plummer T. (1998): Forecasting Financial Markets. Kogan Page limited, London.
9
Morita A. (1994): Made in Japan: Akio Morita and Sony. HarperCollins, London.
10
Eden C., Spender J.C. (2003): Managerial and Organizational Cognition. Theory, Methods and Research.
Sage Publications, London.
11
Weick K. E. (2000): Making Sense of the Organization. Blackwell Publications, New York.
7
objective of this project. This is also the first attempt of this kind in relation to the abovementioned social issues and it should help define a new approach to rationality.
The following research questions have been formulated against the background of the
state-of-the-art:
1. Are information processes of collectivity and the resulting collective behavior, which
make the IS dynamics, phenomena of exclusively qualitative nature, impossible to
structure?
2. Do irrationality or non-rationality, and unpredictability of individual actions determine
irrationality and unpredictability of the whole social system?
3. Is there a research method allowing to identify and analyze a phenomenon of “social
rationality”, from its theoretical to methodological to empirical dimension?
The introductory stage of the research enables to propose the following theses:
1. Collective behavior, which is a basic determinant of the IS dynamics, does not proceed
in a planned and intended manner, but is adaptive and follows certain patterns found in
the world of nature.
2. Collective behavior can be expressed in a model form, which enables to structure this
phenomenon, otherwise considered purely qualitative so far.
3. The identification of quantitative attributes of collective behavior will provide
substantial theoretical and methodological premises for the extension of the
optimizing and individualistic notion of rationality by the social and adaptive aspects.
To solve these issues and prove the proposed theses we have set the following detailed
research objectives:
1. Theoretical objectives, including:
• detailed analysis of the traditional, individualistic and optimizing, approach to
rationality,
• identification of the determinants of rationality in the theory of IS,
• analysis of the theoretical foundations of a model of collective behavior within
an IS,
• proposal of an extension of the traditional, individualistic and optimizing,
paradigm of rationality by a social and adaptive aspect.
2. Methodological objectives, including:
• developing a model of collective behavior within an IS,
• identification of a mathematical dimension of collective behavior.
3. Empirical and utilitarian objectives, including:
• innovative application of a logarithmic spiral, representing the idea of an
isomorphic growth, on the charts of stock indexes, to forecast basic trend
changes on the market,
• generalization of the concept of an isomorphic growth with reference to all
phenomena relating to collective behavior, especially on a global level,
• application of the obtained results to structure forecast and control the social
behavior within a Global Information System, which ought to be a great step
toward an understanding, monitoring, and influencing social processes leading
to the growth of the Global Information Society.
The outlined background and chosen research objectives clearly indicate that the issues
taken up in the project will be the subject of broad multidisciplinary and intersectorial
investigation.
3. THEORETICAL BACKGROUND
The theoretical part of the research will begin with an analysis of the genesis and
evolution of the rationality principle and its influence in micro- and macroeconomic models.
The limitations of the traditional approach to rationality have been discussed for a long time.
One of the research objectives is a detailed analysis of all rationality concepts known in
literature, from those “individualistic-optimizing” to the most recent ones, which try to take
into account social and organizational aspects of human actions. Such a comprehensive
elaboration on the rationality issue has not been found in literature so far. This part of the
study will allow for the identification of the basic determinants of rationality in the Theory of
Information Systems. Owing to the concentration of interests on the technological aspects of
IS, this will be the first attempt to relate the issue of rationality to the IS field.
The next novelty will be the identification of the determinants of an IS dynamics and
relating this phenomenon to the behavior of a social subsystem of a given IS, thus to the issue
of collective behavior. Generally, the point will be to identify a quantitative dimension of this,
otherwise known as purely qualitative, phenomenon.
The research on collective mind and collective behavior will be conducted in a social
subsystem of an IS of a stock exchange. This choice is motivated by the fact that, contrary to
other collectivities, the behavior of a stock market collectivity (investors) is reflected by
relatively simple and concrete indicators, i.e., price changes shown by an index chart and
some “mechanical” indicators of collective activity, such as the volume (the number of shares
changing hands during a session) and the total turnover (money engaged on either side during
a session). In the introductory analysis we found that this social subsystem reveals all 4
attributes of a nonlinear, complex adaptive system. Such a system:
1. Consists of a network of agents (here: investors) acting in a self-managed way without
centralized control.
2. The environment in which the investors operate changes and evolves constantly,
which is the result of continuous fluctuations in economy and the market situation, but
it also is produced by the interactions among the agents.
3. Competition among the agents (investors) leads to a consensus, reflected by a current
market trend.
4. This trend suggests the hidden existence of organized patterns of collective behavior,
which is the result of the emergence of a natural dynamic structure of this social
system.
The classification of a social subsystem among complex adaptive systems allows for the
application of the most recent achievements of Complexity Theory and Chaos Theory.
Complexity Theory can offer a range of new insights into the behavior of social and economic
systems. The idea of self-organization and emergence can be used to identify and explain the
dynamics of individual and collective behavior, e.g., on the stock market. Thousands of
independent and difficult to observe transactions, carried out by individual participants of the
market, generate an emergence of specific and predictable patterns of collective behavior.
These phenomena can only be identified on the higher - collective, not individual - level of
social organization. S. Kauffman’s12 famous phrase “order for free” describes that process of
“crystallization”, also known as the emergence of complexity in complex adaptive systems.
The fundamental challenge of this project will be finding a quantitative measure of that
emergence.
To model and structure the behavior of this complex adaptive system we will use the
elements of Environmental Economics - one of the trendiest areas in Economics. Further
analysis will amplify the approach proposed by F. Capra13, who believed that the new
12
13
Kaufmann S. (1996): At Home in the Universe. Oxford University Press, Oxford.
Capra F. (1995): Turning Point. Ashgate Publishing Group, Aldershot, UK.
paradigm of rationality should take into account the fact that “an economy is a living system,
and one of many aspects of a large ecological and social structure.” This assumption leads to
the notion of an open system (contrary to the Newtonian isolated system) and entropy14. This
enables the introduction of an innovative research concept. The project’s concept is based on
a 3-element system, society-economy-nature, and it replaces the 2-element system, economynature, which has been applied in Economics since the 1960s. The proposed model of
collective behavior will be based on this 3-element system, thus allowing for the identification
of dependencies in its 2-element subsystem, society-nature. This will help identify and
describe phenomena, which are observed in the surrounding world of nature and, to the same
extent, are expected to regulate behavior of the crowd. This is the actual, as opposed to only
formal and declared, introduction of the ideas of open system and entropy to Economic and
Social Sciences.
The initial results point to the possibility of a graphic representation of the analyzed
phenomenon of collective behavior. The most common curve in the world of nature is a
logarithmic spiral, which is isomorphic, self-similar, and based on the Fibonacci ratio
Φ=1.618. The preparatory stage of the research contains the successful application of the
logarithmic spiral to an index chart. Each index chart, according to technical analysis,
represents the crowd sentiment on the market. This is the first attempt of this kind with
reference to the emerging Polish stock market. After the necessary adjustments and tests the
spiral should turn out to be a new and powerful forecasting tool.
The next innovative aspects of the project are revealed with the interpretation of the
identified phenomena, especially with reference to the scientific achievements of Quantum
Mechanics (Heisenberg’s uncertainty principle) and Chaos Theory.
4. RESEARCH METHOD
The outline of the proposed research method can be presented in several basic steps:
1. Identification of the research area, based on the Theory of Information Systems,
Organizational Behavior, Societal Behavior, Complexity Theory.
2. Identification of the mechanism of collective behavior (Organizational Behavior, Theory
of Cycles).
3. Analysis of the identified mechanism (Theory of Cycles, Chaos Theory).
4. Synthesis: a mathematical description of the mechanism (Environmental Economics and
3-element model society-economy-nature).
5. Exemplification and verification of the mechanism (application of a logarithmic spiral
on stock indexes charts).
6. Interpretation and discussion: a new approach to rationality (Theory of Economics,
Rational Choice Theory, Management, Organizational Behavior, Societal Behavior,
Quantum Mechanics).
(1) The identification of the research area, the collectivity of investors, is outlined in B
1.3. Below runs a brief of the remaining stages. We decided to refer the methodological stages
to the key research issues identified in this presentation.
(2) A collectivity is created by information capable of uniting single individuals into a
group. The group, then, lives its own life, a life, which depends on the exchange of
information with the environment. The most significant symptom of this phenomenon is the
collectivity’s fluctuation during this exchange, and it reflects its dynamics. According to the
Theory of Cycles such stable fluctuations between a system and its subsystem can be
presented in a model form as a bounded cycle15 (Fig.1a).
14
15
Gray R.M. (1998): Entropy and Information Theory. Willey & Sons, New York.
Jordon D.W., Smith P. (1999): Nonlinear Ordinary Differential Equations. Oxford University Press, Oxford.
(3) According to Chaos Theory, the bounded cycle is one of the 3 possible forms of an
attractor16. It is also a basic mechanism through which complex adaptive systems react to the
fluctuations of the environment. Because this cycle is stable, it does not represent all adaptive
processes. In reality, the flow of information is not a continuous process. So, when
unexpected information appears (information shock), the collectivity tries to conform to the
new conditions by changing its dynamic structure. It is expressed by a sudden “jump” from
the cycle path. As far as financial markets collectivities are concerned, a jump in both prices
(y) and moods (x) occurs (point A to point B in Fig. 1b). Some time later, the collectivity tries
to return to the basic cycle path and this phenomenon is expressed by a spiral of the
adaptation process (Fig. 1c).
a
b
c
Figure 1. Formation mechanism of a spiral of the adaptation process of collectivity: a) bounded cycle;
b) information shock and “jump” from the cycle path; c) spiral of the adaptation process.
Source: author’s research based on (Plummer, 1998) and (Jordon and Smith, 1999)
(4) The key question is this: What kind of a spiral represents these phenomena (because
there are several different spiral movements)? This base can be found in the world of nature,
because a collectivity also forms a natural system17. Thanks to the proposed 3-element model
(society-economy-nature) we can look for analogies between its two natural subsystems:
society (collectivity) and nature. The most common curve in nature is a logarithmic spiral.
The tail of a comet curves away from the sun in the spiral. Distant galaxies, hurricane clouds,
ocean waves and whirlpools swirl in spirals, as do many other natural phenomena. The
construction of the logarithmic spiral is based on the Fibonacci ratio Φ=1.618, known as the
Golden Ratio or Golden Mean. It defines the ideal proportions.
(5) According to the 3-element model, this natural law, permeating the Universe and
described by the Fibonacci ratio Φ=1.618, should refer to the dynamics of collective behavior
as well. Since adaptations to the exchange of information spiral and financial markets reflect
psychology and the dynamics of the crowd, the spiral identified in price formations also
should be logarithmic. During the introductory stage we confirmed that, indeed, the top of
each successive wave of higher degree on the index chart is the touch point of the logarithmic
expansion (Fig. 2).
16
Peters (1996): Chaos and Order in the Capital Markets: A New View of Cycle, Prices, and Market Volatility.
John Wiley & Sons, New York.
17
Frost A.J., Prechter R. (2001): Elliot Wave Principle. John Wiley & Sons, New York.
Figure 2. Logarithmic spiral on the Warsaw Stock Exchange Index (WIG)
Source: authors’ research
(6) The logarithmic spiral is self-similar and isomorphic. It follows that information
processes of collectivity are also isomorphic. The identification of isomorphism and selfsimilarity in the analyzed system is of great importance in the proposed research procedure.
The spiral in Fig. 1c represents a new, modified form of the attractor presented in Fig. 1a.
This spiral is a metaphorical equivalent of a fractal attractor (strange attractor). This
metaphor has deep theoretical grounds, as a logarithmic spiral actually is a fractal. Most
importantly, however, it suggests the occurrence of a certain form of rationality of collective
behavior (see the sequence: fractal-recurrence-collective mind in B 1.3). Also the identified
phenomena of cyclical recurrence and adaptability can be tentatively recognized as an
expression of collective rationality. Another research thread which will be used to
theoretically explain the identified phenomena and justify the method incorporated in the
project is Heisenberg’s uncertainty principle, with the example of the dual nature of electrons
(unpredictability of behavior on the individual level, predictability on the collective level of
an “electron cloud”).
5. INITIAL FINDINGS AND CONCLUSIONS
The initial observations should be confirmed in the world’s most representative markets and
this will allow to prove the universality of mechanisms controlling the behavior of complex
adaptive systems.
The identification of a fractal attractor (strange attractor) in the model of a social system
carries far reaching theoretical and methodological consequences. It implies self-similarity
and recurrence of system behavior. Recurring patterns of behavior in an organization are
called organizational culture, and the notion of “organizational culture” is used
interchangeably with the concept of “collective mind”18. Thus, the identification of a fractal
attractor in the analyzed social system suggests, on the grounds of Chaos Theory, the
occurrence of rationality of collective behavior and defines the model representation of the
adaptability of collective behavior – a spiral movement. It is worth to stress that the concepts
of collective mind and organizational intelligence add a crucial qualitative dimension to
systems analysis. They add the missing internal social dimension to the technical or
mechanistic dimension, which is the focus of the classical theory of systems and
organizations.
The initial findings suggest that the decision-making process of collectivity is adaptive and
follows specific patterns found in nature. Therefore, unlike the decision-making process of an
individual, this process can be expressed mathematically and ought to be predictable. In other
words, individual behavior, which is often irrational and unpredictable, is expected to
compose an adaptive, spiral and, thus, predictable process of collective decision-making.
If the proposal theses are confirmed, the main scientific result will be the formulation of a
new paradigm of rationality. In the era of globalization and virtualization we shift our interest
from traditionally perceived “physical collectivities” to a “dispersed, virtual crowd”, which is
a totally new social phenomenon. The realization of the project will allow to reach the
theoretical grounds of a new paradigm, which will refer to the behavior of crowd and the
notion of adaptation as a more natural reaction to information stimuli than optimization.
Moreover, adaptation will not exclude traditional optimization. Optimization will remain a
specific case of adaptation, applicable to strictly deterministic decision situations. This way,
the project will contribute to an understanding of mutual interactions between societies and
individuals. It will examine and structure the unique influence that social processes exert on
the decision-making processes of an individual. In this sense we will be able to speak of
system rationality, which should not depend on the rationality or irrationality of the system’s
components.
References
Avison D., Fitzgerald G. (2003): Information Systems Development: Methodologies, Techniques and
Tools. Mc-Graw Hill, New York.
Benson S., Standing C. (2002): Information Systems: A Business Approach. John Wiley & Sons,
Milton.
Capra F. (1995): Turning Point. Ashgate Publishing Group, Aldershot, UK.
Eden C., Spender J.C. (2003): Managerial and Organizational Cognition. Theory, Methods and
Research. Sage Publications, London.
Frost A.J., Prechter R. (2001): Elliot Wave Principle. John Wiley & Sons, New York.
Gray R.M. (1998): Entropy and Information Theory. Willey & Sons, New York.
Halpern J., Stern R. (1998): Debating Rationality: Nonrational Aspects of Organizational Decision
Making. Cornell University Press, London.
Jordon D.W., Smith P. (1999): Nonlinear Ordinary Differential Equations. Oxford University Press,
Oxford.
18
Eden C., Spender J.C. (2003): Managerial and Organizational Cognition. Theory, Methods and Research.
Sage Publications, London.
Kaufmann S. (1996): At Home in the Universe. Oxford University Press, Oxford.
Morita A. (1994): Made in Japan: Akio Morita and Sony. HarperCollins, London.
Nelson D. L., Quick J. C. (2000): Organizational Behavior: Foundations, Realities, and Challenges.
South Western College Publishing, Cincinnati.
Peters (1996): Chaos and Order in the Capital Markets: A New View of Cycle, Prices, and Market
Volatility. John Wiley & Sons, New York.
Plummer T. (1998): Forecasting Financial Markets. Kogan Page limited, London.
Russo N.L., Fitzgerald B., DeGross J.I. (2001): Realigning Research and Practice in Information
Systems Development: The Social and Organizational Perspective. Kluwer Academic Publishers.
Weick K. E. (2000): Making Sense of the Organization. Blackwell Publications, New York.