Enterprise Resource Planning and Business Intelligence Systems For Information Quality (PDFDrive)

Contributions to Management Science
Carlo Caserio · Sara Trucco
Enterprise Resource
Planning and
Business Intelligence
Systems for
Information Quality
An Empirical Analysis in the Italian
Setting
More information about this series at http://www.springer.com/series/1505
Carlo Caserio Sara Trucco
•
Enterprise Resource Planning

and Business Intelligence
Systems for Information
Quality
An Empirical Analysis in the Italian Setting
123
Carlo Caserio Sara Trucco
Faculty of Economics Faculty of Economics
Università degli Studi eCampus Università degli Studi Internazionali
Novedrate di Roma
Italy Rome
Italy
ISSN 1431-1941 ISSN 2197-716X (electronic)

ISBN 978-3-319-77678-1 ISBN 978-3-319-77679-8 (eBook)
https://doi.org/10.1007/978-3-319-77679-8
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To my family
Carlo Caserio
To my Mom and Dad

Sara Trucco
Preface
Nowadays, Information Technology (IT) innovations, the advent of the Internet,

and the ease of finding and sharing information are all elements that contribute to
obtaining overwhelming amounts of data and information. On the one hand,
managers can now easily find and store information, and on the other hand, this
hyper-amount of data does not allow us to distinguish between “good” and “bad”
information. Furthermore, the data and information stored in enterprise databases
may be obsolete, inaccurate, irrelevant, or partial. In other words, companies do not
find it difficult to acquire and store a huge “quantity” of data and information. Their
problem instead is to obtain an adequate level of “quality” of data and information.
The point is that the increased volume of data and information can undermine the
capacity of companies to discern quality from non-quality data and information, and
this difficulty is even more crucial when we consider that we are living in an
information economy where data, information, and knowledge become extremely
strategic for companies. Therefore, the quality of information deserves particular
attention.
Although IT has played a key role in bringing about information overload and
underload, possible solutions to these phenomena are still being sought in the IT
field. Integrated systems, data management systems, data warehousing, data min-
ing, and knowledge discovery tools are some examples of IT solutions that com-
panies are adopting to deal with information overload/underload. One of the most
effective solutions seems to be the implementation of Enterprise Resource Planning
(ERP) systems, which improve data quality, data integrity, and system integration.
In addition to improving data quality and system integration, companies also aim
at improving their capacity to perform data analysis. As a matter of fact, in order to
pursue the objective of improving the quality of information, companies need to
pay attention both to the quality of incoming data and to the capacity to analyze it
and deliver the resulting information to the right person, at the right time. Therefore,
Business Intelligence (BI) systems are another important solution that companies
use to improve their data analysis and processing capabilities and to recognize and
select relevant data for a more effective decision-making process.
vii
viii Preface
This manuscript will examine, through an empirical analysis, the role played by
ERP and BI systems in reducing or managing information overload/underload and
thus in improving the information quality perceived by the Italian manager. The
research is based on the idea that the improvement of information systems,
achievable by means of ERP and BI systems, may reduce or eliminate information
overload/underload. We also investigate whether the combined adoption of ERP
and BI systems is more effective in dealing with information overload/underload
than would be the single adoption of ERP or BI systems. Furthermore, the research
presented in this book examines the influence that ERP and BI systems may have
on the features of information flow—such as information processing capacity,
communication and reporting, the frequency of meetings, and information sharing
—and, in turn, the influence of information flow features on information quality.
The research was made possible by the financial support of the Università degli
Studi Internazionali di Roma (UNINT).
This study is part of a larger project on accounting information systems.
Novedrate, Italy Carlo Caserio

Rome, Italy Sara Trucco
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 A Brief Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Theoretical Contributions of the Present Work . . . . . . . . . . . . . . . 3
1.3 Managerial Implications of the Present Work . . . . . . . . . . . . . . . . 5
1.4 Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Enterprise Resource Planning Systems . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 The Evolution of ERP Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Information Quality and ERP . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Information Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.2 ERP System for Information Quality . . . . . . . . . . . . . . . . . 21
2.4 Critical Success Factor for ERP Implementation . . . . . . . . . . . . . . 23
2.5 Critical Success Factors for ERP Post-implementation . . . . . . . . . 26
2.6 Advantages and Disadvantages of ERPs . . . . . . . . . . . . . . . . . . . 27
2.6.1 Potential Benefits of ERP Adoption . . . . . . . . . . . . . . . . . 27
2.6.2 A Framework for Classifying the Benefits of ERP
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... 30
2.6.3 Potential Disadvantages of ERP Adoption . . . . . . . ...... 31
2.7 ERP as a Driver of Alignment Between Management
Accounting Information and Financial Accounting
Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... 32
2.8 The Managerial Role of the Chief Information Officer . . . ...... 33
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... 34
3 Business Intelligence Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 Business Intelligence and Companies Needs . . . . . . . . . . . . . . . . . 44
3.3 BI for Management Information Systems Needs . . . . . . . . . . . . . . 48
ix
x Contents
3.3.1 Alignment to Group Logics . . . . . . . . . . . . . . . . . . . .... 48

3.3.2 Coordination and Technical-Organizational
Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... 50
3.3.3 Improvement of Data Management and Decision
Support Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3.4 Improvement in Communications . . . . . . . . . . . . . . . . . . . 53
3.4 BI for Strategic Planning Needs . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.4.1 Monitoring of Environmental Signals . . . . . . . . . . . . . . . . 55
3.4.2 Planning and Control Requirements . . . . . . . . . . . . . . . . . 57
3.4.3 Innovative BI Tools for the Adaptation
to Environmental Conditions . . . . . . . . . . . . . . . . . . . . . . 59
3.5 BI for Marketing Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.6 BI for Regulations and Fraud Detection Needs . . . . . . . . . . . . . . . 61
3.7 Critical Success Factors of BI Implementation and Adoption . . . . 62
3.8 BI Maturity Models and Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . 65
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4 ERP and BI as Tools to Improve Information Quality
in the Italian Setting: The Research Design . . . . . . . . . . . . . . . . ... 75
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 75
4.2 Literature Review Supporting the Research Design . . . . . . . . . ... 76
4.2.1 Literature Review on Information Overload
and Information Underload . . . . . . . . . . . . . . . . . . . . . ... 76
4.2.2 Links Between Information Overload/Underload
and ERP Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 78
4.2.3 Links Between Features of Information Flow
and ERP Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 79
and Business Intelligence Systems . . . . . . . . . . . . . . . ... 80
4.2.5 Links Between Features of Information Flow and
Business Intelligence Systems . . . . . . . . . . . . . . . . . . ... 82
4.2.6 The Combined Use of ERP and Business Intelligence:
Information Overload/Underload and Features
of Information Flow . . . . . . . . . . . . . . . . . . . . . . . . . . ... 83
4.2.7 Literature Review on Information Quality . . . . . . . . . . ... 84
4.2.8 Links between Features of Information Flow and
Information Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.3 Sample Selection and Data Collection . . . . . . . . . . . . . . . . . . . . . 89
4.4 Variable Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.4.1 Research Variable Measurement . . . . . . . . . . . . . . . . . . . . 90
4.4.2 Variable Measurement: Control Variables . . . . . . . . . . . . . 94
4.5 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Contents xi
5 ERP and BI as Tools to Improve Information Quality

in the Italian Setting: Empirical Analysis . . . . . . . . . . . . . . . . . . . . . 105
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.2 Descriptive Statistics and Correlation Analysis . . . . . . . . . . . . . . . 106
5.3 Research Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3.1 T-Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3.2 Regression Analysis for Research Variables . . . . . . . . . . . 109
5.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.4.1 T-Test: Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.4.2 Empirical Results for Regression Analysis . . . . . . . . . . . . 117
5.5 Additional Analysis: Empirical Results on the Chief
Information Officer Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.5.1 Regression Analysis for Chief Information Officers . . . . . . 118
5.5.2 Empirical Results of the Regression Analysis
on Chief Information Officers . . . . . . . . . . . . . . . . . . . . . . 118
5.5.3 T-Test: Empirical Results of the Analysis of Chief
Information Officers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.6 Summary Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.6.1 Summary Results for the Entire Dataset
of Respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.6.2 Summary Results for Chief Information Officers . . . . . . . . 130
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.2 ERP, Information Overload/Underload and Features
of Information Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.3 BI, Information Overload/Underload and Features
of Information Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.4 The Combination of ERP and BI for Information
Overload/Underload and Features of Information Flow . . . . . . . . . 137
6.5 Information Quality and Features of Information Flow . . . . . . . . . 137
6.6 Limitations and Further Development . . . . . . . . . . . . . . . . . . . . . 139
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Chapter 1
Introduction
Abstract The manuscript aims at analyzing the role played by ERP, BI systems
and the combined adoption of ERP and BI in reducing or managing information
overload/underload, and thus in improving the information quality perceived by
Italian managers. Furthermore, the manuscript analyzes the effects of information
flow on the perceived information quality. The analysis was carried out through a
survey on a sample of 300 managers who work for Italian listed or non-listed
companies of varying size. The participants—Chief Information Officers, Chief
Technology Officers, Chief Executive Officers and Controllers—were randomly
selected from the LinkedIn social network database, since some scholars have
recently stressed the relevance and widespread use of this social media application.
We received back 79 answers, with a 26% rate of response. A set of regression and
t-test analyses was performed. The main practical implication of our research is that
it helps managers understand the impacts an investment in ERP or BI systems could
have on information management and on the decision-making process. Other
practical implications pertain to the methodology used in our study: in fact, man-
agers may conduct an internal survey similar to that used for this study to assess the
pre-conditions for investing in ERP and/or BI systems by (a) examining the
information quality perceived by employees and managers, (b) analyzing the
employees’ and managers’ perception of information overload/underload, and
(c) investigating the perception of employees and managers regarding the current
IT.
1.1 A Brief Overview of the Book
Nowadays, Information Technology (IT) innovations, the advent of the Internet,

and the ease of finding and sharing information are all elements that contribute to
obtaining overwhelming amounts of data and information. The storage of terabytes
of data and information is becoming commonplace (Abbott 2001), and this huge
volume of easily available information is only apparently a benefit for companies.
In fact, on the one hand, managers can now easily find and store information, and
© Springer International Publishing AG, part of Springer Nature 2018 1

C. Caserio and S. Trucco, Enterprise Resource Planning and Business Intelligence
Systems for Information Quality, Contributions to Management Science,
https://doi.org/10.1007/978-3-319-77679-8_1
2 1 Introduction
on the other this hyper-amount of data does not allow us to distinguish between
“good” and “bad” information. The literature shows that organizations have far
more information than they can possibly use, and at the same time they do not have
the information they would actually need (Abbott 2001; Eckerson 2002).
Furthermore, the data and information stored in enterprise databases may be
obsolete, inaccurate, irrelevant, or partial. In other words, companies do not find it
difficult to acquire and store a huge “quantity” of data and information. Their
problem instead is to obtain an adequate level of “quality” of data and information
(Al-Hakim 2007; Wang et al. 2005). The point is that the increased volume of data
and information can undermine the capacity of companies to discern quality from
non-quality data and information, and this difficulty is even more crucial when we
consider that we are living in an information economy where data, information and
knowledge become extremely strategic for companies (Eckerson 2002).
Therefore, information overload (and underload) deserves particular attention.
Information overload arose in the 1970s as a consequence of the information age
and its widespread use of organizational computing systems (Bettis-Outland 2012).
The initial studies on information overload/underload recognized the lack of rele-
vant information as one of the weaknesses of management information systems
(Ackoff 1967). Other important studies emphasized that information overload
happens every time the quantity of information surpasses an individual’s infor-
mation processing resources, whereas information underload occurs when man-
agers receive less than the amount of information necessary for their job tasks
(O’Reilly 1980). More recent studies confirm that information overload is still a
critical issue affecting decision-making process in several business fields (Soucek
and Moser 2010; Letsholo and Pretorius 2016; Ho and Tang 2001; Rodriguez et al.
2014).
Although IT has played a key role in bringing about information overload and
underload, possible solutions to these phenomena are still being sought in the IT
field. Integrated systems, data management systems, data warehousing, data mining
and knowledge discovery tools are some examples of IT solutions that companies
are adopting to deal with information overload/underload.
One of the most effective solutions seems to be the implementation of Enterprise
Resource Planning (ERP) systems, which improve data quality, data integrity and
system integration. As an example, Markus and Tanis (2000), Rajagopal (2002) and
Karimi et al. (2007) recognize the following benefits from ERP systems:
(1) ERPs eliminate multiple data entry and concomitant errors;
(2) ERPs simplify data analysis;
(3) ERPs improve data integration, since they allow for the management and
sharing of data related to products, services and business activities.
In addition to improving data quality and system integration, companies also aim
at improving their capacity to perform data analysis. As a matter of fact, in order to
pursue the objective of improving the quality of information, companies need to
pay attention both to the quality of incoming data and to the capacity to analyze it
1.1 A Brief Overview of the Book 3
and deliver the resulting information to the right person, at the right time (Agarwal
and Dhar 2014; Herschel and Jones 2005). Therefore, Business Intelligence
(BI) systems are another important solution that companies use to improve their
data analysis and processing capabilities, and to recognize and select relevant data
for a more effective decision-making process.
This manuscript will examine, through an empirical analysis, the role played by
ERP and BI systems in reducing or managing information overload/underload, and
thus in improving the information quality perceived by the Italian manager. The
research is based on the idea that the improvement of information systems,
achievable by means of ERPs and BI systems, may reduce or eliminate information
overload/underload. We also investigate whether the combined adoption of ERP
and BI systems is more effective in dealing with information overload/underload
than would be the single adoption of ERP or BI systems.
ERP and BI systems may play a crucial role in improving the quality of data
management and analysis. The combined use of both ERP and BI systems is
expected to be more effective than the single use of one of them.
Furthermore, the research presented in this book also examines the influence that
ERP and BI systems may have on the features of information flow—such as
information processing capacity, communication and reporting, the frequency of
meetings, and information sharing—and, in turn, the influence of information flow
features on information quality.
1.2 Theoretical Contributions of the Present Work
From a theoretical standpoint, the present work contributes to shedding some light
on:
• The relationship between ERP and information overload/underload and between
ERP and features of information flow. The empirical results of our research
show that ERP systems do not affect the perception of information overload/
underload. However, some effects of the implementation of ERP systems is
recognizable in other items, which are indirectly connected to the quality of
information. For example, empirical results show that respondents adopting
ERP perceive higher data accuracy and system reliability and, in general, a
higher information processing capacity than do respondents not adopting
ERP. Furthermore, the results show that companies adopting ERP have a more
structured reporting system, as information is more frequently communicated on
a monthly or a 6-month basis, with respect to companies that do not adopt
ERP. These perceptions, though probably not connected to the perception of
information overload/underload, indicate that the use of ERP has a positive
impact on information system quality and information quality items. This
supports that part of the literature which supports the idea that ERP improves
data quality, information quality and information system quality in general
4 1 Introduction
(Bingi et al. 1999; Dell’Orco and Giordano 2003; Chapman and Kihn 2009;
Scapens and Jazayeri 2003).
• The relationship between BI and information overload/underload and between
BI and features of information flow. Our results show that respondents adopting
BI systems do not perceive a different level of information overload or under-
load than do respondents who do not adopt BI systems. However, a more
detailed analysis shows that managers of companies adopting BI systems per-
ceive a higher data accuracy, a higher level of information processing capacity,
and a more regular reporting system, based on a systematic monthly frequency.
Furthermore, our empirical results also show that respondents adopting BI
systems perceive a higher information quality with respect to respondents that
do not adopt BI. Therefore, the higher data accuracy and information quality
perceived by BI system adopters can be due to the improvements that BI brings
to the entire data-information-decision cycle. Regarding the perception of
respondents pertaining to the more regular reporting system, this result is
probably an effect of the capacities of BI systems, well-recognized by the lit-
erature, which consists in providing the right information at the right time to the
right person (Burstein and Holsapple 2008). A regular and systematic reporting
system could be, in fact, the effect of an accurate reporting design process
carried out before implementing a BI system. A successful BI implementation
should require managers to define the features of the information and reports
they will need, including the frequency with which they wish to receive them
(Eckerson 2005; Foshay and Kuziemsky 2014; Nita 2015). Moreover, respon-
dents adopting BI perceive a better information processing capacity, due to the
variety of opportunities provided by BI systems regarding data elaboration and
information flow (Boyer et al. 2010; Brien and Marakas 2009; da Costa and
Cugnasca 2010; Smith et al. 2012; Spira 2011).
• The relationship between the combined use of ERP and BI and information
overload/underload and between the combined use of ERP and BI and features
of information flow. The empirical results show that respondents adopting both
an ERP and a BI system do not perceive higher or lower information overload or
information underload than do the other respondents. This is partially aligned
with the literature, which suggests that information problems, caused by a lack
of systematic information collection and processing, make BI tasks more and
more difficult (Li et al. 2009). In other words, this result suggests that in
companies where information collection and processing are not appropriately
managed from the beginning, the potential benefits of BI systems are weakly
perceived or not perceived at all. Interestingly, our results also show that
respondents who have implemented both ERP and BI systems perceive a higher
level of information processing capacity than do respondents who adopt only
ERP or BI. Therefore, despite the fact managers do not perceive that ERP and
BI improve information overload/underload, they recognize that these systems
improve the capacity of the company to process information. Our results are
thus not fully supported by the literature, which suggests that the simultaneous
use of ERP and BI systems should have more of an effect on the information
1.2 Theoretical Contributions of the Present Work 5
flow features than would the single adoption of ERP or BI (Berthold et al. 2010;
Chapman and Kihn 2009; Horvath 2001; Scheer and Habermann 2000).
• The relationship between the information quality perceived by managers and
features of information flow. Our empirical evidence reveals the features which
can affect the information quality perceived by managers. In particular, we
found that information processing capacity and communication and reporting
affect, in different ways, the perceived information quality.
1.3 Managerial Implications of the Present Work
Some implications for practitioners emerge from both the theoretical and empirical
analyses.
The main practical implication of our research is that it helps managers to
understand the impacts an investment in ERP or BI systems could have on infor-
mation management and on the decision-making process. The results of our
research show, in fact, that the use of ERP and BI systems have indirect effects on
information overload and underload.
Our study may also have implications for managers operating in sectors char-
acterized by high uncertainty, since the use of ERP and BI systems is a possible
solution to deal with the ambiguity arising from information overload.
As a consequence, other managerial implications are related to the possibility of
adopting ERP and BI systems to improve information flow, increase information
quality and support strategic decisions.
In addition, further useful insights are provided by our research from a theo-
retical perspective: first, managers could support their decisions to invest in BI
based on the taxonomy of BI needs emerging from the literature and summarized in
this study; second, the understanding of critical success factors for the implemen-
tation of ERP and BI systems may help managers to develop an effective imple-
mentation project; third, the acknowledgement of the effects of ERP and BI on
information quality and on information overload and underload may support
managers in selecting the system they need the most; fourth, the literature analysis
presented in this study may help managers in evaluating the opportunity to maintain
their legacy systems and to invest in ERP or in Extended-ERP and/or in BI systems,
according to their particular needs, characteristics and objectives.
Other practical implications derive from the methodology used in our study: in
fact, managers may conduct an internal survey similar to that used for this study in
order to assess the pre-conditions for investing in ERP and/or in BI systems: (a) by
examining the information quality and the information system quality perceived by
employees and managers; (b) by analyzing the employees’ and managers’ per-
ceptions of information overload/underload; (c) by investigating the perception of
employees and managers regarding the appropriateness of information provided by
the present systems.
6 1 Introduction
1.4 Structure of the Book
The remainder of the book is divided into 5 chapters:

• Chapter 2 deals with the characteristics of ERP systems and their effects on
information quality, according to the literature;
• Chapter 3 refers to BI systems and to the most important aspects which make
these systems crucial for information quality and for companies’ support;
• Chapter 4 presents the research design and the analysis of ERP and BI systems
with respect to information quality;
• Chapter 5 shows the research methodology applied, the empirical analysis and
the results obtained;
• Chapter 6 discusses the results and presents the conclusions of the study.
The conceptual path underlying the structure of the book is to first examine the
characteristics and the usefulness of ERP and BI systems, with the aim of analyzing
their potential capacities to reduce information overload and underload and to
improve information quality. Subsequently, the empirical analysis investigates
whether and how the ERP and BI systems play a role in improving the information
quality for a sample of Italian managers.
Following this path, Chap. 2 analyzes the academic literature on ERP, with
regard to the evolution of ERP systems, which started in the 1960s when the first
reorder point systems were implemented by the companies. Material Requirements
Planning (MRP) and Manufacturing Requirements Planning (MRP II) represent the
next phase in this evolution (Ganesh et al. 2014). In the ‘90s, ERP was born, and
from that year to the present the evolution of ERP has not stopped: Extended ERP
(or ERP II) was developed around the year 2000 and ERP systems based on cloud
computing technologies have been deployed beginning in the 2010s (Chaudhary
2017; Rashid et al. 2002). The evolution of ERP is useful for understanding how
ERP systems have supported, over time, information systems quality and infor-
mation quality. In fact, Chap. 2 also shows that ERP systems can positively impact
information quality in two main ways: first, they are able to directly impact the
quality of information by improving data management and eliminating (or dra-
matically reducing) information redundancy (Sumner 2013); second, ERP systems
are also beneficial to many other characteristics of information systems (Karimi
et al. 2007; Uwizeyemungu and Raymond 2005; Xu et al. 2002), which, indirectly,
impacts the quality of information. Obviously, to obtain these benefits, it is nec-
essary to implement an effective ERP system by following the critical success
factors suggested by the literature. Chapter 2 proposes a list of success factors based
on the main literature, regarding both ERP implementation (Finney and Corbett
2007; Somers and Nelson 2004) and ERP-post implementation (Nicolaou 2004;
Zhu et al. 2010). Finally, Chap. 2 focuses on the managerial role of the Chief
Information Officer, who is responsible for the IT system and the entire information
flow within a firm.
1.4 Structure of the Book 7
Chapter 3 deals with BI systems, trying to follow the complete path of BI

implementation and maintenance, based on the academic literature. Specifically,
this chapter first summarizes the main needs which may lead companies to
implement a BI system; second, it proposes a set of critical success factors which
allow for the implementation of an effective BI system that can satisfy companies’
needs; third, it presents the main maturity models of BI systems by paying par-
ticular attention to the life cycle of BI systems and the need to keep them up do
date. Regarding the first part of the chapter, the summarization of companies’ needs
for BI includes management information system needs (Elbashir et al. 2008;
Levinson 1994; Peters et al. 2016; Rud 2009; Sudarsanam 2003), strategic planning
needs (Alkhafaji 2011; Giesen et al. 2010; Laszlo and Laugel 2000; Malmi and
Brown 2008; Yeoh and Popovič 2016), commercial and marketing needs (Chau and
Xu 2012; He et al. 2013; Olszak 2016; Park et al. 2012), regulation needs (Rutter
et al. 2007; Trill 1993; Williams 1993; Wingate 2016; Yeoh and Popovič 2016) and
fraud detection needs (Bell and Carcello 2000; Dorronsoro et al. 1997; Fanning and
Cogger 1998; Kotsiantis et al. 2006; Ngai et al. 2011). Each category of company
needs is thoroughly analyzed according to the literature and eventually broken
down into sub-needs. The second part of this chapter pertains to the critical success
factors of BI implementation; several authors have proposed a different set of
factors, which allows companies to maximize the effectiveness of BI system
implementation. This part of the chapter takes into account the main critical success
factor studies in the literature and shows the key aspects that a company should
consider for effective BI implementation (Hawking and Sellitto 2010; Vosburg and
Kumar 2001; Yeoh and Koronios 2010; Yeoh and Popovič 2016). Included among
these critical success factors are the ERP systems dealt with in Chap. 2. In addition
to the critical success factors for BI implementation, the literature also recommends
aligning the evolution of BI systems with that of business, considering the life-cycle
of BI models as a driver which affects critical success factors. In this regard, the
third part of the chapter refers to the BI maturity models (Hribar Rajterič 2010;
Moss and Atre 2003; Tan et al. 2011; Watson 2010).
Chapter 4 presents the research design and the research questions of our
empirical research. In particular, we analyze the possible relationships between
ERP, BI and information overload/underload. Furthermore, we investigate whether
ERP and BI systems may also affect information quality by influencing the infor-
mation flow features (i.e., information processing capacity, communication and
reporting, information sharing and frequency of meetings). In fact, the potentialities
of ERP and BI systems may positively contribute to increasing information quality
(and information system quality) by means of an improved management of infor-
mation flow. This quality improvement may indirectly support management in
counteracting, or at least reducing, the information overload/underload. Finally, we
investigate the role played by the features of information flow in improving the
information quality perceived by managers. After presenting the research design,
the chapter describes the sample selection, the data collection, the variable mea-
surement and the factor analysis carried out on the research variables.
8 1 Introduction
Chapter 5 pertains to the results of the empirical research. This chapter presents
the methodology applied to the research, the analyses carried out and the main
results of the research. Empirical results from the entire datasets of respondents
demonstrate that respondents adopting an ERP or a BI system—or both an ERP and
a BI system—do not perceive higher or lower information overload or information
underload. Furthermore, respondents who have implemented an ERP system per-
ceive a higher level of information processing capacity, a higher level of com-
munication and reporting, and a higher level of frequency of meetings than do
respondents who have not implemented an ERP. Respondents who have imple-
mented a BI perceive a higher level of information processing capacity than do
respondents who have not implemented a BI. Respondents who have implemented
both ERP and BI systems perceive a higher level of information processing capacity
than do respondents who have not implemented an ERP or a BI system. Results
from the regression analysis show that information processing capacity has a
positive effect on the information quality perceived by managers; therefore, if the
information processing capacity increases, the information quality perceived by
respondents increases as well. Furthermore, results show that communication and
reporting have a negative effect on the information quality perceived by respon-
dents; as a result, if the communication and reporting increases, the information
quality decreases.
Chapter 6 presents a discussion about the results of the theoretical and empirical
analysis conducted in the manuscript. The chapter also discusses the limitations of
the research and suggests further developments.
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Chapter 2
Enterprise Resource Planning Systems
Abstract The most advanced integrated Information Technology (IT) tools are
represented by Enterprise Resource Planning systems (ERP). These systems can
collect and integrate data using a common database, thereby representing a good
basis for the overall accounting process. This chapter starts with an analysis of the
literature on ERP, with a particular focus on the evolution of ERP systems. The
evolution of ERP is, in fact, useful in understanding how ERP systems may affect,
over time, the quality of information systems and of information. The chapter also
shows that ERP systems can positively impact information quality in two main
ways: first, they can directly impact the quality of information by improving data
management; and second, ERP systems are also beneficial to many other features of
information systems, which indirectly impacts the quality of information. To obtain
these benefits, it is necessary to implement an effective ERP system by following
the critical success factors suggested by the literature. Therefore, the chapter also
proposes a list of success factors based on the main literature, regarding both ERP
implementation and ERP post-implementation. Finally, the chapter focuses on the
managerial role of the Chief Information Officer, who is responsible for the IT
system and the entire information flow within a firm.
2.1 Introduction
The most advanced integrated Information Technology (IT) tools are represented by
Enterprise Resource Planning systems (ERP)1 (Granlund and Malmi 2002). These
systems are able to collect and integrate data using a common database, and thus
1
ERP could be defined as: “enterprise wide packages that tightly integrate business functions into
a single system with a shared database” (Lee and Lee 2000; Quattrone and Hopper 2001; Newell
et al. 2003; Grabski et al. 2011). In a similar vein, Kumar and Hillegersberg defined ERP as:
“information systems packages that integrate information and information-based processes within
and across functional areas in an organization.” Both the aforementioned definitions of ERP
underline the relevance of integrated information across different functional areas of an organi-
zation (Kumar and van Hillegersberg 2000).

https://doi.org/10.1007/978-3-319-77679-8_2
14 2 Enterprise Resource Planning Systems
they represent a good basis for the overall accounting process (Chapman and Kihn
2009).
For their potential benefits, ERPs became popular during the ‘90s in firms all
over the world (Arnold 2006; Sutton 2006). Before that date, companies usually
used different information systems for each functional area within the organization,
which did not allow for an easy and timely exchange of information among
managers. This also discouraged the comparability of accounting information (Rom
and Rohde 2007). To solve these problems and to exploit the potentialities of the
new Information System Integration (ISI), ERPs were introduced especially to
facilitate the exchange of information among managers and, in general, to foster
internal relationships (Davenport 1998a). Therefore, their use is generally justified
by the need to share consistent information across different functional areas of a
company (Robey et al. 2002).
ERP systems play a crucial role in integrating the several business functions and
in improving the quality of data and, thus, of information. Therefore, this chapter
has been divided into seven sections: Sect. 2.2 presents the evolution of ERP
systems, from the first examples of inventory control systems (1960s) to the recent
cloud ERP (2010s); Sect. 2.3 deals with the supporting role of ERP for information
quality; Sects. 2.4 and 2.5 show the Critical Success Factors (CSFs) for ERP
implementation and post-implementation, respectively; Sect. 2.6 highlights the
advantages and disadvantages of ERP systems; Sect. 2.7 illustrates the role of ERP
in aligning management accounting information with financial accounting infor-
mation; and Sect. 2.8 shows the role of the Chief Information Officer (CIO).
2.2 The Evolution of ERP Systems
Enterprise Resource Planning (ERP) systems have evolved from software which
supported companies in Material Requirements Planning (MRP) and
Manufacturing Resource Planning (MRP II). In the ‘60s, only reorder point systems
were developed to support managers in forecasting inventory demand on the basis
of historical data. Attempts to integrate information systems started years before the
birth of ERP; MRP and MRP II, in fact, represent two examples of information
systems integration. MRP was born in the’70s and supported managers in pro-
duction planning and inventory control through a master production schedule and a
bill of materials. Its main aim was to ensure the availability of materials needed for
production, in order to avoid the interruption of the production processes (Sumner
2013). The objectives of MRP are summarized by Ganesh et al. as follows (Ganesh
et al. 2014):
• to ensure the availability of required input material for production;
• to make sure that required products are made from input material and provided
to the customer;
• to maintain an optimal level of investors;
2.2 The Evolution of ERP Systems 15
• to synchronize manufacturing activities with delivery schedules;

• to synchronize purchasing activities with manufacturing activities.
MRP evolved after about ten years into MRP II, which incorporated the financial
accounting system, sales planning functions and customer order processing (Somers
and Nelson 2003). MRP II resolved many of the problems of MRP, which were
mainly due to the latter’s incapacity to manage complex manufacturing business
processes (Ganesh et al. 2014). The main difference between MRP and MRP II is
that the former is a stand-alone software, whereas MRP II is an initial example of an
enterprise-level system aimed at avoiding data duplication by promoting data
integrity and forecast accuracy through customer feedback.
By the ‘90s, the first ERP systems were developed with the aim of integrating
the main business functions and of aligning the business processes to the ERP
software (Brown et al. 2003). For the first time, ERP systems made it possible to
generate a seamless flow of information throughout the company, satisfying not
only the needs of external customers but also those of internal customers (that is,
information users); by doing so, it improved the effectiveness and the timeliness of
the decision-making process (Ross et al. 2003; Ganesh et al. 2014).
From the ‘90s on, vendors added further modules and functions to the basic ERP
modules, thus laying the bases for the “Extended ERPs”, or ERP II (Rashid et al.
2002). By the 2000s, this “extended version” of ERP was made possible also by the
proliferation of the Internet (Lawton 2000), which allowed the integration of ERP
with other external business modules, such as CRM (Customer Relationship
Management), SCM (Supply Chain Management), APS (Advanced Planning and
Scheduling), BI (Business Intelligence), and e-business capabilities (Rashid et al.
2002). The extensions of ERP to CRM and SCM allowed for the effective man-
agement of the relationships among organizations, suppliers and customers, from
the procurement of materials to the delivery of the products, thereby aligning the
supply system with customer demand.
Thus, the evolution from ERP to ERP II has been driven by new business
requirements and new information technologies. The latter do not necessarily
represent an invention of ERP vendors but arise from the market and consist of
single components, such as application frameworks, databases, Decision Support
Systems (DSS), which, once incorporated into the enterprise system, increase
considerably the business benefits (Møller 2005). BI and business analytics are
other examples of IT tools—namely, DSS tools—which have become even more
integrated with the ERP system, as they use ERP data for supporting managers’
decisions. In addition, the eXtended Mark-up Language (XML) has been gradually
implemented in the ERP infrastructures (Møller 2005).
As some studies suggest, ERP II provides benefits to the company only when the
technology available on the market is well integrated in the enterprise system;
hence, it is not sufficient that the technology exists; it also has to be effectively
embedded in the information system (Akkermans et al. 2003; Weston 2003). In this
regard, the definition of ERP II provided by the Gartner Research Group in 2000
states that the extended ERP (or ERP II) is a business strategy and a set of industry
domain-specific applications which create value for customers and shareholders

through collaborative operational and financial processes (Oliver 1999).
A study based on a survey shows that: (a) ERP II increases all the benefits of
ERP, since resources are better managed, and (b) ERP II allows the
decision-making process to be supported even more effectively than would be the
case with a non-extended ERP, as the resources of the whole supply chain are made
available (Wheller 2004).
The innovations explained so far mainly regard the need for data and informa-
tion quality, the integration of ERP with other applications, and the improvement of
the decision-making process. However, more recently, technology has provided
another innovation for managing ERP, which consists in purchasing the system as a
cloud computing service.
Cloud computing is a model of computing which provides access to a shared set
of IT resources by means of the Internet. These resources consist in computer
processing, storage, software, and other services provided in virtualization and
accessible on the basis of an as-needed logic, from any device connected to the
Internet and from any location (Laudon and Laudon 2015).
Cloud computing technology is characterised by the following essential features
(Mell et al. 2011):
• on-demand self-service: consumers can obtain services as needed, automatically
and on their own;
• ubiquitous network access: cloud resources can be accessed through any stan-
dard Internet device;
• location-independent resource pooling: computing resources are assigned to
multiple users, according to their demand. Users do not know where the com-
puting resources are located;
• rapid elasticity: computing resources are rapidly adapted to meet changing user
demand;
• measured service: cloud resource fees are proportional to the amount of
resources used.
Cloud computing consists of three different types of services: Infrastructure as a
Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS), and
it can be private, public and hybrid (Elragal and El Kommos 2012).
Cloud ERP belongs to the SaaS category and allows companies to obtain ERP
services in a cloud environment. The Internet has made it possible to introduce in
the company’s value chain many applications, which are not necessarily owned by
the ERP vendors. Applications, in fact, reside on web servers to which anyone on
the intranet has access using a connected device (from personal computers to
smartphones or tablets). Following this logic, access to the system and to the
information does not imply extra costs, and anyone who needs information can
obtain it with ease. This architecture has advantages also in extending ERP, as it
easily allows for a selective access of suppliers and customers by means of extranets
2.2 The Evolution of ERP Systems 17
Table 2.1 Evolution of ERP 2010s Cloud ERP

(Source authors’ presentation)
2000s Extended ERP
1990s ERP
1980s MRP II
1970s MRP
1960s Reorder point systems and inventory control
or the public Internet (Chaudhary 2017). Scalability, easy upgrades and mobile
access are consequent advantages of this architecture.
Regarding the differences between ERP in cloud and ERP on-premises, some
studies show that cloud ERP requires no capital expenditure and no maintenance
costs, as opposed to on-premises ERP; furthermore, the cloud solution is more
flexible and more easily accessible (Ramasamy and Periasamy 2017).
The disadvantages and concerns regarding cloud ERP are mainly related to:
(1) data security (including privacy issues) and (2) integration. In terms of data
security, business data is likely to be accessed from any smartphone or device,
which potentially compromises data security (Chao Peng and Baptista Nunes
2009). Nevertheless, in this regard data security is completely controlled by the
vendor, as the company only uses the services but does not own the servers where
data is stored, and it has no control over who may access their business data (from
the vendor side) (Peng and Gala 2014). In many cases, the company does not even
know where servers are geographically located and how they are protected; this
lack of transparency may introduce further data privacy concerns. For these reasons,
Service Level Agreements have a crucial role in defining all the conditions, guar-
antees, actions and remedies between vendor and customer (Lenart 2011).
Regarding the second item, integration, it is quite difficult both for companies
and for vendors to customise a cloud ERP and to integrate it with other applications.
For their part, companies have limited control over the cloud and do not have
sufficient freedom and rights to personalize a cloud ERP, whereas vendors, in trying
to make integrations, would have to face the diversity of platforms and technologies
used for developing applications. As a result, until now it has not been feasible for
vendors to customise the ERP package and to provide a seamless integration
between the system and the applications purchased by different client companies
(Peng and Gala 2014).
Table 2.1 summarizes the evolution of ERP over the years, showing how rapidly
information systems innovation is advancing. In fact, over about 50 years, tech-
nology and other drivers such as globalisation, hyper-competition and market
changes have dramatically changed companies’ needs with regard to the integration
of information systems, data storage and elaboration, and decision-making support.
2.3 Information Quality and ERP
The attention paid to information systems quality has gradually increased over time,
given the importance of information systems in providing information to man-
agement. From data acquisition and elaboration to the communication of infor-
mation, several components are involved, since the information system consists of a
set of technical resources, data, people and procedures which interact to produce
information (Kroenke and Boyle 2016) and to generate knowledge (Wijnhoven
2009). The definitions of information systems make it clearly understood that they
are composed of several dimensions. Therefore, the quality of information systems
needs to be assessed through a multidimensional measure, or through frameworks
which take into account the whole set of components (DeLone and McLean 1992).
According to some studies, it is important that managers identify the most critical
aspects of information system quality that can affect the business (Gorla et al.
2010).
The literature provides numerous studies aimed at analyzing how the quality of
information systems could be obtained and measured under different perspectives
and using different methods. The initial studies focused attention mainly on user
satisfaction and system use (Lucas 1978; Ginzberg 1981; Hopelain 1982;
Srinivasan 1985). Following the idea that productivity in the computer context is
related to the sense of satisfaction in using the computer services, some studies
measured user satisfaction through a list of factors identified through a review of the
literature (Bailey and Pearson 1983; King and Epstein 1983), while others focused
on the users’ attitude towards the changes introduced by a system—specifically, by
DSS—to the work environment (Barki and Huff 1985). Barki and Huff discovered
that satisfaction is higher when DSSs bring changes to the work environment as
opposed to when they do not result in substantial changes. Later studies examined
service quality as a driver for information system quality; service quality refers to
the fact that computer users are satisfied only if their expectations meet their per-
ception of the quality they are getting (Pitt et al. 1995); the concept is thus very
similar to that of user satisfaction.
Another study, based on an extensive survey conducted on a sample of 465 data
warehouse users from seven companies, developed a model based on nine deter-
minants of quality in an IT environment, four focused on the output of the system
(i.e., the information quality), and five addressed to the information processing
system needed to produce the output (i.e., the system quality) (Nelson et al. 2005).
It is interesting to note that, according to the authors, information quality—con-
sisting in the accuracy, completeness, currency and format of information—has a
significant role in explaining information system quality—consisting in the
accessibility of the system, its reliability, response time, flexibility and integration;
these nine determinants are also predictive of the general information and system
quality in data warehouse contexts.
Similarly, other studies identified the characteristics that give high quality to an
information system. The literature review conducted by De Lone and McLean
2.3 Information Quality and ERP 19
(1992) identified six factors considered critical for information system quality:
(a) system quality, intended as the information processing system itself; (b) infor-
mation quality, that is, accuracy, timeliness, reliability, completeness, relevance,
precision and currency; (c) information use; (d) user satisfaction; (e) individual
impact; (f) organizational impact. After about 20 years, De Lone and McLean
updated their study, proposing other determinants that can affect information sys-
tem success, divided into four categories: task, user, project, organization (Petter
et al. 2013).
As the more recent literature shows, there is no single determinant which can, on
its own, explain the quality or the success of the information system; instead, it is
necessary to include variables pertaining to the several aspects characterizing
information systems, such as hardware and software quality, service quality,
information quality, communication quality, while also considering that different, or
more specific needs can arise depending on the business and on the evolution of
technology (Xu et al. 2013; Bessa et al. 2016).
Because information systems produce information and knowledge starting from
data and using processing capabilities, the quality of information is related to the
quality of the entire data elaboration process: if the information system allows
companies to acquire and store high quality data (with the support of high quality
hardware), then the processing system will generate high quality information (with
the support of high quality software). This, in turn, will effectively support the
decision-making process, providing a high service quality. These considerations are
recognizable in a wide stream of studies on the role of data and information in
improving the quality of information systems (Redman and Blanton 1997; Kahn
et al. 2002; Pipino et al. 2002; Xu et al. 2002; Madnick et al. 2009). Studies on the
impact of data and information quality have been carried out to promote positive
impacts and provide disincentives to negative ones. Poor data quality, in fact, could
make the retrieval of business records more difficult (Mikkelsen and Aasly 2005),
thereby not allowing the right information to be provided to the right stakeholder.
This misalignment could be even more critical in the performance management
field: as underlined by Redman (Redman 1998), poor data quality can compromise
the achievement of strategic and tactical objectives. Other studies demonstrate that
the quality of the decision-making process depends on the quality of data produced
by the information system (Fisher et al. 2003; Calvasina et al. 2009; Caserio 2011)
and on the coherence between data architecture and business architecture (Vasile
and Mirela 2008). Studies on data quality also involve the Enterprise Architecture
and the IT governance frameworks, both aimed at aligning the information systems
with the business objectives on a strategic level (Schekkerman 2004; Weill and
Ross 2004; Caserio 2017). This is evidence of how important data and information
quality have become, and it explains why companies are investing in IT and
information systems solutions such as ERP and BI systems. The following sections
focus on these issues, in particular on the role that, according to the literature, ERP
systems could play in information quality.
2.3.1 Information Quality
In the field of Management Information Systems, information quality and infor-

mation systems quality are still the most discussed topics. The literature provides
different interpretations of information quality, recognizing that information quality
could be intrinsic, contextual, representational, and related to accessibility (Lee et al.
2002; Zmud 1978; Ballou and Pazer 1985; DeLone and McLean 1992; Goodhue
1995; Wand and Wang 1996; Wang and Strong 1996; Jarke and Vassiliou 1997).
Intrinsic information quality pertains to the accuracy, objectivity and precision of
information; this interpretation derives from the initial theoretical grounds behind
Gorry and Scott Morton’s framework on the accuracy of information for structured
problems (Gorry and Scott Morton 1971a).
The contextual characteristic of information quality refers to the capacity of
information to be relevant, reliable and timely, capable of adding value, useful and
complete. This interpretation refers to information being available in the right
amount, sufficient and informative, and able to create value for the decision-making
process.
The representational characteristic of information quality is related to the
capacity of the information to be understood and effectively implemented in the
decision-making process. Information must be understandable, concise, clear and
meaningful; in other words, it has to be able to represent the problem to which it
refers.
Regarding information accessibility, computer systems must permit an easy and
secure access to the information.
According to other studies, information quality can be defined as the coherence
of information with respect to the specifications of the product or the service to
which it refers and as the capacity to satisfy (or to exceed) consumer expectations
(Zeithaml et al. 1990; Reeves and Bednar 1994; Kahn et al. 2002). Based on this
interpretation, high-quality information provides an accurate representation and
meets the requirements of the final user. Naturally, the coherence and the usefulness
of information also depend on the initial data quality (Piattini et al. 2012).
According to the literature, the quality of information depends on several attri-
butes, divided into three main dimensions (Marchi 1993; O’Brien and Marakas
2006):
• time: the information must be timely, and thus provided when it is needed; it has
to be up-to-date, provided with the needed frequency, and can refer to the past,
present or future;
• content: the information must be accurate, without errors, relevant, complete,
concise; it must also have a scope and be useful in revealing the performance
obtained;
• form: the information must be clear, with the proper detail, ordered in a
sequence as needed, composed of text, images, maps, graphics, etc., as required
by the user in a digital or a printed paper version.
Information quality matters also for economic reasons, as both quality infor-
mation and non-quality information have a cost. The costs of non-quality infor-
mation involve, first of all, a waste of time for people trying to find the most
appropriate information for their needs and to make the most reliable interpretation
of inaccurate information. In addition, inaccurate information may cause several
problems for the business activities depending on the type of error or inaccuracy of
information (which could regard clients, orders, suppliers, internal processes, etc.),
which results in costs. Moreover, data correction, the recovery of process failure,
backup, recovery and other similar activities lead to the consumption of more
computing resources than would be necessary if information were accurate.
Similarly, because of non-quality information, redundant controls on data and
information will need to be activated in order to prevent errors from negatively
affecting the results (English 2002).
2.3.2 ERP System for Information Quality
The implementation of an ERP, when critical success factors are respected (see
Sect. 2.4), has many implications for the information system. As a matter of fact,
ERP is defined by the literature as an information system itself (Sheu et al. 2003; Li
and Olorunniwo 2008; Parthasarathy 2012; Esendemirli et al. 2015). The greatest
benefit of ERP implementation is the reduction of business process complexity,
since ERP aims at integrating business functions, data and processes along the
value chain (Broadbent et al. 1999; Karimi et al. 2007). In most cases, a successful
ERP implementation requires a preventive Business Process Reengineering
(BPR) (Broadbent et al. 1999; Holland and Light 1999; Palaniswamy and Frank
2000; Fui-Hoon Nah et al. 2001) which aims at revising and optimizing the busi-
ness processes. BPR developed in companies with a high business process com-
plexity has more of an impact and is more expensive because of the difficulty in
carrying out standardization (Rosenkranz et al. 2010; Schäfermeyer et al. 2012). In
this regard, Karimi et al. observe that “the higher a firm’s business process com-
plexity, the higher the radicalness of its ERP implementation as a result of its
potential to enable fundamental and radical changes in the firm’s business pro-
cesses and their outcomes” (Karimi et al. 2007: 107). We can consequently deduce
that the higher the business process complexity, the higher the business impact (and
risk of failure) of ERP implementation. In fact, the literature confirms that the
benefits of ERP for information systems can depend on the quality of BPR (Bingi
et al. 1999) and that one of the motivations that lead companies to implement an
ERP is to obtain business process standardization (Al-Mashari et al. 2003). From an
opposing viewpoint, the literature also shows that a more impactful BPR may
engender ERP dissatisfaction (Scheer and Habermann 2000).
In addition to business process complexity, organizational factors play a critical
role in examining the benefits of ERP for information systems. Employees are a
component of information systems, as well as being the end users of ERP; thus, to
obtain information system benefits from an ERP implementation, people should

accept the ERP and recognize its usefulness and support for their tasks. Therefore,
as suggested by many authors, ERP benefits information systems when top man-
agement provides its support (Ein-Dor and Segev 1978; Grover et al. 1995; Grover
and Segars 1996) and mediates between technology and business requirements,
resolving eventual conflicts of interest among stakeholders (Grover et al. 1995).
Furthermore, the alignment between the ERP and the organizational objectives and
needs is a critical condition that enables the ERP to improve the information system
(Cline and Guynes 2001; Gefen and Ragowsky 2005).
Along with business process complexity and organizational factors, another
benefit that the ERP can bring to the information system is the improvement in
information quality. Given the importance recognized by companies and scholars of
the quality of information, the attention paid to the circumstances that may improve
information quality has gradually increased. Moreover, the huge amount of data and
information that companies need to manage has increased the attention on solutions
which could improve the quality of the information system.
ERP systems directly and indirectly support information quality: for example,
they lead to the integrity of the system and permit users to insert data only once (Xu
et al. 2002; Uwizeyemungu and Raymond 2005).
The literature confirms that companies implement ERP systems in order to
resolve information problems related to the legacy systems; in fact, poor produc-
tivity and performance are connected to the poor quality of information, specifically
to the fragmentation of information (Davenport 1998b; Rajagopal 2002).
ERP systems reduce data integration problems as follows (Markus and Tanis
2000a; Rajagopal 2002; Karimi et al. 2007):
(1) by eliminating multiple data entry and concomitant errors;
(2) by simplifying the data analysis;
(3) by managing, integrating and sharing data related to products, services and
business activities that create value.
Data integration improvement allows information to be consistent, thus ensuring
that two (or more) separate systems do not generate two (or more) different versions
of the same information. In other words, data integration allows each decision
maker in the company, and in each subsidiary, to receive the same information; as a
result, the decision-making process is faster (Shanks et al. 2003) and managers can
exchange views on problems and business issues, even when the subsidiaries are
located at a great distance.
As confirmation of this, the relational database on which ERP systems are built
makes information representative throughout the company, which is even more
perceived when a company migrates from legacy systems to an ERP system (Xu
et al. 2002). In fact, legacy systems are built on separate subsystems, and thus the
same data is located in several sources, thereby generating problems of information
inconsistency. The resulting lack of integration makes it difficult for a subsystem to
access data stored in another subsystem and makes the communication between
different subsystems very problematic (Xu et al. 2002).
Literature shows several ERP benefits to the information system, wisely sum-
marized by (Sumner 2013), who recognizes that ERP: (a) allows companies to
move from a stand-alone to an integrated system solution; (b) makes possible a
better internal coordination, particularly among the business functions; (c) improves
the integration of database; (d) allows a more effective maintenance; (e) promotes
common interfaces across the company’s systems; (f) makes information consistent
and available in real-time; (g) introduces a client-server model, more effective than
legacy systems; (h) aligns business processes with an information model; (i) opti-
mizes the number of applications required for managing business functions.
In addition to the benefits which can be obtained by the adoption of ERP, it is
also important to take into consideration other drivers which may lead managers to
implement ERP, specifically (Skok and Legge 2001):
• legacy systems and concerns about the Millennium Bug;
• globalization of the business;
• the more stringent national and international regulatory environment: e.g., the
European Monetary Union;
• BPR and the attention paid to process standardization, such as ISO 9000;
• scalable and flexible emerging client/server infrastructures;
• trend towards collaboration among software vendors.
2.4 Critical Success Factor for ERP Implementation
The first important studies on Critical Success Factors (CSFs) and Critical Failure
Factors (CFFs) of ERP were developed in the US, where the implementation of
ERP occurred for the first time (Wylie 1990); subsequently, several studies have
also been carried out in emerging economies, which has allowed researchers to
draw up frameworks useful in understanding the weight of the several factors. Most
of these studies followed a methodology aimed at: (a) identifying CSFs proposed
by the literature; (b) submitting these CSFs to the attention of experts, professional
operators and users to obtain their judgment; and (c) setting up a sort of ranking
(Ganesh and Mehta 2010; Garg 2010). Other authors have dealt with some of the
CSFs emerging from the literature by examining them on the basis of the industry,
the size of the company and the country (Niu et al. 2011).
The classifications of CSFs proposed by the literature are frequently based on a
study by Davenport (Davenport 1998b), which lays down the first relevant con-
siderations about the complexity of ERP implementation. Markus et al. (2000) also
show the different business strategies to be followed for an effective implementation
of ERP. During the 2000s, two rich literature streams emerged, one aimed at
examining the difficulties in implementing an ERP system and its CFFs (Markus
et al. 2000; Umble and Umble 2002; Gargeya and Brady 2005; Shirouyehzad et al.
2011), the other at identifying the CSFs of ERP implementation (Brown and Vessey
1999; Parr and Shanks 2000; Fui-Hoon Nah et al. 2001; Al-Mashari et al. 2003;
Somers and Nelson 2004; Nah and Delgado 2006; Finney and Corbett 2007). One
of the first studies to summarize the CSFs on the basis of a rigorous literature
review and a cross-sectional analysis carried out on 116 companies was by Somers
and Nelson (Somers and Nelson 2004), which was later adopted as a reference in
several studies. The authors identified 22 CSFs (shown in Table 2.2), whose order
of importance changes according to the phase of ERP implementation (initiation,
adoption, adaptation, acceptance, routinization, infusion).
For example, in the ERP initiation and ERP acceptance phases, the “use of
steering committee” is recognized as the most important factor, whereas, during the
ERP adoption and adaptation phases, the “change management” has the highest
importance. Again, in the ERP routinization phase, the “user training on software”
plays the most important role, whereas in the ERP infusion phase, the most critical
factor is the “use of consultants”.
Another important study, conducted after that by Somers and Nelson, extends
the number of CSFs by identifying 26 items and classifying them into two cate-
gories: strategic and tactical CSFs (Finney and Corbett 2007). Carried out from the
stakeholder perspective, this study underlines the strict connection between
strategic CSFs (e.g., change management) and tactical ones (e.g., how to obtain the
change management). The list of CSFs, collected through an analysis of the liter-
ature, includes the 22 CSFs proposed by Somers and Nelson (2004) and adds some
new aspects, such as the relevance of the implementation strategy, the choice of the
ERP, precautionary crisis management (of the implementation project), and a
preliminary analysis of the existing legacy system.
With regard to the definition of CSFs, some studies have followed a different
approach by identifying CSFs along the various steps of ERP implementation.
However, the results of these studies are very similar to those of Finney and
Corbett. Kronbichler et al. for example, identify CSFs along the three phases of
planning, implementation and stabilization/improvement of an ERP (Kronbichler
et al. 2009). Markus and Tanis considered the factors of success/failure of ERP
implementation, which can occur along one or more of the following implemen-
tation phases (Markus and Tanis 2000b):
• project chartering: that is, the phase in which software, project manager, budget
and scheduling are selected;
• project phase, in which the system is implemented, and thus data conversion is
performed, users are trained, and testing is achieved;
• shakedown phase, where the system begins to run regularly, becomes stabilized,
and is slightly customized;
• the onward-upward phase, consisting of a continuous improvement pursued
through upgrades, the continuous training of users, and the evaluation of
post-implementation benefits.
2.4 Critical Success Factor for ERP Implementation 25
Table 2.2 Critical success factors according to Somers and Nelson

Critical success factors
Use of steering committee Interdepartmental cooperation
Change management Interdepartmental communication
Top management support Education on new BPR
Business process reengineering Dedicated resources
Clear goals and objectives Project management
Management of expectations User training on software
Project champion Vendor support
Project team competence Minimal customization
Partnership with vendor Use of consultants
Use of vendor tools Architecture choices
Data analysis and conversion Careful selection of package
Given the relevance of a successful ERP implementation and the great impact
this has on the business, many studies have focused attention on the Critical Failure
Factors (CFFs): that is, on the main causes of an ERP failure. One of the most
common ideas is that an ERP implementation is likely to fail if its consequences on
the business structure are not accurately evaluated (Markus et al. 2000; Umble et al.
2003).
Analysing the issue in more detail, the causes of the failure could be related to
several aspects, such as ERP software modification: in other words, the tendency of
companies to ask for tailored ERP systems by forcing the vendors to find cus-
tomized solutions which turn out to be counter-productive for an effective func-
tioning of ERP (Shanks et al. 2003). System integration may represent another risk
of failure, as it could lead to technical difficulties related to the integration of the
enterprise software with a package of hardware, software, database management
systems and telecommunications systems appropriate to the size, structure and
geographical dispersion of the company. Furthermore, companies may need to keep
legacy systems which perform operations not included in the ERP package (Tsai
et al. 2005); these systems have to be interfaced with ERP and could give rise to
some complications (Yeo 2002; Shanks et al. 2003; Umble et al. 2003). Other
problems could be due to the coordination of the several firms involved in the
implementation process (applications developers, ERP vendors, vendors of ERP
extensions) and to the turnover of project personnel possessing the necessary skills
for managing ERP system (Shanks et al. 2003).
Other failure factors are related to the shakedown and the onward-upward phase.
Regarding the shakedown, the most important problems are due to the imple-
mentation of ERP following an excessively functional perspective, a scarce defi-
nition of project scope, a poor consideration of end-user training needs, testing
aspects, and problems concerning data quality and reporting needs.
Regarding the onward-upward phase, failure factors are mainly due to the lack of
knowledge of the effects ERP investment has on business results, to the lack of
end-user knowledge of the new system, and to the difficulties related to the upgrade
and maintenance of the ERP system (Shanks et al. 2003). Post-implementation of
ERP thus deserves special attention, since it influences the long-term success of the
ERP system.
2.5 Critical Success Factors for ERP Post-implementation
The implementation stage of ERP has been largely studied by scholars and with
different perspectives. The life of an ERP starts with its adoption and ends when the
ERP has been replaced by a new one (Markus and Tanis 2000b).
One of the most relevant research perspectives is that related to critical success
factors for the implementation of ERP systems. The post-implementation stage
encompasses a number of activities which are pivotal for the success of ERP
implementation (Gelinas et al. 1999). Therefore, the post-implementation success of
ERP is a complex topic due to several dimensions such as organizational perfor-
mance and the financial return on investment in ERP (Sedera and Gable 2004).
An ERP may be considered successful if it can improve the overall performance of
a firm by reducing organizational costs, increasing the firm’s productivity,
increasing employees and customer satisfaction, and so on (Sedera and Gable
2004).
The success of the post-implementation process is heavily affected by the quality
of the phase of ERP implementation itself and by its effectiveness in carrying out
changes and improvements in processes, systems, and the overall performance of
the firm (Nicolaou 2004a). In particular, Zhu et al. argue that the quality of
implementation and organizational readiness affect post-implementation success
(Zhu et al. 2010).
Furthermore, successful business process changes can be considered as facili-
tators for achieving post-implementation performance gains (Guha et al. 1997).
Nicolaou (2004a) associated the critical dimensions of success in
post-implementation with the critical success factors of ERP implementation. The
author identified the following critical success factors for ERP implementation:
(1) top management support and commitment to project and fit to business strategy;
(2) the alignment of people, process, technology; (3) anticipated benefits from the
ERP implementation project; (4) the motivation behind ERP implementation; and
(5) the scope of user training. The author argues that the first factor can be linked to
the following dimensions of success in post-implementation: “evaluation of fit with
strategic vision; review of project planning effectiveness and evaluation of infras-
tructure development”. The second one can be linked to the following dimensions
of post-implementation: “review of fit resolution strategies; evaluation of system
integration attainment and reporting and flexibility”. The third factor can be linked
to “evaluation of level of attainment of expected system benefits”. The fourth factor
can be linked to “review of driving principles for project and review of project
justification practices”. Finally, the fifth success factor may be linked to “review of
2.5 Critical Success Factors for ERP Post-implementation 27
user learning and evaluation of effective knowledge transfer (among project team
members and other users)” (Nicolaou 2004a).
2.6 Advantages and Disadvantages of ERPs
This section analyzes potential benefits and disadvantages that may arise from ERP
adoption within a firm. The literature has focused particular attention on the effects
ERP adoption could produce on both financial and non-financial performance ratios
(Sect. 2.6.1). Section 2.6.2 presents a discussion about the framework that can be
used to classify the potential benefits of an ERP system. Finally, Sect. 2.6.3 ana-
lyzes the potential disadvantages linked to adopting an ERP system.
2.6.1 Potential Benefits of ERP Adoption
The literature about the potential benefits of adopting an ERP has focused on the
effects this could produce on both financial and non-financial performance indi-
cators. Some scholars have even analyzed this topic by referring to tangible and
intangible benefits (Markus et al. 2000; Nicolaou 2004b; Fang and Lin 2006;
Florescu 2007; Skibniewski and Ghosh 2009; Trucco and Corsi 2014).
The main studies focusing on the effects ERP adoption could produce on
financial performance were carried out by Poston and Grabski 2001; Hunton et al.
2002; Hitt et al. 2002; Nicolaou 2004a. These authors found that the introduction of
an ERP can produce important effects on the following financial performance
indicators: (1) Return On Assets (ROA); (2) Return On Investment (ROI);
(3) Return On Sales (ROS); (4) Cost of Goods Sold over Sales (CGSS); and
(5) Employee to Sales (ES). Although they found controversial results, even if they
used a similar method to carry out their studies, they all agreed that ERP adoption is
able to produce all its effects after a certain time-lag (Poston and Grabski 2001; Hitt
et al. 2002; Hunton et al. 2003; Zaino 2004; Nicolaou 2004b).
In particular, Poston and Grabski examined the effects of ERP adoption over a
three-year period, finding no significant improvements in the main key financial
performance indices. However, they found an improvement in the cost of goods to
revenue three years after the ERP system implementation (but not in the first or
second year after implementation). They also found a significant reduction in the
ratio of employee to revenue for each of the three years they examined (Poston and
Grabski 2001).
Nicolaou examined the effects of an ERP on the financial performance of a firm
over four years after implementation. He found that ERP benefits on the firms’
financial performance became evident and strong only after a lag of approximately
two years from ERP implementation, and therefore after two years of continued use
of ERP (Nicolaou 2004b). Hitt et al. found that firms that invest in ERPs have a
higher performance in several ratios than firms with no investment in ERPs. In

particular, they found that the ERP implementation phases take between one and
three years, and the first significant benefits appear on average after 31 months.
Some authors noted that in the first period of ERP implementation there is a
discordant trend between internal and external performance indices; as a matter of
fact, there is a reduction in performance ratios and in productivity and an increase in
stock market evaluation (Hitt et al. 2002). In a similar vein, other authors argued
that the market expects that an ERP implementation may allow firms to improve
their competitive advantage (Stratman 2007). Hunton et al. examined the effects of
ERP adoption on financial performance over a period of 3 years after ERP
implementation, confirming the productivity paradox by comparing firm perfor-
mance of adopters with those of non-adopters. They found that the financial per-
formance for ERP adopters does not change significantly from pre- to
post-adoption, although some performance ratios decline for non-adopters over
the same time-period. They also found that large/unhealthy adopters can expect
greater performance gains than can large/healthy adopters, and that the small/
healthy adopters have better performance in terms of ROA, ROI, and Return On
Sales (ROS) than do small/unhealthy adopters (Hunton et al. 2003). Zaino found
that 60% of firms have financial benefits from ERP implementation, whereas the
remaining 40% have a reduction in ROI (Zaino 2004). Other scholars have
examined the immediate after-effects of ERP adoption, finding that investments due
to ERP implementation might lead to productivity and profitability problems. These
problems can be linked to a change in management during the implementation
phases (Davenport 1998a; Hitt et al. 2002).
More recently, other scholars have studied the potential benefits ERP imple-
mentation may have on non-financial dimensions (Fang and Lin 2006; Qutaishat
et al. 2012; Trucco and Corsi 2014). Fang and Lin investigated Taiwan public firms
that adopted the ERP system to evaluate the effects on non-financial measures by
exploiting the balanced scorecard and the dimensions of the balanced scorecard
(financial, internal process, customer, innovation and learning). The authors
examined whether different corporate ERP aims may affect performance after ERP
implementation. The corporate aims of ERP adoption that they analyzed were
re-engineering processes, performing supply chain management, implementing or
supporting e-commerce, integrating ERP with other business information systems,
reducing inventory costs, changing the existing legacy system, favoring the com-
petitiveness of multinational enterprises, enhancing enterprise images, developing
e-business. They found through a regression analysis that the balance scorecard’s
financial perspectives are closely related to non-financial perspectives (Fang and
Lin 2006). Qutaishat et al. (2012), through users’ interviews, underlined that ERP
adoption could produce benefits in terms of customer satisfaction and employee
productivity (Qutaishat et al. 2012). Trucco and Corsi found that ERP adoption can
produce benefits for the classical financial indicators in terms of ROE and ROI, and
for non-financial ratios such as corporate governance and social and organizational
aspects (Trucco and Corsi 2014). In particular, they found a general benefit from
ERP implementation to the corporate governance score in terms of a company’s
2.6 Advantages and Disadvantages of ERPs 29
systems, processes and management practices. Furthermore, they found an

improvement to a social ratio, which summarizes if and how the company describes
the implementation of its training and development policy. Results from the study
by Trucco and Corsi are in line with other studies that point out that ERP adoption
can bring some improvements to social ratios linked to customer satisfaction and
employee productivity (Markus et al. 2000; Cotteleer and Bendoly 2002; McAfee
2002).
Moreover, prior studies have investigated the market reaction to ERP imple-
mentation announcements, finding that stakeholders perceive the potential advan-
tages of a new ERP system (Wah 2000; Hayes et al. 2001; Hunton et al. 2002).
Specifically, Hunton et al. (2002) found that analysts reacted positively to ERP
announcements. In fact, they found that analysts who participated in the experi-
mental study perceived that a firm may have some benefits due to the use of an
integrated Information Technology (IT) system. Even if most scholars agree that an
integrated ERP produces its effects on financial statement disclosure and has
advantages regarding accounting information, most of the literature focuses on the
external perceptions (analysts and external users at large). Furthermore, Hunton
et al. (2002) have pointed out that one of the main limitations of their study is its
external validity, since they based their results on laboratory experiments.
Therefore, they call for more research regarding the potential quality improvements
correlated to ERP adoption (Hunton et al. 2002).
Another stream of literature on ERP has investigated the complex relationships
between ERP and management control systems (Maccarone 2000; Booth et al.
2000; Granlund and Malmi 2002; Shang and Seddon 2002; Hartmann and Vaassen
2003; Caglio 2003; Scapens and Jazayeri 2003; Dechow and Mouritsen 2005;
Sangster et al. 2009; Chapman and Kihn 2009; Granlund 2011; Kallunki et al.
2011). Most of the above-mentioned literature agrees that ERP systems can produce
their effects on the organization as a whole. In this regard, Shang and Seddon
(2000) emphasized that managerial benefits may arise from a better planning and
management of resources, whereas Maccarone (2000) identified two main classes
of benefits produced by adopting an ERP: (1) a reduction in the time needed to
perform managerial activities, and (2) an improvement in the quality of data and
control activities at large. Sangster et al. (2009) carried out a survey using a
questionnaire addressed to 700 management accountants in large UK firms to
identify the effect of the perceived success of ERP implementation upon the role of
respondents, finding that ERP generally improves the quality of the role of man-
agement accountants if ERP adoption is successful.
Even if most scholars have emphasized the positive, even small, correlation
between the use and implementation of an ERP within an organization and man-
agerial controls (Quattrone and Hopper 2001; Spathis and Constantinides 2004;
Kallunki et al. 2011), others have found a quite limited impact on the improvements
in management control systems and practices due to ERP adoption. Booth et al.
(2000) examined the Chief Financial Officers’ (CFOs) perception about the impact
of ERPs on the adoption of new accounting practices, finding little evidence.
Table 2.3 Literature review on the potential effects of ERP adoption (financial and non-financial
dimensions)
Dimensions Main items in each dimension Literature streams
Financial ROA, ROI, ROS, ROE, Cost of Hitt et al. (2002), Hunton et al.
goods sold over sales, Employee to (2003), Nicolaou (2004b), Poston
sales and Grabski (2001), Zaino (2004)
Non-financial Social ratios, corporate governance, Cotteleer and Bendoly (2002),
customer satisfaction, employee Markus et al. (2000), McAfee (2002),
satisfaction, employee productivity, Fang and Lin (2006), Florescu
internal process, innovation and (2007), Markus et al. (2000),
learning Nicolaou (2004a), Skibniewski and
Ghosh (2009), Trucco and Corsi
(2014)
Specifically, they found that ERPs seem to open the way to data manipulation rather
than lead to an easier collection and elaboration of management data.
According to other scholars, resistance to change on the part of controllers and
the time lag between ERP adoption and the related effects on management control
systems have a limited impact on the success of ERP (Granlund and Malmi 2002;
Scapens and Jazayeri 2003).
Table 2.3 summarizes the literature review on the potential effects of ERP
adoption (financial and non-financial dimensions).
2.6.2 A Framework for Classifying the Benefits of ERP

Systems
Some authors have identified a framework to classify the potential benefits that ERP
adoption can have on the financial and non-financial performance of a firm. In this
regard, Shang and Sheddon (2002) proposed five dimensions to classify the benefits
of ERP systems: (1) operational dimension; (2) managerial dimension; (3) strategic
dimension; (4) IT infrastructure dimension; and (5) organizational dimension
(Shang and Seddon 2002). The operational dimension refers to business processes
and operation volumes (Brynjolfsson and Hitt 1996; Weill and Broadbent 1998).
Within this dimension, an ERP adoption can bring about the following classes of
benefits: (1) cost reduction; (2) cycle time reduction; (3) productivity improvement;
(4) information quality improvement; and (5) customer service improvement. The
managerial dimension pertains to senior managers of information systems (Gorry
and Scott Morton 1971b). Within this dimension, an ERP adoption can bring about
the following classes of benefits to the firm: (1) better resource management;
(2) better decision-making and planning; and (3) better performance. The strategic
dimension is related to competitive advantages (Porter and Millar 1991). Within
this dimension, an ERP implementation can produce the following benefits for the
firm: (1) strategic business growth plan; (2) support business alliance; (3) support
2.6 Advantages and Disadvantages of ERPs 31
business innovation; (4) support cost leadership; (5) support product differentiation;
and (6) support external linkages. The IT infrastructure dimension refers to the
architecture of the IT and produces the following benefits: (1) increased business
flexibility; (2) IT cost reduction; and (3) increased IT infrastructure capability. The
organizational dimension refers to organizational behavior (Baets and Venugopal
1998). Within this dimension, an ERP implementation can produce the following
benefits: (1) support organizational changes; (2) facilitate business learning;
(3) empowerment; and (4) build a common vision.
Within this framework, Gattiker and Goodhue proposed a model in which they
identified the following organizational benefits due to the ERP implementations:
better information quality, more efficient internal business processes, and better
coordination among different units of the firm (Gattiker and Goodhue 2005).
Similarly, Markus et al. proposed different dimensions to analyze the benefits of
ERP implementation, including economic, financial and strategic business ratios;
business process aspects; the organization’s managers; employee and customer
aspects; and supplier and investor dimensions (Markus et al. 2000).
2.6.3 Potential Disadvantages of ERP Adoption
Despite these considerations about the potential positive effects of ERP adoption,
some scholars have found some disadvantages linked to a new ERP.
Brazel and Dang (2005) found a decreased reliability of financial statements for
external users in the years following the adoption of ERP; they measured this
reliability through the value of discretionary accruals. According to their frame-
work, a loss of financial statement reliability could happen because of a potential
increase in the discretion managers have in the use of accounting information
(Brazel and Dang 2008). In fact, ERPs allow managers greater access to and control
over financial information (Dillon 1999).
Furthermore, Davenport and other scholars have revealed the disadvantages,
risks and costs related to ERP adoption (Davenport 1998a). Some authors have
stressed the potential risks that accounting integration due to ERP adoption could
bring to the company. As a matter of fact, even if the ERP system is perceived as a
strategic investment within the firm (Cooke and Peterson 1998), the most relevant
risk related to this strategic investment is the failure of ERP implementation, which
could even lead to firms’ bankruptcy (Davenport 1998a; Markus et al. 2000).
Despite this, an Advanced Market Research (AMR) revealed that firms invest huge
amounts of money in ERP (around $79 billion in 2004), even if some implemen-
tations have failed (Carlino et al. 2000). Some scholars have estimated that only
34% of ERP implementation projects are successful (Nelson 2007). Costs which are
associated with a new ERP may include the purchase of software, hardware, net-
work investments and consulting fees (Beheshti and Beheshti 2010).
Furthermore, an ERP could be viewed as a limitation on the discretion of
managers in changing managerial controls in the future, since it is difficult to
forecast the long-term implications of ERP during its initial phase of implemen-
tation. To overcome this limitation a possible, but not sufficient, solution could be
to adopt a strategic and long-term vision during the ERP implementation phase
(Grabski et al. 2001; Quattrone and Hopper 2001; Grabski et al. 2011).
Hendricks et al. have argued that any or only a few of the financial benefits due
to an IT adoption could depend on high implementation costs (Hendricks et al.
2007). Costs are both monetary and relative to the human resources required to
implement and manage the ERP system and its integration within the organization
(Granlund and Malmi 2002). However, researchers agree that a holistic view of the
effects of ERP implementation is necessary (Jarrar et al. 2000; Markus et al. 2000;
Gattiker and Goodhue 2005), since the long and deep process of ERP adoption
affects the whole organization (Rose and Kræmmergaard 2006).
2.7 ERP as a Driver of Alignment Between Management

Accounting Information and Financial Accounting
Information
Another stream of literature has pointed out the important role that ERP can have in
fostering the relationship between external financial information2 and internal
managerial information3 (Innes and Mitchell 1990; Caglio 2003; Taipaleenmäki
and Ikäheimo 2013). Some authors have argued that an ERP may represent a
facilitator, motivator, even an enabler for the convergence between financial
accounting and management accounting4 (Innes and Mitchell 1990; Cobb et al.
1995; Booth et al. 2000; Lukka 2007). Booth et al. (2000) asserted that IT can set
the premises for high levels of information integration.
2
Financial accounting information is the product of corporate accounting and external reporting
systems that measure and disclose quantitative and qualitative data concerning the financial
position and the overall performance of the firm (Bushman and Smith 2001). Further, financial
accounting information, whose main purpose is to meet the information requirements of external
stakeholders, also fulfils the internal need of a company to correctly disclose information to the
market about its performance, thereby reducing uncertainties for investors and, consequently, the
cost of capital (Lambert and Verrecchia 2014).
3
Management accounting information can be defined by Anthony as the flow of information used
by management for internal purposes such as planning and control (Anthony 1965).
4
The two areas of financial accounting information and management accounting information
represent together the accounting; the existence of financial accounting and management
accounting information tends to create two different circuits of information within a firm
(Popa-Paliu and Godeanu 2007; Taipaleenmäki and Ikäheimo 2013). Even if some authors have
pointed out that, in academia and from a theoretical viewpoint, there is a deep distinction between
financial accounting and management accounting, they have highlighted that there are some
practical overlapping areas between the two, which need to be explored and identified (Lambert
2006).
2.7 ERP as a Driver of Alignment Between Management Accounting … 33
Taipaleenmäki and Ikäheimo state that ERP systems could be a useful basis for
changes in the accounting system. As a matter of fact, they assert that integration
between financial and management accounting information could be linked to the
contemporary need to understand ERP systems and to decrease accounting
resources (Ikäheimo and Taipaleenmäki 2010; Taipaleenmäki and Ikäheimo 2013).
In a similar vein, Caglio (2003) has theorized deep changes in accounting
practices due to the introduction of an ERP system, and in this regard he has
introduced a new hybrid figure of a manager who is somewhere between a financial
accountant and the other professional managers, confirming, through a case study,
the pivotal role of ERP in removing the barriers between financial accounting and
management accounting. Within this framework, Trucco found that the high level
of integration in ERP improves the level of integration of accounting systems
(Trucco 2014, 2015).
2.8 The Managerial Role of the Chief Information Officer
The managerial role of the Chief Information Officer (CIO) was introduced in the
1980s, even if his or her tasks have increased in recent decades (Grover et al. 1993).
The CIO label was created in order to recognize that the Information Systems
function had become critical in many firms (Earl 1996).
The CIO is responsible for the enterprise IT system; indeed he/she covers
technical and organizational areas, such as as IT-business alignment (Gottschalk
1999), IT investment decisions (Earl and Feeny 1994; Mithas et al. 2012), and IT
system quality improvement and evaluation (Spewak and Hill 1993). Therefore, a
CIO should ensure a cost-efficient enterprise IT system in order to create
long-lasting value within the firm in which he/she operates (Gottschalk 1999; Li
and Ye 1999; Sobol and Klein 2009; Lunardi et al. 2014). The CIO is thus a
member of the firm’s C-level executive team, assuming a strategic role and affecting
the organizational and financial performance of the whole firm (Peppard 2007).
Furthermore, the market positively perceives the presence of the new CIO and the
appointment of the CIO as a firm’s leader (Chatterjee et al. 2001).
Some scholars have identified different profiles of CIOs, namely architecture
builder, partner, project coordinator, systems provider and technological leader,
while others have proposed different features of CIOs, such as business visionary,
business system thinker, value configure, entrepreneur, IT architect planner, orga-
nizational designer, relationship builder and informed buyer (Chen and Wu 2011;
Guillemette and Paré 2012).
In accomplishing his/her tasks, the CIO interacts with other top managers, such
as the Chief Executive Officer (CEO), the IT auditor and the Chief Financial Officer
(CFO) (Banker et al. 2011). Other studies focus on the relationship between the
CIO and the other top managers, especially with regard to the Chief Executive
Officer and Chief Financial Officer. In particular, the CIO should report to the CEO
or to the CFO: he/she should report to the CEO if the firm pursues IT initiatives in
order to improve the differentiation strategy; otherwise, he/she should report to the
CFO in order to lead IT initiatives in facilitating cost leadership strategy (Earl and
Feeny 1994; Preston et al. 2006). The CIO should have both a technical background
and managerial and leadership skills in order to support the firm’s long-term goals
(Bharadwaj 2000; Corsi and Trucco 2016).
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Chapter 3
Business Intelligence Systems
Abstract This chapter deals with Business Intelligence (BI) systems, summarizing
the main needs which may lead companies to implement a BI system and proposing
a set of critical success factors which allow for an effective implementation of a BI
system that can satisfy companies’ needs. Finally, it presents the main maturity
models of BI systems, focusing on the life cycle of BI systems and the need to keep
them up-to-date. In the first part of the chapter, the summarization of companies’
needs for BI includes management information system needs, strategic planning
needs, commercial and marketing needs, regulation needs, and fraud detection
needs. The second part of this chapter pertains to the critical success factors of BI
implementation. Several scholars have proposed different sets of factors to allow
companies to maximize the effectiveness of BI system implementation. This part of
the chapter considers the main critical success factor studies in the literature and
shows the key aspects a company should consider for an effective BI implemen-
tation. Among these critical success factors are the ERP systems dealt with in
Chap. 2. In addition to the critical success factors for BI implementation, the
literature also recommends aligning the evolution of BI systems with that of the
business, considering the life-cycle of BI models as a driver which affects critical
success factors. In this regard, the third part of the chapter discusses the BI maturity
models.
3.1 Introduction
This chapter will analyze the main needs for Business Intelligence (BI) which lead
companies to invest in BI solutions (Sect. 3.2). The need to implement a BI system
can arise from very different situations and could depend on internal or external
aspects: for example, BI investment could be achieved to meet information system
needs (Sect. 3.3), strategic planning needs (Sect. 3.4), commercial and marketing
needs (Sect. 3.5), or regulatory and fraud detection needs (Sect. 3.6). After
underlining the companies’ needs for BI, it is important to understand how to
implement a BI system to ensure it will meet the companies’ expectations. For this

https://doi.org/10.1007/978-3-319-77679-8_3
44 3 Business Intelligence Systems
purpose, Sect. 3.7 examines the main literature on the Critical Success Factors
(CSFs) for BI implementation.
The success of BI is also affected by the maturity model and by the lifecycle
approach used by the company; these aspects are explained in Sect. 3.8 and sup-
ported by the main literature streams.
3.2 Business Intelligence and Companies Needs
The main aim of this section is to analyze the factors that lead companies to invest
in BI solutions. The aim, therefore, is to identify the criteria that should be followed
in guiding companies to choose the most suitable system to meet their needs.
The need to invest in BI systems is analyzed from both an academic and pro-
fessional perspective (Yeoh and Popovič 2016). However, there have been few
studies that analyze, in a systematic and in-depth approach, the several factors and
motivations that determine the need for companies to adopt BI systems (Yeoh and
Koronios 2010; Yeoh and Popovič 2016). Only a few studies have been carried out
through surveys specifically aimed at highlighting companies’ needs for BI: the
most representative example is the study of Yeoh and Popovič (2016), which is
aimed at enhancing the understanding of critical success factors in the implemen-
tation of BI systems by analyzing 7 case studies and interviewing 26 business actors
involved in the use of BI tools. Their research proposes an analysis of the perceived
users’ needs at various organizational levels.
Another study that indirectly identifies the needs of companies to implement BI
systems is by Rud (2009), which provides guidance on the many needs that
companies may feel. This study presents evidence from BI professionals, CEOs and
experts during their work experience on the main information and IT requirements
perceived by companies and on aspects that increase (or would increase) such
needs. The lack of studies addressed directly at the needs of BI has made it nec-
essary to extend the analysis of the literature to articles that, although not directly
focused on the analysis of companies’ BI needs, indirectly deal with their BI
requirements, both in empirical and theoretical studies.
A BI system, a term coined in the early twentieth century by Gartner, can be
defined as an integrated set of tools and technologies used to gather, integrate,
aggregate, select, validate, intelligently explore and analyze (structured and
semi-structured) data and information from different sources, making them useful
and usable in different decision-making processes (Reinschmidt and Francoise
2000; Olszak and Ziemba 2007).
Studies show a growing trend in the investments that companies have made in
the latest generation of IT technologies, despite the difficulties associated with the
recent financial crisis. Companies are now faced with the choice of alternative
solutions. On the one hand, they can opt for more “traditional” IT-centric models, in
which BI is controlled by Information Technology (IT). These models are based on
complex technologies that require special skills, and thus the availability of IT
3.2 Business Intelligence and Companies Needs 45
experts who deliver all the reports, making the process lengthy and ineffective. On
the other hand, companies can choose “more innovative” systems, also called
“business-led analytics”, which, based on self-service and visual analytics tools,
allow users to produce custom reports without the need for specific IT skills.
The analyses aimed at monitoring the market trends of BI solutions appear to
show that the entire market is constantly growing at an annual rate of 7.9% and that
the latest generation solution—”business-led”—is in rapid growth, with a rate of
only 63.6% in 2015 (Gartner 2017). The latter market segment seems to be highly
promising since the decision to invest in BI systems seems to be highly influenced
today by the growing need for flexibility in use and the personalization of data and
information.
Below is a list of the critical drivers which lead companies to invest in BI
technologies and the major models that companies may choose to implement these
(see Table 3.1).
Regarding the factors which affect a company’s decision to invest in BI, we can
identify the following three main drivers:
(1) coercive isomorphism (DiMaggio and Powell 1983; Powell and DiMaggio
2012), related to the need to manage a growing amount of data and information.
These needs can be determined by the increasingly stringent and complex
management needs, and by the need to meet the requirements of compliance
with general or sectoral norms (e.g., bank regulations). The diffusion of com-
puter technology and communication tools has greatly increased the amount of
data and information that companies are able to manage daily;
(2) mimetic isomorphism (Haveman 1993), related to the need to cope with an
increasingly competitive, global, turbulent and disrupting environment where
timeliness becomes more and more critical in responding rapidly to market
demands;
(3) decision-making process, associated with the need to deal with more and more
complex processes (Saaty 1990; Turban et al. 2014) due to highly competitive
contexts that require the increasing use of advanced information technologies
and sophisticated decision-making algorithms.
Table 3.1 Summarization of the main drivers of BI needs

Coercive isomorphism Mimetic isomorphism Decision-making
(DiMaggio and Powell 1983; (Haveman 1993) process support (Saaty 1990;
Powell and DiMaggio 2012) Turban et al. 2014)
Need to manage great amount Need to compete on the Need for timeliness
of data market
Need to deal with complex Need to deal with Need for data quality
environment competitors
Need to comply with Need to respond to market Need for reliable
regulations demands information
As a consequence, companies need to invest in innovative BI models that can:

• meet the need of integrating and analyzing data from different applications or,
more generally, from different sources;
• reduce the opacity of certain operations and business functions by increasing
their transparency and sharing;
• increase the timeliness of data access and data processing;
• increase the number of users who have access to data and information while
reducing the technical expertise needed;
• effectively and efficiently manage company-specific life stages (expansion of the
business, mergers, acquisitions, and so forth).
To develop these capacities, companies need to have BI and information
available for several business functions and areas. The main areas where BI needs
are most felt are the following (see Table 3.2):
a. management information systems;
b. strategic planning;
c. marketing;
d. regulations and fraud detection.
The main literature regarding the needs of companies is reported in Table 3.3.
The following sections analyze each of the BI needs reported in Table 3.2 on the
basis of the literature summarized in Table 3.3.
Table 3.2 Main BI needs of companies

Management Strategic Marketing Regulation
information systems planning and fraud
detection
Alignment to group Monitoring of Search new needs Comply with
logics environmental signals and consumers the law
tastes
Coordination and Planning and control Profile new Make the
technical-organizational requirements potential internal
integration customers control
system
auditable
Improvement of data Innovative tools for Enhance existing Detect
management and adapting to relationships with financial
decision support environmental dynamics customers frauds
information
Improvement in Support
communication marketing
strategies
3.2 Business Intelligence and Companies Needs 47
Table 3.3 Main literature on the BI needs of companies

Management Literature review
information systems
Alignment to group logics Sudarsanam (2003), Levinson (1994), Robbins and
Stylianou (1999), Roehl-Anderson (2013), Elbashir
et al. (2008), Peters et al. (2016), Kirlidog (1996)
Coordination and technical-organizational Hou (2012), Popovič et al. (2012), Sparks and McCann
integration (2015), Raymond (1990), Dishman and Calof (2008),
Isik et al. (2011), Hérault et al. (2005), Serain (2002);
Bieberstein (2006), Mendoza et al. (2006), Marjanovic
(2010)
Improvement of data Gilad and Gilad (1986), Turban et al. (2014), Woodside
management and decision support (2011), Chen et al. (2012), O’Reilly (2009), O’Reilly
information and Battelle (2009), Da Xu et al. (2014), Palattella et al.
(2016), Peters et al. (2016), Moss and Atre (2003),
Checkland (1981), Rosenhead and Mingers (2001),
Mackenzie et al. (2006)
Improvement in Rud (2009), Krivda (2008), Coleman and Levine
communication (2008), Caserio and Trucco (2016), Patel and Hancock
(2005), Hribar Rajterič (2010), Burton (2009),
Lahrmann et al. (2011)
Strategic planning
Monitoring of environmental signals Georgantzas and Acar (1995), Malaska et al. (1984),
Bradfield et al. (2005), Rud (2009), Giesen et al.
(2010), Mitchell and Bruckner Coles (2004),
Lindgren and Bandhold (2009), Laszlo and Laugel
(2000), Pearce et al. (1997), Pearce and Robinson
(2005), Alkhafaji (2011), Carpenter and Sanders
(2006), Bose and Mahapatra (2001), Liebowitz (2006),
Bradfield et al. (2005)
Planning and control Anandarajan et al. (2004), Hannula and Pirttimaki
requirements (2003), Mancini and Marchi (2004), Yeoh and Popovič
(2016), Williams and Williams (2010), Howard (2003),
Williams and Williams (2010), Elbashir et al. (2011),
Malmi and Brown (2008), Aronson et al. (2005),
Brignall and Ballantine (2004), Carte et al. (2005),
Robertson et al. (2007), Olszak (2016), Chaudhary
(2004), Hawking et al. (2008), Davenport et al. (2010),
Pranjić (2011)
Innovative tools for adapting to Laszlo and Laugel (2000), Rud (2009), Bäck (2002),
environmental dynamics Bose and Mahapatra (2001), Michalewicz et al. (2006),
Bäck (2002), Wang (2005), Salehie and Tahvildari
(2009)
Marketing
Search for new needs and consumer tastes Olszak (2016), He et al. (2013), Chau and Xu (2012),
Park et al. (2012)
Profile new Olszak (2016), He et al. (2013), Berthon et al. (2012),
potential Hall (2004), Hočevar and Jaklič (2008), Ranjan (2009)
customers
(continued)
Table 3.3 (continued)

Management Literature review
information systems
Enhance existing relationships with Olszak (2016), He et al. (2013), Berthon et al. (2012),
customers and support marketing strategies Ranjan (2009)
Regulation and fraud detection
Comply with the law Yeoh and Popovič (2016), Trill (1993), Williams
(1993), Rutter et al. (2007)
Make the internal control Wingate (2016), Yeoh and Popovič (2016), Trill (1993)
system
auditable
Detect financial frauds Ngai et al. (2011), Dorronsoro et al. (1997), Fanning
and Cogger (1998), Cerullo and Cerullo (1999), Bell
and Carcello (2000), Owusu-Ansah et al. (2002),
Spathis (2002), Viaene et al. (2004), Kotsiantis et al.
(2006)
3.3 BI for Management Information Systems Needs
This section deals with the needs for BI in the information system. First of all, the
needs for BI may emerge from mergers and acquisitions operations, as explained in
Sect. 3.3.1. Another reason why companies perceive the need to invest in BI is to
improve internal coordination and technical-organizational integration (Sect. 3.3.2).
Furthermore, because information systems aim at supporting strategic, managerial
and operational decisions, companies may invest in BI to satisfy the need for data
management and decision support (Sect. 3.3.3) and to improve communications
(Sect. 3.3.4).
3.3.1 Alignment to Group Logics
Extraordinary finance operations, in particular Mergers and Acquisitions (M&A),

play an increasingly important role in the current economy, due in part to global-
ization; such operations effect different business areas, such as the financial struc-
ture of companies, equity ownership, the business model, size and organizational
set-up. Therefore, given the increased frequency and scope of these operations, due
diligence activities should highlight the key success factors of M&A, such as the
competitive position of the company to be acquired, the system and production
processes, the human resources available, the accounting, contractual and fiscal
situation, organizational features and information systems (Sudarsanam 2003). This
latter point is particularly relevant in the context of this study, as M&A involve the
integration, and thus the alignment, of corporate information systems, with the aim
of pursuing strategic and tactical objectives (Levinson 1994). The literature shows
3.3 BI for Management Information Systems Needs 49
that such finance operations can fail if they are carried out by attributing excessive
attention to financial variables at the expense of technical, informational and
organizational ones. Empirical studies also suggest that accurate planning of M&A
operations is crucial for their success (Robbins and Stylianou 1999). One of the
most common causes of M&A failure is the scant attention paid to their effects on
the integration of the IT resources of the companies involved (McKiernan and
Merali 1995; Roehl-Anderson 2013).
This integration should instead be one of the primary objectives of M&A
operations, as it would provide quality, accurate, useful and timely information
(Buck-Lew et al. 1992) and an effective system with the characteristics of selec-
tivity, flexibility, reliability, timeliness and acceptability (Marchi 1993) that can
support decision-making at the operational, managerial and strategic levels
(Anthony 1967; Mancini 2010). According to a slightly different analytical per-
spective, for M&A operations to be successful, an alignment should be pursued
between the business strategy and the technological tools available in the company
(Roehl-Anderson 2013). Therefore, the impact of M&A operations on BI systems is
twofold: on the one hand, the success of the integration of information systems
improves the effectiveness of BI systems, as these analyze and process data pro-
vided by the information systems (Elbashir et al. 2008; Peters et al. 2016); on the
other hand, M&A operations should be carried out by aligning the company’s
strategic needs with its BI needs in order to create value (Henningsson and
Kettinger 2016).
With regard to the alignment of information systems, the need to implement a BI
system may derive from the need to align the purchasing company’s BI system to
that of the acquired company, in case the latter is considered more effective (Yeoh
and Popovič 2016). In this context, the most recent literature clearly demonstrates
that the effective integration of information and IT systems of companies involved
in M&A operations is essential for the achievement of the expected benefits
(Wijnhoven et al. 2006; Graebner et al. 2016; Henningsson and Kettinger 2016).
Globalization has undoubtedly favored the implementation of M&A operations,
and therefore the need for companies to implement or update BI systems to align
them with business strategies. The compatibility of the information and IT systems
of companies is thus one of the most important success factors for M&A operations,
as it influences the ability of BI systems to provide adequate decision support
(Sudarsanam 2003).
In the case of company groups, the parent company may feel the need to
implement or adapt its BI systems to make them available to other companies of the
group that, for organizational, structural or budget reasons, are lacking in BI tools.
On the contrary, the need to invest in BI systems may be perceived by companies
belonging to the corporate group due to the implementation and transfer strategies
of hardware and software pursued by the parent company (Kirlidog 1996).
3.3.2 Coordination and Technical-Organizational

Integration
The need to improve internal, technical and organizational coordination is very

commonly discussed in the literature on BI integration systems. One of the needs
emphasized in some studies is that perceived by BI end-users, who feel that BI
investment is creating value only if the tools meet their needs and improve their job
performance (Hou 2012).
The literature shows that end users’ needs generally require that BI tools
(Popovič et al. 2012; Sparks and McCann 2015):
• support the end-users’ job tasks;
• are well structured and integrated so as to facilitate the achievement of
end-users’ objectives;
• have intuitive interfaces;
• facilitate the access to data;
• increase the timeliness of data, information and communication.
In other words, BI systems should provide an adequate level of user satisfaction,
which consists in the end-users perception of an acceptable alignment between the
systems used and the perceived needs (Raymond 1990; Dishman and Calof 2008).
Such satisfaction can be achieved through the enhancement and integration of
existing and/or new BI systems; but this improvement, in turn, generates the need to
manage the technical issues underlying the integration. This latter mainly concerns
two closely-related factors (Isik et al. 2011):
a. technical-IT integration of internal business applications, which involves data,
information and people;
b. enhancement of the ability to provide the information and knowledge needed to
support end-user decisions.
With reference to the first point, obtaining a satisfactory level of integration
between the business applications has become increasingly complex over time,
given the high heterogeneity of databases, information platforms, software and
interfaces. Furthermore, the advent of the Internet has brought new problems related
to public communications, communications security and interoperability: i.e., the
ability of systems to interact reliably with other systems (Hérault et al. 2005).
To cope with these complexities and to foster a link between business appli-
cations, middleware solutions were designed, which consist in software systems
and interfaces that act as mediators among a lot of different applications (Serain
2002; Bieberstein 2006).
With regard to point (b), namely, the ability of the system to provide decision
support to end-users, it is still thanks to the integration (and the updating) of the
systems that the decision makers can meet this need. In addition to allowing very
different applications to interact, the integration of the systems also makes it pos-
sible to unify information and data management systems, thus improving the
alignment of information flows with business needs (Markus 2000). Consequently,

the integration of the system allows for the provision of “unified” information
which supports managerial decision-making (Mendoza et al. 2006).
In addition to providing “unified” information, an effective decision support to
end-users also requires the adoption of data mining and knowledge discovery tools
to transform data into knowledge; in doing so, these tools allow management to
obtain insights and to make interpretations regarding a vast amount of data (Shim
et al. 2002; Chou et al. 2014).
System integration has not only technical but also organizational implications.
Integration makes it necessary to identify the most appropriate and effective solu-
tions for end-user training and the definition of rules and best practices that
end-users could follow and share (Marjanovic 2010).
However, the need for information system coordination and integration can also
be perceived by the same top management, especially in companies that have
already implemented BI systems and used them to support key business processes.
Companies of this type recognize a considerable strategic value for BI systems, and
they are generally the most inclined to invest in new BI solutions, since they wish to
derive the maximum possible value (Marjanovic 2010).
3.3.3 Improvement of Data Management and Decision

Support Information
The implementation of BI systems inevitably responds to the need to obtain data

and information that support the decision-making process. The main phases of a BI
system are (Gilad and Gilad 1986; Turban et al. 2014):
a. collection of data;
b. evaluation of the validity and reliability of the data;
c. data analysis;
d. data storing and processing;
e. dissemination and communication.
Communication precedes the acquisition and use of the information by the
recipients to support their decisions. Following this path, raw data will be converted
into information and therefore into knowledge, which is useful in providing deci-
sion makers with the appropriate support for their strategic choices. The need to
have a BI system is thus perceived even more intensely when information is critical
and impacts the decision-making process (Woodside 2011). Companies’ needs for
BI to support decisions can be explored under two main aspects:
• technological, based on data and tools available for data analysis;
• informational, associated with the specific business-related decision-making
needs.
Regarding the technological aspect, companies’ needs for data processing tools
have evolved in line with the evolution of data complexity. Consequently, these
needs have led to an evolution in BI tools (Chen et al. 2012). According to this
interpretation, the needs for BI can be correlated with the types of data that man-
agers need to analyze. The more structured is the data—that is, the more it comes
from corporate databases and management systems—the more companies will
effectively use BI tools such as data warehousing, Extracting, Transforming and
Loading technologies (ETL), On-Line Analytical Processing (OLAP) and reporting,
which allow them to extrapolate useful information through statistical analyses such
as regression, segmentation and clustering, and to visualize information using
multidimensional tools such as scorecards and dashboards (Chen et al. 2012).
However, BI tools needed by companies may change according to the nature and
to the characteristics of the data. For example, if the data to be analyzed comes from
the web in very large amounts and is not structured, companies will need Big Data
and Web 2.0 tools, which would allow managers to analyze large amounts of data
—such as, sites, social media, forums, blogs, and online resources in general. These
tools, through advanced analyses, can provide a measurement of relevant aspects,
such as online user activity (through web analytics and web intelligence tools), the
frequency of use of certain terms (text mining, web mining), and the “moods”
emerging from the text analysis (tone analysis, sensitivity analysis) (O’Reilly 2009;
O’Reilly and Battelle 2009; Chen et al. 2012).
Companies that rely particularly on innovative tools, such as the Internet of
Things, or mobile web applications, may perceive the need for BI tools that have
recently begun to spread on the market (Da Xu et al. 2014; Palattella et al. 2016;
Peters et al. 2016).
In addition to relying on the data typology and tools, BI needs also depend on
the specific decision-making requirements that managers must meet. Hence,
investing in BI requires the identification of the company’s real technical and
informational needs to ensure the new BI resources are not acquired only to upgrade
the processing capabilities, neglecting the alignment of technology to the business.
Following this idea, the need for BI tools depends on the type of business activity,
the industry, the complexity of the internal processes and the external environment.
In other words, it depends on all those elements that contribute to creating the
problems and, consequently, it affects the type of decision support needed to solve
them (Moss and Atre 2003). The study by Mackenzie et al. (2006) is also in line
with these ideas: the authors distinguish the DSSs according to the decision-making
needs the company has to satisfy, recognizing two types of DSSs:
a. substantive systems, which provide support for the resolution of specific kinds
of problems and the management of specific decisions through processing,
calculation and design capabilities;
b. procedural systems, which instead provide support for the assessment of the
consequences of a decision.
Therefore, substantive systems will be implemented by companies that need an

“operational” decision-making support, and thus aid in understanding how to reach
a certain goal that is already known (Checkland 1981). This type of support is based
on providing the decision maker with a set of alternatives, mostly based on
mathematical calculations and simulations.
On the other hand, procedural systems will be adopted by companies that need to
understand why a specific action is required, or to look for the best alternative that
could solve a particular problem. In these systems—unlike the previous ones—the
problems to be solved are not known in advance and are generally strategic,
unstructured and not well-documented (Rosenhead and Mingers 2001; Mackenzie
et al. 2006).
3.3.4 Improvement in Communications
In addition to acquiring data and information needed to gain decision support,

companies also perceive the need for BI tools to enhance their communicative skills
(Rud 2009). BI tools, therefore, can be used to improve the frequency, clarity and
timeliness of communication. In some cases, the choice to implement a BI system
arises from the specific need to improve the effectiveness of communications
between management and customers, with the aim of improving economic and
financial performance (Krivda 2008). Of course, the tools that can be used for this
purpose include those of Web 2.0.
Some factors that may affect a company’s need for BI and the type of BI tools
required are:
• the type of business activity;
• the number of sectors and/or markets in which the company operates;
• the structure of the reporting system;
• the maturity model of BI.
Regarding the first point, some companies may adopt tools useful for promoting
internal real-time collaboration, setting up collaborative teams that can discuss and
share ideas and solutions for several issues (Rud 2009). The type of business
activity can also lead companies to prefer BI systems for synchronous communi-
cation or asynchronous communication: the first fulfills the need for quick com-
parison between the interested parties (both internal and external), while the latter is
generally suitable for the resolution of less urgent problems (Coleman and Levine
2008).
As for the second point, a high number of sectors and/or markets in which the
company operates may cause information overload, which could bring about a loss
in decision-making accuracy, and low-quality and delayed communications (Rud
2009). In these cases, companies would need a high-quality information system
(Caserio and Trucco 2016), which requires both an integrated transactional system
(e.g. ERP systems) and a BI system well-aligned with business goals (Patel and
Hancock 2005).
Concerning the third point—the structure of the reporting system—communi-
cation and collaboration needs can also be met through the implementation of a
reporting system, which is one of the first investments that companies carry out in
BI. Companies with this system ensure that they can select, represent and com-
municate data by choosing only the most relevant data and dividing it into multi-
dimensional views. In other cases, companies invest in BI reporting systems
because they are interested in analyzing trends and historical data, creating fore-
casts, and conducting scenario analysis (Hribar Rajterič 2010). If company feels the
need to enhance the reporting system, it can introduce more advanced solutions,
such as personalized Key Performance Indicators (KPIs) and scorecards, both of
which are multidimensional tools that generate reports for performance measure-
ment, control and monitoring.
Regarding the fourth point—the BI maturity model—the need for an upgrade in
BI could be influenced by the maturity of BI and that of the company: in fact,
according to the literature on the maturity model of BI systems—used to describe,
explain and evaluate the life cycle of Business Intelligence—for a BI system
upgrade to produce the benefits expected, the equilibrium between the level of
maturity of BI and the level of maturity of the company has to be maintained
(Burton 2009; Hribar Rajterič 2010; Lahrmann et al. 2011). Therefore, in investing
in BI, a company could postpone or anticipate its purchase according to the con-
dition of equilibrium (or disequilibrium) between the BI and the company maturity
level.
3.4 BI for Strategic Planning Needs
As previously explained, BI systems support information system management by

improving data and information management, data and system integration, and
reporting systems. One of the most frequent needs companies try to satisfy by
implementing a BI system is that of supporting strategic decisions, planning and
control. Accordingly, this section deals with the following needs of companies:
• BI needs for monitoring weak environmental signals (Sect. 3.4.1); BI systems
may allow companies to understand the environmental dynamics and to even-
tually anticipate strategic competitors;
• planning and control needs (Sect. 3.4.2); BI may aid companies in improving
the management control system through dashboards, forecasting models,
reward/compensation systems, and by aligning the various business functions
objectives;
• needs for advanced tools which support the alignment between the business
model and the changing environment (Sect. 3.4.3).
3.4 BI for Strategic Planning Needs 55
3.4.1 Monitoring of Environmental Signals
Environmental changes require companies to rapidly and effectively obtain the

information needed to support the decision-making process. From this perspective,
the implementation of BI systems can respond to the need for tools that can support
the company in the strategic planning phases and in analyzing complex problems
through planning, simulation and scenario analysis tools (Georgantzas and Acar
1995; Marchi 1997; Caserio 2015). The need to analyze unstructured problems,
typically associated with environmental turbulence, has gradually been perceived
since the 1970s, when the environmental complexity began to affect corporate
decisions more intensely (Malaska et al. 1984; Bradfield et al. 2005).
According to the literature, companies which implement BI tools to support
strategic planning and control activities seek to satisfy the following needs:
• obtain better monitoring of the external environment and recognize weak change
signals (Rud 2009);
• periodic re-evaluations of the adopted business model (Giesen et al. 2010);
• identify and monitor over time the drivers which affect economic and financial
results (Mitchell and Bruckner Coles 2004);
• create alternative scenarios for solving complex problems (Lindgren and
Bandhold 2009);
• obtain a quick and coordinated decision-making process (Laszlo and Laugel
2000).
In these areas, an effective BI system would play a crucial role for companies, as
it would provide environmental turbulence signals necessary to evaluate a possible
revision of the business model. The analysis of the external environment is as
crucial as it is difficult to carry out, considering that it examines several qualitative
and quantitative factors of multiple resources, including elements that, in many
cases, are not known beforehand. This could make it difficult for companies to
adapt to the new environmental conditions in a timely manner (Pearce et al. 1997;
Pearce and Robinson 2005).
In addition, external information, useful for analyzing the strategic problems and
for supporting decisions, may be available only in certain limited periods of time;
furthermore, its acquisition could be costly and time-expensive, and the time
available for procuring it could be insufficient. Moreover, even in cases of easy
information accessibility, it would still be unlikely to know, a priori and accurately,
what information is needed to effectively support decisions; instead, it is much more
common for management to become aware of the necessary information only when
it is really needed, which usually happens when it is too late (Alkhafaji 2011).
In addition to the need for BI to anticipate potential changes in the external
environment, companies also need to continuously align their business model to the
environmental changes. Therefore, BI tools that help to recognize external change
signals are as important as those that support management (Alkhafaji 2011):
• in understanding whether the alignment between the business model and the
environment has been achieved (or is happening) effectively;
• in verifying if there are any inconsistencies between the desired change and the
actual change;
• in providing alternatives that enhance alignment, minimizing the negative
effects of external pressures.
However, the external environment consists of several forces and conditions
related to the industry, the market and the economic system, some of which are not
measurable. Thus, BI tools are useful for analyzing only measurable data, infor-
mation and resources which pertain to the external environment. This analysis is
usually based on the principles of SWOT analysis, which identifies the strengths,
the weaknesses, the opportunities and the threats that characterize the company and
its environment (Hunger and Wheelen 2010). Within a turbulent environmental
system, BI can effectively support managers in identifying environmental elements
that have changed, in recognizing which elements have a direct influence on
business processes and which ones, instead, do not engender any modification of
the business model (Carpenter and Sanders 2006).
In the absence of BI tools, it is likely that the difficulties in recognizing the weak
signals of change will be perceived to the maximum extent possible, while an
effective BI system could help to reduce them. BI tools, in fact, can support
business management in several ways:
a. by providing in-depth data mining, which enables companies to acquire
non-detectable knowledge (Bose and Mahapatra 2001);
b. by creating forecasts based on historical trends (Liebowitz 2006);
c. by acquiring and processing data in a timely fashion and visualizing it through
multidimensional reporting systems (Chen et al. 2012);
d. by providing scenarios, simulating the effects of possible future decisions
(Bradfield et al. 2005).
In addition to the support in recognizing environmental change signals, BI tools
also allow companies to continuously monitor the ongoing process of alignment
between the business model and the new, changed environmental conditions. These
tools allow the company both to monitor external information that most likely will
affect the business model and to monitor the internal key variables that are most
sensitive to external influences.
A BI system capable of managing such continuous monitoring and controls
provides undoubted benefits to business management, as it permits periodic reviews
of strategic objectives and the alignment of business policies with environmental
changes. The need to adapt to the changed external conditions forces companies to
promptly change the business model. Consequently, there emerges the need for
tools that guide and optimize business operations on a daily basis and that enable
the monitoring of internal operations activities. These tools, called operational BI,
allow instant visibility of the most critical business operations (Nesamoney 2004)
and, in pursuing the aim of making the decision-making process as quick as
possible, operational BI systems must be matched by the continuous, on-line

monitoring of business operations (White 2005). BI systems are particularly suited
for such operations because they are able to acquire information directly from
original business processes, or during the process implementation, in a disaggre-
gated form that allows managers to measure and monitor the performances in
real-time, with a high level of detail (Alles et al. 2006). Because business processes
are often a response to environmental turbulence, continuous monitoring of these
processes must necessarily be coordinated with the constant monitoring of the
external environment.
Therefore, BI systems satisfy the need to monitor the external signals of change,
the need to assess the adherence of the business model to the mutable environ-
mental conditions, and the need to promptly reveal any discrepancy.
3.4.2 Planning and Control Requirements
Originally, BI systems were defined as a collective term for data analysis tools
(Anandarajan et al. 2004) that allow for timely, relevant and easy-to-use informa-
tion (Hannula and Pirttimaki 2003). Today, this definition is still valid, as BI
provides significant improvements to the traditional DSS (Mancini and Marchi
2004; Yeoh and Popovič 2016).
In addition to the support provided to the strategic level, BI systems are also
helpful in enhancing the planning and control activities performed by firms. BI
systems, in fact, refer to a set of systems for data analysis and reporting that provide
decision support at various levels of the organization (Hannula and Pirttimaki 2003)
through business applications that include scorecards, dashboards, customer ana-
lytics and supply chain analytics (Williams and Williams 2010).
With regard to planning and control activities, the main need that leads managers
to acquire BI systems is to improve the effectiveness of Corporate Performance
Management (CPM), which mainly consists in resource management, cost
accounting, financial planning and budgeting (Howard 2003; Williams and
Williams 2010; Elbashir et al. 2011). BI tools provide powerful capabilities which
support planning, cybernetic controls, and administrative and reward/compensation
controls (Elbashir et al. 2011). According to Malmi and Brown’s definition (2008):
• cybernetic controls provide quantitative measures for activities and processes,
fix performance standards, and provide feedback on the business goals,
assessing variances between goals and results;
• planning controls designate functional area goals, establish standards for
assessing the business function results, and assure alignment between the var-
ious functional goals throughout the company;
• reward/compensation controls seek to motivate the individuals, thereby
increasing their performance;
• administrative controls set organizational structures, lines of accountability, and

establish policies and procedures.
Several planning and control activities are supported by the BI tools, since they
help to monitor, scan and interpret the collected information. For example, BI tools
allow firms to perform financial simulations, what-if analyses and scenario analysis
before defining strategic (and tactical) goals (Mancini and Marchi 2004; Aronson
et al. 2005; Marchi and Caserio 2010).
Hence, using BI solutions can dramatically improve the planning and control
activities. In an integrated enterprise system, the solidity of databases is ensured by
the set of relations that connect the data to each other; but in many cases the
individual ability of end-users to extract relevant information without using spe-
cialized applications is quite limited. This plays against the need to effectively
manage planning and control activities, which require a timely response of the
system and accurate enterprise data, which is useful in making estimates, forecasts,
simulations, and the like. In this sense, BI systems play a crucial role as they are
specifically designed to facilitate users in performing detailed analyses of enterprise
data (Brignall and Ballantine 2004; Carte et al. 2005; Robertson et al. 2007).
Furthermore, the availability of timely and accurate information and data positively
affect both cost management and the reporting system (Rud 2009). In fact, BI
systems are able to produce, from the underlying enterprise databases, a wide range
of pre-specified reports useful for supporting planning and control activities
(Elbashir et al. 2011).
The importance of BI tools for planning and control activities is also confirmed
by some empirical studies: a survey conducted by Olszak (2016), along with other
case study analyses (Chaudhary 2004; Hawking et al. 2008; Davenport et al. 2010;
Olszak 2015), reveal that BI systems can be acquired for:
• supporting demand forecasting;
• informing about the realization of enterprise goals;
• increasing the effectiveness of strategic, tactical and operational planning;
• improving the quality of information related to trends and the realization of
plans;
• providing analyses of deviations from the realization of plans.
The benefits of BI for planning and control activities are sometimes neither
directly measurable nor explicit. This happens when the improved strategic deci-
sions and the increased information quality due to the adoption of BI tools allow
managers to prevent losses, without the possibility of quantifying the effects
(Pranjić 2011).
3.4.3 Innovative BI Tools for the Adaptation

to Environmental Conditions
To support strategic decisions, companies need BI tools that are sufficiently inno-
vative and capable of recognizing the signs of change. Some studies argue that
traditional BI tools, as part of strategic planning, usually consist of applications that
use historical trends to derive future values, supported by statistical models (Laszlo
and Laugel 2000; Rud 2009). The same studies argue that the mathematical rigor of
such models, although useful for the initial formulation of the strategy, may be an
obstacle to the creativity which could be necessary for suggesting alternative sce-
nario hypotheses and for proposing business model changes. Traditional BI tools
are, therefore, likely to undermine the strategic adaptability of the company (Laszlo
and Laugel 2000). This strategic adaptability should be supported by an equally
adaptable BI infrastructure, or by adaptive BI tools (Bäck 2002). These systems
inherit the features of expert systems and belong to the area of machine learning,
which examines the ability to provide software with the capacity to learn without a
specific programming. The learning process, in fact, derives from the recognition of
patterns and algorithms which analyze source data to make predictions (Bose and
Mahapatra 2001).
The goals of traditional BI systems are to access data from a variety of sources,
transform them into information and, through data mining algorithms, into knowledge
that supports decision-making. The knowledge is made available to the decision
makers through user-friendly interfaces. Unlike these traditional systems, adaptive BI
not only provides support for decisions but is also able to recognize the best decisions
to take on the basis of the knowledge available (Michalewicz et al. 2006).
Therefore, in addition to transforming the data into information and information
into knowledge, these systems elaborate the knowledge through optimization
models and predictive algorithms, proposing decisions that are continuously
updated based on the data and knowledge acquired in input. These tools are thus
based on an iterative “input-output-output” logic, as shown in Fig. 3.1.
Over time, the adaptive BI system contributes to a self-learning process that
gradually updates the knowledge about problems and proposes solutions aligned
with the input data.
The adaptive BI system is based on a logic in which:
• the input is represented by the problem to be solved and its related data;
• the output consists of the additional knowledge that the system generates
regarding the problem, utilizing the combination of optimization and prediction
models;
• the generated output (i.e., the solution proposed for the problem) represents both
a decision-making support and an internal data source which returns as a sys-
tem’s input; this input is also composed of new updated data acquired from
external sources;
• the process is repeated cyclically.
Adaptability
Optimization
models
Knowledge
Information
Decisions
Data
Data
Data-mining
preparation
Prediction
models
Input - Problem Output - Solution
Fig. 3.1 Adaptive business intelligence (Source Adapted from Michalewicz et al. (2006))
The iterative approach of these systems allows companies to keep the knowledge
of the problems up-to-date by taking into account the effects of environmental
turbulence on the input data (Bäck 2002; Wang 2005; Salehie and Tahvildari 2009).
Therefore, companies which use traditional BI systems and operate in a turbulent
environment perceive the need to adopt more innovative and flexible systems,
thereby favoring the adaptability necessary to cope with mutable environmental
dynamics (Rud 2009).
3.5 BI for Marketing Needs
One of the strongest needs that lead companies to invest in BI tools is to create new
relationships with customers or to enhance existing relations (Olszak 2016). BI
provides valuable support for processing large amounts of structured or unstruc-
tured data, thereby helping companies to analyze data related to active customers.
Furthermore, BI tools may also be useful in identifying and profiling new potential
customers through data analysis carried out, for example, on social media.
Companies can thus invest in BI to meet several types of needs relating to the
commercial and marketing fields (He et al. 2013; Olszak 2016):
• market analysis to search for new needs and consumer tastes;
• market analysis to identify the target audience and profile potential new
customers;
• internal data analysis on existing customers to enhance existing relationships;
• classifying active customers based on common characteristics (creating clusters
and segments);
• implementing targeted marketing strategies.
3.5 BI for Marketing Needs 61
BI systems support companies in identifying new market opportunities in order

to meet the needs of customers and increase their satisfaction levels (Chau and Xu
2012; Park et al. 2012). The combination of these tools with Web 2.0 can even
improve customer analysis. Web 2.0 tools, in fact, provide valuable support to the
management of business and marketing activities, since they enable managers to
interact directly with effective or potential customers and to gain information about
their preferences and interests. For example, through the combination of
data-mining techniques—namely, the text-mining applied to social networks—and
Web 2.0, it is possible to obtain information implicit in the exchange of messages
and opinions expressed by social media users (He et al. 2013). These combined
tools could also be used by companies operating in international markets; however,
in these cases they should customize their global marketing strategy to meet the
diverse needs of clients from many different countries (Berthon et al. 2012).
With regard to internal customers, data analysis can only be conducted where BI
tools are closely related to Customer Relationship Management (CRM) systems.
The latter, in fact, represent the source from which BI tools acquire customer data
related to buying habits (channel used, quantity purchased, typology of products,
and so on), degree of loyalty, customer age, and customer preferences. Once the
data has been acquired, BI tools process it through statistical analysis, clustering
and segmentation (Olszak 2016).
Therefore, from a commercial and marketing perspective companies invest in BI
tools to meet the following needs (Hall 2004; Hočevar and Jaklič 2008):
• monitoring the market demand;
• deepening knowledge of clients (actual and potential);
• proposing solutions to maximize customer satisfaction and loyalty;
• consequently, increasing economic returns and competitive advantage.
Hence, the use of BI tools in the marketing field helps improve the customer
experience as a whole by providing customers with the most timely and aligned
responses to their problems and priorities (Ranjan 2009).
3.6 BI for Regulations and Fraud Detection Needs
The study conducted by Yeoh and Popovič (2016) suggests that one of the reasons
why companies adopt BI systems is to comply with regulations. In certain cases, in
fact, the peculiarities of the industry and the unique characteristics of the envi-
ronment in which the company operates may lead managers to acquire BI systems
that make business activities easy to audit. In these cases, companies require BI
tools that can create auditable reporting (in support of institutional inspections
performed by the control organisms) and perform business activities in compliance
with regulations. In this regard, some studies conducted in the pharmaceutical
sectors suggest that companies have to make sure that their software allows them to
keep audit trails and to apply access restrictions to specific fields of the enterprise
database (Wingate 2016). Therefore, the choice of BI tools and, more generally, of
the BI model is sometimes influenced by the expectations of the regulatory
authorities, which could require that (Trill 1993; Williams 1993):
• software be programmed and maintained under version and change control;
• companies use good programming practices;
• data integrity issues be well implemented in the database management systems.
In other cases, BI model modifications could arise from compliance with the
changes in International Accounting Standards (IAS) and International Financial
Reporting Standards (IFRS). For example, the passage from the IAS 39 to the IFRS
9—related to the evaluation of financial instruments—obliged companies to adopt
new classification and measurement methodologies. These changes are likely to
imply some modifications also in the business applications used in accounting
information systems (Corsi and Mancini 2010; Trucco 2014, 2015).
In some cases, companies need to adopt BI tools to provide disabled people with
the same quality of web services and the same effectiveness that other users receive.
In such cases, Web accessibility tools, PDF accessibility tools and other specific
technologies have to be adopted (Rutter et al. 2007).
Furthermore, the need for advanced BI tools is perceived by companies that have
to perform Financial Fraud Detection (FFD) activities, mainly banks and insurance
firms. In this area, data-mining plays an important role as it allows companies to
extract and uncover the hidden information behind large amounts of data and
identify interesting patterns in databases and useful knowledge from business data
(Ngai et al. 2011). Fraud detection seems to be one of the most common and
well-established applications of data-mining, as confirmed by the existence of
several data-mining techniques such as neural networks (Dorronsoro et al. 1997;
Fanning and Cogger 1998; Cerullo and Cerullo 1999), logistic regression (Bell and
Carcello 2000; Owusu-Ansah et al. 2002; Spathis 2002), naïve Bayes methods
(Viaene et al. 2004), and decision trees (Kotsiantis et al. 2006), among others.
3.7 Critical Success Factors of BI Implementation

and Adoption
The previous sections summarized the main reasons that lead companies to adopt
BI systems and BI tools. Once the needs for BI have been recognized, it is
important to understand how BI systems are implemented within the company.
For this purpose, several authors propose a set of Critical Success Factors
(CSF) considered essential for the successful implementation of BI. Yeoh and
Koronios (2010), through a multiple case study analysis, distinguish three main
aspects of BI implementation—organization, process and technology—recognizing
the CSFs pertaining to each of them. Regarding organization, they identify “vision
3.7 Critical Success Factors of BI Implementation and Adoption 63
and business case related factors” and “management and championship related
factors”; with regard to process, they find “team related factors”, “project man-
agement and methodology related factors” and “change management related fac-
tors”; regarding technology, they identify “data related factors” and “infrastructure
related factors”. In their framework, the authors also consider the relevance of
process performance, which includes the budget and time schedule, and infras-
tructure performance, represented by system quality, information quality and sys-
tem use. A follow-up study by (Yeoh and Popovič 2016) suggests that
organizational factors play the most crucial role in influencing the success of BI
implementation.
According to Rud (2009), CSFs for BI are classifiable into five elements:
effective communication, which could be obtained by sharing goals, knowledge and
using different communication styles and different BI tools; collaboration, achiev-
able through teamwork using specific tools, which allows people to exchange
opinions about problems in a timely manner; innovation, which is the capability to
develop creativity and propose new solutions to meet market needs; adaptability,
attainable by keeping the organization open to change and by promoting flexibility
in organizational structures and interactions; and leadership, a term which sum-
marizes a wide range of characteristics that leaders should have to motivate per-
sonnel and to obtain the awareness of the internal needs (for example, empathy,
attunement, organizational awareness, inspiration, among others). Other studies
underline the fact that BI systems are an extension of ERP systems, and thus the
presence of an effective ERP is one of the CSFs for BI implementation (Vosburg
and Kumar 2001). In this regard, Hawking and Sellitto (2010) propose a list of
CSFs for BI implementation in an ERP system environment, identified through a
content analysis performed on presentations of industry practitioners involved in the
implementation, use and maintenance of BI in an ERP environment. The most
frequent CSFs found through this content analysis are: management support,
resources, user participation, and team skills. Other factors, such as champion,
source systems and development technology, had minor frequency and thus less
importance.
Another qualitative study, carried out through a literature analysis and a set of
interviews conducted with executives, managers and staff before, during and after
the BI implementation, suggest there are two types of CSFs for BI: implementation
CSFs—collaborative culture, customization, communication, project management,
resources, top management support, training, vertical integration—and success
factors related to the post-BI implementation phase—perceived success, timely
implementation and satisfaction. (Woodside 2011). Other studies deal with the
CSFs of BI implementation in specific industries or places. For example, Olszak
and Ziemba (2012) examine the CSFs for BI implementation in small-medium
enterprises in Poland, while Dawson and Van Belle (2013) focus on CSFs for
Business Intelligence in South African financial services.
Table 3.4 summarizes the main CSFs related to BI implementation emerging
from the literature.
Table 3.4 Main critical success factors for BI implementation

Yeoh and Koronios Rud (2009) Hawking and Sellitto Woodside (2011)
(2010) (2010)
Vision and Effective Management support Collaborative
business case communication culture
related factors
Management and Collaboration Resources Customization
championship
related factors
Team related Innovation User Communication
factors participation
Project Adaptability Team skills Project
management and management
methodology
related factors
Change Leadership Champion Resources
management
related factors
Data related Source Top management
factors systems support
Infrastructure Development Training
related factors technology
Budget and time Vertical
schedule integration
System quality Perceived
success
Information Timely
quality implementation
System use Satisfaction
It is also important to underline that several studies also recognize ERP systems
as critical BI success factors, since ERP systems provide the starting data that are
processed by BI systems. If the quality of data is high and the data comes from an
integrated and reliable ERP system, then the BI system will also be able to provide
effective decision support (Karim et al. 2007; Laudon et al. 2012).
In examining the CSFs of BI implementation, some authors also take into
account the BI maturity model, which allows the position of the company along the
BI development process to be identified. Consequently, the BI maturity model
enables companies to understand which are the CSFs for upgrading BI, coherent
with the maturity of the company (Hribar Rajterič 2010; Popovič et al. 2012). In
other words, these studies consider the lifecycle of BI as one of the drivers that
influence the CSFs.
3.8 BI Maturity Models and Lifecycle 65
3.8 BI Maturity Models and Lifecycle
Over time, the role of BI systems has gradually changed, from that of a single
analytical application view to being an essential component of information systems
which contributes to overall company success (Watson 2010). To make BI systems
effective, it is necessary to follow its design and structure changes. To this end, the
literature provides the concept of a maturity model, which consists in a sequence of
multiple archetypal levels of maturity of a certain domain that can be used to assess
the degree of the model development (Lahrmann et al. 2011).
Each level of maturity is very different from the others and composed of features
that differ from those of the other levels. Key Process Areas (KPAs) are defined for
each level of maturity and are distinctive for that particular level. KPAs represent
phases that the company needs to complete in order to achieve a certain level of
maturity (Hribar Rajterič 2010).
Maturity models allow companies to understand the overall development of a
certain domain, and therefore are useful in understanding the characteristics needed
by the company to evolve from one level to another. Regarding BI maturity models,
the literature provides a series of examples, the main ones summarized by
Lahrmann et al. (2010) and Hribar Rajterič (2010). An example of maturity model
is shown in Fig. 3.2.
Figure 3.2 shows an example of a BI Maturity Model, developed by The Data
Warehouse Institute (TDWI), mainly focuses on technical issues. Maturity levels
are graded through eight key process areas: scope, sponsorship, funding, value,
architecture, data, development and delivery.
Business
value
Sage
Adult
Teenager
Child
Infant
Prenatal
BI Sofistication
Fig. 3.2 TDWI’s business intelligence maturity model (Source Adapted from Eckerson (2007))
The starting level, “prenatal”, represents the situation in which organizations

have rigid management reporting systems, mainly based on standard static reports,
distributed to employees on a regular time basis. Reports are hand-coded against
legacy systems, therefore IT staff cannot satisfy rapidly the requests for custom
reports.
The “infant” phase, represents the situation in which companies are faced with
several partial and non-integrated data sources, which provide contrasting business
views and do not allow for an effective decision-making process. When companies
are at a “child” level, some data warehouse starts to be implemented in single
business units, which, however, are not linked to each other. At the “teenager”
level, companies recognize the importance of BI systems and of the centralized
Data Warehouse (DW) for improving data integration and data quality. Companies
make an extensive use of BI consultants to acquire BI tools that provide users with
dashboards based on KPIs and with interactive reporting and analysis tools. At the
“adult” level, companies implement centralized data management and standardize
the architecture of DWs, the language and the metrics rules. The companies start
using DWs integrated with data sources in real time. When the company reaches the
“sage” level, the BI system capabilities are turned into technical services and an ad
hoc information management group is responsible for managing the company’s
entire data warehouse as a source for all enterprise information (Hribar Rajterič
2010). At this level, the BI system allows for the creation of customized reports, the
monitoring of KPIs, and the provision of services with high added value.
The main aim of maturity models is to allow companies to assess their BI
technologies: first, companies can understand what the BI maturity level of their
system is and, second, ascertain the coherence between BI maturity and company
maturity. Furthermore, maturity models support companies in comparing their
system with those of competitors, in recognizing possible weak points in their BI
system, and in identifying possible improvement strategies to achieve a certain
maturity level (Tan et al. 2011). Therefore, the passage from one level to another is
generally dictated by strategic alignment needs.
The maturity models also support the understanding of critical success factors
for BI implementation since, according to Hawking and Sellitto (2010), success
factors may vary depending on the life stage of BI in which the company is
involved. Similarly, Dinter et al. (2011) suggest that a lifecycle-oriented approach
allows companies to anticipate potential project risks in a timely fashion and to
identify possible interventions in the early implementation stage.
This latter consideration introduces another analytical perspective, which also
refers to the dynamics of BI systems over time. In addition to the maturity models,
in fact, BI may also be analyzed from a lifecycle perspective. Moss and Atre
(2003), for example, state that an integrated BI system needs considerable time to
be deployed. Almost all the implementations of engineering projects need to follow
these phases:
• justification, in which the developer analyses the business needs which gave rise
to the project;
3.8 BI Maturity Models and Lifecycle 67
• planning, useful in defining strategies and examining how to achieve the

objectives;
• business analysis, which deeply and analytically investigates the business
requirements and the possible problems, in order to understand which product
could fulfill the business needs;
• design, in which the developer defines the product features which could solve
the problems and satisfy the business needs;
• construction, in which the product is created;
• deployment, which allows the company to implement the product and evaluate
whether it meets the business needs.
However, this approach is effective only when the external (and internal)
environment of the company is relatively static. Since BI models tend to evolve
following continuous environmental changes, the traditional deployment process
become inadequate for the continual support of business decisions and needs. An
effective BI deployment approach should develop iterative releases, as each
deployment is likely to give rise to new requirements for the next release (Moss and
Atre 2003).
Therefore, the process to be followed, shown in Fig. 3.3, requires that initially—
the justification phase—the business opportunities have to be clearly identified and
the problems that the (new) BI application release could solve and/or the benefits
that it could bring to the company highlighted; subsequently, the planning stage
Business
Justification Planning Design Construction Deployment
Analysis
Business ETL
opportunities development
Database design Implementation
Technical and
Project
Problems to be nontechnical Application
requirements
solved enterprise development
definition
infrastructure
ETL design Evaluation

New benefits to
Data mining
bring
Changes for
next release
Environment
Review of tools
and processes
New business
needs
Fig. 3.3 Lifecycle of BI applications (Source Adapted from Moss and Atre (2003))
evaluates the technical and nontechnical enterprise infrastructure, after which the
business needs are analyzed in detail to identify the requirements of the company
regarding data, information, tools and internal processes. After the design and
construction have been accomplished, the release is deployed. This iterative
approach is important for two main reasons: (a) it keeps the functionalities of new
releases constantly aligned with business needs, and (b) it learns from the previous
releases, as every tool, technique and process which was not helpful in the previous
project will be modified or discarded in the next one.
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Chapter 4
ERP and BI as Tools to Improve
Information Quality in the Italian
Setting: The Research Design
Abstract This chapter presents the research design and the research questions of
our empirical research. We specifically analyze the possible relationships between
ERP, BI and information overload/underload. Furthermore, we wonder whether
ERP and BI systems may also affect the information quality by influencing the
information flow features (i.e., information processing capacity, communication
and reporting, information sharing and frequency of meeting). In fact, the poten-
tialities of ERP and BI systems may positively contribute to increasing the infor-
mation quality (and the information system quality) by means of an improved
management of information flow. This quality improvement may indirectly support
management in counteracting, or at least reducing, the information overload/
underload. Finally, we investigate the role played by the features of information
flow in improving the information quality perceived by managers. After discussing
the research design, the chapter describes the sample selection, the data collection,
the variable measurement, and the factor analysis carried out on the research
variables.
4.1 Introduction
The present section analyzes the literature review on information quality, infor-
mation overload, information underload, and the features of information flow, along
with the research design and the research questions of our research.
We provide the research design of this study in Sect. 4.2, the sample selection
and data collection in Sect. 4.3, the variable measurement in Sect. 4.4, and the
factor analysis carried out on our research variables in Sect. 4.5.

https://doi.org/10.1007/978-3-319-77679-8_4
76 4 ERP and BI as Tools to Improve Information Quality …
4.2 Literature Review Supporting the Research Design
4.2.1 Literature Review on Information Overload

and Information Underload
The initial studies on information overload and information underload were con-
ducted in the ‘70s. Ackoff (1967) described the weaknesses of management
information systems, stating that managers usually operate under a system defi-
ciency: the lack of relevant information (Ackoff 1967). Prior studies on information
overload were conducted in the ‘80s by O’Reilly (1980), who realized that infor-
mation overload happens every time the quantity of information surpasses the
individual’s information processing resources (O’Reilly 1980). Galbraith (1977),
Tushman and Nadler (1978) laid down the theoretical bases of information over-
load, asserting that companies facing uncertainty need to adjust their information
processing capacities to adapt successfully to the different environments. However,
when the information processing capacity is not aligned anymore to the information
processing requirements, companies enter into a condition of information overload
(Galbraith 1977; Tushman and Nadler 1978). More recent research supports the
basic assumptions of information overload, both from a theoretical and an empirical
perspective. With regard to the theoretical perspective, scholars have analyzed the
main literature on information overload by highlighting the features, causes, con-
sequences, and possible solutions to information overload (Edmunds and Morris
2000; Eppler and Mengis 2004a; Melinat et al. 2014). According to these studies,
the solutions to information overload would be the adoption of personal information
management strategies, the selection of only relevant information, a reduction in the
amount of information selected for the decision-making process, the use of push
technology solutions and intelligent agents, and the use of value-added information.
The initial empirical research on information overload confirms the theories about
the effects of information overload on managerial decisions (Duncan 1973;
Tushman 1977). More recent empirical studies have analyzed different aspects of
information overload, such as its effect on email communication (Soucek and
Moser 2010), on the decision-making process (Letsholo and Pretorius 2016), and
on social media communication (Ho and Tang 2001; Rodriguez et al. 2014). Both
theoretical and empirical studies suggest the following actions for preventing
information overload and underload: (a) avoid assuming that more information is
always needed by managers; (b) do not provide more information to managers but
make better use of information already available; (c) consider that, while a larger
amount of relevant information leads to better decision making process, a larger
amount of irrelevant information reduces the manager’s capacity to recognize the
problem and thus to carry out an effective decision-making process; (d) make a
better use of technology in order to select only relevant and quality information.
As the literature shows, many authors recognize the cause of information
overload in the amount of information (Hiltz and Turoff 1985; Miller 1994), while
others believe that information overload is due to the lack of information processing
4.2 Literature Review Supporting the Research Design 77
capacity needed to manage the information load (Eppler and Mengis 2004a;
Tushman and Nadler 1978). Information processing capacity is, in turn, affected by
information quality and format (Stvilia et al. 2005).
However, the literature shows that information overload could be due to several
causes, such as the use of technology, the decision-making approach and the
characteristics of the tasks performed. With regard to technology, managers tend to
invest in Information Technology (IT) and in Business Intelligence (BI) to meet
workers’ needs and increase their productivity, but they do not realize that tech-
nology itself could give raise to “technology overload”, which is a combination of
information, communication and system feature overload (Karr-Wisniewski and Lu
2010). Investing in technology beyond a certain level might not increase produc-
tivity; on the contrary, it could lead to a loss of productivity.
In terms of the decision-making approach, managers sometimes tend to produce
information overload by seeking more information than they need and, at the same
time, not using the information that they already possess (Feldman and March
1981). This behavior could depend on two main reasons: first, more available
information reduces the perception of uncertainties and increases the manager’s
feeling of having better control of the situation (Milliken 1987, 1990); second,
managers feel more confident and satisfied if they collect more information, even
when it causes overload (Connolly 1977). The paradox, as explained by O’Reilly
(1980), is that, on the one hand, managers lose decision-making accuracy as a
consequence of the information overload, while on the other they feel more con-
fident and secure. Therefore, beyond a certain level of information load, further
information does not provide any improvement in decision-making accuracy; on the
contrary, the performance tends to decrease. Some studies focus attention on the
decision-making approach. For example, Bettis-Outland (2012) asserts that incre-
mental decision-making produces less information overload than does compre-
hensive decision making, as the former considers fewer alternatives in solving a
problem, whereas the latter by nature leads managers to seek for all the possible
alternative solutions (Bettis-Outland 2012). However, it is very difficult for com-
panies operating under uncertain conditions to benefit from the advantages of an
incremental decision-making style.
The problem of information overload might not depend only on the accumula-
tion of information; managers, in fact, could feel information overloaded because of
the time pressure to accomplish their tasks and the inability to prioritize tasks
optimally (Kock 2000a). Moreover, communication is another relevant aspect
which could give rise to information overload. In fact, as shown by Meglio and
Kleiner (1990), in many cases information users contribute to information overload,
since their communication is not effective enough (Meglio and Kleiner 1990); this
point supports the idea that information overload could depend on the individuals
(knowledge base, decision style) and on task-related factors (amount of information
processes, task complexity, number of information exchange interactions) (Kock
2000a). Another study on task-related factors shows that tasks characterized by
frequent interruptions are more likely to produce information overload than are
uninterrupted tasks (Speier et al. 1999).
There is broad agreement in the literature on the fact that managers, in pursuing
the aim of feeling more confident in solving problems or making decisions, seek
more information than they need, a behavior favored by the emergence of the
Internet (Kiley 2005). Nowadays, in fact, acquiring, communicating and storing
huge amounts of information is much easier and faster than in the past. Hence,
whereas IT can help managers to support their decisions, it could also be used
improperly (overused or misused), and by doing so increasing the information
overload/underload.

and ERP Systems
Following the literature, it seems quite clear that the capacity of companies to
efficiently manage data and information—which is a possible cause of information
overload and information underload—largely depends on the quality of Information
Systems (IS) (Bera 2016; Melchor and Julián 2008; Petter et al. 2013a; Simperl
et al. 2010). Even though an IS represents a symbiotic relationship between system
users and the system itself (Chandler 1982; Taylor 1982) aimed at processing
information to support decisions, the literature shows that very often managers are
faced with the paradoxical situation of having a lot of information available but
finding it difficult to select the information which is useful for supporting decisions
(Edmunds and Morris 2000).
To our knowledge the literature does not provide specific studies on the rela-
tionship between ERP systems and information overload/underload; however,
considering the main causes of information overload/underload described above,
and taking into consideration the results of a preliminary study which partially
confirms that IS quality could affect information overload (Caserio and Trucco
2016), we can hypothesize that a well-integrated IS based on an ERP system can
also play an important role in dealing with information overload and underload. As
a matter of fact, from an analysis of the literature we can deduce that information
overload/underload could be managed through an effective system which: (a) al-
lows for the collection and integration of data in a single database (Chandler 1982;
Chapman and Kihn 2009); (b) permits consistent information to be shared across
different functional areas of a company (Robey et al. 2002a); (c) improves the
reliability, timeliness, comparability and relevance of accounting information for
external and internal users (Hitt et al. 2002; Mauldin and Richtermeyer 2004;
Poston and Grabski 2001), enhancing the capacity to plan and manage the
resources, thereby reducing the time needed to perform managerial activities and
bringing benefits to the quality of data and control activities in general (Caglio
2003; Quattrone and Hopper 2001); and (d) meets the users’ expectations,
improving job performance and increasing job satisfaction (Morris and Venkatesh
2010; Thatcher et al. 2002).
The aforementioned points are all made possible by the capacities of an ERP
system. Some studies, which compare legacy systems with ERP systems, confirm
that ERP capacities could improve data and information management, and thus
counteract information overload and underload through:
• an increase in system quality, as ERP systems allow companies to manage
several business functions with a comprehensive software, using shared infor-
mation and data flows (Lee and Lee 2000) and replacing legacy systems
characterized by incompatible software;
• an increase in data quality, as ERP systems solve the typical problems of legacy
systems (Xu et al. 2002a): (a) keeping the same data in different subsystems
(i.e., in different sources); (b) difficulties and slowness in accessing the data kept
in another subsystem; (c) a lack of communication capacity.
Considering that ERP systems are implemented to improve data accuracy and
data management through a comprehensive relational database which connects all
aspects of the business and allows data and information to be shared inside the
company, these systems could play an important role in reducing or in managing
information overload and underload. Thus, our first research question is:
• RQ 1a: “Do ERP systems matter to information overload and information
underload?”

and ERP Systems
In addition to the possible relationship between ERP and information overload/

underload, ERP systems may also affect the information flow. In fact, an ERP
system brings integration into the company, favoring a real-time sharing of infor-
mation. However, while ERP solutions ensure a smooth flow of information across
the company, they also cause a real-time sharing of possible data entry mistakes,
which would have a domino effect that impacts other business units (Bingi et al.
1999).
Another reason why ERP might improve information flow is that its imple-
mentation often requires a preliminary Business Process Reengineering
(BPR) (Scheer and Habermann 2000), which allows firms to assess, revise and
reorganize their internal processes. In the context of business process optimization,
ERP systems are generally adopted to achieve flexible information flows, obtain
short planning cycles, make available up-to-date information and more timely
communications, and eliminate data redundancy. As a result, they improve infor-
mation processing capacity, enhance organizational communications and data vis-
ibility (Dell’Orco and Giordano 2003), and increase the productivity of work
processes (Gupta and Kohli 2006).
The most important potentialities of ERP emerging from the literature pertaining
to information flow refer to: (a) the capacity to improve data management and
permit data integration and sharing (Chapman and Kihn 2009); (b) the possibility to
share consistent information across different functional areas (Robey et al. 2002b);
(c) the capacity to improve the reliability, timeliness, comparability and relevance
of information (Mauldin and Richtermeyer 2004; Poston and Grabski 2001); (d) the
possibility to achieve flexible information flows (Scheer and Habermann 2000).
These potentialities may also affect the reporting system, which is part of the
information flow. In the absence of ERP systems, business units work in silos, each
of them managing its own data. ERP-implementing companies instead take
advantage of data integration, which could affect reporting system management.
Joseph et al. (1996), in a study of the relationship between external reporting and
management accounting, suggest that information system integration, made pos-
sible by technological innovation, allows managers to have online access to the
information required for carrying out control tasks. Because managers can access a
huge amount of information for both control and decision-making support, they do
not need to wait for the periodic reports produced by management accountants.
Even if this study did not specifically focus on ERP systems, Scapens and Jazayeri
(2003) propose using the same line of reasoning: they suggest that the introduction
of ERP systems may have important implications for the nature of management
accounting, and thus of internal reporting.
The effects of ERP on internal reporting have also been studied by Sangster et al.
(2009), who found a slight decrease in the time spent on internal reporting by
management accountants. Furthermore, they found that management accountants
spend significantly less time on data collection when the ERP has been successfully
implemented. In these circumstances, management accountants have more time for
data analysis, performance issues, control activities, and more time to produce a
larger amount of reports than before (Sangster et al. 2009).
Based on the above considerations, we pose the following research question,
which aims at understanding whether the ERP system may (and in what way) affect
the information flow of the company. For “information flow” we refer to a set of
features emerging from the literature cited above, such as information processing
capacity, communication and reporting, frequency of meeting, and information
sharing.
• RQ1b: “Do ERP systems matter to the features of information flow?”

and Business Intelligence Systems
In addition to studies on ERP, legacy or integrated systems in general, scholars have

also investigated the role of Business Intelligence (BI) system in counteracting
information overload. In fact, while ERP systems may help to prevent information
overload/underload by assuring data quality and system integrity, BI systems could
contribute to the solution of information overload/underload by addressing the right
information to the right decision maker at the right time (Burstein and Holsapple
2008).
Unlike ERP systems, the literature provides several studies which analyze the
link between information overload/underload and BI, supporting the idea that an
effective BI system may allow companies to manage, counteract or eliminate
information overload/underload. Boyer et al. (2010), for example, recognizes the
use of analytics, BI and performance management systems as a solution to this
problem (Boyer et al. 2010). Similarly, Spira (2011) proposes a solution based on
the use of data mining, digital mapping, geographical information systems and
online preservations to reveal patterns that would otherwise remain undiscovered
(Spira 2011). O’Brien and Marakas (2009) attribute great importance to the
reporting system, stating that to reduce information overload/underload, companies
should adopt exception reports, issued only when exceptional conditions occur,
instead of periodical standard reports (Brien and Marakas 2009).
Other studies recognize the critical role of BI in reducing these anomalies on the
Web. In particular, they recognize the role of knowledge discovery tools on the
Web, which would help managers who need to analyze business data on the
Internet, such as the competitive environment of their company, the market situa-
tion, or the competitors that most resemble their company (Chung et al. 2005). In
the Web research field, several studies support the relevance of visualization
techniques in reducing information overload and supporting managers in exploring
the knowledge on the Web (Lin 1997) by means of Web-mining and webpage
clustering (Hartigan 1985; Marshall et al. 2004), while other studies support the
importance of social media analytics. The attention paid to social media analysis
has increased considerably in recent years given the growing importance of Web
2.0, which provides a great source of information about consumer preferences,
opinions, behavior and market trends (Stieglitz and Dang-Xuan 2013; Zeng et al.
2010).
According to other studies, information overload/underload could be counter-
acted using an effective corporate portal, which allows companies to easily access
all the digital enterprise information and knowledge by integrating structured with
unstructured information through the Intranet (Detlor 2000; Dias 2001).
However, the idea that BI could represent a solution is not unanimously shared
by the literature. Li et al. (2009), for example, believe that information problems
caused by a lack of systematic information collection and processing makes BI
tasks more and more difficult (Li et al. 2009). Following this idea, information
overload and underload can be prevented rather than solved, and this prevention
would be obtained by collecting proper data or information and applying data
mining for the succeeding elaborations. Similarly, another study underlines that BI
studies often ignore the importance of information selection and pay too much
attention to the capacity of BI to gather and elaborate data and to exhaustively
define information requirements (Blanco and Lesca 1998). According to this study,
information overload and underload could be reduced by selecting the early

warning signals, that is, information which is anticipatory in nature and thus has the
ability to inform managers about future environmental and business changes.
The above literature about the role of BI in reducing, counteracting or managing
information overload and underload allows us to define our second research
question:
• RQ 2a: “Does Business Intelligence matter to information overload and infor-
mation underload?”

and Business Intelligence Systems
The literature on BI systems shows that BI tools are also able to improve the
information flow within the company. In fact, the features of information flow—
information processing capacity, communication and reporting, frequency of
meeting and information sharing—have often been examined in the literature. BI
tools play an important role in improving both internal and external information
flow.
Regarding the latter, the study by Rud (2009) suggests that BI systems allow
companies to shift from a reactive to a proactive management of customer needs.
Unlike traditional BI tools, which forecast yearly demand and then plan the pro-
duction, most recent BI solutions enable firms to achieve a real-time monitoring of
customer needs, which, in turn, allows for a more effective and timely communi-
cation between the market and the company.
Regarding the internal information flow, there are many studies in the literature
which demonstrate that BI makes it possible to facilitate real-time interactions
between team members (Berthold et al. 2010; Horvath 2001; Matei 2010; Rizzi
2012; Rud 2009). The role of collaborative ad hoc BI solutions is crucial for
developing infrastructures which permit an information self-service for final users
and, consequently, a collaborative decision-making process (Berthold et al. 2010).
Collaboration is vital for creating business value: according to Horvath (2001),
in the context of Supply Chain Management (SCM), intelligent e-business networks
provide the competitive advantage which allows the participants in a value chain to
predominate and grow. Technological solutions applied to promote collaboration
among the actors involved in the value chain are the key driver for the effectiveness
of SCM, since they allow companies to follow the demand chain and to respond to
the changing needs of customers (Horvath 2001).
The effectiveness of the information flow has also been improved by the impact
of Web 2.0 platforms and of collaborative BI solutions. The former has changed the
knowledge management paradigm from the “conventional” to the “conversational”
(Lee and Lan 2007) by means of synchronous and asynchronous technologies that
enhance the collaboration among people and allow information to be easily shared.
The latter is a set of tools which promotes cooperation and data sharing not only
within a company but also with other companies by means of more flexible
warehouse approaches (Rizzi 2012).
The literature suggests that BI systems improve information flow also through
OLAP (On-Line Analytical Processing) reporting technologies, which are able to
extract pertinent information based on data analysis and to turn it into knowledge
and rapidly-generated reports (Ranjan 2009). The above considerations lead to the
following research question:
• RQ 2b: “Does a Business Intelligence system matter to the features of infor-
mation flow?”
4.2.6 The Combined Use of ERP and Business Intelligence:

Information Overload/Underload and Features
of Information Flow
Summarizing the analysis performed so far, there are four different situations, as
shown in Table 4.1: some companies may decide to adopt both an ERP system and
a BI system (case 1); other companies can adopt an ERP system but not a BI system
(case 2); conversely, other companies may decide to adopt a BI system but not an
ERP system (case 3); others may adopt neither of the two systems (case 4).
With reference to the above table, we wonder whether companies that adopt both
an ERP and a BI system (case 1) are better able to handle the information overload
and information underload than are companies that adopt only an ERP system or a
BI system. The idea is that the combined use of the two systems may provide
greater support to the information overload and underload problems. Thus, we
formulate our third research question as follows:
• RQ 3a: “Does the combined adoption of ERP and BI systems matter more to
information overload and information underload than does the single adoption
of an ERP or BI system?”
Similarly, the simultaneous use of ERP and BI systems is expected to be more
impactful on the information flow features than the single adoption of ERP and BI
would be. In fact, ERP systems may affect information flow through:
Table 4.1 Possible cases of ERP and BI system adoption (Source Our presentation)
Case 1 Case 2 Case 3 Case 4
Adopting ERP Adopting ERP Non-adopting ERP Non-adopting ERP
system system system system
Adopting BI Non-adopting BI Adopting BI system Non-adopting BI
system system system
(a) the BPR needed for the ERP implementation (Scheer and Habermann 2000),
since BPR allows firms to restructure the internal processes and thus the
internal and external communication flow;
(b) the enhancement of organizational communications, data visibility and the
productivity of work processes (Dell’Orco and Giordano 2003; Gupta and
Kohli 2006);
(c) the capacity to improve data management and allow data integration and
sharing (Chapman and Kihn 2009);
(d) the possibility of sharing consistent information across different functional
areas (Robey et al. 2002b);
(e) the capacity to improve information quality (Mauldin and Richtermeyer 2004;
Poston and Grabski 2001);
(f) the achievement of flexible information flows (Scheer and Habermann 2000);
(g) the improvement of data integration and reporting systems (Scapens and
Jazayeri 2003).
On the other hand, BI systems can improve the information flow within the
company because they:
(a) support companies in proactively managing customer needs (Rud 2009);
(b) provide tools for real-time monitoring of customer needs;
(c) allow real-time interactions among team members (Berthold et al. 2010;
Horvath 2001; Matei 2010; Rizzi 2012; Rud 2009);
(d) support a collaborative decision-making process (Berthold et al. 2010);
(e) promote business value creation by enabling collaboration among the actors
involved in the value chain;
(f) allow companies to adapt the demand to the changing needs of customers
(Horvath 2001);
(g) improve knowledge management and the knowledge repository (Lee and Lan
2007; Rizzi 2012);
(h) improve the information flow through OLAP reporting technologies (Ranjan
2009).
Based on the above considerations, we pose the following research question:
• RQ 3b: “Does the combined adoption of ERP and BI systems matter more to the
features of information flow than does the single adoption of an ERP or BI
system?”
4.2.7 Literature Review on Information Quality
The literature shows several contributions involving information quality: part of the
literature considers information quality as a feature—or a driver—of information
systems quality (DeLone and McLean 1992a; Nelson et al. 2005), whereas other
contributions in the literature attempt to assess the information quality by proposing

frameworks or methodologies (Bovee et al. 2003; Lee et al. 2002; Stvilia et al.
2005). Lee et al. (2002), for example, try to identify the business areas which have a
need for information quality improvements by analyzing five major organizations.
The aim of their research is to propose a methodology for assessing and improving
information quality within a company. Stvilia et al. (2005) propose an information
quality assessment framework by considering several typologies of information
quality problems, related activities, and a taxonomy of information quality.
DeLone and McLean (1992a), along with Nelson et al. (2005), are instead
examples of studies which involve information quality as an item affecting infor-
mation system quality. Nelson et al. (2005) conducted a study on data warehouse
users, developing a model based on nine determinants of IT environment quality,
four of which were related to information quality. The interesting thing is that the
information quality features—accuracy, completeness, currency and format of
information—are believed to play a significant role in explaining information
system quality. Other studies, aimed at identifying the characteristics that make an
information system of high-quality, emphasize the crucial role played by infor-
mation quality (DeLone and McLean 1992b, Petter et al. 2013b).
Bessa et al. (2016) and Xu et al. (2013), examine the determinants of information
system quality, including among these information quality, also underlining that
different, or more specific, needs can arise depending on the business and on the
evolution of technology. Therefore, they indirectly suggest that information quality,
along with information system quality, is a contingent factor.
These considerations are recognizable in a wide stream of studies on the role of
data and information quality in improving the quality of information systems (Kahn
et al. 2002; Madnick et al. 2009; Pipino et al. 2002; Redman and Blanton 1997; Xu
et al. 2002b). Studies on the impacts of data and information quality have been
carried out to trigger positive impacts and disable negative ones. Scant data quality
could, in fact, cause difficulties in the retrieval of business records (Mikkelsen and
Aasly 2005), as it obstructs the possibility of providing the right information to the
right stakeholder. Other studies underline that information quality is the basis for a
quality decision-making process (Calvasina et al. 2009; Caserio 2011; Fisher et al.
2003).
Information quality is thus a key factor for several reasons. The literature sug-
gests several definitions of information quality, which, for example, could be
considered as the fitness of user needs, as defined by Juran (1992) and Strong et al.
(1997). Other studies, in proposing a definition of information quality, focus
attention on information users by considering that information quality is the
capacity to meet or exceed information users’ expectations (Evans and Lindsay
2002; McClave et al. 1998). Information quality can also be defined as the
coherence of information with regard to the specifications of the product or the
service to which it refers (Kahn et al. 2002; Reeves and Bednar 1994; Zeithaml
et al. 1990). According to this interpretation, high-quality information provides an
accurate representation and meets the requirements of the final user. Naturally, the
coherence and the usefulness of information also depend on the initial data quality
(Piattini et al. 2012). The importance of the information user perspective is also
confirmed by the theory of information use environments suggested by Taylor
(1991), which states that the long-term information needs of users are directly
linked to their professional activities. The focus of Redman (1992) is still on the
information user, even though he uses a more structured definition: information is
of higher quality if it is more satisfactory than other information the user needs. In
an earlier work, Eppler (2003) defines information quality under a subjective and an
objective dimension; the former is the fitness of user expectations and the latter is
the satisfaction of activity requirements.
In addition to the definitions of information quality, the literature provides some
interpretations and dimensions of information quality. Information quality could be
intrinsic, contextual, representational and related to accessibility (Lee et al. 2002;
Ballou and Pazer 1985; DeLone and McLean 1992b; Goodhue 1995; Jarke and
Vassiliou 1997; Wand and Wang 1996; Wang and Strong 1996; Zmud 1978). The
intrinsic information quality includes dimensions related to the accuracy, objectivity
and precision of information; this interpretation derives from the initial theoretical
bases of Gorry and Scott Morton’s (1971) framework regarding the information
features of structured problems. Information quality could be contextual, as it
depends on the capacity of information to be relevant, useful, complete, reliable and
timely, able to add value and to meet users’ expectations, despite the continuous
changes in the (external and internal) context. Information quality could be rep-
resentational, that is, related to the capacity of the information to represent the
problem to which it refers; information has to be understood and effectively
implemented in the decision-making process.
According to some authors, the quality of information depends on several
attributes, which could be summarized in three main dimensions (Marchi 1993;
O’Brien and Marakas 2006):
• time: the information must be provided when it is needed;
• content: information must be accurate, correct, relevant, complete, concise, and
reveal knowledge about what it refers to;
• form: the information must be clear, detailed, formatted as required, and ordered
in a sequence as needed.
Following another study, the dimensions of information quality could be listed
in a more detailed manner, as shown in Table 4.2, which is adapted from Kahn
et al. (2002).
Interesting to note among the features of information quality are “appropriate
amount of information”, “completeness” and “concise representation”, which, in a
certain way, pertain to the information overload/underload issues. For our purposes,
we will consider information quality as the capacity of information the meet the
decision-maker’s needs.
Table 4.2 Features of information quality (Source Adapted from Kahn et al. 2002)
Features Definitions
Accessibility The possibility to easily retrieve the information
Appropriate amount of The coherence between the amount of information and the task to
information be carried out
Believability The truthfulness of the information
Completeness The capacity of information to provide all the details useful for
the task
Concise representation The correct synthesis/analysis level of information
Consistent representation The capacity of information to be provided in the same format
Ease of manipulation The possibility to use the same information for different tasks
Free of error The correctness and reliability of information
Interpretability The information is represented using an appropriate format,
language, symbols
Objectivity The information is unbiased, neutral, impartial
Relevancy The helpfulness of information for the tasks
Reputation The information is highly regarded in terms of its source or
content
Security The access restrictions to information are appropriately managed
Timeliness The capacity of information to be up-do-date
Understandability The capacity to easily comprehend the information
Value-added The capacity of information to be beneficial and to bring
advantages from its use
4.2.8 Links between Features of Information Flow

and Information Quality
Information quality has significant economic implications for a company since

non-quality information generates costs. These costs mainly pertain to the waste of
time for decision makers trying to find the most appropriate information for their
needs. Poor information quality forces decision makers to interpret the inaccurate
information, and inaccuracy may cause problems for the business activities
(Farhoomand and Drury 2002). The magnitude of the problem depends on the type
of errors that inaccuracy may cause.
The costs of non-quality information also consist in data correction, the recovery
of process failure and other similar activities, which consume more computing
resources than would be necessary if the information were accurate. Similarly,
because of non-quality information, redundant controls on data and information will
need to be activated to prevent decision-making errors (English 2002).
In this regard, the literature suggests that information overload is also one of the
costs of poor information quality. In the presence of information overload, man-
agers might not be able to effectively manage their decision-making process, as the
information available is too abundant, or irrelevant. In these situations, managers

are not able to prioritize their tasks, and thus their decision-making process col-
lapses (Kock 2000b). Interestingly, some studies recognize the costs of information
overload as similar to those of non-quality information; in fact, information over-
load is considered as a phenomenon which affects individuals, organizations and
decision-making processes because it causes a waste of time in processing redun-
dant information from multiple sources on the same topic (Farhoomand and Drury
2002; Yang et al. 2009). When a manager receives (a lot of) irrelevant information
instead of (a small amount of) relevant information, he/she is not able to accomplish
his/her job. Therefore, a situation of information underload happens when managers
receive less than the amount of information they would need to accomplish their
decision-making process and when they receive irrelevant instead of relevant
information (Kock 2000b; O’Reilly 1980). Information overload often results from
poor quality information; that is, uncertain, ambiguous and complex information
(Schneider 1987).
The link between information quality and information overload is also recog-
nizable from the countermeasures suggested by some authors. For example, Eppler
and Mengis (2004b) propose that, to avoid information overload, companies should
implement intelligent information management, e-tools, decision support systems
and information quality filters that can prioritize information and reduce a wide set
of alternatives to a more manageable size. This implies that by improving some
dimensions of information quality (such as relevancy, accessibility, credibility),
information overload should also decrease. Other authors belong to this line of
thought: they suggest that information overload could be reduced by investing in
information visualization systems, which simplify the retrieval, recognition, pro-
cessing and recall of information (Strother et al. 2012; Chen and Yu 2000). Larkin
and Simon (1987) show that visualization techniques dramatically improve peo-
ple’s capacity to recognize patterns, distinguish various pieces of information, and
focus on the most relevant ones. The authors show that these systems allow people
to process information as experts could. The literature also reveals that experts are
less subject to information overload than novices are when facing the same volume
of information (Agnew and Szykman 2005; Swain and Haka 2000). Taken together,
these studies suggest that visualization is a possible countermeasure to information
overload, as it increases information quality and the features of information flow.
Information processing capacity would increase by means of the synthetic and
systemic representation of information; communication would improve because
messages would be more selective and, consequently, reports would better signal
the relevant information. As a confirmation of this, the literature shows that
information visualization techniques can improve the quality of information, as they
reduce information complexity and help to focus on the relevant details (Burkhard
and Meier 2005; Shneiderman 1996).
Furthermore, another study states that to reduce information distortion, and thus
to improve the quality of shared information, it is necessary that the information
shared be as accurate as possible (Li and Lin 2006). In other words, this confirms
that features of information quality (such as accuracy) are linked to the features of
information flow (such as the sharing of information). Other authors consider the
information flow as an important dimension of information quality; in fact, an
effective information flow allows information system users to receive (Al-Hakim
2007):
• complete information; that is, the correct amount of information;
• information selected on the base of relevancy;
• timely information;
• up-to-date information;
• information at the required time;
• accessible information.
Similarly, Bosset (1991), Evans and Lindsay (2002) and Fadlalla and
Wickramasinghe (2004) emphasize the importance of information flow in
improving the effectiveness of the decision-making process. Based on these con-
siderations, we posit the following research question:
• RQ4: “Do the features of information flow affect the information quality per-
ceived by managers?”
4.3 Sample Selection and Data Collection
To answer the research questions, we conducted a survey on a sample of 300 Italian

managers of Italian listed and non-listed companies of different size. The partici-
pants—Chief Information Officers, Chief Technology Officers, Chief Executive
Officers and Controllers—were randomly selected from the LinkedIn social net-
work database, since some scholars have recently stressed the relevance and
widespread use of this social media application (Albrecht 2011). Furthermore, the
growing interest paid to LinkedIn by practitioners has also been documented by the
Association of Accounting Marketing (AAM 2011).
The main aim of the survey is to test the research design and elicit preliminary
evidence from our study (Gable 1994). The survey was divided into 6 sections as
follows: (1) personal data of the interviewees; (2) features of the firms; (3) the
quality features of the accounting information system; (4) communication and
reporting; (5) information overload and underload; and (6) overall judgement on IS
and suggestions. Since the empirical analysis is based on a survey, most of the
research variables measure the managers’ perceptions, which could be interpreted
as their satisfaction with the survey issues (Dillman 2008). We received back 79
answers, with a 26% rate of response.
A test on an early-late response was conducted on the control variables “gender”,
“type of firms” and “sector” to check for differences in the two groups following a
wave analysis proposed by Rogelberg and Stanton (Rogelberg and Stanton 2007).
The results of a two-sample t test with equal variances showed that the mean
differences of the variables were not statistically significant, and therefore the
hypothesis of bias between early and late respondents in the surveyed sample can
be rejected. All the statistical analyses were performed with SPSS 20.0.
4.4 Variable Measurement
4.4.1 Research Variable Measurement
Surveys are useful in defining the research and control variables. In our study, the
survey allows us to detect:
• ERP adoption (the survey question is “Does your firm adopt an ERP system?”
1 = Yes; 0 = No); and
• Business Intelligence adoption (the survey question is “Does your firm use
systems of Business Intelligence?” 1 = Yes, 0 = No).
We created the research variable “ERP and Business Intelligence”, which
assumes a value of 1 when respondents adopt both ERP and Business Intelligence
and a value of 0 when respondents do not adopt any of the two systems.
With reference to information processing capacity (Tables 4.3 and 4.4), we use
the following items:
• Data accuracy (the survey question is “What is your perception of the accuracy
of data to perform your tasks?” 1 very low,…, 7 very high);
• Timeliness of data; (the survey question is “What is your perception of the
timeliness of data to perform your tasks?” 1 very low,…, 7 very high);
• System reliability (the survey question is “What is your perception of the
capacity of the information system to address the right choice to the right person
at the right moment?” 1 very low,…, 7 very high).
Regarding communication and reporting (Tables 4.5 and 4.6), the items are:
• Monthly reporting frequency (the survey question is “What is, on average, the
number of reports issued in one month? 1 very low,…, 7 very high);
• 6-month reporting frequency (the survey question is “What is, on average, the
number of reports issued over a six-months period? 1 very low,…, 7 very high);
• Annual reporting frequency (the survey question is “What is, on average, the
number of reports issued annually?, 1 very low,…, 7 very high);
Table 4.3 Items included in the information processing capacity research variable
Research variable Items in the research variable
Information processing capacity Data accuracy
System reliability
Timeliness of data
4.4 Variable Measurement 91
Table 4.4 Measurement of Items in the research variable Scale of measurement

the items included in the
information processing Data accuracy Value on a scale of 1–7
capacity research variable System reliability Value on a scale of 1–7
Timeliness of data Value on a scale of 1–7
Table 4.5 Items included in Research variable Items in the research variable
the communication and
reporting research variable Communication and Flash reporting frequency
reporting Monthly reporting frequency
6-month reporting frequency
Annual reporting frequency

communication and reporting Flash reporting frequency Value on a scale of 1–7
research variable Monthly reporting frequency Value on a scale of 1–7
6-month reporting frequency Value on a scale of 1–7
Annual reporting frequency Value on a scale of 1–7
• Flash reporting frequency (the survey question is “How often are flash reports
issued? 1 very low,…, 7 very high).
With regard to Information Sharing (Tables 4.7 and 4.8), we consider the fol-
lowing items:
• Satisfaction about the sharing of information with colleagues at the same
hierarchical level (the survey question is “What is your satisfaction about the
information sharing with colleagues at the same hierarchical level?” 1 very low,
…, 7 very high);
• Satisfaction about the sharing of information with colleagues at higher hierar-
chical levels (the survey question is “What is your satisfaction about the
information sharing with colleagues at higher hierarchical levels?” 1 very low,
…, 7 very high).
With regard to Frequency of Meeting (Tables 4.9 and 4.10), the items are:
Table 4.7 Items included in the information sharing research variable

Research Items in the research variable
variable
Information Satisfaction about the sharing of information with colleagues at the same
sharing hierarchical level
Satisfaction about the sharing of information with colleagues at higher
hierarchical levels
Table 4.8 Measurement of the items included in information sharing research variable
Items in the research variable Scale of
measurement
Satisfaction about the sharing of information with colleagues at the same Value on a scale of
hierarchical level 1–7
Satisfaction about the sharing of information with colleagues at higher Value on a scale of
hierarchical levels 1–7
• Frequency of meetings with colleagues at the same hierarchical level (the survey
question is “How often do you have meetings with colleagues at the same
hierarchical level?” 1 very rarely,…, 7 very often);
• Frequency of meetings with colleagues at higher hierarchical levels (the survey
question is “How often do you have meetings with colleagues who belong to
higher hierarchical levels?” 1 very rarely,…, 7 very often).
The perception of information overload and underload is measured according to
the prior literature (O’Reilly 1980; Karr-Wisniewski and Lu 2010). In particular,
information underload is measured through the following items (Tables 4.11 and
4.12):
• Less information (the survey question is “How often do you realize you have
less than the amount of information you would need to make the best possible
decision? 1 very rarely,…, 7 very often);
• Fewer IT resources (the survey question is “How often do you realize you have
fewer than the amount of IT resources you would need to make the best possible
decision? 1 very rarely,…, 7 very often);
• No information (the survey question is “How often do you feel you are not
receiving all the information you need? 1 very rarely,…, 7 very often).
The information overload is measured through the following items (Tables 4.13
and 4.14):
• More information (the survey question is “How often do you realize you have
more than the amount of information you would need?” 1 very rarely,…, 7 very
often);
• Too many IT resources (the survey question is “How often do you realize you
have too many alternative technologies to use for the same problem?” 1 very
rarely,…, 7 very often);
• Too much information (the survey question is “How often do you realize you
are receiving too much information with respect to the amount you would need?
1 very rarely,…, 7 very often);
Table 4.9 Items included in the frequency of meeting research variable

Research variable Items in the research variable
Frequency of meeting Frequency of meetings with colleagues at the same hierarchical level
Frequency of meetings with colleagues at higher hierarchical levels
4.4 Variable Measurement 93
Table 4.10 Measurement of the items included in the frequency of meeting research variable
Items in the research variable Scale of measurement
Frequency of meetings with colleagues at the same hierarchical Value on a scale of 1–7
level
Frequency of meetings with colleagues at higher hierarchical levels Value on a scale of 1–7
Table 4.11 Items included Research variable Items in the research variable
in the information underload
research variable Information underload Fewer IT resources
Less information
No information

information underload Fewer IT Resources Value on a scale of 1–7
research variable Less Information Value on a scale of 1–7
No Information Value on a scale of 1–7
Table 4.13 Items included Research variable Items in the research variable
in the information overload
research variable Information overload More IT resources
More information
Too many IT resources
Too much information

information overload research More IT resources Value on a scale of 1–7
variable More information Value on a scale of 1–7
Too many IT resources Value on a scale of 1–7
Too much information Value on a scale of 1–7
• More IT resources (the survey question is “How often do you realize you have
more IT resources than you would need?” 1 very rarely,…, 7 very often);
We also use another research variable that measures the perception of respon-
dents about the absence of both information overload and information underload.
This variable is called “Perceived Information Quality” (the survey question is “To
what extent do you perceive that the amount of information you receive is
appropriate to allow you to optimally execute your tasks?)”1 very low,…, 7 very
high). Therefore, we assume that this variable measures the respondents’ perception
of information quality (Table 4.15).
Table 4.15 Measurement of the perceived information quality research variable

Items in the research variable Scale of measurement
Perceived information quality Value on a scale of 1–7
4.4.2 Variable Measurement: Control Variables
The control variables, shown in Tables 4.16 and 4.17, regard either respondents’
features or firms’ features. The control variables for the former are the following:
• Role (1 if the respondent is a controller, 2 if the respondent is a Chief
Information Officer, 3 if the respondent is a Chief Executive Officer, 4 if the
respondent is head of the accounting information system, 5 if the respondent is
Chief Technology Officer, and 6 if the respondent is a Chief Financial Officer);
• Education (1 if the respondent has only a secondary school diploma, 2 if the
respondent has a bachelor or master degree, 3 if the respondent has a
post-master degree, 4 if the respondent has a PhD).
• Gender (0 if the respondent is a male and 1 if the respondent is a female).
The control variables for the firms’ features are:
• Firm size (1) if the respondent works in a small firm, (2) if the respondent works
in a medium firm, (3) if the respondent works in a medium/large firm, and (4) if
the respondent works in a large firm);
• Sector (1) if the firm belongs to the service sector, (2) if the firm belongs to the
industrial sector, (3) if the firm belongs to the financial sector, and (4) if the firm
belongs to the public sector);
• Type of firm (1) if the firm is listed, (2) if the firm is non-listed, (3) if the firm is
non-profit, and (4) if the firm is public).
Table 4.16 Control variables Dimensions Control variables

Respondents’ features Role
Gender
Education
Firms’ features Firm size
Sector
Type of firm
Table 4.17 Measurement of Control variables Scale of measurement

the control variables
Role Value on a scale of 1–6
Gender Dichotomous variable
Education Value on a scale of 1–4
Firm size Value on a scale of 1–4
Sector Value on a scale of 1–4
Type of firm Value on a scale of 1–4
4.5 Factor Analysis 95
4.5 Factor Analysis
The first step in our empirical analysis was to perform a principal component
analysis (Tables 4.18, 4.19, 4.20, 4.21, 4.22 and 4.23) to construct the research
variables and their components (Williams et al. 2012). A principal component
analysis is a statistical procedure which uses an orthogonal transformation to
convert a set of observations of possibly correlated variables into a set of values of
linearly uncorrelated variables called principal components (Niculescu et al. 2016).
To test the validity and reliability of the factor analysis, we performed a
Keiser-Meyer-Olkin (KMO) test to determine the sampling adequacy (Kaiser
1960), a Bartlett’s sphericity test (Snedecor and Cochran 1989), and we used the
analysis of Cronbach’s alpha to assess the scale reliability (Nunnally and Bernstein
1994). We also checked for the eigenvalue of each item in order to determine how
many factors should be retained in the analysis (Hayton et al. 2004).
Eigenvalues greater than 1 are associated with retained factors (Kaiser 1960). As
shown in Tables 4.18, 4.19, 4.20, 4.21, 4.22 and 4.23, the reliability of the factor
analysis is satisfactory for each item. The KMO measure of sampling adequacy
Table 4.18 Factor analysis for the information processing capacity research variable
Item Factor Communality Eigen % of Cronbach’s Bartlett’s KMO
loading value variance alpha sphericity test
Data 0.912 0.832 2.363 78.754 0.865 Chi2 = 113.570 0.719
accuracy p-value = 0.000***
Timeliness 0.897 0.804 0.398 13.263
of data
System 0.853 0.727 0.239 7.983
reliability
*, **, *** indicate a significance degree between 0.10 and 0.05, 0.05 and 0.01, and 0.01 and 0, respectively
Table 4.19 Factor analysis for the communication and reporting research variable
Monthly 0.788 0.622 2.537 63.432 0.803 Chi2 = 137.257 0.628
reporting p-value = 0.000***
frequency
6-month 0.921 0.847 0.785 19.626
reporting
frequency
Annual 0.789 0.622 0.520 12.989
reporting
frequency
Flash 0.668 0.446 0.158 3.954
reporting
frequency
96
Table 4.20 Factor analysis for the information sharing research variable
Loading value variance alpha sphericity test
Satisfaction about the sharing of information with colleagues 0.868 0.754 1.508 75.412 0.674 Chi2 = 22.860 0.500
at the same hierarchical level p-value = 0.000***
Satisfaction about the sharing of information with colleagues 0.868 0.754 0.492 24.588
at higher hierarchical levels
4 ERP and BI as Tools to Improve Information Quality …
4.5 Factor Analysis
Table 4.21 Factor analysis for frequency of meeting research variable

Loading value variance alpha sphericity test
Frequency of meetings with colleagues at the 0.898 0.807 1.615 80.727 0.761 Chi2 = 36.281 0.500
same hierarchical level p-value = 0.000***
Frequency of meetings with colleagues at higher 0.898 0.807 0.385 19.273
hierarchical levels
97
Table 4.22 Factor analysis for the information overload research variable
More 0.649 0.421 2.454 61.361 0.648 Chi2 = 104.011 0.784
information p-value = 0.000***
Too much 0.824 0.678 0.752 18.793
information
More IT 0.733 0.538 0.585 14.622
resources
Too many 0.904 0.817 0.209 5.224
IT resources
Table 4.23 Factor analysis for the information underload research variable
No 0.905 0.820 2.347 78.226 0.860 Chi2 = 103.028 0.714
information p-value = 0.000***
Fewer IT 0.841 0.707 0.419 13.966
resources
Less 0.906 0.820 0.234 7.808
information
achieves satisfactory levels, as it is higher than 0.5 (Hair et al. 2006) in all cases.
Similarly, the Bartlett’s test reports a satisfactory level of goodness-of-fit for each
component of the research variables (Snedecor and Cochran 1989). Communality
values are consistently higher than the threshold level of 0.50. The only two items
showing a communality value below the threshold level are Flash Reporting
Frequency, inside the research variable Communication and Reporting, and More
Information, inside the research variable Information Overload. Both items are
considered important in the accounting information literature: daily flash reports are
of increasing importance, especially in sectors when managers need to constantly
track sales performance and related trends in order to define appropriate marketing
policies (Bog et al. 2011) and when management requires a quick presentation of
the overall financial strength of the company (Basile et al. 2002). The item “more
information” represents the situation in which managers feel they need to receive
more information than they would need to accomplish their tasks; this situation is
considered by the literature as a condition of information overload to be avoided
(Galbraith 1974; Wärzner et al. 2017). However, searching for more information
makes managers feel more confident and secure about the problems to be solved
(O’Reilly 1980).
4.5 Factor Analysis 99
Moreover, the scale reliability for each component is very good, achieving a
level of 0.865 for Information Processing Capacity, 0.803 for Communication and
Reporting, 0.674 for Information Sharing, 0.761 for Frequency of Meeting, 0.648
for Information Overload, and 0.860 for Information Underload.
The factor analysis confirms the previous literature and frameworks by identi-
fying which items could be encompassed in each research variable.
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Chapter 5
ERP and BI as Tools to Improve
Information Quality in the Italian
Setting: Empirical Analysis
Abstract This chapter presents the results of our survey. Empirical results from the
entire datasets of respondents demonstrated that respondents adopting an ERP or a
BI system—or both an ERP and a BI system—do not perceive higher or lower
information overload or information underload. Furthermore, respondents who
have implemented an ERP perceive a higher level of information processing
capacity, a higher level of communication and reporting, and a higher level of
frequency of meeting than do respondents who have not implemented an
ERP. Respondents who have implemented a BI perceive a higher level of infor-
mation processing capacity compared to respondents who have not implemented a
BI. Respondents who have implemented both an ERP and a BI system perceive a
higher level of information processing capacity than do respondents who have not
implemented an ERP or a BI. Results from the regression analysis show that
information processing capacity has a positive effect on the information quality
perceived by managers; therefore, if the information processing capacity increases,
the information quality perceived by respondents increases as well. Furthermore,
results show that communication and reporting has a negative effect on the infor-
mation quality perceived by respondents, so that if communication and reporting
increases, the information quality decreases.
5.1 Introduction
This chapter presents descriptive statistics and a correlation analysis of the research
and control variables for the entire dataset (Sect. 5.2). Section 5.3 proposes the
research method, the t-test and the regression analysis; Sect. 5.4 presents empirical
results from the regression analysis and the t-test applied to the entire dataset of
respondents. These analyses allow us to answer the research questions presented in
Chap. 4. Section 5.5 presents the empirical results for the sub-sample of Chief
Information Officers. Finally, Sect. 5.6 presents the summary results for the whole
dataset of respondents and for the dataset of CIOs.

https://doi.org/10.1007/978-3-319-77679-8_5
5.2 Descriptive Statistics and Correlation Analysis
Table 5.1 shows the frequency distribution of the dichotomous research variables
(ERP Adoption, Business Intelligence Adoption, and ERP and Business
Intelligence); Table 5.2 shows some descriptive statistics of the research variables.
In particular, Table 5.1 indicates that 78.5% of respondents declare they adopt ERP
in their firms, 68.4% of respondents adopt Business Intelligence, and 60.7% of
respondents adopt both ERP and Business Intelligence in their firms.
Table 5.2 shows that the highest mean value is linked to Information Sharing,
whereas the lowest mean value is linked to Information Overload.
Furthermore, we ranked research variables according to their mean value in a
descending order, applying a one-sample t-test (Table 5.3) to determine whether the
mean response was significantly different from the indifference level (whose value
is 4). Table 5.3 shows that, among the research variables, Information Sharing and
Information Overload show a significant mean difference from the indifference
level.
Table 5.4 shows the descriptive statistics for the items included in the
Information Processing Capacity variables. Regarding the items included in this
variable, the highest mean value is linked to Data Accuracy, whereas the lowest
mean value is linked to Timeliness of Data. Table 5.5 shows the descriptive
statistics for the items included in the Communication and Reporting variable. For
items included in this variable, the highest mean value is linked to Flash Reporting
frequency, whereas the lowest mean value is linked to 6-month reporting frequency.
Table 5.1 Frequency distribution of the dichotomous research variables (Number of observa-
tions: 79)
Research variables Frequency distribution for
survey questions (in %)
0 1
ERP adoption 21.5 78.5
Business intelligence adoption 31.6 68.4
ERP and business intelligence 39.2 60.8
Table 5.2 Descriptive statistics of the research variables (Number of observations: 79)
Research variables Minimum Maximum Mean Standard
deviation
Information processing capacity 1 7 4.215 1.501
Communication and reporting 1 7 4.003 1.484
Information sharing 1 7 4.455 1.474
Frequency of meeting 1 7 4.000 1.494
Information underload 1 7 3.900 1.591
Information overload 1 5.25 2.493 1.082
Perceived information quality 2 7 4.18 1.326
5.2 Descriptive Statistics and Correlation Analysis 107
Table 5.3 One-sample t-test on the research variables (Number of observations: 79)
deviation
Information sharing 1 7 4.455*** 1.474
Information processing capacity 1 7 4.215 1.501
Perceived information quality 2 7 4.18 1.326
Communication and reporting 1 7 4.003 1.484
Frequency of meeting 1 7 4.000 1.494
Information underload 1 7 3.900 1.591
Information overload 1 5.25 2.493*** 1.082
*, **, *** indicate a significance degree between 0.10 and 0.05, 0.05 and 0.01, and 0.01 and 0,
respectively
Table 5.4 Descriptive statistics of items included in the information processing capacity variable
(Number of observations: 79)
deviation
Data accuracy 1 7 4.43 1.707
System reliability 1 7 4.18 1.623
Timeliness of data 1 7 4.04 1.743
Table 5.5 Descriptive statistics of items included in the communication and reporting variable
(Number of observations: 79)
deviation
Flash reporting frequency 1 7 4.32 2.01
Monthly reporting frequency 1 7 4.13 1.793
6-month reporting frequency 1 7 3.75 1.815
Annual reporting frequency 1 7 3.82 1.893
Table 5.6 shows the descriptive statistics for the items included in the Frequency
of Meeting variable. For items included in this variable, the highest mean value is
linked to Frequency of meetings with colleagues at the same hierarchical Level.
Information Sharing variable. Regarding items included in this variable, the highest
mean value is linked to Satisfaction about the sharing of information with col-
leagues at higher hierarchical levels.
Information Underload variable. The highest mean value for items included in this
variable is linked to Fewer IT resources, whereas the lowest mean value is linked to
No information.
Table 5.6 Descriptive statistics of items included in the frequency of meeting variable (Number
of observations: 79)
deviation
Frequency of meetings with colleagues at 1 7 4.16 1.589
the same hierarchical level
Frequency of meetings with colleagues at 1 7 3.84 1.735
higher hierarchical levels
Table 5.7 Descriptive statistics of items included in the information sharing variable (Number of
observations: 79)
deviation
Satisfaction about the sharing of information with 1 7 4.28 1.632
colleagues at the same hierarchical level
Satisfaction about the sharing of information with 1 7 4.63 1.763
colleagues at higher hierarchical levels
Table 5.8 Descriptive statistics of items included in the information underload variable (Number
deviation
Fewer IT resources 1 7 4.14 1.946
No information 1 7 3.7 1.793
Less information 1 7 3.86 1.669
Information overload variable. The highest mean value for items included in this
variable is linked to More information, whereas the lowest mean value is linked to
Too much information.
Table 5.10 shows the frequency distribution of the control variables.
Results from the zero-order correlation analysis of the research variables are
presented in Table 5.11. Information Processing Capacity has moderate positive
correlations with Communication and Reporting, Information Sharing, Frequency of
Table 5.9 Descriptive statistics of items included in the information overload variable (Number
deviation
More information 1 7 2.67 1.395
More IT resources 1 6 2.51 1.324
Too many IT resources 1 7 2.45 1.395
Too much information 1 7 2.34 1.436
5.2 Descriptive Statistics and Correlation Analysis 109
Table 5.10 Frequency distribution of the control variables (Number of observations: 79)
Control Frequency distribution for survey questions (in %)
variables 0 1 2 3 4 5 6
Gender 96.2 3.8
Role 10.1 46.8 8.9 10.1 16.5 7.6
Education 21.5 49.4 24.1 5.1
Type of firm 24.1 65.8 5.1 5.1
Firm size 8.9 36.7 25.3 29.1
Sector 58.2 38.0 1.3 2.5
Meeting, and Perceived Information Quality. Furthermore, Information Processing

Capacity has moderate negative correlations with Information Underload and
Gender. Communication and Reporting has moderate positive correlations with
Information Sharing, Frequency of Meeting and Firm Size. Information Sharing has
moderate positive correlations with Frequency of Meeting, Perceived Information
Quality and Firm Size, and a negative moderate correlation with Information
Underload. Frequency of Meeting has moderate positive correlations with Perceived
Information Quality and Firm Size and a negative moderate correlation with Gender.
Information Underload has a moderate negative correlation with Perceived
Information Quality. Information Overload has positive moderate correlations with
Perceived Information Quality and Sector. Firm Size has a positive moderate cor-
relation with Sector and a negative moderate correlation with Type of Firms.
The zero-order correlation analysis demonstrates that our results are not affected
by issues of collinearity (Cohen et al. 2013).
5.3 Research Models
5.3.1 T-Test
We used non-parametric tests (Beattie and Pratt 2003; Beattie and Smith 2012) to
answer our research questions and performed a t-test analysis to check for any
differences between the perception of respondents (1) who adopted ERP and those
who did not; (2) who adopted Business Intelligence and those who did not; and
(3) who adopted both Business Intelligence and ERP and the other respondents. By
doing so, we were able to answer RQ1a, RQ1b, RQ2a, RQ2b, RQ3a and RQ3b.
5.3.2 Regression Analysis for Research Variables
Furthermore, to answer research question 4 (RQ4) we performed a regression

analysis on the research variables (identified in Chap. 4) created based on the factor
analysis (Chap. 4).
Table 5.11 Correlation matrix of the research variables (79 observations)
110
Information Communication Information Frequency Information Information Perceived Gender Education Role Firm size Sector Type
processing and reporting sharing of meeting underload overload informa-tion of firm
capacity quality
Information 1
processing
capacity
Communication 0.336** 1
and reporting 0.002
Information 0.633** 0.238* 1
sharing 0.000 0.035
Frequency of 0.564** 0.329** 0.562** 1
meeting 0.000 0.003 0.000
Information −0.484** −0.130 −0.337** −0.175 1
underload 0.000 0.274 0.004 0.139
Information 0.038 0.086 0.184 0.132 0.090 1
overload 0.751 0.470 0.119 0.267 0.451
Perceived 0.579** 0.104 0.426** 0.392** −0.384** 0.286* 1
information 0.000 0.380 0.000 0.001 0.001 0.014
quality
Gender −0.265* −0.113 −0.197 −0.223* 0.064 0.001 −0.150 1
0.018 0.323 0.081 0.048 0.592 0.993 0.205
Role 0.185 0.174 0.181 0.081 −0.099 −0.124 0.260* −0.216 0.188 1
0.102 0.126 0.110 0.477 0.403 0.294 0.027 0.056 0.097
Education −0.182 −0.057 −0.195 −0.117 0.050 −0.084 −0.164 0.051 1
0.109 0.620 0.086 0.304 0.673 0.479 0.166 0.654
Firm size 0.142 0.269* 0.187 0.328** −0.064 0.171 0.083 −0.084 0.090 −0.130 1
0.212 0.016 0.098 0.003 0.591 0.149 0.483 0.460 0.432 0.253
Sector −0.046 −0.080 −0.097 0.065 0.076 0.273* 0.028 0.056 0.05 −0.172 0.231* 1
0.690 0.481 0.396 0.568 0.521 0.019 0.816 0.621 0.968 0.129 0.040
Type of firm −0.112 −0.092 −0.128 −0.122 0.123 −0.048 −0.086 −0.086 −0.139 0.035 −0.350** 0.121 1
0.328 0.419 0.262 0.283 0.301 0.685 0.469 0.469 0.223 0.761 0.002 0.287
5 ERP and BI as Tools to Improve Information Quality …
p values are in brackets
5.3 Research Models 111
(1) Y = b0 + b1 Information Processing Capacity + b2 Communication and

Reporting + b3 Information Sharing + b4 Frequency of Meeting + b5
Information Underload + b6 Information Overload + b7 Gender + b8
Role + b9 Education + b10 Sector + b11 Type of firms + b12 Firm Size + e
The dependent variable (Y) is the Perceived Information Quality and represents
the information quality perceived by respondents; the independent variables are the
research variables identified in Chap. 4, which represent the features of the infor-
mation flow. These variables are: Information Processing Capacity, Communication
and Reporting, Information Sharing and Frequency of Meeting. The control vari-
ables are: Information Underload, Information Overload, Gender, Education, Role,
Sector, Type of Firms, and Firm Size.
5.4 Empirical Results
The following sub-sections present the t-test analysis (Sect. 5.4.1) and the empirical
results (Sect. 5.4.2).
5.4.1 T-Test: Empirical Results
We performed a t-test analysis to check for any differences between the perception
of respondents (1) who adopted ERP and those who did not; (2) who adopted
Business Intelligence and those who did not; and (3) who adopted both Business
Intelligence and ERP and the other respondents. We carried out the same analysis
for the research variables identified in Chap. 4 (Sect. 5.4.1.1) and for the survey
items (Sect. 5.4.1.2).
5.4.1.1 T-Test: Empirical Results for the Research Variables
Regarding the research variables, Table 5.12 shows that respondents who imple-
mented an ERP perceived a higher level of Information Processing Capacity than
those who did not (t-test is statistically significant, p value = 0.051). Furthermore,
respondents adopting an ERP perceived a higher level of Communication and
Reporting than did respondents who did not adopt an ERP (t-test is statistically
significant, p value = 0.028) and a higher level of Frequency of Meeting compared
to respondents who did not adopt an ERP (t-test is statistically significant,
p value = 0.099). These results allow us to answer RQ 1b: “Do ERP systems matter
to the features of information flow?”
With regard to RQ 1a: “Do ERP systems matter to information overload and
information underload?”, the results demonstrated that respondents adopting an
Table 5.12 Results of the t-test analysis for research variables comparing respondents who
adopted ERP and those who did not
Item Number of Mean Standard deviation T-test
observations (p value)
Information processing 62 4.38 1.455 0.051*
capacity (with ERP)
Information processing 17 3.58 1.543
capacity (without ERP)
Communication and 62 4.19 1.452 0.028**
Reporting (with ERP)
Communication and 17 3.31 1.427
reporting (without ERP)
Information sharing 62 4.48 1.417 0.748
(with ERP)
Information sharing 17 4.35 1.712
(without ERP)
Frequency of meeting 62 4.14 1.412 0.099*
(with ERP)
Frequency of meeting 17 3.47 1.700
(without ERP)
Information underload 57 3.83 1.623 0.487
(with ERP)
Information underload 16 4.15 1.495
(without ERP)
(with ERP)
Information overload 16 2.70 1.415
(without ERP)
Perceived information 57 4.26 1.330 0.304
quality (with ERP)
Perceived information 16 3.88 1.310
quality (without ERP)
*. **. *** indicate a significance degree between 0.10 and 0.05. 0.05 and 0.01. and 0.01 and 0,
respectively
ERP did not perceive higher or lower information overload or information under-
load compared to the other respondents (t-test for both research variables, infor-
mation overload and information underload, are not statistically significant)
(Table 5.12).
Table 5.13 shows that respondents who have implemented a BI perceive a
higher level of Information Processing Capacity than do respondents who have not
implemented a BI (t-test is statistically significant, p value = 0.075). These results
allow us to answer RQ 2b: “Does a Business Intelligence system matter to the
features of information flow?”.
With regard to RQ 2a: “Does Business Intelligence matter to information
overload and information underload?”, the results demonstrate that respondents
5.4 Empirical Results 113
Table 5.13 Results of the t-test analysis for the research variables comparing respondents who
adopt BI and those who do not
Item Number of Mean Standard deviation T-test
observations (p value)
Information processing 54 4.42 1.443 0.075*
capacity (with BI)
Information processing 25 3.77 1.560
capacity (without BI)
Communication and 54 4.10 1.427 0.411
reporting (with BI)
reporting (without BI)
(with BI)
(without BI)
Frequency of meeting 54 4.12 1.352 0.295
(with BI)
Frequency of meeting 25 3.74 1.762
(without BI)
Information underload 50 3.71 1.592 0.128
(with BI)
Information underload 23 4.32 1.542
(without BI)
(with BI)
Information overload 23 2.29 1.215
(without BI)
Perceived information 50 4.44 1.280 0.012**
quality (with BI)
quality (without BI)
*. **. *** indicate a significance degree between 0.10 and 0.05. 0.05 and 0.01. and 0.01 and 0,
respectively
adopting a BI do not perceive higher or lower information overload or information

underload compared to the other respondents (t-test for both research variables,
information overload and information underload, are not statistically significant)
(Table 5.13). Moreover, respondents using Business Intelligence perceive a higher
level of Perceived Information Quality than do those who do not use Business
Intelligence systems (t-test is statistically significant, p value = 0.012).
Table 5.14 shows that respondents who have implemented both ERP and BI
perceive a higher level of Information Processing Capacity than do respondents
who have not implemented an ERP or a BI (t-test is statistically significant,
p value = 0.058). These results allow us to answer RQ 3b: “Does the combined
Table 5.14 Results of the t-test analysis for the research variables comparing respondents who
adopted both business intelligence and ERP and the other respondents
Item Number of Mean Standard T-test
observations deviation (p value)
Information processing capacity (with ERP and 48 4.47 1.437 0.058*
business intelligence)
Information processing capacity (without ERP 31 3.81 1.534
or business intelligence)
Communication and reporting (with ERP and 48 4.18 1.404 0.197
Communication and reporting (without ERP or 31 3.73 1.585
Information sharing (with ERP and business 48 4.60 1.337 0.268
intelligence)
Information sharing (without ERP or business 31 4.23 1.662
intelligence)
Frequency of meeting (with ERP and business 48 4.13 1.324 0.319
intelligence)
Frequency of meeting (without ERP or business 31 3.79 1.726
intelligence)
Information underload (with ERP and business 44 3.69 1.592 0.166
intelligence)
Information underload (without ERP or business 29 4.22 1.564
intelligence)
Information overload (with ERP and business 44 2.52 1.250 0.818
intelligence)
Information overload (without ERP or business 29 2.46 0.969
intelligence)
Perceived information quality (with ERP and 44 4.39 1.351 0.099*
Perceived information quality (without ERP or 29 3.86 1.246
respectively
adoption of ERP and BI systems matter more to the features of information flow
than does the single adoption of an ERP or BI system?”.
With reference to RQ 3a: “Does the combined adoption of ERP and BI systems
matter more to information overload and information underload than does the
single adoption of an ERP or BI system?”, the results demonstrate that respondents
who adopted an ERP and a BI do not perceive higher or lower information overload
or information underload than do the other respondents (t-test for both research
variables, information overload and information underload, are not statistically
significant) (Table 5.14). Finally, respondents who adopted both ERP and Business
Intelligence perceive a higher level of Perceived Information Quality than do the
other respondents (t-test is statistically significant, p value = 0.099).
5.4.1.2 T-Test: Empirical Results for Survey Items
Regarding the survey items related to ERP system adoption, Table 5.15 shows that
respondents who have implemented an ERP perceive a higher level of data accu-
racy than do respondents who have not (t-test is statistically significant,
p value = 0.013). Furthermore, respondents adopting an ERP perceive a higher
level of System reliability than do respondents who have not (t-test is statistically
significant, p value = 0.017) and a higher frequency of monthly reports, 6-month
reports and flash reports than those who do not adopt an ERP (t-test are statistically
significant, p value = 0.063, 0.078 and 0.049, respectively). Finally, respondents
who have adopted an ERP perceive a higher level of frequency of meetings with
colleagues at the same hierarchical level compared to respondents who have not
(t-test is statistically significant, p value = 0.062). In terms of the other survey
items, Table 5.15 shows that there are no statistically significant differences in the
perception of respondents who have or have not adopted an ERP system.
Table 5.16 shows that respondents who adopt a Business Intelligence system in
their firms perceive a higher level of data accuracy than do respondents who do not
use Business Intelligence (t-test is statistically significant, p value = 0.025).
Furthermore, respondents who adopt Business Intelligence perceive a higher level
of monthly reports than do respondents who have not (t-test is statistically signif-
icant, p value = 0.040) and attribute a lower score to the No information variable
with respect to respondents who have not adopted a Business Intelligence system
(t-test is statistically significant, p value = 0.035). Therefore, respondents who
adopt Business Intelligence perceive they are not receiving all the information they
need more rarely than do those who do not adopt Business Intelligence. Regarding
the other survey items in Table 5.16, the results show that there are no statistically
significant differences in the perception of respondents who adopt or do not adopt
Business Intelligence systems.
Table 5.17 shows that respondents who adopt both an ERP and a Business
Intelligence system perceive a higher level of data accuracy than do the other
respondents in the sample (t-test is statistically significant, p value = 0.012) and a
higher level of system reliability (t-test is statistically significant, p value = 0.076).
Additionally, respondents adopting both ERP and Business Intelligence perceive a
higher number of reports issued in one month than do the other respondents (t-test
is statistically significant, p value = 0.029) and a lower level of no information
(t-test is statistically significant, p value = 0.089). The results show that there are no
other statistically significant differences in the perception of respondents regarding
the other survey items.
Table 5.15 Results of the t-test analysis for items comparing respondents who adopt ERP with
those who do not
Data accuracy (with ERP) 62 4.68 1.647 0.013**
Data accuracy (without ERP) 17 3.53 1.663
Timeliness of data (with ERP) 62 4.08 1.692 0.68
Timeliness of data (without ERP) 17 3.88 1.965
System reliability (with ERP) 62 4.4 1.573 0.017**
System reliability (without ERP) 17 3.35 1.579
Monthly reporting frequency (with ERP) 62 4.32 1.818 0.063*
Monthly reporting frequency (without ERP) 17 3.41 1.543
6-month reporting frequency (with ERP) 62 3.94 1.854 0.078*
6-month reporting frequency (without ERP) 17 3.06 1.519
Annual reporting frequency (with ERP) 62 3.97 1.89 0.196
Annual reporting frequency (without ERP) 17 3.29 1.863
Flash reporting frequency (with ERP) 62 4.55 1.964 0.049**
Flash reporting frequency (without ERP) 17 3.47 2.004
Satisfaction about the information sharing with 62 4.37 1.591 0.339
colleagues at the same hierarchical level (with
ERP)
Satisfaction about the information sharing with 17 3.94 1.784
(without ERP)
colleagues at higher hierarchical levels (with
ERP)
colleagues at higher hierarchical levels (without
ERP)
Frequency of meetings with colleagues at the 62 4.34 1.536 0.062*
same hierarchical level (with ERP)
Frequency of meetings with colleagues at the 17 3.53 1.663
same hierarchical level (without ERP)
Frequency of meetings with colleagues at 62 3.95 1.634 0.258
higher hierarchical levels (with ERP)
Frequency of meetings with colleagues at 17 3.41 2.063
higher hierarchical levels (without ERP)
Less information (with ERP) 57 3.75 1.714 0.297
Less information (without ERP) 16 4.25 1.483
Fewer IT resources (with ERP) 57 4.11 1.934 0.795
Fewer IT resources (without ERP) 16 4.25 2.049
No information (with ERP) 57 3.63 1.867 0.55
No information (without ERP) 16 3.94 1.526
More information (with ERP) 57 2.63 1.345 0.65
(continued)

More information (without ERP) 16 2.81 1.601
More IT resources (with ERP) 57 2.51 1.283 0.982
More IT resources (without ERP) 16 2.5 1.506
Too much information (with ERP) 57 2.25 1.272 0.28
Too much information (without ERP) 16 2.69 1.922
Too many IT resources (with ERP) 57 2.35 1.203 0.245
Too many IT resources (without ERP) 16 2.81 1.94
respectively
5.4.2 Empirical Results for Regression Analysis
The results of the regression analysis for the entire dataset of respondents are
reported in Table 5.18. This analysis allows us to answer RQ4 (Do the features of
information flow affect the information quality perceived by managers?).
Empirical results from the entire datasets of respondents show that the
Information Quality perceived by respondents (Perceived Information Quality) is
affected by Information Processing Capacity, Communication and Reporting,
Information Underload, Information Overload and Education. In particular, the
results show that Information Processing Capacity has a positive effect on the
information quality perceived by managers (b: 0.467, p value: 0.001); therefore, if
the information processing capacity increases, the information quality perceived by
respondents increases as well. Furthermore, results show that Communication and
Reporting has a negative effect on the information quality perceived by respondents
(b: −0.169, p value: 0.096), so that if Communication and Reporting increases, the
information quality decreases.
Among the control variables, Information Underload has a negative effect on
perceived information quality; therefore, if the Information Underload increases, the
perceived information quality decreases. Moreover, results show that Information
Overload has a positive effect on the information quality perceived by managers, so
that if the information overload increases the perceived information quality
increases as well.
Results from the regression analysis allow us to answer RQ4 (Do the features of
information flow affect the information quality perceived by managers?). We found
that some features of information flow can affect the information quality perceived
by managers; specifically, that Information Processing Capacity and
Communication and Reporting affect the perceived information quality differently.
The power of the model fit is high (R2 = 51.9%).
Table 5.19 presents a multicollinearity check for the regression analysis carried
out in this section. The Variance Inflation Factor (VIF) allows us to check for the
presence of multicollinearity. Low values for the VIF index (VIF < 10) and the
correlation matrix entries allow us to reject the hypothesis of multicollinearity for
the entire dataset of non-financial firms (Cohen et al. 2013).
5.5 Additional Analysis: Empirical Results on the Chief

Information Officer Dataset
We carried out an additional analysis on Chief Information Officers (CIOs), since

this professional figure is responsible for the company’s IT system, and thus of the
entire information flow within a firm (Gottschalk 1999). The relevance of his/her
role has particularly increased over the last few years (Bharadwaj 2000; Corsi and
Trucco 2016). Furthermore, CIOs represent the majority (46,8%) of respondents
within our dataset. Therefore, we carried out the regression and the t-test analysis
specifically on this category of interviewees, adopting the same model shown
above.
5.5.1 Regression Analysis for Chief Information Officers
To deepen our research, we performed a regression analysis on the research vari-

ables for CIOs. The regression model can be expressed as follows:
Y = b0 + b1 Information Processing Capacity + b2 Communication and
Reporting + b3 Information Sharing + b4 Frequency of Meeting + b5 Information
Underload + b6 Information Overload + b7 Gender + b8 Education + b9
Sector + b10 Type of firms + b11 Firm Size + e
The dependent variable (Y) represents the Perceived Information Quality,
whereas the independent variables are the research variables identified in Chap. 4,
which represent the features of the information flow. These variables are:
Information Processing Capacity, Communication and Reporting, Information
Sharing, and Frequency of Meeting. The control variables are: Information
Underload, Information Overload, Gender, Role, Sector, Type of Firms, and Firm
Size.
5.5.2 Empirical Results of the Regression Analysis on Chief

Information Officers
The results of the regression analysis on CIOs are reported in Table 5.20. This
analysis allows us to provide a more in-depth answer to RQ4 (Do the features of
information flow affect the information quality perceived by managers?).
5.5 Additional Analysis: Empirical Results … 119
Table 5.16 Results of the t-test analysis for items comparing respondents who adopt business
intelligence with those who do not
Data accuracy (with business intelligence) 54 4.72 1.583 0.025**
Data accuracy (without business intelligence) 25 3.8 1.826
Timeliness of data (with business intelligence) 54 4.19 1.661 0.273
Timeliness of data (without business 25 3.72 1.904
intelligence)
System reliability (with business intelligence) 54 4.35 1.544 0.161
System reliability (without business 25 3.8 1.756
intelligence)
Monthly reporting frequency (with business 54 4.41 1.775 0.040**
intelligence)
Monthly reporting frequency (without business 25 3.52 1.711
intelligence)
6-month reporting frequency (with business 54 3.74 1.855 0.965
intelligence)
6-month reporting frequency (without business 25 3.76 1.763
intelligence)
Annual reporting frequency (with business 54 3.74 1.875 0.575
intelligence)
Annual reporting frequency (without business 25 4 1.958
intelligence)
Flash reporting frequency (with business 54 4.5 1.901 0.235
intelligence)
Flash reporting frequency (without business 25 3.92 2.216
intelligence)
(without business intelligence)
Frequency of meetings with colleagues at the 54 4.31 1.503 0.219
same hierarchical level (with business
intelligence)
same hierarchical level (without business
intelligence)
(continued)

higher hierarchical levels (with business
intelligence)
higher hierarchical levels (without business
intelligence)
Less information (with business intelligence) 50 3.72 1.703 0.284
Less information (without business intelligence) 23 4.17 1.586
Fewer IT resources 50 4 1.969 0.379
respectively
Empirical results from the dataset of respondents among the CIOs demonstrate
that the Information Quality perceived by CIOs is affected by Information
Processing Capacity and Communication and Reporting, confirming results which
refer to the entire dataset of respondents. In particular, the results show that
Information Processing Capacity has a positive effect on the information quality
perceived by CIOs (b: 0.380, p value: 0.093); therefore if the information pro-
cessing capacity increases, the information quality perceived by respondents
increases as well. Furthermore, results show that Communication and Reporting has
a negative effect on the information quality perceived by respondents (b: −0.330,
p value: 0.038), and thus if the Communication and Reporting increases, the
information quality decreases.
Results allow us to better answer RQ4 (Do the features of information flow affect
the information quality perceived by managers?). We found that some features of
information flow can affect the information quality perceived by managers,
specifically that Information Processing Capacity and Communication and
Reporting affect the Perceived Information Quality differently. Similar considera-
tions could arise from an analysis of the entire dataset of respondents. The power of
the model fit is high (R2 = 55.1%).
Table 5.21 presents a multicollinearity check for the regression analysis carried
out in this section. The Variance Inflation Factor (VIF) allows us to check for the
presence of multicollinearity. Low values for the VIF index (VIF < 10) and the
correlation matrix entries allow us to reject the hypothesis of multicollinearity for
the entire dataset of non-financial firms (Cohen et al. 2013).
Table 5.17 Results of the t-test analysis for items comparing respondents who adopt both
Business Intelligence and ERP with those who single adopted an ERP or BI system
Data accuracy (with ERP and business 48 4.81 1.58 0.012**
intelligence)
Data accuracy (without ERP or business 31 3.84 1.753
intelligence)
Timeliness of data (with ERP and business 48 4.17 1.667 0.418
intelligence)
Timeliness of data (without ERP or business 31 3.84 1.864
intelligence)
System reliability (with ERP and business 48 4.44 1.515 0.076*
intelligence)
System reliability (without ERP or business 31 3.77 1.726
intelligence)
Monthly reporting frequency (with ERP and 48 4.48 1.798 0.029**
Monthly reporting frequency (without ERP or 31 3.58 1.669
6-month reporting frequency (with ERP and 48 3.79 1.89 0.787
Business Intelligence)
6-month reporting frequency (without ERP or 31 3.68 1.72
Annual reporting frequency (with ERP and 48 3.85 1.868 0.856
Annual reporting frequency (without ERP or 31 3.77 1.961
Flash reporting frequency (with ERP and 48 4.58 1.866 0.143
Flash reporting frequency (without ERP or 31 3.9 2.181
ERP and business intelligence)
Satisfaction about the information sharing with 31 4 1.713
(without ERP or business intelligence)
ERP or business intelligence)
(continued)

Frequency of meetings with colleagues at the 48 4.35 1.48 0.189
same hierarchical level (with ERP and business
intelligence)
same hierarchical level (without ERP or
higher hierarchical levels (with ERP and
higher hierarchical levels (without ERP or
Less information (with ERP and business 44 3.66 1.711 0.201
intelligence)
Less information (without ERP or business 29 4.17 1.583
intelligence)
Fewer IT resources (with ERP and business 44 4 1.953 0.463
intelligence)
Fewer IT resources (without ERP or business 29 4.34 1.951
intelligence)
No information (with ERP and business 44 3.41 1.808 0.089*
intelligence)
No information (without ERP or business 29 4.14 1.706
intelligence)
More information (with ERP and business 44 2.82 1.419 0.271
intelligence)
More information (without ERP or business 29 2.45 1.352
intelligence)
More IT resources (with ERP and business 44 2.59 1.3 0.508
intelligence)
More IT resources (without ERP or business 29 2.38 1.374
intelligence)
Too much information (with ERP and business 44 2.2 1.25 0.315
intelligence)
Too much information (without ERP or 29 2.55 1.682
Too many IT resources (with ERP and business 44 2.45 1.229 0.985
intelligence)
Too many IT resources (without ERP or 29 2.45 1.639
respectively
Table 5.18 Results of the regression analysis (the dependent variable is perceived information
quality)a
b p value Standard T
error
Information 0.467 0.001*** 0.139 3.352
processing capacity
Communication −0.169 0.096* 0.100 −1.691
and reporting
Information sharing −0.134 0.317 0.132 −1.010
Frequency of meeting 0.156 0.194 0.119 1.314
Information underload −0.191 0.074* 0.105 −1.820
Information overload 0.334 0.001*** 0.099 3.392
Gender −0.064 0.912 0.572 −0.111
Role 0.278 0.268 0.098 2.831
Education −0.106 0.006** 0.095 −1.118
Sector −0.039 0.701 0.102 −0.386
Type of firms 0.009 0.931 0.109 0.087
Firm size 0.046 0.683 0.111 0.410
a 2
R = 51.9%, F-test (F) = 5.385, p value = 0.000, Number of observations (N) = 72
respectively
Table 5.19 Multicollinearity check

Y = perceived information quality
Information processing capacity 2.393
Communication and reporting 1.285
Information sharing 2.280
Frequency of meeting 1.810
Information underload 1.366
Information overload 1.212
Gender 1.100
Role 1.246
Education 1.192
Sector 1.350
Type of firms 1.387
Firm size 1.633
Mean of VIF 1.521
Table 5.20 Results of the regression analysis on CIOs (the dependent variable is the Information
Quality perceived by CIOs)a
b p value Standard T
error
Information 0.380 0.093* 0.217 1.754
processing capacity
Communication −0.330 0.038** 0.150 −2.206
and reporting
Information sharing −0.010 0.953 0.173 −0.060
Frequency of meeting 0.180 0.283 0.164 1.099
Information underload −0.222 0.195 0.167 −1.335
Information overload 0.193 0.144 0.192 1.343
Gender −0.533 0.486 0.754 −0.708
Education −0.112 0.405 0.132 −0.849
Sector −0.151 0.281 0.137 −1.104
Type of firms −0.109 0.431 −0.802 0.136
Firm size 0.041 0.807 0.166 0.247
a 2
R = 55.1%, F-test (F) = 2.569, p value = 0.027, Number of observations (N) = 34
respectively
Table 5.21 Multicollinearity check

Y = Perceived information quality
Information processing capacity 2.782
Communication and reporting 1.462
Information sharing 1.781
Frequency of meeting 1.465
Information underload 1.463
Information overload 1.351
Gender 1.313
Education 1.333
Sector 1.519
Type of firms 1.264
Firm size 2.248
Mean of VIF 1.635
5.5.3 T-Test: Empirical Results of the Analysis of Chief

Information Officers
We also performed a t-test analysis on CIOs to provide more in-depth results from
the analysis of the entire dataset of respondents. Therefore, with regard to RQ 1a:
“Do ERP systems matter to information overload and information underload?”, the
Table 5.22 Results of the t-test analysis for research variables comparing CIOs who adopt ERP
with those who do not
Information processing capacity 28 4.51 1.142 0.414
(with ERP)
Information processing capacity 9 4.11 1.616
(without ERP)
Communication and 28 4.03 1.140 0.107
reporting (with ERP)
reporting (without ERP)
(with ERP)
(without ERP)
Frequency of 28 4.39 1.083 0.147
meeting (with ERP)
Frequency of 9 3.72 1.460
meeting (without ERP)
Information 26 3.96 1.273 0.717
underload (with ERP)
Information 9 3.78 1.384
underload (without ERP)
Information 26 2.39 0.855 0.010**
overload (with ERP)
Information 9 3.33 1.000
overload (without ERP)
Perceived information 26 4.00 1.020 0.594
quality (with ERP)
quality (without ERP)
respectively
results demonstrate that CIOs who adopt an ERP perceive lower information
overload than those who do not (t-test is statistically significant, p value = 0.010)
(Table 5.22).
With regard to RQ1b: “Do ERP systems matter to the features of information
flow?, the results demonstrate that there is no differences in the perception of the
features of information flow between CIOs who adopt ERP with those who do not.
Regarding RQ2b: “Does a Business Intelligence system matter to the features of
information flow?”, Table 5.23 shows there is no differences in the perception of
the features of information flow between CIOs who adopt a BI with those who do
not.
Table 5.23 Results of the t-test analysis for research variables comparing respondents who adopt
BI with those who do not (dataset of CIO respondents)
Item Number of Mean Standard T-test (p
observations deviation value)
Information processing 29 4.36 1.269 0.601
capacity (with BI)
(without BI)
Communication and reporting 29 3.96 1.295 0.267
(with BI)
Communication and reporting 8 3.37 1.369
(without BI)
Information sharing (with BI) 29 4.52 1.214 0.834
Information sharing (without BI) 8 4.62 1.506
Frequency of meeting (with BI) 29 4.22 1.25 0.958
Frequency of meeting (without BI) 8 4.25 1.069
Information underload (with BI) 27 3.93 1.409 0.923
Information underload (without 8 3.87 0.796
BI)
Information overload (with BI) 27 2.67 0.934 0.735
Information overload (without BI) 8 2.53 1.145
Perceived information quality 27 4.15 1.064 0.356
(with BI)
Perceived information quality 8 3.75 1.035
(without BI)
respectively
In terms of RQ 2a: “Does Business Intelligence matter to information overload

and information underload?”, the results demonstrate that respondents who adopt a
BI do not perceive a higher or lower information overload or information underload
compared to the other respondents (t-test for both research variables, information
overload and information underload, are not statistically significant) (Table 5.23).
With regard to RQ 3b: “Does the combined adoption of ERP and BI systems
matter more to the features of information flow than does the single adoption of an
ERP or BI system?”, Table 5.24 shows that CIOs who have implemented both BI
and ERP do not perceive greater differences in the features of information flow than
do the other CIOs.
With regard to RQ 3a: “Does the combined adoption of ERP and BI systems
matter more to information overload and information underload than does the
single adoption of an ERP or BI system?”, the results demonstrate that respondents
adopting an ERP and a BI system do not perceive a higher or lower information
overload or information underload than do the other CIOs (t-test for both research
variables, information overload and information underload, are not statistically
significant) (Table 5.24).
5.6 Summary Results 127
Table 5.24 Results of the t-test analysis for research variables comparing respondents who
adopted both Business Intelligence and ERP systems with those who single adopted an ERP or BI
system (dataset of CIO respondents)
Information processing capacity (with 24 4.41 1.193 0.988
(without ERP or business intelligence)
Communication and reporting (with 24 4.08 1.888 0.109
Communication and reporting (without 13 3.36 1.453
Information sharing (with ERP and 24 4.56 1.182
Information sharing (without ERP or 13 4.5 1.443 0.888
Frequency of meeting (with ERP and 24 4.33 1.148 0.483
Frequency of meeting (without ERP or 13 4.04 1.314
Information underload (with ERP and 22 4 1.338 0.615
Information underload (without ERP or 13 3.77 1.228
Information overload (with ERP and 22 2.49 0.84 0.25
Information overload (without ERP or 13 2.88 1.157
Perceived information quality (with ERP 22 3.95 1.09 0.463
and business intelligence)
Perceived information quality (without 13 4.23 1.013
respectively
5.6 Summary Results
5.6.1 Summary Results for the Entire Dataset

of Respondents
Table 5.25 summarizes the research questions outlined in Chap. 4. Chapter 6 will
discuss the results of this study.
Table 5.25 Summary results for the entire dataset of respondents

Research questions Summary results for research variables
RQ 1a: “Do ERP systems matter to Results demonstrated that respondents
information overload and information adopting an ERP do not perceive higher or
underload?” lower information overload or information
underload (t-test for both research variables,
information overload and information
underload, are not statistically significant)
RQ 1b: “Do ERP systems matter to the Respondents who have implemented an ERP
features of information flow?” perceive a higher level of Information
Processing Capacity than do respondents who
have not implemented an ERP (t-test is
statistically significant, p value = 0.051).
Furthermore, respondents adopting an ERP
perceive a higher level of Communication
and Reporting than do respondents without an
ERP (t-test is statistically significant,
p value = 0.028), as well as a higher level of
Frequency of Meeting (t-test is statistically
significant, p value = 0.099)
RQ 2a: “Does Business Intelligence matter to Results demonstrated that respondents
information overload and information adopting a BI do not perceive higher or lower
underload?” information overload or information
underload compared to the other respondents
(t-test for both research variables, information
overload and information underload, are not
statistically significant)
RQ 2b: “Does a Business Intelligence system Respondents who have implemented a BI
matter to the features of information flow?” perceive a higher level of Information
Processing Capacity than do respondents who
have not implemented a BI (t-test is
statistically significant, p value = 0.075)
RQ 3a: “Does the combined adoption of ERP Results demonstrated that respondents
and BI systems matter more to information adopting an ERP and a BI do not perceive
overload and information underload than higher or lower information overload or
does the single adoption of an ERP or BI information underload compared to the other
system?” respondents (t-test for both research variables,
information overload and information
underload, are not statistically significant)
RQ 3b: “Does the combined adoption of ERP Respondents who have implemented both
and BI systems matter more to the features of ERP and BI perceive a higher level of
information flow than does the single Information Processing Capacity than do
adoption of an ERP or BI system?” respondents who have not implemented an
ERP or a BI (t-test is statistically significant,
p value = 0.058)
(continued)
5.6 Summary Results 129

Research questions Summary results for research variables
RQ 4: “Do the features of information flow Results show that Information Processing
affect the information quality perceived by Capacity has a positive effect on the
managers?” information quality perceived by managers
(b: 0.467, p value: 0.001); therefore, if the
information processing capacity increases, the
information quality perceived by respondents
increases as well. Furthermore, results show
that Communication and Reporting has a
negative effect on the information quality
perceived by respondents (b: −0.169, p value:
0.096), so that if Communication and
Reporting increases, the information quality
decreases
Table 5.26 Summary results for chief information officers

Research Questions Summary results for research variables
RQ 1a: “Do ERP systems matter to Results demonstrated that respondents
information overload and information adopting an ERP perceive lower information
underload?” overload than do the other respondents (t-test
is statistically significant, p value = 0.010)
RQ 1b: “Do ERP systems matter to the Results demonstrated that CIOs adopting an
features of information flow?” ERP do not perceive greater differences in the
features of information flow than do the other
CIOs
RQ 2a: “Does Business Intelligence matter to Results demonstrated that respondents
information overload and information adopting a BI do not perceive a higher or
underload?” lower information overload or information
underload compared to the other respondents
(t-test for both research variables, information
RQ 2b: “Does a Business Intelligence system CIOs who have implemented a BI do not
matter to the features of information flow?” perceive greater differences in the features of
information flow compared to the other CIOs
RQ 3a: “Does the combined adoption of ERP Results demonstrated that respondents
and BI systems matter more to information adopting an ERP and a BI do not perceive a
overload and information underload than higher or lower information overload or
does the single adoption of an ERP or BI information underload than do the other CIOs
system?” (t-test for both research variables, information
RQ 3b: “Does the combined adoption of ERP CIOs who have implemented both BI and
and BI systems matter more to the features of ERP do not perceive greater differences in the
information flow than does the single features of information flow than do the other
adoption of an ERP or BI system?” CIOs
(continued)

Research Questions Summary results for research variables
RQ 4: “Do the features of information flow Results show that Information Processing
affect the information quality perceived by Capacity has a positive effect on the
managers?” information quality perceived by CIOs (b:
0.380, p value: 0.093); therefore, if the
information processing capacity increases, the
information quality perceived by respondents
increases as well. Furthermore, results show
that Communication and Reporting has a
negative effect on the information quality
perceived by respondents (b: −0.330, p value:
0.038), so that if Communication and
Reporting increases, the information quality
decreases
5.6.2 Summary Results for Chief Information Officers
Table 5.26 summarizes the research questions outlined in Chap. 4 for a sub-sample
of respondents, namely CIOs. Chapter 6 will discuss the results of this study.
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the behavioral sciences. Routledge
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pp 217–236
Chapter 6
Concluding Remarks
Abstract This chapter discusses the results of the theoretical and empirical anal-
ysis presented in the previous chapters of the manuscript. The limitations and
further developments of the research were also presented. In general, our results
show that information overload is less perceived than information underload in all
the comparisons performed in the research. The empirical results of our research
concerning the relationship between ERP systems and information overload/
underload show that ERP systems do not affect the perception of information
overload/underload. However, the empirical results show that respondents who
adopt ERP perceive higher data accuracy, system reliability and, in general, a
higher information processing capacity than do respondents who do not adopt an
ERP. Furthermore, our results show that respondents who adopt BI systems do not
perceive a different level of information overload/underload compared with
respondents who do not adopt. However, a more detailed analysis shows that
managers of companies adopting BI systems perceive a higher data accuracy, a
higher level of information processing capacity, and a more regular reporting
system, based on more systematic frequency. Empirical evidence on the effects of
the simultaneous adoption of ERP and BI on information overload/underload and
on the features of information flow show that respondents adopting both an ERP
and a BI system do not perceive higher or lower information overload or infor-
mation underload than do the other respondents. Finally, our results confirm prior
studies on information processing capacity and information quality and suggest that
reporting is one of the drivers of information quality.
6.1 Introduction
This section presents the results of the theoretical and empirical analysis conducted
in the previous chapters of this manuscript.
Information overload and information underload could represent a serious limit
for a company, as they can compromise the effectiveness of the decision-making
process. The literature shows quite clearly that information overload and underload

https://doi.org/10.1007/978-3-319-77679-8_6
132 6 Concluding Remarks
reduce decision accuracy (Eppler and Mengis 2004) and, consequently, the per-
formance of managers. Information overload happens whenever the quantity of
information the individual receives surpasses her/his capacity to process it
(O’Reilly 1980); therefore, it happens more frequently in companies which face
high environmental uncertainty, as they would need to adapt their information
processing capacity to the changing conditions of the environment. Information
underload occurs when the individual receives less information than she/he would
need to accomplish a task (O’Reilly 1980; Eppler and Mengis 2004). Information
underload may also occur when managers receive a large amount of irrelevant
information. In this case, both information overload and information underload may
occur as, on the one hand, the amount of information does not allow managers to
perform their actions in a timely and effective manner, and on the other irrelevant
information does not provide managers with the solutions to their problems, thereby
causing information underload (Melinat et al. 2014; Letsholo and Pretorius 2016).
Information overload and information underload are also related to the time
pressure managers feel in performing their job and to their possible incapacity to
prioritize tasks optimally (Kock 2000). According to the literature, information
overload and underload may even be due to the wrong use of technology (Lee et al.
2016). In fact, two contrasting types of behavior have been observed: on the one
hand, companies invest in powerful IT tools in order to look for data, elaborate data,
extract information, and undertake data mining, and in doing so produce plenty of
data and information. On the other hand, companies invest in IT tools to deal with
the information overload caused by the huge amount of data and information they
need to manage. Therefore, if not appropriately used and not well-aligned with the
management information needs and strategic goals, technology may even worsen
the information overload and underload (Karr-Wisniewski and Lu 2010).
The literature shows that managers’ perception of information overload/
underload may represent a signal of poor information quality and, consequently,
a signal of low quality of information flow (Kock 2000; Farhoomand and Drury
2002; Yang et al. 2009).
There have been several studies on information quality: part of the literature
considers information quality as a feature—or a driver—of the quality of information
systems (DeLone and McLean 1992; Nelson et al. 2005), whereas other contributions
attempt to assess the information quality by proposing frameworks or methodologies
(Lee et al. 2002; Bovee et al. 2003; Stvilia et al. 2005). Furthermore, the literature
suggests several definitions of information quality, which, for example, could be
considered as the fitness of user needs, as defined by Juran (1992) and Strong et al.
(1997). Other studies, in proposing a definition of information quality, focus attention
on information users by viewing information quality as the capacity to meet or
exceed information users’ expectations (McClave et al. 1998; Evans and Lindsay
2002). Information quality can also be defined as the coherence of information with
regard to the specifications of the product or the service to which it refers (Zeithaml
et al. 1990; Reeves and Bednar 1994; Kahn et al. 2002). According to this inter-
pretation, high-quality information provides an accurate representation and meets the
6.1 Introduction 133
requirements of the final user. Naturally, the coherence and the usefulness of
information also depend on the initial data quality (Piattini et al. 2012).
According to some authors, the quality of information depends on several
attributes, which could be summarized in three main dimensions (Marchi 1993;
O’Brien and Marakas 2006): time, content and form.
Our research provides an investigation of information overload and underload,
information quality and features of information flow conducted through a survey on
a sample of 79 Italian managers. Special focus is given to Chief Information
Officers (CIOs), since this role is responsible for a company’s IT system, and thus
for the entire information flow within a firm (Gottschalk 1999). The role of the CIO
has noticeably increased in the last few years (Bharadwaj 2000; Corsi and Trucco
2016). Furthermore, among our respondents, CIOs represent the majority of our
dataset, with 46.8% of respondents.
In general, our results show that the information overload is less perceived than
is information underload in all the comparisons performed in our research (i.e.,
(a) between respondents working in companies which adopt ERP systems and those
working in companies that do not; (b) between respondents working in companies
which adopt BI systems and those working in companies that do not; (c) between
respondents working in companies which adopt both ERP and BI systems and the
other respondents).
6.2 ERP, Information Overload/Underload and Features

of Information Flow
The empirical results of our research on the relationship between ERP systems and
information overload/underload show that ERP systems do not affect the perception
of information overload/underload. The research variables used to measure the
perception of information overload and underload are defined on the basis of
previous studies (O’Reilly 1980; Karr-Wisniewski and Lu 2010) and aim at
investigating whether managers perceive a lack of information—or even an absence
of information—or a lack of IT resources. T-test analyses demonstrate that the
perceptions of managers regarding these variables does not change whether or not
they adopt ERP systems. The same is also true for research variables which
investigate whether managers perceive information overload and whether they
perceive having received appropriate information. It thus seems thus that the
presence of an ERP system within the company does not alter the perception of
managers about the quantity and the quality of information they receive to
accomplish their tasks.
However, some effects of the implementation of ERP systems is recognizable in
other items, which are indirectly connected to the quality of information. For
example, empirical results show that respondents adopting ERP perceive higher
data accuracy and system reliability and, in general, a higher information processing
capacity than do respondents who have not adopted ERP. Furthermore, the results
show that companies adopting ERP have a more structured reporting system, as
information is more frequently communicated on a monthly or a 6-month basis,
than do companies that do not adopt ERP.
These perceptions, although probably not connected to the perception of infor-
mation overload/underload, reveal that the use of ERP has a positive impact on
information system quality and on the information quality items. This confirms that
part of the literature which supports the idea that ERP improves data quality,
information quality and information system quality in general (Bingi et al. 1999;
Dell’Orco and Giordano 2003; Chapman and Kihn 2009; Scapens and Jazayeri
2003).
For respondents adopting ERP, the perception that they are using a more reliable
system may be due to the impact that a comprehensive software like ERP has on
data and system quality, as suggested by Lee and Lee 2000; Xu et al. 2002. In
addition, empirical results show that flash reporting is more frequently used in
companies adopting ERP than in firms that do not; this is in line with the idea that
in the presence of ERP systems management accountants can dedicate more time to
data analysis and performance measurement, and thus have more time to produce a
larger amount of reports (Sangster et al. 2009).
Another significant perception arising from our results is that respondents
adopting an ERP perceive better internal job coordination, probably because the
implementation of an ERP often requires a thorough reorganization (along with a
Business Process Reengineering), which may result in more frequent meetings with
colleagues at the same hierarchical level. This effect is also confirmed by the
literature, under different perspectives (Scheer and Habermann 2000). Respondents’
perceptions on the quality of information flow are also useful in understanding the
effects of ERP implementation on information issues. In fact, respondents adopting
ERP perceive a better capacity to process information, along with better commu-
nication; these results are in line with the literature on the impacts of ERP on
information flow issues (Sangster et al. 2009; Poston and Grabski 2001; Mauldin
and Richtermeyer 2004; Scheer and Habermann 2000).
Interestingly, the empirical results described above show that, in general,
respondents adopting ERP perceive a general improvement in several items directly
or indirectly connected to information quality (such as data accuracy, system reli-
ability, information processing capacity, and reporting system quality) compared
with those who do not adopt ERP; but at the same time, respondents who adopt
ERP do not perceive either an information overload or an information underload.
Consequently, these results allow us to speculate that the absence of a perception of
information overload or underload could be due to the improved information and
reporting quality brought about by the ERP system.
Similar results were also obtained in the sub-sample of CIOs.
Table 6.1 reports our research questions regarding the relationships between
ERP systems and information overload/underload and between ERP and informa-
tion flow, our empirical evidence, and the results from the main literature, which
either confirm or contradict our results.
6.3 BI, Information Overload/Underload and Features of Information Flow 135
Table 6.1 ERP, information overload/underload and information flow: empirical evidence and
the main literature
Research questions Empirical evidence Main literature
RQ 1a: “Do ERP systems • No information overload/ The results are in line with the
matter to information underload perceptions either main literature on the effects of
overload and information for respondents adopting ERP on data and information
underload?” ERP or for those not quality issues (Chandler 1982;
RQ 1b: “Do ERP systems adopting ERP Chapman and Kihn 2009;
matter to the features of • Respondents adopting ERP Robey et al. 2002; Hitt et al.
information flow?” perceive higher information 2002; Mauldin and
quality and better Richtermeyer 2004; Poston
communication and and Grabski 2001; Lee and
information flow Lee 2000; Xu et al. 2002)
6.3 BI, Information Overload/Underload and Features

of Information Flow
The empirical evidence of our research on the relationship between BI systems and
information overload/underload show that BI systems do not affect the perception
of information overload/underload. Our results show that respondents adopting BI
systems do not perceive a different level of information overload or underload than
do respondents who do not adopt BI systems. However, a more detailed analysis
shows that managers of companies adopting BI systems perceive higher data
accuracy, a higher level of information processing capacity and a more regular
reporting system, based on a systematic monthly frequency.
Regarding data accuracy, the literature shows that BI systems allow companies
to collect data in data warehouses, to manage and analyse it, and to carry out data
cleansing to improve data accuracy and completeness by supporting managers in
selecting only the relevant data, and thus in providing appropriate information
(Boyer et al. 2010; Brien and Marakas 2009; da Costa and Cugnasca 2010; Smith
et al. 2012). In terms of the capacity of BI systems to provide appropriate infor-
mation, our empirical results also show that respondents who adopt BI systems
perceive a higher information quality than do respondents who do not adopt BI.
Therefore, the higher data accuracy and information quality perceived by BI
system adopters may be due to the improvements BI brings to the entire
data-information-decision cycle.
Regarding the perception of respondents pertaining to the more regular reporting
system, this result is probably an effect of BI system capacities, well-recognized by
the literature, which consist in addressing the right information at the right time to
the right person (Burstein and Holsapple 2008). In fact, a regular and systematic
reporting system could be the effect of an accurate reporting design process carried
out before implementing a BI system. Successful BI implementation should, in fact,
require managers to define the features of information and reports they will need,
Table 6.2 Business Intelligence, information overload/underload and information flow: empirical
evidence and the main literature
RQ 2a: “Does Business • No information overload/ Results are not aligned with
Intelligence matter to underload perceptions the main literature: BI was
information overload and either for respondents who expected to improve
information underload?” adopt BI or those who do information overload, as
RQ 2b: “Does a Business not suggested by the literature
Intelligence system matter • Respondents adopting BI (Boyer et al. 2010, Brien and
to the features of perceive higher data Marakas 2009)
information flow?” accuracy, better At the same time, the results
information processing confirm the literature
capacity, higher regarding the role of BI in
information quality, and a improving data accuracy,
more structured reporting information processing
system capabilities, and information
quality (Burstein and
Holsapple 2008; Foshay and
Kuziemsky 2014; Nita 2015;
Eckerson 2005; Smith et al.
2012)
including the frequency with which they wish to receive them (Eckerson 2005;
Foshay and Kuziemsky 2014; Nita 2015).
Another interesting result of our research is that respondents who do not adopt
BI systems perceive more frequently that they are not receiving all the information
they would need to accomplish their tasks. This probably occurs because, without a
BI system, respondents are not provided with support in collecting, selecting,
managing and analysing data. As a result, business data is probably disseminated in
the company, but because it is not well-organized, collected and stored, managers
perceive that data does not exist at all, or is insufficient to meet their
decision-making needs. This is confirmed by some studies which assert that without
BI, obtaining information would require a long manual process (Kelly 2005);
furthermore, other studies state that companies, because of environmental turbu-
lence, are obliged to use business information more effectively than before, which is
not possible without systematic information management (Imran and Tanveer
2015).
As a confirmation of the above result, on the other hand, respondents adopting
BI perceive a better information processing capacity due to the various opportu-
nities BI systems provide for data elaboration and information flow (Brien and
Marakas 2009; Boyer et al. 2010; da Costa and Cugnasca 2010; Spira 2011; Smith
et al. 2012).
Table 6.2 summarizes our research questions pertaining to the role of BI in
affecting information overload/underload and information flow, the empirical evi-
dence obtained and the main literature, which confirms or contradicts our results.
6.4 The Combination of ERP and BI for Information Overload/Underload … 137
6.4 The Combination of ERP and BI for Information

Overload/Underload and Features of Information
Flow
The empirical evidence on the effects of the simultaneous adoption of ERP and BI
on information overload/underload and on the features of information flow show
that respondents adopting both an ERP and a BI system do not perceive higher or
lower information overload or information underload than do the other respondents.
Similar considerations arise from the analysis of the CIO dataset. This is partially
aligned with the literature, suggesting that information problems caused by a lack of
systematic information collection and processing make BI tasks more and more
difficult (Li et al. 2009). In other words, this result suggests that in companies where
information collection and processing are not appropriately managed from the
beginning, the potential benefits of BI systems are weakly perceived or not per-
ceived at all.
Interestingly, our results also show that respondents who have implemented both
ERP and BI systems perceive a higher level of information processing capacity than
do respondents who adopt only ERP or BI. Therefore, although managers do not
perceive that ERP and BI improve information overload/underload, they recognize
that these systems improve the capacity of the company to process information.
This evidence suggests that: either information overload and information underload
are not perceived as problems, even if the benefits of ERP and BI are clearly
recognized, or information overload and underload are indeed problems, but remain
implicit in the perception of managers, who instead find it easier to recognize the
improvement in a more tangible aspect such as information processing capacity.
Our results are thus not fully supported by the literature, which argues that the
simultaneous use of ERP and BI systems is expected to have more influence on the
information flow features than would the single adoption of ERP or BI (Scheer and
Habermann 2000; Horvath 2001; Chapman and Kihn 2009; Berthold et al. 2010)
(Table 6.3).
6.5 Information Quality and Features of Information

Flow
The literature on this topic suggests that features of information flow are relevant
for improving information quality (Swain and Haka 2000; Agnew and Szykman
2005). In particular, information processing capacity would increase by means of
the synthetic and systemic representation of information, and communication would
improve by means of more selective messages (Shneiderman 1996; Burkhard and
Meier 2005). Other authors consider the information flow as an important dimen-
sion of information quality; in fact, an effective information flow allows information
Table 6.3 The simultaneous use of ERP and BI for information overload/underload and
information flow: empirical evidence and the main literature
RQ 3a: “Does the combined • The results demonstrated Results are partially aligned
adoption of ERP and BI that respondents adopting an with the literature, suggesting
systems matter more to ERP and a BI do not that information problems
information overload and perceive higher or lower caused by a lack of systematic
information underload than information overload or information collection and
does the single adoption of an information underload than processing make BI tasks
ERP or BI system?” do the other respondents more and more difficult (Li
RQ 3b: “Does the combined • Respondents who have et al. 2009). BI studies often
adoption of ERP and BI implemented both ERP and ignore the importance of
systems matter more to the BI perceive a higher level of information selection and pay
features of information flow Information Processing too much attention to the
than does the single adoption Capacity than do capacity of BI to gather and
of an ERP or BI system?” respondents who have not elaborate data (Blanco and
implemented an ERP or a BI Lesca 1998)
system Results confirm those in the
literature concerning the role
of ERP and BI systems in
improving information
processing capacity (Lee and
Lan 2007; Ranjan 2009;
Chapman and Kihn 2009)
system users to receive complete, relevant, timely, up-to-date and accessible

information (Al-Hakim 2007).
Our empirical evidence on the relationship between information quality and the
features of information flow show which of these features are able to affect the
information quality perceived by managers. In particular, we found that information
processing capacity and communication and reporting affect, in different ways, the
perceived information quality. In fact, the results reveal that information processing
capacity has a positive effect on the information quality perceived by managers;
therefore, if the information processing capacity increases, the information quality
perceived by respondents increases as well. On the contrary, the results show that
communication and reporting has a negative effect on the information quality
perceived by respondents; therefore, if the communication and reporting increases,
the information quality decreases. This result shows a reduction in the quality of
information due to an increase in reporting frequency. However, it would be nec-
essary to carry out more in-depth investigations to understand the type of reporting
that leads to a reduction in the perceived quality. For example, an increase in flash
reporting frequency denotes greater timeliness of communication; instead, an
increase in the frequency of annual reports could mean a lack of timeliness (but a
greater level of accuracy). In spite of this, the results are useful in understanding
that reporting frequency is actually one of the drivers of information quality
(Table 6.4).
Similar considerations arise from an analysis of the dataset of CIOs.
6.6 Limitations and Further Development 139
Table 6.4 Information quality and features of information flow

RQ 4: “Do the features of Results show that Our results confirm prior
information flow affect the Information Processing studies on information
information quality Capacity has a positive effect processing capacity and
perceived by managers?” on the information quality information quality
perceived by managers; (Shneiderman 1996;
therefore, if the information Burkhard and Meier 2005)
processing capacity Our results suggest that
increases, the information reporting is one of the drivers
quality perceived by of information quality
respondents increases as well (Al-Hakim 2007; Sangster
Furthermore, results show et al. 2009)
that Communication and
Reporting has a negative
effect on the information
quality perceived by
respondents; therefore, if the
Communication and
Reporting increases, the
information quality decreases
6.6 Limitations and Further Development
This section presents some limitations of our research. The first limitation is related
to the choice of the manager sample, which is not based on the industry. The
literature actually suggests that firms belonging to industries characterized by high
uncertainty are more likely to face information overload and underload compared
with companies operating in more stable industries (Ho and Tang 2001). Another
limitation is linked to the small number of observations: the perception of
respondents about information overload and underload and about information
quality in general may depend on several endogenous factors such as the size of the
company, the experience of the interviewees, and their role inside the company.
In addition to extending the sample, it would be useful for future research to
submit the survey to companies at two different moments: immediately before and
immediately after the company makes an ERP/BI investment. By doing so, it would
be possible to compare the management perception of information overload/
underload before and after the adoption of the new software. This would allow for a
better perception of the effects of ERP and BI on the topic we have investigated.
Furthermore, a more in-depth analysis of the relationship between the reporting
system and information quality could be carried out by analyzing the role played by
the single items of our research variable “Communication and Reporting” on
information quality.
Acknowledgements The authors gratefully acknowledge the anonymous reviewers for the
insightful suggestions provided to enhance the quality of this manuscript.
The authors also acknowledge the assistant editor of this book series, Maria Cristina Acocella,
along with the editorial staff of Springer for their professional and proficient involvement in the
production of this book.
The authors also gratefully acknowledge the Università degli Studi Internazionali di Roma
(UNINT), which has made this study possible by providing financial support.
This study is part of a larger project on accounting information systems.
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Contributions to Management Science

Enterprise Resource Planning

ISSN 1431-1941 ISSN 2197-716X (electronic)

© Springer International Publishing AG, part of Springer Nature 2018

Printed on acid-free paper

To my Mom and Dad

Nowadays, Information Technology (IT) innovations, the advent of the Internet,

Novedrate, Italy Carlo Caserio

3.3.1 Alignment to Group Logics . . . . . . . . . . . . . . . . . . . .... 48

5 ERP and BI as Tools to Improve Information Quality

1.1 A Brief Overview of the Book

Nowadays, Information Technology (IT) innovations, the advent of the Internet,

© Springer International Publishing AG, part of Springer Nature 2018 1

1.2 Theoretical Contributions of the Present Work

1.3 Managerial Implications of the Present Work

1.4 Structure of the Book

The remainder of the book is divided into 5 chapters:

Chapter 3 deals with BI systems, trying to follow the complete path of BI

Burstein F, Holsapple C (2008) Handbook on decision support systems 2: variations. Springer

Li X, Qu H, Zhu Z, Han Y (2009) A systematic information collection method for business

Uwizeyemungu S, Raymond L (2005) Essential characteristics of an ERP system: conceptual-

© Springer International Publishing AG, part of Springer Nature 2018 13

2.2 The Evolution of ERP Systems

• to synchronize manufacturing activities with delivery schedules;

domain-speciﬁc applications which create value for customers and shareholders

Table 2.1 Evolution of ERP 2010s Cloud ERP

2.3 Information Quality and ERP

2.3.1 Information Quality

In the ﬁeld of Management Information Systems, information quality and infor-

2.3.2 ERP System for Information Quality

obtain information system beneﬁts from an ERP implementation, people should

2.4 Critical Success Factor for ERP Implementation

Table 2.2 Critical success factors according to Somers and Nelson

2.5 Critical Success Factors for ERP Post-implementation

2.6 Advantages and Disadvantages of ERPs

2.6.1 Potential Beneﬁts of ERP Adoption

higher performance in several ratios than ﬁrms with no investment in ERPs. In

systems, processes and management practices. Furthermore, they found an

2.6.2 A Framework for Classifying the Beneﬁts of ERP

2.6.3 Potential Disadvantages of ERP Adoption

2.7 ERP as a Driver of Alignment Between Management

2.8 The Managerial Role of the Chief Information Ofﬁcer

Wand Y, Wang RY (1996) Anchoring data quality dimensions in ontological foundations.

© Springer International Publishing AG, part of Springer Nature 2018 43

3.2 Business Intelligence and Companies Needs

Table 3.1 Summarization of the main drivers of BI needs

As a consequence, companies need to invest in innovative BI models that can:

Table 3.2 Main BI needs of companies

Table 3.3 Main literature on the BI needs of companies

Table 3.3 (continued)

3.3 BI for Management Information Systems Needs

3.3.1 Alignment to Group Logics

Extraordinary ﬁnance operations, in particular Mergers and Acquisitions (M&A),

3.3.2 Coordination and Technical-Organizational

The need to improve internal, technical and organizational coordination is very

alignment of information flows with business needs (Markus 2000). Consequently,

3.3.3 Improvement of Data Management and Decision

The implementation of BI systems inevitably responds to the need to obtain data

Therefore, substantive systems will be implemented by companies that need an