Description
Business Intelligence Success An Empirical Evaluation Of The Role Of BI
APPROVED:
Mary C. Jones, Major Professor and Chair of
the Department of Information
Technology and Decision Sciences
Audesh Paswan, Minor Professor
Anna Sidorova, Committee Member
Nicholas Evangelopoulos, Committee
Member
Andy Wu, Committee Member
O. Finley Graves, Dean of the College of
Business
James D. Meernik, Acting Dean of the Robert
B. Toulouse School of Graduate
Studies
BUSINESS INTELLIGENCE SUCCESS: AN EMPIRICAL EVALUATION OF THE ROLE OF BI
CAPABILITIES AND THE DECISION ENVIRONMENT
Öykü I??k, B.S., M.B.A.
Dissertation Prepared for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
August 2010
I??k, Öykü. Business Intelligence Success: An Empirical Evaluation of the Role of BI
Capabilities and the Decision Environment. Doctor of Philosophy (Business Computer
Information Systems), August 2010, 170 pp., 54 tables, 6 figures, references, 220 titles.
Since the concept of business intelligence (BI) was introduced in the late 1980s, many
organizations have implemented BI to improve performance but not all BI initiatives have been
successful. Practitioners and academicians have discussed the reasons for success and failure,
yet, a consistent picture about how to achieve BI success has not yet emerged.
The purpose of this dissertation is to help fill the gap in research and provide a better
understanding of BI success by examining the impact of BI capabilities on BI success, in the
presence of different decision environments. The decision environment is a composition of the
decision types and the way the required information is processed to aid in decision making. BI
capabilities are defined as critical functionalities that help an organization improve its
performance, and they are examined in terms of organizational and technological capabilities.
An online survey is used to obtain the data and partial least squares path modeling (PLS)
is used for analysis. The results of this dissertation suggest that all technological capabilities as
well as one of the organizational capabilities, flexibility, significantly impact BI success. Results
also indicate that the moderating effect of decision environment is significant for quantitative
data quality. These findings provide richer insight in the role of the decision environment in BI
success and a framework with which future research on the relationship between BI capabilities
and BI success can be conducted. Findings may also contribute to practice by presenting
information for managers and users of BI to consider about their decision environment in
assessing BI success.
ii
Copyright 2010
by
Öykü I??k
iii
ACKNOWLEDGEMENTS
I would like to thank my dissertation chair, Dr. Mary Jones, for her support and patience.
Without her feedback and advice, I would not be able to complete my dissertation on a timely
fashion. I would like to express my gratitude to the members of my committee, Dr. Sidorova,
Dr. Wu, Dr. Paswan and Dr. Evangelopoulos, for their support and valuable comments towards
improving my dissertation. I also would like to thank the Department of Information
Technology and Decision Sciences for funding my dissertation.
My thanks and gratitude also goes to my family. My mom, although thousands of miles
away, has been even more anxious than me and has supported me in every step of the
program. I am also forever thankful to my two wonderful aunts and the greatest grandma of all
times for always inspiring me to reach further. Their unconditional love and prayers helped me
through the difficult times, and I am glad that I could make them proud by being the first to
pursue a Ph.D. in the family. Last but not least, I would like to thank my husband, Baris Isik, who
has left his career behind just to support me during my Ph.D. journey. He has been extremely
understanding and supportive, and without him, I would not be where I am right now. I would
like to dedicate my dissertation to him.
iv
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS .............................................................................................................. iii
LIST OF TABLES ........................................................................................................................... vi
LIST OF FIGURES ......................................................................................................................... ix
Chapters
1. INTRODUCTION .................................................................................................... 1
2. LITERATURE REVIEW ............................................................................................ 9
BI Success ............................................................................................... 14
Measuring BI Success .................................................................. 20
Relationship between BI Capabilities and the Decision Environment ...... 22
Decision Environment ................................................................. 23
Organizational Information Processing Theory ............................ 25
Decision Types ........................................................................................ 30
BI Capabilities ......................................................................................... 35
Data Sources ............................................................................... 38
Data Types .................................................................................. 39
Interaction with Other Systems ................................................... 39
User Access ................................................................................. 40
Data Reliability ............................................................................ 41
Risk Level .................................................................................... 42
Flexibility..................................................................................... 43
Intuition Involved in Analysis ...................................................... 44
Research Model and Hypotheses ............................................................ 46
3. METHODOLOGY ................................................................................................. 60
Research Population and Sample ............................................................ 60
Research Design ..................................................................................... 61
Instrument Design and Development ..................................................... 62
v
BI Success ................................................................................... 63
BI Capabilities ............................................................................. 64
Decision Environment ................................................................. 65
Survey Administration ............................................................................ 65
Reliability and Validity Issues .................................................................. 67
Data Analysis Procedures ....................................................................... 69
4. DATA ANALYSIS AND RESULTS ............................................................................ 73
Response Rate and Non-Response Bias .................................................. 73
Treatment of Missing Data and Outliers ................................................. 82
Demographics ........................................................................................ 83
Exploratory Factor Analysis and Internal Consistency ............................. 87
PLS Analysis and Assessment of Validity ............................................... 105
Hypotheses Testing Results .................................................................. 109
Hypothesis 1 and Hypothesis 2 ................................................. 109
Hypothesis 3 and Hypothesis 4 ................................................. 111
5. DISCUSSION AND CONCLUSIONS ...................................................................... 122
Discussion of Research Findings............................................................ 122
Technological BI Capabilities and BI Success.............................. 122
Organizational BI Capabilities and BI Success ............................ 124
Technological BI Capabilities and the Decision Environment ..... 126
Organizational BI Capabilities and the Decision Environment .... 127
Limitations............................................................................................ 128
Research Contributions ........................................................................ 130
Conclusion and Future Research Directions .......................................... 134
Appendices
A. COVER LETTER.................................................................................................. 137
B. SURVEY INSTRUMENT ...................................................................................... 139
REFERENCES ............................................................................................................................ 147
vi
LIST OF TABLES
Page
1. Selected BI Definitions ................................................................................................... 10
2. Concepts Examined in Research about BI Success .......................................................... 17
3. Examples of Organizational Information Processing Theory in Information Systems
Research ........................................................................................................................ 28
4. A Framework for Information Systems, Adapted from Gorry and Scott Morton (1971).. 34
5. BI Capabilities and Their Levels Associated with the Four BI Worlds, Adapted from
Hostmann et al. (2007) .................................................................................................. 46
6. Research Variables Used in Prior Research .................................................................... 66
7. Hypotheses and Statistical Tests .................................................................................... 70
8a. Independent Samples t-Tests for Non-response Bias ..................................................... 75
8b. Independent Samples t-Tests for Non-response Bias - Demographics ............................ 76
9a Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Main Data Set ......... 77
9b Independent Samples t-Tests for Response Bias on Demographics: Pilot Data Set vs.
Main Data Set ................................................................................................................ 78
10a Independent Samples t-Test: Pilot Data Set vs. Operational Managers in the Main Data
Set ................................................................................................................................. 79
10b Independent Samples t-Test on Demographics: Pilot Data Set vs. Operational Managers
in the Main Data Set ...................................................................................................... 80
11a Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Non-Operational
Managers in the Main Data Set...................................................................................... 81
11b Independent Samples t-Test on Demographics: Pilot Data Set vs. Non-Operational
Managers in the Main Data Set...................................................................................... 82
12. Descriptive Statistics on Organizational Size .................................................................. 84
13. Descriptive Statistics on Annual Organizational Revenue ............................................... 84
14. Descriptive Statistics on Organizational Industry ........................................................... 85
vii
15. Descriptive Statistics on Functional Area ....................................................................... 86
16. Descriptive Statistics on Level in the Organization ......................................................... 86
17. Descriptive Statistics on BI User Levels .......................................................................... 86
18. Factor Analysis for the Independent Variable ................................................................ 88
19. Factor Analysis for the Data Quality ............................................................................... 89
20. Factor Analysis for the Data Source Quality ................................................................... 89
21. Factor Analysis for the User Access Quality .................................................................... 90
22. Factor Analysis for the Data Reliability ........................................................................... 91
23. Factor Analysis for the Interaction with Other Systems.................................................. 91
24a Factor Analysis for Flexibility - I ...................................................................................... 92
24b Factor Analysis for Flexibility - II ..................................................................................... 92
25a Factor Analysis for Intuition - I ....................................................................................... 94
25b Factor Analysis for Intuition - II ...................................................................................... 94
26. Factor Analysis for the Risk Level ................................................................................... 94
27a Factor Analysis for the Organizational BI Capability Variables - I .................................... 96
27b Factor Analysis for the Organizational BI Capability Variables - II ................................... 97
28. Factor Analysis for Risk and Intuition ............................................................................. 98
29. Factor Analysis for the Technological BI Capability Variables ......................................... 99
30. Factor Analysis for the Dependent Variables - External Data Reliability and External Data
Source Quality ............................................................................................................. 100
31a Factor Analysis for the Moderator Variable - I ............................................................. 101
31b Factor Analysis for the Moderator Variable - II ............................................................ 102
31c Factor Analysis for the Moderator Variable - III............................................................ 102
31d Factor Analysis for the Moderator Variable - IV ........................................................... 103
31e Factor Analysis for the Moderator Variable - V ............................................................ 104
viii
31f Correlations for Decision Type Items ........................................................................... 104
32. Item Statistics and Loadings ......................................................................................... 107
33. Inter-Construct Correlations: Consistency and Reliability Tests .................................... 108
34. Hypotheses 1 & 2 ........................................................................................................ 109
35. Path Coefficients, t Values and p Values for BI Capabilities (H1 & H2) .......................... 110
36. Hypothesis 3 ................................................................................................................ 113
37. Multiple Regression Results - H3.................................................................................. 115
38. Descriptive Statistics for the Decision Environment ..................................................... 116
39. Regression Equations for High and Low Values of the Decision Environment ............... 116
40. Hypotheses 4 ............................................................................................................... 118
41. Multiple Regression Results - H4.................................................................................. 119
42. Summary of Hypothesis Testing ................................................................................... 120
ix
LIST OF FIGURES
Page
1. High level overview of the model .................................................................................. 14
2. The four worlds of BI adopted from Hostmann et al. (2007) .......................................... 37
3. Conceptual model ......................................................................................................... 47
4. Research model ............................................................................................................. 59
5. PLS results - H1 and H2 ................................................................................................ 111
6. Interaction effect on the quantitative data quality ...................................................... 117
1
CHAPTER 1
INTRODUCTION
Since the concept of business intelligence (BI) was introduced in the late 1980s by
Howard Dresner, a Gartner Research Group analyst (Power, 2003; Buchanan and O’Connell,
2006), the information systems (IS
1
) field has witnessed the rapid development of systems and
software applications providing support for business decision making. Organizations started
migrating to complete BI environments so that they could have a “single version of the truth”
through the use of cross-organizational data, provided by an integrated architecture (Eckerson,
2003; Negash, 2004). The total investment of organizations in BI tools is estimated to be $50
billion a year and is steadily growing with the introduction of new desktop data analysis tools,
data warehousing technologies, data extraction middleware and many other tools and
techniques into the market by BI vendors (Weier, 2007).
Organizations need these new tools and techniques to improve performance and profits
(Watson et al., 2002; Eckerson, 2003; Williams and Williams, 2007). Organizations need to meet
or exceed the expectations of their customers in order to stay competitive in today’s highly
aggressive business world, and managers are increasingly relying on BI to do so (Clark et al.,
2007). Although many organizations have implemented BI, not all BI initiatives have been
successful. Practitioners and academicians have discussed the reasons for success and failure
extensively (Wixom and Watson, 2001; Watson et al., 2002; Solomon, 2005; Watson et al.,
2006). Unfortunately, a consistent picture about how to achieve success with BI has not yet
1
Research has used IS and IT interchangeably. While IT represents computer hardware, software and
telecommunication technologies, IS implies a broader context that is composed of processes, people and
information. This dissertation uses IS rather than IT.
2
emerged. This suggests that there are gaps in the research to be filled, and that research has
perhaps overlooked one or more key constructs for a BI success model.
Various approaches to examining BI capabilities may be one of the reasons behind the
gaps in the research about BI success. A lack of fit between the organization and its BI
capabilities is one of the reasons for lack of success (Watson et al., 2002; Watson et al., 2006).
Although research has defined the concept of fit differently in several areas of research
(Venkatraman, 1989), for the purposes of this dissertation it is defined as the relationship
between different BI capabilities and BI success, in the presence of different decision
environments. The decision environment is defined as the combination of different types of
decisions made and the information processing needs of the decision maker to make those
decisions (Munro and Davis, 1977).
Although BI capabilities have been studied from organizational (Eckerson, 2003; Watson
and Wixom, 2007) and technological (Manglik and Mehra, 2005; Watson and Wixom, 2007)
perspectives, some organizations still fail to achieve BI success (Jourdan et al., 2008). This may
be because the influence of the decision environment on BI capabilities has remained largely
unexamined. Examining this relationship is, however, appropriate because the primary purpose
of BI is to support decision-making in organizations (Eckerson, 2003; Buchanan and O’Connell,
2006). The purpose of this dissertation is to help fill this gap in research and provide a better
understanding of BI success by examining the impact of BI capabilities on BI success, in the
presence of different decision environments.
There is an extensive amount of research on the success of information technology in
organizations that draws on organizational design theory. Some researchers examine this from
3
an individual perspective (Lovelace and Rosen, 1996; Ryan and Schmit, 1996), while others
investigate the organization as the level of analysis (Premkumar et al., 2005; Setia et al., 2008).
Because the main interest of this dissertation is to examine BI success in light of different
decision environments and BI capabilities, the organization is used as the unit of analysis.
The suitability of BI capabilities and the decision environment includes the match
between organizational structure and the technology (Galbraith, 1977; Alexander and
Randolph, 1985), and the match between information processing needs and information
processing capabilities (Tushman and Nadler, 1978; Premkumar et al., 2005). Organizational
structure and information processing needs are part of the decision environment (Munro and
Davis, 1977; Zack, 2007). Capabilities provided by the BI include both the technology used by
the BI and the information processing capabilities of the BI. Although existing research
improves knowledge about BI, little or no research examines how BI capabilities influence BI
success in light of the decision environment of an organization. Little research examines the
decisions made in the organization as well as the information processing needs of the decision
maker. This dissertation examines this by using a theoretical lens grounded in decision making
and information processing. Specifically, Galbraith’s (1977) organizational information
processing theory and Gorry and Scott Morton’s (1971) decision support framework are used to
examine the decision environment of an organization.
The decision environment of an organization is defined as a composition of the decision
types and the way the required information is accessed and processed to aid in decision making
in that organization (Galbraith, 1977; Beach and Mitchell, 1978; Eisenhardt, 1989). Decisions
are largely distinguished by the type of problem that needs to be solved and who needs to
4
make the decision (Power, 2002). The problem addressed by a decision impacts the decision
making approach. Problems can be classified as programmed or nonprogrammed (Simon,
1960). A decision is programmed if it is repetitive and routine, and it is nonprogrammed when
there is no fixed method of handling it and the decision is consequential (Simon, 1960). In
general, programmed and nonprogrammed decisions are referred to as “structured” and
“unstructured” respectively, because these terms “imply less dependence on the computer
and relate more directly to the basic nature of the problem-solving activity in question” (Keen
and Scott Morton, 1978, p. 86). An example of a structured decision is a sales order or an
airline reservation, whereas choosing a location for a new plant is an example of an
unstructured decision.
In addition to Simon’s (1960) two decision types, Gorry and Scott Morton’s (1971)
framework for information systems includes a third type of decision: semistructured.
Semistructured decisions are decisions that cannot be solved by only autonomous decision
making or only human judgment (Gorry and Scott Morton, 1971). Semistructured decisions
require both. Gorry and Scott Morton’s (1971) framework includes nine categories of decisions
based on the decision type and management activity. Although this model has been applied to
various IS scenarios (Kirs et al., 1989; Ashill and Jobber, 2001; Millet and Gogan, 2005), it has
not been applied to the BI context. It is appropriate to do so, however, because BI is developed
to support decision making (Eckerson, 2003; Buchanan and O’Connell, 2006).
Different decisions need different types of information, depending on the managerial
activities with which they are associated (Gorry and Scott Morton, 1971). Thus, the way
information is processed for decision making purposes is also a part of the decision
5
environment of an organization (Tushman and Nadler, 1978). Galbraith’s (1977) organizational
information processing theory spawned much work on the role of information processing in
organizations. Subsequently, research indicates that the information processing capabilities of
an organization directly impact organizational effectiveness (Tushman and Nadler, 1978; Keller,
1994; Premkumar et al., 2005). Research has also examined the relationship between
technology and information processing capabilities and showed that organizational
performance increases when the technology that suits the organization’s information
processing capabilities is used (Keller, 1994; Premkumar et al., 2005).
BI helps organizations meet their information processing needs by facilitating
organizational information processing capacity (Gallegos, 1999; Nelson et al., 2005). BI does so
by combining data collection, data storage and knowledge management with analytical tools so
that decision makers can convert complex information into effective decisions (Negash, 2004).
BI capabilities within an organization can be divided into two groups; technological (e.g.,, data
sources used and data reliability) and organizational (Feeney and Willcocks, 1998; Bharadwaj et
al., 1999). Organizational capabilities are those that impact the way the BI is used within an
organization (e.g., flexibility and risk-taking level of the organization).
Technology is critical to BI success, although it is not the only driving force (Cooper et
al., 2000; Wixom and Watson, 2001; Clark et al., 2007). Research has extensively examined how
technology impacts BI success (Rouibah and Ould-ali, 2002; Watson et al., 2006). Findings
suggest that having the right technology for supporting decision making can help an
organization increase its decision-making capabilities (Arnott and Pervan, 2005). For example,
6
the appropriateness of the technology employed affects the efficiency and effectiveness of the
data warehouse implementation and usage (Wixom and Watson, 2001).
BI organizational capabilities also impact BI success and include BI flexibility, level of
acceptable risk for the organization, and the level of intuition the decision maker can involve in
the decision making process with BI (Hostmann et al., 2007; Bell, 2007; Loftis, 2008). One of the
reasons why organizations employ BI is the support it provides for decision making (Eckerson,
2003). The strictness of business process rules and regulations in an organization as well as the
level of risk tolerated impacts the way BI supports decision making in an organization
(Hostmann et al., 2007). Research suggests that organizations where employees use hard data
rather than intuition to make decisions are more likely to succeed in BI (Eckerson, 2003). Using
the collected data, BI can provide notifications to users and run predictive analytics to help
users make well informed decisions. Although making decisions based on facts as opposed to
gut feelings has become an approach preferred by many (Watson and Wixom, 2007), decision
makers still use their intuition while making decisions, especially for decisions that are not
straightforward to make (Harding, 2003).
To better support emerging BI user needs and best practices, a coordinated effort across
users, technology, business processes and data is required (Bonde and Kuckuk, 2004). This
endeavor, if successful, can improve the fit between BI and the organization within which it is
implemented. The primary research question that this dissertation addresses is how BI
capabilities influence BI success for different decision environments. BI capabilities include both
technological and organizational capabilities. The decision environment is defined as the
organizational decision types and information processing needs of the organization. The goal of
7
this study is to examine the extent to which these two constructs moderate the impact of BI
capabilities on BI success.
This study is relevant to both researchers and practitioners. This dissertation proposes
to extend current research in BI and provide a parsimonious and intuitive model for explaining
the relationship between BI success and BI capabilities in the presence of different decision
environments, based on theories from decision making and organizational information
processing. This dissertation contributes to academic research by providing richer insight in the
role of the decision environment in BI success and providing a framework with which future
research on the relationship between BI capabilities and BI success can be conducted. The
practitioner oriented contribution of this study is that it helps users and developers of BI
understand how to better align their BI capabilities with their decision environments and
presents information for managers and users of BI to consider about their decision
environment in assessing BI success.
The results of this dissertation suggest that all technological capabilities as well as one
of the organizational capabilities (flexibility) studied in this dissertation significantly impact BI
success. This may indicate that technology drives the BI initiative, rather than the organizational
capabilities. Results also indicate that the moderating effect of decision environment is
significant for quantitative data quality. This means that the quality of quantitative data impact
BI success stronger for operational control activities.
The remainder of the dissertation is organized as follows. Chapter 2 includes a review of
prior research about BI, BI success measures, BI capabilities and the role of the decision
environment. This chapter also presents a conceptual model and the proposed hypotheses.
8
Chapter 3 contains a detailed description of the methodology employed. The chapter also
discusses the sampling frame, the operationalization of constructs, and how validity and
reliability issues are addressed. Chapter 4 presents the detailed analysis process and the results
of the analysis. This dissertation concludes with Chapter 5, which provides a discussion of the
findings, presents the limitations of the study as well as its implications for both managers and
academics, and concludes by providing future research directions.
9
CHAPTER 2
LITERATURE REVIEW
Business intelligence (BI) is the top priority for many organizations and the promises of
BI are rapidly attracting many others (Evelson et al., 2007). Gartner Group’s BI user survey
reports suggest that BI is also a top priority for many chief information officers (CIOs) (Sommer,
2008). More than one-quarter of CIOs surveyed estimated that they will spend at least $1
million on BI and information infrastructure in 2008 (Sommer, 2008). Organizations today
collect enormous amounts of data from numerous sources, and using BI to collect, organize,
and analyze this data can add great value to a business (Gile et al., 2006). BI can also provide
executives with real time data and allow them to make informed decisions to put them ahead
of their competitors (Gile et al., 2006). Although BI matters so much to so many organizations,
there are still inconsistencies in research findings about BI and BI success.
Various definitions of BI have emerged in the academic and practitioner literature.
While some broadly define BI as a holistic and sophisticated approach to cross-organizational
decision support (Moss and Atre, 2003; Alter, 2004), others approach BI from a more technical
point of view (White, 2004; Burton and Hostmann, 2005). Table 1 provides some of the more
prevalent definitions of BI.
10
Table 1
Selected BI Definitions
BI Definition Author(s) Definition Focus
An umbrella term to describe the set of
concepts and methods used to improve
business decision-making by using fact-
based support systems
Dresner (1989) Technological
A system that takes data and transforms
into various information products
Eckerson (2003) Technological
An architecture and a collection of
integrated operational as well as decision
support applications and databases that
provide the business community easy
access to business data
Moss and Atre (2003) Technological
Organized and systemic processes which
are used to acquire, analyze and
disseminate information to support the
operative and strategic decision making
Hannula and Pirttimaki
(2003)
Technological
A set of concepts, methods and processes
that aim at not only improving business
decisions but also at supporting realization
of an enterprise’s strategy
Olszak and Ziemba
(2003)
Organizational
An umbrella term for decision support
Alter (2004)
Organizational
Results obtained from collecting,
analyzing, evaluating and utilizing
information in the business domain.
Chung et al. (2004) Organizational
A system that combines data collection,
data storage and knowledge management
with analytical tools so that decision
makers can convert complex information
into competitive advantage
Negash (2004) Technological
A system designed to help individual users
manage vast quantities of data and help
them make decisions about organizational
processes
Watson et al. (2004) Organizational
(table continues)
11
Table 1 (continued).
BI Definition Author(s) Definition Focus
An umbrella term that encompasses data
warehousing (DW), reporting, analytical
processing, performance management
and predictive analytics
White (2004) Technological
The use and analysis of information that
enable organizations to achieve efficiency
and profit through better decisions,
management, measurement and
optimization
Burton and Hostmann
(2005)
Organizational
A managerial philosophy and tool that
helps organizations manage and refine
information with the objective of making
more effective decisions
Lonnqvist and
Pirttimaki (2006)
Organizational
Extraction of insights from structured data Seeley and Davenport
(2006)
Technological
A combination of products, technology
and methods to organize key information
that management needs to improve profit
and performance
Williams and Williams
(2007)
Organizational
Both a process and a product, that is used
to develop useful information to help
organizations survive in the global
economy and predict the behavior of the
general business environment
Jourdan et al. (2008) Organizational
These definitions largely reflect either a technologically or organizationally driven
perspective. BI, however, is comprised of both technical and organizational elements (Watson
et al., 2006). In the most general sense, BI presents historical information to its users for
analysis to enable effective decision making and for management support (Eckerson, 2003). For
the purpose of this dissertation, BI is defined as a system comprised of both technical and
organizational elements that presents historical information to its users for analysis, to enable
12
effective decision making and management support, for the overall purpose of increasing
organizational performance.
One of the goals of BI is to support management activities. Computer based systems
that support management activities and provide functionality to summarize and analyze
business information are called management support systems (MSS) (Scott Morton, 1984;
Gelderman, 2002; Clark et al., 2007; Hartono et al., 2007). Decision support systems (DSS),
knowledge management systems (KMS), and executive information systems (EIS) are examples
of MSS (Forgionne and Kohli, 2000; Clark et al., 2007; Hartono et al., 2007). These systems have
commonalities that make them all MSS (Clark et al., 2007). These common properties include
providing decision support for managerial activities, (Forgionne and Kohli, 2000; Gelderman,
2002; Clark et al., 2007), using and supporting a data repository for decision-making needs
(Cody et al., 2002; Arnott and Pervan, 2005; Clark et al., 2007), and improving individual user
performance (Gelderman, 2002; Hartono et al., 2005; Clark et al., 2007).
BI can also be included in the MSS set (Clark et al., 2007). First, BI supports decision
making for managerial activities (Eckerson, 2003; Hannula and Pirttimaki, 2003; Burton and
Hostmann, 2005). Second, BI uses a data repository (usually a data warehouse) to store past
and present data and to run data analyses (Eckerson, 2003; Moss and Atre, 2003; Anderson-
Lehman et al., 2004; Clark et al., 2007). BI is also aimed at improving individual user
performance through helping individual users manage enormous amounts of data while making
decisions (Watson et al., 2004; Burton et al., 2006; Clark et al., 2007). Thus, BI can be classified
as an MSS (Clark et al., 2007; Baars and Kemper, 2008). Examining BI in the light of research
based on other types of MSS may lead to better decision support and a higher quality of BI
13
systems (Clark et al., 2007). Findings of this dissertation may also be applied to other types of
MSS that exist now and that may emerge in the future.
The MSS classification of BI may also help research address gaps that result from
examining MSS separately, without considering their common properties. Research examines
success antecedents of many MSS extensively (Hartono et al., 2006), but consistent factors that
help organizations achieve a successful BI have not yet emerged. Research suggests that fit
between an MSS and the decision environment in which it is used is an MSS success antecedent
(Hartono et al., 2006; Clark et al., 2007). For example, using appropriate information technology
for knowledge management systems provides more successful decision support (Baloh, 2007).
The complexity level of the technology also impacts MSS effectiveness and success (Srinivasan,
1985). However, research has not looked specifically at the role of the decision environment in
BI success. It is important to do so because although it is an MSS, BI has requirements that are
significantly different from those of other MSS (Wixom and Watson, 2001).
The purpose of this dissertation is to help fill this gap in BI research by examining how BI
capabilities impact BI success and how the decision environment influences this relationship.
The decision environment is composed of the types of decisions made in the organization and
the information processing needs of the decision maker (Galbraith, 1977; Beach and Mitchell,
1978; Eisenhardt, 1989). BI capabilities include both organizational and technological
capabilities (Feeney and Willcocks, 1998; Bharadwaj et al., 1999). Figure 1 provides a high level
overview to help orient the reader to the model this dissertation addresses.
14
Figure 1. High level overview of the model.
The following sections review the literature for each construct of the model provided
above. After BI success, discussions on the decision environment and BI capabilities follow.
BI Success
BI success is the positive value an organization obtains from its BI investment (Wells,
2003). The organizations that have BI also have a competitive advantage, but how an
organization defines BI success depends on what benefits that organization needs from its BI
initiative (Miller, 2007). BI success may represent attainment of benefits such as improved
profitability (Eckerson, 2003), reduced costs (Pirttimaki et al., 2006), and improved efficiency
(Wells, 2003). For the purpose of this dissertation, BI success is defined as the positive benefits
organizations achieve through use of their BI.
BI
Success
Decision Environment
BI Capabilities
Technological BI
Capabilities
Organizational
BI Capabilities
Decision Types
Information
Processing Needs
15
Most organizations struggle to measure BI success. Some of them want to see tangible
benefits, so they use explicit measures such as return on investment (ROI) (Howson, 2006). BI
success can also be measured with the improvement in the operational efficiency or
profitability of the organization (Vitt et al., 2002; Eckerson, 2003). If the “costs are reasonable in
relation to the benefits accruing” (Pirttimaki et al., 2006, p. 83), then organizations may
conclude that their BI is successful. Other companies are interested in measuring intangible
benefits; these include whether users perceive the BI as mission critical, how much
stakeholders support BI and the percentage of active users (Howson, 2006). Specific BI success
measures differ across organizations and even across BI instances within an organization. For
example, one firm may implement to achieve better management of its supply chain, while
another may implement to achieve better customer service.
Research, however, does consistently point to at least one high level commonality
among successful BI implementations. Organizations that have achieved success with their BI
implementations have created a strategic approach to BI to help ensure that their BI is
consistent with corporate business objectives (Eckerson, 2003; Watson et al., 2002; McMurchy,
2008). How Continental Airlines improved its processes and profitability through successful
implementation and use of BI is a good example of aligning BI with business needs (Watson et
al., 2006). Cardinal Health Care is also a good example of the importance of BI and business
alignment because this organization has shaped its BI according to its business requirements
(Malone, 2005).
Research provides valuable insight into how to align BI with business objectives and
offers explanations for failures to do so (Eckerson, 2003; McMurchy, 2008). However, much of
16
this research is derived from a small number of cases and/or it is not strongly grounded in
theory (e.g., Cody et al., 2002; Watson, 2005). Other research provides a solid theoretical
foundation for examining BI success, yet provides limited empirical evidence (e.g., Gessner and
Volonino, 2005; Clark et al., 2007). Research that provides a sound theoretical background as
well as empirical evidence focuses on specific technologies of BI, such as data warehousing
(e.g., Cooper et al., 2000; Nelson et al., 2005) or web BI (e.g., Srivastava and Cooley, 2003;
Chung et al., 2004), rather than a more holistic model.
Finally, although research suggests several success models for MSS (Forgionne and
Kohli, 1995; Gelderman, 2002; Clark et al., 2007; Hartono et al., 2007), there is little theory-
based research solely focusing on understanding BI success from the perspective of BI
capabilities and the influence of the decision environment in which the BI is used. DSS and its
success factors, for example, have been studied comprehensively in the literature (e.g., Sanders
and Courtney, 1985; Guimaraes et al., 1992; Finlay and Forghani, 1998; Alter, 2003; Hung et al.,
2007). KMS success factors have also been widely examined using various theories from IS (e.g.,
Wu and Wang, 2006; Kulkarni et al., 2007; Tsai and Chen, 2007) as well as the management
literature (e.g., Al-Busaidi and Olfman, 2005; Oltra, 2005). Common features among these MSS
success studies is that they all suggest research models on how to increase organizational and
financial benefits obtained from these systems by testing the impact of various factors such as
user satisfaction (e.g., Wu and Wang, 2006), system quality (e.g., Tsai and Chen, 2007), or
management support (e.g., Al-Busaidi and Olfman, 2005).
Research has identified some of the factors that influence BI success as well (Negash,
2004; Solomon, 2005; Clark et al., 2007). For example, BI usability is an important determinant
17
of system performance and user satisfaction (Bonde and Kuckuk, 2004; Chung et al., 2005).
Other important performance indicators include technology and infrastructure (Negash, 2004;
Gessner and Volonino, 2005) and management support (Cooper et al., 2000; Anderson-Lehman
et al., 2004). Table 2 summarizes research on factors that affect BI success.
Table 2
Concepts Examined in Research About BI Success
Success Factors Author(s) Key Findings
Organizational
strategy
Cooper et
al. (2000)
This article presents how a data warehousing technology can
transform an organization by improving its performance and
increasing its competitive advantage. The authors have observed
the First American Cooperation changing its corporate strategy and
provide lessons for managers who plan to use BI to increase
competitive advantage.
Raymond
(2003)
This article provides a conceptual framework for business
intelligence activities in small and medium enterprises. Authors
suggest that the framework they propose can guide the design and
specification of BI projects. Based on their framework, authors
divide the BI project into 5 phases; including searching for strategic
information that provide competitive advantage.
Watson
et al.
(2004)
This article discusses how companies justify and assess data
warehousing investments. They examine the approval process and
post-implementation review for data warehouses. They discuss
that benefits gained can be tangible or intangible; operational,
informational or strategic; revenue enhancing or cost saving; and
time savings or improved decision making.
Technology &
Infrastructure
Wixom
and
Watson
(2001)
This article investigates data warehousing success factors. The
authors argue that a data warehouse is different from a regular IS
project and various implementation factors affect data
warehousing success. Findings indicate that project, organizational
and technical implementation successes are positively related to
data quality and system quality.
Nelson et
al. (2005)
In this article, the authors’ main goal is to find out the determinants
of the quality in data warehouses. Findings indicate that reliability,
flexibility, accessibility and integration are significant determinants
of system quality for BI tools. Also, they present that information
and system quality are success factors for data warehouses.
(table continues)
18
Table 2 (continued).
Success Factors Author(s) Key Findings
Technology &
infrastructure
Solomon
(2005)
This article gives a guideline for successful data warehouse
implementation and suggestions to managers on how to avoid
pitfalls and overcome challenges in enterprise-level projects.
These guidelines are mostly technical-oriented, such as; ETL
tool selection, data transport and data conversion methods.
Presentation &
usability
Alter
(2003)
Defining BI as a new umbrella term for decision support, Alter
suggests that structure of business processes, participants,
technology, information quality, availability and presentation,
product and services, infrastructure, environment and business
strategy are success factors for better decision support.
Lönnqvist
and
Pirttimaki
(2006)
This article is a literature review that discusses various
methods used for measuring business intelligence. Among the
reasons to measure BI is to show that it is worth the
investment. It also helps manage the BI process by ensuring
that BI products satisfy the users’ needs and the process is
efficient. They use total cost of ownership and subjective
measurements of effectiveness as examples of BI measures.
Management
support
Eckerson
(2003)
Based on a TDWI survey, this article provides an overview of BI
concepts and components and also examines the key success
factors of BI. One of these factors emphasizes the top
management commitment and mentions that it is the
commitment and support from the business sponsors and
managers that drives an organization’s BI initiative and
furthers its strategic objectives.
McMurchy
(2008)
This article identifies several factors for success in developing
BI business cases. His key findings indicate that organizations
need to tie BI strategy to overall strategy, sustain top
management support and user enthusiasm to maximizing the
ROI on their BI.
Performance
measures
Watson et
al. (2001)
This article assesses the benefits of the data warehousing and
provided a taxonomy. They group benefits as easy and hard to
measure as one dimension, and their impact being local and
global as the other dimension. An interesting result of this
study shows that there is an inverse relationship between the
expected and received benefits, and the potential impact of
the benefits.
(table continues)
19
Table 2 (continued).
Success Factors Author(s) Key Findings
Performance
measures
Gessner
and
Volonino
(2005)
This article discusses how right timing can improve ROI on BI,
specifically for marketing applications. They argue that, if BI
process does not increase the customer value, it would only
increase the expenses. They measure BI success through ROI,
and examine the change in ROI by maximizing Customer
Lifetime Value (CLV), where the change in CLV is the link
between technology infrastructure investments and profits.
Pirttimaki
et al.
(2006)
This article discusses available measurement methods for BI.
Since there is not enough measure available for the BI process;
business performance measurement literature can be used as
a reference for this purpose. They suggest a measurement
system that can be used as a tool to develop and improve BI
activities.
Information &
decision quality
Dennis et
al. (2001)
This article develops a model for interpreting Group Support
Systems effects on performance, and they test the fit between
the task and the GSS structures selected for use. The findings
indicate the importance of information and decision quality on
performance.
Clark et al.
(2007)
This article proposes a conceptual model for MSS. Mainly from
the IS success literature, 20 variables are selected and formed
the basis of the model. Some of them that are; perceived MSS
benefits, management decision quality, usability of MSS, MSS
costs, MSS functionality, MSS training, and MSS quality.
Structure of
business
processes
Yoon et al.
(1995)
The goal of this article is to identify and empirically test the
determinants of Expert Systems success. The authors have
come up with 8 major success determinants, and measured the
relationship between them and user satisfaction; problem
characteristics, developer skill, end-user characteristics, impact
on job, expert characteristics, shell characteristics, user
involvement and manager support.
Watson et
al. (2002)
This article investigates why some organizations receive more
benefits from data warehousing. It presents a framework that
shows how data warehouses can transform an organization
through time savings for both data suppliers and users, more
and better information, better decisions, improvement of
business processes and support for the accomplishment of
strategic business objectives.
20
Common characteristics of successful BI solutions are business sponsors who are highly
committed and actively involved; business users and the BI technical team working together; BI
being viewed as an enterprise resource and given enough funding to ensure long-term growth;
static and interactive online views of data being provided to the users; an experienced BI team
assisted by vendor and independent consultants; and, organizational culture reinforcing the BI
solution (Eckerson, 2003; Howson, 2006). Fit between BI strategy and business objectives,
commitment from top management with long-term funding, and a realistic BI strategy with
expected benefits and key metrics are also important characteristics of a successful BI
(McMurchy, 2008). In addition, sound infrastructure and appropriate technology are
characteristics of a successful BI (Solomon, 2005; Lönnqvist and Pirttimaki, 2006).
To succeed, organizations must develop their own measures for BI success (Howson,
2006) because BI success can have more than one meaning depending on the context in which
it is being used. The following section reviews measures of BI success.
Measuring BI Success
BI success can be measured by an increase in an organization’s profits (Williams and
Williams, 2007) or enhancement to competitive advantage (Herring, 1996). Return on
investment (ROI), however, is the most frequently used measure of BI success (McKnight,
2004). For example, Gessner and Volonino (2005) use ROI to measure BI success for marketing
applications. They argue that if BI does not increase customer value, it only increases expenses
and therefore does not produce an adequate ROI. ROI is also used in approving and assessing
data warehouses (Watson et al., 2001; Watson et al., 2004). ROI, however, is often difficult to
measure (Watson et al., 2004). Thus, revenue enhancement, time savings, cost savings, cost
21
avoidance and value contribution are variables that are also used to measure BI effectiveness in
addition to ROI (Herring, 1996, Sawka, 2000).
The Competitive Intelligence Measurement Model (CIMM) has been suggested as an
alternative approach to ROI to measure BI success (Davison, 2001). This model calculates the
return on BI investment by considering completion of objectives, satisfaction of decision
makers, and the costs associated with the project (Lonnqvist and Pirttimaki, 2006).The
suitability of the technology, whether business users like the BI, and how satisfied business
sponsors are with BI are other measures used to assess BI success (Moss and Atre, 2003;
Lonnqvist and Pirttimaki, 2006).
Another approach to measure BI success is subjective measurement (Lonnqvist and
Pirttimaki, 2006). This involves measuring the satisfaction of the decision maker with BI by
asking questions regarding the effectiveness of the BI (Davison, 2001). This way, it is possible to
learn what users think of various aspects of the system, such as ease of use, timeliness, and
usefulness. With this method, it is also possible to understand the perceptions of the extent to
which the users realized their expected benefits with BI.
This dissertation employs the subjective measurement method to measure BI success.
Many of the commonly used success measures mentioned above require that quantitative
data, such as ROI, be collected from various operations of the organization. In many cases it is
difficult, if not impossible, to measure the necessary constructs (Kemppila and Lonnqvist, 2003).
For example, many benefits provided by BI are intangible and non-financial, such as improved
quality and timeliness of information (Hannula and Pirttimaki, 2003). Although it may transfer
into financial benefits in the form of cost savings or profit increase, the time lag between the
22
actual production of intelligence and financial gain makes it difficult to measure the benefits
(Lonnqvist and Pirttimaki, 2006). Also, using subjective measurement based on the satisfaction
of the decision makers and their perception of the extent to which they realized their expected
benefits with BI shows how effective the BI is considered by its users (Davison, 2001; Lonnqvist
and Pirttimaki, 2006). As suggested by the CIMM model, measuring user satisfaction regarding
timeliness, relevancy and quality of the information provided by the BI also gives insight
regarding how successful the BI is (Lonnqvist and Pirttimaki, 2006).
Relationship between BI Capabilities and the Decision Environment
This dissertation posits that a key antecedent of BI success is having the right BI
capabilities, and right BI capabilities depend on the decision environment in which the BI is
used. The match between the decision environment and what an MSS provides has been
studied as an indicator of success, and is widely recognized as an organizational requirement
(Arnott, 2004; Clark et al., 2007).
This has also been examined as the match between MSS and the problem space within
which it is implemented (Clark et al., 2007). This match is defined as “how closely the designed
MSS reflects the goals of the organization in decision outcomes” (Clark et al., 2007, p. 586).
Complexity of the decisions that organizations face every day impacts the level of this match
(Clark et al., 2007). MSS are developed to address a variety of decisions and MSS effectiveness
is a direct outcome of how well these decisions are supported (Gessner and Volonino, 2005).
For example, various BI applications are developed to help organizations decide on the best
time to present offers to customers, and the effectiveness of BI is judged according to the
23
effectiveness of these decisions (Gessner and Volonino, 2005). Thus, understanding how the
decision environment affects the impact of BI capabilities is useful and important.
Organizational structure and strategy are two significant components of the decision
environment of an organization (Duncan, 1974). The appropriateness of an MSS to an
organization’s structure and strategy is a significant factor that impacts MSS success (Cooper
and Zmud, 1990; Hong and Kim, 2002; Setia et al., 2008). For example, Setia et al.’s (2008)
findings indicate that supply chain systems provide enhanced agility if there is a strategy and
task fit between supply chain systems and the organizational elements. As the match between
MSS and organizational structure increases, the performance of the organization improves
(Weil and Olson, 1989). The strategic alignment model developed by Henderson and
Venkatraman (1993) suggests that the fit among business strategy, organizational structure and
technology infrastructure increases the ability to obtain value from IS investments.
As can be seen from the examples above, research examines how MSS capabilities
moderated by the decision environment impacts MSS success. However, this concept has not
been used specifically to examine BI success. Focusing on the decision environment and BI
capabilities, this dissertation examines the effect of the BI capabilities on BI success, moderated
by the decision environment.
Decision Environment
The decision environment can be defined as “the totality of physical and social factors
that are taken directly into consideration in the decision-making behavior of individuals in the
organization” (Duncan, 1974, p. 314). This definition considers both internal and external
factors. Internal factors include people, functional units and organization factors (Duncan,
24
1974). External factors include customers, suppliers, competitors, sociopolitical issues and
technological issues (Duncan, 1974; Power, 2002).
Decision types are a part of the decision environment because the extent to which
decisions within the decision environment are structured or unstructured influences the
performance of the analytical methods used for decision making (Munro and Davis, 1977). The
types of decisions supported by the decision environment should be considered in selecting
techniques for determining information requirements for that decision (Munro and Davis,
1977).
The information processing needs of the decision maker are also a part of the decision
environment, provided that decision making involves processing and applying information
gathered (Zack, 2007). Because appropriate information depends on the characteristics of the
decision making context (Zack, 2007), it is hard to separate the information processing needs
from decision making. This indicates that information processing needs are also a part of the
decision environment.
Information processing and decision making are the central functions of organizations.
They are topics of interest in research and have been discussed from both technical and
managerial perspectives (Soelberg, 1967; Galbraith, 1977; Tushman and Nadler, 1978; Saaty
and Kearns, 1985). According to the behavioral theory of the firm, decision making in
organizations is a reflection of people’s limited ability to process information (Galbraith, 1977).
Contradictory to this, the operations research/management science perspective argues that
decision making can be improved by rationalizing the process, formulating the decision
problem as a mathematical problem, and testing alternatives on the model before actually
25
applying one to a real world problem (Galbraith, 1977). This approach opened the way for
computer applications and information technology that support decision making processes.
With the great information processing power of computers, information systems such as MSS
were developed.
IS research has used various information processing theories to explain the impact of
information processing on organizational performance, but organizational information
processing theory is one of the most frequently used theories (Premkumar et al., 2005;
Fairbank et al., 2006). The following section provides an overview of organizational information
processing theory including definition, constructs and its use in IS research.
Organizational Information Processing Theory
Organizational Information Processing (OIP) theory emerged as a result of an increasing
understanding among organizational researchers that information is possibly the most
important element of today’s organizations (Fairbank et al., 2006). The first researcher that
proposed this theory was Galbraith (1973). He suggested that specific structural characteristics
and behaviors can be associated with information requirements, and various empirical studies
have found support for his propositions (Tushman and Nadler, 1978; Daft and Lengel, 1986;
Karimi et al., 2004).
In OIP theory, organizations are structured around information. The relationship
between information and how it is used is a direct antecedent of organizational performance.
OIP focuses on information processing needs, information processing capability, and the fit
between them to obtain the best possible performance in an organization (Premkumar et al.,
2005). In this context, information processing is defined as the “gathering, interpreting, and
26
synthesis of information in the context of organizational decision making” (Tushman and
Nadler, 1978, p. 614), and information processing needs are the means to reduce uncertainty
and equivocality (Daft and Lengel, 1986).
OIP theory assumes that organizations are open social systems that deal with work-
related uncertainty (Tushman and Nadler, 1978) and equivocality (Daft and Macintosh, 1981).
Uncertainty is the difference between information acquired and information needed to
complete a task (Galbraith, 1973; Tushman and Nadler, 1978; Premkumar et al., 2005). Task
characteristics, task environment and task interdependence are among the sources of
uncertainty (Tushman and Nadler, 1978). Equivocality can be defined as multiple and conflicting
interpretations about an organizational situation (Daft and Macintosh, 1981; Daft and Lengel,
1986). It refers to an unclear situation where new and/or more data may not be enough to
clarify (Daft and Lengel, 1986).
One reason why organizations process information is to reduce uncertainty and
equivocality (Daft and Lengel, 1986). Organizations that face uncertainty must acquire more
information to learn more about their environment (Daft and Lengel, 1986). When tasks are
non-routine or highly complex, uncertainty is high; hence, information processing requirements
are greater for effective performance (Daft and Macintosh, 1981). Equivocality is very similar to
uncertainty. However, rather than lack of information, it is associated with lack of
understanding (Daft and Lengel, 1986). In other words, a decision maker may process the
required data, but not clearly understand what it means or how to use it. For example, a
problem may be perceived differently by managers from different functional departments in an
organization; an accounting manager may interpret some specific information different than a
27
system analyst. Both uncertainty and equivocality impact information processing in an
organization and should be minimized to achieve performance (Daft and Lengel, 1986; Keller,
1994).
OIP theory has important implications for organizational design because different
organizational structures are more effective in different situations (Tushman and Nadler, 1978;
Daft and Lengel, 1986). Specifically, the degree of uncertainty and equivocality may imply how
organizational structure should be designed (Daft and Lengel, 1986; Lewis, 2004). Here,
organizational structure is defined as the “allocation of tasks and responsibilities to individuals
and groups within the organization, and the design of systems to ensure effective
communication and integration of effort” (Daft and Lengel, 1986, p. 559). Thus, it is important
for organizations to have a structure that fits their uncertainty and equivocality levels, so that
they can perform well.
Organizations must develop information processing systems capable of dealing with
uncertainty (Zaltman et al., 1973). IS provides a way of managing uncertainty and equivocality
in organizations (Daft and Lengel, 1986; Keller, 1994; Premkumar et al., 2005). Various
researchers have studied how IS impacts uncertainty and equivocality (Tushman and Nadler,
1978; Jarvenpaa and Ives, 1993; Premkumar et al., 2005), and also how this affects
organizational effectiveness (Tuggle and Gerwin, 1980; Wang, 2003).
Several IS studies use OIP as the central theory in their models to explain how to obtain
effectiveness in organizations through the use of information technologies (Galbraith, 1977;
Tushman and Nadler, 1978; Daft and Lengel, 1986). For example, Premkumar et al. (2005)
suggest that the fit between information processing needs and information processing
28
capabilities has a significant impact on organizational performance. The fit between
organizational structure and information technology is an important contributor to
organizational effectiveness as well (Sauer and Willcocks, 2003). Table 3 provides examples
from IS research that have used OIP theory.
Table 3
Examples of Organizational Information Processing Theory in Information Systems
Concept Author(s) Key Findings
IS Fit
Jarvenpaa
and Ives
(1993)
This study examines various organizational designs for IS in
globally competing organizations. Findings show that there are
inconsistencies among how the organizations are structured and
how they manage their IS capabilities, revealing that there is a
lack of fit between organizational environment and IT.
Premkumar
et al. (2005)
This study examines the fit between information processing needs
and information processing capability in a supply chain context
and examines its effect on performance. Findings indicate that the
fit of information needs and IS capability has a significant impact
on performance.
Stock and
Tatikonda
(2008)
This study suggests a conceptual model on the fit of IS adopted
from an external source. Authors base their arguments on
organizational information processing theory and their findings
show that the fit between IS and information processing
requirements affect IS effectiveness.
IS Design &
Development
Tatikonda
and
Rosenthal
(2000)
Using information processing theory, this paper examines the
relationship between product development project characteristics
and project outcomes. Results show that technology novelty and
project complexity characteristics contribute to project task
uncertainty, which impacts project execution outcomes.
Jain et al.
(2003)
This study suggests that when compared to the traditional
approach, component-based software development (CBSD)
improves the requirements identification process. They use the
information processing theory to show how CBSD could facilitate
the identification of user requirements.
(table continues)
29
Table 3 (continued).
Concept Author(s) Key Findings
IS
Architecture
&
Management
Anandarajan
and Arinze
(1998)
This study uses information processing theory to examine the
match between an organization's information processing
requirements and its client/server architectures, and its impact on
effectiveness. The results indicate that a fit between task
characteristics and architectures directly affects system
effectiveness.
Douglas
(1998)
This study examines the fit between organizational structures and
information processing needs, specifically in the health care
industry. Findings suggest that vertical and horizontal information
systems offer the best opportunity for information processing
capability.
Cooper and
Wolfe
(2005)
This study uses information processing theory to examine the IS
adaptation process in organizations. Authors suggest that the fit
between information processing volume and, uncertainty and
equivocality reduction contributes to successful IS adaptation.
Organizational
Performance
Tuggle and
Gerwin
(1980)
This study suggests a simulation model that integrates the
processes of key environmental factors, strategy formulation by
the organization, routine operating decision executions and
standard operating procedures. Findings suggest that uncertainty
and sensitivity to changes impacts organizational effectiveness
negatively.
Fairbank et
al. (2006)
This study examines the relationship between IS and
organizational performance in the health insurance industry.
Authors examine how IS is deployed in organizations through
information processing design choices. Results show that
information processing design choices are generally related to
organizational performance.
IS Costs &
Benefits
Tatikonda
and
Montoya
Weiss
(2001)
This study examines relationships among organizational process
factors, product development capabilities, critical uncertainties,
and operational/market performance in product development
projects. The findings show that the organizational process factors
are associated with achievement of operational outcome targets
for product quality, unit-cost and time-to-market.
Gattiker and
Goodhue
(2004)
Using organizational information processing theory, this study
suggests factors that influence enterprise resource planning (ERP)
costs and benefits. The organizational characteristics they focus
on are interdependence and differentiation. While high
interdependence among organizational units is found to be
contributing to the positive ERP effects, high differentiation seems
to increase costs.
30
Although there is IS research using OIP theory to explain various phenomena, there is
very little research focusing on BI through the lens of OIP theory. BI is an information
processing mechanism that allows each user to process, analyze, and share information and to
turn it into useful knowledge (Hannula and Pirttimaki, 2003), thus it seems important to study
BI from OIP perspective.
In the BI context, the extent of information processing is a direct result of BI capabilities
(both technological and organizational). Employing the right capabilities for information
processing is an important issue for effective decision making and organizational performance
(Daft and Lengel, 1986; Fairbank et al., 2006), hence it is important to understand the dynamics
of information processing for BI.
Processing information allows organizations to develop a more effective decision
making process and an acceptable level of performance. Decision making is a key part of
managers’ jobs because it involves taking actions on behalf of their organization, and the
managers are evaluated based on the effectiveness of their decisions (Simon, 1960; Power,
2002). Thus, it is important to understand the underlying decision making mechanism, and how
decisions differ based on their characteristics. The next section provides a literature review of
the second component of the decision environment; decision types made in the organization.
Decision Types
Decisions types are different problems that are distinguished based on who needs to
make the decision and the steps the decision maker needs to follow to solve the problem
(Power, 2002). A problem is a structured decision if it is repetitive and routine, and it is
unstructured if there is no fixed method of handling it and the decision is consequential (Simon,
31
1960). Any other type of problem that falls between these two types is a semi-structured
decision (Keen and Scott Morton, 1978).
Simon’s framework distinguishes between different types of decisions based on
different techniques that are required to handle them (Simon, 1965; Gorry and Scott Morton,
1971; Adam et al., 1998). For example, while structured decisions are mostly made with
standard operating procedures using well-defined organizational channels, unstructured
decisions require judgment, creativity and training of executives (Simon, 1965; Kirs et al., 1989).
Semistructured decisions fall in between these two and require managerial judgment as well as
the support system (Keen and Scott Morton, 1978; Teng and Calhoun, 1996). Structured
decisions can largely be automated therefore do not involve a decision maker. Unstructured
decisions require judgment; hence the involvement of a decision maker at all times (Gorry and
Scott Morton, 1971; Teng and Calhoun, 1996).
Another categorization of decision making activities was suggested by Anthony (1965).
To categorize managerial activities according to their decision-making requirements, Anthony
(1965) developed a framework of decision types, associating decisions with organizational
levels. This framework includes three categories; strategic planning, management control, and
operational control. The strategic planning category involves decisions related to long term
plans, strategic plans and policies that may change direction of the organization (Anthony,
1965; Shim et al., 2002). This typically involves senior managers and analysts because the
problems are highly complex, nonroutine, and require creativity (Gorry and Scott Morton,
1971). Anthony defines strategic planning as “the process of deciding on objectives of the
organization, on changes in these objectives, on the resources used to attain these objectives,
32
and on the policies that are to govern the acquisition, use, and disposition of these resources”
(p. 24). Introducing a new product line can be given as an example of a decision in this category.
In Anthony’s (1965) framework, the management control category includes both
planning and control, involves making decisions about what to do in the future based on the
guidelines established in the strategic planning (Otley et al., 1995; Shim et al., 2002). Anthony
defines management control as “the process by which managers assure that resources are
obtained and used effectively and efficiently in the accomplishment of the organization’s
objectives” (p. 27). For instance, planning upon next year’s budget is an example of a
management control activity. The operational control category involves decisions related to
operational control, which is “the process of assuring that specific tasks are carried out
effectively and efficiently” (Anthony, 1965, p. 69). Here, individual tasks and transactions are
considered, such as a sales order or inventory procurement.
The boundaries between Anthony’s three categories are not always clear. There can be
overlaps between them, forming a continuum between highly complex activities and routine
activities (Anthony, 1965; Gorry and Scott Morton, 1971; Shim et al., 2002). When information
requirements of Anthony’s (1965) three managerial activities are considered, it can be seen
that they are very different from one another. This difference is attributable to the
fundamental characteristics of the information needs at different managerial levels (Gorry and
Scott Morton, 1971). Thus, Anthony’s (1965) framework also represents different information
processing needs of the decision makers at different management levels (Gorry and Scott
Morton, 1971).
33
Similar to Anthony’s (1965) classification, Simon’s (1965) classification of business
decisions as structured and unstructured also form a continuum between these two types of
decisions. Simon (1960) classifies decisions based on the ways used to handle them, and
Anthony’s (1965) categorization is based on the purpose and requirements of the managerial
activity that involves the decision (Shim et al., 2002). Gorry and Scott Morton (1971) combine
these two views and suggest a broader framework for decision support for managerial
activities. A table representation of this framework as adapted from Gorry and Scott Morton
(1971) is shown in Table 4.
The framework that results from the combination of Anthony’s (1965) and Simon’s
(1960) frameworks includes nine categories. Cell (1), the structured operational control,
involves decisions like inventory reordering which can be done through a computer-based
system without requiring any judgment. Decisions in cells (2) and (3) differ from cell (1) on the
level of system support they require. For example, while bond trading is an example of
semistructured operational control, cash management is an unstructured operational control
decision (Gorry and Scott Morton, 1971). In a similar fashion, while the degree of
automatization reduces from cell (4) to cell (6), the decisions involved in management control
are at the tactical level rather than the operational level. Examples of cells (4), (5) and (6) are
budget analysis, variance analysis, and hiring new managers, respectively. In strategic planning
(cells 7, 8, 9), the decisions are made at the executive level. Warehouse location, mergers, and
R&D planning are examples of cells (7), (8), (9) respectively.
34
Table 4
A Framework for Information Systems, Adapted From Gorry and Scott Morton (1971)
Management Activity
Decision Type Operational
Control
Management
Control
Strategic
Planning
Structured (1) (4) (7)
Semistructured (2) (5) (8)
Unstructured (3) (6) (9)
Gorry and Scott Morton’s (1971) framework has implications for both system design and
organizational structure (Shim et al., 2002). Because information requirements differ among
different types of decisions, the data collection and maintenance techniques for decision types
are also different. Information differences among the three decision areas imply related
differences in hardware and software requirements (Gorry and Scott Morton, 1971; Parikh et
al., 2001). For example, techniques used for operational control are rarely useful for strategic
planning, and the records in the operational control database may be too detailed to be used
for strategic decision making (Gorry and Scott Morton, 1971).
Organizational structure related implications of this framework are that managerial and
analytical skills for each type of decision are different. For example, decision makers involved in
the operational control area usually have different backgrounds and training than the ones in
management control. Thus, the skills and the decision making styles of managers in strategic,
operational and managerial areas differ significantly (Gorry and Scott Morton, 1971; Parikh et
al., 2001).
In summary, for the purposes of this dissertation, Gorry and Scott Morton’s (1971; 1989)
framework represents the decision environment because it categorizes both internal and
35
external factors related to the decision-making activities in an organization (Duncan, 1974),
such as the different technological requirements of different decisions and different
information needs of managerial activities. This framework groups decisions according to the
managerial activities with which they are associated and the methods used to handle them.
Different decision types require different methods, techniques and skills to be handled. These
differences lead to variations in technology infrastructure as well as organizational
characteristics that best handle specific types of decisions. This dissertation argues that BI
should be employed in accordance with these differences.
BI Capabilities
Adapting to today’s rapidly changing business environment requires agility from
organizations and BI has an important role in providing this agility with the capabilities it
provides (Watson and Wixom, 2007). BI capabilities are critical functionalities of BI that help an
organization improve its adaptation to change as well as improve its performance (Watson and
Wixom, 2007). With the right capabilities, BI can help an organization predict changes in
product demand or detect an increase in a competitor’s new product market share and
respond quickly by introducing a competing product (Watson and Wixom, 2007).
BI capabilities have been examined by practitioner-oriented research, especially from
the BI maturity model perspective (Eckerson, 2004; Watson and Wixom, 2007). Yet, BI
capabilities have remained largely unexamined in academic IS research. IS research has
examined IS capabilities extensively to explain the role of IS in organizational performance and
competitive advantage (Bharadwaj, 2000; Bhatt and Grover, 2005; Ray et al., 2005; Zhang and
Tansuhaj, 2007). IS capabilities are the functionalities that organize and deploy IS-based
36
resources in combination with other resources and capabilities (Bharadwaj, 2000). While some
research conceptualizes IS capabilities in managerial terms (Sambamurthy and Zmud, 1992;
Ross et al., 1996), other research focuses on technological capabilities (Sabherwal and Kirs,
1994; Teo and King, 1997). More recent models incorporate both managerial and technical
aspects of IS (Bharadwaj, 2000; Ray et al., 2005).
Similarly, BI capabilities can be examined from both organizational and technological
perspectives (Howson, 2004; Watson and Wixom, 2007). Technological BI capabilities are
sharable technical platforms and databases that ideally include a well-defined technology
architecture and data standards (Ross et al., 1996). Organizational BI capabilities are assets for
the effective application of IS in the organization, such as the shared risks and responsibilities as
well as flexibility (Ross et al., 1996; Howson, 2004). For example, while the data sources and
data types used by BI are technological BI capabilities, BI flexibility and level of risk supported
by BI are organizational BI capabilities (Hostmann et al., 2007).
Gartner Group’s research report about the evolution of BI groups organizations into four
categories based on their BI capabilities (Hostmann et al., 2007). Figure 2 shows the categories
as adopted from Hostmann et al. (2007).
Based on the exponential increase of accessible information and the increasing need for
skilled business users, different types of BI applications and their evolution can be characterized
with two dimensions, (1) information access and analysis, and (2) decision making style
(Hostmann et al., 2007). The first dimension of information access and analysis includes
methods and technologies used to collect and analyze the information. The second dimension,
decision style, includes the decision structure, i.e. unstructured or structured. Based on the
37
information access and analysis methods and the types of decisions made, an organization can
be characterized as the decision factory, the information buffet, the brave new world or the
hypothesis explored. Which quadrant an organization belongs to in this model depends on
capabilities such as the sources the data is obtained from, data types that can be analyzed, data
reliability, user access in terms of authorization and/or authentication, flexibility of the system,
interaction with other systems, acceptable risk level by the system, and how much intuition can
be involved in the analysis process.
Figure 2. The four worlds of BI adopted from Hostmann et al. (2007).
As organizations take advantage of these capabilities, their BI use increases, and so does
the maturity level of BI (Watson and Wixom, 2007). Mature BI increases organizational
responsiveness, which positively affects organizational performance. Thus, it is important to
recognize BI capabilities to better apply it to strategic needs (Ross et al., 1996).
Information Access & Analysis
Controlled/Qualified
Information Access & Analysis
Open/Unqualified
Decision Making
Process
Structured
Decision Making
Process
Unstructured
The
Decision
Factory
The
Information
Buffet
The
Brave
New World
The
Hypothesis
Explored
38
Data Sources
A data source can be defined as the place where the data that is used for analysis
resides and is retrieved (Hostmann et al., 2007). BI requires the collection of data from both
internal and external sources (Harding, 2003; Kanzier, 2002). Internal data is generally
integrated and managed within a traditional BI application information management
infrastructure, such as a data warehouse, a data mart, or an online analytical processing (OLAP)
cube (Hostmann et al., 2007). External data includes the data that organizations exchange with
customers, suppliers and vendors (Kanzier, 2002). This is rarely inserted into a data warehouse.
Often, external data is retrieved from web sites, spreadsheets, audio files, and video files
(Kanzier, 2002).
Organizations may use internal, external, or both types of data for BI analysis purposes.
For example, Unicredit built a sophisticated BI environment and created an OLAP architecture
composed of data warehouse and data marts, to aggregate all the information used for analysis
(Schlegel, 2007). Although they were using external data sources, the data collected from these
sources were internalized first. In the case of Richmond Police Department, the BI collected
crime data from untraditional data sources and used text mining to analyze that data
(Hostmann et al., 2007). Other examples are pharmaceutical and medical researchers who
analyze experimental data or legal information related to suspicious activities or individuals
(Hostmann et al., 2007). Because of its direct connection to BI infrastructure and software
characteristics, the data source is a technological capability for BI.
39
Data Types
Data type refers to the nature of the data; numerical or non-numerical and dimensional
or non-dimensional. Numerical data is data that can be measured or identified on a numerical
scale, and analyzed with statistical methods, such as measurements, percentages, and
monetary values (Sukumaran and Sureka, 2006). If data is non-numerical, then it cannot be
used for mathematical calculations. Non-numerical refers to data in text, image or sound
format that needs to be interpreted for analysis purposes. For example, financial data is
categorized as numerical data, whereas data collected from online news agencies is categorized
as non-numerical data.
Dimensional data refers to data that is organized and kept within relational data
structure and is a core concept for data warehouse implementations (Ferguson, 2007).
Dimensional data is subject oriented (Hostmann et al., 2007). Examples are customer-centric
dimensions such as product category, service area, sales channel or time period (Ferguson,
2007). Non-dimensional data refers to unorganized and unstructured data (Hostmann et al.,
2007). Non-dimensional data might be obtained from a website, for example. Because BI
infrastructure directly impacts the data types supported by the system, it is a technological BI
capability. In this dissertation, numerical and dimensional data is referred to as quantitative
data and non-numerical and non-dimensional data as qualitative data.
Interaction with Other Systems
Many organizations prefer having IS applications interacting at multiple levels so that
enterprise business integration can occur (White, 2005). This integration can be at the data
level, application level, business process level, or user level, yet these four levels are not
40
isolated from each other (White, 2005). Although data integration provides a unified view of
business data, application integration unifies business applications by managing the flow of
events (White, 2005). User interaction integration provides a single personalized interface to
the user and business process integration provides a unified view of organization’s business
processes (White, 2005). There are different technologies available for these integration types.
For example, enterprise information integration (EII) enables applications to see dispersed data
as though it resided in a single database and enterprise application integration (EAI) enables
applications to communicate with each other using standard interfaces (Swaminatha, 2006).
Data integration is very important especially for organizations that collect data from
multiple data sources; techniques such as EAI makes it possible to quickly and efficiently
integrate heterogeneous sources (Swaminatha, 2006). These technologies also provide benefits
for end users. For example, Constellation Energy Company integrated their BI system with
Microsoft Excel because it was a popular application frequently used throughout the company.
Since employees were using excel for data entry, they could continue using it even after the
roll-out of BI. As a result of this integration, change management issues and time spent on
training was reduced significantly (Briggs, 2006). Interaction with other systems is a
technological BI capability because of its reliance on BI infrastructure.
User Access
Because one size does not fit all with BI, there are different BI tools with different
capabilities, serving different purposes (Eckerson, 2003). Organizations may need to employ
these different BI tools from different vendors because different groups of users have different
reporting and analysis needs as well as different information needs (Howson, 2004). In contrast,
41
some organizations may choose to deploy a BI that provides unlimited access to data analysis
and reporting tools to all users (Havenstein, 2006). Because user access depends on BI
infrastructure and application characteristics, it is a technological BI capability.
Whether the organization prefers to use best-of-breed applications or a single BI suite,
matching the tool capabilities with user types is always a good strategy (Howson, 2006). While
some organizations limit user access through practicing authorization/authentication and
access control, others prefer to allow full access to all types of users through a web-centric
approach (Hostmann et al., 2007). For example, BI tools provided by Lyzasoft Inc. is an all-in-
one tool that includes integrated reporting, ad hoc query and analysis, dashboards, and
connectivity to data sources as a client-side desktop application (Swoyer, 2008). On the other
hand, QlikTech International developed QlikView, a web-centric BI application that provides
analytical and reporting capabilities for all types of users, especially easier to use for
nontechnical users (Havenstein, 2006). While web-centric systems are generally shared by large
numbers of users, desktop applications are mostly dedicated to specific users (Hostmann et al.,
2007).
Data Reliability
Organizations make critical decisions based on the data they collect every day, so it is
vital for them to have accurate and reliable data. Yet, there is evidence that organizations of all
sizes are all negatively impacted by imperfection, duplication and inaccuracy of the data they
use (Damianakis, 2008). Gartner Group estimates that more than 50% of BI projects through
2007 would fail because of data quality issues and TDWI estimates that customer data quality
issues alone cost U.S. businesses over $600 billion dollars a year (Graham, 2008).
42
Data that organizations collect from sources that are unqualified or uncontrolled also
give rise to errors. For example, the data from a Web site or from spreadsheets throughout the
organization contains errors that may not be caught prior to use in the BI (Hostmann et al.,
2007). Data reliability may be a problem for externally sourced data because there is no control
mechanism validating and integrating it; for example, getting the data from web blogs or RSS
feeds. Internal data is also prone to error. Poor data handling processes, poor data
maintenance procedures, and errors in the migration process from one system to another can
cause poor data reliability (Fisher, 2008). If the information analyzed is not accurate or
consistent, organizations cannot satisfy their customers’ expectations and cannot keep up with
new information-centric regulations (Parikh and Haddad, 2008). The technological capability of
BI delivering accurate, consistent and timely information across its users can enable the
organization improve its business agility (Parikh and Haddad, 2008).
Risk Level
Risk can be defined as making decisions when all the facts are not known (Harding,
2003). Risk and uncertainty exist in every business decision; some organizations use BI to
minimize uncertainty and make better decisions. Thus this is an organizational BI capability. For
risk-taking organizations, the decisions supported by the BI are entrepreneurial and motivated
by exploration and discovery of new opportunities as well as new risks (Hostmann et al., 2007).
Typically, innovative organizations tolerate high levels of risk but organizations that have
specific and well-defined problems to solve have a low tolerance for risk (Hostmann et al.,
2007).
43
People, processes, technology and even external events can cause risks for an
organization (Imhoff, 2005). The capabilities of the BI impact how successfully the organization
manages risk. BI can help the organization manage risk by monitoring the financial and
operational health of the organization and by regulating the operations of the organization
through key performance indicators (KPIs), alerts and dashboards (Imhoff, 2005). For example,
the Richmond Police Department deployed a number of analytical and predictive tools to
determine likely areas of criminal activity in Virginia, so that officers could take action early to
prevent crimes, rather than respond to criminal activity after it happened. Other than analytical
and predictive tools, modeling and simulation techniques also enable companies make
decisions that balance risk and obtain higher value (Business Wire, 2007).
Flexibility
An IS needs to be flexible in order to be effective (Applegate et al., 1999). Flexibility can
be defined as the capability of an IS to “accommodate a certain amount of variation regarding
the requirements of the supported business process” (Gebauer and Schober, 2006, p. 123). The
amount of flexibility directly impacts the success of an IS; while insufficient flexibility may
prevent the IS use for certain situations, too much flexibility may increase complexity and
reduce usability (Silver, 1991; Gebauer and Schober, 2006).
To achieve competitive advantages provided by BI, organizations need to select the
underlying technology to support the BI operations carefully (Dreyer, 2006), and flexibility is
one of the important factors to consider. Ideally, the system must be compatible with existing
tools and applications to minimize cost and complexity to the organization (Dreyer, 2006). The
strictness of business process rules and regulations supported by the BI directly impacts the
44
flexibility of BI. If there are strict sets of policies and rules embedded in the applications, then BI
has relatively low flexibility, because as the regulations get stricter, dealing with exceptions and
urgencies gets harder. Technology does not always support exceptional situations although
organizations need the flexibility and robust functionality to obtain the optimum potential from
BI (Antebi, 2007). Because flexibility is a direct result of organizational rules and regulations, it is
an organizational BI capability (Martinich, 2002).
For example, Richmond Police Department in Virginia, United States, deployed a BI
system to help them organize their fight against crime, and find out areas that criminal activity
is likely to occur (Hostmann et al., 2007). They used a wide variety of non-traditional data
sources rather than a single and traditional one such as a data warehouse, and analyzed that
collected data with different types of analytical tools. Through the flexibility of data sources and
data analysis methods, they were able to reduce the crime rate significantly and became
proactive in deterring crime (Hostmann et al., 2007).
Intuition Involved in Analysis
Intuition, in the context of analysis, can be described as rapid decision making with a
low level of cognitive control and high confidence in the recommendation (Gonzales, 2005).
Although BI has improved significantly with the developing technology, its core processes have
rarely changed. People use their intuition to manage their businesses whether they have a
technology accompanying it or not (Harding, 2003). Thus, intuition is an organizational BI
capability. Research, however, suggests that intuition by itself is not enough to competitively
run a business in today’s business world (Gonzales, 2005). Making decisions based on facts and
numbers as opposed to decision making based on gut feelings has become a suggested
45
approach for more successful BI applications and improved enterprise agility (Watson and
Wixom, 2007). On the opposite side to intuition is using the analytic process for decision
making; it is slower, requires a high level of cognitive control, and the recommended solution is
often chosen with a low level of confidence (Gonzales, 2005).
Although most of the applications using BI do not involve intuition at all in their analysis
(Hostmann et al., 2007), using intuition has not been totally drawn out of the BI scene.
Technology can monitor events, provide notifications and run predictive analysis, even
automate a response in straightforward cases, but for the decisions requiring human thought
intuition is still required (Bell, 2007). For example, the City of Richmond Police Department’s
use of BI to predict crimes is a good example how BI can also help officers and other field
personnel compare their expectations and intuitions against actual demographic trends
(Swoyer, 2008). With the help of BI, the police department covers areas that are likely to have
high crime while empowering the officers to include their instincts to figure out what actually in
happening at the location (Swoyer, 2008). There are other organizations that do not involve
intuition in the decision making process as much as in the case of Richmond Police Department,
but rather use it only for executive level decision making.
In summary, BI provides both technological and organizational capabilities to
organizations. These capabilities impact the way organization processes information and the
performance of the organization (Bharadwaj, 2000; Ray et al., 2005; Zhang and Tansuhaj, 2007).
Thus, it is imperative that these capabilities should match the decision environment. Table 5
summarizes the above mentioned BI capabilities and their levels associated with the four
quadrants of BI worlds.
46
Table 5
BI Capabilities and Their Levels Associated with the Four BI Worlds, Adapted From Hostmann et
al. (2007)
The Decision
Factory
The Information
Buffet
The Brave New
World
The Hypothesis
Explored
Data Source Internal Internal Mostly external Mostly external
Data Type quantitative Both qualitative Both
Data Reliability System
System and
Individual
Individual System
Flexibility Low High High Low
Intuition Involved
in Analysis
None Sometimes Always Always
Interaction with
Other Systems
Low High High High
Risk Level Low Low High High
User Access Web-centric Specific Web-centric Specific
Research Model and Hypotheses
Although BI success is widely addressed, there are still many inconsistencies in findings
about achieving success with BI. This is partly because one size does not fit all. Therefore, this
dissertation suggests that examining BI from a capabilities perspective, considering the
presence of different decision environments may provide better guidance on achieving BI
success. This study suggests that organizations should be aware of their needs based on their
decision environments and tailor BI solutions accordingly. Specifically, this dissertation argues
that as long as BI capabilities that fit the decision environment are in place, the BI initiative will
be successful. Below Figure 3 provides the conceptual model.
47
Figure 3. Conceptual model.
The amount of information available to users increases exponentially and it is not
possible to examine every piece of information to sort out what is useful or not (Clark et al.,
2007). Thus, identifying the appropriate information for the decision environment in a timely
manner is critical (Chung et al., 2005; Clark et al., 2007). Information system is a key concept in
identifying useful information (Eckerson, 2003; Clark et al., 2007). But, if IS is employed in the
organization just for the sake of using technology, and its capabilities do not match the decision
environment, then success may be limited (Clark et al., 2007).
Research suggests that a lack of fit between an organization and its BI is one of the
reasons for lack of success (Watson et al., 2002; Watson et al., 2006; Eckerson, 2006). It is not
Decision environment
BI
Success
BI Capabilities
Technological BI Capabilities
? Data Source
? Data Type
? Data Reliability
? Interaction with Other Systems
? User Access
Organizational BI Capabilities
? Flexibility
? Intuition Involved in Analysis
? Risk Level
Decision Types
Information
Processing Needs
48
only appropriate but necessary to examine the relationship between BI capabilities and BI
success, and how this relationship is affected by different decision environments. BI capabilities
include technological capabilities as well as organizational capabilities (Feeney and Willcocks,
1998; Bharadwaj et al., 1999). Technological capabilities are important success factors for any IS
(Watson and Wixom, 2007). Research shows that having a well-defined technology architecture
and data standards positively affect IS success (Ross et al., 1996). This is also true for BI; having
an effective infrastructure, reliable and high quality data, as well as pervasiveness are
important factors that influence BI maturity and success (Watson and Wixom, 2007). The
quality of technological BI capabilities in an organization has a positive influence on its BI
success.
Technological BI capabilities studied in this dissertation are data sources used to obtain
data for BI, data types used with BI, reliability of the data, interaction of BI with other systems
used in the organization, and BI user access methods supported by the organization. Although
these capabilities are present in every BI, their quality differs from organization to organization
(Hostmann et al., 2007). The difference in the quality of these capabilities is one of the factors
that may explain why some organizations are successful with their BI initiative while some are
not. For example, clean and relevant data is one of the most important BI success factors
(Eckerson, 2003; Howson, 2006). Organizations that have earned awards due to successful BI
initiatives, such as Allstate insurance company and 1-800-Contacts retailer, pay critical
attention to the sources from which they obtain their data, the type of data they use, and the
reliability of their data by acting early during their BI initiative and dedicating a working group
to data related issues (Howson, 2006).
49
The quality of interaction of BI with other systems in the organization is another critical
factor for BI success (White, 2005). For organizations that use data from multiple sources and
feed the data to multiple information systems, the quality of communication between these
systems directly affects the overall performance (Swaminatha, 2006). Likewise, BI user access
methods are critical for BI success. Because organizations have multiple purposes and user
groups with BI, they may employ different BI applications with different access methods
(Howson, 2004). While most of the web-centric applications are relatively easier to use,
especially for non-technical users, desktop applications are mostly dedicated to specific users
and provide specialized functionalities for more effective analysis (Hostmann et al., 2007). Thus,
the former may increase BI success with faster analysis, while the latter may increase it with
more effective decision making. Based on the above discussions, the following are
hypothesized:
H1a: The better the quality of data sources in an organization, the greater its BI success.
H1b: The better the quality of different types of data in an organization, the greater its
BI success.
H1c: The higher the data reliability in an organization, the greater its BI success.
H1d: The higher the interaction of BI with other systems in an organization, the greater
its BI success.
H1e: The higher the quality of user access methods to BI in an organization, the greater
its BI success.
Organizational BI capabilities include the level of intuition involved in analysis by the
decision maker, flexibility of the system, the level of risk that can be tolerated by the system
50
(Hostmann et al., 2007). The levels of these capabilities change from organization to
organization, depending on different business requirements and organizational structures
(Watson and Wixom, 2007). Regardless of their levels, these organizational capabilities
significantly impact BI success (Hostmann et al., 2007; Watson and Wixom, 2007). For example,
risk exists in every type of business, but there is evidence that entrepreneurial organizations are
motivated by it and can handle it better (Busenitz, 1999). Thus, an entrepreneurial organization
has a more successful BI if it can tolerate high levels of risk as one of their organizational BI
capabilities, compared to having a risk-averse system (Hostmann et al., 2007). On the other
hand, organizations that have specific and well-defined problems to solve may have a low
tolerance for risk and may have a more successful BI with a risk-averse system (Hostmann et al.,
2007). Flexibility is similar to the risk level in the sense that innovative and dynamic
organizations have a more successful BI if the system provides high flexibility (Dreyer, 2006;
Antebi, 2007). For organizations that shape their business with strict rules and regulations, high
flexibility may even become problematic by complicating business. Thus, a system with low
flexibility provides a more successful BI for these type of organizations (Hostmann et al., 2007).
The level of intuition involved in analysis by the decision maker depends on the type of
decision being made (Simon, 1965; Hostmann et al., 2007). For decisions that do not have a cut-
and-dried solution, the decision maker involves his intuition, which involves his experience, gut
feeling and judgment as well as creativity. Thus, BI that enables the decision maker to
incorporate his intuition in the decision making process is beneficial in these type of situations
and results in greater success (Harding, 2003). In opposition, organizations develop specific
processes for handling routine and repetitive decisions, so that the decision maker does not
51
need to use his intuition while making the decision, but only the information that is available
(Watson and Wixom, 2007). Based on the above discussion, the following hypotheses are
proposed;
H2a: The level of BI flexibility positively influences BI success.
H2b: The level of intuition allowed in analysis by BI positively influences BI success.
H2c: The level of risk supported by BI positively influences BI success.
The primary purpose of BI is to support decision-making in organizations (Eckerson, 2003;
Buchanan and O’Connell, 2006), and different decision types have different technology
requirements (Gorry and Scott Morton, 1971). Hence, employing the right technological
capabilities to provide support for the right type of decisions is critical for organizational
performance. For example, for structured decisions the decision making process can mostly be
automated, which is generally handled by computer-based systems, like transaction processing
systems (TPS) (Kirs et al., 1989). At the same time, DSS are better suited for semi-structured
decisions (Kirs et al., 1989) while BI is suitable for all types of decision structures (Blumberg and
Atre, 2003; Negash, 2004).
IS should be centered on the important decisions of the organization (Gorry and Scott
Morton, 1971). Thus, the types of decisions to be made should be taken into consideration
while using an MSS. For example, strategic planning decisions may require a database which
requires a complex interface although it is not frequently used (Gorry and Scott Morton, 1971).
On the other hand, operational control decisions may need a larger database which is
frequently used and requires continuous updating (Gorry and Scott Morton, 1971). Thus, the
52
relationship between technological BI capabilities and BI success is influenced by the decision
environment.
The data source used to retrieve information is one of the technological capabilities of BI
and it can be either internal or external (Harding, 2003; Kanzier, 2002). Internal data is
generated within the organization and it is managed through organizational structures
(Hostmann et al., 2007). Because internal data is ideally validated and integrated, it significantly
impacts the outcome of structured decisions and operational control activities (Keen and Scott
Morton, 1978). Because structured decisions are best handled with routine procedures and
operational control activities involve individual tasks or transactions, they all require accurate,
detailed and current information; and this need is best addressed with internal data (Keen and
Scott Morton, 1978). On the other hand, unstructured decisions have no set procedure for
handling because they are complex, and strategic planning activities involve mostly
unstructured decisions and require creativity. So, just internal data is almost never enough to
handle them. They need a wide scope of information, and external data sources are used to
retrieve what is needed from web sites, spreadsheets, audio and video files (Hostmann et al.,
2007). Whether the data is internal or external, its quality is a key to success with BI (Friedman
et al., 2006). Thus, the following is hypothesized:
H3a: The influence of high quality internal data sources on BI success is moderated by
the decision environment such that the effect is stronger for structured decision types
and operational control activities.
53
H3b: The influence of high quality external data sources on BI success is moderated by
the decision environment such that the effect is stronger for unstructured decision types
and strategic planning activities.
Besides the data sources, data types are also among technological BI capabilities and
their quality may impact BI success differently for different decisions and different
management activities. Because operational control activities are about assuring that core
business tasks are carried out effectively and efficiently, and that they are carried out rather
frequently, they require data that is easily analyzable (Anthony, 1965). Similarly, structured
decisions require detailed and accurate information (Keen and Scott Morton, 1978). Both for
structured decisions and operational management activities, quantitative data is used (Keen
and Scott Morton, 1978; Hostmann et al., 2007). Because non-numerical or qualitative data is
generally not detailed and its accuracy open to discussion, it is not appropriate for structured
decisions and operational activities. Rather, qualitative data is best used for unstructured
decisions because they are complex, they include non-routine problems and quantitative data
is not enough for solving those (Hostmann et al., 2007). Furthermore because strategic
planning activities need a wide scope of information with an aggregate level of detail, data used
better be qualitative so that it can be interpreted and used for subjective judgment (Keen and
Scott Morton, 1978). As mentioned in the data sources discussion, the quality of data is a key to
success with BI (Friedman et al., 2006). Thus, the following is hypothesized:
H3c: The positive influence of high quality quantitative data on BI success is moderated
by the decision environment such that the effect is stronger for structured decision types
and operational control activities.
54
H3d: The positive influence of high quality quantitative data on BI success is moderated
by the decision environment such that the effect is stronger for unstructured decision
types and strategic planning activities.
Data reliability is another factor that influences BI success, whether at the system level
or at the individual level. Operational control activities are related to basic operations that are
critical for an organization’s survival, so the data being used should be consistent and accurate
throughout the organization, requiring system-level reliability. Structured decisions also require
system-level reliability because they require consistent and current information for routine
processes (Keen and Scott Morton, 1978). On the other hand, strategic planning activities and
unstructured decisions are complex, non-routine and mostly solved by individuals or a small
group of people who use their subjective judgment and intuition (Keen and Scott Morton,
1978). This kind of information must be reliable at the individual level. The required
information for these activities is generally obtained from external and multiple sources in
addition to internal sources. This makes it harder to obtain system-level reliability. Low data
reliability leads to confusion and lack of understanding in analysis (Drummord, 2007). It is
important to use highly reliable data in BI, whether it is system-level or individual-level
reliability. Thus, the following is hypothesized:
H3e: The positive influence of high data reliability at the system level on BI success is
moderated by the decision environment such that the effect is stronger for structured
decision types and operational control activities.
55
H3f: The positive influence of high data reliability at the individual level on BI success is
moderated by the decision environment such that the effect is stronger for unstructured
decision types and strategic planning activities.
Many organizations implement multiple information systems or multiple applications
for different purposes. These applications often need to interact at multiple levels for the
enterprise business integration and data integration to occur (White, 2005). This interaction of
BI with other systems is especially critical to unstructured decision making and strategic
planning activities, because they collect data from multiple data sources (Swaminatha, 2006).
Thus, the following is hypothesized;
H3g: The positive influence of high quality interaction of BI with other systems in the
organization on BI success is moderated by the decision environment, such that the
effect is stronger for unstructured decision types and strategic planning activities.
How users access and use BI is another factor that influences BI success. User access can
be either shared, where large numbers of users access the same system through a web-based
application, or individual, where the tools are used with desktop computers and dedicated to a
specific user (Hostmann et al., 2007). For structured decisions and operational activities, shared
user access methods provide greater BI success. This is because decision makers need access to
real-time and transaction-level details to support their day-to-day work activities at these
levels, and a single integrated user interface to access the data eliminates the burden of
accessing multiple BI applications and saves time for the decision maker, which is vital for
operational activities (Manglik, 2006). The situation is different for unstructured decisions and
strategic planning activities. They require cross-functional business views that span
56
heterogeneous data sources and a more aggregated view (Fryman, 2007). Because these types
of activities are not as frequently handled as operational activities, the performance is not as
vital and due to the fact that users are executives, complexity is rarely an issue. That is why a
user-specific desktop application applies better. Thus, the following is hypothesized:
H3h: The positive influence of high quality shared user access methods to BI on BI
success is moderated by the decision environment, such that the effect is stronger for
structured decision types and operational control activities.
H3i: The positive influence of high quality individual user access methods to BI on BI
success is moderated by the decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Different types of decisions and management activities also require different
organizational BI capabilities, such as using intuition while making decisions and the level of risk
the organization tolerates. The decision maker involved in structured decisions and operational
activities needs to be different in terms of skills and attitudes from the decision maker involved
in unstructured decisions and strategic planning activities (Keen and Scott Morton, 1978). For
example, a system analyst who is involved in the development of a new transaction processing
system as a decision maker (structured operational control decision) may not be as successful
as a decision maker in an R&D portfolio development (unstructured strategic decision). While
structured decisions do not require intuition, decision makers need involve their intuition while
making unstructured decisions (Khatri and Ng, 2000). The decision environment influences the
impact of organizational BI capabilities on BI success.
57
The required level of BI flexibility, one of the organizational BI capabilities, is different
for different decision types and managerial activities. For example, if there is a need for
information that requires little processing (e.g., structured operational decisions) then rules and
regulations within the organization’s structure can provide a well-established response to
problems. For situations that require rich information and equivocality reduction (e.g.,
unstructured strategic decisions), then group meetings (which is a more flexible communication
method) where decision makers can exchange opinions and judgments face-to-face can help
them define a solution (Daft and Lengel, 1986). Therefore, the information processing and
decision making capabilities of an organization are directly related to the flexibility of the IS the
organization is using (Burns and Stalker, 1967). As the organization becomes more flexible, its
information processing capacity increases (Tushman and Nadler, 1978). This is useful for
strategic and unstructured decisions because they need a lot of information that is not always
easy to process. On the other hand, too much flexibility may result in complexity and reduced
usability (Silver, 1991; Gebauer and Schober, 2006). Thus, it is important to use the right level
of flexibility for the right decision types and activities. Therefore, the following is hypothesized:
H4a: The influence of BI flexibility on BI success is moderated by decision environment
such that the effect is stronger for unstructured decision types and strategic planning
activities.
Most of the decision makers use their intuition to manage their businesses whether
they have a technology accompanying it or not (Harding, 2003). This is especially necessary for
unstructured decisions and strategic planning activities because they need the decision maker
use his experiences, creativity and gut feeling due to their nature (Kirs et al., 1989). These
58
problems need more than the available data, so BI would be more successful if the decision
maker uses intuition for decision making. Yet, this is not the case for structured decisions and
operational control activities; the decision maker solely relies on data, logic and quantitative
analysis for these problems. When subjective judgment is involved, it is very difficult to apply
rational reasoning and doing so may even jeopardize the quality of the outcome (Hostmann et
al., 2007). Accuracy and consistency required for operational decision making may not be
provided. Thus, the following is hypothesized:
H4b: The influence of the intuition allowed in analysis on BI success is moderated by the
decision environment, such that the effect is stronger for unstructured decision types and
strategic planning activities.
In addition to the decision making process, the level of risk taken by the decision maker
may also differ for different decision types and different managerial activities. For example, as
organizations become more innovative, they also become more risk-tolerant and the decisions
they make become more and more unstructured (Hostmann et al., 2007). On the other hand,
organizations that generally make structured decisions tend to have routine and well-defined
problems to solve, and, they are more risk-averse (Hostmann et al., 2007). It is important to
tolerate the appropriate level of risk depending on the existing types of decisions and
managerial activities within an organization. Thus, the following is hypothesized:
H4c: The influence of tolerating risk on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured decision types and
strategic planning activities.
The research model is provided in Figure 4.
59
Figure 4. Research model.
H
1a
H
1e
H
1d
H
1c
H
1b
H
2a
H
2c
H
2b
H
3a-b
H
3c-d
H
3e-f
H
3g
H
4a
H
4b
H
4c
H
3h-i
BI
Success
Decision Environment
Decision Types
Information Processing Needs
Organizational BI Capabilities
Flexibility
Intuition Involved in Analysis
Risk Level
Technological BI Capabilities
Data Source
Data Type
User Access
Interaction with Other Systems
Data Reliability
60
CHAPTER 3
METHODOLOGY
This chapter describes the research methodology used to test the dissertation’s
hypotheses. How the data were collected and analyzed is explained, as are the research
methods employed and the development of the research instrument. Reliability and validity
issues are discussed and the data analysis procedures employed are described. The chapter is
composed of the following sections: description of the research population and sample,
description of the research design, discussion of instrument design and development, survey
administration, reliability and validity issues, and data analysis procedures.
Research Population and Sample
Business Intelligence (BI) success research largely draws from the population of business
managers, including IS professionals and business sponsors (Eckerson, 2003). This study draws
from a similar population because the goal is to measure BI success by examining BI capabilities
and decision environment. The research population for this dissertation consists of business
managers who use BI for strategic, tactical and operational decision making across a range of
organizations and industries. Data are collected from business firms located in the United
States. The firms are randomly selected, and the names and contact information of decision
makers are obtained from a publicly available mailing list of a market research company, L.I.S.T.
Inc., which maintains the Business Intelligence Network e-mail list from B-EYE-Network.com
web community, which is a collection of over 60,000 corporate and IS buyers of BI.
61
Research Design
The research design used in this dissertation is a field study. The research method used
is a formal survey. Using a survey helps the researcher gather information from a
representative sample and generalize those findings back to a population, within the limits of
random error (Bartlett et al., 2001). Advantages of survey research include flexibility in reaching
respondents from a broad scope (Kerlinger and Lee, 2000). In this dissertation, the data is
collected through a web-based survey. Advantages of using web-based surveys are the
elimination of paper, postage, mail out, and data entry costs, and reduction in time required for
implementation (Dillman, 2000). Web-based surveys also make it easier to send reminders,
follow-ups and importing collected data into data analysis programs (Dillman, 2000).
Two consistent flaws in business research are the lack of attention to sampling error
when determining sample size and the lack of attention to response and nonresponse bias
(Wunsch, 1986). Determining sample size and dealing with nonresponse bias is essential for
research based on survey methodology (Bartlett et al., 2001). This dissertation investigates
nonresponse bias by comparing the average values for dependent, independent and
demographic variables between early and late respondents, depending on the time of the
completed surveys are received, with t-tests (Armstrong and Overton, 1977; Kearns and
Lederer, 2003). In addition, t-tests are also performed between the pilot study respondents and
main data collection respondents.
Depending on the research design of the study, various strategies can be used to
determine an adequate sample size. A priori power analysis is recommended to find out the
appropriate sample size (Cohen, 1988). The power of a statistical test of a null hypothesis is the
62
probability that it will be rejected, meaning that the phenomenon of interest exists (Cohen,
1988). Power is related to Type I error (?), Type II error (?), sample size (N) and effect size (ES).
With a priori power analysis, the required sample size is calculated by holding the other three
elements of power analysis constant.
The first step in a priori power analysis is to specify the amount of power desired. The
recommended level of power to achieve is .80 (Chin, 1998). The second step is to specify the
criterion for statistical significance, ? level, which typically is .05 (Chin, 1998). The third step is
to estimate the effect size. In new areas of research inquiry, effect sizes are likely to be small
and it is common practice to estimate a small effect size, which corresponds to .2 (Cohen,
1988). Using these statistics, sample size is calculated using a free, general power analysis
software application, G*Power 3 (Erdfelder et al., 1996). Assuming an effect size of .2, an ?
level of .05, and a power of .8, a minimum sample size of 132 is needed.
Instrument Design and Development
The content and the wording of the questions in a survey are among the factors that
impact the effectiveness of surveys. Research suggests various methods to improve a survey
questionnaire. Brief and concise questions (Armstrong and Overton, 1971), careful ordering of
questions (Schuman and Pressor, 1981), and use of terminology that is clearly understood by
the respondents (Mangione, 1995) are methods suggested for survey improvement.
The survey used in this dissertation was refined in several steps. First, several IS
academic experts reviewed the survey. Based on their suggestions, I addressed ambiguity,
sequencing and flow of the questions. Second, a pilot study was conducted with 24 BI
professionals who have experience with BI implementation and use. The appropriateness of the
63
questions was assessed based on the results of the pilot study. The survey instrument was
finalized after making the necessary changes based on the feedback from pilot study
participants.
The survey instrument used in this dissertation consists of four parts. The first part
contains items used to collect demographic information from the respondents. The second part
measures the dependent variable, BI success. The third part includes items measuring the
independent variable, BI capabilities, and the fourth part includes items used to measure the
moderator variable, the decision environment. Decision environment is operationalized as the
types of decisions made (decision types) and the information processing needs of the decision
maker. BI capabilities are operationalized as organizational and technological BI capabilities.
Refer to Appendix A for a copy of the instrument.
BI Success
In this study, user satisfaction is used as a surrogate measure for BI success. User
satisfaction has been frequently used as a surrogate for IS success (Rai et al., 2002; Hartono et
al., 2006). The reason behind measuring user satisfaction as the surrogate measure is the direct
relationship among IS user satisfaction, IS use and decisional or organizational effectiveness
that IS research shows to exist (DeLone and McLean, 1992; Rai et al., 2002). Items measuring
user satisfaction are selected from Hartono et al.’s (2006) Management Support System (MSS)
success dimensions and Doll and Torkzadeh’s (1988) end-user satisfaction measure. Hartono et
al. (2006) identify and collect empirical studies that examine only MSS success measures from
peer-reviewed IS journals, which are then synthesized using DeLone and McLean’s (1992; 2003)
taxonomy of IS success measures. The items that measure satisfaction are developed based on
64
construct definitions stated in quantitative studies on MSS, published in peer-reviewed
information systems (IS) journals. Doll and Torkzadeh’s (1988) instrument merges ease of use
and information product items, focusing on end users interacting with a specific application for
decision making (Doll and Torkzadeh, 1988). From both studies, survey items measuring user’s
satisfaction regarding decision making, information obtained, and user friendliness are adapted
for this study.
BI Capabilities
BI capabilities of an organization directly impact BI effectiveness and success (Clark et
al., 2007; Watson and Wixom, 2007). BI capabilities were first identified in eight dimensions
extracted from the Gartner Group report on the evolution of BI (Hostmann et al., 2007). Three
of these dimensions were identified as organizational BI capabilities; level of risk tolerated, BI
flexibility, and level of intuition decision makers use during analysis. Five of the dimensions
were identified as technological BI capabilities; data sources used, data types analyzed, data
reliability, interaction with other systems and user access methods. Both technological and
organizational BI capabilities were operationalized with questions developed based on the
same Gartner Group report as well as other practitioner oriented publications from the Data
Warehousing Institute (TDWI) related to the eight BI capabilities (Harding, 2003; Gonzales,
2005; Sukumaran and Sureka, 2006; Ferguson, 2007; Damianakis, 2008).
The quality of technological BI capabilities, specifically quality of data sources and data
types, are measured with questions adapted from Wixom and Watson’s (2001) model that
measures data warehousing implementation success. Responses to each item are recorded on
a 5-point Likert scale.
65
Decision Environment
Decision environment was operationalized based on the two dimensional decision
support framework suggested by Gorry and Scott Morton (1971), which was later validated by
Kirs et al. (1989) and Klein et al. (1997).The first dimension addresses decision types and the
second dimension addresses the level of the management with which the decision is associated
and the information processing needs. To measure the first dimension, I ask respondents
questions pertaining to the nature of the decisions they make, such as the repetitiveness of the
decision or the managerial involvement in the decision making process. The objective of these
questions is to understand whether the decisions they make are structured, semistructured or
unstructured. For the second dimension, respondents indicate the organizational level with
which their decisions are associated; operational, tactical or strategic. Based on the
respondents’ answers, each decision is categorized as one of nine decision possibilities in Gorry
and Scott Morton’s (1971) framework. The questions measuring these were developed based
on Gorry and Scott Morton (1971), Kirs et al. (1989), Klein et al. (1997) and Shim et al. (2002).
Responses to each item are recorded on a 5-point Likert scale. Table 6 lists the
operationalization and measurement properties of the constructs measured in the survey.
Survey Administration
The response rate is a reflection of the cooperation of all potential respondents included
in the sample (Kviz, 1977). A low response rate may affect the quality of the results by
impacting the reliability or generalizability of findings. In order to increase the response rate,
some recommended methods are used in this study, including oofering an executive report on
the findings of the survey and providing anonymity to the respondents (Dillman, 2000). Survey
66
instructions also clearly stated that participation is voluntary and that no identifying
information is gathered by the administrator of the survey. To encourage participation, a final
analysis and executive summary of findings was provided upon the completion of the
dissertation to those who request them.
Table 6
Research Variables Used in Prior Research
Construct
Names
Sources Number
of items
Reliability
(Cronbach’s
? )
Validity
Assessed?
Directly
incorporated
/adapted /
developed
Decision
Environment
Gorry and Scott Morton
(1971),
Kirs et al. (1989),
Klein et al. (1997),
Shim et al. (2002)
10 No No Developed*
BI success
Hartono et al. (2006) 2 No No Adapted
Doll and Torkzadeh
(1988)
3 >.80 Yes Adapted
Organizational
BI capabilities
Hostmann et al. (2007)
Imhoff (2005)
Gonzales (2005)
9 No No Developed*
Technological
BI capabilities
Hostmann et al. (2007)
White (2005)
Eckerson (2003)
15 No No Developed*
Quality of
data types and
data sources
Watson and Wixom
(2001)
5 > .70 Yes Adapted
* The research cited did not use survey items to measure decision environment and BI capabilities. The
items used in this dissertation are developed based on their writings.
The sample data was obtained through a web-based survey. The procedure was
completed in two steps. First, the hyperlink to the instrument was e-mailed along with a
personalized cover letter explaining the purpose of the study. See Appendix B for a copy of the
cover letter. I did not have the chance to send a reminder to the same group of recipients.
67
Thus, to increase the number of respondents, the hyperlink to the instrument was e-mailed to a
different but smaller group of recipients two weeks after the first e-mail.
Reliability and Validity Issues
An instrument has adequate reliability if (1) it yields the same results when applied to
the same set of objects, (2) it reflects the true measures of the property measured, and (3)
there is a relative absence of measurement error in the instrument (Kerlinger and Lee, 2000).
Internal consistency is one of the most frequently used indicators of reliability (Cronbach,
1951). Internal consistency assesses how consistently individuals respond to items within a
scale. Cronbach’s coefficient alpha is widely used as the criterion to assess the reliability of a
multi-item measurement. A set of items with a coefficient alpha greater than or equal to 0.80
is considered to be internally consistent (Nunnally and Bernstein, 1994). This dissertation uses
Cronbach’s coefficient to assess the reliability of multi-item measurement scales.
Validity refers to the accuracy of the instrument. Content validity concerns the degree
to which various items collectively cover the material that the instrument is supposed to cover
(Huck, 2004). Content validity is judgmental (Kerlinger and Lee, 2000) and is generally
determined by having experts compare the content of the measure to the instrument’s domain
(Churchill, 1979; Huck, 2004). One step taken to ensure content validity in this dissertation is
that some of the items are adapted from prior research. Content validity is also addressed by
asking BI experts both in academia and industry to review the instrument and provide feedback
on whether the items adequately cover the relevant dimensions of the topic being examined.
Experts evaluate the content of the questions, their wording, and their ordering as well as the
instrument’s format. The instrument is modified based on their feedback.
68
Construct validity refers to the correspondence between the results obtained from an
instrument and the meaning attributed to those results (Schwab, 1980). Construct validity links
psychometric notions to theoretical notions; it shows that inferences can be made from
operationalizations to theoretical constructs (Kerlinger and Lee, 2000). Dimensionality is one
psychometric property used to assess construct validity. It relates to whether the items thought
to measure a given construct measure only that construct (Hair et al., 1998). Exploratory factor
analysis is a frequently used method to assess construct validity when the measurement
properties of the items are unknown. Because many of the items in this study are developed by
the researcher, exploratory factor analysis is used to assess the dimensionality of the items
used to measure a given construct.
In this dissertation, principle axis factor analysis with an orthogonal rotation was used to
assess all the dependent variables and the moderators. Dimensionality of each factor is
assessed by examining the factor loading. According to Hair et al. (1998), factor loadings over
0.3 meet the minimal level, over 0.4 are considered more important, and 0.5 and greater
practically significant. It is also suggested that the loadings over 0.71 are excellent, over 0.55
good, and over 0.45 are fair (Tabachnick and Fidell, 2000; Komiak and Benbasat, 2006). The
factor analyses conducted in this study are assessed according to these criteria. Then
confirmatory factor analysis was applied to the resulting factor structure to further assess
dimensionality and confirm that the items result in the number of factors specified.
Convergence and discriminability are also aspects of construct validity (Hair et al., 1998).
Convergent validity indicates that there is a significant relationship between constructs that are
thought to have a relationship, and that items purporting to measure the same thing are highly
69
correlated (Kerlinger and Lee, 2000). Discriminant validity indicates that there is no significant
relationship between constructs that are not thought to have a relationship, and that items
measuring different variables have a low correlation (Kerlinger and Lee, 2000). Correlations
among constructs were used to assess these two types of validities.
External validity refers to the validity with which a casual relationship can be generalized
to various populations of persons, settings and times (Kerlinger and Lee, 2000). It refers to the
degree to which the findings of a single study from a sample can be generalized to the
population. Sample of this study are BI users who reasonably represent the population of
business managers who use BI for strategic, tactical and operational decision making across a
range of organizations and industries. Thus, results from this dissertation can be generalized to
the population of BI users.
Data Analysis Procedures
A moderator variable affects the strength of the relationship between an independent
variable and a dependent variable (Baron and Kenny, 1986). Two methods of testing a model
that includes a moderator variable are suggested (Baron and Kenny, 1986). One method
involves multiple regression analysis and regressing the dependent variable on both the
independent variable and the interaction of the independent variable with the moderator
(Baron and Kenny, 1986). Research shows, however, that measuring multiplicative interactions
results in low power when measurement error exists (Busemeyer and Jones, 1983). Thus, Baron
and Kenny (1986) recommend an alternate approach, Structural Equation Modeling (SEM), if
measurement error is expected in the moderating variable, which is often the case in
psychological and behavioral variables. SEM is a covariance-based modeling technique is
70
capable of dealing with the measurement error, in contrast to regression analysis (Hair et al.,
1998).
The characteristics that distinguish SEM from other multivariate techniques are the
estimation of multiple and interrelated dependence relationships and its ability to represent
unobserved concepts in these relationships (Hair et al., 1998). SEM estimates a series of
multiple regression equations simultaneously by specifying the structural model. The
advantages of SEM include flexibility in modeling relationships with multiple predictor and
criterion variables, use of confirmatory factor analysis to reduce measurement error, and the
ability to test models overall rather than coefficients individually (Chin, 1998; Hair et al., 1998).
This dissertation employs SEM to test the research hypotheses. The research model
suggests that there is a relationship between BI capabilities and BI success, and that this
relationship is moderated by the decision environment. Table 7 shows the statistical tests
associated with each hypothesis.
Table 7
Hypotheses and Statistical Tests
Hypotheses Statistical Tests
H1a: The better the quality of data sources in an organization, the greater its BI
success.
Ysucc = ?
0
+?
1
ds+?
H1b: The better the quality of different types of data in an organization, the
greater its BI success.
Ysucc = ?
0
+?
1
dt+?
H1c: The higher the data reliability in an organization, the greater its BI success. Ysucc = ?
0
+?
1
dr+?
H1d: The higher the quality of interaction of BI with other systems in an
organization, the greater its BI success.
Ysucc = ?
0
+?
1
inr+?
H1e: The higher the quality of user access methods to BI in an organization, the
greater its BI success.
Ysucc = ?
0
+?
1
ua+?
H2a: The level of BI flexibility positively influences BI success. Ysucc = ?
0
+?
1
fx+?
H2b: The level of intuition allowed in analysis by BI positively influences BI
success.
Ysucc = ?
0
+?
1
intu+?
(table continues)
71
Table 7 (continued).
H2c: The level of risk supported by BI positively influences BI
success.
Ysucc = ?
0
+?
1
rsk+?
H3a: The influence of high quality internal data sources on BI
success is moderated by the decision environment such that the
effect is stronger for structured decision types and operational
control activities.
Ysucc =
?
0
+?
1
ds+?
2
(ds*dty)+?
3
(ds*inf)+?
H3b: The influence of high quality external data sources on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Ysucc =
?
0
+?
1
ds+?
2
(ds*dty)+?
3
(ds*inf)+?
H3c: The positive influence of high quality quantitative data on BI
success is moderated by the decision environment such that the
effect is stronger for structured decision types and operational
control activities.
Ysucc =
?
0
+?
1
dt+?
2
(dt*dty)+?
3
(dt*inf)+?
H3d: The positive influence of high quality qualitative data on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Ysucc =
?
0
+?
1
dt+?
2
(dt*dty)+?
3
(dt*inf)+?
H3e: The positive influence of high data reliability at the system
level on BI success is moderated by the decision environment such
that the effect is stronger for structured decision types and
operational control activities.
Ysucc =
?
0
+?
1
dr+?
2
(dr*dty)+?
3
(dr*inf)+?
H3f: The positive influence of high data reliability at the individual
level on BI success is moderated by the decision environment such
that the effect is stronger for unstructured decision types and
strategic planning activities.
Ysucc =
?
0
+?
1
dr+?
2
(dr*dty)+?
3
(dr*inf)+?
H3g: The positive influence of high quality interaction of BI with
other systems in the organization on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Ysucc =
?
0
+?
1
inr+?
2
(inr*dty)+?
3
(inr*inf)+?
H3h: The positive influence of high quality shared user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for structured decision
types and operational control activities.
Ysucc =
?
0
+?
1
ua+?
2
(ua*dty)+?
3
(ua*inf)+?
H3i: The positive influence of high quality individual user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured
decision types and strategic planning activities.
Ysucc =
?
0
+?
1
ua+?
2
(ua*dty)+?
3
(ua*inf)+?
(table continues)
72
Table 7 (continued).
H4a: The influence of BI flexibility on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Ysucc = ?
0
+?
1
fx +?
2
(fx*dty)
+?
3
(fx*inf)+?
H4b: The influence of the intuition allowed in analysis on BI success
is moderated by the decision environment, such that the effect is
stronger for unstructured decision types and strategic planning
activities.
Ysucc =
?
0
+?
1
int+?
2
(int*dty)+?
3
(int*inf)+?
H4c: The influence of tolerating risk on BI success is moderated by
the decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Ysucc =
?
0
+?
1
rsk+?
2
(rsk*dty)+?
3
(rsk*inf)+?
*** Notations
suc – BI Success dty- decision types
ds – data sources inf – information processing needs
dt – data types fx- flexibility
dr- data reliability intu – intuition involved in analysis
inr- interaction with other systems
ua – user access
rsk- risk level
73
CHAPTER 4
DATA ANALYSIS AND RESULTS
This chapter describes the data analysis and results of the dissertation. The first section
discusses response rate and analysis of non-response bias. The next section reports the sample
characteristics, followed by a discussion on the validity and reliability of the data and the survey
instrument. Finally, the statistical tests that are performed to test the research framework and
hypotheses are discussed and results of these tests are presented.
Response Rate and Non-Response Bias
The research population for this dissertation consisted of business managers who use BI
for strategic, tactical and operational decision making across a range of organizations and
industries. Data are collected from business firms located in the United States. The firms are
randomly selected, and contact information of decision makers are obtained from a publicly
available mailing list of a market research company, L.I.S.T. Inc., which maintains the Business
Intelligence Network e-mail list from B-EYE-Network.com web community, which is a collection
of over 60,000 corporate and IS buyers of business intelligence (BI).
As the first step of the data collection process, a pilot study was conducted. For this pilot
study, the survey was sent out to mailing list, which consists of operational managers using SAS
software for data analysis purposes. A total of 24 responses were received, all were complete
and usable.
After purchasing the right to use the e-mail addresses from L-I-S-T Inc., the survey was
administered to 8,843 BI users through two e-mails. Although the content of the e-mails was
the same, the second e-mail was sent three weeks after the first e-mail was sent. In the case of
74
the first e-mail, twenty-nine %of the mailing was undeliverable, and hence, 6281 were
delivered to potential respondents. Out of 6281 professionals, 1.7% clicked the survey link, but
only 29 respondents actually completed the survey. The second e-mail was sent out to
compensate for the high undeliverable rate of the first e-mail, and it was delivered to another
2,500 recipients.
Overall, a total of 97 responses were collected during the data collection process. This
corresponds to a response rate lower than 1%. This result is not necessarily surprising for web-
based surveys (Basi, 1999). Among the reasons for not completing the survey could be time
constraints, dislike of surveys and lack of incentives (Basi, 1999). Of the 97 responses, 5 were
incomplete and hence were dropped from subsequent analyses, yielding 92 usable responses.
To assess the non-response bias early respondents were compared to late respondents,
with respect to dependent, independent, moderator variables and demographics. With this
approach, it is assumed that subjects who respond less readily are more like those who do not
respond at all compared to subjects who respond readily (Kanuk and Berenson, 1975). This
method has been shown to be a useful way to assess non-response bias and has been adopted
by IS researchers frequently (Karahanna, Straub and Chervany 1999; Ryan, Harrison and
Schkade 2002). The differences between the responses to the first e-mail (n = 53) and the
responses to the second e-mail (n = 39) were examined with t-tests. There were no significant
differences between groups for dependent, independent or moderating variables at the .05
significance level. Table 8a shows the results of the t-tests. For the variables where the Levene’s
Test was significant (BI success, decision type and data sources), the t-values reflect the
assumption of unequal variances between groups.
75
I also performed t-tests to see if there were any significant differences in terms of
demographics. Table 8b shows the results of these t-tests. For the variables where the Levene’s
Test was significant (highest education level and number of employees), the t-values reflect the
assumption of unequal variances between groups. No significant differences were observed
among the variables.
Table 8a
Independent Samples t-Tests for Non-response Bias
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
Dependent
Variable
BI Success 7.015 .010 -1.938 85.977 .056 -.34168 .17629
Moderator
Decision
Type
4.487 .037 1.406 56.052 .165 .14256 .10138
Information
Processing
Needs
.059 .808 -.365 86 .716 -.04594 .12589
Independent
Variables
Data
Sources
8.677 .004 -1.693 56.028 .096 -.26078 .15401
Data Types .682 .411 -.104 86 .918 -.01388 .13402
Reliability 1.668 .2 -.785 83 .435 -.09237 .11772
Interaction
with Other
Systems
.061 .805 -1.321 85 .190 -.25234 .19100
User Access 3.704 .058 .586 83 .559 .06923 .11805
Flexibility .155 .695 -1.291 82 .200 -.23882 .18502
Intuition
Involved in
Analysis
.166 .685 -.412 86 .681 -.04011 .09735
Risk Level .001 .980 -1.620 79 .109 -.27990 .17281
76
Table 8b
Independent Samples t-Tests for Non-response Bias - Demographics
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel 5.890 .017 .664 65.273 .509 .167 .252
Gender .339 .562 .290 90 .773 .017 .060
TimeInOrg 2.200 .141 .503 90 .616 .731 1.453
ManagerialPosition .321 .573 .458 90 .648 .055 .119
FunctArea .613 .436 .280 90 .780 .181 .646
LevelInOrg .421 .518 -.089 90 .929 -.016 .184
NumEmployees 4.438 .038 -.604 73.305 .548 -.250 .414
TotalRevenue .000 .995 .422 90 .674 .129 .305
Industry .126 .724 .324 90 .747 .767 2.365
BIclass 1.495 .225 -1.237 90 .219 -.173 .140
The data collected from the pilot group was analyzed to check if there are any
anomalies or unexpected factor loadings were present and nothing unexpected was found.
Then, this data set was compared with the data collected from the e-mail recipients. The t-tests
were used to examine the differences between pilot group of users, who responded between
May 6, 2009 and May 27, 2009, and the rest of the respondents. There were no significant
differences between groups for dependent or independent variables but there were significant
differences in terms of the moderator(Table 9a). In terms of demographics, some significant
differences were observed (Table 9b). In both tables, for the variables where the Levene’s Test
was significant, the t-value reflects the assumption of unequal variances between groups.
The reason for significant difference between the pilot group respondents versus other
respondents for the moderator and for the differences in functional area and level in
organization can be explained by the differences in the respondent outlets. The first set of
77
respondents belongs to North Texas SAS Users Group, while the second set was recruited from
a BI professionals mailing list. The SAS Users Group is composed of operational managers that
are responsible for generating and using advanced BI applications, while the mailing list was
comprised of a broader segment of BI users and managers. This may explain the significant
difference in terms of the types of decisions made and the information characteristics required
to make those decisions. Furthermore, total revenue and number of employees was greater for
the mailing list group. This group was comprised of a broader segment of industries and
companies, and thus may have tapped more of the larger firms than the pilot group from North
Texas.
Table 9a
Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Main Data Set
Levene's Test for
Equality of Variances t-Test for Equality of Means
F Sig. t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference
BI Success .232 .631 -.732 110 .466 -.14545 .19881
Decision Type 1.413 .237 -3.825 111 .000 -.36788 .09619
Information
Processing Needs
4.092 .046 -4.892 53.771 .000 -.47159 .09641
Data Sources .203 .653 .245 111 .807 .03710 .15137
Data Types .990 .322 1.035 110 .303 .14015 .13535
Reliability .195 .660 -.846 106 .400 -.10377 .12269
Interaction with
Other Systems
.286 .594 .638 108 .525 .12806 .20077
User Access .803 .372 -.775 107 .440 -.09931 .12807
Flexibility .134 .715 -1.012 105 .314 -.20018 .19784
Intuition Involved
in Analysis
3.336 .070 1.444 110 .152 .16061 .11124
Risk Level .023 .879 -.359 101 .720 -.06411 .17836
78
Table 9b
Independent Samples t-Tests for Response Bias on Demographics: Pilot Data Set vs. Main Data
Set
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel 1.140 .288 1.086 114 .280 .266 .245
Gender 1.207 .274 -.562 114 .575 -.038 .068
TimeInOrg .910 .342 1.087 114 .279 1.636 1.504
ManagerialPosition 1.145 .287 -.916 114 .361 -.116 .127
FunctArea .133 .716 -2.737 114 .007 -1.902 .695
LevelInOrg 1.458 .230 -4.773 114 .000 -.971 .203
NumEmployees 1.577 .212 -2.175 114 .032 -.929 .427
TotalRevenue 1.128 .291 -2.652 114 .009 -.871 .329
Industry 1.434 .234 1.252 114 .213 2.926 2.337
BIclass 2.527 .115 .761 114 .448 .121 .160
Further analysis was conducted to see if there were significant differences between the
pilot group and the operational managers who were members of the mailing list. There were no
significant differences in any of the independent, dependent or moderator constructs (Table
10a). There were also no significant differences found in demographic variables (Table 10b). For
the variables where the Levene’s Test was significant, the t-value reflects the assumption of
unequal variances between groups.
79
Table 10a
Independent Samples t-Test: Pilot Data Set vs. Operational Managers in the Main Data Set
Levene's Test for
Equality of Variances t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
BI Success
.425 .520 -.444 27 .661 -.217 .488
Decision Type .006 .939 -.479 27 .636 -.117 .244
Information
Processing Needs
4.557 .042 -1.107 7.240 .304 -.258 .233
Data Sources .120 .731 1.047 27 .305 .425 .406
Data Types 2.800 .106 .443 27 .661 .133 .301
Reliability 3.511 .072 .573 27 .571 .175 .305
Interaction with
Other Systems
.203 .656 .572 27 .572 .258 .451
User Access .934 .342 -1.262 27 .218 -.317 .251
Flexibility 1.746 .197 -.058 27 .954 -.025 .432
Intuition Involved
in Analysis
.004 .950 -.401 27 .692 -.108 .270
Risk Level 1.022 .321 .074 27 .942 .025 .339
Next, the operational manager respondents were removed from the main data set, and
the remaining group was compared to the pilot data set to see if there were still significant
differences found between the pilot group respondents and other respondents who were non-
operational managers. There were significant differences in the two dimensions for the
moderator (decision type and information needs). There was also a significant difference for
the intuition construct although it was not significant for any of the other t-tests performed.
See Table 11a for the results of this t-test. Table 11b shows the results of the t-test for
demographics. For the variables where the Levene’s Test was significant (Decision type and
information processing needs), the t-values reflect the assumption of unequal variances
between groups.
80
Table 10b
Independent Samples t-Test on Demographics: Pilot Data Set vs. Operational Managers in the
Main Data Set
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel .195 .662 .471 27 .641 .342 .725
Gender 2.048 .164 .651 27 .521 .083 .128
TimeInOrg 4.915 .035 -.044 4.330 .967 -.183 4.201
ManagerialPosition .220 .642 1.103 27 .280 .267 .242
FunctArea .584 .451 -1.850 27 .075 -2.825 1.527
LevelInOrg .785 .383 -.367 27 .716 -.325 .885
NumEmployees .003 .960 -.747 27 .462 -.508 .681
TotalRevenue .507 .482 .479 27 .636 4.158 8.685
Industry 1.022 .321 -.074 27 .942 -.025 .339
BIclass .195 .662 .471 27 .641 .342 .725
There were significant differences between groups for the highest education level, level
in organization, number of employees in the organization and total revenue of the organization.
Because I am comparing operational managers to non-operational managers, the significant
difference in the level in the organization is expected. The difference in the highest education
level can also be explained by the groups being operational managers versus non-operational
managers. One possible explanation for the difference between the number of employees and
the total revenue may be because the pilot group consisted of operational managers from
companies in the North Texas group, and is not as diverse as the mail data set.
81
Table 11a
Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Non-Operational Managers in
the Main Data Set
Levene's Test for
Equality of
Variances t-Test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
BI Success
.384 .537 -.711 90 .479 -.142 .200
Decision Type 6.053 .016 -3.058 39.744 .004 -.348 .114
Information
Processing Needs
36.360 .000 -4.911 76.272 .000 -.542 .110
Data Sources .041 .840 .509 90 .612 .083 .164
Data Types 1.076 .302 1.409 90 .162 .216 .153
Reliability .197 .658 -.399 90 .691 -.059 .148
Interaction with
Other Systems
.512 .476 .841 90 .403 .181 .216
User Access 1.431 .235 -.103 90 .918 -.015 .143
Flexibility .116 .734 -.679 90 .499 -.147 .216
Intuition Involved
in Analysis
1.895 .172 2.095 90 .039 .292 .139
Risk Level .117 .733 -.815 90 .417 -.162 .199
These t-tests provide support for the idea that the significant differences found between
the pilot group data set versus the main data set is because all of the respondents in the pilot
group are operational managers whereas the main data set includes a diverse group of
respondents with only 5 operational managers. The difference in the level of intuition involved
in analysis also is not surprising considering that I hypothesize that non-operational managers
use their intuition while making decisions more than operational managers would. The mean
for the intuition for non-operational managers is higher than the mean for the intuition for
operational managers. Considering that there were only five operational managers in the main
82
data set, to be able to represent the operational managers equally, the pilot data set was added
to main data set. Because I am interested in responses that represent all these groups, and
because I made no changes to the survey from the pilot group, the responses from both sets
were combined for subsequent data analysis without any discrepancies. This provided 116
usable responses.
Table 11b
Independent Samples t-Test on Demographics: Pilot Data Set vs. Non-Operational Managers in
the Main Data Set
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel 4.323 .040 -5.665 36.171 .000 -1.255 .222
Gender .025 .876 1.803 90 .075 .390 .216
TimeInOrg 1.030 .313 -.516 90 .607 -.037 .071
ManagerialPosition 1.893 .172 1.317 90 .191 2.199 1.669
FunctArea .274 .602 -1.404 90 .164 -.186 .133
LevelInOrg .463 .498 -2.936 90 .004 -2.088 .711
NumEmployees 2.173 .144 -2.446 90 .016 -1.081 .442
TotalRevenue .461 .499 -3.362 90 .001 -1.120 .333
Industry 6.436 .013 1.640 54.285 .107 2.047 1.248
BIclass 2.830 .096 .816 90 .417 .135 .165
Treatment of Missing Data and Outliers
The data was examined for missing values. There were five cases that did not answer
any of the questions, thus they were dropped. The rest of the cases that include missing values
were not dropped due to the sample size concerns. Instead, missing values were imputed using
SAS Enterprise Miner Decision Tree imputation algorithm. Decision tree algorithms are useful
83
for missing data completion due to their high accuracy for single value prediction
(Lakshminarayan et al., 1996).
The data was examined for normality and tests were run for all independent and
dependent variables. Results show that the data is skewed to the right. To learn more about the
distribution of the data, skewness and kurtosis values were examined. Skewness values for the
dependent, independent and moderator variables were all between -1 and +1, within the
acceptable range (Huck, 2004). All kurtosis values were between -1 and +2, again all in the
acceptable range (Huck, 2004), thus the data were not judged to be significantly skewed or
kurtotic (Kline, 1997).
Demographics
The respondent pool for the survey has made up of 90.4% male and 9.6% female
professionals. While 47.8% of the respondents had a graduate degree, the highest education
level was post graduate (25.2 %). The respondents represent a broad sample with respect to
organizational size, annual total revenue, and the organizational industry. The descriptive
statistics for the size, annual revenue and the industry of the organization is summarized below
in Tables 12, 13 and 14 respectively.
84
Table 12
Descriptive Statistics on Organizational Size
Number of responses Percentage
Less than 100 27 23.3
100-499 11 9.5
500-999 10 8.6
1,000-4,999 27 23.3
5,000-9,999 11 9.5
10,000 or more 30 25.9
Total
116 100.0
Table 13
Descriptive Statistics on Annual Organizational Revenue
Number of responses Percentage
Less than $100 million 38 32.8
$100 million to $499 million 15 12.9
$500 million to $1 billion 11 9.5
More than $1 billion 40 34.5
Don’t know/not sure 12 10.3
Total
116 100.0
Almost 50% of the respondents indicated information technology as their functional
area in the organization, while the rest of the respondents belong to various other functional
areas. Forty %of the respondents are middle managers and 18% are executive level managers.
The descriptive statistics for the functional area and the organizational level of the respondents
is summarized below in Table 15 and Table 16 respectively.
85
Table 14
Descriptive Statistics on Organizational Industry
Number of the responses Percentage
Aerospace 1 .9
Manufacturing 12 10.3
Banking 6 5.2
Finance / Accounting 3 2.6
Insurance / Real Estate / Legal 11 9.5
Federal Government (Including Military) 2 1.7
State / Local Government 2 1.7
Medical / Dental / Health 10 8.6
Internet Access Providers / ISP 1 .9
Transportation / Utilities 9 7.8
Data Processing Services 5 4.3
Wholesale / Resale / Distribution 9 7.8
Education 13 11.2
Marketing / Advertising / Entertainment 3 2.6
Research / Development Lab 3 2.6
Business Service / Consultant 17 14.7
Computer Manufacturer 3 2.6
Computer / Network Consultant 2 1.7
Computer Related Retailer / Wholesaler / Distributor 2 1.7
VAR/VAD/Systems or Network Integrators 1 .9
Missing 1 .9
Total
116 100.0
58% of the respondents had worked at their respective organizations for five or fewer
years, and 5.3% had twenty or more years of experience. The average organizational
experience of all respondents is approximately seven years. 54% of the respondents held a
managerial position. 51% of the respondents identify themselves as advanced BI users, and 12%
see themselves as new to BI. Therefore, the respondents represent a range of users and
experience. Thus, they are appropriate for answering questions in this study. Table 17 below
shows the descriptive statistics on BI user experience levels.
86
Table 15
Descriptive Statistics on Functional Area
Number of responses Percentage
Management 11 9.5
Finance / Accounting / Planning 9 7.8
Information technology 54 46.6
Manufacturing / Operations 1 .9
Marketing 9 7.8
Sales 6 5.2
Supply chain 3 2.6
Other 23 19.8
Total
116 100.0
Table 16
Descriptive Statistics on Level in the Organization
Number of responses Percentage
Executive 21 18.1
Middle 47 40.5
Operational 29 25.0
Other 19 16.4
Total
116 100.0
Table 17
Descriptive Statistics on BI User Levels
Number of responses Percentage
New BI user 14 12.1
Intermediate BI user 43 37.1
Advanced BI user 59 50.9
Total
116 100.0
87
Exploratory Factor Analysis and Internal Consistency
In this dissertation, the number of factors extracted with exploratory factor analysis was
based on the criteria that the Eigenvalue should be greater than one. To extract the factors,
principal component analysis with a Varimax rotation was used. According to Hair et al. (1998),
factor loadings over 0.3 meet the minimal level, over 0.4 are considered more important, and
0.5 and greater practically significant. It is also suggested that the loadings over 0.71 are
excellent, over 0.55 good, and over 0.45 are fair (Tabachnick and Fidell, 2000; Komiak and
Benbasat, 2006). The factor analyses conducted in this study are assessed according to these
criteria.
A separate factor analysis was conducted for independent variables, dependent
variables and moderator variables, instead of one factor analysis where all indicators on
multiple factors are analyzed. Factor analyzing all 68 indicators together would result in a
correlation matrix of over 2000 relationships, thus, would not produce meaningful outcomes
(Jones and Beatty, 2001; Gefen and Straub, 2005).
For the dependent variable, BI success, five items were hypothesized to load on a single
factor, and all items loaded on one factor with 0.783 or higher. Following the factor analysis,
internal consistency of the BI success factor was examined. Cronbach’s alpha is the most widely
used measure to assess the internal consistency of a scale (Huck, 2004). A Cronbach’s alpha of
0.7 is generally considered acceptable (Hair et al, 1998). Yet, literature suggests that 0.6 may be
accepted for newly created measurement scales (Nunnally, 1978; Robinson, Shaver, and
Wrightsman, 1994). Cronbach’s alpha for the BI success factor was .914 and this is good,
88
considered to be an internally consistent measure. Table 18 below shows the factor loadings for
BI success along with the Cronbach’s alpha value.
Table 18
Factor Analysis for the Independent Variable
Items Components
BIsat5 0.927
BIsat2 0.889
BIsat3 0.869
BIsat1 0.863
BIsat4 0.783
Mean 3.716
Variance Explained 75.254%
Cronbach's Alpha 0.914
Factor analysis of independent variables was carried out in two steps. First each
construct was factor analyzed individually, to see if the items loaded as posited for each
construct, because items were largely developed by the researcher and there is no prior
validation. In the second step, the constructs were factor analyzed together. This dissertation
examines five technological BI capabilities (data quality, data sources quality, user access
methods, data reliability and interaction with other systems), three organizational BI
capabilities (flexibility, intuition involved in analysis and the level of risk supported by BI). First,
technological BI capabilities were factor analyzed individually.
Data quality has two dimensions, quantitative and qualitative data quality. All items
measuring both qualitative and quantitative data quality were retained (Table 19). Qualitative
data quality had an internal consistency of 0.970 and quantitative data quality had an internal
consistency of 0.926.
89
Table 19
Factor Analysis for the Data Quality
Items
Components
Qualitative Data
Quality
Quantitative
Data Quality
QualDataQuality4 .943 .225
QualDataQuality2 .934 .222
QualDataQuality3 .929 .201
QualDataQuality1 .929 .222
QuantDataQuality3 .189 .908
QuantDataQuality1 .182 .896
QuantDataQuality4 .204 .881
QuantDataQuality2 .251 .843
Mean 3.291 3.830
Variance Explained 62.885% 24.174%
Cronbach's Alpha .970 .926
Data sources have two dimensions, internal and external data sources. All four items
measuring internal data source quality and all three items measuring external data source
quality were retained, with internal consistencies of 0.828 and 0.916, respectively (Table 20).
Table 20
Factor Analysis for the Data Source Quality
Items
Components
External Data
Source Quality
Internal Data
Source Quality
ExtDataSrcQ3 .930 .084
ExtDataSrcQ2 .915 .131
ExtDataSrcQ1 .872 .154
IntDataSrcQ2 .085 .894
IntDataSrcQ1 -.083 .881
IntDataSrcQ3 .316 .727
IntDataSrcQ4 .466 .641
Mean 2.888 3.532
Variance Explained 50.703% 25.822%
Cronbach's Alpha .916 .828
90
User access quality was measured with three items. Factor analyzing these items
resulted in a single factor as expected, with an internal consistency of 0.768. Table 21 shows
the results.
Table 21
Factor Analysis for the User Access Quality
Items Components
UserAccess_qual3 .898
UserAccess_qual1 .879
UserAccess_qual2 .716
Mean 3.739
Variance Explained 69.989%
Cronbach's Alpha .768
Data reliability has two dimensions, internal and external data reliability. Each of these
dimensions is measured by four items. Factor analysis of these eight items yielded two separate
factors as expected (Table 22). One of the items measuring external data reliability had a
negative low loading of -0.372, thus was dropped from the scale. The remaining three items
(ExtDataReliability1, 3 & 4) had an internal consistency of 0.829. All items measuring internal
data reliability were retained with an internal consistency of 0.815.
Interaction with other systems was measured with four items. All items were retained
with loadings above .702 and have an internal consistency of 0.803. Table 23 shows the results.
91
Table 22
Factor Analysis for the Data Reliability
Items
Components
Internal Data Reliability External Data Reliability
IntDataReliability1 .892 .074
IntDataReliability3 .883 .145
IntDataReliability4 .752 .094
IntDataReliability2_Coded .705 -.196
ExtDataReliability3 -.019 .896
ExtDataReliability4 -.060 .870
ExtDataReliability1 .181 .816
Mean
3.599 3.230
Variance Explained 39.513% 31.547%
Cronbach's Alpha .815 .829
Table 23
Factor Analysis for the Interaction with Other Systems
Items Components
interaction3 .875
interaction1 .820
interaction4 .769
interaction2 .702
Mean 3.353
Variance Explained 63.119%
Cronbach's Alpha .803
Next, organizational BI capabilities are factor analyzed individually. Eight items were
used to measure flexibility. They loaded on two factors, yet the items were designed to
measure one dimension (Table 24a). Careful examination of questions indicated that one of the
factors measures scalability. Scalability relates to the flexibility of BI to operate in a larger
environment. Because the purpose is to measure flexibility in a given environment, questions
92
measuring scalability were dropped. The remaining four items (flex1, 2, 3 & 8) had loadings
greater than 0.60, with an internal consistency of 0.837. Table 24b shows the results.
Table 24a
Factor Analysis for Flexibility - I
Items Components
flex6_sca3 .903 .141
flex7_sca4 .873 .194
flex5_sca2 .800 .439
flex4_sca1 .788 .426
flex8 .079 .864
flex2 .313 .848
flex3 .349 .789
flex1 .409 .532
Mean 3.442 3.619
Variance Explained 60.491% 14.921%
Table 24b
Factor Analysis for Flexibility - II
Items Components
flex2 .910
flex3 .866
flex8 .801
flex1 .696
Mean 3.442
Variance Explained 67.612%
Cronbach's Alpha .837
Intuition involved in analysis was measured by five items. They loaded on two factors,
yet the items were designed to measure one dimension (Table 25a). Items 5, 2, and 3 loaded
together and items 1 and 4 loaded together. I first examine item 1 (Intuition1-coded). Careful
consideration of this question (Using my BI, I make decisions based on facts and numbers)
93
reveals that it may not actually tap the level of intuition involved in analysis. The extent to
which the decision maker is using facts and numbers to make decisions may not be an indicator
of the extent to which he/she uses intuition while making decisions. Consideration of Item 4
(The decisions I make require a high level of thought) indicates that it is appropriate. Before re-
running the factor analysis, however, I re-considered each of the other items to ascertain
whether they indeed seemed to be appropriate indicators of the use of intuition in decision
making. The third item, Intuition3, (With my BI, it is easier to use my intuition to make better
informed decisions) seems to tap how much BI supports intuitive decision making, rather than
the extent to which intuition is used. Thus, items 1 and 3 were removed and the factor analysis
was rerun (Table 25b). Only one factor emerges in this assessment. The loadings are
acceptable, although the reliability is borderline. I examined whether adding item 3 back would
result in a substantively stronger Cronbach’s alpha, but it did not. Therefore, I chose to use the
three items for the Intuition construct.
Table 25a
Factor Analysis for Intuition - I
Items Components
intuition5 .782 .165
intuition2 .781 .038
intuition3 .702 .024
intuition1_coded .112 -.870
intuition4 .353 .659
Mean 3.739 2.892
Variance Explained 39.620% 21.868%
94
Table 25b
Factor Analysis for Intuition - II
Items Components
intuition5 .791
intuition2 .778
intuition4 .671
Mean 3.807
Variance Explained 56.079%
Cronbach's Alpha .605
The Cronbach’s alpha for intuition is .605. Although this is lower than the suggested
level, reliability values as low as 0.5 are acceptable for new instruments (O'Leary-Kelly and
Vokurka, 1998). Therefore, because the items measuring intuition was newly developed based
on the literature, this new instrument was concluded as reliable for this study.
Level of risk was measured with four items. All items were retained with loadings above
.76 and have an internal consistency of 0.802. Table 26 shows the results.
Table 26
Factor Analysis for the Risk Level
Items Components
risk3 .821
risk4 .812
risk2 .774
risk1 .766
Mean 3.560
Variance Explained 62.992%
Cronbach's Alpha .802
These individual analyses lend support for the strength of the measurement properties
of these items and factors. To further assess measurement properties of these, exploratory
95
factor analysis was conducted, assessing these items in the presence of others. Factor analyzing
all 68 indicators at the same time would result in a correlation matrix of over 2000
relationships, thus, would not produce meaningful outcomes (Jones and Beatty, 2001; Gefen
and Straub, 2005). After careful examination of the dimensions that resulted in the prior factor
analyses, it was determined to divide this assessment into two groups. One set of factors all
relate to data oriented issues; data quality, data reliability and data source quality. Thus, these
are more closely related to technological BI capabilities. The other factors all relate to
organizational or user behavior/perceptions of the system, and thus are more closely related to
organizational BI capabilities. I first discuss the organizational BI capability factors; Table 27a
shows the initial results.
One of the items measuring interaction with other systems (interaction2) was dropped
from the analysis due to its cross-loading with user access quality. The remaining items were
factor analyzed again and Table 27b shows the results.
96
Table 27a
Factor Analysis for the Organizational BI Capability Variables - I
Items
Components
Flexibility Interaction Risk Intuition User Access Quality
flex2 .769 .121 .277 .305 .012
flex3 .760 .145 .111 .388 .000
flex1 .703 .061 .217 -.013 .146
flex8 .655 .288 .195 .313 -.058
risk1 .603 .488 .128 -.102 -.155
risk2 .541 .529 .146 -.049 -.071
risk4 .189 .720 .331 .159 -.096
intuition4 .032 .629 -.265 .055 .527
risk3 .239 .609 .318 .285 .045
UserAccess_qual3 .291 .550 .259 .476 -.122
UserAccess_qual1 .220 .515 .336 .452 -.084
interaction3 .263 .159 .827 .045 .110
interaction4 .232 .103 .752 .087 -.018
interaction1 .123 .423 .707 .129 -.101
UserAccess_qual2 .132 .160 .007 .847 .012
interaction2 .221 .038 .504 .540 .129
intuition2 -.071 -.071 .042 .059 .822
intuition5 .082 -.030 .043 -.054 .808
97
Table 27b
Factor Analysis for the Organizational BI Capability Variables - II
Items
Components
Flexibility Risk Interaction User Access Quality Intuition
flex2 .777 .103 .277 .308 .017
flex3 .773 .162 .087 .344 -.004
flex1 .698 .101 .222 -.018 .145
flex8 .650 .258 .190 .353 -.054
risk4 .149 .696 .341 .277 -.084
risk3 .224 .646 .282 .290 .040
risk2 .500 .593 .132 .009 -.080
intuition4 -.012 .578 -.249 .190 .538
risk1 .559 .564 .117 -.046 -.164
interaction3 .269 .157 .822 .056 .112
interaction4 .236 .033 .788 .157 .003
interaction1 .113 .429 .693 .161 -.099
UserAccess_qual2 .172 .037 .002 .824 .036
UserAccess_qual3 .272 .398 .301 .639 -.088
UserAccess_qual1 .202 .337 .390 .635 -.043
intuition2 -.046 -.046 .026 -.027 .819
intuition5 .088 -.064 .055 -.039 .813
Mean 3.442 3.560 3.230 3.739 3.807
Variance Explained 38.380% 10.177% 7.750% 7.166% 6.148%
Cronbach's Alpha .837 .802 .804 .768 .605
Flexibility, interaction and user access quality factors loaded clearly as expected. One of
the items measuring intuition (intuition4) cross-loaded with the items measuring risk. This item
is “The decisions I make require a high level of thought.” Decisions that involve high level of
uncertainty also involve a high level of risk associated with them, and they require high level
thinking by the decision maker. To further understand the relationship among these items,
another factor analysis was conducted including only intuition and risk items, and the analysis
was forced to produce two factors. Results, as presented in Table 28, show clear loadings for
98
two factors, with both eigenvalues greater than 1. The level of risk and intuition had 0.802 and
0.605 internal consistency values, respectively. Therefore, in subsequent analyses the four
items measuring risk were used together to measure risk and three items measuring intuition
were used together to measure intuition.
Table 28
Factor Analysis for Risk and Intuition
Items
Components
Risk Intuition
risk4 .810 .005
risk3 .807 .123
risk2 .776 -.004
risk1 .760 -.102
intuition5 -.099 .795
intuition2 -.112 .787
intuition4 .334 .648
Mean 3.560 3.807
Variance Explained 37.512% 24.168%
Cronbach's Alpha
Eigenvalues
.802
2.626
.605
1.692
Next, the technological BI capability items, (data quality, data source quality and data
reliability) were factor analyzed. This resulted in five rather than the expected six factors. Items
measuring external data reliability and external data source quality loaded together. All other
items loaded as expected. Table 29 shows the factor loadings as well as the reliability statistics.
99
Table 29
Factor Analysis for the Technological BI Capability Variables
Items
Components
External
Data
Source
Quality &
External
Data
Reliability
Qualitative
Data
Quality
Quantitative
Data Quality
Internal
Data
Source
Quality
Internal
Data
Reliability
ExtDataSrcQ2 .856 .199 .021 .053 .163
ExtDataSrcQ3 .820 .135 -.123 -.077 .245
ExtDataSrcQ1 .817 .110 .020 -.045 .234
ExtDataReliability1 .804 .013 .243 .171 -.062
ExtDataReliability3 .792 -.100 .095 .011 .002
ExtDataReliability4 .723 -.119 .262 -.116 .000
QualDataQuality4 .056 .927 .216 .102 .113
QualDataQuality1 .041 .919 .203 .053 .141
QualDataQuality3 .071 .915 .189 .099 .108
QualDataQuality2 -.003 .902 .216 .160 .151
QuantDataQuality3 .093 .180 .860 .180 .130
QuantDataQuality1 .111 .191 .850 .189 .065
QuantDataQuality4 .144 .196 .830 .135 .167
QuantDataQuality2 .149 .265 .811 .059 .074
IntDataReliability1 .048 .137 .273 .815 .145
IntDataReliability3 .082 .173 .323 .792 .149
IntDataReliability2_Coded -.082 .039 -.052 .769 .187
IntDataReliability4 -.062 .141 .531 .536 .168
IntDataSrcQ3
.188 .247 .056 .055 .803
IntDataSrcQ2
.074 .106 .297 .295 .738
IntDataSrcQ4
.363 .215 -.008 .154 .702
IntDataSrcQ1
-.054 -.035 .341 .421 .677
Mean 3.059 3.291 3.830 3.256 3.532
Variance Explained 34.518% 17.175% 11.217% 8.990% 5.059%
Cronbach's Alpha .900 .970 .926 .836 .828
One possible explanation for double loading in the first factor may be due to the nature
of the constructs. The items measuring the other four factors may have been perceived by
100
respondents as relating to internal issues. The items measuring internal data source quality and
internal data reliability are clearly focused on internal issues. However, the items measuring
qualitative and quantitative data quality do not specify internal or external. Given that majority
of data in most organizations originates internally, it is reasonable that respondents answer
with internal data in mind. Another possible explanation is that external data source quality and
external data reliability were comingled in the respondents’ perceptions as they answered.
To further understand the relationship for this external factor, another factor analysis
was conducted including only external data reliability and external data source quality items,
forcing the analysis to produce two factors. Results including the eigenvalues are presented in
Table 30. They show clear loadings for two factors as expected, with 0.916 and 0.829 internal
consistency values for external data reliability and external data source quality, respectively.
Thus, the items were separated in survey analysis.
Table 30
Factor Analysis for the Dependent Variables - External Data reliability and External Data Source
Quality
Items
Components
External Data
Source Quality
External Data
Reliability
ExtDataSrcQ2 .905 .298
ExtDataSrcQ3 .880 .244
ExtDataSrcQ1 .837 .338
ExtDataReliability4 .215 .874
ExtDataReliability3 .299 .856
ExtDataReliability1 .530 .633
Mean 2.888 3.230
Variance Explained 66.779% 14.398%
Cronbach's Alpha
Eigen values
.916
4.007
.829
.864
101
Next, exploratory factor analysis was conducted for the moderator variable. It is posited
to have two dimensions; information processing needs and decision types. Six items measured
information needs (InfoChar1-6), and five items were used to measure decision types
(DecType1-5). Initial factor analysis resulted in five factors rather than the expected two
factors. Table 31a shows the results of this initial factor analysis.
Table 31a
Factor Analysis for the Moderator Variable - I
Items
Components
Information
Needs 1
Decision
Types 1
Decision
Types 2
Information
Needs 2
Decision
Types 3
InfoChar5 .780 .067 .151 .114 .077
InfoChar2 .733 .015 -.049 -.033 -.058
InfoChar6 .639 .030 -.205 .009 .180
InfoChar1 .460 -.187 .036 .198 .259
DecType2_coded
-.027 .832 -.169 .227 .161
DecType4_coded
-.066 -.785 -.225 .119 .336
DecType1
.051 -.141 .824 .097 -.223
DecType3
-.162 .237 .757 .009 .393
InfoChar3
-.055 .009 .085 .852 -.049
InfoChar4
.199 .084 .005 .728 .082
DecType5
.248 -.072 -.009 .008 .887
Careful examination of the items loading for the Information Needs 2 factor (InfoChar3
and InfoChar4) indicated that this factor refers to the general type of information collected,
whereas the Information Needs 1 factor (InfoChar1, 2, 5 & 6) represents the different
characteristics of the information used. Because the intention of this dissertation is to examine
different characteristics of information collected, items InfoChar3 & 4 were dropped from the
scale. The new factor analysis resulted in four factors (Table 31b).
102
Table 31b
Factor Analysis for the Moderator Variable - II
Items
Components
Information
Needs 1
Decision
Types 1
Decision
Types 2
Decision
Types 3
InfoChar5 .781 .067 .157 .074
InfoChar2 .737 .017 -.067 -.055
InfoChar6 .629 .032 -.201 .174
InfoChar1 .490 -.136 .047 .313
DecType2_coded
-.004 .865 -.139 .153
DecType4_coded
-.053 -.753 -.221 .394
DecType1
.067 -.156 .827 -.219
DecType3
-.166 .238 .762 .360
DecType5
.239 -.035 .002 .882
Examining the questions measuring decision types, DecType 4 item was dropped due to
possible cross loading between Decision Types 1 and Decision Types 3. Table 31c shows the
new factor analysis after dropping this item.
Table 31c
Factor Analysis for the Moderator Variables - III
Items
Components
Information
Needs 1
Decision
Types 1
Decision
Types 2
InfoChar5 .741 .119 .027
InfoChar2 .663 -.126 -.058
InfoChar6 .639 -.203 .108
InfoChar1 .600 .046 -.082
DecType5
.523 .167 .436
DecType3
-.055 .837 .270
DecType1
.003 .759 -.349
DecType2_coded
-.065 -.054 .850
103
Item DecType 2_coded (I make decision without higher level manager involvement)
loaded as a single factor. Its wording was deemed to be ambiguous because involvement from
the higher level managers in a decision may not imply the decision type made by the decision
maker. After dropping this item, another factor analysis was run; table 31d shows the results.
Table 31d
Factor Analysis for the Moderator Variables - IV
Items
Components
Information
Needs
Decision
Types
InfoChar5 .739 .099
InfoChar2 .642 -.143
InfoChar6 .640 -.223
DecType5 .590 .138
InfoChar1
.585 .023
DecType3
.012 .836
DecType1
-.024 .764
Although this analysis resulted in two factors, one of the items thought to measure
decision types (DecType5) loaded with the items thought to measure information needs. This
item (the decisions I make require computational complexity and precision) was dropped from
the scale because it seemed to tap something other than information needs and because it also
seems to tap two different things; precision and computational complexity. Thus, it was
deemed to be a poor indicator. The resulting factors for the moderator shows high factor
loadings, yet low internal consistency (Table 31e). Reporting Cronbach’s Alpha for two-item
scales have been criticized (Cudeck, 2001), thus the correlations between items and their
significance is also reported (Table31f). Although the correlations are significant, they and the
104
Cronbach’s Alpha for Decision Types were deemed too low to retain the factor. Thus, only
Information Needs is used in subsequent analyses.
Table 31e
Factor Analysis for the Moderator Variables - V
Items
Components
Information
Needs
Decision
Types
InfoChar5 .768 .146
InfoChar2 .711 -.083
InfoChar6 .651 -.199
InfoChar1 .578 .036
DecType1
.027 .809
DecType3
-.071 .804
Mean
3.819 2.806
Variance Explained 31.260% 22.570%
Cronbach's Alpha 0.601 0.494
Table 31f
Correlations for Decision Type Items
DecType1 DecType3
DecType1
Pearson Correlation 1 .330**
Sig. (2-tailed) .000
DecType3
Pearson Correlation .330** 1
Sig. (2-tailed) .000
** Correlation is significant at the 0.01 level (2-tailed).
105
PLS Analysis and Assessment of Validity
PLS path modeling was used to analyze and assess the proposed research model and to
test the hypotheses suggested. PLS has several advantages compared to other statistical
techniques such as regression and analysis of variance. PLS has the capability to concurrently
test the measurement and structural model and does not require the homogeneity and normal
distribution of the data set (Chin et al., 2003). PLS can also handle smaller sample sizes better
than other techniques, although PLS is not a panacea for unacceptably low sample sizes
(Marcoulides and Saunders, 2006). PLS requires a minimum sample size that is 10 times greater
of either the number of independent constructs influencing a single dependent construct, or
the number of items comprising the most formative construct (Chin, 1998; Wixom and Watson,
2001; Garg et al., 2005). This dissertation examines eight BI capabilities as independent
variables, thus requires 80 as the minimum sample size. Although a priori power analysis
yielded that for an effect size of .2, an ? level of .05, and a power of .8, a minimum sample size
of 132 is needed, the collected and cleaned data of 116 respondents satisfies the PLS
requirement. SmartPLS version 2.0.M3 (Ringle, Wende & Will, 2005) is used to analyze the
research model.
The acceptability of the measurement model was assessed by the model’s construct
validity as well as the internal consistency between the items (Au et al., 2008). Internal
consistency, a form of reliability, was assessed using Cronbach’s alpha and exploratory factor
analysis was used to assess dimensionality (Beatty et al., 2001). All Cronbach’s alpha values
were satisfactory after item purifications, as presented in the previous section.
106
The independent and dependent variables were assessed for construct validity through
convergent and discriminant validity as well as composite reliability (Hair et al, 1998; Kerlinger
and Lee, 2000). Convergent validity is assessed by the average variance extracted (AVE) and
communality. Both communality and AVE values for all constructs are suggested to be higher
than the recommended threshold value of 0.5 (Rossiter, 2002; Fornell and Larker, 1981). This
required further item purifications in the model. The items that share a high degree of residual
variance with other items in the instrument were eliminated (Au et al., 2008; Gefen et al., 2000;
Gerbing and Anderson, 1988) to increase the AVE and communality values above 0.5. The
resulting item loadings and related statistics are given in Table 32 below.
Discriminant validity was assessed by comparing the square root of AVE associated with
each construct with the correlations among the constructs and observing that square root of
AVE is a greater value (Chin, 1998). As suggested for discriminant validity, the values on the
diagonal were all larger than the off-diagonal values. Composite reliability measures “the
internal consistency of the constructs and the extent to which each item indicates the
underlying construct” (Moores and Chang, 2006, p. 173). Composite reliability values were well
above the recommended level (0.70) for all constructs (Bagozzi and Yi, 1988; Fornell and Larker,
1981). Table 33 shows the composite reliability, average variance extracted (AVE), the square
root of AVE, and the correlations between constructs.
107
Table 32
Item Statistics and Loadings
Item <- Construct it measures Loading Std. dev. Mean
BIsat1 <- BI Success 0.85663 0.035 3.767
BIsat2 <- BI Success 0.889508 0.022 3.879
BIsat3 <- BI Success 0.86678 0.030 3.646
BIsat4 <- BI Success 0.786284 0.043 3.620
BIsat5 <- BI Success 0.93116 0.015 3.663
InfoChar1 <- Decision Environment 0.437519 1.099 3.470
InfoChar2 <- Decision Environment 0.803038 0.829 4.010
InfoChar5 <- Decision Environment 0.847287 0.938 3.910
InfoChar6 <- Decision Environment 0.406642 0.934 3.880
ExtDataReliability1 <- Data Reliability 0.632047 0.204 3.207
ExtDataReliability3 <- Data Reliability 0.43458 0.258 3.293
ExtDataSrcQ2 <- Data Source Quality 0.648588 0.164 2.828
ExtDataSrcQ3 <- Data Source Quality 0.587863 0.202 2.819
IntDataReliability1 <- Data Reliability 0.82271 0.147 3.871
IntDataReliability3 <- Data Reliability 0.857347 0.135 3.733
IntDataSrcQ2 <- Data Source Quality 0.806969 0.078 3.698
IntDataSrcQ3 <- Data Source Quality 0.781245 0.101 3.379
IntDataSrcQ4 <- Data Source Quality 0.774343 0.120 3.198
QualDataQuality2 <- Data Quality 0.590387 0.105 3.336
QuantDataQuality1 <- Data Quality 0.909218 0.023 3.931
QuantDataQuality3 <- Data Quality 0.89763 0.038 3.845
QuantDataQuality4 <- Data Quality 0.916249 0.025 3.776
UserAccess_qual1 <- user access quality 0.903284 0.021 3.586
UserAccess_qual2 <- user access quality 0.660236 0.114 3.853
UserAccess_qual3 <- user access quality 0.91113 0.019 3.776
flex1 <- Flex 0.676308 0.075 3.853
flex2 <- Flex 0.913582 0.014 3.293
flex3 <- Flex 0.86401 0.027 3.259
flex8 <- Flex 0.814945 0.045 3.362
interaction1 <- interaction 0.858283 0.033 3.414
interaction3 <- interaction 0.87544 0.032 3.233
interaction4 <- interaction 0.803407 0.056 3.043
intuition4 <- intuition 0.780321 0.295 3.974
intuition5 <- intuition 0.832241 0.268 3.759
risk2 <- risk 0.726962 0.088 3.440
risk3 <- risk 0.896195 0.030 3.767
risk4 <- risk 0.854442 0.039 3.741
108
Table 33
Inter-Construct Correlations: Consistency and Reliability Tests
Construct
Composite
Reliability
*AVE Risk Flexibility Intuition
Data
Quality
Data
Source
Quality
Data
Reliability
Interaction
User
Access
Quality
Decision
Environment
BI
Success
Risk 0.867 0.687 0.829
Flexibility 0.892 0.676 0.591 0.822
Intuition 0.788 0.651 0.133 0.127 0.807
Data Quality 0.903 0.705 0.453 0.411 0.127 0.840
Data Source
Quality
0.845 0.526 0.414 0.496 0.007 0.426 0.725
Data
Reliability
0.791 0.500 0.419 0.442 0.063 0.551 0.521 0.707
Interaction 0.883 0.716 0.565 0.517 0.020 0.447 0.472 0.454 0.846
User Access
Quality
0.870 0.694 0.608 0.599 0.126 0.674 0.605 0.548 0.536 0.833
Decision
Environment
0.747 0.518 0.158 0.069 0.123 0.385 0.078 0.224 0.536 0.181 0.720
BI Success 0.938 0.752 0.523 0.569 0.144 0.546 0.385 0.336 0.526 0.719 0.192 0.867
The shaded numbers on the diagonal are the square root of the variance shared between the constructs and their
measures.
Off-diagonal elements are correlations among constructs. For discriminant validity, diagonal elements should be
larger than off-diagonal elements.
* Average Variance Extracted
109
Hypotheses Testing Results
Hypothesis 1 and Hypothesis 2
Hypotheses 1a-e and 2a-c posit that technological and organizational BI capabilities
impact BI success (Table 34).
Table 34
Hypotheses 1 & 2
H1a The better the quality of data sources in an organization, the greater its BI success.
H1b The better the quality of different types of data in an organization, the greater its BI success.
H1c The higher the data reliability in an organization, the greater its BI success.
H1d
The higher the quality of interaction of BI with other systems in an organization, the greater its
BI success.
H1e
The higher the quality of user access methods to BI in an organization, the greater its BI
success.
H2a The level of BI flexibility positively influences BI success.
H2b The level of intuition allowed in analysis by BI positively influences BI success.
H2c The level of risk supported by BI positively influences BI success.
In order to obtain reliable results and t-values, 500 random samples of 116 responses
(Chin, 1998) were generated using the bootstrapping procedure available in the SmartPLS
software. The significance of the hypotheses was evaluated by assessing the significance and
the sign of the inner model path coefficients using t-tests. To evaluate the predictive validity of
the relationship between the constructs, R
2
values were assessed. Table 35 shows the path
coefficients between BI capabilities and BI success, as well as the t values associated with these
paths. Figure 5 shows the PLS results along with the t values of both the inner and the outer
models. Figure 5 also shows the R
2
value for the dependent variable, BI success. Results show
that the total variance (R
2
) for BI success explained by eight constructs is 60 percent.
110
Table 35
Path Coefficients, t Values and p Values for BI Capabilities (H1 & H2)
Constructs Path coefficients t value p-value
Flexibility 0.197927 2.918918 0.003671***
Intuition 0.046332 0.426293 0.670146
Risk 0.027724 0.183228 0. 854716
Data Source Quality -0.103309 1.506560 0.066286 **
Data Quality 0.130787 1.475176 0.070408 **
Data Reliability -0.131662 1.862048 0.031588*
Interaction with Other Systems 0.175194 2.367860 0.009137***
User Access Quality 0.537448 5.407056 0.000000***
* Significant at the p = 0.5 level
** Significant at the p = 0.1 level
*** Significant at the p = 0.01 level
Results show that H1a-e and H2a are supported. This means that the higher the quality
of data sources, data types, user access methods, higher the interaction with other systems,
data reliability and flexibility, the better the BI success. But results do not show any support
that the level of intuition used in analysis and level of risk supported by BI influences BI success.
111
Figure 5. PLS results – H1 and H2.
Hypothesis 3 and Hypothesis 4
H3 and H4 posit that the decision environment moderates the relationship between the
BI capabilities and BI success. As explained above, one dimension of the moderator was
retained for subsequent analysis; information processing needs. Information processing needs
are operationalized based on Anthony’s (1965) management activities framework, and the
items measuring this construct were developed based on Gorry and Scott Morton (1971), Kirs
R
2
= 0.60
112
et al. (1989), Klein et al. (1997) and Shim et al. (2002). As recommended by Goodhue et al.
(2007), a multiple regression approach was employed to test whether significant interactions
exist. Although PLS was stated as the main analysis method for this dissertation, using
regression is suggested instead of PLS in the case of sample size or statistical significance is of
concern (Goodhue et al., 2007). Although the sample size for this study exceeds the minimum
sample size requirements for PLS analysis (calculated as 80), the requirement set by the a priori
power analysis is not met (calculated as 132). Hence, because of the sample size is of concern
for testing a moderator effect, a multiple regression approach was employed to test H3 and H4.
The interactions between BI capabilities and the decision environment are tested by
creating cross-product variables and testing the statistical significance of these cross-product
variables in the regression equation (Keith, 2006). The cross-product variables are created by
multiplying the moderator variable with each BI capability. Before the multiplication, all BI
capabilities and the decision environment measures were centered by subtracting the mean
score of the variable from that variable (Aiken and West, 1991; Cohen et al., 2003). Centering
continuous variables helps with reducing the multicollinearity (Keith, 2006; Aiken and West,
1991).
The moderator related hypotheses, H3a-i and H4a-c, were tested with separate
regression models. Rather than testing all possible interactions, it is suggested that one should
focus on a single interaction and test one hypothesis at a time (Keith, 2006). Thus, to test the
statistical significance of the interaction, BI success was regressed on each BI capability and the
decision environment variables as the first step in a sequential regression i.e., H3a was tested
separately, then H3b, and so on. However, for clarity, the set of H3 hypotheses is presented and
113
discussed first, then the set of H4 hypotheses is presented and discussed. As the second step,
the interaction term was added to the equation. Then, the change in R
2
between the two
equations was examined. A significant change in R
2
means a significant interaction term (Keith,
2006). This method of testing interaction is equivalent to dividing the sample into two groups
based on the moderator, conducting separate regressions for each group, and comparing the
regression coefficients (Keith, 2006). Table 36 shows the hypotheses H3a-i.
Table 36
Hypothesis 3
H3a
The influence of high quality internal data sources on BI success is moderated by the
decision environment such that the effect is stronger for structured decision types and
operational control activities.
H3b
The influence of high quality external data sources on BI success is moderated by the
decision environment such that the effect is stronger for unstructured decision types and
strategic planning activities.
H3c
The positive influence of high quality quantitative data on BI success is moderated by the
decision environment such that the effect is stronger for structured decision types and
operational control activities.
H3d
The positive influence of high quality qualitative data on BI success is moderated by the
decision environment such that the effect is stronger for unstructured decision types and
strategic planning activities.
H3e
The positive influence of high data reliability at the system level on BI success is
moderated by the decision environment such that the effect is stronger for structured
decision types and operational control activities.
H3f
The positive influence of high data reliability at the individual level on BI success is
moderated by the decision environment such that the effect is stronger for unstructured
decision types and strategic planning activities.
H3g
The positive influence of high quality interaction of BI with other systems in the
organization on BI success is moderated by the decision environment, such that the effect
is stronger for unstructured decision types and strategic planning activities.
H3h
The positive influence of high quality shared user access methods to BI on BI success is
moderated by the decision environment, such that the effect is stronger for structured
decision types and operational control activities.
H3i The positive influence of high quality individual user access methods to BI on BI success is
moderated by the decision environment, such that the effect is stronger for unstructured
decision types and strategic planning activities.
114
Only H3c was supported. High quality quantitative data has a greater impact on BI
success for operational control activities. Because these activities are largely based on
quantifiable data, the quality of that data is critical to the guidance that a BI provides the
decision maker. However, H3d, which posits that higher quality qualitative data has a greater
impact on BI success in a strategic decision environment, was not supported. One possible
explanation is that these respondents rely more heavily on quantitative or quantifiable data
than on qualitative data. Thus, they are not as concerned with the quality of qualitative data in
the strategic decision environment.
None of the other hypothesized moderator effects were significant for this set of
hypotheses. This suggests that the decision environment does not moderate the ability of BI to
support decision making. It does not moderate the relationship between BI success and the
influence of data sources (H3a & b), data reliability (H3 e & f), or user access methods (H3h & i)
regardless of whether the environment is one of operational activities or strategic activities.
One possible explanation for this is that the data sources are consistent across respondents i.e.,
the data they use is drawn from transactional data that is filtered into data warehouses and
data marts, regardless of the decision environment. Similarly, although data reliability impacts
BI success, all decisions must be based on reliable data regardless of the decision environment.
With regard to user access methods, these findings indicate that higher quality user access
methods positively impact BI success regardless of decision environment. Table 37 shows
regression results for H3, where the significant hypotheses are highlighted.
115
Table 37
Multiple Regression Results – H3
Variables ?
t-
value
p-
value
R Square
Change
F
Change
Sig. F
Change
Internal Data Source Quality .311 3.559 .001
.118 7.546 .001
H3a
Decision Environment .180 1.551 .124
Internal Data Source Quality .317 3.596 .000
.003 .362 .549 Decision Environment .172 1.472 .144
IntDatSrcQ X DecEnv -.075 -.602 .549
External Data Source Quality .165 2.142 0.34
.057 3.428 .036
H3b
Decision Environment .165 1.371 .173
External Data Source Quality .164 2.120 .036
.004 .528 .469 Decision Environment .171 1.418 .159
ExtDatSrcQ X DecEnv -.084 -.726 .469
Quantitative Data Quality .557 6.311 .000
.275 21.390 .000
H3c
Decision Environment -.067 -.592 .555
Quantitative Data Quality .509 5.779 .000
.041 6.759 .011 Decision Environment -.065 -.593 .554
QuantDatQ X DecEnv .298 2.600 .011
Qualitative Data Quality .242 3.156 .002
.098 6.165 .003
H3d
Decision Environment .128 1.083 .281
Qualitative Data Quality .225 2.798 .006
.005 .565 .454 Decision Environment .138 1.155 .250
QualDatQ X DecEnv .077 .752 .454
Internal Data Reliability .404 4.047 .000
.143 9.436 .000
H3e
Decision Environment .112 .966 .336
Internal Data Reliability .369 3.468 .001
.007 .900 .345 Decision Environment .126 1.079 .283
IntDatRel X DecEnv .153 .949 .345
External Data Reliability .164 1.760 .081
.045 2.668 .074
H3f
Decision Environment .159 1.309 .193
External Data Reliability .170 1.801 .074
.002 .215 .644 Decision Environment .155 1.270 .207
ExtDatRel X DecEnv .069 .464 .644
Interaction .479 6.605 .000
.292 23.322 .000
H3g
Decision Environment .232 2.222 .028
Interaction .478 6.523 .000
.000 .003 .956 Decision Environment .232 2.208 .029
Interaction X DecEnv .006 .056 .956
User Access Quality .671 9.912 .000
.475 51.156 .000
H3h/H3i
Decision Environment .069 .767 .445
User Access Quality .668 9.385 .000
.000 .032 .858 Decision Environment .067 .736 .463
UserAccQ X DecEnv .021 .180 .858
116
To further assess the substantive impact of the significant moderator effect in H3c,
regression equations were calculated for low and high values of independent variables by
substituting the desired values in the overall regression equation. Research suggests
substituting the value of -1 standard deviation, the mean, and +1 standard deviation on the
moderator variable (Aiken and West, 1991). Because I am specifically interested in the
implications of this research for operational and strategic decision environments (low and high
values of the decision environment variable), a regression equation was calculated using -1
standard deviation, mean and +1 standard deviation of the decision environment. The mean
and the standard deviation for the decision environment are shown below in Table 38. Table 39
presents the calculated regression equations.
Table 38
Descriptive Statistics for the Decision Environment
N Minimum Maximum Mean Std. Deviation
Average Decision
Environment - Centered
116 -1.818966 1.181034 .00000000 .644024546
Table 39
Regression Equations for High and Low Values of the Decision Environment
Moderator
Values
Corresponding
Decision
Environment
Independent
Variable
Regression Equation for BI Success
+1 Standard
Deviation
Operational
Quantitative
Data Quality
BI Success = 3.596 + (0.807 * QuantitDataQ)
Mean
Between Operational
and Strategic
Quantitative
Data Quality
BI Success = 3.661 + (0.509 * QuantitDataQ)
-1 Standard
Deviation
Strategic
Quantitative
Data Quality
BI Success = 3.726 + (0.211 * QuantitDataQ)
117
The above regression equations show that quantitative data quality has stronger effect
on BI success for operational decision environments, where the decisions are structured and
management activities are operational. Below Figure 6 show the graphical representation of
the above mentioned regression lines. This figure depicts that quantitative data quality appears
to have a substantive positive effect on BI success. As this variable increases, BI success for an
operational decision environment exhibits greater increase than for a strategic decision
environment. Thus, the effect of moderation is significantly and substantively greater for the
operational decision environment.
Figure 6. Interaction effect on the quantitative data quality.
The interaction effect of the decision environment on the relationship between
organizational BI capabilities and BI success (H4a - c) were each tested separately using multiple
regression. These hypotheses examine only the moderator effect of an unstructured/strategic
0
1
2
3
4
5
6
7
8
9
1 2 3 4 5 6 7 8 9
BI Success for
strategic Dec Env
BI Success for
operational Dec
Env
Quantitative Data Quality
B
I
S
u
c
c
e
s
s
118
decision environment (Table 40). Results show that none of the R
2
changes are significant, thus
the interaction effects are not significant (Table 41). The strength of the impact of flexibility,
risk, and intuition on BI success is not impacted by the decision environment. Only flexibility
and risk impact BI success in the absence of the moderator. This suggests that the degree of
intuition involved in the decision is not related to the success of the BI in supporting decisions.
One reason for this may be that BI users do not heavily rely on intuition for decision making.
This is consistent with research that indicates that BI helps to reduce the amount of intuition
involved in decision making (Howson, 2006). A possible explanation for the findings
surrounding flexibility and risk may also relate to the way BI is used. Research suggests that BI
may be more useful in helping decision makers grapple with decisions involving higher risk and
where flexibility is needed (Clark et al 2007). Therefore, the impact of flexibility and risk on BI
success is strong across decision environments.
Table 40
Hypotheses 4
H4a The influence of BI flexibility on BI success is moderated by the decision environment, such that
the effect is stronger for unstructured decision types and strategic planning activities.
H4b The influence of the intuition allowed in analysis on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured decision types and strategic
planning activities.
H4c The influence of tolerating risk on BI success is moderated by the decision environment, such that
the effect is stronger for unstructured decision types and strategic planning activities.
A summary of all hypotheses testing results is provided in Table 42. Overall, BI success is
greater with higher quality data sources, data types, data reliability, interaction of BI with other
systems, user access methods, and higher flexibility. Thus, technological BI capabilities are
largely more influential in BI success than organizational. This is somewhat surprising given the
119
importance of organization readiness (capabilities) called for in much of the BI literature (Clark,
et al. 2007; Watson and Wixom, 2007). The implications of these findings are discussed further
in Chapter 5.
Table 41
Multiple Regression Results – H4
Variables ?
t-
value
p-
value
R
2
Change
F
Change
Sig. F
Change
Flexibility .554 7.344 .000
.336 28.575 .000
H4a
Decision Environment .178 1.769 .080
Flexibility .558 7.321 .000
.001 .211 .647 Decision Environment .182 1.795 .075
Flexibility X DecEnv -.050 -.460 .647
Risk .490 6.208 .000
.268 20.728 .000
H4b
Decision Environment .176 1.664 .099
Risk .485 5.825 .000
.000 .035 .852 Decision Environment .174 1.622 .108
Risk X DecEnv .022 .187 .852
Intuition .095 .733 .465
.024 1.363 .260
H4c
Decision Environment .176 1.438 .153
Intuition .089 .668 .505
.000 .049 .825 Decision Environment .173 1.403 .163
Intuition X DecEnv .043 .222 .222
120
Table 42
Summary of Hypothesis Testing
Hypothesis Results
T
e
c
h
n
o
l
o
g
i
c
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
D
i
r
e
c
t
E
f
f
e
c
t
s
H1a: The better the quality of data sources in an organization, the
greater its BI success.
Supported
H1b: The better the quality of different types of data in an
organization, the greater its BI success.
Supported
H1c: The higher the data reliability in an organization, the greater its
BI success.
Supported
H1d: The higher the quality of interaction of BI with other systems in
an organization, the greater its BI success.
Supported
H1e: The higher the quality of user access methods to BI in an
organization, the greater its BI success.
Supported
O
r
g
a
n
i
z
a
t
i
o
n
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
D
i
r
e
c
t
E
f
f
e
c
t
s
H2a: The level of BI flexibility positively influences BI success. Supported
H2b: The level of intuition allowed in analysis by BI positively
influences BI success.
Not
Supported
H2c: The level of risk supported by BI positively influences BI
success.
Not
Supported
T
e
c
h
n
o
l
o
g
i
c
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
I
n
t
e
r
a
c
t
i
o
n
E
f
f
e
c
t
s
H3a: The influence of high quality internal data sources on BI success
is moderated by the decision environment such that the effect is
stronger for structured decision types and operational control
activities.
Not
Supported
H3b: The influence of high quality external data sources on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Not
Supported
H3c: The positive influence of high quality quantitative data on BI
success is moderated by the decision environment such that the
effect is stronger for structured decision types and operational
control activities.
Supported
(table continues)
121
Table 42 (continued).
T
e
c
h
n
o
l
o
g
i
c
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
I
n
t
e
r
a
c
t
i
o
n
E
f
f
e
c
t
s
H3d: The positive influence of high quality qualitative data on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Not
Supported
H3e: The positive influence of high data reliability at the system level
on BI success is moderated by the decision environment such that
the effect is stronger for structured decision types and operational
control activities.
Not
Supported
H3f: The positive influence of high data reliability at the individual
level on BI success is moderated by the decision environment such
that the effect is stronger for unstructured decision types and
strategic planning activities.
Not
Supported
H3g: The positive influence of high quality interaction of BI with
other systems in the organization on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Not
Supported
H3h: The positive influence of high quality shared user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for structured decision
types and operational control activities.
Not
Supported
H3i: The positive influence of high quality individual user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured
decision types and strategic planning activities.
Not
Supported
O
r
g
a
n
i
z
a
t
i
o
n
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
I
n
t
e
r
a
c
t
i
o
n
E
f
f
e
c
t
s
H4a: The influence of BI flexibility on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Not
Supported
H4b: The influence of the intuition allowed in analysis on BI success
is moderated by the decision environment, such that the effect is
stronger for unstructured decision types and strategic planning
activities.
Not
Supported
H4c: The influence of tolerating risk on BI success is moderated by
the decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Not
Supported
* Significant at the p = 0.1 level
** Significant at the p = 0.05 level
*** Significant at the p = 0.01 level
122
CHAPTER 5
DISCUSSION AND CONCLUSIONS
This dissertation studies the relationship between various business intelligence (BI)
capabilities and BI success, and whether this relationship is affected by different decision
environments. This chapter starts with providing a discussion of the findings and presenting the
limitations of the study. It then proceeds with theoretical and managerial implications of the
study, and concludes by providing future research directions.
Discussion of Research Findings
This dissertation proposes a framework for examining technological and organizational
BI capabilities and how they impact BI success. This framework also considers that different BI
capabilities may have a more significant impact on BI success for different decision
environments. The decision environment consists of different information characteristics
required to make decisions. Each of the constructs and their relevant findings are discussed
below.
Technological BI Capabilities and BI Success
Hypotheses 1a-e propose that the quality of technological BI capabilities positively
impact BI success. The technological BI capabilities examined in this dissertation are data
sources (H1a), different types of data (H1b), data reliability (H1c), interaction of BI with other
systems (H1d) and user access methods to BI (H1e). These hypotheses suggest that the higher
the quality of technological BI capabilities, the greater the BI success. All of these hypotheses
(H1a-e) were confirmed by the positive significant relationship between all technological BI
capabilities and BI success.
123
These results suggest that technological BI capabilities are critical elements for a
successful BI. Organizations, as they are going through BI implementations, should make sure
that they have these technological capabilities implemented. But, just implementing these
capabilities is not enough; the difference in the quality of these capabilities is one of the factors
that may explain why some organizations are successful with their BI initiative while some are
not. Organizations should work towards maintaining the quality of these capabilities, because
as the quality of technological BI capabilities increases, the BI success in an organization also
increases.
These results are also consistent with prior research. Research shows that clean, high
quality and reliable data is one of the most important BI success factors (Eckerson, 2003;
Howson, 2006). Research also implies that the sources where the organizations obtain their
data from play a critical role for the success of a BI initiative (Howson, 2006). Especially for
organizations that use multiple data sources and multiple information systems, it is critical to
integrate these technologies and information to avoid inconsistencies and inaccuracies
(Swaminatha, 2006; Sabherwal and Becerra-Fernandez, 2010). Different user access methods
are also critical for BI success; providing high quality user access methods increases the decision
making effectiveness (Hostmann et al., 2007) as well as the effectiveness of presenting the
appropriate information based on user specific needs and tasks. Because the overall goal is to
enable users access and navigate through data based on their requirements (Sabherwal, 2007,
2008).
124
Organizational BI Capabilities and BI Success
Hypotheses 2a-c propose that organizational BI capabilities positively impact BI success.
The organizational BI capabilities examined in this dissertation are flexibility (H2a), the level of
intuition involved in analysis (H2b) and level of risk supported by BI (H2c). These hypotheses
suggest, regardless of their levels, these organizational BI capabilities significantly impact BI
success. Results of data analysis showed that H2a was confirmed by the positive significant
relationship between flexibility and BI success, but H2b and H2c were not confirmed. There was
not a significant relationship between BI success and intuition involved in analysis or level of
risk supported by BI. This is somewhat surprising considering research emphasizing the
importance of organizational readiness (Clark, et al. 2007; Watson and Wixom, 2007). Although
organizational readiness and organizational capabilities are not the same thing, organizational
capabilities play a critical role in achieving organizational BI readiness (Williams and Williams,
2007).
The significance of flexibility as an organizational BI capability suggests that in order to
be successful, a BI initiative should be able to accommodate a certain amount of variation in
the business processes, environment or the technology (Gebauer and Schober, 2006; Clark et
al., 2007). This finding is also consistent with the literature. Prior research suggests that
flexibility is one of the most important factors to consider while selecting a BI application
(Dreyer, 2006). Considering that change is inevitable in the current business environment, the
organization should be able to modify its BI easily and quickly to adapt to the changing business
(Sabherwal and Becerra-Fernandez, 2010).
125
The non-significance of the level of intuition involved in analysis may indicate two
things. First, it may mean that decision makers do not involve their intuition in their decision
making process with BI and make decisions purely on data and analysis. In support of this
argument, prior research suggests that organizations making decisions based on data and
analysis are more likely to succeed with their BI initiative compared to the organizations making
decisions based on intuition (Howson, 2008; Sabherwal and Becerra-Fernandez, 2010). The
non-significance of the level of intuition involved in analysis may also mean that BI success is
more dependent on how decision makers use the system rather than “what is going on in their
head.” While experience based intuition is important, gut instinct based on experiences is not
as useful as it used to be in less dynamic events (Bresnahan, 1999). This is consistent with
research that indicates that BI helps to reduce the amount of intuition involved in decision
making (Howson, 2006).
A possible explanation for the findings about risk may also relate to the way BI is used.
Research suggests that BI may be more useful in helping decision makers deal with decisions
involving higher risk (Clark et al 2007). BI has been studied as a risk analysis and mitigation
platform, with the overall goal of managing and reducing it (Azvine et al., 2007). Given internal
and external risks that an organization deals with and how they can harm organizational
performance, the role of BI should be to manage risk by attempting to minimize it and
providing an integrated view of performance and risk (Azvine et al., 2007). Thus, users may not
be aware of the level of risk surrounding the decisions they make because their BI is already
managing that risk. It is also possible that different organizations as well as different groups
within an organization may be facing different levels of risk during their decision making
126
process, and the majority of respondents to the survey were from a group that does not have
to deal with a lot of risk. This is not surprising considering that majority of the respondents are
middle and operational level managers. By definition, middle and operational level managers
deal with less risky situations compared to strategic level managers.
Technological BI Capabilities and the Decision Environment
Hypotheses 3a-i propose that the relationship between technological BI capabilities and
BI success is moderated by the decision environment. Results of data analysis showed that only
the influence of high quality quantitative data on BI success is moderated by the decision
environment such that the effect is stronger for operational decision environments (H3c). This
finding is not surprising considering that operational management activities largely rely on
quantifiable data (Gorry and Scott Morton, 1971; Anthony, 1965; Keen and Scott-Morton,
1978), and that the quality of that data is critical for the decision maker. The rest of the
hypotheses positing interaction effects were not supported. More specifically, the findings
suggest that high quality internal data sources (H3a), high quality quantitative data (H3c), high
data reliability at the system level (H3e), and high quality shared user access methods (H3h) do
not have a stronger impact on BI success for operational decision environments.
The results also suggest that high quality external data sources (H3b), high quality
qualitative data (H3d), high data reliability at the individual level (H3f), high quality interaction
(H3g), and high quality individual user access methods (H3i) do not have a stronger impact on BI
success for strategic decision environments. One possible explanation for the non-significance
of H3d is that respondents rely more heavily on quantitative or quantifiable data than on
qualitative data. Thus, they are not as concerned with the quality of qualitative data in the
127
strategic decision environment. It is also a possibility that there were not enough respondents
representing the strategic decision environment to account for a significant statistical impact.
The non-significance of the other hypothesized moderator effects suggests that the
decision environment does not moderate the relationship between technological BI capabilities
and BI success. One possible explanation for this is that respondents refer to the same data
sources, use consistently reliable data, access the BI and experience same level of interaction
with other systems, regardless of decision environment. Thus, these technological BI
capabilities influence BI success regardless of whether the environment is one of structured
decisions/operational activities or unstructured decisions/strategic activities.
Organizational BI Capabilities and the Decision Environment
Hypotheses 4a-c proposes the relationship between organizational BI capabilities and BI
success is moderated by the decision environment. More specifically, they suggest that the
positive impact of flexibility (H4a), intuition allowed in analysis (H4b) and level of risk supported
by BI (H4c) on BI success is stronger for strategic decision environments. Results of data analysis
showed that none of the interaction effects hypothesized is significant. This indicated that the
decision environment does not impact the strength of the relationship between BI success and
organizational BI capabilities.
The non-significance of the interaction effect associated with intuition is not surprising
in the light of the non-significance of its main effects (H2b). Thus, it may mean that decision
makers do not involve their intuition in the decision making process, regardless of the decision
environment. Research also suggests that the role of BI is to minimize the use of intuition in the
decision making process (Howson, 2006; Sabherwal and Becerra-Fernandez, 2010). Similarly,
128
the main effect of the level of risk was not significant, and the interaction effect associated with
risk is not significant either. It means that there is no difference between decision
environments in terms of the impact level of risk supported by BI has on BI success. This may
indicate that regardless of the decision environment, BI is more successful as long as it can
support high risk decisions.
Flexibility impacts BI success in the absence of the moderator. The interaction effect of
the decision environment on the relationship between flexibility and BI success is not
significant. This indicates that the level of BI flexibility is as important for operational decision
environments as it is for strategic decision environments. This is consistent with research
suggesting that BI is more successful dealing with situations where flexibility is needed (Clark et
al 2007).
Limitations
This study is subject to several possible limitations in terms of sample size and scales
used. First, the sample size does not allow for a more comprehensive analysis. The results might
have been more significant if the sample size had been larger and a more thorough analysis
could have been employed. Also, the respondents are not as diverse as I would like. For
instance, only 11 respondents are female, and only 24 of the respondents are executive level
managers. Response rate is another limitation for this study. Although the survey link was
broadcasted to over 8,000 people, less than 1% actually filled out the survey. There can be
possible reasons for the low response rate. First of all, there was no incentive for taking the
survey and considering the busy business life, recipients possibly did not feel compelled to take
129
the survey. Also, the length of the survey (20 to 30 minutes) might be another reason why the
recipients did not want to complete the survey.
Common method variance is another possible limitation of the study. Common method
variance refers to the fact that potential respondent biases might constitute a systematic error.
This is common when using survey responses from the same source because a single
respondent for each survey can only yield one perspective. Thus, there might be spurious
correlation (Bagozzi, 1980). Several precautions were taken to minimize the effects of common
method variance. The dependent and independent variables were separated into different
sections of the survey instrument, using different question formats.
Another possible limitation is the items used to measure some of the constructs. The
reliability analysis was not satisfactory for the level of intuition involved in analysis and decision
types constructs. It is possible that items measuring intuition was not clear enough or did not
tap well enough into the level of intuition the respondents use during their decision making
process. Although the analysis of the responses show that more than 65% of the respondents
involve their gut feeling and put emphasis on their past experiences when making decisions,
this was not reflected in the BI success factor. Similarly, three out of five items that were
supposed to measure decision types were dropped from the scale, because they were either
tapping into multiple different things (e.g., DecType5, “the decisions I make require
computational complexity and precision”) or their wording was deemed to be ambiguous (e.g.,
DecType2_coded, “I make decision without higher level manager involvement”) because they
were not necessarily measuring the decision type made by the decision maker.
130
The items measuring user access quality were deemed problematic. It was posited that
the user access methods consist of two types; individual user access and shared user access.
The goal with the survey was to measure the extent of satisfaction of the BI user with both user
access methods. Yet, the items in the survey only measure the overall quality of the user access
methods. Thus, whether these two different user access methods have different impacts on BI
success, for different decision environments could not be measured. Instead, the impact of the
overall user access quality on BI success for different decision environments was measured.
Although scale related issues may pose as limitations for the current study, this may
also be considered as a starting point for developing a BI success model and its scale. The
wording of some of the questions was ambiguous and mistakes such as using conjunctives have
been made. There are some questions that should have been divided into two and asked as two
different questions.
Research Contributions
The BI success model suggested in this study contributes to the information systems (IS)
literature in several ways. First, it proposes to extend current research in BI and provide a
parsimonious and intuitive model for explaining the relationship between BI success and BI
capabilities in the presence of different decision environments, based on theories from decision
making and organizational information processing. This dissertation contributes to academic
research by providing richer insight in the role of the decision environment in BI success and
providing a framework with which future research on the relationship between BI capabilities
and BI success can be conducted.
131
Another research contribution is the inclusion of the decision environment in the BI
success model. The moderating effect of the decision environment has not been studied in the
IS literature before. The decision environment is operationalized based on Gorry and Scott
Morton’s (1971) framework for DSS and Anthony’s (1965) framework for management
activities. Although these are two established theories, they have not been used for BI research
before and also have not been operationalized to measure with survey items. This study is a
first attempt in creating survey items to operationalize these frameworks. Also, this dissertation
is a first attempt to develop a scale for BI capabilities and BI success. The BI capabilities have
not been measured to date and they all have shown good validity and reliability. All capabilities
has an internal consistency of .768 or above, with the exception of the intuition involved in
analysis, which had an acceptable level of internal consistency of .605 for newly developed
instruments (O’Leary-Kelly and Vokurka, 1998). Similarly, the BI success scale was a first
attempt and had a Cronbach’s alpha of .914, indicating an internally consistent scale (Nunnally
and Bernstein, 1994).
The findings of this study indicate that technological BI capabilities impact BI success
significantly, regardless of the decision environment. This may imply that technology drives the
BI initiatives. While the technologies used or the platform BI is built upon is undeniable critical
for BI success, factors such as top management support, alignment between business strategy
and BI, a strong BI team and available resource are as important (Eckerson, 2006; Wason and
Wixom, 2007). These non-technological capabilities are mostly referred to as organizational
readiness issues and discussed widely in the IT literature as critical success factors for IT
implementation (Rud, 2009; Williams and Williams, 2007; Abdinnour-Helm et al., 2002).
132
Although these have substantial impact on how BI is used within an organization, there are still
enabling technologies that need to be implemented in order to benefit from these factors
(Sabherwal and Becerra-Fernandez, 2010). This may be the reason behind findings suggesting
that technological capabilities impact BI success more significantly. For example, intuition was
non-significant in the results, implying that it does not substantially impact BI success.
Consistent with this finding, literature suggests not to make decisions based on intuition, yet,
both academic and practitioners’ literature emphasize that so many business decisions today
are made based on the decision maker’s gut feeling (Davenport and Harris, 2007; Bonabeau,
2003). As a solution, converting intuition into a tangible strategy is suggested, through using
decision support tools (Bonabeau, 2003) and analytics (Davenport and Harris, 2007). This
exemplifies that it is critical to have the necessary enabling technologies to be able to benefit
from the organizational capabilities.
Only one of the three organizational BI capabilities, flexibility, was found to be
significant by the analysis. This indicates that technology is the most eminent factor that
decision makers associate with BI success, and that BI success is mostly driven by technology
rather than organizational factors. Although research suggests that organizational BI
capabilities are important for BI success (Watson and Wixom, 2007; Watson, 2008) the results
of this study suggest that some organizational BI capabilities are more important than the
others. The significance of flexibility as an organizational BI capability shows that it is a
strategically important element for managing the unpredictable, especially in the technology-
intensive settings (Evans, 1991). This suggests that flexibility can be tied to the frequently
sought after agility by the companies. Agility can be defined as a measure of an organization’s
133
ability to change and adapt to new environments (Neumann, 1994). The more change in the
business environment, the more the organization requires agility and BI provides the
opportunities for the organization to be more agile and adopt innovation (Sabherwal and
Becerra-Fernandez, 2010). It is possible that organizations strive to achieve agility, and
flexibility of BI may be the most important capability of BI in order to achieve that agility.
Literature has suggested IT capabilities as a potential source for agility (Weill et al., 2002; Fink
and Neumann, 2007), and findings of this study is consistent with the previous research findings
about flexibility being one of the most important factors for achieving agility (Swafford et al.,
2008; Erol et al., 2009).
Another contribution of this dissertation is that it shows the relationship between
quantitative data quality, decision environment and BI success. The results show that the
influence of high quality quantitative data on BI success is moderated by the information
processing needs such that the effect is stronger for operational control activities. Literature
suggests that operational management activities largely rely on quantifiable data (Gorry and
Scott Morton, 1971; Anthony, 1965; Keen and Scott-Morton, 1978), and that the quality of that
data is critical for the decision maker. This indicates that, based on the information
requirements of a decision maker, the quality of the quantitative data significantly impacts BI
success. Especially for those decision makers who deal with operational control related
management activities, this impact becomes even more obvious because they mostly rely on
this type of data. Although the importance of data quality and to be more specific, the quality
of the quantitative data has been studied (Baars and Kemper, 2008; Sabherwal and Becerra-
134
Fernandez, 2010), the fact that it is more critical for the operational control activities has not
been investigated previously.
This study provides significant findings for practitioners. The practitioner oriented
contribution of this study is that it helps users and developers of BI understand how to better
align their BI capabilities with their decision environments and presents information for
managers and users of BI to consider about their decision environment in assessing BI success.
Although it is the only significant interaction effect, the fact that quantitative data quality has a
stronger effect on BI success for operational decision environments rather than strategic
decision environments provides an important insight for BI users and managers. Also, the scale
used for this study can be worked up and extended into a much broader BI success survey,
which can be used in the industry to assess organizations’ BI success.
Conclusion and Future Research Directions
Research on BI success and its relationship with BI capabilities is scarce. This study
introduces a new BI success model and provides understanding regarding how different BI
capabilities can improve BI success within an organization. Prior to this study, BI success
research included topics such as critical success factors for BI implementation (Wixom and
Watson, 2001; Solomon, 2005), measurement of BI success (Gessner and Volonino, 2005;
Lonnqvist and Pirttimaki, 2006), and case studies focusing on success or failure stories of
specific BI technologies implemented by specific companies (Cooper et al., 2000; Watson and
Donkin, 2005; Anderson-Lehman et al., 2004).
This study adds to the existing body of knowledge by introducing technological and
organizational BI capabilities and how they can impact BI success. In addition, this study also
135
introduces the decision environment as a moderator for BI success. The findings of this
dissertation suggest that technological capabilities positively impact BI success. However,
hypotheses testing the moderating effect of the decision environment are not supported with
one exception. Results show that the quality of quantitative data indeed impacts BI success
stronger for operational decision environment than strategic decision environments, as
hypothesized. Using a different sampling frame and a larger sample size may yield more
significant findings. Thus, this is one of the future research directions. Another future research
direction may be to expand the capabilities. The technological and organizational BI capabilities
studied in this research are by no means exhaustive. Reexamining the ones studied in this
dissertation, expanding capabilities and even possible redefining grouping of the constructs
maybe another future research direction.
Having the right BI capabilities within the proper decision environment is important for
an organization to realize maximum benefits from its BI investment. This study may serve as a
starting point in investigating how different BI capabilities may impact BI success, for different
decision types and different information requirements for those decisions. Future research on
BI success would benefit from the inclusion of different BI capabilities as well as the inclusion of
other organizational characteristics, such as the organizational structure or organizational
culture. Incorporating environmental characteristics such as the uncertainty and equivocality
(Tushman and Nadler, 1978) in the model may also increase understanding of BI success.
136
APPENDIX A
COVER LETTER
137
Dear Participant,
I would like to invite you to participate in this research project, which is being conducted as part
of the requirements for me to earn my Ph.D. in Business Computer Information Systems from
the University of North Texas. The project aims to measure Business Intelligence (BI) success by
examining the BI capabilities used in your organization and how they are influenced by your
decision environment.
Your honest responses to each statement and question are extremely important to this
project’s outcome. You can be assured of complete confidentiality – no individual responses
will be published and the raw information will be accessible only to me and the University of
North Texas faculty on my dissertation committee. This survey contains sections addressing
your satisfaction with BI, the types of decisions you make, your information processing needs,
the capabilities of BI you use, and some information about yourself.
It will take you approximately 30 minutes to complete the survey. In addition, your
participation is voluntary. You may decline to answer any particular question that you are
uncomfortable with or feel is not appropriate. Submitting the survey will indicate that you have
given your consent for us to use your data. The study has been reviewed and approved by the
UNT Committee for the Protection of Human Subjects (940.565.3940). If you have questions
concerning this study, please feel free to contact me.
Thank you again for your consideration.
Sincerely,
Oyku Isik
138
APPENDIX B
SURVEY INSTRUMENT
139
1. What is the highest education level you have completed? (Analysis Label: HighestEdLevel)
High School
Some college
Two-year college degree
Four-year college degree
Graduate degree
Post-graduate degree
2. What is your gender? (Analysis Label: Gender)
Male
Female
3. How long have you been in your current organization? ___ years (Analysis Label: TimeInOrg)
4. Do you hold a managerial position? (Analysis Label: ManagerialPosition)
Yes
No
5. What is your functional area? (Analysis Label: FunctArea)
General management
Corporate communications
Finance / Accounting / Planning
Human resources / Personnel
Information technology
Legal
Manufacturing / Operations
Marketing
Sales
Supply chain
Other (please specify) __________________________
6. What is your level in the organization? (Analysis Label: LevelInOrg)
Executive management
Middle management
Operational management
Other (please specify) __________________________
7. What is your job title? __________ (Analysis Label: JobTitle)
140
8. What is the approximate number of employees in your organization? (Analysis Label:
NumEmployees)
Less than 100
100-499
500-999
1,000 -4,999
5,000- 9,999
10,000 or more
9. Which below best describes your industry? (Analysis Label: Industry)
Manufacturing
Finance
Education
Wholesale & retail trade
Transportation
Banking
Manufacturing
Utilities
Government
Insurance
Other (Please specify) ______
141
For the purposes of this research, Business Intelligence (BI) is defined as the
following;
"BI is a system comprised of both technical and organizational elements that
presents historical information to its users for analysis, to enable effective decision
making and management support, for the overall purpose of increasing organizational
performance."
Please answer the following questions about a specific BI application you use for your
everyday business decision making purposes. If you are using more than one BI
application, please focus only on one of them and answer the questions only based on
that specific application.
Please choose the response which best describes your satisfaction with each of the
following:
LABEL
CONSTRUCT: BI Success
Strongly
dissatisfied
Somewhat
dissatisfied
Neither
satisfied
or dissatisfied
Somewhat
satisfied
Strongly
satisfied
BIsat1
How well the BI that I am using
supports my decision making
BIsat2
How well the BI that I am using
provides precise information I need
BIsat3
How well the BI that I am using
provides information I need in time
BIsat4
How user friendly the BI that I am
using is
BIsat5 The BI that I am using overall
Please indicate how well each statement below describes the decisions you make:
LABEL
CONSTRUCT: Decision Types Almost never Rarely Sometimes Frequently Almost always
DecType1
I make routine, repetitive
decisions
DecType2_coded
I make decisions without higher
level manager involvement
DecType3
The decisions I make could be
automated
DecType4_coded
The decisions I make require
judgment and intuition
DecType5
The decisions I make require
computational complexity and
precision
142
Please answer the following about the nature of the information you use to make
decisions;
LABEL
CONSTRUCT: Information
Processing Needs
Low 1 2 3 4 5 High
InfoChar1 The granularity is ...
InfoChar2 Accuracy of information is …
Wide 1 2 3 4 5 Narrow
InfoChar3 The scope of information is…
1 Qualitative 2 3 4
5
Quantitative
InfoChar4 Type of information is …
1 Infrequent 2 3 4 5 Frequent
InfoChar5 Frequency of use is…
1 Older 2 3 4 5 Current
InfoChar6 Age of information is …
143
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: Data
Sources
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
IntDataSrcQuality1
The internal data sources
used for my BI are readily
available
IntDataSrcQuality2
The internal data sources
used for my BI are readily
usable
IntDataSrcQuality3
The internal data sources
used for my BI are easy to
understand
IntDataSrcQuality4
The internal data sources
used for my BI are concise
ExtDataSrcQuality1
The external data sources
used for my BI are readily
available
ExtDataSrcQuality2
The external data sources
used for my BI are readily
usable
ExtDataSrcQuality3
The external data sources
used for my BI are easy to
understand
LABEL
CONSTRUCT: Data Types
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewha
t agree
Strongl
y agree
QuantDataQuality1
My BI provides accurate
quantitative data
QuantDataQuality2
My BI provides comprehensive
quantitative data
QuantDataQuality3
My BI provides consistent
quantitative data
QuantDataQuality4
My BI provides high quality
quantitative data
QualDataQuality1
My BI provides high quality
qualitative data
QualDataQuality2
My BI provides accurate
qualitative data
QualDataQuality3
My BI provides comprehensive
qualitative data
QualDataQuality4
My BI provides consistent
qualitative data
144
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: Intuition Involved
Strongly
disagree
Somewhat
disagree
Neither
agree nor
disagree
Somewhat
agree
Strongly
agree
intuition1_c
oded
Using my BI, I make decisions based on
facts and numbers
intuition2
Although I use my BI for decision making, I
still involve my gut feeling for the decisions
I make
intuition3
With my BI, it is easier to use my intuition
to make better informed decisions
intuition4
The decisions I make require a high level of
thought
intuition5
Although I use my BI for decision making , I
still put emphasis on my past experiences
for the decisions I make
LABEL
CONSTRUCT: Data Reliability
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
IntDataQuality1
Internal data collected for my BI
is reliable
IntDataQuality2_Cod
ed
There are inconsistencies and
conflicts in the internal data for
my BI
IntDataQuality3
Internal data collected for my BI
is accurate
IntDataQuality4
Internal data for my BI is
updated regularly
ExtDataQuality1
External data collected for my BI
is reliable
ExtDataQuality2_Co
ded
There are inconsistencies and
conflicts in the external data for
my BI
ExtDataQuality3
External data collected for my BI
is accurate
ExtDataQuality4
External data for my BI is
updated regularly
145
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: User Access
Strongly
disagree
Somewhat
disagree
Neither
agree nor
disagree
Somewhat
agree
Strongly
agree
UserAccess
_qual1
I am satisfied with the quality of the way I
access my BI
UserAccess
_qual2
I am authorized to access to all information
I need with BI
UserAccess
_qual3
The way I access my BI is fits well to the
types of decisions I make using my BI
LABEL
CONSTRUCT: Interaction with
Other Systems
My BI provides ...
Strongly
disagree
Somewhat
disagree
Neither
agree nor
disagree
Somewhat
agree
Strongly
agree
interaction1
… a unified view of business data
and processes
interaction2
… links among multiple business
applications
interaction3 … a comprehensive electronic
catalog of the various enterprise
information resources in the
organization
interaction4 … easy and seamless access to
data from other applications and
systems
146
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: Flexibility
My BI ...
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
flex1
… is compatible with other tools that I use
(e.g., Microsoft Office Suite, security
infrastructure, portal technology or databases)
flex2
… can accommodate changes in business
requirements quickly
flex3
… makes it easier to deal with exceptional
situations
flex4
… is highly scalable with regards to
transactions
flex5 … is highly scalable with regards to data
flex6 … is highly scalable with regards to users
flex7
… is highly scalable with regards to
infrastructure
Flex8
The manner in which the components of my BI
are organized and integrated allows for rapid
changes
LABEL
CONSTRUCT: Risk Level
My BI ...
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
risk1
… supports decisions associated with high level
of risk (e.g., entering a new market, hiring a
new manager)
risk2
… supports decisions motivated by exploration
and discovery of new opportunities (e.g.,
starting a new business line, creating a new
product design)
risk3
… helps me minimize uncertainties in my
decision making process
risk4
… helps me manage risk by monitoring and
regulating the operations (e.g., monitoring key
performance indicators (KPIs), customizing
alerts or creating dashboards)
147
REFERENCES
Abdinnour-Helm, S., Lengnick-Hall, M. L. and Lengnick-Hall, C. A. (2003). Pre-implementation
attitudes and organizational readiness for implementing an enterprise resource planning
system. European Journal of Operational Research, 146 (2), 258-273.
Aiken, L. S., and West, S. G. (1991). Multiple regression: testing and interpreting interactions.
Newbury Park, CA: Sage.
Al-Busaidi, K. A. and Olfman, L. (2005). An investigation of the determinants of knowledge
management systems success in omani organizations. Journal of Global Information
Technology Management, 8 (3), 6-27.
Alexander, J. W. and Randolph, W. A. (1985). The fit between technology and structure as a
predictor of performance in nursing subunits. Academy of Management Journal, 28 (4),
844-859.
Alter, A. (2004). A work system view of DSS in its forth decade. Decision Support Systems, 38 (3),
319-327.
Anandarajan, M. and Arinze, B. (1998). Matching client/server processing architectures with
information processing requirements: A contingency study. Information &
Management, 34 (5), 265-276.
Anderson-Lehman, R., Watson, H. J., Wixom, B. H., and Hoffer, J. A. (2004). Continental Airlines
flies high with real-time business intelligence. MIS Quarterly Executive 3 (4), 163-176.
Antebi, O., (2007, January 16). Managing by exception [web log post]. Retrieved from
http://www.panorama.com/blog/?p=13.
148
Anthony, R. N. (1965). Planning and control systems: A framework for analysis. Boston, MA:
Harvard Business School Press.
Applegate, L. M., F. W. McFarlan, and J. L. McKenney. (1999). Corporate information systems
management: The challenges of managing in an information age (5
th
ed). Boston, MA:
Irwin/McGraw-Hill.
Armstrong, J. S., and Overton, T. S. (1971). Brief vs. comprehensive descriptions in measuring
intentions to purchase. Journal of Marketing Research, 8 (1), 114-117.
Armstrong, J. S., and Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal
of Marketing Research, 14, 396-402.
Arnott, D. (2004). Decision support systems evolution: Framework, case study and research
agenda,” European Journal of Information Systems, 13 (4), 247-259.
Arnott, D., and Pervan, G. (2005). A critical analysis of decision support systems research.
Journal of Information Technology, 20 (2), 67-87.
Ashill, N. J. and Jobber, D. (2001). Defining the information needs of senior marketing
executives: An exploratory study. Qualitative Market Research, 4 (1), 52-61.
Au, N., Ngai, E. W. T. and Cheng, T. C. E. (2008). Extending the understanding of end user
information systems satisfaction formation: An equitable needs fulfillment model
approach,” MIS Quarterly, 32 (1), 43-66.
Azvine, B., Cui, Z., Majeed, B. and Spott, M. (2007). Operational risk management with real-time
business intelligence. BT Technology Journal, 25 (1), 154-167.
149
Baars, H. and Kemper, H.G. (2008). Management support with structured and unstructured
data-an integrated business intelligence framework. Information Systems Management,
45 (2), 132-148.
Bagozzi, P. (1980). Causal methods in marketing. New York, NY: John Wiley & Sons.
Baloh, P. (2007). The role of fit in knowledge management systems: Tentative propositions of
the KMS design. Journal of Organizational and End User Computing. 19 (4), 22-41.
Baron, R. M. and Kenny, D. A. (1986). The moderator-mediator variable distinction in social
psychological research: conceptual, strategic, and statistical considerations,” Journal of
Personality and Social Psychology. 51 (6), 1173-1182.
Bartlett, J. E., Kotrlik, J. W., and Higgins, C. C. (2001). Organizational research: determining
appropriate sample size in survey research. Information Technology, Learning, and
Performance Journal, 19 (1), 43-50.
Basi, R. K. (1999). WWW response rates to socio-demographic items. Journal of the Market
Research Society, 41 (4), 1999, 397-401.
Beach, L. R. and Mitchell, T. R. (1978). A contingency model for the selection of decision
strategies. The Academy of Management Review, 3 (3), 439 -451.
Bell, R. (2007, July 11). Gut instincts give business intelligence a new flavor. Financial Times, p.2.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and
firm performance: An empirical investigation. MIS Quarterly, 24 (1), 169–196.
Bharadwaj, A. S., Sambamurthy, V. and Zmud, R. W. (1999, December). IT Capabilities:
Theoretical perspectives and empirical operationalization. Paper presented at the 20th
International Conference on Information Systems (ICIS), Charlotte, North Carolina.
150
Bhatt, G. D. and Grover, V. (2005). Types of information technology capabilities and their role in
competitive advantage: An empirical study. Journal of Management Information
Systems, 22 (2), 253-277.
Blumberg, R. and Atre, S. (2003). The problem with unstructured data. DM Review. Retrieved
from http://dmreview.com/master.cfm?NavID=55&EdID=6287.
Bonde, A. and Kuckuk, M. (2004, April). Real world business intelligence: The implementation
perspective. DM Review. Retrieved from http://www.dmreview.com.
Bonabeau, E. (2003). Don't trust your gut. Harvard Business Review, 81 (5), 116-123.
Bresnahan, J. (1998, July). Legal espionage. CIO Enterprise.
Briggs, L. L. (2006). BI case study: Power company draws new energy. Bi Solution Third, 11 (3).
Retrieved from
http://www.tdwi.org/Publications/BIJournal/display.aspx?ID=8119m_page=1.
Buchanan, L. and O'Connell, A. (2006). A brief history of decision making. Harvard Business
Review, 84 (1), 32-40.
Burns, T., and Stalker, G. M. (1961). The Management of Innovation. London: Tavistock.
Burton, B., Geishecker, L., Schelegel, K., Hostmann, B., Austin, T., Herschel, G., Soejarto, A., and
Rayner, N. (2006). Business intelligence focus shifts from tactical to strategic. Retrieved
from Gartner database.
Burton, B. and Hostmann, B. (2005). Findings from Sydney symposium: Perceptions of business
intelligence. Retrieved from Gartner database.
Busemeyer, J. R. and Jones, L. E. (1983). Analysis of multiplicative combination rules when the
causal variables are measured with error. Psychological Bulletin, 93, 549-563.
151
Busenitz, L. W. (1999). Entrepreneurial risk and strategic decision making. The Journal of
Applied Behavioral Science, 35 (3), 325-340.
Chin, W.W. (1998). Issues and opinions on structural equation modeling. MIS Quarterly, 22 (1),
7-16.
Chin, W. W. (2004). Frequently asked questions – partial least squares & PLS graph [Web log
post]. Retrieved from: http://disc-nt.cba.uh.edu/chin/plsfaq/multigroup.htm.
Chung W., Zhang, Y., Huang, Z., Wang, G., Ong, T. and Chen H. (2004). Internet searching and
browsing in a multilingual world: An experiment on the Chinese business intelligence
portal (CbizPort). Journal of the American Society for Information Science and
Technology, 55 (9), 818-831.
Chung, W., Chen, H., and Nunamaker, J. F., Jr. (2005). A visual framework for knowledge
discovery on the web: An empirical study of business intelligence exploration. Journal of
Management Information Systems 21 (4), 57-84.
Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs.
Journal of Marketing Research, 16, 64-73.
Clark T. D., Jones, M. C., and Armstrong, C.P. (2007). The dynamic structure of management
support systems: Theory development, research focus and direction. MIS Quarterly, 31
(3), 579-615.
Cody, W. F., Kreulen, J. T., Krishna, V. and Spangler. W. S. (2002). The integration of business
intelligence and knowledge management. IBM Systems Journal, 41 (4), 697-715.
Cohen, J. (1998). Statistical power analysis for the behavioral sciences (2
nd
ed). Hillsdale, NJ:
Erlbaum.
152
Cooper, B. L., Watson, H. J., Wixom, B. H., and Goodhue, D. L. (2000). Data warehousing
supports corporate strategy at first American corporation. MIS Quarterly, 24 (4), 547-
567.
Cooper, R. B. and Wolfe, R. A. (2005). Information processing model of information technology
adaptation: An intra-organizational diffusion perspective. Database for advances in
information systems, 36 (1), 30-48.
Cooper, R. B. and Zmud, R. W. (1990). Information technology implementation research: A
technological diffusion approach. Management Science, 36 (2), 123-141.
Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16,
297-334.
Cudeck R. (2001). Cronbach's Alpha on two-item scales. Journal of Consumer Psychology, 10
(1/2), 55-69.
Daft, R. L. and Lengel, R. H. (1986). Organizational information requirements, media richness
and structural design. Management Science, 32 (5), 554-571.
Daft, R. L. and Machintosh, N. B. (1981). A tentative exploration into the amount and
equivocality of information processing in organizational work units. Administrative
Science Quarterly, 26 (2), 207-224.
Damianakis, S. (2008, June). The ins and outs of imperfect data. DM Direct. Retrieved from
http://www.dmreview.com/dmdirect/2008_77/10001491-1.html?portal=data_quality.
Davenport, T. H. and Harris, J. G. (2007). Competing on analytics: The new science of winning.
Boston, MA: Harvard Business School Press.
153
Davison, L. (2001). Measuring competitive intelligence effectiveness: Insights from the
advertising industry. Competitive Intelligence Review, 12 (4), 25-38.
DeLone, W.H. and McLean, E.R. (1992) “Information system success: The quest for the
dependent variable. Information Systems Research, 3 (1), 60–95.
Delone, W.H. and McLean, E.R. (2003). The DeLone and McLean model of information system
success: A ten-year update. Journal of Management Information Systems, 19 (4), 9–30.
Dennis, A. R., Wixom, B. H., and Vandenberg, R. J. (2001). Understanding fit and appropriation
effects in group support systems via meta-analysis. MIS Quarterly, 25 (2), 167-193.
Dillman, D.A. (2000). Mail and internet surveys: The tailored design. New York: John Wiley and
Sons.
Doll, W. J. and Torkzadeh, G (1988). The measurement of end-user computing satisfaction. MIS
Quarterly, 12 (2), 259-274.
Douglas, B. S. (1998). Information processing theory: implications for health care organizations.
International Journal of Technology Management, 15 (3-5), 211-223.
Dreyer, L. (2006, May). The “right time” for operational business intelligence? TDWI What
Works. Retrieved from
http://www.tdwi.org/Publications/WhatWorks/display.aspx?id=7976.
Duncan, R. B. (1972). Characteristics of organizational environment and perceived
environmental uncertainty. Administrative Science Quarterly, 17, 313-327.
Eckerson, W. (2003). Smart companies in the 21st century: The secrets of creating successful
business intelligence solutions. TDWI The Data Warehousing Institute Report Series, 1-
35. Retrieved from http://www.tdwi.org.
154
Eckerson, W. W. (2004). Gauge your data warehouse maturity. DM Review, 51, 34–37.
Retrieved from http://www.tdwi.org/publications/display.aspx?ID=7199.
Eckerson, W. W. (2006). Performance dashboards: Measuring, monitoring, and managing your
business. Hoboken, NJ: Wiley & Sons.
Eisenhardt, K. M. (1989). Agency theory: An assessment and review. The Academy of
Management Review, 14 (1), 57-76.
Erdfelder, E., Faul, F., and Buchner, A. (1996). GPOWER: A general power analysis program.
Behavior Research Methods, Instruments, & Computers, 28, 1-11.
Erol, O., Sauser, B. J., and Boardman, J. T. (2009). Creating enterprise flexibility through service
oriented architecture. Global Journal of Flexible Systems Management, 10 (1), 11-16.
Evans, J. S. (1991). Strategic flexibility for high technology manoeuvres: A conceptual
framework. Journal of Management Studies, 28 (1), 69-89.
Evelson, B., McNabb, K., Karel, R., and Barnett, J. (2007). It's time to reinvent your BI strategy.
Retrieved from Forrester database.
Fairbank, J.F., Labianca, G., Steensma, H.K. and Metters, R.D. (2006). Information processing
design choices, strategy and risk management performance. Journal of Management
Information Systems, 23, 293-319.
Feeney, D. and Willcocks, L. (1998). Core IS capabilities for exploiting information technology.
Sloan Management Review, 39 (3), 9–21.
Fink, L. and Neumann, S. (2007). Gaining agility through IT personnel capabilities: The mediating
role of IT infrastructure capabilities. Journal of the Association for Information Systems,
8 (8), 440-458.
155
Finlay, P. N. and Forghani, M. (1998). A classification of success factors for decision support
systems. The Journal of Strategic Information Systems, 7 (1), 53-70.
Forgionne, G. A. and Kohli, R. (2000). Management support system effectiveness: further
empirical evidence. Journal of the Association of Information Systems 1 (3), 1-37.
Fryman, H. (2007). Taking a user-centric approach to the information challenge. Business
Intelligence Journal, 12 (2). Retrieved from
http://www.tdwi.org/Publications/BIJournal/display.aspx?ID=8475.
Galbraith, J. (1977). Organizational design. Reading, MA: Addison-Wesley.
Gallegos, F. (1999). Decision support systems: An overview. Information Strategy, 15 (2), 42-47.
Garg, A.K., Joubert, R.J.O., and Pelisser, R. (2005). Information systems environmental
alignment and business performance: A case study. South African Journal of Business
Management, 36 (4), 33-53.
Gattiker, T. F. and Goodhue, D. L. (2004). Understanding the local-level costs and benefits of
ERP through organizational information processing theory. Information & Management,
41 (4), 431.
Gebauer, J. and Schober, F. (2006). Information system flexibility and the cost efficiency of
business processes. Journal of the Association for Information Systems, 7 (3), 122-145.
Gefen, D., Straub, D. W., and Boudreau, M. C. (2000). Structural equation modeling and
regression: Guidelines for research practice. Communications of the Association for
Information Systems, 4 (7), 1-70.
Gefen, D., and Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial
and annotated example. Communications of the AIS, 16, 91-109.
156
Gelderman, M. (2002). Task difficulty, task variability and satisfaction with management
support systems. Information & Management, 39 (7), 593-604.
Gerbing, D. W., and Anderson, J. C. (1988). An updated paradigm for scale development
incorporating unidimensionality and its assessment. Journal of Marketing Research, 25
(2), 186-192.
Gessner, G.H., and Volonino, L. (2005). Quick response improves on business intelligence
investments. Information Systems Management, 22 (3), 66-74.
Gile, K., Kirby, J. P., Karel, R., Teubner, C., Driver, E. and Murphy, B. (2006). Topic overview:
business intelligence. Retrieved from Forrester database.
Gonzales, M. L. (2005, August). What’s your BI environment IQ? DM Review Magazine.
Retrieved from http://www.dmreview.com/issues/20050801/1033572-1.html.
Goodhue, L, D., Wybo, D, M. and Kirsch, J. L. (1992). The impact of data integration on the costs
and benefits of information systems. MIS Quarterly, 16 (3), 293.
Dale Goodhue, D., Lewis, W., and Thompson, R. (2007). Statistical power in analyzing
interaction effects: Questioning the advantage of PLS with product indicators.
Information Systems Research, 18 (2), 211-227.
Gorry, G. A., Scott Morton, M. S. (1971). A framework for management information systems.
Sloan Management Review, 13 (1), 55-72.
Graham, P. (2008, August). Data quality: You don’t just need a dashboard! Strategy execution.
DM Review Magazine. Retrieved from
http://www.dmreview.com/issues/2007_50/10001727-1.html?portal=data_quality.
157
Guimaraes, T., Igbaria, M. and Lu, M.T. (1992). The determinants of DSS success: An integrated
model. Decision Sciences, 23 (2), 409-432.
Hair, J.F, Anderson, R.L., Tatham, R., and Black, W. (1998). Multivariate data analysis (5
th
ed).
New York: Prentice Hall.
Hannula, M. and Pirttimaki V. (2003). Business intelligence empirical study on the top 50
Finnish companies. Journal of American Academy of Business, 2 (2), 593-599.
Harding, W. (2003). BI crucial to making the right decision. Financial Executive, 19 (2), 49-50.
Hartono, E., Santhanam, R. and Holsapple, C.W. (2007). Factors that contribute to management
support system success: An analysis of field studies. Decision Support Systems, 43 (1),
256-268.
Havenstein, H. (2006, October). QlikTech looks to broaden access to BI data. ComputerWorld,
Retrieved from
http://www.computerworld.com/action/article.do?command=viewArticleBasic&articleI
d=9004369.
Henderson, J. C, and Venkatraman, N. (1993). Strategic alignment: Leveraging information
technology for transforming organizations. IBM Systems Journal, 32 (1), 4-19.
Herring, J. (1996). Measuring the Value of Competitive Intelligence: Accessing &
Communicating CI’s Value to Your Organization. Alexandria, VA: SCIP Monograph Series.
Hong, K and Kim, Y. (2002). The critical success factors for ERP implementation: An
organizational fit perspective. Information and Management, 40, 25-40.
Hostmann, B.,Herschel, G. and Rayner, N. (2007). The evolution of business intelligence: The
four worlds. Retrieved from Gartner database.
158
Howson, C. (2004, 2
nd
quarter). Ten mistakes to avoid when selecting and deploying BI tools.
TDWI Quarterly Ten Mistakes to Avoid Series. Retrieved from http://www.bi-
bestpractices.com/view-articles/4741.
Howson, C. (2006, September). The seven pillars of BI success. Intelligent Enterprise. Retrieved
from http://www.intelligententerprise.com/showArticle.jhtml?articleID=191902420.
Howson, C. (2008). Successful business intelligence: Secrets to making BI a killer app. New York,
NY: McGraw-Hill.
Huck, S. W. (2004). Reading statistics and research, (4
th
ed). Boston, MA: Pearson Education.
Hung, S.Y., Ku, Y. C., Liang, T. P. and Lee, C. J. (2007). Regret avoidance as a measure of DSS
success: An exploratory study. Decision Support Systems, 42 (4), 2093-2106.
Imhoff, C. (2005, August). Risky business! Using business intelligence to mitigate operational
risk. DM Review Magazine. Retrieved from
http://www.dmreview.com/issues/20050801/1033577-1.html.
Jain, H., Vitharana, P. and Zahedi, F. (2003). An assessment model for requirements
identification in component-based software development. Database for Advances in
Information Systems, 34 (4), 48-63.
Jarvenpaa, L. S. and Ives, B. (1993). Organizing for global competition: The fit of information
technology. Decision Sciences, 24 (3), 547-580.
Jourdan, Z., Rainer, R. K. and Marshall, T. E. (2008). Business intelligence: An analysis of the
literature. Information Systems Management, 25 (2), 121–131.
Kanuk, L. and Berenson, C. (1975). Mail surveys and response rates: A literature review. Journal
of Marketing Research, 12 (4), 440-453.
159
Karahanna, E., Straub, D.W., and Chervany, N.L. (1999). Information technology adoption across
time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS
Quarterly, 23 (2), 183-213.
Karimi, J., Somers, T.M. and Gupta, Y. P. (2004). Impact of environmental uncertainty and task
characteristics on user satisfaction with data. Information Systems Research, 15 (2), 175-
195.
Kearns, G. S. and Lederer, A. L. (2003). A resource-based view of strategic IT alignment: How
knowledge sharing creates competitive advantage. Decision Sciences, 34 (1), 1-29.
Keen, P. G. W. and Scott Morton, M. S. (1978). Decision support systems: An organizational
perspective. Reading, MA: Addison-Wesley.
Keith, T. Z. (2006). Multiple regression and beyond. Boston, MA: Allyn & Bacon.
Keller, R. T. (1994). Technology-information processing fit and the performance of R&D project
groups: A test of contingency theory. Academy of Management Journal, 37 (1), 167-179.
Kerlinger, F.N. and Lee, H.B. (2000). Foundations of behavioral research (4
th
ed). New York:
Thomson Learning.
Kirs, P. J., Sanders, G. L., Cerveny, R. P., and Robey, D. (1989). Experimental validation of the
Gorry and Scott Morton framework. MIS Quarterly, 13 (2), 183-197.
Klein, G., Jiang, J. J. and Balloun, J. (1997). Information system evaluation by system typology.
Journal of Systems and Software, 37 (3), 181-186.
Kulkarni, U. R., Ravindran, S. and Freeze, R. (2006). A knowledge management success model:
Theoretical development and empirical validation. Journal of Management Information
Systems, 23 (3), 309-347.
160
Kviz, F. J. (1977). Toward a standard definition of response rate. The Public Opinion Quarterly,
41 (2), 265-267.
Lakshminarayan, K., Harp, S.A., Goldman, R. and Samad, T. (1996). Imputation of missing data
using machine learning techniques. Proceedings of the Second International Conference
on Knowledge Discovery and Data Mining. Portland, OR. Retrieved from
http://www.aaai.org/Papers/KDD/1996/KDD96-023.pdf.
Lewis, G. J. (2004). Uncertainty and equivocality in the commercial and natural environments:
The implications for organizational design. Corporate Social Responsibility and
Environmental Management, 11 (3), 167-177.
Loftis, L. (2008, November). Getting data in, getting information out. DM Review Magazine,
2008. Retrieved from http://www.dmreview.com/issues/2007_53/10002146-1.html.
Lonnqvist, A., and Pirttimaki, V. (2006). The measurement of business intelligence. Business
Intelligence, 23 (1), 32-40.
Lovelace, K. and Rosen, B. (1996). Differences in achieving person-organization fit among
diverse groups of managers. Journal of Management, 22 (5), 703-722.
Malone, R. (2005). Data warehousing: Information under control. Forbes. Retrieved from
http://www.forbes.com/logistics/2005/12/23/cardinal-data-warehouse-
cx_rm_1222cardinal.html.
Mangione, T. W. (1995). Mail surveys: Improving the quality. Thousand Oaks, CA: Sage
Publications.
161
Manglik, A. (2006). Increasing BI adoption: An enterprise approach. Business Intelligence
Journal, 11 (2). Retrieved from
http://www.tdwi.org/Publications/BIJournal/display.aspx?ID=8038.
Manglik, A. and Mehra, V. (2005). Extending enterprise BI capabilities: New patterns for data
integration. Business Intelligence Journal, 10 (1). Retrieved from:
http://www.tdwi.org/research/display.aspx?ID=7486.
Marcoulides, G. A. and Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30 (2), iii-x.
Martinich, L. (2002). Managing innovations, standards and organizational capabilities. IEEE
International Engineering Management Conference, 1, 58- 63.
McMurchy, N. (2008). Take these steps to develop successful BI business cases. Retrieved from
Gartner database.
McKnight, W. (2004, April). Business intelligence return on investment issues. DM Review, 62.
Retrieved from: www.dmreview.com.
Miller, D. (2007, October). Measuring BI success: business goals and business requirements. DM
Review. Retrieved from: http://www.dmreview.com/news/10000100-1.html.
Millet, I. and Gogan, J. L. (2006). A dialectical framework for problem structuring and
information technology. The Journal of the Operational Research Society, 57 (4), 434-
442.
Moss, L. T. and Atre, S. (2003). Business intelligence roadmap: The complete project lifecycle for
decision-support applications. Boston, MA: Addison-Wesley.
Munro, M. C. and Davis, G. B. (1977). Determining management information needs: A
comparison of methods. MIS Quarterly, 1 (2), 55-67.
162
Negash, S. (2004). Business intelligence. Communications of the Association for Information
Systems, 13, 177-195.
Nelson, R. R., Todd, P. A., and Wixom, B. H. (2005). Antecedents of information and system
quality: Within the context of data warehousing. Journal of Management Information
Systems, 21 (4), 199-235.
Neumann, S. (1994). Strategic information systems: Competition through information
technologies. New York, NY: Macmillan College Publishing.
Nunnally, J.C. and Bernstein, I.H. (1998). Psychometric theory, New York: McGraw-Hill.
O'Leary-Kelly, S.W. and Vokurka, R.J. (1998). The empirical assessment of construct validity.
Journal of Operations Management, 16 (4), 387-405.
Olszak, C. M. and Ziemba, E. (2003). Business intelligence as a key to management of an
enterprise. Proceedings of Informing Science And IT Education. Santa Rosa, CA.
Retrieved from
http://proceedings.informingscience.org/IS2003Proceedings/docs/109Olsza.pdf.
Oltra, V. (2005). Knowledge management effectiveness factors: The role of HRM. Journal of
Knowledge Management, 9 (4), 70-86.
Parikh, A. A. and Haddad, J. (2008, October). Right-Time information for the real-time
enterprise timely information drives business. DM Direct. Retrieved from
http://www.dmreview.com/dmdirect/2008_92/10002003-1.html?portal=data_quality.
Pirttimaki, V., Lonnqvist, A., and Karjaluoto, A. (2006). Measurement of business intelligence in
a Finnish telecommunications company. Electronic Journal of Knowledge Management,
4 (1), 83-90.
163
Power, D. J. (2002). Decision support systems: Concepts and resources for managers. Westport,
CT: Quorum Books.
Power, D. J. (2003, May). A brief history of decision support systems [Web log post]. Retrieved
from http://dssresources.com/history/dsshistory.html.
Premkumar, G., Ramamurthy, K., and Saunders, C. S. (2005). Information processing view of
organizations: An exploratory examination of fit in the context of interorganizational
relationships. Journal of Management Information Systems, 22 (1), 257-294.
Rai, A., Lang, S. S., and Welker, R. B. (2002). Assessing the validity of IS success models: An
empirical test and theoretical analysis. Information Systems Research, 13 (1), 50-69.
Ray, G., Muharma W. A., and Barney J. B. (2005). Information technology and the performance
of the customer service process: A resource-based analysis. MIS Quarterly 29 (4), 625-
652.
Raymond L. (2003). Globalization, the knowledge economy, and competitiveness: A business
intelligence framework for the development SMES. Journal of American Academy of
Business, 3 (1/2), 260-269.
Ross, J. W., Beath C. M., and Goodhue D. L. (1996). Develop long-term competitiveness through
IT assets. Sloan Management Review, 38 (1), 31-44.
Rouibah, K., and Ould-ali, S. (2002). Puzzle: A concept and prototype for linking business
intelligence to business strategy. Journal of Strategic Information Systems, 11 (2), 133-
152.
Rud, O. P. (2009). Business intelligence success factors: Tools for aligning your business in the
global economy. Hoboken, NJ: John Wiley and Sons.
164
Ryan, A. M. and Schmit, M. J. (1996). An assessment of organizational climate and P-E fit: A tool
for organizational change. International Journal of Organizational Analysis, 4 (1), 75-95.
Ryan, S.D., Harrison, D.W., and Schkade, L.L. (2002). Information-technology investment
decisions: When do costs and benefits in the social subsystem matter?” Journal of
Management Information Systems, 19 (2), 85-127.
Saaty, T. L. and Kearns, K. P. (1985). Analytical planning: The organization of systems. Oxford:
Pergamon Press.
Sabherwal, R. and Kirs, P. (1994). The alignment between organizational critical success factors
and information technology capability in academic institutions. Decision Sciences, 25 (2),
301-331.
Sabherwal, R. (2007). Succeeding with business intelligence: Some insights and
recommendations. Cutter Benchmark Review, 7 (9), 5-15.
Sabherwal, R. (2008). KM and BI: From mutual isolation to complementarity and synergy. Cutter
Consortium Executive Report, 8 (8), 1-18.
Sabherwal, R. and Becerra-Fernandez, I. (2010). Business intelligence: Practices, technologies,
and management. Hoboken, NJ: John Wiley & Sons.
Sambamurthy, V., and Zmud, R. W. (1992). Managing IT for success: The empowering business
partnership. Morristown, NJ: Financial Executives Research Foundation.
Sanders, G. L. and Courtney, J. F. (1985). A field study of organizational factors influencing DSS
success. MIS Quarterly, 9 (1), 77-95.
Sanders, N. R., Premus, R. (2005). Modeling the relationship between firm IT capability,
collaboration, and performance. Journal of Business Logistics, 26 (1), 1-25.
165
Sauer, C. and Willcocks, L. (2003). Establishing the business of the future: The role of
organizational architecture and information technologies. European Management
Journal, 21 (4), 497–508.
Sawka, K. (2000). Are we valuable? Competitive Intelligence Magazine, 3 (2). Retrieved from
http://www.scip.org/Publications/CIMArticleDetail.cfm?ItemNumber=1191.
Schuman, H., and Pressor, S. (1981). Questions and answers in attitude survey. New York, NY:
Academic Press.
Schwab, D. P. (1980). Construct validity in organizational behavior. In L. L. Cummings & B. M.
Staw (Eds.), Research in Organizational Behavior (pp. 3-43). Greenwich, CT: JAI Press.
Scott Morton, M. S. (1984). The state of the art of research. In F. W. McFarlan (Ed.), The
Information Research Challenge (pp. 13-41). Boston, MA: Harvard University Press.
Seeley, C.P. and Davenport, T.H. (2006). KM meets business intelligence. Knowledge
Management Review, 8 (6), 10-15.
Setia, P., Sambamurthy, V., and Closs, D. J. (2008). Realizing business value of agile IT
applications: Antecedents in the supply chain networks. Information Technology and
Management, 9 (1), 5-19.
Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R. and Carlsson, C. (2002). Past,
present, and future of decision support technology. Decision Support Systems, 33 (2),
111–126.
Silver, M. S. (1991). Systems that support decision makers: Description and analysis. Chichester,
United Kingdom: Wiley & Sons.
Simon, H. A. (1960). The new science of management decision. New York: Harper and Row.
166
Soelberg, P. O. (1967). Unprogrammed decision making. Industrial Management Review, 8 (2),
19-29.
Solomon, M.D. (2005). Ensuring a successful data warehouse initiative. Information Systems
Management, 22 (1), 26-36.
Sommer, D. (2008). Report highlight for market trends: Business intelligence, worldwide, 2008.
Retrieved from Gartner database.
Srinivasan, A. (1985). Alternative measures of system effectiveness: Associations and
implications. MIS Quarterly, 9 (3), 243-253.
Srivastava, J. and Cooley, R. (2003). Web business intelligence: Mining the web for actionable
knowledge. INFORMS Journal on Computing, 15 (2), 191-207.
Stock, G. N. and Tatikonda, M. V. (2008). The joint influence of technology uncertainty and
interorganizational interaction on external technology integration success. Journal of
Operations Management, 26 (1), 65-80.
Swafford, P. M., Ghosh, S. and Murthy, N. (2008). Achieving supply chain agility through IT
integration and flexibility. International Journal of Production Economics, 116 (2), 288-
297.
Swoyer, S. (2008, September 24). Lyza empowers new class of BI consumers. TDWI. Retrieved
from http://www.tdwi.org/News/display.aspx?id=9129.
Tatikonda, M. V. and Rosenthal, S. R. (2000). Technology novelty, project complexity, and
product development project execution success: A deeper look at task uncertainty in
product innovation. IEEE Transactions on Engineering Management, 47 (1), 74-87.
167
Tatikonda, M. V. and Montoya-Weiss, M. M. (2001). Integrating operations and marketing
perspectives of product innovation: The influence of organizational process factors and
capabilities on development performance. Management Science, 47 (1), 151-172.
Teo, T. S. H. and King, W. R. (1997). Integration between business planning and information
systems planning: An evolutionary-contingency perspective. Journal of Management
Information Systems, 14 (1), 185-216.
Tsai, C.H. and Chen, H. Y. (2007). Assessing knowledge management system success: An
empirical study in Taiwan's high-tech industry. Journal of American Academy of
Business, 10 (2), 257-264.
Tuggle, F. D, and Gerwin, D. (1980). An information processing model of organizational
perception, strategy and choice. Management Science, 26 (6), 575-592.
Tushman, M. L. and Nadler, D. A. (1978). Information processing as an integrating concept in
organizational design. The Academy of Management Review, 3 (3), 613-624.
Vitt, E., Luckevich, M, and Misner, S. (2002). Business intelligence: Making better decisions
faster, Redmond, WA: Microsoft Corporation.
Wang, E.T.G. (2003). Effect of the fit between information processing requirements and
capacity on organizational performance. International Journal of Information
Management, 23 (3), 239-247.
Watson, H. J. (2008). Why some firms’ BI efforts lag. Business Intelligence Journal, 13 (3), 4-7.
Watson, H. J. (2005). Are data warehouses prone to failure? Business Intelligence Journal, 10
(4), 4-7.
168
Watson, H. J., Annino, D. A., Wixom, B. H., Avery, K. L., and Rutherford, M. (2001). Current
practices in data warehousing. Information Systems Management, 18 (1), 47-55.
Watson, H.J. and Donkin, D. (2005). Editorial preface: Outstanding BI and data warehousing
practice exists around the world: The Absa Bank in South Africa. Journal of Global
Information Technology Management, 8 (4), 1-6.
Watson, H. J., Goodhue, D. L., and Wixom, B. H. (2002). The benefits of data warehousing: Why
some organizations realize exceptional payoffs. Information and Management, 39 (6),
491-502.
Watson, H.J., Abraham, D.L., Chen, D., Preston, D., and Thomas, D. (2004). Data warehousing
ROI: Justifying and assessing a data warehouse. Business Intelligence Journal, 9 (2), 6-17.
Watson, H. J., Fuller, C., and Ariyachandra, T. (2004). Data warehouse governance: Best
practices at Blue Cross and Blue Shield of North Carolina. Decision Support Systems, 38
(3), 435-450.
Watson, H.J., Wixom, B.H., Hoffer, J.A., Anderson-Lehman, R., and Reynolds, A. M. (2006). Real-
time business intelligence: Best practices in Continental Airlines. Business Intelligence,
23 (1), 7-18.
Watson, H. J., and Wixom, B. H. (2007). The current state of business intelligence. Computer, 40
(9), 96-99.
Watson, H. J. and Wixom, B. H. (2007). Enterprise agility and mature BI capabilities. Business
Intelligence Journal, 12 (3), 13-28.
Weier, M.H. (2007). QUERY: What's next in BI? Information Week, 1128, 27-29.
169
Weill, P., M. Subramani, and Broadbent, M. (2002). Building IT infrastructure for strategic
agility. MIT Sloan Management Review, 44 (1), 57-65.
Wells, D. (2003, April). Ten best practices in business intelligence and data warehousing. TDWI
FlashPoint. Retrieved from
https://www.tdwi.org/Publications/display.aspx?id=6638&t=y.
White, C. (2005, May). The next generation of business intelligence: Operational BI. Information
Management Magazine. Retrieved from http://www.dmreview.com.
White, C. (2004, September). Now is the right time for real-time BI. Information Management
Magazine. Retrieved from http://www.dmreview.com.
Williams, S. and Williams, N. (2007). The profit impact of business intelligence, San Francisco,
CA: Morgan Kaufmann.
Wixom, H. and Watson, H. J. (2001). An empirical investigation of the factors affecting data
warehousing success. The Journal of Business Strategy, 25 (1), 17-41.
Wu, J. H. and Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and
McLean's model. Information & Management, 43 (6), 728-739.
Wunsch, D. (1986). Survey research: Determining sample size and representative response.
Business Education Forum, 40 (5), 31-34.
Yoon, Y., Guimaraes, T. and O’Neal, Q. (1995). Exploring the factors associating with expert
systems success. MIS Quarterly, 19 (1), 83-106.
Zack, M. H. (2007). The role of decision support systems in an indeterminate world. Decision
Support Systems, 43 (4), 1664-1674.
170
Zaltman, G., Duncan, R. and Holbek, J. (1973). Innovation and organizations. New York: John
Wiley and Sons.
Zhang, M. and Tansuhaj, P. (2007). Organizational culture, information technology capability,
and performance: The case of born global firms. Multinational Business Review, 15 (3),
43-77.
doc_542063161.pdf
Business Intelligence Success An Empirical Evaluation Of The Role Of BI
APPROVED:
Mary C. Jones, Major Professor and Chair of
the Department of Information
Technology and Decision Sciences
Audesh Paswan, Minor Professor
Anna Sidorova, Committee Member
Nicholas Evangelopoulos, Committee
Member
Andy Wu, Committee Member
O. Finley Graves, Dean of the College of
Business
James D. Meernik, Acting Dean of the Robert
B. Toulouse School of Graduate
Studies
BUSINESS INTELLIGENCE SUCCESS: AN EMPIRICAL EVALUATION OF THE ROLE OF BI
CAPABILITIES AND THE DECISION ENVIRONMENT
Öykü I??k, B.S., M.B.A.
Dissertation Prepared for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
August 2010
I??k, Öykü. Business Intelligence Success: An Empirical Evaluation of the Role of BI
Capabilities and the Decision Environment. Doctor of Philosophy (Business Computer
Information Systems), August 2010, 170 pp., 54 tables, 6 figures, references, 220 titles.
Since the concept of business intelligence (BI) was introduced in the late 1980s, many
organizations have implemented BI to improve performance but not all BI initiatives have been
successful. Practitioners and academicians have discussed the reasons for success and failure,
yet, a consistent picture about how to achieve BI success has not yet emerged.
The purpose of this dissertation is to help fill the gap in research and provide a better
understanding of BI success by examining the impact of BI capabilities on BI success, in the
presence of different decision environments. The decision environment is a composition of the
decision types and the way the required information is processed to aid in decision making. BI
capabilities are defined as critical functionalities that help an organization improve its
performance, and they are examined in terms of organizational and technological capabilities.
An online survey is used to obtain the data and partial least squares path modeling (PLS)
is used for analysis. The results of this dissertation suggest that all technological capabilities as
well as one of the organizational capabilities, flexibility, significantly impact BI success. Results
also indicate that the moderating effect of decision environment is significant for quantitative
data quality. These findings provide richer insight in the role of the decision environment in BI
success and a framework with which future research on the relationship between BI capabilities
and BI success can be conducted. Findings may also contribute to practice by presenting
information for managers and users of BI to consider about their decision environment in
assessing BI success.
ii
Copyright 2010
by
Öykü I??k
iii
ACKNOWLEDGEMENTS
I would like to thank my dissertation chair, Dr. Mary Jones, for her support and patience.
Without her feedback and advice, I would not be able to complete my dissertation on a timely
fashion. I would like to express my gratitude to the members of my committee, Dr. Sidorova,
Dr. Wu, Dr. Paswan and Dr. Evangelopoulos, for their support and valuable comments towards
improving my dissertation. I also would like to thank the Department of Information
Technology and Decision Sciences for funding my dissertation.
My thanks and gratitude also goes to my family. My mom, although thousands of miles
away, has been even more anxious than me and has supported me in every step of the
program. I am also forever thankful to my two wonderful aunts and the greatest grandma of all
times for always inspiring me to reach further. Their unconditional love and prayers helped me
through the difficult times, and I am glad that I could make them proud by being the first to
pursue a Ph.D. in the family. Last but not least, I would like to thank my husband, Baris Isik, who
has left his career behind just to support me during my Ph.D. journey. He has been extremely
understanding and supportive, and without him, I would not be where I am right now. I would
like to dedicate my dissertation to him.
iv
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS .............................................................................................................. iii
LIST OF TABLES ........................................................................................................................... vi
LIST OF FIGURES ......................................................................................................................... ix
Chapters
1. INTRODUCTION .................................................................................................... 1
2. LITERATURE REVIEW ............................................................................................ 9
BI Success ............................................................................................... 14
Measuring BI Success .................................................................. 20
Relationship between BI Capabilities and the Decision Environment ...... 22
Decision Environment ................................................................. 23
Organizational Information Processing Theory ............................ 25
Decision Types ........................................................................................ 30
BI Capabilities ......................................................................................... 35
Data Sources ............................................................................... 38
Data Types .................................................................................. 39
Interaction with Other Systems ................................................... 39
User Access ................................................................................. 40
Data Reliability ............................................................................ 41
Risk Level .................................................................................... 42
Flexibility..................................................................................... 43
Intuition Involved in Analysis ...................................................... 44
Research Model and Hypotheses ............................................................ 46
3. METHODOLOGY ................................................................................................. 60
Research Population and Sample ............................................................ 60
Research Design ..................................................................................... 61
Instrument Design and Development ..................................................... 62
v
BI Success ................................................................................... 63
BI Capabilities ............................................................................. 64
Decision Environment ................................................................. 65
Survey Administration ............................................................................ 65
Reliability and Validity Issues .................................................................. 67
Data Analysis Procedures ....................................................................... 69
4. DATA ANALYSIS AND RESULTS ............................................................................ 73
Response Rate and Non-Response Bias .................................................. 73
Treatment of Missing Data and Outliers ................................................. 82
Demographics ........................................................................................ 83
Exploratory Factor Analysis and Internal Consistency ............................. 87
PLS Analysis and Assessment of Validity ............................................... 105
Hypotheses Testing Results .................................................................. 109
Hypothesis 1 and Hypothesis 2 ................................................. 109
Hypothesis 3 and Hypothesis 4 ................................................. 111
5. DISCUSSION AND CONCLUSIONS ...................................................................... 122
Discussion of Research Findings............................................................ 122
Technological BI Capabilities and BI Success.............................. 122
Organizational BI Capabilities and BI Success ............................ 124
Technological BI Capabilities and the Decision Environment ..... 126
Organizational BI Capabilities and the Decision Environment .... 127
Limitations............................................................................................ 128
Research Contributions ........................................................................ 130
Conclusion and Future Research Directions .......................................... 134
Appendices
A. COVER LETTER.................................................................................................. 137
B. SURVEY INSTRUMENT ...................................................................................... 139
REFERENCES ............................................................................................................................ 147
vi
LIST OF TABLES
Page
1. Selected BI Definitions ................................................................................................... 10
2. Concepts Examined in Research about BI Success .......................................................... 17
3. Examples of Organizational Information Processing Theory in Information Systems
Research ........................................................................................................................ 28
4. A Framework for Information Systems, Adapted from Gorry and Scott Morton (1971).. 34
5. BI Capabilities and Their Levels Associated with the Four BI Worlds, Adapted from
Hostmann et al. (2007) .................................................................................................. 46
6. Research Variables Used in Prior Research .................................................................... 66
7. Hypotheses and Statistical Tests .................................................................................... 70
8a. Independent Samples t-Tests for Non-response Bias ..................................................... 75
8b. Independent Samples t-Tests for Non-response Bias - Demographics ............................ 76
9a Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Main Data Set ......... 77
9b Independent Samples t-Tests for Response Bias on Demographics: Pilot Data Set vs.
Main Data Set ................................................................................................................ 78
10a Independent Samples t-Test: Pilot Data Set vs. Operational Managers in the Main Data
Set ................................................................................................................................. 79
10b Independent Samples t-Test on Demographics: Pilot Data Set vs. Operational Managers
in the Main Data Set ...................................................................................................... 80
11a Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Non-Operational
Managers in the Main Data Set...................................................................................... 81
11b Independent Samples t-Test on Demographics: Pilot Data Set vs. Non-Operational
Managers in the Main Data Set...................................................................................... 82
12. Descriptive Statistics on Organizational Size .................................................................. 84
13. Descriptive Statistics on Annual Organizational Revenue ............................................... 84
14. Descriptive Statistics on Organizational Industry ........................................................... 85
vii
15. Descriptive Statistics on Functional Area ....................................................................... 86
16. Descriptive Statistics on Level in the Organization ......................................................... 86
17. Descriptive Statistics on BI User Levels .......................................................................... 86
18. Factor Analysis for the Independent Variable ................................................................ 88
19. Factor Analysis for the Data Quality ............................................................................... 89
20. Factor Analysis for the Data Source Quality ................................................................... 89
21. Factor Analysis for the User Access Quality .................................................................... 90
22. Factor Analysis for the Data Reliability ........................................................................... 91
23. Factor Analysis for the Interaction with Other Systems.................................................. 91
24a Factor Analysis for Flexibility - I ...................................................................................... 92
24b Factor Analysis for Flexibility - II ..................................................................................... 92
25a Factor Analysis for Intuition - I ....................................................................................... 94
25b Factor Analysis for Intuition - II ...................................................................................... 94
26. Factor Analysis for the Risk Level ................................................................................... 94
27a Factor Analysis for the Organizational BI Capability Variables - I .................................... 96
27b Factor Analysis for the Organizational BI Capability Variables - II ................................... 97
28. Factor Analysis for Risk and Intuition ............................................................................. 98
29. Factor Analysis for the Technological BI Capability Variables ......................................... 99
30. Factor Analysis for the Dependent Variables - External Data Reliability and External Data
Source Quality ............................................................................................................. 100
31a Factor Analysis for the Moderator Variable - I ............................................................. 101
31b Factor Analysis for the Moderator Variable - II ............................................................ 102
31c Factor Analysis for the Moderator Variable - III............................................................ 102
31d Factor Analysis for the Moderator Variable - IV ........................................................... 103
31e Factor Analysis for the Moderator Variable - V ............................................................ 104
viii
31f Correlations for Decision Type Items ........................................................................... 104
32. Item Statistics and Loadings ......................................................................................... 107
33. Inter-Construct Correlations: Consistency and Reliability Tests .................................... 108
34. Hypotheses 1 & 2 ........................................................................................................ 109
35. Path Coefficients, t Values and p Values for BI Capabilities (H1 & H2) .......................... 110
36. Hypothesis 3 ................................................................................................................ 113
37. Multiple Regression Results - H3.................................................................................. 115
38. Descriptive Statistics for the Decision Environment ..................................................... 116
39. Regression Equations for High and Low Values of the Decision Environment ............... 116
40. Hypotheses 4 ............................................................................................................... 118
41. Multiple Regression Results - H4.................................................................................. 119
42. Summary of Hypothesis Testing ................................................................................... 120
ix
LIST OF FIGURES
Page
1. High level overview of the model .................................................................................. 14
2. The four worlds of BI adopted from Hostmann et al. (2007) .......................................... 37
3. Conceptual model ......................................................................................................... 47
4. Research model ............................................................................................................. 59
5. PLS results - H1 and H2 ................................................................................................ 111
6. Interaction effect on the quantitative data quality ...................................................... 117
1
CHAPTER 1
INTRODUCTION
Since the concept of business intelligence (BI) was introduced in the late 1980s by
Howard Dresner, a Gartner Research Group analyst (Power, 2003; Buchanan and O’Connell,
2006), the information systems (IS
1
) field has witnessed the rapid development of systems and
software applications providing support for business decision making. Organizations started
migrating to complete BI environments so that they could have a “single version of the truth”
through the use of cross-organizational data, provided by an integrated architecture (Eckerson,
2003; Negash, 2004). The total investment of organizations in BI tools is estimated to be $50
billion a year and is steadily growing with the introduction of new desktop data analysis tools,
data warehousing technologies, data extraction middleware and many other tools and
techniques into the market by BI vendors (Weier, 2007).
Organizations need these new tools and techniques to improve performance and profits
(Watson et al., 2002; Eckerson, 2003; Williams and Williams, 2007). Organizations need to meet
or exceed the expectations of their customers in order to stay competitive in today’s highly
aggressive business world, and managers are increasingly relying on BI to do so (Clark et al.,
2007). Although many organizations have implemented BI, not all BI initiatives have been
successful. Practitioners and academicians have discussed the reasons for success and failure
extensively (Wixom and Watson, 2001; Watson et al., 2002; Solomon, 2005; Watson et al.,
2006). Unfortunately, a consistent picture about how to achieve success with BI has not yet
1
Research has used IS and IT interchangeably. While IT represents computer hardware, software and
telecommunication technologies, IS implies a broader context that is composed of processes, people and
information. This dissertation uses IS rather than IT.
2
emerged. This suggests that there are gaps in the research to be filled, and that research has
perhaps overlooked one or more key constructs for a BI success model.
Various approaches to examining BI capabilities may be one of the reasons behind the
gaps in the research about BI success. A lack of fit between the organization and its BI
capabilities is one of the reasons for lack of success (Watson et al., 2002; Watson et al., 2006).
Although research has defined the concept of fit differently in several areas of research
(Venkatraman, 1989), for the purposes of this dissertation it is defined as the relationship
between different BI capabilities and BI success, in the presence of different decision
environments. The decision environment is defined as the combination of different types of
decisions made and the information processing needs of the decision maker to make those
decisions (Munro and Davis, 1977).
Although BI capabilities have been studied from organizational (Eckerson, 2003; Watson
and Wixom, 2007) and technological (Manglik and Mehra, 2005; Watson and Wixom, 2007)
perspectives, some organizations still fail to achieve BI success (Jourdan et al., 2008). This may
be because the influence of the decision environment on BI capabilities has remained largely
unexamined. Examining this relationship is, however, appropriate because the primary purpose
of BI is to support decision-making in organizations (Eckerson, 2003; Buchanan and O’Connell,
2006). The purpose of this dissertation is to help fill this gap in research and provide a better
understanding of BI success by examining the impact of BI capabilities on BI success, in the
presence of different decision environments.
There is an extensive amount of research on the success of information technology in
organizations that draws on organizational design theory. Some researchers examine this from
3
an individual perspective (Lovelace and Rosen, 1996; Ryan and Schmit, 1996), while others
investigate the organization as the level of analysis (Premkumar et al., 2005; Setia et al., 2008).
Because the main interest of this dissertation is to examine BI success in light of different
decision environments and BI capabilities, the organization is used as the unit of analysis.
The suitability of BI capabilities and the decision environment includes the match
between organizational structure and the technology (Galbraith, 1977; Alexander and
Randolph, 1985), and the match between information processing needs and information
processing capabilities (Tushman and Nadler, 1978; Premkumar et al., 2005). Organizational
structure and information processing needs are part of the decision environment (Munro and
Davis, 1977; Zack, 2007). Capabilities provided by the BI include both the technology used by
the BI and the information processing capabilities of the BI. Although existing research
improves knowledge about BI, little or no research examines how BI capabilities influence BI
success in light of the decision environment of an organization. Little research examines the
decisions made in the organization as well as the information processing needs of the decision
maker. This dissertation examines this by using a theoretical lens grounded in decision making
and information processing. Specifically, Galbraith’s (1977) organizational information
processing theory and Gorry and Scott Morton’s (1971) decision support framework are used to
examine the decision environment of an organization.
The decision environment of an organization is defined as a composition of the decision
types and the way the required information is accessed and processed to aid in decision making
in that organization (Galbraith, 1977; Beach and Mitchell, 1978; Eisenhardt, 1989). Decisions
are largely distinguished by the type of problem that needs to be solved and who needs to
4
make the decision (Power, 2002). The problem addressed by a decision impacts the decision
making approach. Problems can be classified as programmed or nonprogrammed (Simon,
1960). A decision is programmed if it is repetitive and routine, and it is nonprogrammed when
there is no fixed method of handling it and the decision is consequential (Simon, 1960). In
general, programmed and nonprogrammed decisions are referred to as “structured” and
“unstructured” respectively, because these terms “imply less dependence on the computer
and relate more directly to the basic nature of the problem-solving activity in question” (Keen
and Scott Morton, 1978, p. 86). An example of a structured decision is a sales order or an
airline reservation, whereas choosing a location for a new plant is an example of an
unstructured decision.
In addition to Simon’s (1960) two decision types, Gorry and Scott Morton’s (1971)
framework for information systems includes a third type of decision: semistructured.
Semistructured decisions are decisions that cannot be solved by only autonomous decision
making or only human judgment (Gorry and Scott Morton, 1971). Semistructured decisions
require both. Gorry and Scott Morton’s (1971) framework includes nine categories of decisions
based on the decision type and management activity. Although this model has been applied to
various IS scenarios (Kirs et al., 1989; Ashill and Jobber, 2001; Millet and Gogan, 2005), it has
not been applied to the BI context. It is appropriate to do so, however, because BI is developed
to support decision making (Eckerson, 2003; Buchanan and O’Connell, 2006).
Different decisions need different types of information, depending on the managerial
activities with which they are associated (Gorry and Scott Morton, 1971). Thus, the way
information is processed for decision making purposes is also a part of the decision
5
environment of an organization (Tushman and Nadler, 1978). Galbraith’s (1977) organizational
information processing theory spawned much work on the role of information processing in
organizations. Subsequently, research indicates that the information processing capabilities of
an organization directly impact organizational effectiveness (Tushman and Nadler, 1978; Keller,
1994; Premkumar et al., 2005). Research has also examined the relationship between
technology and information processing capabilities and showed that organizational
performance increases when the technology that suits the organization’s information
processing capabilities is used (Keller, 1994; Premkumar et al., 2005).
BI helps organizations meet their information processing needs by facilitating
organizational information processing capacity (Gallegos, 1999; Nelson et al., 2005). BI does so
by combining data collection, data storage and knowledge management with analytical tools so
that decision makers can convert complex information into effective decisions (Negash, 2004).
BI capabilities within an organization can be divided into two groups; technological (e.g.,, data
sources used and data reliability) and organizational (Feeney and Willcocks, 1998; Bharadwaj et
al., 1999). Organizational capabilities are those that impact the way the BI is used within an
organization (e.g., flexibility and risk-taking level of the organization).
Technology is critical to BI success, although it is not the only driving force (Cooper et
al., 2000; Wixom and Watson, 2001; Clark et al., 2007). Research has extensively examined how
technology impacts BI success (Rouibah and Ould-ali, 2002; Watson et al., 2006). Findings
suggest that having the right technology for supporting decision making can help an
organization increase its decision-making capabilities (Arnott and Pervan, 2005). For example,
6
the appropriateness of the technology employed affects the efficiency and effectiveness of the
data warehouse implementation and usage (Wixom and Watson, 2001).
BI organizational capabilities also impact BI success and include BI flexibility, level of
acceptable risk for the organization, and the level of intuition the decision maker can involve in
the decision making process with BI (Hostmann et al., 2007; Bell, 2007; Loftis, 2008). One of the
reasons why organizations employ BI is the support it provides for decision making (Eckerson,
2003). The strictness of business process rules and regulations in an organization as well as the
level of risk tolerated impacts the way BI supports decision making in an organization
(Hostmann et al., 2007). Research suggests that organizations where employees use hard data
rather than intuition to make decisions are more likely to succeed in BI (Eckerson, 2003). Using
the collected data, BI can provide notifications to users and run predictive analytics to help
users make well informed decisions. Although making decisions based on facts as opposed to
gut feelings has become an approach preferred by many (Watson and Wixom, 2007), decision
makers still use their intuition while making decisions, especially for decisions that are not
straightforward to make (Harding, 2003).
To better support emerging BI user needs and best practices, a coordinated effort across
users, technology, business processes and data is required (Bonde and Kuckuk, 2004). This
endeavor, if successful, can improve the fit between BI and the organization within which it is
implemented. The primary research question that this dissertation addresses is how BI
capabilities influence BI success for different decision environments. BI capabilities include both
technological and organizational capabilities. The decision environment is defined as the
organizational decision types and information processing needs of the organization. The goal of
7
this study is to examine the extent to which these two constructs moderate the impact of BI
capabilities on BI success.
This study is relevant to both researchers and practitioners. This dissertation proposes
to extend current research in BI and provide a parsimonious and intuitive model for explaining
the relationship between BI success and BI capabilities in the presence of different decision
environments, based on theories from decision making and organizational information
processing. This dissertation contributes to academic research by providing richer insight in the
role of the decision environment in BI success and providing a framework with which future
research on the relationship between BI capabilities and BI success can be conducted. The
practitioner oriented contribution of this study is that it helps users and developers of BI
understand how to better align their BI capabilities with their decision environments and
presents information for managers and users of BI to consider about their decision
environment in assessing BI success.
The results of this dissertation suggest that all technological capabilities as well as one
of the organizational capabilities (flexibility) studied in this dissertation significantly impact BI
success. This may indicate that technology drives the BI initiative, rather than the organizational
capabilities. Results also indicate that the moderating effect of decision environment is
significant for quantitative data quality. This means that the quality of quantitative data impact
BI success stronger for operational control activities.
The remainder of the dissertation is organized as follows. Chapter 2 includes a review of
prior research about BI, BI success measures, BI capabilities and the role of the decision
environment. This chapter also presents a conceptual model and the proposed hypotheses.
8
Chapter 3 contains a detailed description of the methodology employed. The chapter also
discusses the sampling frame, the operationalization of constructs, and how validity and
reliability issues are addressed. Chapter 4 presents the detailed analysis process and the results
of the analysis. This dissertation concludes with Chapter 5, which provides a discussion of the
findings, presents the limitations of the study as well as its implications for both managers and
academics, and concludes by providing future research directions.
9
CHAPTER 2
LITERATURE REVIEW
Business intelligence (BI) is the top priority for many organizations and the promises of
BI are rapidly attracting many others (Evelson et al., 2007). Gartner Group’s BI user survey
reports suggest that BI is also a top priority for many chief information officers (CIOs) (Sommer,
2008). More than one-quarter of CIOs surveyed estimated that they will spend at least $1
million on BI and information infrastructure in 2008 (Sommer, 2008). Organizations today
collect enormous amounts of data from numerous sources, and using BI to collect, organize,
and analyze this data can add great value to a business (Gile et al., 2006). BI can also provide
executives with real time data and allow them to make informed decisions to put them ahead
of their competitors (Gile et al., 2006). Although BI matters so much to so many organizations,
there are still inconsistencies in research findings about BI and BI success.
Various definitions of BI have emerged in the academic and practitioner literature.
While some broadly define BI as a holistic and sophisticated approach to cross-organizational
decision support (Moss and Atre, 2003; Alter, 2004), others approach BI from a more technical
point of view (White, 2004; Burton and Hostmann, 2005). Table 1 provides some of the more
prevalent definitions of BI.
10
Table 1
Selected BI Definitions
BI Definition Author(s) Definition Focus
An umbrella term to describe the set of
concepts and methods used to improve
business decision-making by using fact-
based support systems
Dresner (1989) Technological
A system that takes data and transforms
into various information products
Eckerson (2003) Technological
An architecture and a collection of
integrated operational as well as decision
support applications and databases that
provide the business community easy
access to business data
Moss and Atre (2003) Technological
Organized and systemic processes which
are used to acquire, analyze and
disseminate information to support the
operative and strategic decision making
Hannula and Pirttimaki
(2003)
Technological
A set of concepts, methods and processes
that aim at not only improving business
decisions but also at supporting realization
of an enterprise’s strategy
Olszak and Ziemba
(2003)
Organizational
An umbrella term for decision support
Alter (2004)
Organizational
Results obtained from collecting,
analyzing, evaluating and utilizing
information in the business domain.
Chung et al. (2004) Organizational
A system that combines data collection,
data storage and knowledge management
with analytical tools so that decision
makers can convert complex information
into competitive advantage
Negash (2004) Technological
A system designed to help individual users
manage vast quantities of data and help
them make decisions about organizational
processes
Watson et al. (2004) Organizational
(table continues)
11
Table 1 (continued).
BI Definition Author(s) Definition Focus
An umbrella term that encompasses data
warehousing (DW), reporting, analytical
processing, performance management
and predictive analytics
White (2004) Technological
The use and analysis of information that
enable organizations to achieve efficiency
and profit through better decisions,
management, measurement and
optimization
Burton and Hostmann
(2005)
Organizational
A managerial philosophy and tool that
helps organizations manage and refine
information with the objective of making
more effective decisions
Lonnqvist and
Pirttimaki (2006)
Organizational
Extraction of insights from structured data Seeley and Davenport
(2006)
Technological
A combination of products, technology
and methods to organize key information
that management needs to improve profit
and performance
Williams and Williams
(2007)
Organizational
Both a process and a product, that is used
to develop useful information to help
organizations survive in the global
economy and predict the behavior of the
general business environment
Jourdan et al. (2008) Organizational
These definitions largely reflect either a technologically or organizationally driven
perspective. BI, however, is comprised of both technical and organizational elements (Watson
et al., 2006). In the most general sense, BI presents historical information to its users for
analysis to enable effective decision making and for management support (Eckerson, 2003). For
the purpose of this dissertation, BI is defined as a system comprised of both technical and
organizational elements that presents historical information to its users for analysis, to enable
12
effective decision making and management support, for the overall purpose of increasing
organizational performance.
One of the goals of BI is to support management activities. Computer based systems
that support management activities and provide functionality to summarize and analyze
business information are called management support systems (MSS) (Scott Morton, 1984;
Gelderman, 2002; Clark et al., 2007; Hartono et al., 2007). Decision support systems (DSS),
knowledge management systems (KMS), and executive information systems (EIS) are examples
of MSS (Forgionne and Kohli, 2000; Clark et al., 2007; Hartono et al., 2007). These systems have
commonalities that make them all MSS (Clark et al., 2007). These common properties include
providing decision support for managerial activities, (Forgionne and Kohli, 2000; Gelderman,
2002; Clark et al., 2007), using and supporting a data repository for decision-making needs
(Cody et al., 2002; Arnott and Pervan, 2005; Clark et al., 2007), and improving individual user
performance (Gelderman, 2002; Hartono et al., 2005; Clark et al., 2007).
BI can also be included in the MSS set (Clark et al., 2007). First, BI supports decision
making for managerial activities (Eckerson, 2003; Hannula and Pirttimaki, 2003; Burton and
Hostmann, 2005). Second, BI uses a data repository (usually a data warehouse) to store past
and present data and to run data analyses (Eckerson, 2003; Moss and Atre, 2003; Anderson-
Lehman et al., 2004; Clark et al., 2007). BI is also aimed at improving individual user
performance through helping individual users manage enormous amounts of data while making
decisions (Watson et al., 2004; Burton et al., 2006; Clark et al., 2007). Thus, BI can be classified
as an MSS (Clark et al., 2007; Baars and Kemper, 2008). Examining BI in the light of research
based on other types of MSS may lead to better decision support and a higher quality of BI
13
systems (Clark et al., 2007). Findings of this dissertation may also be applied to other types of
MSS that exist now and that may emerge in the future.
The MSS classification of BI may also help research address gaps that result from
examining MSS separately, without considering their common properties. Research examines
success antecedents of many MSS extensively (Hartono et al., 2006), but consistent factors that
help organizations achieve a successful BI have not yet emerged. Research suggests that fit
between an MSS and the decision environment in which it is used is an MSS success antecedent
(Hartono et al., 2006; Clark et al., 2007). For example, using appropriate information technology
for knowledge management systems provides more successful decision support (Baloh, 2007).
The complexity level of the technology also impacts MSS effectiveness and success (Srinivasan,
1985). However, research has not looked specifically at the role of the decision environment in
BI success. It is important to do so because although it is an MSS, BI has requirements that are
significantly different from those of other MSS (Wixom and Watson, 2001).
The purpose of this dissertation is to help fill this gap in BI research by examining how BI
capabilities impact BI success and how the decision environment influences this relationship.
The decision environment is composed of the types of decisions made in the organization and
the information processing needs of the decision maker (Galbraith, 1977; Beach and Mitchell,
1978; Eisenhardt, 1989). BI capabilities include both organizational and technological
capabilities (Feeney and Willcocks, 1998; Bharadwaj et al., 1999). Figure 1 provides a high level
overview to help orient the reader to the model this dissertation addresses.
14
Figure 1. High level overview of the model.
The following sections review the literature for each construct of the model provided
above. After BI success, discussions on the decision environment and BI capabilities follow.
BI Success
BI success is the positive value an organization obtains from its BI investment (Wells,
2003). The organizations that have BI also have a competitive advantage, but how an
organization defines BI success depends on what benefits that organization needs from its BI
initiative (Miller, 2007). BI success may represent attainment of benefits such as improved
profitability (Eckerson, 2003), reduced costs (Pirttimaki et al., 2006), and improved efficiency
(Wells, 2003). For the purpose of this dissertation, BI success is defined as the positive benefits
organizations achieve through use of their BI.
BI
Success
Decision Environment
BI Capabilities
Technological BI
Capabilities
Organizational
BI Capabilities
Decision Types
Information
Processing Needs
15
Most organizations struggle to measure BI success. Some of them want to see tangible
benefits, so they use explicit measures such as return on investment (ROI) (Howson, 2006). BI
success can also be measured with the improvement in the operational efficiency or
profitability of the organization (Vitt et al., 2002; Eckerson, 2003). If the “costs are reasonable in
relation to the benefits accruing” (Pirttimaki et al., 2006, p. 83), then organizations may
conclude that their BI is successful. Other companies are interested in measuring intangible
benefits; these include whether users perceive the BI as mission critical, how much
stakeholders support BI and the percentage of active users (Howson, 2006). Specific BI success
measures differ across organizations and even across BI instances within an organization. For
example, one firm may implement to achieve better management of its supply chain, while
another may implement to achieve better customer service.
Research, however, does consistently point to at least one high level commonality
among successful BI implementations. Organizations that have achieved success with their BI
implementations have created a strategic approach to BI to help ensure that their BI is
consistent with corporate business objectives (Eckerson, 2003; Watson et al., 2002; McMurchy,
2008). How Continental Airlines improved its processes and profitability through successful
implementation and use of BI is a good example of aligning BI with business needs (Watson et
al., 2006). Cardinal Health Care is also a good example of the importance of BI and business
alignment because this organization has shaped its BI according to its business requirements
(Malone, 2005).
Research provides valuable insight into how to align BI with business objectives and
offers explanations for failures to do so (Eckerson, 2003; McMurchy, 2008). However, much of
16
this research is derived from a small number of cases and/or it is not strongly grounded in
theory (e.g., Cody et al., 2002; Watson, 2005). Other research provides a solid theoretical
foundation for examining BI success, yet provides limited empirical evidence (e.g., Gessner and
Volonino, 2005; Clark et al., 2007). Research that provides a sound theoretical background as
well as empirical evidence focuses on specific technologies of BI, such as data warehousing
(e.g., Cooper et al., 2000; Nelson et al., 2005) or web BI (e.g., Srivastava and Cooley, 2003;
Chung et al., 2004), rather than a more holistic model.
Finally, although research suggests several success models for MSS (Forgionne and
Kohli, 1995; Gelderman, 2002; Clark et al., 2007; Hartono et al., 2007), there is little theory-
based research solely focusing on understanding BI success from the perspective of BI
capabilities and the influence of the decision environment in which the BI is used. DSS and its
success factors, for example, have been studied comprehensively in the literature (e.g., Sanders
and Courtney, 1985; Guimaraes et al., 1992; Finlay and Forghani, 1998; Alter, 2003; Hung et al.,
2007). KMS success factors have also been widely examined using various theories from IS (e.g.,
Wu and Wang, 2006; Kulkarni et al., 2007; Tsai and Chen, 2007) as well as the management
literature (e.g., Al-Busaidi and Olfman, 2005; Oltra, 2005). Common features among these MSS
success studies is that they all suggest research models on how to increase organizational and
financial benefits obtained from these systems by testing the impact of various factors such as
user satisfaction (e.g., Wu and Wang, 2006), system quality (e.g., Tsai and Chen, 2007), or
management support (e.g., Al-Busaidi and Olfman, 2005).
Research has identified some of the factors that influence BI success as well (Negash,
2004; Solomon, 2005; Clark et al., 2007). For example, BI usability is an important determinant
17
of system performance and user satisfaction (Bonde and Kuckuk, 2004; Chung et al., 2005).
Other important performance indicators include technology and infrastructure (Negash, 2004;
Gessner and Volonino, 2005) and management support (Cooper et al., 2000; Anderson-Lehman
et al., 2004). Table 2 summarizes research on factors that affect BI success.
Table 2
Concepts Examined in Research About BI Success
Success Factors Author(s) Key Findings
Organizational
strategy
Cooper et
al. (2000)
This article presents how a data warehousing technology can
transform an organization by improving its performance and
increasing its competitive advantage. The authors have observed
the First American Cooperation changing its corporate strategy and
provide lessons for managers who plan to use BI to increase
competitive advantage.
Raymond
(2003)
This article provides a conceptual framework for business
intelligence activities in small and medium enterprises. Authors
suggest that the framework they propose can guide the design and
specification of BI projects. Based on their framework, authors
divide the BI project into 5 phases; including searching for strategic
information that provide competitive advantage.
Watson
et al.
(2004)
This article discusses how companies justify and assess data
warehousing investments. They examine the approval process and
post-implementation review for data warehouses. They discuss
that benefits gained can be tangible or intangible; operational,
informational or strategic; revenue enhancing or cost saving; and
time savings or improved decision making.
Technology &
Infrastructure
Wixom
and
Watson
(2001)
This article investigates data warehousing success factors. The
authors argue that a data warehouse is different from a regular IS
project and various implementation factors affect data
warehousing success. Findings indicate that project, organizational
and technical implementation successes are positively related to
data quality and system quality.
Nelson et
al. (2005)
In this article, the authors’ main goal is to find out the determinants
of the quality in data warehouses. Findings indicate that reliability,
flexibility, accessibility and integration are significant determinants
of system quality for BI tools. Also, they present that information
and system quality are success factors for data warehouses.
(table continues)
18
Table 2 (continued).
Success Factors Author(s) Key Findings
Technology &
infrastructure
Solomon
(2005)
This article gives a guideline for successful data warehouse
implementation and suggestions to managers on how to avoid
pitfalls and overcome challenges in enterprise-level projects.
These guidelines are mostly technical-oriented, such as; ETL
tool selection, data transport and data conversion methods.
Presentation &
usability
Alter
(2003)
Defining BI as a new umbrella term for decision support, Alter
suggests that structure of business processes, participants,
technology, information quality, availability and presentation,
product and services, infrastructure, environment and business
strategy are success factors for better decision support.
Lönnqvist
and
Pirttimaki
(2006)
This article is a literature review that discusses various
methods used for measuring business intelligence. Among the
reasons to measure BI is to show that it is worth the
investment. It also helps manage the BI process by ensuring
that BI products satisfy the users’ needs and the process is
efficient. They use total cost of ownership and subjective
measurements of effectiveness as examples of BI measures.
Management
support
Eckerson
(2003)
Based on a TDWI survey, this article provides an overview of BI
concepts and components and also examines the key success
factors of BI. One of these factors emphasizes the top
management commitment and mentions that it is the
commitment and support from the business sponsors and
managers that drives an organization’s BI initiative and
furthers its strategic objectives.
McMurchy
(2008)
This article identifies several factors for success in developing
BI business cases. His key findings indicate that organizations
need to tie BI strategy to overall strategy, sustain top
management support and user enthusiasm to maximizing the
ROI on their BI.
Performance
measures
Watson et
al. (2001)
This article assesses the benefits of the data warehousing and
provided a taxonomy. They group benefits as easy and hard to
measure as one dimension, and their impact being local and
global as the other dimension. An interesting result of this
study shows that there is an inverse relationship between the
expected and received benefits, and the potential impact of
the benefits.
(table continues)
19
Table 2 (continued).
Success Factors Author(s) Key Findings
Performance
measures
Gessner
and
Volonino
(2005)
This article discusses how right timing can improve ROI on BI,
specifically for marketing applications. They argue that, if BI
process does not increase the customer value, it would only
increase the expenses. They measure BI success through ROI,
and examine the change in ROI by maximizing Customer
Lifetime Value (CLV), where the change in CLV is the link
between technology infrastructure investments and profits.
Pirttimaki
et al.
(2006)
This article discusses available measurement methods for BI.
Since there is not enough measure available for the BI process;
business performance measurement literature can be used as
a reference for this purpose. They suggest a measurement
system that can be used as a tool to develop and improve BI
activities.
Information &
decision quality
Dennis et
al. (2001)
This article develops a model for interpreting Group Support
Systems effects on performance, and they test the fit between
the task and the GSS structures selected for use. The findings
indicate the importance of information and decision quality on
performance.
Clark et al.
(2007)
This article proposes a conceptual model for MSS. Mainly from
the IS success literature, 20 variables are selected and formed
the basis of the model. Some of them that are; perceived MSS
benefits, management decision quality, usability of MSS, MSS
costs, MSS functionality, MSS training, and MSS quality.
Structure of
business
processes
Yoon et al.
(1995)
The goal of this article is to identify and empirically test the
determinants of Expert Systems success. The authors have
come up with 8 major success determinants, and measured the
relationship between them and user satisfaction; problem
characteristics, developer skill, end-user characteristics, impact
on job, expert characteristics, shell characteristics, user
involvement and manager support.
Watson et
al. (2002)
This article investigates why some organizations receive more
benefits from data warehousing. It presents a framework that
shows how data warehouses can transform an organization
through time savings for both data suppliers and users, more
and better information, better decisions, improvement of
business processes and support for the accomplishment of
strategic business objectives.
20
Common characteristics of successful BI solutions are business sponsors who are highly
committed and actively involved; business users and the BI technical team working together; BI
being viewed as an enterprise resource and given enough funding to ensure long-term growth;
static and interactive online views of data being provided to the users; an experienced BI team
assisted by vendor and independent consultants; and, organizational culture reinforcing the BI
solution (Eckerson, 2003; Howson, 2006). Fit between BI strategy and business objectives,
commitment from top management with long-term funding, and a realistic BI strategy with
expected benefits and key metrics are also important characteristics of a successful BI
(McMurchy, 2008). In addition, sound infrastructure and appropriate technology are
characteristics of a successful BI (Solomon, 2005; Lönnqvist and Pirttimaki, 2006).
To succeed, organizations must develop their own measures for BI success (Howson,
2006) because BI success can have more than one meaning depending on the context in which
it is being used. The following section reviews measures of BI success.
Measuring BI Success
BI success can be measured by an increase in an organization’s profits (Williams and
Williams, 2007) or enhancement to competitive advantage (Herring, 1996). Return on
investment (ROI), however, is the most frequently used measure of BI success (McKnight,
2004). For example, Gessner and Volonino (2005) use ROI to measure BI success for marketing
applications. They argue that if BI does not increase customer value, it only increases expenses
and therefore does not produce an adequate ROI. ROI is also used in approving and assessing
data warehouses (Watson et al., 2001; Watson et al., 2004). ROI, however, is often difficult to
measure (Watson et al., 2004). Thus, revenue enhancement, time savings, cost savings, cost
21
avoidance and value contribution are variables that are also used to measure BI effectiveness in
addition to ROI (Herring, 1996, Sawka, 2000).
The Competitive Intelligence Measurement Model (CIMM) has been suggested as an
alternative approach to ROI to measure BI success (Davison, 2001). This model calculates the
return on BI investment by considering completion of objectives, satisfaction of decision
makers, and the costs associated with the project (Lonnqvist and Pirttimaki, 2006).The
suitability of the technology, whether business users like the BI, and how satisfied business
sponsors are with BI are other measures used to assess BI success (Moss and Atre, 2003;
Lonnqvist and Pirttimaki, 2006).
Another approach to measure BI success is subjective measurement (Lonnqvist and
Pirttimaki, 2006). This involves measuring the satisfaction of the decision maker with BI by
asking questions regarding the effectiveness of the BI (Davison, 2001). This way, it is possible to
learn what users think of various aspects of the system, such as ease of use, timeliness, and
usefulness. With this method, it is also possible to understand the perceptions of the extent to
which the users realized their expected benefits with BI.
This dissertation employs the subjective measurement method to measure BI success.
Many of the commonly used success measures mentioned above require that quantitative
data, such as ROI, be collected from various operations of the organization. In many cases it is
difficult, if not impossible, to measure the necessary constructs (Kemppila and Lonnqvist, 2003).
For example, many benefits provided by BI are intangible and non-financial, such as improved
quality and timeliness of information (Hannula and Pirttimaki, 2003). Although it may transfer
into financial benefits in the form of cost savings or profit increase, the time lag between the
22
actual production of intelligence and financial gain makes it difficult to measure the benefits
(Lonnqvist and Pirttimaki, 2006). Also, using subjective measurement based on the satisfaction
of the decision makers and their perception of the extent to which they realized their expected
benefits with BI shows how effective the BI is considered by its users (Davison, 2001; Lonnqvist
and Pirttimaki, 2006). As suggested by the CIMM model, measuring user satisfaction regarding
timeliness, relevancy and quality of the information provided by the BI also gives insight
regarding how successful the BI is (Lonnqvist and Pirttimaki, 2006).
Relationship between BI Capabilities and the Decision Environment
This dissertation posits that a key antecedent of BI success is having the right BI
capabilities, and right BI capabilities depend on the decision environment in which the BI is
used. The match between the decision environment and what an MSS provides has been
studied as an indicator of success, and is widely recognized as an organizational requirement
(Arnott, 2004; Clark et al., 2007).
This has also been examined as the match between MSS and the problem space within
which it is implemented (Clark et al., 2007). This match is defined as “how closely the designed
MSS reflects the goals of the organization in decision outcomes” (Clark et al., 2007, p. 586).
Complexity of the decisions that organizations face every day impacts the level of this match
(Clark et al., 2007). MSS are developed to address a variety of decisions and MSS effectiveness
is a direct outcome of how well these decisions are supported (Gessner and Volonino, 2005).
For example, various BI applications are developed to help organizations decide on the best
time to present offers to customers, and the effectiveness of BI is judged according to the
23
effectiveness of these decisions (Gessner and Volonino, 2005). Thus, understanding how the
decision environment affects the impact of BI capabilities is useful and important.
Organizational structure and strategy are two significant components of the decision
environment of an organization (Duncan, 1974). The appropriateness of an MSS to an
organization’s structure and strategy is a significant factor that impacts MSS success (Cooper
and Zmud, 1990; Hong and Kim, 2002; Setia et al., 2008). For example, Setia et al.’s (2008)
findings indicate that supply chain systems provide enhanced agility if there is a strategy and
task fit between supply chain systems and the organizational elements. As the match between
MSS and organizational structure increases, the performance of the organization improves
(Weil and Olson, 1989). The strategic alignment model developed by Henderson and
Venkatraman (1993) suggests that the fit among business strategy, organizational structure and
technology infrastructure increases the ability to obtain value from IS investments.
As can be seen from the examples above, research examines how MSS capabilities
moderated by the decision environment impacts MSS success. However, this concept has not
been used specifically to examine BI success. Focusing on the decision environment and BI
capabilities, this dissertation examines the effect of the BI capabilities on BI success, moderated
by the decision environment.
Decision Environment
The decision environment can be defined as “the totality of physical and social factors
that are taken directly into consideration in the decision-making behavior of individuals in the
organization” (Duncan, 1974, p. 314). This definition considers both internal and external
factors. Internal factors include people, functional units and organization factors (Duncan,
24
1974). External factors include customers, suppliers, competitors, sociopolitical issues and
technological issues (Duncan, 1974; Power, 2002).
Decision types are a part of the decision environment because the extent to which
decisions within the decision environment are structured or unstructured influences the
performance of the analytical methods used for decision making (Munro and Davis, 1977). The
types of decisions supported by the decision environment should be considered in selecting
techniques for determining information requirements for that decision (Munro and Davis,
1977).
The information processing needs of the decision maker are also a part of the decision
environment, provided that decision making involves processing and applying information
gathered (Zack, 2007). Because appropriate information depends on the characteristics of the
decision making context (Zack, 2007), it is hard to separate the information processing needs
from decision making. This indicates that information processing needs are also a part of the
decision environment.
Information processing and decision making are the central functions of organizations.
They are topics of interest in research and have been discussed from both technical and
managerial perspectives (Soelberg, 1967; Galbraith, 1977; Tushman and Nadler, 1978; Saaty
and Kearns, 1985). According to the behavioral theory of the firm, decision making in
organizations is a reflection of people’s limited ability to process information (Galbraith, 1977).
Contradictory to this, the operations research/management science perspective argues that
decision making can be improved by rationalizing the process, formulating the decision
problem as a mathematical problem, and testing alternatives on the model before actually
25
applying one to a real world problem (Galbraith, 1977). This approach opened the way for
computer applications and information technology that support decision making processes.
With the great information processing power of computers, information systems such as MSS
were developed.
IS research has used various information processing theories to explain the impact of
information processing on organizational performance, but organizational information
processing theory is one of the most frequently used theories (Premkumar et al., 2005;
Fairbank et al., 2006). The following section provides an overview of organizational information
processing theory including definition, constructs and its use in IS research.
Organizational Information Processing Theory
Organizational Information Processing (OIP) theory emerged as a result of an increasing
understanding among organizational researchers that information is possibly the most
important element of today’s organizations (Fairbank et al., 2006). The first researcher that
proposed this theory was Galbraith (1973). He suggested that specific structural characteristics
and behaviors can be associated with information requirements, and various empirical studies
have found support for his propositions (Tushman and Nadler, 1978; Daft and Lengel, 1986;
Karimi et al., 2004).
In OIP theory, organizations are structured around information. The relationship
between information and how it is used is a direct antecedent of organizational performance.
OIP focuses on information processing needs, information processing capability, and the fit
between them to obtain the best possible performance in an organization (Premkumar et al.,
2005). In this context, information processing is defined as the “gathering, interpreting, and
26
synthesis of information in the context of organizational decision making” (Tushman and
Nadler, 1978, p. 614), and information processing needs are the means to reduce uncertainty
and equivocality (Daft and Lengel, 1986).
OIP theory assumes that organizations are open social systems that deal with work-
related uncertainty (Tushman and Nadler, 1978) and equivocality (Daft and Macintosh, 1981).
Uncertainty is the difference between information acquired and information needed to
complete a task (Galbraith, 1973; Tushman and Nadler, 1978; Premkumar et al., 2005). Task
characteristics, task environment and task interdependence are among the sources of
uncertainty (Tushman and Nadler, 1978). Equivocality can be defined as multiple and conflicting
interpretations about an organizational situation (Daft and Macintosh, 1981; Daft and Lengel,
1986). It refers to an unclear situation where new and/or more data may not be enough to
clarify (Daft and Lengel, 1986).
One reason why organizations process information is to reduce uncertainty and
equivocality (Daft and Lengel, 1986). Organizations that face uncertainty must acquire more
information to learn more about their environment (Daft and Lengel, 1986). When tasks are
non-routine or highly complex, uncertainty is high; hence, information processing requirements
are greater for effective performance (Daft and Macintosh, 1981). Equivocality is very similar to
uncertainty. However, rather than lack of information, it is associated with lack of
understanding (Daft and Lengel, 1986). In other words, a decision maker may process the
required data, but not clearly understand what it means or how to use it. For example, a
problem may be perceived differently by managers from different functional departments in an
organization; an accounting manager may interpret some specific information different than a
27
system analyst. Both uncertainty and equivocality impact information processing in an
organization and should be minimized to achieve performance (Daft and Lengel, 1986; Keller,
1994).
OIP theory has important implications for organizational design because different
organizational structures are more effective in different situations (Tushman and Nadler, 1978;
Daft and Lengel, 1986). Specifically, the degree of uncertainty and equivocality may imply how
organizational structure should be designed (Daft and Lengel, 1986; Lewis, 2004). Here,
organizational structure is defined as the “allocation of tasks and responsibilities to individuals
and groups within the organization, and the design of systems to ensure effective
communication and integration of effort” (Daft and Lengel, 1986, p. 559). Thus, it is important
for organizations to have a structure that fits their uncertainty and equivocality levels, so that
they can perform well.
Organizations must develop information processing systems capable of dealing with
uncertainty (Zaltman et al., 1973). IS provides a way of managing uncertainty and equivocality
in organizations (Daft and Lengel, 1986; Keller, 1994; Premkumar et al., 2005). Various
researchers have studied how IS impacts uncertainty and equivocality (Tushman and Nadler,
1978; Jarvenpaa and Ives, 1993; Premkumar et al., 2005), and also how this affects
organizational effectiveness (Tuggle and Gerwin, 1980; Wang, 2003).
Several IS studies use OIP as the central theory in their models to explain how to obtain
effectiveness in organizations through the use of information technologies (Galbraith, 1977;
Tushman and Nadler, 1978; Daft and Lengel, 1986). For example, Premkumar et al. (2005)
suggest that the fit between information processing needs and information processing
28
capabilities has a significant impact on organizational performance. The fit between
organizational structure and information technology is an important contributor to
organizational effectiveness as well (Sauer and Willcocks, 2003). Table 3 provides examples
from IS research that have used OIP theory.
Table 3
Examples of Organizational Information Processing Theory in Information Systems
Concept Author(s) Key Findings
IS Fit
Jarvenpaa
and Ives
(1993)
This study examines various organizational designs for IS in
globally competing organizations. Findings show that there are
inconsistencies among how the organizations are structured and
how they manage their IS capabilities, revealing that there is a
lack of fit between organizational environment and IT.
Premkumar
et al. (2005)
This study examines the fit between information processing needs
and information processing capability in a supply chain context
and examines its effect on performance. Findings indicate that the
fit of information needs and IS capability has a significant impact
on performance.
Stock and
Tatikonda
(2008)
This study suggests a conceptual model on the fit of IS adopted
from an external source. Authors base their arguments on
organizational information processing theory and their findings
show that the fit between IS and information processing
requirements affect IS effectiveness.
IS Design &
Development
Tatikonda
and
Rosenthal
(2000)
Using information processing theory, this paper examines the
relationship between product development project characteristics
and project outcomes. Results show that technology novelty and
project complexity characteristics contribute to project task
uncertainty, which impacts project execution outcomes.
Jain et al.
(2003)
This study suggests that when compared to the traditional
approach, component-based software development (CBSD)
improves the requirements identification process. They use the
information processing theory to show how CBSD could facilitate
the identification of user requirements.
(table continues)
29
Table 3 (continued).
Concept Author(s) Key Findings
IS
Architecture
&
Management
Anandarajan
and Arinze
(1998)
This study uses information processing theory to examine the
match between an organization's information processing
requirements and its client/server architectures, and its impact on
effectiveness. The results indicate that a fit between task
characteristics and architectures directly affects system
effectiveness.
Douglas
(1998)
This study examines the fit between organizational structures and
information processing needs, specifically in the health care
industry. Findings suggest that vertical and horizontal information
systems offer the best opportunity for information processing
capability.
Cooper and
Wolfe
(2005)
This study uses information processing theory to examine the IS
adaptation process in organizations. Authors suggest that the fit
between information processing volume and, uncertainty and
equivocality reduction contributes to successful IS adaptation.
Organizational
Performance
Tuggle and
Gerwin
(1980)
This study suggests a simulation model that integrates the
processes of key environmental factors, strategy formulation by
the organization, routine operating decision executions and
standard operating procedures. Findings suggest that uncertainty
and sensitivity to changes impacts organizational effectiveness
negatively.
Fairbank et
al. (2006)
This study examines the relationship between IS and
organizational performance in the health insurance industry.
Authors examine how IS is deployed in organizations through
information processing design choices. Results show that
information processing design choices are generally related to
organizational performance.
IS Costs &
Benefits
Tatikonda
and
Montoya
Weiss
(2001)
This study examines relationships among organizational process
factors, product development capabilities, critical uncertainties,
and operational/market performance in product development
projects. The findings show that the organizational process factors
are associated with achievement of operational outcome targets
for product quality, unit-cost and time-to-market.
Gattiker and
Goodhue
(2004)
Using organizational information processing theory, this study
suggests factors that influence enterprise resource planning (ERP)
costs and benefits. The organizational characteristics they focus
on are interdependence and differentiation. While high
interdependence among organizational units is found to be
contributing to the positive ERP effects, high differentiation seems
to increase costs.
30
Although there is IS research using OIP theory to explain various phenomena, there is
very little research focusing on BI through the lens of OIP theory. BI is an information
processing mechanism that allows each user to process, analyze, and share information and to
turn it into useful knowledge (Hannula and Pirttimaki, 2003), thus it seems important to study
BI from OIP perspective.
In the BI context, the extent of information processing is a direct result of BI capabilities
(both technological and organizational). Employing the right capabilities for information
processing is an important issue for effective decision making and organizational performance
(Daft and Lengel, 1986; Fairbank et al., 2006), hence it is important to understand the dynamics
of information processing for BI.
Processing information allows organizations to develop a more effective decision
making process and an acceptable level of performance. Decision making is a key part of
managers’ jobs because it involves taking actions on behalf of their organization, and the
managers are evaluated based on the effectiveness of their decisions (Simon, 1960; Power,
2002). Thus, it is important to understand the underlying decision making mechanism, and how
decisions differ based on their characteristics. The next section provides a literature review of
the second component of the decision environment; decision types made in the organization.
Decision Types
Decisions types are different problems that are distinguished based on who needs to
make the decision and the steps the decision maker needs to follow to solve the problem
(Power, 2002). A problem is a structured decision if it is repetitive and routine, and it is
unstructured if there is no fixed method of handling it and the decision is consequential (Simon,
31
1960). Any other type of problem that falls between these two types is a semi-structured
decision (Keen and Scott Morton, 1978).
Simon’s framework distinguishes between different types of decisions based on
different techniques that are required to handle them (Simon, 1965; Gorry and Scott Morton,
1971; Adam et al., 1998). For example, while structured decisions are mostly made with
standard operating procedures using well-defined organizational channels, unstructured
decisions require judgment, creativity and training of executives (Simon, 1965; Kirs et al., 1989).
Semistructured decisions fall in between these two and require managerial judgment as well as
the support system (Keen and Scott Morton, 1978; Teng and Calhoun, 1996). Structured
decisions can largely be automated therefore do not involve a decision maker. Unstructured
decisions require judgment; hence the involvement of a decision maker at all times (Gorry and
Scott Morton, 1971; Teng and Calhoun, 1996).
Another categorization of decision making activities was suggested by Anthony (1965).
To categorize managerial activities according to their decision-making requirements, Anthony
(1965) developed a framework of decision types, associating decisions with organizational
levels. This framework includes three categories; strategic planning, management control, and
operational control. The strategic planning category involves decisions related to long term
plans, strategic plans and policies that may change direction of the organization (Anthony,
1965; Shim et al., 2002). This typically involves senior managers and analysts because the
problems are highly complex, nonroutine, and require creativity (Gorry and Scott Morton,
1971). Anthony defines strategic planning as “the process of deciding on objectives of the
organization, on changes in these objectives, on the resources used to attain these objectives,
32
and on the policies that are to govern the acquisition, use, and disposition of these resources”
(p. 24). Introducing a new product line can be given as an example of a decision in this category.
In Anthony’s (1965) framework, the management control category includes both
planning and control, involves making decisions about what to do in the future based on the
guidelines established in the strategic planning (Otley et al., 1995; Shim et al., 2002). Anthony
defines management control as “the process by which managers assure that resources are
obtained and used effectively and efficiently in the accomplishment of the organization’s
objectives” (p. 27). For instance, planning upon next year’s budget is an example of a
management control activity. The operational control category involves decisions related to
operational control, which is “the process of assuring that specific tasks are carried out
effectively and efficiently” (Anthony, 1965, p. 69). Here, individual tasks and transactions are
considered, such as a sales order or inventory procurement.
The boundaries between Anthony’s three categories are not always clear. There can be
overlaps between them, forming a continuum between highly complex activities and routine
activities (Anthony, 1965; Gorry and Scott Morton, 1971; Shim et al., 2002). When information
requirements of Anthony’s (1965) three managerial activities are considered, it can be seen
that they are very different from one another. This difference is attributable to the
fundamental characteristics of the information needs at different managerial levels (Gorry and
Scott Morton, 1971). Thus, Anthony’s (1965) framework also represents different information
processing needs of the decision makers at different management levels (Gorry and Scott
Morton, 1971).
33
Similar to Anthony’s (1965) classification, Simon’s (1965) classification of business
decisions as structured and unstructured also form a continuum between these two types of
decisions. Simon (1960) classifies decisions based on the ways used to handle them, and
Anthony’s (1965) categorization is based on the purpose and requirements of the managerial
activity that involves the decision (Shim et al., 2002). Gorry and Scott Morton (1971) combine
these two views and suggest a broader framework for decision support for managerial
activities. A table representation of this framework as adapted from Gorry and Scott Morton
(1971) is shown in Table 4.
The framework that results from the combination of Anthony’s (1965) and Simon’s
(1960) frameworks includes nine categories. Cell (1), the structured operational control,
involves decisions like inventory reordering which can be done through a computer-based
system without requiring any judgment. Decisions in cells (2) and (3) differ from cell (1) on the
level of system support they require. For example, while bond trading is an example of
semistructured operational control, cash management is an unstructured operational control
decision (Gorry and Scott Morton, 1971). In a similar fashion, while the degree of
automatization reduces from cell (4) to cell (6), the decisions involved in management control
are at the tactical level rather than the operational level. Examples of cells (4), (5) and (6) are
budget analysis, variance analysis, and hiring new managers, respectively. In strategic planning
(cells 7, 8, 9), the decisions are made at the executive level. Warehouse location, mergers, and
R&D planning are examples of cells (7), (8), (9) respectively.
34
Table 4
A Framework for Information Systems, Adapted From Gorry and Scott Morton (1971)
Management Activity
Decision Type Operational
Control
Management
Control
Strategic
Planning
Structured (1) (4) (7)
Semistructured (2) (5) (8)
Unstructured (3) (6) (9)
Gorry and Scott Morton’s (1971) framework has implications for both system design and
organizational structure (Shim et al., 2002). Because information requirements differ among
different types of decisions, the data collection and maintenance techniques for decision types
are also different. Information differences among the three decision areas imply related
differences in hardware and software requirements (Gorry and Scott Morton, 1971; Parikh et
al., 2001). For example, techniques used for operational control are rarely useful for strategic
planning, and the records in the operational control database may be too detailed to be used
for strategic decision making (Gorry and Scott Morton, 1971).
Organizational structure related implications of this framework are that managerial and
analytical skills for each type of decision are different. For example, decision makers involved in
the operational control area usually have different backgrounds and training than the ones in
management control. Thus, the skills and the decision making styles of managers in strategic,
operational and managerial areas differ significantly (Gorry and Scott Morton, 1971; Parikh et
al., 2001).
In summary, for the purposes of this dissertation, Gorry and Scott Morton’s (1971; 1989)
framework represents the decision environment because it categorizes both internal and
35
external factors related to the decision-making activities in an organization (Duncan, 1974),
such as the different technological requirements of different decisions and different
information needs of managerial activities. This framework groups decisions according to the
managerial activities with which they are associated and the methods used to handle them.
Different decision types require different methods, techniques and skills to be handled. These
differences lead to variations in technology infrastructure as well as organizational
characteristics that best handle specific types of decisions. This dissertation argues that BI
should be employed in accordance with these differences.
BI Capabilities
Adapting to today’s rapidly changing business environment requires agility from
organizations and BI has an important role in providing this agility with the capabilities it
provides (Watson and Wixom, 2007). BI capabilities are critical functionalities of BI that help an
organization improve its adaptation to change as well as improve its performance (Watson and
Wixom, 2007). With the right capabilities, BI can help an organization predict changes in
product demand or detect an increase in a competitor’s new product market share and
respond quickly by introducing a competing product (Watson and Wixom, 2007).
BI capabilities have been examined by practitioner-oriented research, especially from
the BI maturity model perspective (Eckerson, 2004; Watson and Wixom, 2007). Yet, BI
capabilities have remained largely unexamined in academic IS research. IS research has
examined IS capabilities extensively to explain the role of IS in organizational performance and
competitive advantage (Bharadwaj, 2000; Bhatt and Grover, 2005; Ray et al., 2005; Zhang and
Tansuhaj, 2007). IS capabilities are the functionalities that organize and deploy IS-based
36
resources in combination with other resources and capabilities (Bharadwaj, 2000). While some
research conceptualizes IS capabilities in managerial terms (Sambamurthy and Zmud, 1992;
Ross et al., 1996), other research focuses on technological capabilities (Sabherwal and Kirs,
1994; Teo and King, 1997). More recent models incorporate both managerial and technical
aspects of IS (Bharadwaj, 2000; Ray et al., 2005).
Similarly, BI capabilities can be examined from both organizational and technological
perspectives (Howson, 2004; Watson and Wixom, 2007). Technological BI capabilities are
sharable technical platforms and databases that ideally include a well-defined technology
architecture and data standards (Ross et al., 1996). Organizational BI capabilities are assets for
the effective application of IS in the organization, such as the shared risks and responsibilities as
well as flexibility (Ross et al., 1996; Howson, 2004). For example, while the data sources and
data types used by BI are technological BI capabilities, BI flexibility and level of risk supported
by BI are organizational BI capabilities (Hostmann et al., 2007).
Gartner Group’s research report about the evolution of BI groups organizations into four
categories based on their BI capabilities (Hostmann et al., 2007). Figure 2 shows the categories
as adopted from Hostmann et al. (2007).
Based on the exponential increase of accessible information and the increasing need for
skilled business users, different types of BI applications and their evolution can be characterized
with two dimensions, (1) information access and analysis, and (2) decision making style
(Hostmann et al., 2007). The first dimension of information access and analysis includes
methods and technologies used to collect and analyze the information. The second dimension,
decision style, includes the decision structure, i.e. unstructured or structured. Based on the
37
information access and analysis methods and the types of decisions made, an organization can
be characterized as the decision factory, the information buffet, the brave new world or the
hypothesis explored. Which quadrant an organization belongs to in this model depends on
capabilities such as the sources the data is obtained from, data types that can be analyzed, data
reliability, user access in terms of authorization and/or authentication, flexibility of the system,
interaction with other systems, acceptable risk level by the system, and how much intuition can
be involved in the analysis process.
Figure 2. The four worlds of BI adopted from Hostmann et al. (2007).
As organizations take advantage of these capabilities, their BI use increases, and so does
the maturity level of BI (Watson and Wixom, 2007). Mature BI increases organizational
responsiveness, which positively affects organizational performance. Thus, it is important to
recognize BI capabilities to better apply it to strategic needs (Ross et al., 1996).
Information Access & Analysis
Controlled/Qualified
Information Access & Analysis
Open/Unqualified
Decision Making
Process
Structured
Decision Making
Process
Unstructured
The
Decision
Factory
The
Information
Buffet
The
Brave
New World
The
Hypothesis
Explored
38
Data Sources
A data source can be defined as the place where the data that is used for analysis
resides and is retrieved (Hostmann et al., 2007). BI requires the collection of data from both
internal and external sources (Harding, 2003; Kanzier, 2002). Internal data is generally
integrated and managed within a traditional BI application information management
infrastructure, such as a data warehouse, a data mart, or an online analytical processing (OLAP)
cube (Hostmann et al., 2007). External data includes the data that organizations exchange with
customers, suppliers and vendors (Kanzier, 2002). This is rarely inserted into a data warehouse.
Often, external data is retrieved from web sites, spreadsheets, audio files, and video files
(Kanzier, 2002).
Organizations may use internal, external, or both types of data for BI analysis purposes.
For example, Unicredit built a sophisticated BI environment and created an OLAP architecture
composed of data warehouse and data marts, to aggregate all the information used for analysis
(Schlegel, 2007). Although they were using external data sources, the data collected from these
sources were internalized first. In the case of Richmond Police Department, the BI collected
crime data from untraditional data sources and used text mining to analyze that data
(Hostmann et al., 2007). Other examples are pharmaceutical and medical researchers who
analyze experimental data or legal information related to suspicious activities or individuals
(Hostmann et al., 2007). Because of its direct connection to BI infrastructure and software
characteristics, the data source is a technological capability for BI.
39
Data Types
Data type refers to the nature of the data; numerical or non-numerical and dimensional
or non-dimensional. Numerical data is data that can be measured or identified on a numerical
scale, and analyzed with statistical methods, such as measurements, percentages, and
monetary values (Sukumaran and Sureka, 2006). If data is non-numerical, then it cannot be
used for mathematical calculations. Non-numerical refers to data in text, image or sound
format that needs to be interpreted for analysis purposes. For example, financial data is
categorized as numerical data, whereas data collected from online news agencies is categorized
as non-numerical data.
Dimensional data refers to data that is organized and kept within relational data
structure and is a core concept for data warehouse implementations (Ferguson, 2007).
Dimensional data is subject oriented (Hostmann et al., 2007). Examples are customer-centric
dimensions such as product category, service area, sales channel or time period (Ferguson,
2007). Non-dimensional data refers to unorganized and unstructured data (Hostmann et al.,
2007). Non-dimensional data might be obtained from a website, for example. Because BI
infrastructure directly impacts the data types supported by the system, it is a technological BI
capability. In this dissertation, numerical and dimensional data is referred to as quantitative
data and non-numerical and non-dimensional data as qualitative data.
Interaction with Other Systems
Many organizations prefer having IS applications interacting at multiple levels so that
enterprise business integration can occur (White, 2005). This integration can be at the data
level, application level, business process level, or user level, yet these four levels are not
40
isolated from each other (White, 2005). Although data integration provides a unified view of
business data, application integration unifies business applications by managing the flow of
events (White, 2005). User interaction integration provides a single personalized interface to
the user and business process integration provides a unified view of organization’s business
processes (White, 2005). There are different technologies available for these integration types.
For example, enterprise information integration (EII) enables applications to see dispersed data
as though it resided in a single database and enterprise application integration (EAI) enables
applications to communicate with each other using standard interfaces (Swaminatha, 2006).
Data integration is very important especially for organizations that collect data from
multiple data sources; techniques such as EAI makes it possible to quickly and efficiently
integrate heterogeneous sources (Swaminatha, 2006). These technologies also provide benefits
for end users. For example, Constellation Energy Company integrated their BI system with
Microsoft Excel because it was a popular application frequently used throughout the company.
Since employees were using excel for data entry, they could continue using it even after the
roll-out of BI. As a result of this integration, change management issues and time spent on
training was reduced significantly (Briggs, 2006). Interaction with other systems is a
technological BI capability because of its reliance on BI infrastructure.
User Access
Because one size does not fit all with BI, there are different BI tools with different
capabilities, serving different purposes (Eckerson, 2003). Organizations may need to employ
these different BI tools from different vendors because different groups of users have different
reporting and analysis needs as well as different information needs (Howson, 2004). In contrast,
41
some organizations may choose to deploy a BI that provides unlimited access to data analysis
and reporting tools to all users (Havenstein, 2006). Because user access depends on BI
infrastructure and application characteristics, it is a technological BI capability.
Whether the organization prefers to use best-of-breed applications or a single BI suite,
matching the tool capabilities with user types is always a good strategy (Howson, 2006). While
some organizations limit user access through practicing authorization/authentication and
access control, others prefer to allow full access to all types of users through a web-centric
approach (Hostmann et al., 2007). For example, BI tools provided by Lyzasoft Inc. is an all-in-
one tool that includes integrated reporting, ad hoc query and analysis, dashboards, and
connectivity to data sources as a client-side desktop application (Swoyer, 2008). On the other
hand, QlikTech International developed QlikView, a web-centric BI application that provides
analytical and reporting capabilities for all types of users, especially easier to use for
nontechnical users (Havenstein, 2006). While web-centric systems are generally shared by large
numbers of users, desktop applications are mostly dedicated to specific users (Hostmann et al.,
2007).
Data Reliability
Organizations make critical decisions based on the data they collect every day, so it is
vital for them to have accurate and reliable data. Yet, there is evidence that organizations of all
sizes are all negatively impacted by imperfection, duplication and inaccuracy of the data they
use (Damianakis, 2008). Gartner Group estimates that more than 50% of BI projects through
2007 would fail because of data quality issues and TDWI estimates that customer data quality
issues alone cost U.S. businesses over $600 billion dollars a year (Graham, 2008).
42
Data that organizations collect from sources that are unqualified or uncontrolled also
give rise to errors. For example, the data from a Web site or from spreadsheets throughout the
organization contains errors that may not be caught prior to use in the BI (Hostmann et al.,
2007). Data reliability may be a problem for externally sourced data because there is no control
mechanism validating and integrating it; for example, getting the data from web blogs or RSS
feeds. Internal data is also prone to error. Poor data handling processes, poor data
maintenance procedures, and errors in the migration process from one system to another can
cause poor data reliability (Fisher, 2008). If the information analyzed is not accurate or
consistent, organizations cannot satisfy their customers’ expectations and cannot keep up with
new information-centric regulations (Parikh and Haddad, 2008). The technological capability of
BI delivering accurate, consistent and timely information across its users can enable the
organization improve its business agility (Parikh and Haddad, 2008).
Risk Level
Risk can be defined as making decisions when all the facts are not known (Harding,
2003). Risk and uncertainty exist in every business decision; some organizations use BI to
minimize uncertainty and make better decisions. Thus this is an organizational BI capability. For
risk-taking organizations, the decisions supported by the BI are entrepreneurial and motivated
by exploration and discovery of new opportunities as well as new risks (Hostmann et al., 2007).
Typically, innovative organizations tolerate high levels of risk but organizations that have
specific and well-defined problems to solve have a low tolerance for risk (Hostmann et al.,
2007).
43
People, processes, technology and even external events can cause risks for an
organization (Imhoff, 2005). The capabilities of the BI impact how successfully the organization
manages risk. BI can help the organization manage risk by monitoring the financial and
operational health of the organization and by regulating the operations of the organization
through key performance indicators (KPIs), alerts and dashboards (Imhoff, 2005). For example,
the Richmond Police Department deployed a number of analytical and predictive tools to
determine likely areas of criminal activity in Virginia, so that officers could take action early to
prevent crimes, rather than respond to criminal activity after it happened. Other than analytical
and predictive tools, modeling and simulation techniques also enable companies make
decisions that balance risk and obtain higher value (Business Wire, 2007).
Flexibility
An IS needs to be flexible in order to be effective (Applegate et al., 1999). Flexibility can
be defined as the capability of an IS to “accommodate a certain amount of variation regarding
the requirements of the supported business process” (Gebauer and Schober, 2006, p. 123). The
amount of flexibility directly impacts the success of an IS; while insufficient flexibility may
prevent the IS use for certain situations, too much flexibility may increase complexity and
reduce usability (Silver, 1991; Gebauer and Schober, 2006).
To achieve competitive advantages provided by BI, organizations need to select the
underlying technology to support the BI operations carefully (Dreyer, 2006), and flexibility is
one of the important factors to consider. Ideally, the system must be compatible with existing
tools and applications to minimize cost and complexity to the organization (Dreyer, 2006). The
strictness of business process rules and regulations supported by the BI directly impacts the
44
flexibility of BI. If there are strict sets of policies and rules embedded in the applications, then BI
has relatively low flexibility, because as the regulations get stricter, dealing with exceptions and
urgencies gets harder. Technology does not always support exceptional situations although
organizations need the flexibility and robust functionality to obtain the optimum potential from
BI (Antebi, 2007). Because flexibility is a direct result of organizational rules and regulations, it is
an organizational BI capability (Martinich, 2002).
For example, Richmond Police Department in Virginia, United States, deployed a BI
system to help them organize their fight against crime, and find out areas that criminal activity
is likely to occur (Hostmann et al., 2007). They used a wide variety of non-traditional data
sources rather than a single and traditional one such as a data warehouse, and analyzed that
collected data with different types of analytical tools. Through the flexibility of data sources and
data analysis methods, they were able to reduce the crime rate significantly and became
proactive in deterring crime (Hostmann et al., 2007).
Intuition Involved in Analysis
Intuition, in the context of analysis, can be described as rapid decision making with a
low level of cognitive control and high confidence in the recommendation (Gonzales, 2005).
Although BI has improved significantly with the developing technology, its core processes have
rarely changed. People use their intuition to manage their businesses whether they have a
technology accompanying it or not (Harding, 2003). Thus, intuition is an organizational BI
capability. Research, however, suggests that intuition by itself is not enough to competitively
run a business in today’s business world (Gonzales, 2005). Making decisions based on facts and
numbers as opposed to decision making based on gut feelings has become a suggested
45
approach for more successful BI applications and improved enterprise agility (Watson and
Wixom, 2007). On the opposite side to intuition is using the analytic process for decision
making; it is slower, requires a high level of cognitive control, and the recommended solution is
often chosen with a low level of confidence (Gonzales, 2005).
Although most of the applications using BI do not involve intuition at all in their analysis
(Hostmann et al., 2007), using intuition has not been totally drawn out of the BI scene.
Technology can monitor events, provide notifications and run predictive analysis, even
automate a response in straightforward cases, but for the decisions requiring human thought
intuition is still required (Bell, 2007). For example, the City of Richmond Police Department’s
use of BI to predict crimes is a good example how BI can also help officers and other field
personnel compare their expectations and intuitions against actual demographic trends
(Swoyer, 2008). With the help of BI, the police department covers areas that are likely to have
high crime while empowering the officers to include their instincts to figure out what actually in
happening at the location (Swoyer, 2008). There are other organizations that do not involve
intuition in the decision making process as much as in the case of Richmond Police Department,
but rather use it only for executive level decision making.
In summary, BI provides both technological and organizational capabilities to
organizations. These capabilities impact the way organization processes information and the
performance of the organization (Bharadwaj, 2000; Ray et al., 2005; Zhang and Tansuhaj, 2007).
Thus, it is imperative that these capabilities should match the decision environment. Table 5
summarizes the above mentioned BI capabilities and their levels associated with the four
quadrants of BI worlds.
46
Table 5
BI Capabilities and Their Levels Associated with the Four BI Worlds, Adapted From Hostmann et
al. (2007)
The Decision
Factory
The Information
Buffet
The Brave New
World
The Hypothesis
Explored
Data Source Internal Internal Mostly external Mostly external
Data Type quantitative Both qualitative Both
Data Reliability System
System and
Individual
Individual System
Flexibility Low High High Low
Intuition Involved
in Analysis
None Sometimes Always Always
Interaction with
Other Systems
Low High High High
Risk Level Low Low High High
User Access Web-centric Specific Web-centric Specific
Research Model and Hypotheses
Although BI success is widely addressed, there are still many inconsistencies in findings
about achieving success with BI. This is partly because one size does not fit all. Therefore, this
dissertation suggests that examining BI from a capabilities perspective, considering the
presence of different decision environments may provide better guidance on achieving BI
success. This study suggests that organizations should be aware of their needs based on their
decision environments and tailor BI solutions accordingly. Specifically, this dissertation argues
that as long as BI capabilities that fit the decision environment are in place, the BI initiative will
be successful. Below Figure 3 provides the conceptual model.
47
Figure 3. Conceptual model.
The amount of information available to users increases exponentially and it is not
possible to examine every piece of information to sort out what is useful or not (Clark et al.,
2007). Thus, identifying the appropriate information for the decision environment in a timely
manner is critical (Chung et al., 2005; Clark et al., 2007). Information system is a key concept in
identifying useful information (Eckerson, 2003; Clark et al., 2007). But, if IS is employed in the
organization just for the sake of using technology, and its capabilities do not match the decision
environment, then success may be limited (Clark et al., 2007).
Research suggests that a lack of fit between an organization and its BI is one of the
reasons for lack of success (Watson et al., 2002; Watson et al., 2006; Eckerson, 2006). It is not
Decision environment
BI
Success
BI Capabilities
Technological BI Capabilities
? Data Source
? Data Type
? Data Reliability
? Interaction with Other Systems
? User Access
Organizational BI Capabilities
? Flexibility
? Intuition Involved in Analysis
? Risk Level
Decision Types
Information
Processing Needs
48
only appropriate but necessary to examine the relationship between BI capabilities and BI
success, and how this relationship is affected by different decision environments. BI capabilities
include technological capabilities as well as organizational capabilities (Feeney and Willcocks,
1998; Bharadwaj et al., 1999). Technological capabilities are important success factors for any IS
(Watson and Wixom, 2007). Research shows that having a well-defined technology architecture
and data standards positively affect IS success (Ross et al., 1996). This is also true for BI; having
an effective infrastructure, reliable and high quality data, as well as pervasiveness are
important factors that influence BI maturity and success (Watson and Wixom, 2007). The
quality of technological BI capabilities in an organization has a positive influence on its BI
success.
Technological BI capabilities studied in this dissertation are data sources used to obtain
data for BI, data types used with BI, reliability of the data, interaction of BI with other systems
used in the organization, and BI user access methods supported by the organization. Although
these capabilities are present in every BI, their quality differs from organization to organization
(Hostmann et al., 2007). The difference in the quality of these capabilities is one of the factors
that may explain why some organizations are successful with their BI initiative while some are
not. For example, clean and relevant data is one of the most important BI success factors
(Eckerson, 2003; Howson, 2006). Organizations that have earned awards due to successful BI
initiatives, such as Allstate insurance company and 1-800-Contacts retailer, pay critical
attention to the sources from which they obtain their data, the type of data they use, and the
reliability of their data by acting early during their BI initiative and dedicating a working group
to data related issues (Howson, 2006).
49
The quality of interaction of BI with other systems in the organization is another critical
factor for BI success (White, 2005). For organizations that use data from multiple sources and
feed the data to multiple information systems, the quality of communication between these
systems directly affects the overall performance (Swaminatha, 2006). Likewise, BI user access
methods are critical for BI success. Because organizations have multiple purposes and user
groups with BI, they may employ different BI applications with different access methods
(Howson, 2004). While most of the web-centric applications are relatively easier to use,
especially for non-technical users, desktop applications are mostly dedicated to specific users
and provide specialized functionalities for more effective analysis (Hostmann et al., 2007). Thus,
the former may increase BI success with faster analysis, while the latter may increase it with
more effective decision making. Based on the above discussions, the following are
hypothesized:
H1a: The better the quality of data sources in an organization, the greater its BI success.
H1b: The better the quality of different types of data in an organization, the greater its
BI success.
H1c: The higher the data reliability in an organization, the greater its BI success.
H1d: The higher the interaction of BI with other systems in an organization, the greater
its BI success.
H1e: The higher the quality of user access methods to BI in an organization, the greater
its BI success.
Organizational BI capabilities include the level of intuition involved in analysis by the
decision maker, flexibility of the system, the level of risk that can be tolerated by the system
50
(Hostmann et al., 2007). The levels of these capabilities change from organization to
organization, depending on different business requirements and organizational structures
(Watson and Wixom, 2007). Regardless of their levels, these organizational capabilities
significantly impact BI success (Hostmann et al., 2007; Watson and Wixom, 2007). For example,
risk exists in every type of business, but there is evidence that entrepreneurial organizations are
motivated by it and can handle it better (Busenitz, 1999). Thus, an entrepreneurial organization
has a more successful BI if it can tolerate high levels of risk as one of their organizational BI
capabilities, compared to having a risk-averse system (Hostmann et al., 2007). On the other
hand, organizations that have specific and well-defined problems to solve may have a low
tolerance for risk and may have a more successful BI with a risk-averse system (Hostmann et al.,
2007). Flexibility is similar to the risk level in the sense that innovative and dynamic
organizations have a more successful BI if the system provides high flexibility (Dreyer, 2006;
Antebi, 2007). For organizations that shape their business with strict rules and regulations, high
flexibility may even become problematic by complicating business. Thus, a system with low
flexibility provides a more successful BI for these type of organizations (Hostmann et al., 2007).
The level of intuition involved in analysis by the decision maker depends on the type of
decision being made (Simon, 1965; Hostmann et al., 2007). For decisions that do not have a cut-
and-dried solution, the decision maker involves his intuition, which involves his experience, gut
feeling and judgment as well as creativity. Thus, BI that enables the decision maker to
incorporate his intuition in the decision making process is beneficial in these type of situations
and results in greater success (Harding, 2003). In opposition, organizations develop specific
processes for handling routine and repetitive decisions, so that the decision maker does not
51
need to use his intuition while making the decision, but only the information that is available
(Watson and Wixom, 2007). Based on the above discussion, the following hypotheses are
proposed;
H2a: The level of BI flexibility positively influences BI success.
H2b: The level of intuition allowed in analysis by BI positively influences BI success.
H2c: The level of risk supported by BI positively influences BI success.
The primary purpose of BI is to support decision-making in organizations (Eckerson, 2003;
Buchanan and O’Connell, 2006), and different decision types have different technology
requirements (Gorry and Scott Morton, 1971). Hence, employing the right technological
capabilities to provide support for the right type of decisions is critical for organizational
performance. For example, for structured decisions the decision making process can mostly be
automated, which is generally handled by computer-based systems, like transaction processing
systems (TPS) (Kirs et al., 1989). At the same time, DSS are better suited for semi-structured
decisions (Kirs et al., 1989) while BI is suitable for all types of decision structures (Blumberg and
Atre, 2003; Negash, 2004).
IS should be centered on the important decisions of the organization (Gorry and Scott
Morton, 1971). Thus, the types of decisions to be made should be taken into consideration
while using an MSS. For example, strategic planning decisions may require a database which
requires a complex interface although it is not frequently used (Gorry and Scott Morton, 1971).
On the other hand, operational control decisions may need a larger database which is
frequently used and requires continuous updating (Gorry and Scott Morton, 1971). Thus, the
52
relationship between technological BI capabilities and BI success is influenced by the decision
environment.
The data source used to retrieve information is one of the technological capabilities of BI
and it can be either internal or external (Harding, 2003; Kanzier, 2002). Internal data is
generated within the organization and it is managed through organizational structures
(Hostmann et al., 2007). Because internal data is ideally validated and integrated, it significantly
impacts the outcome of structured decisions and operational control activities (Keen and Scott
Morton, 1978). Because structured decisions are best handled with routine procedures and
operational control activities involve individual tasks or transactions, they all require accurate,
detailed and current information; and this need is best addressed with internal data (Keen and
Scott Morton, 1978). On the other hand, unstructured decisions have no set procedure for
handling because they are complex, and strategic planning activities involve mostly
unstructured decisions and require creativity. So, just internal data is almost never enough to
handle them. They need a wide scope of information, and external data sources are used to
retrieve what is needed from web sites, spreadsheets, audio and video files (Hostmann et al.,
2007). Whether the data is internal or external, its quality is a key to success with BI (Friedman
et al., 2006). Thus, the following is hypothesized:
H3a: The influence of high quality internal data sources on BI success is moderated by
the decision environment such that the effect is stronger for structured decision types
and operational control activities.
53
H3b: The influence of high quality external data sources on BI success is moderated by
the decision environment such that the effect is stronger for unstructured decision types
and strategic planning activities.
Besides the data sources, data types are also among technological BI capabilities and
their quality may impact BI success differently for different decisions and different
management activities. Because operational control activities are about assuring that core
business tasks are carried out effectively and efficiently, and that they are carried out rather
frequently, they require data that is easily analyzable (Anthony, 1965). Similarly, structured
decisions require detailed and accurate information (Keen and Scott Morton, 1978). Both for
structured decisions and operational management activities, quantitative data is used (Keen
and Scott Morton, 1978; Hostmann et al., 2007). Because non-numerical or qualitative data is
generally not detailed and its accuracy open to discussion, it is not appropriate for structured
decisions and operational activities. Rather, qualitative data is best used for unstructured
decisions because they are complex, they include non-routine problems and quantitative data
is not enough for solving those (Hostmann et al., 2007). Furthermore because strategic
planning activities need a wide scope of information with an aggregate level of detail, data used
better be qualitative so that it can be interpreted and used for subjective judgment (Keen and
Scott Morton, 1978). As mentioned in the data sources discussion, the quality of data is a key to
success with BI (Friedman et al., 2006). Thus, the following is hypothesized:
H3c: The positive influence of high quality quantitative data on BI success is moderated
by the decision environment such that the effect is stronger for structured decision types
and operational control activities.
54
H3d: The positive influence of high quality quantitative data on BI success is moderated
by the decision environment such that the effect is stronger for unstructured decision
types and strategic planning activities.
Data reliability is another factor that influences BI success, whether at the system level
or at the individual level. Operational control activities are related to basic operations that are
critical for an organization’s survival, so the data being used should be consistent and accurate
throughout the organization, requiring system-level reliability. Structured decisions also require
system-level reliability because they require consistent and current information for routine
processes (Keen and Scott Morton, 1978). On the other hand, strategic planning activities and
unstructured decisions are complex, non-routine and mostly solved by individuals or a small
group of people who use their subjective judgment and intuition (Keen and Scott Morton,
1978). This kind of information must be reliable at the individual level. The required
information for these activities is generally obtained from external and multiple sources in
addition to internal sources. This makes it harder to obtain system-level reliability. Low data
reliability leads to confusion and lack of understanding in analysis (Drummord, 2007). It is
important to use highly reliable data in BI, whether it is system-level or individual-level
reliability. Thus, the following is hypothesized:
H3e: The positive influence of high data reliability at the system level on BI success is
moderated by the decision environment such that the effect is stronger for structured
decision types and operational control activities.
55
H3f: The positive influence of high data reliability at the individual level on BI success is
moderated by the decision environment such that the effect is stronger for unstructured
decision types and strategic planning activities.
Many organizations implement multiple information systems or multiple applications
for different purposes. These applications often need to interact at multiple levels for the
enterprise business integration and data integration to occur (White, 2005). This interaction of
BI with other systems is especially critical to unstructured decision making and strategic
planning activities, because they collect data from multiple data sources (Swaminatha, 2006).
Thus, the following is hypothesized;
H3g: The positive influence of high quality interaction of BI with other systems in the
organization on BI success is moderated by the decision environment, such that the
effect is stronger for unstructured decision types and strategic planning activities.
How users access and use BI is another factor that influences BI success. User access can
be either shared, where large numbers of users access the same system through a web-based
application, or individual, where the tools are used with desktop computers and dedicated to a
specific user (Hostmann et al., 2007). For structured decisions and operational activities, shared
user access methods provide greater BI success. This is because decision makers need access to
real-time and transaction-level details to support their day-to-day work activities at these
levels, and a single integrated user interface to access the data eliminates the burden of
accessing multiple BI applications and saves time for the decision maker, which is vital for
operational activities (Manglik, 2006). The situation is different for unstructured decisions and
strategic planning activities. They require cross-functional business views that span
56
heterogeneous data sources and a more aggregated view (Fryman, 2007). Because these types
of activities are not as frequently handled as operational activities, the performance is not as
vital and due to the fact that users are executives, complexity is rarely an issue. That is why a
user-specific desktop application applies better. Thus, the following is hypothesized:
H3h: The positive influence of high quality shared user access methods to BI on BI
success is moderated by the decision environment, such that the effect is stronger for
structured decision types and operational control activities.
H3i: The positive influence of high quality individual user access methods to BI on BI
success is moderated by the decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Different types of decisions and management activities also require different
organizational BI capabilities, such as using intuition while making decisions and the level of risk
the organization tolerates. The decision maker involved in structured decisions and operational
activities needs to be different in terms of skills and attitudes from the decision maker involved
in unstructured decisions and strategic planning activities (Keen and Scott Morton, 1978). For
example, a system analyst who is involved in the development of a new transaction processing
system as a decision maker (structured operational control decision) may not be as successful
as a decision maker in an R&D portfolio development (unstructured strategic decision). While
structured decisions do not require intuition, decision makers need involve their intuition while
making unstructured decisions (Khatri and Ng, 2000). The decision environment influences the
impact of organizational BI capabilities on BI success.
57
The required level of BI flexibility, one of the organizational BI capabilities, is different
for different decision types and managerial activities. For example, if there is a need for
information that requires little processing (e.g., structured operational decisions) then rules and
regulations within the organization’s structure can provide a well-established response to
problems. For situations that require rich information and equivocality reduction (e.g.,
unstructured strategic decisions), then group meetings (which is a more flexible communication
method) where decision makers can exchange opinions and judgments face-to-face can help
them define a solution (Daft and Lengel, 1986). Therefore, the information processing and
decision making capabilities of an organization are directly related to the flexibility of the IS the
organization is using (Burns and Stalker, 1967). As the organization becomes more flexible, its
information processing capacity increases (Tushman and Nadler, 1978). This is useful for
strategic and unstructured decisions because they need a lot of information that is not always
easy to process. On the other hand, too much flexibility may result in complexity and reduced
usability (Silver, 1991; Gebauer and Schober, 2006). Thus, it is important to use the right level
of flexibility for the right decision types and activities. Therefore, the following is hypothesized:
H4a: The influence of BI flexibility on BI success is moderated by decision environment
such that the effect is stronger for unstructured decision types and strategic planning
activities.
Most of the decision makers use their intuition to manage their businesses whether
they have a technology accompanying it or not (Harding, 2003). This is especially necessary for
unstructured decisions and strategic planning activities because they need the decision maker
use his experiences, creativity and gut feeling due to their nature (Kirs et al., 1989). These
58
problems need more than the available data, so BI would be more successful if the decision
maker uses intuition for decision making. Yet, this is not the case for structured decisions and
operational control activities; the decision maker solely relies on data, logic and quantitative
analysis for these problems. When subjective judgment is involved, it is very difficult to apply
rational reasoning and doing so may even jeopardize the quality of the outcome (Hostmann et
al., 2007). Accuracy and consistency required for operational decision making may not be
provided. Thus, the following is hypothesized:
H4b: The influence of the intuition allowed in analysis on BI success is moderated by the
decision environment, such that the effect is stronger for unstructured decision types and
strategic planning activities.
In addition to the decision making process, the level of risk taken by the decision maker
may also differ for different decision types and different managerial activities. For example, as
organizations become more innovative, they also become more risk-tolerant and the decisions
they make become more and more unstructured (Hostmann et al., 2007). On the other hand,
organizations that generally make structured decisions tend to have routine and well-defined
problems to solve, and, they are more risk-averse (Hostmann et al., 2007). It is important to
tolerate the appropriate level of risk depending on the existing types of decisions and
managerial activities within an organization. Thus, the following is hypothesized:
H4c: The influence of tolerating risk on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured decision types and
strategic planning activities.
The research model is provided in Figure 4.
59
Figure 4. Research model.
H
1a
H
1e
H
1d
H
1c
H
1b
H
2a
H
2c
H
2b
H
3a-b
H
3c-d
H
3e-f
H
3g
H
4a
H
4b
H
4c
H
3h-i
BI
Success
Decision Environment
Decision Types
Information Processing Needs
Organizational BI Capabilities
Flexibility
Intuition Involved in Analysis
Risk Level
Technological BI Capabilities
Data Source
Data Type
User Access
Interaction with Other Systems
Data Reliability
60
CHAPTER 3
METHODOLOGY
This chapter describes the research methodology used to test the dissertation’s
hypotheses. How the data were collected and analyzed is explained, as are the research
methods employed and the development of the research instrument. Reliability and validity
issues are discussed and the data analysis procedures employed are described. The chapter is
composed of the following sections: description of the research population and sample,
description of the research design, discussion of instrument design and development, survey
administration, reliability and validity issues, and data analysis procedures.
Research Population and Sample
Business Intelligence (BI) success research largely draws from the population of business
managers, including IS professionals and business sponsors (Eckerson, 2003). This study draws
from a similar population because the goal is to measure BI success by examining BI capabilities
and decision environment. The research population for this dissertation consists of business
managers who use BI for strategic, tactical and operational decision making across a range of
organizations and industries. Data are collected from business firms located in the United
States. The firms are randomly selected, and the names and contact information of decision
makers are obtained from a publicly available mailing list of a market research company, L.I.S.T.
Inc., which maintains the Business Intelligence Network e-mail list from B-EYE-Network.com
web community, which is a collection of over 60,000 corporate and IS buyers of BI.
61
Research Design
The research design used in this dissertation is a field study. The research method used
is a formal survey. Using a survey helps the researcher gather information from a
representative sample and generalize those findings back to a population, within the limits of
random error (Bartlett et al., 2001). Advantages of survey research include flexibility in reaching
respondents from a broad scope (Kerlinger and Lee, 2000). In this dissertation, the data is
collected through a web-based survey. Advantages of using web-based surveys are the
elimination of paper, postage, mail out, and data entry costs, and reduction in time required for
implementation (Dillman, 2000). Web-based surveys also make it easier to send reminders,
follow-ups and importing collected data into data analysis programs (Dillman, 2000).
Two consistent flaws in business research are the lack of attention to sampling error
when determining sample size and the lack of attention to response and nonresponse bias
(Wunsch, 1986). Determining sample size and dealing with nonresponse bias is essential for
research based on survey methodology (Bartlett et al., 2001). This dissertation investigates
nonresponse bias by comparing the average values for dependent, independent and
demographic variables between early and late respondents, depending on the time of the
completed surveys are received, with t-tests (Armstrong and Overton, 1977; Kearns and
Lederer, 2003). In addition, t-tests are also performed between the pilot study respondents and
main data collection respondents.
Depending on the research design of the study, various strategies can be used to
determine an adequate sample size. A priori power analysis is recommended to find out the
appropriate sample size (Cohen, 1988). The power of a statistical test of a null hypothesis is the
62
probability that it will be rejected, meaning that the phenomenon of interest exists (Cohen,
1988). Power is related to Type I error (?), Type II error (?), sample size (N) and effect size (ES).
With a priori power analysis, the required sample size is calculated by holding the other three
elements of power analysis constant.
The first step in a priori power analysis is to specify the amount of power desired. The
recommended level of power to achieve is .80 (Chin, 1998). The second step is to specify the
criterion for statistical significance, ? level, which typically is .05 (Chin, 1998). The third step is
to estimate the effect size. In new areas of research inquiry, effect sizes are likely to be small
and it is common practice to estimate a small effect size, which corresponds to .2 (Cohen,
1988). Using these statistics, sample size is calculated using a free, general power analysis
software application, G*Power 3 (Erdfelder et al., 1996). Assuming an effect size of .2, an ?
level of .05, and a power of .8, a minimum sample size of 132 is needed.
Instrument Design and Development
The content and the wording of the questions in a survey are among the factors that
impact the effectiveness of surveys. Research suggests various methods to improve a survey
questionnaire. Brief and concise questions (Armstrong and Overton, 1971), careful ordering of
questions (Schuman and Pressor, 1981), and use of terminology that is clearly understood by
the respondents (Mangione, 1995) are methods suggested for survey improvement.
The survey used in this dissertation was refined in several steps. First, several IS
academic experts reviewed the survey. Based on their suggestions, I addressed ambiguity,
sequencing and flow of the questions. Second, a pilot study was conducted with 24 BI
professionals who have experience with BI implementation and use. The appropriateness of the
63
questions was assessed based on the results of the pilot study. The survey instrument was
finalized after making the necessary changes based on the feedback from pilot study
participants.
The survey instrument used in this dissertation consists of four parts. The first part
contains items used to collect demographic information from the respondents. The second part
measures the dependent variable, BI success. The third part includes items measuring the
independent variable, BI capabilities, and the fourth part includes items used to measure the
moderator variable, the decision environment. Decision environment is operationalized as the
types of decisions made (decision types) and the information processing needs of the decision
maker. BI capabilities are operationalized as organizational and technological BI capabilities.
Refer to Appendix A for a copy of the instrument.
BI Success
In this study, user satisfaction is used as a surrogate measure for BI success. User
satisfaction has been frequently used as a surrogate for IS success (Rai et al., 2002; Hartono et
al., 2006). The reason behind measuring user satisfaction as the surrogate measure is the direct
relationship among IS user satisfaction, IS use and decisional or organizational effectiveness
that IS research shows to exist (DeLone and McLean, 1992; Rai et al., 2002). Items measuring
user satisfaction are selected from Hartono et al.’s (2006) Management Support System (MSS)
success dimensions and Doll and Torkzadeh’s (1988) end-user satisfaction measure. Hartono et
al. (2006) identify and collect empirical studies that examine only MSS success measures from
peer-reviewed IS journals, which are then synthesized using DeLone and McLean’s (1992; 2003)
taxonomy of IS success measures. The items that measure satisfaction are developed based on
64
construct definitions stated in quantitative studies on MSS, published in peer-reviewed
information systems (IS) journals. Doll and Torkzadeh’s (1988) instrument merges ease of use
and information product items, focusing on end users interacting with a specific application for
decision making (Doll and Torkzadeh, 1988). From both studies, survey items measuring user’s
satisfaction regarding decision making, information obtained, and user friendliness are adapted
for this study.
BI Capabilities
BI capabilities of an organization directly impact BI effectiveness and success (Clark et
al., 2007; Watson and Wixom, 2007). BI capabilities were first identified in eight dimensions
extracted from the Gartner Group report on the evolution of BI (Hostmann et al., 2007). Three
of these dimensions were identified as organizational BI capabilities; level of risk tolerated, BI
flexibility, and level of intuition decision makers use during analysis. Five of the dimensions
were identified as technological BI capabilities; data sources used, data types analyzed, data
reliability, interaction with other systems and user access methods. Both technological and
organizational BI capabilities were operationalized with questions developed based on the
same Gartner Group report as well as other practitioner oriented publications from the Data
Warehousing Institute (TDWI) related to the eight BI capabilities (Harding, 2003; Gonzales,
2005; Sukumaran and Sureka, 2006; Ferguson, 2007; Damianakis, 2008).
The quality of technological BI capabilities, specifically quality of data sources and data
types, are measured with questions adapted from Wixom and Watson’s (2001) model that
measures data warehousing implementation success. Responses to each item are recorded on
a 5-point Likert scale.
65
Decision Environment
Decision environment was operationalized based on the two dimensional decision
support framework suggested by Gorry and Scott Morton (1971), which was later validated by
Kirs et al. (1989) and Klein et al. (1997).The first dimension addresses decision types and the
second dimension addresses the level of the management with which the decision is associated
and the information processing needs. To measure the first dimension, I ask respondents
questions pertaining to the nature of the decisions they make, such as the repetitiveness of the
decision or the managerial involvement in the decision making process. The objective of these
questions is to understand whether the decisions they make are structured, semistructured or
unstructured. For the second dimension, respondents indicate the organizational level with
which their decisions are associated; operational, tactical or strategic. Based on the
respondents’ answers, each decision is categorized as one of nine decision possibilities in Gorry
and Scott Morton’s (1971) framework. The questions measuring these were developed based
on Gorry and Scott Morton (1971), Kirs et al. (1989), Klein et al. (1997) and Shim et al. (2002).
Responses to each item are recorded on a 5-point Likert scale. Table 6 lists the
operationalization and measurement properties of the constructs measured in the survey.
Survey Administration
The response rate is a reflection of the cooperation of all potential respondents included
in the sample (Kviz, 1977). A low response rate may affect the quality of the results by
impacting the reliability or generalizability of findings. In order to increase the response rate,
some recommended methods are used in this study, including oofering an executive report on
the findings of the survey and providing anonymity to the respondents (Dillman, 2000). Survey
66
instructions also clearly stated that participation is voluntary and that no identifying
information is gathered by the administrator of the survey. To encourage participation, a final
analysis and executive summary of findings was provided upon the completion of the
dissertation to those who request them.
Table 6
Research Variables Used in Prior Research
Construct
Names
Sources Number
of items
Reliability
(Cronbach’s
? )
Validity
Assessed?
Directly
incorporated
/adapted /
developed
Decision
Environment
Gorry and Scott Morton
(1971),
Kirs et al. (1989),
Klein et al. (1997),
Shim et al. (2002)
10 No No Developed*
BI success
Hartono et al. (2006) 2 No No Adapted
Doll and Torkzadeh
(1988)
3 >.80 Yes Adapted
Organizational
BI capabilities
Hostmann et al. (2007)
Imhoff (2005)
Gonzales (2005)
9 No No Developed*
Technological
BI capabilities
Hostmann et al. (2007)
White (2005)
Eckerson (2003)
15 No No Developed*
Quality of
data types and
data sources
Watson and Wixom
(2001)
5 > .70 Yes Adapted
* The research cited did not use survey items to measure decision environment and BI capabilities. The
items used in this dissertation are developed based on their writings.
The sample data was obtained through a web-based survey. The procedure was
completed in two steps. First, the hyperlink to the instrument was e-mailed along with a
personalized cover letter explaining the purpose of the study. See Appendix B for a copy of the
cover letter. I did not have the chance to send a reminder to the same group of recipients.
67
Thus, to increase the number of respondents, the hyperlink to the instrument was e-mailed to a
different but smaller group of recipients two weeks after the first e-mail.
Reliability and Validity Issues
An instrument has adequate reliability if (1) it yields the same results when applied to
the same set of objects, (2) it reflects the true measures of the property measured, and (3)
there is a relative absence of measurement error in the instrument (Kerlinger and Lee, 2000).
Internal consistency is one of the most frequently used indicators of reliability (Cronbach,
1951). Internal consistency assesses how consistently individuals respond to items within a
scale. Cronbach’s coefficient alpha is widely used as the criterion to assess the reliability of a
multi-item measurement. A set of items with a coefficient alpha greater than or equal to 0.80
is considered to be internally consistent (Nunnally and Bernstein, 1994). This dissertation uses
Cronbach’s coefficient to assess the reliability of multi-item measurement scales.
Validity refers to the accuracy of the instrument. Content validity concerns the degree
to which various items collectively cover the material that the instrument is supposed to cover
(Huck, 2004). Content validity is judgmental (Kerlinger and Lee, 2000) and is generally
determined by having experts compare the content of the measure to the instrument’s domain
(Churchill, 1979; Huck, 2004). One step taken to ensure content validity in this dissertation is
that some of the items are adapted from prior research. Content validity is also addressed by
asking BI experts both in academia and industry to review the instrument and provide feedback
on whether the items adequately cover the relevant dimensions of the topic being examined.
Experts evaluate the content of the questions, their wording, and their ordering as well as the
instrument’s format. The instrument is modified based on their feedback.
68
Construct validity refers to the correspondence between the results obtained from an
instrument and the meaning attributed to those results (Schwab, 1980). Construct validity links
psychometric notions to theoretical notions; it shows that inferences can be made from
operationalizations to theoretical constructs (Kerlinger and Lee, 2000). Dimensionality is one
psychometric property used to assess construct validity. It relates to whether the items thought
to measure a given construct measure only that construct (Hair et al., 1998). Exploratory factor
analysis is a frequently used method to assess construct validity when the measurement
properties of the items are unknown. Because many of the items in this study are developed by
the researcher, exploratory factor analysis is used to assess the dimensionality of the items
used to measure a given construct.
In this dissertation, principle axis factor analysis with an orthogonal rotation was used to
assess all the dependent variables and the moderators. Dimensionality of each factor is
assessed by examining the factor loading. According to Hair et al. (1998), factor loadings over
0.3 meet the minimal level, over 0.4 are considered more important, and 0.5 and greater
practically significant. It is also suggested that the loadings over 0.71 are excellent, over 0.55
good, and over 0.45 are fair (Tabachnick and Fidell, 2000; Komiak and Benbasat, 2006). The
factor analyses conducted in this study are assessed according to these criteria. Then
confirmatory factor analysis was applied to the resulting factor structure to further assess
dimensionality and confirm that the items result in the number of factors specified.
Convergence and discriminability are also aspects of construct validity (Hair et al., 1998).
Convergent validity indicates that there is a significant relationship between constructs that are
thought to have a relationship, and that items purporting to measure the same thing are highly
69
correlated (Kerlinger and Lee, 2000). Discriminant validity indicates that there is no significant
relationship between constructs that are not thought to have a relationship, and that items
measuring different variables have a low correlation (Kerlinger and Lee, 2000). Correlations
among constructs were used to assess these two types of validities.
External validity refers to the validity with which a casual relationship can be generalized
to various populations of persons, settings and times (Kerlinger and Lee, 2000). It refers to the
degree to which the findings of a single study from a sample can be generalized to the
population. Sample of this study are BI users who reasonably represent the population of
business managers who use BI for strategic, tactical and operational decision making across a
range of organizations and industries. Thus, results from this dissertation can be generalized to
the population of BI users.
Data Analysis Procedures
A moderator variable affects the strength of the relationship between an independent
variable and a dependent variable (Baron and Kenny, 1986). Two methods of testing a model
that includes a moderator variable are suggested (Baron and Kenny, 1986). One method
involves multiple regression analysis and regressing the dependent variable on both the
independent variable and the interaction of the independent variable with the moderator
(Baron and Kenny, 1986). Research shows, however, that measuring multiplicative interactions
results in low power when measurement error exists (Busemeyer and Jones, 1983). Thus, Baron
and Kenny (1986) recommend an alternate approach, Structural Equation Modeling (SEM), if
measurement error is expected in the moderating variable, which is often the case in
psychological and behavioral variables. SEM is a covariance-based modeling technique is
70
capable of dealing with the measurement error, in contrast to regression analysis (Hair et al.,
1998).
The characteristics that distinguish SEM from other multivariate techniques are the
estimation of multiple and interrelated dependence relationships and its ability to represent
unobserved concepts in these relationships (Hair et al., 1998). SEM estimates a series of
multiple regression equations simultaneously by specifying the structural model. The
advantages of SEM include flexibility in modeling relationships with multiple predictor and
criterion variables, use of confirmatory factor analysis to reduce measurement error, and the
ability to test models overall rather than coefficients individually (Chin, 1998; Hair et al., 1998).
This dissertation employs SEM to test the research hypotheses. The research model
suggests that there is a relationship between BI capabilities and BI success, and that this
relationship is moderated by the decision environment. Table 7 shows the statistical tests
associated with each hypothesis.
Table 7
Hypotheses and Statistical Tests
Hypotheses Statistical Tests
H1a: The better the quality of data sources in an organization, the greater its BI
success.
Ysucc = ?
0
+?
1
ds+?
H1b: The better the quality of different types of data in an organization, the
greater its BI success.
Ysucc = ?
0
+?
1
dt+?
H1c: The higher the data reliability in an organization, the greater its BI success. Ysucc = ?
0
+?
1
dr+?
H1d: The higher the quality of interaction of BI with other systems in an
organization, the greater its BI success.
Ysucc = ?
0
+?
1
inr+?
H1e: The higher the quality of user access methods to BI in an organization, the
greater its BI success.
Ysucc = ?
0
+?
1
ua+?
H2a: The level of BI flexibility positively influences BI success. Ysucc = ?
0
+?
1
fx+?
H2b: The level of intuition allowed in analysis by BI positively influences BI
success.
Ysucc = ?
0
+?
1
intu+?
(table continues)
71
Table 7 (continued).
H2c: The level of risk supported by BI positively influences BI
success.
Ysucc = ?
0
+?
1
rsk+?
H3a: The influence of high quality internal data sources on BI
success is moderated by the decision environment such that the
effect is stronger for structured decision types and operational
control activities.
Ysucc =
?
0
+?
1
ds+?
2
(ds*dty)+?
3
(ds*inf)+?
H3b: The influence of high quality external data sources on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Ysucc =
?
0
+?
1
ds+?
2
(ds*dty)+?
3
(ds*inf)+?
H3c: The positive influence of high quality quantitative data on BI
success is moderated by the decision environment such that the
effect is stronger for structured decision types and operational
control activities.
Ysucc =
?
0
+?
1
dt+?
2
(dt*dty)+?
3
(dt*inf)+?
H3d: The positive influence of high quality qualitative data on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Ysucc =
?
0
+?
1
dt+?
2
(dt*dty)+?
3
(dt*inf)+?
H3e: The positive influence of high data reliability at the system
level on BI success is moderated by the decision environment such
that the effect is stronger for structured decision types and
operational control activities.
Ysucc =
?
0
+?
1
dr+?
2
(dr*dty)+?
3
(dr*inf)+?
H3f: The positive influence of high data reliability at the individual
level on BI success is moderated by the decision environment such
that the effect is stronger for unstructured decision types and
strategic planning activities.
Ysucc =
?
0
+?
1
dr+?
2
(dr*dty)+?
3
(dr*inf)+?
H3g: The positive influence of high quality interaction of BI with
other systems in the organization on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Ysucc =
?
0
+?
1
inr+?
2
(inr*dty)+?
3
(inr*inf)+?
H3h: The positive influence of high quality shared user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for structured decision
types and operational control activities.
Ysucc =
?
0
+?
1
ua+?
2
(ua*dty)+?
3
(ua*inf)+?
H3i: The positive influence of high quality individual user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured
decision types and strategic planning activities.
Ysucc =
?
0
+?
1
ua+?
2
(ua*dty)+?
3
(ua*inf)+?
(table continues)
72
Table 7 (continued).
H4a: The influence of BI flexibility on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Ysucc = ?
0
+?
1
fx +?
2
(fx*dty)
+?
3
(fx*inf)+?
H4b: The influence of the intuition allowed in analysis on BI success
is moderated by the decision environment, such that the effect is
stronger for unstructured decision types and strategic planning
activities.
Ysucc =
?
0
+?
1
int+?
2
(int*dty)+?
3
(int*inf)+?
H4c: The influence of tolerating risk on BI success is moderated by
the decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Ysucc =
?
0
+?
1
rsk+?
2
(rsk*dty)+?
3
(rsk*inf)+?
*** Notations
suc – BI Success dty- decision types
ds – data sources inf – information processing needs
dt – data types fx- flexibility
dr- data reliability intu – intuition involved in analysis
inr- interaction with other systems
ua – user access
rsk- risk level
73
CHAPTER 4
DATA ANALYSIS AND RESULTS
This chapter describes the data analysis and results of the dissertation. The first section
discusses response rate and analysis of non-response bias. The next section reports the sample
characteristics, followed by a discussion on the validity and reliability of the data and the survey
instrument. Finally, the statistical tests that are performed to test the research framework and
hypotheses are discussed and results of these tests are presented.
Response Rate and Non-Response Bias
The research population for this dissertation consisted of business managers who use BI
for strategic, tactical and operational decision making across a range of organizations and
industries. Data are collected from business firms located in the United States. The firms are
randomly selected, and contact information of decision makers are obtained from a publicly
available mailing list of a market research company, L.I.S.T. Inc., which maintains the Business
Intelligence Network e-mail list from B-EYE-Network.com web community, which is a collection
of over 60,000 corporate and IS buyers of business intelligence (BI).
As the first step of the data collection process, a pilot study was conducted. For this pilot
study, the survey was sent out to mailing list, which consists of operational managers using SAS
software for data analysis purposes. A total of 24 responses were received, all were complete
and usable.
After purchasing the right to use the e-mail addresses from L-I-S-T Inc., the survey was
administered to 8,843 BI users through two e-mails. Although the content of the e-mails was
the same, the second e-mail was sent three weeks after the first e-mail was sent. In the case of
74
the first e-mail, twenty-nine %of the mailing was undeliverable, and hence, 6281 were
delivered to potential respondents. Out of 6281 professionals, 1.7% clicked the survey link, but
only 29 respondents actually completed the survey. The second e-mail was sent out to
compensate for the high undeliverable rate of the first e-mail, and it was delivered to another
2,500 recipients.
Overall, a total of 97 responses were collected during the data collection process. This
corresponds to a response rate lower than 1%. This result is not necessarily surprising for web-
based surveys (Basi, 1999). Among the reasons for not completing the survey could be time
constraints, dislike of surveys and lack of incentives (Basi, 1999). Of the 97 responses, 5 were
incomplete and hence were dropped from subsequent analyses, yielding 92 usable responses.
To assess the non-response bias early respondents were compared to late respondents,
with respect to dependent, independent, moderator variables and demographics. With this
approach, it is assumed that subjects who respond less readily are more like those who do not
respond at all compared to subjects who respond readily (Kanuk and Berenson, 1975). This
method has been shown to be a useful way to assess non-response bias and has been adopted
by IS researchers frequently (Karahanna, Straub and Chervany 1999; Ryan, Harrison and
Schkade 2002). The differences between the responses to the first e-mail (n = 53) and the
responses to the second e-mail (n = 39) were examined with t-tests. There were no significant
differences between groups for dependent, independent or moderating variables at the .05
significance level. Table 8a shows the results of the t-tests. For the variables where the Levene’s
Test was significant (BI success, decision type and data sources), the t-values reflect the
assumption of unequal variances between groups.
75
I also performed t-tests to see if there were any significant differences in terms of
demographics. Table 8b shows the results of these t-tests. For the variables where the Levene’s
Test was significant (highest education level and number of employees), the t-values reflect the
assumption of unequal variances between groups. No significant differences were observed
among the variables.
Table 8a
Independent Samples t-Tests for Non-response Bias
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
Dependent
Variable
BI Success 7.015 .010 -1.938 85.977 .056 -.34168 .17629
Moderator
Decision
Type
4.487 .037 1.406 56.052 .165 .14256 .10138
Information
Processing
Needs
.059 .808 -.365 86 .716 -.04594 .12589
Independent
Variables
Data
Sources
8.677 .004 -1.693 56.028 .096 -.26078 .15401
Data Types .682 .411 -.104 86 .918 -.01388 .13402
Reliability 1.668 .2 -.785 83 .435 -.09237 .11772
Interaction
with Other
Systems
.061 .805 -1.321 85 .190 -.25234 .19100
User Access 3.704 .058 .586 83 .559 .06923 .11805
Flexibility .155 .695 -1.291 82 .200 -.23882 .18502
Intuition
Involved in
Analysis
.166 .685 -.412 86 .681 -.04011 .09735
Risk Level .001 .980 -1.620 79 .109 -.27990 .17281
76
Table 8b
Independent Samples t-Tests for Non-response Bias - Demographics
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel 5.890 .017 .664 65.273 .509 .167 .252
Gender .339 .562 .290 90 .773 .017 .060
TimeInOrg 2.200 .141 .503 90 .616 .731 1.453
ManagerialPosition .321 .573 .458 90 .648 .055 .119
FunctArea .613 .436 .280 90 .780 .181 .646
LevelInOrg .421 .518 -.089 90 .929 -.016 .184
NumEmployees 4.438 .038 -.604 73.305 .548 -.250 .414
TotalRevenue .000 .995 .422 90 .674 .129 .305
Industry .126 .724 .324 90 .747 .767 2.365
BIclass 1.495 .225 -1.237 90 .219 -.173 .140
The data collected from the pilot group was analyzed to check if there are any
anomalies or unexpected factor loadings were present and nothing unexpected was found.
Then, this data set was compared with the data collected from the e-mail recipients. The t-tests
were used to examine the differences between pilot group of users, who responded between
May 6, 2009 and May 27, 2009, and the rest of the respondents. There were no significant
differences between groups for dependent or independent variables but there were significant
differences in terms of the moderator(Table 9a). In terms of demographics, some significant
differences were observed (Table 9b). In both tables, for the variables where the Levene’s Test
was significant, the t-value reflects the assumption of unequal variances between groups.
The reason for significant difference between the pilot group respondents versus other
respondents for the moderator and for the differences in functional area and level in
organization can be explained by the differences in the respondent outlets. The first set of
77
respondents belongs to North Texas SAS Users Group, while the second set was recruited from
a BI professionals mailing list. The SAS Users Group is composed of operational managers that
are responsible for generating and using advanced BI applications, while the mailing list was
comprised of a broader segment of BI users and managers. This may explain the significant
difference in terms of the types of decisions made and the information characteristics required
to make those decisions. Furthermore, total revenue and number of employees was greater for
the mailing list group. This group was comprised of a broader segment of industries and
companies, and thus may have tapped more of the larger firms than the pilot group from North
Texas.
Table 9a
Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Main Data Set
Levene's Test for
Equality of Variances t-Test for Equality of Means
F Sig. t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference
BI Success .232 .631 -.732 110 .466 -.14545 .19881
Decision Type 1.413 .237 -3.825 111 .000 -.36788 .09619
Information
Processing Needs
4.092 .046 -4.892 53.771 .000 -.47159 .09641
Data Sources .203 .653 .245 111 .807 .03710 .15137
Data Types .990 .322 1.035 110 .303 .14015 .13535
Reliability .195 .660 -.846 106 .400 -.10377 .12269
Interaction with
Other Systems
.286 .594 .638 108 .525 .12806 .20077
User Access .803 .372 -.775 107 .440 -.09931 .12807
Flexibility .134 .715 -1.012 105 .314 -.20018 .19784
Intuition Involved
in Analysis
3.336 .070 1.444 110 .152 .16061 .11124
Risk Level .023 .879 -.359 101 .720 -.06411 .17836
78
Table 9b
Independent Samples t-Tests for Response Bias on Demographics: Pilot Data Set vs. Main Data
Set
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel 1.140 .288 1.086 114 .280 .266 .245
Gender 1.207 .274 -.562 114 .575 -.038 .068
TimeInOrg .910 .342 1.087 114 .279 1.636 1.504
ManagerialPosition 1.145 .287 -.916 114 .361 -.116 .127
FunctArea .133 .716 -2.737 114 .007 -1.902 .695
LevelInOrg 1.458 .230 -4.773 114 .000 -.971 .203
NumEmployees 1.577 .212 -2.175 114 .032 -.929 .427
TotalRevenue 1.128 .291 -2.652 114 .009 -.871 .329
Industry 1.434 .234 1.252 114 .213 2.926 2.337
BIclass 2.527 .115 .761 114 .448 .121 .160
Further analysis was conducted to see if there were significant differences between the
pilot group and the operational managers who were members of the mailing list. There were no
significant differences in any of the independent, dependent or moderator constructs (Table
10a). There were also no significant differences found in demographic variables (Table 10b). For
the variables where the Levene’s Test was significant, the t-value reflects the assumption of
unequal variances between groups.
79
Table 10a
Independent Samples t-Test: Pilot Data Set vs. Operational Managers in the Main Data Set
Levene's Test for
Equality of Variances t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
BI Success
.425 .520 -.444 27 .661 -.217 .488
Decision Type .006 .939 -.479 27 .636 -.117 .244
Information
Processing Needs
4.557 .042 -1.107 7.240 .304 -.258 .233
Data Sources .120 .731 1.047 27 .305 .425 .406
Data Types 2.800 .106 .443 27 .661 .133 .301
Reliability 3.511 .072 .573 27 .571 .175 .305
Interaction with
Other Systems
.203 .656 .572 27 .572 .258 .451
User Access .934 .342 -1.262 27 .218 -.317 .251
Flexibility 1.746 .197 -.058 27 .954 -.025 .432
Intuition Involved
in Analysis
.004 .950 -.401 27 .692 -.108 .270
Risk Level 1.022 .321 .074 27 .942 .025 .339
Next, the operational manager respondents were removed from the main data set, and
the remaining group was compared to the pilot data set to see if there were still significant
differences found between the pilot group respondents and other respondents who were non-
operational managers. There were significant differences in the two dimensions for the
moderator (decision type and information needs). There was also a significant difference for
the intuition construct although it was not significant for any of the other t-tests performed.
See Table 11a for the results of this t-test. Table 11b shows the results of the t-test for
demographics. For the variables where the Levene’s Test was significant (Decision type and
information processing needs), the t-values reflect the assumption of unequal variances
between groups.
80
Table 10b
Independent Samples t-Test on Demographics: Pilot Data Set vs. Operational Managers in the
Main Data Set
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel .195 .662 .471 27 .641 .342 .725
Gender 2.048 .164 .651 27 .521 .083 .128
TimeInOrg 4.915 .035 -.044 4.330 .967 -.183 4.201
ManagerialPosition .220 .642 1.103 27 .280 .267 .242
FunctArea .584 .451 -1.850 27 .075 -2.825 1.527
LevelInOrg .785 .383 -.367 27 .716 -.325 .885
NumEmployees .003 .960 -.747 27 .462 -.508 .681
TotalRevenue .507 .482 .479 27 .636 4.158 8.685
Industry 1.022 .321 -.074 27 .942 -.025 .339
BIclass .195 .662 .471 27 .641 .342 .725
There were significant differences between groups for the highest education level, level
in organization, number of employees in the organization and total revenue of the organization.
Because I am comparing operational managers to non-operational managers, the significant
difference in the level in the organization is expected. The difference in the highest education
level can also be explained by the groups being operational managers versus non-operational
managers. One possible explanation for the difference between the number of employees and
the total revenue may be because the pilot group consisted of operational managers from
companies in the North Texas group, and is not as diverse as the mail data set.
81
Table 11a
Independent Samples t-Tests for Response Bias: Pilot Data Set vs. Non-Operational Managers in
the Main Data Set
Levene's Test for
Equality of
Variances t-Test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
BI Success
.384 .537 -.711 90 .479 -.142 .200
Decision Type 6.053 .016 -3.058 39.744 .004 -.348 .114
Information
Processing Needs
36.360 .000 -4.911 76.272 .000 -.542 .110
Data Sources .041 .840 .509 90 .612 .083 .164
Data Types 1.076 .302 1.409 90 .162 .216 .153
Reliability .197 .658 -.399 90 .691 -.059 .148
Interaction with
Other Systems
.512 .476 .841 90 .403 .181 .216
User Access 1.431 .235 -.103 90 .918 -.015 .143
Flexibility .116 .734 -.679 90 .499 -.147 .216
Intuition Involved
in Analysis
1.895 .172 2.095 90 .039 .292 .139
Risk Level .117 .733 -.815 90 .417 -.162 .199
These t-tests provide support for the idea that the significant differences found between
the pilot group data set versus the main data set is because all of the respondents in the pilot
group are operational managers whereas the main data set includes a diverse group of
respondents with only 5 operational managers. The difference in the level of intuition involved
in analysis also is not surprising considering that I hypothesize that non-operational managers
use their intuition while making decisions more than operational managers would. The mean
for the intuition for non-operational managers is higher than the mean for the intuition for
operational managers. Considering that there were only five operational managers in the main
82
data set, to be able to represent the operational managers equally, the pilot data set was added
to main data set. Because I am interested in responses that represent all these groups, and
because I made no changes to the survey from the pilot group, the responses from both sets
were combined for subsequent data analysis without any discrepancies. This provided 116
usable responses.
Table 11b
Independent Samples t-Test on Demographics: Pilot Data Set vs. Non-Operational Managers in
the Main Data Set
Levene's Test for
Equality of Variances
t-Test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
HighestEdLevel 4.323 .040 -5.665 36.171 .000 -1.255 .222
Gender .025 .876 1.803 90 .075 .390 .216
TimeInOrg 1.030 .313 -.516 90 .607 -.037 .071
ManagerialPosition 1.893 .172 1.317 90 .191 2.199 1.669
FunctArea .274 .602 -1.404 90 .164 -.186 .133
LevelInOrg .463 .498 -2.936 90 .004 -2.088 .711
NumEmployees 2.173 .144 -2.446 90 .016 -1.081 .442
TotalRevenue .461 .499 -3.362 90 .001 -1.120 .333
Industry 6.436 .013 1.640 54.285 .107 2.047 1.248
BIclass 2.830 .096 .816 90 .417 .135 .165
Treatment of Missing Data and Outliers
The data was examined for missing values. There were five cases that did not answer
any of the questions, thus they were dropped. The rest of the cases that include missing values
were not dropped due to the sample size concerns. Instead, missing values were imputed using
SAS Enterprise Miner Decision Tree imputation algorithm. Decision tree algorithms are useful
83
for missing data completion due to their high accuracy for single value prediction
(Lakshminarayan et al., 1996).
The data was examined for normality and tests were run for all independent and
dependent variables. Results show that the data is skewed to the right. To learn more about the
distribution of the data, skewness and kurtosis values were examined. Skewness values for the
dependent, independent and moderator variables were all between -1 and +1, within the
acceptable range (Huck, 2004). All kurtosis values were between -1 and +2, again all in the
acceptable range (Huck, 2004), thus the data were not judged to be significantly skewed or
kurtotic (Kline, 1997).
Demographics
The respondent pool for the survey has made up of 90.4% male and 9.6% female
professionals. While 47.8% of the respondents had a graduate degree, the highest education
level was post graduate (25.2 %). The respondents represent a broad sample with respect to
organizational size, annual total revenue, and the organizational industry. The descriptive
statistics for the size, annual revenue and the industry of the organization is summarized below
in Tables 12, 13 and 14 respectively.
84
Table 12
Descriptive Statistics on Organizational Size
Number of responses Percentage
Less than 100 27 23.3
100-499 11 9.5
500-999 10 8.6
1,000-4,999 27 23.3
5,000-9,999 11 9.5
10,000 or more 30 25.9
Total
116 100.0
Table 13
Descriptive Statistics on Annual Organizational Revenue
Number of responses Percentage
Less than $100 million 38 32.8
$100 million to $499 million 15 12.9
$500 million to $1 billion 11 9.5
More than $1 billion 40 34.5
Don’t know/not sure 12 10.3
Total
116 100.0
Almost 50% of the respondents indicated information technology as their functional
area in the organization, while the rest of the respondents belong to various other functional
areas. Forty %of the respondents are middle managers and 18% are executive level managers.
The descriptive statistics for the functional area and the organizational level of the respondents
is summarized below in Table 15 and Table 16 respectively.
85
Table 14
Descriptive Statistics on Organizational Industry
Number of the responses Percentage
Aerospace 1 .9
Manufacturing 12 10.3
Banking 6 5.2
Finance / Accounting 3 2.6
Insurance / Real Estate / Legal 11 9.5
Federal Government (Including Military) 2 1.7
State / Local Government 2 1.7
Medical / Dental / Health 10 8.6
Internet Access Providers / ISP 1 .9
Transportation / Utilities 9 7.8
Data Processing Services 5 4.3
Wholesale / Resale / Distribution 9 7.8
Education 13 11.2
Marketing / Advertising / Entertainment 3 2.6
Research / Development Lab 3 2.6
Business Service / Consultant 17 14.7
Computer Manufacturer 3 2.6
Computer / Network Consultant 2 1.7
Computer Related Retailer / Wholesaler / Distributor 2 1.7
VAR/VAD/Systems or Network Integrators 1 .9
Missing 1 .9
Total
116 100.0
58% of the respondents had worked at their respective organizations for five or fewer
years, and 5.3% had twenty or more years of experience. The average organizational
experience of all respondents is approximately seven years. 54% of the respondents held a
managerial position. 51% of the respondents identify themselves as advanced BI users, and 12%
see themselves as new to BI. Therefore, the respondents represent a range of users and
experience. Thus, they are appropriate for answering questions in this study. Table 17 below
shows the descriptive statistics on BI user experience levels.
86
Table 15
Descriptive Statistics on Functional Area
Number of responses Percentage
Management 11 9.5
Finance / Accounting / Planning 9 7.8
Information technology 54 46.6
Manufacturing / Operations 1 .9
Marketing 9 7.8
Sales 6 5.2
Supply chain 3 2.6
Other 23 19.8
Total
116 100.0
Table 16
Descriptive Statistics on Level in the Organization
Number of responses Percentage
Executive 21 18.1
Middle 47 40.5
Operational 29 25.0
Other 19 16.4
Total
116 100.0
Table 17
Descriptive Statistics on BI User Levels
Number of responses Percentage
New BI user 14 12.1
Intermediate BI user 43 37.1
Advanced BI user 59 50.9
Total
116 100.0
87
Exploratory Factor Analysis and Internal Consistency
In this dissertation, the number of factors extracted with exploratory factor analysis was
based on the criteria that the Eigenvalue should be greater than one. To extract the factors,
principal component analysis with a Varimax rotation was used. According to Hair et al. (1998),
factor loadings over 0.3 meet the minimal level, over 0.4 are considered more important, and
0.5 and greater practically significant. It is also suggested that the loadings over 0.71 are
excellent, over 0.55 good, and over 0.45 are fair (Tabachnick and Fidell, 2000; Komiak and
Benbasat, 2006). The factor analyses conducted in this study are assessed according to these
criteria.
A separate factor analysis was conducted for independent variables, dependent
variables and moderator variables, instead of one factor analysis where all indicators on
multiple factors are analyzed. Factor analyzing all 68 indicators together would result in a
correlation matrix of over 2000 relationships, thus, would not produce meaningful outcomes
(Jones and Beatty, 2001; Gefen and Straub, 2005).
For the dependent variable, BI success, five items were hypothesized to load on a single
factor, and all items loaded on one factor with 0.783 or higher. Following the factor analysis,
internal consistency of the BI success factor was examined. Cronbach’s alpha is the most widely
used measure to assess the internal consistency of a scale (Huck, 2004). A Cronbach’s alpha of
0.7 is generally considered acceptable (Hair et al, 1998). Yet, literature suggests that 0.6 may be
accepted for newly created measurement scales (Nunnally, 1978; Robinson, Shaver, and
Wrightsman, 1994). Cronbach’s alpha for the BI success factor was .914 and this is good,
88
considered to be an internally consistent measure. Table 18 below shows the factor loadings for
BI success along with the Cronbach’s alpha value.
Table 18
Factor Analysis for the Independent Variable
Items Components
BIsat5 0.927
BIsat2 0.889
BIsat3 0.869
BIsat1 0.863
BIsat4 0.783
Mean 3.716
Variance Explained 75.254%
Cronbach's Alpha 0.914
Factor analysis of independent variables was carried out in two steps. First each
construct was factor analyzed individually, to see if the items loaded as posited for each
construct, because items were largely developed by the researcher and there is no prior
validation. In the second step, the constructs were factor analyzed together. This dissertation
examines five technological BI capabilities (data quality, data sources quality, user access
methods, data reliability and interaction with other systems), three organizational BI
capabilities (flexibility, intuition involved in analysis and the level of risk supported by BI). First,
technological BI capabilities were factor analyzed individually.
Data quality has two dimensions, quantitative and qualitative data quality. All items
measuring both qualitative and quantitative data quality were retained (Table 19). Qualitative
data quality had an internal consistency of 0.970 and quantitative data quality had an internal
consistency of 0.926.
89
Table 19
Factor Analysis for the Data Quality
Items
Components
Qualitative Data
Quality
Quantitative
Data Quality
QualDataQuality4 .943 .225
QualDataQuality2 .934 .222
QualDataQuality3 .929 .201
QualDataQuality1 .929 .222
QuantDataQuality3 .189 .908
QuantDataQuality1 .182 .896
QuantDataQuality4 .204 .881
QuantDataQuality2 .251 .843
Mean 3.291 3.830
Variance Explained 62.885% 24.174%
Cronbach's Alpha .970 .926
Data sources have two dimensions, internal and external data sources. All four items
measuring internal data source quality and all three items measuring external data source
quality were retained, with internal consistencies of 0.828 and 0.916, respectively (Table 20).
Table 20
Factor Analysis for the Data Source Quality
Items
Components
External Data
Source Quality
Internal Data
Source Quality
ExtDataSrcQ3 .930 .084
ExtDataSrcQ2 .915 .131
ExtDataSrcQ1 .872 .154
IntDataSrcQ2 .085 .894
IntDataSrcQ1 -.083 .881
IntDataSrcQ3 .316 .727
IntDataSrcQ4 .466 .641
Mean 2.888 3.532
Variance Explained 50.703% 25.822%
Cronbach's Alpha .916 .828
90
User access quality was measured with three items. Factor analyzing these items
resulted in a single factor as expected, with an internal consistency of 0.768. Table 21 shows
the results.
Table 21
Factor Analysis for the User Access Quality
Items Components
UserAccess_qual3 .898
UserAccess_qual1 .879
UserAccess_qual2 .716
Mean 3.739
Variance Explained 69.989%
Cronbach's Alpha .768
Data reliability has two dimensions, internal and external data reliability. Each of these
dimensions is measured by four items. Factor analysis of these eight items yielded two separate
factors as expected (Table 22). One of the items measuring external data reliability had a
negative low loading of -0.372, thus was dropped from the scale. The remaining three items
(ExtDataReliability1, 3 & 4) had an internal consistency of 0.829. All items measuring internal
data reliability were retained with an internal consistency of 0.815.
Interaction with other systems was measured with four items. All items were retained
with loadings above .702 and have an internal consistency of 0.803. Table 23 shows the results.
91
Table 22
Factor Analysis for the Data Reliability
Items
Components
Internal Data Reliability External Data Reliability
IntDataReliability1 .892 .074
IntDataReliability3 .883 .145
IntDataReliability4 .752 .094
IntDataReliability2_Coded .705 -.196
ExtDataReliability3 -.019 .896
ExtDataReliability4 -.060 .870
ExtDataReliability1 .181 .816
Mean
3.599 3.230
Variance Explained 39.513% 31.547%
Cronbach's Alpha .815 .829
Table 23
Factor Analysis for the Interaction with Other Systems
Items Components
interaction3 .875
interaction1 .820
interaction4 .769
interaction2 .702
Mean 3.353
Variance Explained 63.119%
Cronbach's Alpha .803
Next, organizational BI capabilities are factor analyzed individually. Eight items were
used to measure flexibility. They loaded on two factors, yet the items were designed to
measure one dimension (Table 24a). Careful examination of questions indicated that one of the
factors measures scalability. Scalability relates to the flexibility of BI to operate in a larger
environment. Because the purpose is to measure flexibility in a given environment, questions
92
measuring scalability were dropped. The remaining four items (flex1, 2, 3 & 8) had loadings
greater than 0.60, with an internal consistency of 0.837. Table 24b shows the results.
Table 24a
Factor Analysis for Flexibility - I
Items Components
flex6_sca3 .903 .141
flex7_sca4 .873 .194
flex5_sca2 .800 .439
flex4_sca1 .788 .426
flex8 .079 .864
flex2 .313 .848
flex3 .349 .789
flex1 .409 .532
Mean 3.442 3.619
Variance Explained 60.491% 14.921%
Table 24b
Factor Analysis for Flexibility - II
Items Components
flex2 .910
flex3 .866
flex8 .801
flex1 .696
Mean 3.442
Variance Explained 67.612%
Cronbach's Alpha .837
Intuition involved in analysis was measured by five items. They loaded on two factors,
yet the items were designed to measure one dimension (Table 25a). Items 5, 2, and 3 loaded
together and items 1 and 4 loaded together. I first examine item 1 (Intuition1-coded). Careful
consideration of this question (Using my BI, I make decisions based on facts and numbers)
93
reveals that it may not actually tap the level of intuition involved in analysis. The extent to
which the decision maker is using facts and numbers to make decisions may not be an indicator
of the extent to which he/she uses intuition while making decisions. Consideration of Item 4
(The decisions I make require a high level of thought) indicates that it is appropriate. Before re-
running the factor analysis, however, I re-considered each of the other items to ascertain
whether they indeed seemed to be appropriate indicators of the use of intuition in decision
making. The third item, Intuition3, (With my BI, it is easier to use my intuition to make better
informed decisions) seems to tap how much BI supports intuitive decision making, rather than
the extent to which intuition is used. Thus, items 1 and 3 were removed and the factor analysis
was rerun (Table 25b). Only one factor emerges in this assessment. The loadings are
acceptable, although the reliability is borderline. I examined whether adding item 3 back would
result in a substantively stronger Cronbach’s alpha, but it did not. Therefore, I chose to use the
three items for the Intuition construct.
Table 25a
Factor Analysis for Intuition - I
Items Components
intuition5 .782 .165
intuition2 .781 .038
intuition3 .702 .024
intuition1_coded .112 -.870
intuition4 .353 .659
Mean 3.739 2.892
Variance Explained 39.620% 21.868%
94
Table 25b
Factor Analysis for Intuition - II
Items Components
intuition5 .791
intuition2 .778
intuition4 .671
Mean 3.807
Variance Explained 56.079%
Cronbach's Alpha .605
The Cronbach’s alpha for intuition is .605. Although this is lower than the suggested
level, reliability values as low as 0.5 are acceptable for new instruments (O'Leary-Kelly and
Vokurka, 1998). Therefore, because the items measuring intuition was newly developed based
on the literature, this new instrument was concluded as reliable for this study.
Level of risk was measured with four items. All items were retained with loadings above
.76 and have an internal consistency of 0.802. Table 26 shows the results.
Table 26
Factor Analysis for the Risk Level
Items Components
risk3 .821
risk4 .812
risk2 .774
risk1 .766
Mean 3.560
Variance Explained 62.992%
Cronbach's Alpha .802
These individual analyses lend support for the strength of the measurement properties
of these items and factors. To further assess measurement properties of these, exploratory
95
factor analysis was conducted, assessing these items in the presence of others. Factor analyzing
all 68 indicators at the same time would result in a correlation matrix of over 2000
relationships, thus, would not produce meaningful outcomes (Jones and Beatty, 2001; Gefen
and Straub, 2005). After careful examination of the dimensions that resulted in the prior factor
analyses, it was determined to divide this assessment into two groups. One set of factors all
relate to data oriented issues; data quality, data reliability and data source quality. Thus, these
are more closely related to technological BI capabilities. The other factors all relate to
organizational or user behavior/perceptions of the system, and thus are more closely related to
organizational BI capabilities. I first discuss the organizational BI capability factors; Table 27a
shows the initial results.
One of the items measuring interaction with other systems (interaction2) was dropped
from the analysis due to its cross-loading with user access quality. The remaining items were
factor analyzed again and Table 27b shows the results.
96
Table 27a
Factor Analysis for the Organizational BI Capability Variables - I
Items
Components
Flexibility Interaction Risk Intuition User Access Quality
flex2 .769 .121 .277 .305 .012
flex3 .760 .145 .111 .388 .000
flex1 .703 .061 .217 -.013 .146
flex8 .655 .288 .195 .313 -.058
risk1 .603 .488 .128 -.102 -.155
risk2 .541 .529 .146 -.049 -.071
risk4 .189 .720 .331 .159 -.096
intuition4 .032 .629 -.265 .055 .527
risk3 .239 .609 .318 .285 .045
UserAccess_qual3 .291 .550 .259 .476 -.122
UserAccess_qual1 .220 .515 .336 .452 -.084
interaction3 .263 .159 .827 .045 .110
interaction4 .232 .103 .752 .087 -.018
interaction1 .123 .423 .707 .129 -.101
UserAccess_qual2 .132 .160 .007 .847 .012
interaction2 .221 .038 .504 .540 .129
intuition2 -.071 -.071 .042 .059 .822
intuition5 .082 -.030 .043 -.054 .808
97
Table 27b
Factor Analysis for the Organizational BI Capability Variables - II
Items
Components
Flexibility Risk Interaction User Access Quality Intuition
flex2 .777 .103 .277 .308 .017
flex3 .773 .162 .087 .344 -.004
flex1 .698 .101 .222 -.018 .145
flex8 .650 .258 .190 .353 -.054
risk4 .149 .696 .341 .277 -.084
risk3 .224 .646 .282 .290 .040
risk2 .500 .593 .132 .009 -.080
intuition4 -.012 .578 -.249 .190 .538
risk1 .559 .564 .117 -.046 -.164
interaction3 .269 .157 .822 .056 .112
interaction4 .236 .033 .788 .157 .003
interaction1 .113 .429 .693 .161 -.099
UserAccess_qual2 .172 .037 .002 .824 .036
UserAccess_qual3 .272 .398 .301 .639 -.088
UserAccess_qual1 .202 .337 .390 .635 -.043
intuition2 -.046 -.046 .026 -.027 .819
intuition5 .088 -.064 .055 -.039 .813
Mean 3.442 3.560 3.230 3.739 3.807
Variance Explained 38.380% 10.177% 7.750% 7.166% 6.148%
Cronbach's Alpha .837 .802 .804 .768 .605
Flexibility, interaction and user access quality factors loaded clearly as expected. One of
the items measuring intuition (intuition4) cross-loaded with the items measuring risk. This item
is “The decisions I make require a high level of thought.” Decisions that involve high level of
uncertainty also involve a high level of risk associated with them, and they require high level
thinking by the decision maker. To further understand the relationship among these items,
another factor analysis was conducted including only intuition and risk items, and the analysis
was forced to produce two factors. Results, as presented in Table 28, show clear loadings for
98
two factors, with both eigenvalues greater than 1. The level of risk and intuition had 0.802 and
0.605 internal consistency values, respectively. Therefore, in subsequent analyses the four
items measuring risk were used together to measure risk and three items measuring intuition
were used together to measure intuition.
Table 28
Factor Analysis for Risk and Intuition
Items
Components
Risk Intuition
risk4 .810 .005
risk3 .807 .123
risk2 .776 -.004
risk1 .760 -.102
intuition5 -.099 .795
intuition2 -.112 .787
intuition4 .334 .648
Mean 3.560 3.807
Variance Explained 37.512% 24.168%
Cronbach's Alpha
Eigenvalues
.802
2.626
.605
1.692
Next, the technological BI capability items, (data quality, data source quality and data
reliability) were factor analyzed. This resulted in five rather than the expected six factors. Items
measuring external data reliability and external data source quality loaded together. All other
items loaded as expected. Table 29 shows the factor loadings as well as the reliability statistics.
99
Table 29
Factor Analysis for the Technological BI Capability Variables
Items
Components
External
Data
Source
Quality &
External
Data
Reliability
Qualitative
Data
Quality
Quantitative
Data Quality
Internal
Data
Source
Quality
Internal
Data
Reliability
ExtDataSrcQ2 .856 .199 .021 .053 .163
ExtDataSrcQ3 .820 .135 -.123 -.077 .245
ExtDataSrcQ1 .817 .110 .020 -.045 .234
ExtDataReliability1 .804 .013 .243 .171 -.062
ExtDataReliability3 .792 -.100 .095 .011 .002
ExtDataReliability4 .723 -.119 .262 -.116 .000
QualDataQuality4 .056 .927 .216 .102 .113
QualDataQuality1 .041 .919 .203 .053 .141
QualDataQuality3 .071 .915 .189 .099 .108
QualDataQuality2 -.003 .902 .216 .160 .151
QuantDataQuality3 .093 .180 .860 .180 .130
QuantDataQuality1 .111 .191 .850 .189 .065
QuantDataQuality4 .144 .196 .830 .135 .167
QuantDataQuality2 .149 .265 .811 .059 .074
IntDataReliability1 .048 .137 .273 .815 .145
IntDataReliability3 .082 .173 .323 .792 .149
IntDataReliability2_Coded -.082 .039 -.052 .769 .187
IntDataReliability4 -.062 .141 .531 .536 .168
IntDataSrcQ3
.188 .247 .056 .055 .803
IntDataSrcQ2
.074 .106 .297 .295 .738
IntDataSrcQ4
.363 .215 -.008 .154 .702
IntDataSrcQ1
-.054 -.035 .341 .421 .677
Mean 3.059 3.291 3.830 3.256 3.532
Variance Explained 34.518% 17.175% 11.217% 8.990% 5.059%
Cronbach's Alpha .900 .970 .926 .836 .828
One possible explanation for double loading in the first factor may be due to the nature
of the constructs. The items measuring the other four factors may have been perceived by
100
respondents as relating to internal issues. The items measuring internal data source quality and
internal data reliability are clearly focused on internal issues. However, the items measuring
qualitative and quantitative data quality do not specify internal or external. Given that majority
of data in most organizations originates internally, it is reasonable that respondents answer
with internal data in mind. Another possible explanation is that external data source quality and
external data reliability were comingled in the respondents’ perceptions as they answered.
To further understand the relationship for this external factor, another factor analysis
was conducted including only external data reliability and external data source quality items,
forcing the analysis to produce two factors. Results including the eigenvalues are presented in
Table 30. They show clear loadings for two factors as expected, with 0.916 and 0.829 internal
consistency values for external data reliability and external data source quality, respectively.
Thus, the items were separated in survey analysis.
Table 30
Factor Analysis for the Dependent Variables - External Data reliability and External Data Source
Quality
Items
Components
External Data
Source Quality
External Data
Reliability
ExtDataSrcQ2 .905 .298
ExtDataSrcQ3 .880 .244
ExtDataSrcQ1 .837 .338
ExtDataReliability4 .215 .874
ExtDataReliability3 .299 .856
ExtDataReliability1 .530 .633
Mean 2.888 3.230
Variance Explained 66.779% 14.398%
Cronbach's Alpha
Eigen values
.916
4.007
.829
.864
101
Next, exploratory factor analysis was conducted for the moderator variable. It is posited
to have two dimensions; information processing needs and decision types. Six items measured
information needs (InfoChar1-6), and five items were used to measure decision types
(DecType1-5). Initial factor analysis resulted in five factors rather than the expected two
factors. Table 31a shows the results of this initial factor analysis.
Table 31a
Factor Analysis for the Moderator Variable - I
Items
Components
Information
Needs 1
Decision
Types 1
Decision
Types 2
Information
Needs 2
Decision
Types 3
InfoChar5 .780 .067 .151 .114 .077
InfoChar2 .733 .015 -.049 -.033 -.058
InfoChar6 .639 .030 -.205 .009 .180
InfoChar1 .460 -.187 .036 .198 .259
DecType2_coded
-.027 .832 -.169 .227 .161
DecType4_coded
-.066 -.785 -.225 .119 .336
DecType1
.051 -.141 .824 .097 -.223
DecType3
-.162 .237 .757 .009 .393
InfoChar3
-.055 .009 .085 .852 -.049
InfoChar4
.199 .084 .005 .728 .082
DecType5
.248 -.072 -.009 .008 .887
Careful examination of the items loading for the Information Needs 2 factor (InfoChar3
and InfoChar4) indicated that this factor refers to the general type of information collected,
whereas the Information Needs 1 factor (InfoChar1, 2, 5 & 6) represents the different
characteristics of the information used. Because the intention of this dissertation is to examine
different characteristics of information collected, items InfoChar3 & 4 were dropped from the
scale. The new factor analysis resulted in four factors (Table 31b).
102
Table 31b
Factor Analysis for the Moderator Variable - II
Items
Components
Information
Needs 1
Decision
Types 1
Decision
Types 2
Decision
Types 3
InfoChar5 .781 .067 .157 .074
InfoChar2 .737 .017 -.067 -.055
InfoChar6 .629 .032 -.201 .174
InfoChar1 .490 -.136 .047 .313
DecType2_coded
-.004 .865 -.139 .153
DecType4_coded
-.053 -.753 -.221 .394
DecType1
.067 -.156 .827 -.219
DecType3
-.166 .238 .762 .360
DecType5
.239 -.035 .002 .882
Examining the questions measuring decision types, DecType 4 item was dropped due to
possible cross loading between Decision Types 1 and Decision Types 3. Table 31c shows the
new factor analysis after dropping this item.
Table 31c
Factor Analysis for the Moderator Variables - III
Items
Components
Information
Needs 1
Decision
Types 1
Decision
Types 2
InfoChar5 .741 .119 .027
InfoChar2 .663 -.126 -.058
InfoChar6 .639 -.203 .108
InfoChar1 .600 .046 -.082
DecType5
.523 .167 .436
DecType3
-.055 .837 .270
DecType1
.003 .759 -.349
DecType2_coded
-.065 -.054 .850
103
Item DecType 2_coded (I make decision without higher level manager involvement)
loaded as a single factor. Its wording was deemed to be ambiguous because involvement from
the higher level managers in a decision may not imply the decision type made by the decision
maker. After dropping this item, another factor analysis was run; table 31d shows the results.
Table 31d
Factor Analysis for the Moderator Variables - IV
Items
Components
Information
Needs
Decision
Types
InfoChar5 .739 .099
InfoChar2 .642 -.143
InfoChar6 .640 -.223
DecType5 .590 .138
InfoChar1
.585 .023
DecType3
.012 .836
DecType1
-.024 .764
Although this analysis resulted in two factors, one of the items thought to measure
decision types (DecType5) loaded with the items thought to measure information needs. This
item (the decisions I make require computational complexity and precision) was dropped from
the scale because it seemed to tap something other than information needs and because it also
seems to tap two different things; precision and computational complexity. Thus, it was
deemed to be a poor indicator. The resulting factors for the moderator shows high factor
loadings, yet low internal consistency (Table 31e). Reporting Cronbach’s Alpha for two-item
scales have been criticized (Cudeck, 2001), thus the correlations between items and their
significance is also reported (Table31f). Although the correlations are significant, they and the
104
Cronbach’s Alpha for Decision Types were deemed too low to retain the factor. Thus, only
Information Needs is used in subsequent analyses.
Table 31e
Factor Analysis for the Moderator Variables - V
Items
Components
Information
Needs
Decision
Types
InfoChar5 .768 .146
InfoChar2 .711 -.083
InfoChar6 .651 -.199
InfoChar1 .578 .036
DecType1
.027 .809
DecType3
-.071 .804
Mean
3.819 2.806
Variance Explained 31.260% 22.570%
Cronbach's Alpha 0.601 0.494
Table 31f
Correlations for Decision Type Items
DecType1 DecType3
DecType1
Pearson Correlation 1 .330**
Sig. (2-tailed) .000
DecType3
Pearson Correlation .330** 1
Sig. (2-tailed) .000
** Correlation is significant at the 0.01 level (2-tailed).
105
PLS Analysis and Assessment of Validity
PLS path modeling was used to analyze and assess the proposed research model and to
test the hypotheses suggested. PLS has several advantages compared to other statistical
techniques such as regression and analysis of variance. PLS has the capability to concurrently
test the measurement and structural model and does not require the homogeneity and normal
distribution of the data set (Chin et al., 2003). PLS can also handle smaller sample sizes better
than other techniques, although PLS is not a panacea for unacceptably low sample sizes
(Marcoulides and Saunders, 2006). PLS requires a minimum sample size that is 10 times greater
of either the number of independent constructs influencing a single dependent construct, or
the number of items comprising the most formative construct (Chin, 1998; Wixom and Watson,
2001; Garg et al., 2005). This dissertation examines eight BI capabilities as independent
variables, thus requires 80 as the minimum sample size. Although a priori power analysis
yielded that for an effect size of .2, an ? level of .05, and a power of .8, a minimum sample size
of 132 is needed, the collected and cleaned data of 116 respondents satisfies the PLS
requirement. SmartPLS version 2.0.M3 (Ringle, Wende & Will, 2005) is used to analyze the
research model.
The acceptability of the measurement model was assessed by the model’s construct
validity as well as the internal consistency between the items (Au et al., 2008). Internal
consistency, a form of reliability, was assessed using Cronbach’s alpha and exploratory factor
analysis was used to assess dimensionality (Beatty et al., 2001). All Cronbach’s alpha values
were satisfactory after item purifications, as presented in the previous section.
106
The independent and dependent variables were assessed for construct validity through
convergent and discriminant validity as well as composite reliability (Hair et al, 1998; Kerlinger
and Lee, 2000). Convergent validity is assessed by the average variance extracted (AVE) and
communality. Both communality and AVE values for all constructs are suggested to be higher
than the recommended threshold value of 0.5 (Rossiter, 2002; Fornell and Larker, 1981). This
required further item purifications in the model. The items that share a high degree of residual
variance with other items in the instrument were eliminated (Au et al., 2008; Gefen et al., 2000;
Gerbing and Anderson, 1988) to increase the AVE and communality values above 0.5. The
resulting item loadings and related statistics are given in Table 32 below.
Discriminant validity was assessed by comparing the square root of AVE associated with
each construct with the correlations among the constructs and observing that square root of
AVE is a greater value (Chin, 1998). As suggested for discriminant validity, the values on the
diagonal were all larger than the off-diagonal values. Composite reliability measures “the
internal consistency of the constructs and the extent to which each item indicates the
underlying construct” (Moores and Chang, 2006, p. 173). Composite reliability values were well
above the recommended level (0.70) for all constructs (Bagozzi and Yi, 1988; Fornell and Larker,
1981). Table 33 shows the composite reliability, average variance extracted (AVE), the square
root of AVE, and the correlations between constructs.
107
Table 32
Item Statistics and Loadings
Item <- Construct it measures Loading Std. dev. Mean
BIsat1 <- BI Success 0.85663 0.035 3.767
BIsat2 <- BI Success 0.889508 0.022 3.879
BIsat3 <- BI Success 0.86678 0.030 3.646
BIsat4 <- BI Success 0.786284 0.043 3.620
BIsat5 <- BI Success 0.93116 0.015 3.663
InfoChar1 <- Decision Environment 0.437519 1.099 3.470
InfoChar2 <- Decision Environment 0.803038 0.829 4.010
InfoChar5 <- Decision Environment 0.847287 0.938 3.910
InfoChar6 <- Decision Environment 0.406642 0.934 3.880
ExtDataReliability1 <- Data Reliability 0.632047 0.204 3.207
ExtDataReliability3 <- Data Reliability 0.43458 0.258 3.293
ExtDataSrcQ2 <- Data Source Quality 0.648588 0.164 2.828
ExtDataSrcQ3 <- Data Source Quality 0.587863 0.202 2.819
IntDataReliability1 <- Data Reliability 0.82271 0.147 3.871
IntDataReliability3 <- Data Reliability 0.857347 0.135 3.733
IntDataSrcQ2 <- Data Source Quality 0.806969 0.078 3.698
IntDataSrcQ3 <- Data Source Quality 0.781245 0.101 3.379
IntDataSrcQ4 <- Data Source Quality 0.774343 0.120 3.198
QualDataQuality2 <- Data Quality 0.590387 0.105 3.336
QuantDataQuality1 <- Data Quality 0.909218 0.023 3.931
QuantDataQuality3 <- Data Quality 0.89763 0.038 3.845
QuantDataQuality4 <- Data Quality 0.916249 0.025 3.776
UserAccess_qual1 <- user access quality 0.903284 0.021 3.586
UserAccess_qual2 <- user access quality 0.660236 0.114 3.853
UserAccess_qual3 <- user access quality 0.91113 0.019 3.776
flex1 <- Flex 0.676308 0.075 3.853
flex2 <- Flex 0.913582 0.014 3.293
flex3 <- Flex 0.86401 0.027 3.259
flex8 <- Flex 0.814945 0.045 3.362
interaction1 <- interaction 0.858283 0.033 3.414
interaction3 <- interaction 0.87544 0.032 3.233
interaction4 <- interaction 0.803407 0.056 3.043
intuition4 <- intuition 0.780321 0.295 3.974
intuition5 <- intuition 0.832241 0.268 3.759
risk2 <- risk 0.726962 0.088 3.440
risk3 <- risk 0.896195 0.030 3.767
risk4 <- risk 0.854442 0.039 3.741
108
Table 33
Inter-Construct Correlations: Consistency and Reliability Tests
Construct
Composite
Reliability
*AVE Risk Flexibility Intuition
Data
Quality
Data
Source
Quality
Data
Reliability
Interaction
User
Access
Quality
Decision
Environment
BI
Success
Risk 0.867 0.687 0.829
Flexibility 0.892 0.676 0.591 0.822
Intuition 0.788 0.651 0.133 0.127 0.807
Data Quality 0.903 0.705 0.453 0.411 0.127 0.840
Data Source
Quality
0.845 0.526 0.414 0.496 0.007 0.426 0.725
Data
Reliability
0.791 0.500 0.419 0.442 0.063 0.551 0.521 0.707
Interaction 0.883 0.716 0.565 0.517 0.020 0.447 0.472 0.454 0.846
User Access
Quality
0.870 0.694 0.608 0.599 0.126 0.674 0.605 0.548 0.536 0.833
Decision
Environment
0.747 0.518 0.158 0.069 0.123 0.385 0.078 0.224 0.536 0.181 0.720
BI Success 0.938 0.752 0.523 0.569 0.144 0.546 0.385 0.336 0.526 0.719 0.192 0.867
The shaded numbers on the diagonal are the square root of the variance shared between the constructs and their
measures.
Off-diagonal elements are correlations among constructs. For discriminant validity, diagonal elements should be
larger than off-diagonal elements.
* Average Variance Extracted
109
Hypotheses Testing Results
Hypothesis 1 and Hypothesis 2
Hypotheses 1a-e and 2a-c posit that technological and organizational BI capabilities
impact BI success (Table 34).
Table 34
Hypotheses 1 & 2
H1a The better the quality of data sources in an organization, the greater its BI success.
H1b The better the quality of different types of data in an organization, the greater its BI success.
H1c The higher the data reliability in an organization, the greater its BI success.
H1d
The higher the quality of interaction of BI with other systems in an organization, the greater its
BI success.
H1e
The higher the quality of user access methods to BI in an organization, the greater its BI
success.
H2a The level of BI flexibility positively influences BI success.
H2b The level of intuition allowed in analysis by BI positively influences BI success.
H2c The level of risk supported by BI positively influences BI success.
In order to obtain reliable results and t-values, 500 random samples of 116 responses
(Chin, 1998) were generated using the bootstrapping procedure available in the SmartPLS
software. The significance of the hypotheses was evaluated by assessing the significance and
the sign of the inner model path coefficients using t-tests. To evaluate the predictive validity of
the relationship between the constructs, R
2
values were assessed. Table 35 shows the path
coefficients between BI capabilities and BI success, as well as the t values associated with these
paths. Figure 5 shows the PLS results along with the t values of both the inner and the outer
models. Figure 5 also shows the R
2
value for the dependent variable, BI success. Results show
that the total variance (R
2
) for BI success explained by eight constructs is 60 percent.
110
Table 35
Path Coefficients, t Values and p Values for BI Capabilities (H1 & H2)
Constructs Path coefficients t value p-value
Flexibility 0.197927 2.918918 0.003671***
Intuition 0.046332 0.426293 0.670146
Risk 0.027724 0.183228 0. 854716
Data Source Quality -0.103309 1.506560 0.066286 **
Data Quality 0.130787 1.475176 0.070408 **
Data Reliability -0.131662 1.862048 0.031588*
Interaction with Other Systems 0.175194 2.367860 0.009137***
User Access Quality 0.537448 5.407056 0.000000***
* Significant at the p = 0.5 level
** Significant at the p = 0.1 level
*** Significant at the p = 0.01 level
Results show that H1a-e and H2a are supported. This means that the higher the quality
of data sources, data types, user access methods, higher the interaction with other systems,
data reliability and flexibility, the better the BI success. But results do not show any support
that the level of intuition used in analysis and level of risk supported by BI influences BI success.
111
Figure 5. PLS results – H1 and H2.
Hypothesis 3 and Hypothesis 4
H3 and H4 posit that the decision environment moderates the relationship between the
BI capabilities and BI success. As explained above, one dimension of the moderator was
retained for subsequent analysis; information processing needs. Information processing needs
are operationalized based on Anthony’s (1965) management activities framework, and the
items measuring this construct were developed based on Gorry and Scott Morton (1971), Kirs
R
2
= 0.60
112
et al. (1989), Klein et al. (1997) and Shim et al. (2002). As recommended by Goodhue et al.
(2007), a multiple regression approach was employed to test whether significant interactions
exist. Although PLS was stated as the main analysis method for this dissertation, using
regression is suggested instead of PLS in the case of sample size or statistical significance is of
concern (Goodhue et al., 2007). Although the sample size for this study exceeds the minimum
sample size requirements for PLS analysis (calculated as 80), the requirement set by the a priori
power analysis is not met (calculated as 132). Hence, because of the sample size is of concern
for testing a moderator effect, a multiple regression approach was employed to test H3 and H4.
The interactions between BI capabilities and the decision environment are tested by
creating cross-product variables and testing the statistical significance of these cross-product
variables in the regression equation (Keith, 2006). The cross-product variables are created by
multiplying the moderator variable with each BI capability. Before the multiplication, all BI
capabilities and the decision environment measures were centered by subtracting the mean
score of the variable from that variable (Aiken and West, 1991; Cohen et al., 2003). Centering
continuous variables helps with reducing the multicollinearity (Keith, 2006; Aiken and West,
1991).
The moderator related hypotheses, H3a-i and H4a-c, were tested with separate
regression models. Rather than testing all possible interactions, it is suggested that one should
focus on a single interaction and test one hypothesis at a time (Keith, 2006). Thus, to test the
statistical significance of the interaction, BI success was regressed on each BI capability and the
decision environment variables as the first step in a sequential regression i.e., H3a was tested
separately, then H3b, and so on. However, for clarity, the set of H3 hypotheses is presented and
113
discussed first, then the set of H4 hypotheses is presented and discussed. As the second step,
the interaction term was added to the equation. Then, the change in R
2
between the two
equations was examined. A significant change in R
2
means a significant interaction term (Keith,
2006). This method of testing interaction is equivalent to dividing the sample into two groups
based on the moderator, conducting separate regressions for each group, and comparing the
regression coefficients (Keith, 2006). Table 36 shows the hypotheses H3a-i.
Table 36
Hypothesis 3
H3a
The influence of high quality internal data sources on BI success is moderated by the
decision environment such that the effect is stronger for structured decision types and
operational control activities.
H3b
The influence of high quality external data sources on BI success is moderated by the
decision environment such that the effect is stronger for unstructured decision types and
strategic planning activities.
H3c
The positive influence of high quality quantitative data on BI success is moderated by the
decision environment such that the effect is stronger for structured decision types and
operational control activities.
H3d
The positive influence of high quality qualitative data on BI success is moderated by the
decision environment such that the effect is stronger for unstructured decision types and
strategic planning activities.
H3e
The positive influence of high data reliability at the system level on BI success is
moderated by the decision environment such that the effect is stronger for structured
decision types and operational control activities.
H3f
The positive influence of high data reliability at the individual level on BI success is
moderated by the decision environment such that the effect is stronger for unstructured
decision types and strategic planning activities.
H3g
The positive influence of high quality interaction of BI with other systems in the
organization on BI success is moderated by the decision environment, such that the effect
is stronger for unstructured decision types and strategic planning activities.
H3h
The positive influence of high quality shared user access methods to BI on BI success is
moderated by the decision environment, such that the effect is stronger for structured
decision types and operational control activities.
H3i The positive influence of high quality individual user access methods to BI on BI success is
moderated by the decision environment, such that the effect is stronger for unstructured
decision types and strategic planning activities.
114
Only H3c was supported. High quality quantitative data has a greater impact on BI
success for operational control activities. Because these activities are largely based on
quantifiable data, the quality of that data is critical to the guidance that a BI provides the
decision maker. However, H3d, which posits that higher quality qualitative data has a greater
impact on BI success in a strategic decision environment, was not supported. One possible
explanation is that these respondents rely more heavily on quantitative or quantifiable data
than on qualitative data. Thus, they are not as concerned with the quality of qualitative data in
the strategic decision environment.
None of the other hypothesized moderator effects were significant for this set of
hypotheses. This suggests that the decision environment does not moderate the ability of BI to
support decision making. It does not moderate the relationship between BI success and the
influence of data sources (H3a & b), data reliability (H3 e & f), or user access methods (H3h & i)
regardless of whether the environment is one of operational activities or strategic activities.
One possible explanation for this is that the data sources are consistent across respondents i.e.,
the data they use is drawn from transactional data that is filtered into data warehouses and
data marts, regardless of the decision environment. Similarly, although data reliability impacts
BI success, all decisions must be based on reliable data regardless of the decision environment.
With regard to user access methods, these findings indicate that higher quality user access
methods positively impact BI success regardless of decision environment. Table 37 shows
regression results for H3, where the significant hypotheses are highlighted.
115
Table 37
Multiple Regression Results – H3
Variables ?
t-
value
p-
value
R Square
Change
F
Change
Sig. F
Change
Internal Data Source Quality .311 3.559 .001
.118 7.546 .001
H3a
Decision Environment .180 1.551 .124
Internal Data Source Quality .317 3.596 .000
.003 .362 .549 Decision Environment .172 1.472 .144
IntDatSrcQ X DecEnv -.075 -.602 .549
External Data Source Quality .165 2.142 0.34
.057 3.428 .036
H3b
Decision Environment .165 1.371 .173
External Data Source Quality .164 2.120 .036
.004 .528 .469 Decision Environment .171 1.418 .159
ExtDatSrcQ X DecEnv -.084 -.726 .469
Quantitative Data Quality .557 6.311 .000
.275 21.390 .000
H3c
Decision Environment -.067 -.592 .555
Quantitative Data Quality .509 5.779 .000
.041 6.759 .011 Decision Environment -.065 -.593 .554
QuantDatQ X DecEnv .298 2.600 .011
Qualitative Data Quality .242 3.156 .002
.098 6.165 .003
H3d
Decision Environment .128 1.083 .281
Qualitative Data Quality .225 2.798 .006
.005 .565 .454 Decision Environment .138 1.155 .250
QualDatQ X DecEnv .077 .752 .454
Internal Data Reliability .404 4.047 .000
.143 9.436 .000
H3e
Decision Environment .112 .966 .336
Internal Data Reliability .369 3.468 .001
.007 .900 .345 Decision Environment .126 1.079 .283
IntDatRel X DecEnv .153 .949 .345
External Data Reliability .164 1.760 .081
.045 2.668 .074
H3f
Decision Environment .159 1.309 .193
External Data Reliability .170 1.801 .074
.002 .215 .644 Decision Environment .155 1.270 .207
ExtDatRel X DecEnv .069 .464 .644
Interaction .479 6.605 .000
.292 23.322 .000
H3g
Decision Environment .232 2.222 .028
Interaction .478 6.523 .000
.000 .003 .956 Decision Environment .232 2.208 .029
Interaction X DecEnv .006 .056 .956
User Access Quality .671 9.912 .000
.475 51.156 .000
H3h/H3i
Decision Environment .069 .767 .445
User Access Quality .668 9.385 .000
.000 .032 .858 Decision Environment .067 .736 .463
UserAccQ X DecEnv .021 .180 .858
116
To further assess the substantive impact of the significant moderator effect in H3c,
regression equations were calculated for low and high values of independent variables by
substituting the desired values in the overall regression equation. Research suggests
substituting the value of -1 standard deviation, the mean, and +1 standard deviation on the
moderator variable (Aiken and West, 1991). Because I am specifically interested in the
implications of this research for operational and strategic decision environments (low and high
values of the decision environment variable), a regression equation was calculated using -1
standard deviation, mean and +1 standard deviation of the decision environment. The mean
and the standard deviation for the decision environment are shown below in Table 38. Table 39
presents the calculated regression equations.
Table 38
Descriptive Statistics for the Decision Environment
N Minimum Maximum Mean Std. Deviation
Average Decision
Environment - Centered
116 -1.818966 1.181034 .00000000 .644024546
Table 39
Regression Equations for High and Low Values of the Decision Environment
Moderator
Values
Corresponding
Decision
Environment
Independent
Variable
Regression Equation for BI Success
+1 Standard
Deviation
Operational
Quantitative
Data Quality
BI Success = 3.596 + (0.807 * QuantitDataQ)
Mean
Between Operational
and Strategic
Quantitative
Data Quality
BI Success = 3.661 + (0.509 * QuantitDataQ)
-1 Standard
Deviation
Strategic
Quantitative
Data Quality
BI Success = 3.726 + (0.211 * QuantitDataQ)
117
The above regression equations show that quantitative data quality has stronger effect
on BI success for operational decision environments, where the decisions are structured and
management activities are operational. Below Figure 6 show the graphical representation of
the above mentioned regression lines. This figure depicts that quantitative data quality appears
to have a substantive positive effect on BI success. As this variable increases, BI success for an
operational decision environment exhibits greater increase than for a strategic decision
environment. Thus, the effect of moderation is significantly and substantively greater for the
operational decision environment.
Figure 6. Interaction effect on the quantitative data quality.
The interaction effect of the decision environment on the relationship between
organizational BI capabilities and BI success (H4a - c) were each tested separately using multiple
regression. These hypotheses examine only the moderator effect of an unstructured/strategic
0
1
2
3
4
5
6
7
8
9
1 2 3 4 5 6 7 8 9
BI Success for
strategic Dec Env
BI Success for
operational Dec
Env
Quantitative Data Quality
B
I
S
u
c
c
e
s
s
118
decision environment (Table 40). Results show that none of the R
2
changes are significant, thus
the interaction effects are not significant (Table 41). The strength of the impact of flexibility,
risk, and intuition on BI success is not impacted by the decision environment. Only flexibility
and risk impact BI success in the absence of the moderator. This suggests that the degree of
intuition involved in the decision is not related to the success of the BI in supporting decisions.
One reason for this may be that BI users do not heavily rely on intuition for decision making.
This is consistent with research that indicates that BI helps to reduce the amount of intuition
involved in decision making (Howson, 2006). A possible explanation for the findings
surrounding flexibility and risk may also relate to the way BI is used. Research suggests that BI
may be more useful in helping decision makers grapple with decisions involving higher risk and
where flexibility is needed (Clark et al 2007). Therefore, the impact of flexibility and risk on BI
success is strong across decision environments.
Table 40
Hypotheses 4
H4a The influence of BI flexibility on BI success is moderated by the decision environment, such that
the effect is stronger for unstructured decision types and strategic planning activities.
H4b The influence of the intuition allowed in analysis on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured decision types and strategic
planning activities.
H4c The influence of tolerating risk on BI success is moderated by the decision environment, such that
the effect is stronger for unstructured decision types and strategic planning activities.
A summary of all hypotheses testing results is provided in Table 42. Overall, BI success is
greater with higher quality data sources, data types, data reliability, interaction of BI with other
systems, user access methods, and higher flexibility. Thus, technological BI capabilities are
largely more influential in BI success than organizational. This is somewhat surprising given the
119
importance of organization readiness (capabilities) called for in much of the BI literature (Clark,
et al. 2007; Watson and Wixom, 2007). The implications of these findings are discussed further
in Chapter 5.
Table 41
Multiple Regression Results – H4
Variables ?
t-
value
p-
value
R
2
Change
F
Change
Sig. F
Change
Flexibility .554 7.344 .000
.336 28.575 .000
H4a
Decision Environment .178 1.769 .080
Flexibility .558 7.321 .000
.001 .211 .647 Decision Environment .182 1.795 .075
Flexibility X DecEnv -.050 -.460 .647
Risk .490 6.208 .000
.268 20.728 .000
H4b
Decision Environment .176 1.664 .099
Risk .485 5.825 .000
.000 .035 .852 Decision Environment .174 1.622 .108
Risk X DecEnv .022 .187 .852
Intuition .095 .733 .465
.024 1.363 .260
H4c
Decision Environment .176 1.438 .153
Intuition .089 .668 .505
.000 .049 .825 Decision Environment .173 1.403 .163
Intuition X DecEnv .043 .222 .222
120
Table 42
Summary of Hypothesis Testing
Hypothesis Results
T
e
c
h
n
o
l
o
g
i
c
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
D
i
r
e
c
t
E
f
f
e
c
t
s
H1a: The better the quality of data sources in an organization, the
greater its BI success.
Supported
H1b: The better the quality of different types of data in an
organization, the greater its BI success.
Supported
H1c: The higher the data reliability in an organization, the greater its
BI success.
Supported
H1d: The higher the quality of interaction of BI with other systems in
an organization, the greater its BI success.
Supported
H1e: The higher the quality of user access methods to BI in an
organization, the greater its BI success.
Supported
O
r
g
a
n
i
z
a
t
i
o
n
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
D
i
r
e
c
t
E
f
f
e
c
t
s
H2a: The level of BI flexibility positively influences BI success. Supported
H2b: The level of intuition allowed in analysis by BI positively
influences BI success.
Not
Supported
H2c: The level of risk supported by BI positively influences BI
success.
Not
Supported
T
e
c
h
n
o
l
o
g
i
c
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
I
n
t
e
r
a
c
t
i
o
n
E
f
f
e
c
t
s
H3a: The influence of high quality internal data sources on BI success
is moderated by the decision environment such that the effect is
stronger for structured decision types and operational control
activities.
Not
Supported
H3b: The influence of high quality external data sources on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Not
Supported
H3c: The positive influence of high quality quantitative data on BI
success is moderated by the decision environment such that the
effect is stronger for structured decision types and operational
control activities.
Supported
(table continues)
121
Table 42 (continued).
T
e
c
h
n
o
l
o
g
i
c
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
I
n
t
e
r
a
c
t
i
o
n
E
f
f
e
c
t
s
H3d: The positive influence of high quality qualitative data on BI
success is moderated by the decision environment such that the
effect is stronger for unstructured decision types and strategic
planning activities.
Not
Supported
H3e: The positive influence of high data reliability at the system level
on BI success is moderated by the decision environment such that
the effect is stronger for structured decision types and operational
control activities.
Not
Supported
H3f: The positive influence of high data reliability at the individual
level on BI success is moderated by the decision environment such
that the effect is stronger for unstructured decision types and
strategic planning activities.
Not
Supported
H3g: The positive influence of high quality interaction of BI with
other systems in the organization on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Not
Supported
H3h: The positive influence of high quality shared user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for structured decision
types and operational control activities.
Not
Supported
H3i: The positive influence of high quality individual user access
methods to BI on BI success is moderated by the decision
environment, such that the effect is stronger for unstructured
decision types and strategic planning activities.
Not
Supported
O
r
g
a
n
i
z
a
t
i
o
n
a
l
B
I
C
a
p
a
b
i
l
i
t
i
e
s
I
n
t
e
r
a
c
t
i
o
n
E
f
f
e
c
t
s
H4a: The influence of BI flexibility on BI success is moderated by the
decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Not
Supported
H4b: The influence of the intuition allowed in analysis on BI success
is moderated by the decision environment, such that the effect is
stronger for unstructured decision types and strategic planning
activities.
Not
Supported
H4c: The influence of tolerating risk on BI success is moderated by
the decision environment, such that the effect is stronger for
unstructured decision types and strategic planning activities.
Not
Supported
* Significant at the p = 0.1 level
** Significant at the p = 0.05 level
*** Significant at the p = 0.01 level
122
CHAPTER 5
DISCUSSION AND CONCLUSIONS
This dissertation studies the relationship between various business intelligence (BI)
capabilities and BI success, and whether this relationship is affected by different decision
environments. This chapter starts with providing a discussion of the findings and presenting the
limitations of the study. It then proceeds with theoretical and managerial implications of the
study, and concludes by providing future research directions.
Discussion of Research Findings
This dissertation proposes a framework for examining technological and organizational
BI capabilities and how they impact BI success. This framework also considers that different BI
capabilities may have a more significant impact on BI success for different decision
environments. The decision environment consists of different information characteristics
required to make decisions. Each of the constructs and their relevant findings are discussed
below.
Technological BI Capabilities and BI Success
Hypotheses 1a-e propose that the quality of technological BI capabilities positively
impact BI success. The technological BI capabilities examined in this dissertation are data
sources (H1a), different types of data (H1b), data reliability (H1c), interaction of BI with other
systems (H1d) and user access methods to BI (H1e). These hypotheses suggest that the higher
the quality of technological BI capabilities, the greater the BI success. All of these hypotheses
(H1a-e) were confirmed by the positive significant relationship between all technological BI
capabilities and BI success.
123
These results suggest that technological BI capabilities are critical elements for a
successful BI. Organizations, as they are going through BI implementations, should make sure
that they have these technological capabilities implemented. But, just implementing these
capabilities is not enough; the difference in the quality of these capabilities is one of the factors
that may explain why some organizations are successful with their BI initiative while some are
not. Organizations should work towards maintaining the quality of these capabilities, because
as the quality of technological BI capabilities increases, the BI success in an organization also
increases.
These results are also consistent with prior research. Research shows that clean, high
quality and reliable data is one of the most important BI success factors (Eckerson, 2003;
Howson, 2006). Research also implies that the sources where the organizations obtain their
data from play a critical role for the success of a BI initiative (Howson, 2006). Especially for
organizations that use multiple data sources and multiple information systems, it is critical to
integrate these technologies and information to avoid inconsistencies and inaccuracies
(Swaminatha, 2006; Sabherwal and Becerra-Fernandez, 2010). Different user access methods
are also critical for BI success; providing high quality user access methods increases the decision
making effectiveness (Hostmann et al., 2007) as well as the effectiveness of presenting the
appropriate information based on user specific needs and tasks. Because the overall goal is to
enable users access and navigate through data based on their requirements (Sabherwal, 2007,
2008).
124
Organizational BI Capabilities and BI Success
Hypotheses 2a-c propose that organizational BI capabilities positively impact BI success.
The organizational BI capabilities examined in this dissertation are flexibility (H2a), the level of
intuition involved in analysis (H2b) and level of risk supported by BI (H2c). These hypotheses
suggest, regardless of their levels, these organizational BI capabilities significantly impact BI
success. Results of data analysis showed that H2a was confirmed by the positive significant
relationship between flexibility and BI success, but H2b and H2c were not confirmed. There was
not a significant relationship between BI success and intuition involved in analysis or level of
risk supported by BI. This is somewhat surprising considering research emphasizing the
importance of organizational readiness (Clark, et al. 2007; Watson and Wixom, 2007). Although
organizational readiness and organizational capabilities are not the same thing, organizational
capabilities play a critical role in achieving organizational BI readiness (Williams and Williams,
2007).
The significance of flexibility as an organizational BI capability suggests that in order to
be successful, a BI initiative should be able to accommodate a certain amount of variation in
the business processes, environment or the technology (Gebauer and Schober, 2006; Clark et
al., 2007). This finding is also consistent with the literature. Prior research suggests that
flexibility is one of the most important factors to consider while selecting a BI application
(Dreyer, 2006). Considering that change is inevitable in the current business environment, the
organization should be able to modify its BI easily and quickly to adapt to the changing business
(Sabherwal and Becerra-Fernandez, 2010).
125
The non-significance of the level of intuition involved in analysis may indicate two
things. First, it may mean that decision makers do not involve their intuition in their decision
making process with BI and make decisions purely on data and analysis. In support of this
argument, prior research suggests that organizations making decisions based on data and
analysis are more likely to succeed with their BI initiative compared to the organizations making
decisions based on intuition (Howson, 2008; Sabherwal and Becerra-Fernandez, 2010). The
non-significance of the level of intuition involved in analysis may also mean that BI success is
more dependent on how decision makers use the system rather than “what is going on in their
head.” While experience based intuition is important, gut instinct based on experiences is not
as useful as it used to be in less dynamic events (Bresnahan, 1999). This is consistent with
research that indicates that BI helps to reduce the amount of intuition involved in decision
making (Howson, 2006).
A possible explanation for the findings about risk may also relate to the way BI is used.
Research suggests that BI may be more useful in helping decision makers deal with decisions
involving higher risk (Clark et al 2007). BI has been studied as a risk analysis and mitigation
platform, with the overall goal of managing and reducing it (Azvine et al., 2007). Given internal
and external risks that an organization deals with and how they can harm organizational
performance, the role of BI should be to manage risk by attempting to minimize it and
providing an integrated view of performance and risk (Azvine et al., 2007). Thus, users may not
be aware of the level of risk surrounding the decisions they make because their BI is already
managing that risk. It is also possible that different organizations as well as different groups
within an organization may be facing different levels of risk during their decision making
126
process, and the majority of respondents to the survey were from a group that does not have
to deal with a lot of risk. This is not surprising considering that majority of the respondents are
middle and operational level managers. By definition, middle and operational level managers
deal with less risky situations compared to strategic level managers.
Technological BI Capabilities and the Decision Environment
Hypotheses 3a-i propose that the relationship between technological BI capabilities and
BI success is moderated by the decision environment. Results of data analysis showed that only
the influence of high quality quantitative data on BI success is moderated by the decision
environment such that the effect is stronger for operational decision environments (H3c). This
finding is not surprising considering that operational management activities largely rely on
quantifiable data (Gorry and Scott Morton, 1971; Anthony, 1965; Keen and Scott-Morton,
1978), and that the quality of that data is critical for the decision maker. The rest of the
hypotheses positing interaction effects were not supported. More specifically, the findings
suggest that high quality internal data sources (H3a), high quality quantitative data (H3c), high
data reliability at the system level (H3e), and high quality shared user access methods (H3h) do
not have a stronger impact on BI success for operational decision environments.
The results also suggest that high quality external data sources (H3b), high quality
qualitative data (H3d), high data reliability at the individual level (H3f), high quality interaction
(H3g), and high quality individual user access methods (H3i) do not have a stronger impact on BI
success for strategic decision environments. One possible explanation for the non-significance
of H3d is that respondents rely more heavily on quantitative or quantifiable data than on
qualitative data. Thus, they are not as concerned with the quality of qualitative data in the
127
strategic decision environment. It is also a possibility that there were not enough respondents
representing the strategic decision environment to account for a significant statistical impact.
The non-significance of the other hypothesized moderator effects suggests that the
decision environment does not moderate the relationship between technological BI capabilities
and BI success. One possible explanation for this is that respondents refer to the same data
sources, use consistently reliable data, access the BI and experience same level of interaction
with other systems, regardless of decision environment. Thus, these technological BI
capabilities influence BI success regardless of whether the environment is one of structured
decisions/operational activities or unstructured decisions/strategic activities.
Organizational BI Capabilities and the Decision Environment
Hypotheses 4a-c proposes the relationship between organizational BI capabilities and BI
success is moderated by the decision environment. More specifically, they suggest that the
positive impact of flexibility (H4a), intuition allowed in analysis (H4b) and level of risk supported
by BI (H4c) on BI success is stronger for strategic decision environments. Results of data analysis
showed that none of the interaction effects hypothesized is significant. This indicated that the
decision environment does not impact the strength of the relationship between BI success and
organizational BI capabilities.
The non-significance of the interaction effect associated with intuition is not surprising
in the light of the non-significance of its main effects (H2b). Thus, it may mean that decision
makers do not involve their intuition in the decision making process, regardless of the decision
environment. Research also suggests that the role of BI is to minimize the use of intuition in the
decision making process (Howson, 2006; Sabherwal and Becerra-Fernandez, 2010). Similarly,
128
the main effect of the level of risk was not significant, and the interaction effect associated with
risk is not significant either. It means that there is no difference between decision
environments in terms of the impact level of risk supported by BI has on BI success. This may
indicate that regardless of the decision environment, BI is more successful as long as it can
support high risk decisions.
Flexibility impacts BI success in the absence of the moderator. The interaction effect of
the decision environment on the relationship between flexibility and BI success is not
significant. This indicates that the level of BI flexibility is as important for operational decision
environments as it is for strategic decision environments. This is consistent with research
suggesting that BI is more successful dealing with situations where flexibility is needed (Clark et
al 2007).
Limitations
This study is subject to several possible limitations in terms of sample size and scales
used. First, the sample size does not allow for a more comprehensive analysis. The results might
have been more significant if the sample size had been larger and a more thorough analysis
could have been employed. Also, the respondents are not as diverse as I would like. For
instance, only 11 respondents are female, and only 24 of the respondents are executive level
managers. Response rate is another limitation for this study. Although the survey link was
broadcasted to over 8,000 people, less than 1% actually filled out the survey. There can be
possible reasons for the low response rate. First of all, there was no incentive for taking the
survey and considering the busy business life, recipients possibly did not feel compelled to take
129
the survey. Also, the length of the survey (20 to 30 minutes) might be another reason why the
recipients did not want to complete the survey.
Common method variance is another possible limitation of the study. Common method
variance refers to the fact that potential respondent biases might constitute a systematic error.
This is common when using survey responses from the same source because a single
respondent for each survey can only yield one perspective. Thus, there might be spurious
correlation (Bagozzi, 1980). Several precautions were taken to minimize the effects of common
method variance. The dependent and independent variables were separated into different
sections of the survey instrument, using different question formats.
Another possible limitation is the items used to measure some of the constructs. The
reliability analysis was not satisfactory for the level of intuition involved in analysis and decision
types constructs. It is possible that items measuring intuition was not clear enough or did not
tap well enough into the level of intuition the respondents use during their decision making
process. Although the analysis of the responses show that more than 65% of the respondents
involve their gut feeling and put emphasis on their past experiences when making decisions,
this was not reflected in the BI success factor. Similarly, three out of five items that were
supposed to measure decision types were dropped from the scale, because they were either
tapping into multiple different things (e.g., DecType5, “the decisions I make require
computational complexity and precision”) or their wording was deemed to be ambiguous (e.g.,
DecType2_coded, “I make decision without higher level manager involvement”) because they
were not necessarily measuring the decision type made by the decision maker.
130
The items measuring user access quality were deemed problematic. It was posited that
the user access methods consist of two types; individual user access and shared user access.
The goal with the survey was to measure the extent of satisfaction of the BI user with both user
access methods. Yet, the items in the survey only measure the overall quality of the user access
methods. Thus, whether these two different user access methods have different impacts on BI
success, for different decision environments could not be measured. Instead, the impact of the
overall user access quality on BI success for different decision environments was measured.
Although scale related issues may pose as limitations for the current study, this may
also be considered as a starting point for developing a BI success model and its scale. The
wording of some of the questions was ambiguous and mistakes such as using conjunctives have
been made. There are some questions that should have been divided into two and asked as two
different questions.
Research Contributions
The BI success model suggested in this study contributes to the information systems (IS)
literature in several ways. First, it proposes to extend current research in BI and provide a
parsimonious and intuitive model for explaining the relationship between BI success and BI
capabilities in the presence of different decision environments, based on theories from decision
making and organizational information processing. This dissertation contributes to academic
research by providing richer insight in the role of the decision environment in BI success and
providing a framework with which future research on the relationship between BI capabilities
and BI success can be conducted.
131
Another research contribution is the inclusion of the decision environment in the BI
success model. The moderating effect of the decision environment has not been studied in the
IS literature before. The decision environment is operationalized based on Gorry and Scott
Morton’s (1971) framework for DSS and Anthony’s (1965) framework for management
activities. Although these are two established theories, they have not been used for BI research
before and also have not been operationalized to measure with survey items. This study is a
first attempt in creating survey items to operationalize these frameworks. Also, this dissertation
is a first attempt to develop a scale for BI capabilities and BI success. The BI capabilities have
not been measured to date and they all have shown good validity and reliability. All capabilities
has an internal consistency of .768 or above, with the exception of the intuition involved in
analysis, which had an acceptable level of internal consistency of .605 for newly developed
instruments (O’Leary-Kelly and Vokurka, 1998). Similarly, the BI success scale was a first
attempt and had a Cronbach’s alpha of .914, indicating an internally consistent scale (Nunnally
and Bernstein, 1994).
The findings of this study indicate that technological BI capabilities impact BI success
significantly, regardless of the decision environment. This may imply that technology drives the
BI initiatives. While the technologies used or the platform BI is built upon is undeniable critical
for BI success, factors such as top management support, alignment between business strategy
and BI, a strong BI team and available resource are as important (Eckerson, 2006; Wason and
Wixom, 2007). These non-technological capabilities are mostly referred to as organizational
readiness issues and discussed widely in the IT literature as critical success factors for IT
implementation (Rud, 2009; Williams and Williams, 2007; Abdinnour-Helm et al., 2002).
132
Although these have substantial impact on how BI is used within an organization, there are still
enabling technologies that need to be implemented in order to benefit from these factors
(Sabherwal and Becerra-Fernandez, 2010). This may be the reason behind findings suggesting
that technological capabilities impact BI success more significantly. For example, intuition was
non-significant in the results, implying that it does not substantially impact BI success.
Consistent with this finding, literature suggests not to make decisions based on intuition, yet,
both academic and practitioners’ literature emphasize that so many business decisions today
are made based on the decision maker’s gut feeling (Davenport and Harris, 2007; Bonabeau,
2003). As a solution, converting intuition into a tangible strategy is suggested, through using
decision support tools (Bonabeau, 2003) and analytics (Davenport and Harris, 2007). This
exemplifies that it is critical to have the necessary enabling technologies to be able to benefit
from the organizational capabilities.
Only one of the three organizational BI capabilities, flexibility, was found to be
significant by the analysis. This indicates that technology is the most eminent factor that
decision makers associate with BI success, and that BI success is mostly driven by technology
rather than organizational factors. Although research suggests that organizational BI
capabilities are important for BI success (Watson and Wixom, 2007; Watson, 2008) the results
of this study suggest that some organizational BI capabilities are more important than the
others. The significance of flexibility as an organizational BI capability shows that it is a
strategically important element for managing the unpredictable, especially in the technology-
intensive settings (Evans, 1991). This suggests that flexibility can be tied to the frequently
sought after agility by the companies. Agility can be defined as a measure of an organization’s
133
ability to change and adapt to new environments (Neumann, 1994). The more change in the
business environment, the more the organization requires agility and BI provides the
opportunities for the organization to be more agile and adopt innovation (Sabherwal and
Becerra-Fernandez, 2010). It is possible that organizations strive to achieve agility, and
flexibility of BI may be the most important capability of BI in order to achieve that agility.
Literature has suggested IT capabilities as a potential source for agility (Weill et al., 2002; Fink
and Neumann, 2007), and findings of this study is consistent with the previous research findings
about flexibility being one of the most important factors for achieving agility (Swafford et al.,
2008; Erol et al., 2009).
Another contribution of this dissertation is that it shows the relationship between
quantitative data quality, decision environment and BI success. The results show that the
influence of high quality quantitative data on BI success is moderated by the information
processing needs such that the effect is stronger for operational control activities. Literature
suggests that operational management activities largely rely on quantifiable data (Gorry and
Scott Morton, 1971; Anthony, 1965; Keen and Scott-Morton, 1978), and that the quality of that
data is critical for the decision maker. This indicates that, based on the information
requirements of a decision maker, the quality of the quantitative data significantly impacts BI
success. Especially for those decision makers who deal with operational control related
management activities, this impact becomes even more obvious because they mostly rely on
this type of data. Although the importance of data quality and to be more specific, the quality
of the quantitative data has been studied (Baars and Kemper, 2008; Sabherwal and Becerra-
134
Fernandez, 2010), the fact that it is more critical for the operational control activities has not
been investigated previously.
This study provides significant findings for practitioners. The practitioner oriented
contribution of this study is that it helps users and developers of BI understand how to better
align their BI capabilities with their decision environments and presents information for
managers and users of BI to consider about their decision environment in assessing BI success.
Although it is the only significant interaction effect, the fact that quantitative data quality has a
stronger effect on BI success for operational decision environments rather than strategic
decision environments provides an important insight for BI users and managers. Also, the scale
used for this study can be worked up and extended into a much broader BI success survey,
which can be used in the industry to assess organizations’ BI success.
Conclusion and Future Research Directions
Research on BI success and its relationship with BI capabilities is scarce. This study
introduces a new BI success model and provides understanding regarding how different BI
capabilities can improve BI success within an organization. Prior to this study, BI success
research included topics such as critical success factors for BI implementation (Wixom and
Watson, 2001; Solomon, 2005), measurement of BI success (Gessner and Volonino, 2005;
Lonnqvist and Pirttimaki, 2006), and case studies focusing on success or failure stories of
specific BI technologies implemented by specific companies (Cooper et al., 2000; Watson and
Donkin, 2005; Anderson-Lehman et al., 2004).
This study adds to the existing body of knowledge by introducing technological and
organizational BI capabilities and how they can impact BI success. In addition, this study also
135
introduces the decision environment as a moderator for BI success. The findings of this
dissertation suggest that technological capabilities positively impact BI success. However,
hypotheses testing the moderating effect of the decision environment are not supported with
one exception. Results show that the quality of quantitative data indeed impacts BI success
stronger for operational decision environment than strategic decision environments, as
hypothesized. Using a different sampling frame and a larger sample size may yield more
significant findings. Thus, this is one of the future research directions. Another future research
direction may be to expand the capabilities. The technological and organizational BI capabilities
studied in this research are by no means exhaustive. Reexamining the ones studied in this
dissertation, expanding capabilities and even possible redefining grouping of the constructs
maybe another future research direction.
Having the right BI capabilities within the proper decision environment is important for
an organization to realize maximum benefits from its BI investment. This study may serve as a
starting point in investigating how different BI capabilities may impact BI success, for different
decision types and different information requirements for those decisions. Future research on
BI success would benefit from the inclusion of different BI capabilities as well as the inclusion of
other organizational characteristics, such as the organizational structure or organizational
culture. Incorporating environmental characteristics such as the uncertainty and equivocality
(Tushman and Nadler, 1978) in the model may also increase understanding of BI success.
136
APPENDIX A
COVER LETTER
137
Dear Participant,
I would like to invite you to participate in this research project, which is being conducted as part
of the requirements for me to earn my Ph.D. in Business Computer Information Systems from
the University of North Texas. The project aims to measure Business Intelligence (BI) success by
examining the BI capabilities used in your organization and how they are influenced by your
decision environment.
Your honest responses to each statement and question are extremely important to this
project’s outcome. You can be assured of complete confidentiality – no individual responses
will be published and the raw information will be accessible only to me and the University of
North Texas faculty on my dissertation committee. This survey contains sections addressing
your satisfaction with BI, the types of decisions you make, your information processing needs,
the capabilities of BI you use, and some information about yourself.
It will take you approximately 30 minutes to complete the survey. In addition, your
participation is voluntary. You may decline to answer any particular question that you are
uncomfortable with or feel is not appropriate. Submitting the survey will indicate that you have
given your consent for us to use your data. The study has been reviewed and approved by the
UNT Committee for the Protection of Human Subjects (940.565.3940). If you have questions
concerning this study, please feel free to contact me.
Thank you again for your consideration.
Sincerely,
Oyku Isik
138
APPENDIX B
SURVEY INSTRUMENT
139
1. What is the highest education level you have completed? (Analysis Label: HighestEdLevel)
High School
Some college
Two-year college degree
Four-year college degree
Graduate degree
Post-graduate degree
2. What is your gender? (Analysis Label: Gender)
Male
Female
3. How long have you been in your current organization? ___ years (Analysis Label: TimeInOrg)
4. Do you hold a managerial position? (Analysis Label: ManagerialPosition)
Yes
No
5. What is your functional area? (Analysis Label: FunctArea)
General management
Corporate communications
Finance / Accounting / Planning
Human resources / Personnel
Information technology
Legal
Manufacturing / Operations
Marketing
Sales
Supply chain
Other (please specify) __________________________
6. What is your level in the organization? (Analysis Label: LevelInOrg)
Executive management
Middle management
Operational management
Other (please specify) __________________________
7. What is your job title? __________ (Analysis Label: JobTitle)
140
8. What is the approximate number of employees in your organization? (Analysis Label:
NumEmployees)
Less than 100
100-499
500-999
1,000 -4,999
5,000- 9,999
10,000 or more
9. Which below best describes your industry? (Analysis Label: Industry)
Manufacturing
Finance
Education
Wholesale & retail trade
Transportation
Banking
Manufacturing
Utilities
Government
Insurance
Other (Please specify) ______
141
For the purposes of this research, Business Intelligence (BI) is defined as the
following;
"BI is a system comprised of both technical and organizational elements that
presents historical information to its users for analysis, to enable effective decision
making and management support, for the overall purpose of increasing organizational
performance."
Please answer the following questions about a specific BI application you use for your
everyday business decision making purposes. If you are using more than one BI
application, please focus only on one of them and answer the questions only based on
that specific application.
Please choose the response which best describes your satisfaction with each of the
following:
LABEL
CONSTRUCT: BI Success
Strongly
dissatisfied
Somewhat
dissatisfied
Neither
satisfied
or dissatisfied
Somewhat
satisfied
Strongly
satisfied
BIsat1
How well the BI that I am using
supports my decision making
BIsat2
How well the BI that I am using
provides precise information I need
BIsat3
How well the BI that I am using
provides information I need in time
BIsat4
How user friendly the BI that I am
using is
BIsat5 The BI that I am using overall
Please indicate how well each statement below describes the decisions you make:
LABEL
CONSTRUCT: Decision Types Almost never Rarely Sometimes Frequently Almost always
DecType1
I make routine, repetitive
decisions
DecType2_coded
I make decisions without higher
level manager involvement
DecType3
The decisions I make could be
automated
DecType4_coded
The decisions I make require
judgment and intuition
DecType5
The decisions I make require
computational complexity and
precision
142
Please answer the following about the nature of the information you use to make
decisions;
LABEL
CONSTRUCT: Information
Processing Needs
Low 1 2 3 4 5 High
InfoChar1 The granularity is ...
InfoChar2 Accuracy of information is …
Wide 1 2 3 4 5 Narrow
InfoChar3 The scope of information is…
1 Qualitative 2 3 4
5
Quantitative
InfoChar4 Type of information is …
1 Infrequent 2 3 4 5 Frequent
InfoChar5 Frequency of use is…
1 Older 2 3 4 5 Current
InfoChar6 Age of information is …
143
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: Data
Sources
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
IntDataSrcQuality1
The internal data sources
used for my BI are readily
available
IntDataSrcQuality2
The internal data sources
used for my BI are readily
usable
IntDataSrcQuality3
The internal data sources
used for my BI are easy to
understand
IntDataSrcQuality4
The internal data sources
used for my BI are concise
ExtDataSrcQuality1
The external data sources
used for my BI are readily
available
ExtDataSrcQuality2
The external data sources
used for my BI are readily
usable
ExtDataSrcQuality3
The external data sources
used for my BI are easy to
understand
LABEL
CONSTRUCT: Data Types
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewha
t agree
Strongl
y agree
QuantDataQuality1
My BI provides accurate
quantitative data
QuantDataQuality2
My BI provides comprehensive
quantitative data
QuantDataQuality3
My BI provides consistent
quantitative data
QuantDataQuality4
My BI provides high quality
quantitative data
QualDataQuality1
My BI provides high quality
qualitative data
QualDataQuality2
My BI provides accurate
qualitative data
QualDataQuality3
My BI provides comprehensive
qualitative data
QualDataQuality4
My BI provides consistent
qualitative data
144
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: Intuition Involved
Strongly
disagree
Somewhat
disagree
Neither
agree nor
disagree
Somewhat
agree
Strongly
agree
intuition1_c
oded
Using my BI, I make decisions based on
facts and numbers
intuition2
Although I use my BI for decision making, I
still involve my gut feeling for the decisions
I make
intuition3
With my BI, it is easier to use my intuition
to make better informed decisions
intuition4
The decisions I make require a high level of
thought
intuition5
Although I use my BI for decision making , I
still put emphasis on my past experiences
for the decisions I make
LABEL
CONSTRUCT: Data Reliability
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
IntDataQuality1
Internal data collected for my BI
is reliable
IntDataQuality2_Cod
ed
There are inconsistencies and
conflicts in the internal data for
my BI
IntDataQuality3
Internal data collected for my BI
is accurate
IntDataQuality4
Internal data for my BI is
updated regularly
ExtDataQuality1
External data collected for my BI
is reliable
ExtDataQuality2_Co
ded
There are inconsistencies and
conflicts in the external data for
my BI
ExtDataQuality3
External data collected for my BI
is accurate
ExtDataQuality4
External data for my BI is
updated regularly
145
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: User Access
Strongly
disagree
Somewhat
disagree
Neither
agree nor
disagree
Somewhat
agree
Strongly
agree
UserAccess
_qual1
I am satisfied with the quality of the way I
access my BI
UserAccess
_qual2
I am authorized to access to all information
I need with BI
UserAccess
_qual3
The way I access my BI is fits well to the
types of decisions I make using my BI
LABEL
CONSTRUCT: Interaction with
Other Systems
My BI provides ...
Strongly
disagree
Somewhat
disagree
Neither
agree nor
disagree
Somewhat
agree
Strongly
agree
interaction1
… a unified view of business data
and processes
interaction2
… links among multiple business
applications
interaction3 … a comprehensive electronic
catalog of the various enterprise
information resources in the
organization
interaction4 … easy and seamless access to
data from other applications and
systems
146
Please choose the response that best describes each of the following statements;
Please choose the response that best describes each of the following statements;
LABEL
CONSTRUCT: Flexibility
My BI ...
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
flex1
… is compatible with other tools that I use
(e.g., Microsoft Office Suite, security
infrastructure, portal technology or databases)
flex2
… can accommodate changes in business
requirements quickly
flex3
… makes it easier to deal with exceptional
situations
flex4
… is highly scalable with regards to
transactions
flex5 … is highly scalable with regards to data
flex6 … is highly scalable with regards to users
flex7
… is highly scalable with regards to
infrastructure
Flex8
The manner in which the components of my BI
are organized and integrated allows for rapid
changes
LABEL
CONSTRUCT: Risk Level
My BI ...
Strongly
disagree
Somewhat
disagree
Neither agree
nor disagree
Somewhat
agree
Strongly
agree
risk1
… supports decisions associated with high level
of risk (e.g., entering a new market, hiring a
new manager)
risk2
… supports decisions motivated by exploration
and discovery of new opportunities (e.g.,
starting a new business line, creating a new
product design)
risk3
… helps me minimize uncertainties in my
decision making process
risk4
… helps me manage risk by monitoring and
regulating the operations (e.g., monitoring key
performance indicators (KPIs), customizing
alerts or creating dashboards)
147
REFERENCES
Abdinnour-Helm, S., Lengnick-Hall, M. L. and Lengnick-Hall, C. A. (2003). Pre-implementation
attitudes and organizational readiness for implementing an enterprise resource planning
system. European Journal of Operational Research, 146 (2), 258-273.
Aiken, L. S., and West, S. G. (1991). Multiple regression: testing and interpreting interactions.
Newbury Park, CA: Sage.
Al-Busaidi, K. A. and Olfman, L. (2005). An investigation of the determinants of knowledge
management systems success in omani organizations. Journal of Global Information
Technology Management, 8 (3), 6-27.
Alexander, J. W. and Randolph, W. A. (1985). The fit between technology and structure as a
predictor of performance in nursing subunits. Academy of Management Journal, 28 (4),
844-859.
Alter, A. (2004). A work system view of DSS in its forth decade. Decision Support Systems, 38 (3),
319-327.
Anandarajan, M. and Arinze, B. (1998). Matching client/server processing architectures with
information processing requirements: A contingency study. Information &
Management, 34 (5), 265-276.
Anderson-Lehman, R., Watson, H. J., Wixom, B. H., and Hoffer, J. A. (2004). Continental Airlines
flies high with real-time business intelligence. MIS Quarterly Executive 3 (4), 163-176.
Antebi, O., (2007, January 16). Managing by exception [web log post]. Retrieved from
http://www.panorama.com/blog/?p=13.
148
Anthony, R. N. (1965). Planning and control systems: A framework for analysis. Boston, MA:
Harvard Business School Press.
Applegate, L. M., F. W. McFarlan, and J. L. McKenney. (1999). Corporate information systems
management: The challenges of managing in an information age (5
th
ed). Boston, MA:
Irwin/McGraw-Hill.
Armstrong, J. S., and Overton, T. S. (1971). Brief vs. comprehensive descriptions in measuring
intentions to purchase. Journal of Marketing Research, 8 (1), 114-117.
Armstrong, J. S., and Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal
of Marketing Research, 14, 396-402.
Arnott, D. (2004). Decision support systems evolution: Framework, case study and research
agenda,” European Journal of Information Systems, 13 (4), 247-259.
Arnott, D., and Pervan, G. (2005). A critical analysis of decision support systems research.
Journal of Information Technology, 20 (2), 67-87.
Ashill, N. J. and Jobber, D. (2001). Defining the information needs of senior marketing
executives: An exploratory study. Qualitative Market Research, 4 (1), 52-61.
Au, N., Ngai, E. W. T. and Cheng, T. C. E. (2008). Extending the understanding of end user
information systems satisfaction formation: An equitable needs fulfillment model
approach,” MIS Quarterly, 32 (1), 43-66.
Azvine, B., Cui, Z., Majeed, B. and Spott, M. (2007). Operational risk management with real-time
business intelligence. BT Technology Journal, 25 (1), 154-167.
149
Baars, H. and Kemper, H.G. (2008). Management support with structured and unstructured
data-an integrated business intelligence framework. Information Systems Management,
45 (2), 132-148.
Bagozzi, P. (1980). Causal methods in marketing. New York, NY: John Wiley & Sons.
Baloh, P. (2007). The role of fit in knowledge management systems: Tentative propositions of
the KMS design. Journal of Organizational and End User Computing. 19 (4), 22-41.
Baron, R. M. and Kenny, D. A. (1986). The moderator-mediator variable distinction in social
psychological research: conceptual, strategic, and statistical considerations,” Journal of
Personality and Social Psychology. 51 (6), 1173-1182.
Bartlett, J. E., Kotrlik, J. W., and Higgins, C. C. (2001). Organizational research: determining
appropriate sample size in survey research. Information Technology, Learning, and
Performance Journal, 19 (1), 43-50.
Basi, R. K. (1999). WWW response rates to socio-demographic items. Journal of the Market
Research Society, 41 (4), 1999, 397-401.
Beach, L. R. and Mitchell, T. R. (1978). A contingency model for the selection of decision
strategies. The Academy of Management Review, 3 (3), 439 -451.
Bell, R. (2007, July 11). Gut instincts give business intelligence a new flavor. Financial Times, p.2.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and
firm performance: An empirical investigation. MIS Quarterly, 24 (1), 169–196.
Bharadwaj, A. S., Sambamurthy, V. and Zmud, R. W. (1999, December). IT Capabilities:
Theoretical perspectives and empirical operationalization. Paper presented at the 20th
International Conference on Information Systems (ICIS), Charlotte, North Carolina.
150
Bhatt, G. D. and Grover, V. (2005). Types of information technology capabilities and their role in
competitive advantage: An empirical study. Journal of Management Information
Systems, 22 (2), 253-277.
Blumberg, R. and Atre, S. (2003). The problem with unstructured data. DM Review. Retrieved
from http://dmreview.com/master.cfm?NavID=55&EdID=6287.
Bonde, A. and Kuckuk, M. (2004, April). Real world business intelligence: The implementation
perspective. DM Review. Retrieved from http://www.dmreview.com.
Bonabeau, E. (2003). Don't trust your gut. Harvard Business Review, 81 (5), 116-123.
Bresnahan, J. (1998, July). Legal espionage. CIO Enterprise.
Briggs, L. L. (2006). BI case study: Power company draws new energy. Bi Solution Third, 11 (3).
Retrieved from
http://www.tdwi.org/Publications/BIJournal/display.aspx?ID=8119m_page=1.
Buchanan, L. and O'Connell, A. (2006). A brief history of decision making. Harvard Business
Review, 84 (1), 32-40.
Burns, T., and Stalker, G. M. (1961). The Management of Innovation. London: Tavistock.
Burton, B., Geishecker, L., Schelegel, K., Hostmann, B., Austin, T., Herschel, G., Soejarto, A., and
Rayner, N. (2006). Business intelligence focus shifts from tactical to strategic. Retrieved
from Gartner database.
Burton, B. and Hostmann, B. (2005). Findings from Sydney symposium: Perceptions of business
intelligence. Retrieved from Gartner database.
Busemeyer, J. R. and Jones, L. E. (1983). Analysis of multiplicative combination rules when the
causal variables are measured with error. Psychological Bulletin, 93, 549-563.
151
Busenitz, L. W. (1999). Entrepreneurial risk and strategic decision making. The Journal of
Applied Behavioral Science, 35 (3), 325-340.
Chin, W.W. (1998). Issues and opinions on structural equation modeling. MIS Quarterly, 22 (1),
7-16.
Chin, W. W. (2004). Frequently asked questions – partial least squares & PLS graph [Web log
post]. Retrieved from: http://disc-nt.cba.uh.edu/chin/plsfaq/multigroup.htm.
Chung W., Zhang, Y., Huang, Z., Wang, G., Ong, T. and Chen H. (2004). Internet searching and
browsing in a multilingual world: An experiment on the Chinese business intelligence
portal (CbizPort). Journal of the American Society for Information Science and
Technology, 55 (9), 818-831.
Chung, W., Chen, H., and Nunamaker, J. F., Jr. (2005). A visual framework for knowledge
discovery on the web: An empirical study of business intelligence exploration. Journal of
Management Information Systems 21 (4), 57-84.
Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs.
Journal of Marketing Research, 16, 64-73.
Clark T. D., Jones, M. C., and Armstrong, C.P. (2007). The dynamic structure of management
support systems: Theory development, research focus and direction. MIS Quarterly, 31
(3), 579-615.
Cody, W. F., Kreulen, J. T., Krishna, V. and Spangler. W. S. (2002). The integration of business
intelligence and knowledge management. IBM Systems Journal, 41 (4), 697-715.
Cohen, J. (1998). Statistical power analysis for the behavioral sciences (2
nd
ed). Hillsdale, NJ:
Erlbaum.
152
Cooper, B. L., Watson, H. J., Wixom, B. H., and Goodhue, D. L. (2000). Data warehousing
supports corporate strategy at first American corporation. MIS Quarterly, 24 (4), 547-
567.
Cooper, R. B. and Wolfe, R. A. (2005). Information processing model of information technology
adaptation: An intra-organizational diffusion perspective. Database for advances in
information systems, 36 (1), 30-48.
Cooper, R. B. and Zmud, R. W. (1990). Information technology implementation research: A
technological diffusion approach. Management Science, 36 (2), 123-141.
Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16,
297-334.
Cudeck R. (2001). Cronbach's Alpha on two-item scales. Journal of Consumer Psychology, 10
(1/2), 55-69.
Daft, R. L. and Lengel, R. H. (1986). Organizational information requirements, media richness
and structural design. Management Science, 32 (5), 554-571.
Daft, R. L. and Machintosh, N. B. (1981). A tentative exploration into the amount and
equivocality of information processing in organizational work units. Administrative
Science Quarterly, 26 (2), 207-224.
Damianakis, S. (2008, June). The ins and outs of imperfect data. DM Direct. Retrieved from
http://www.dmreview.com/dmdirect/2008_77/10001491-1.html?portal=data_quality.
Davenport, T. H. and Harris, J. G. (2007). Competing on analytics: The new science of winning.
Boston, MA: Harvard Business School Press.
153
Davison, L. (2001). Measuring competitive intelligence effectiveness: Insights from the
advertising industry. Competitive Intelligence Review, 12 (4), 25-38.
DeLone, W.H. and McLean, E.R. (1992) “Information system success: The quest for the
dependent variable. Information Systems Research, 3 (1), 60–95.
Delone, W.H. and McLean, E.R. (2003). The DeLone and McLean model of information system
success: A ten-year update. Journal of Management Information Systems, 19 (4), 9–30.
Dennis, A. R., Wixom, B. H., and Vandenberg, R. J. (2001). Understanding fit and appropriation
effects in group support systems via meta-analysis. MIS Quarterly, 25 (2), 167-193.
Dillman, D.A. (2000). Mail and internet surveys: The tailored design. New York: John Wiley and
Sons.
Doll, W. J. and Torkzadeh, G (1988). The measurement of end-user computing satisfaction. MIS
Quarterly, 12 (2), 259-274.
Douglas, B. S. (1998). Information processing theory: implications for health care organizations.
International Journal of Technology Management, 15 (3-5), 211-223.
Dreyer, L. (2006, May). The “right time” for operational business intelligence? TDWI What
Works. Retrieved from
http://www.tdwi.org/Publications/WhatWorks/display.aspx?id=7976.
Duncan, R. B. (1972). Characteristics of organizational environment and perceived
environmental uncertainty. Administrative Science Quarterly, 17, 313-327.
Eckerson, W. (2003). Smart companies in the 21st century: The secrets of creating successful
business intelligence solutions. TDWI The Data Warehousing Institute Report Series, 1-
35. Retrieved from http://www.tdwi.org.
154
Eckerson, W. W. (2004). Gauge your data warehouse maturity. DM Review, 51, 34–37.
Retrieved from http://www.tdwi.org/publications/display.aspx?ID=7199.
Eckerson, W. W. (2006). Performance dashboards: Measuring, monitoring, and managing your
business. Hoboken, NJ: Wiley & Sons.
Eisenhardt, K. M. (1989). Agency theory: An assessment and review. The Academy of
Management Review, 14 (1), 57-76.
Erdfelder, E., Faul, F., and Buchner, A. (1996). GPOWER: A general power analysis program.
Behavior Research Methods, Instruments, & Computers, 28, 1-11.
Erol, O., Sauser, B. J., and Boardman, J. T. (2009). Creating enterprise flexibility through service
oriented architecture. Global Journal of Flexible Systems Management, 10 (1), 11-16.
Evans, J. S. (1991). Strategic flexibility for high technology manoeuvres: A conceptual
framework. Journal of Management Studies, 28 (1), 69-89.
Evelson, B., McNabb, K., Karel, R., and Barnett, J. (2007). It's time to reinvent your BI strategy.
Retrieved from Forrester database.
Fairbank, J.F., Labianca, G., Steensma, H.K. and Metters, R.D. (2006). Information processing
design choices, strategy and risk management performance. Journal of Management
Information Systems, 23, 293-319.
Feeney, D. and Willcocks, L. (1998). Core IS capabilities for exploiting information technology.
Sloan Management Review, 39 (3), 9–21.
Fink, L. and Neumann, S. (2007). Gaining agility through IT personnel capabilities: The mediating
role of IT infrastructure capabilities. Journal of the Association for Information Systems,
8 (8), 440-458.
155
Finlay, P. N. and Forghani, M. (1998). A classification of success factors for decision support
systems. The Journal of Strategic Information Systems, 7 (1), 53-70.
Forgionne, G. A. and Kohli, R. (2000). Management support system effectiveness: further
empirical evidence. Journal of the Association of Information Systems 1 (3), 1-37.
Fryman, H. (2007). Taking a user-centric approach to the information challenge. Business
Intelligence Journal, 12 (2). Retrieved from
http://www.tdwi.org/Publications/BIJournal/display.aspx?ID=8475.
Galbraith, J. (1977). Organizational design. Reading, MA: Addison-Wesley.
Gallegos, F. (1999). Decision support systems: An overview. Information Strategy, 15 (2), 42-47.
Garg, A.K., Joubert, R.J.O., and Pelisser, R. (2005). Information systems environmental
alignment and business performance: A case study. South African Journal of Business
Management, 36 (4), 33-53.
Gattiker, T. F. and Goodhue, D. L. (2004). Understanding the local-level costs and benefits of
ERP through organizational information processing theory. Information & Management,
41 (4), 431.
Gebauer, J. and Schober, F. (2006). Information system flexibility and the cost efficiency of
business processes. Journal of the Association for Information Systems, 7 (3), 122-145.
Gefen, D., Straub, D. W., and Boudreau, M. C. (2000). Structural equation modeling and
regression: Guidelines for research practice. Communications of the Association for
Information Systems, 4 (7), 1-70.
Gefen, D., and Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial
and annotated example. Communications of the AIS, 16, 91-109.
156
Gelderman, M. (2002). Task difficulty, task variability and satisfaction with management
support systems. Information & Management, 39 (7), 593-604.
Gerbing, D. W., and Anderson, J. C. (1988). An updated paradigm for scale development
incorporating unidimensionality and its assessment. Journal of Marketing Research, 25
(2), 186-192.
Gessner, G.H., and Volonino, L. (2005). Quick response improves on business intelligence
investments. Information Systems Management, 22 (3), 66-74.
Gile, K., Kirby, J. P., Karel, R., Teubner, C., Driver, E. and Murphy, B. (2006). Topic overview:
business intelligence. Retrieved from Forrester database.
Gonzales, M. L. (2005, August). What’s your BI environment IQ? DM Review Magazine.
Retrieved from http://www.dmreview.com/issues/20050801/1033572-1.html.
Goodhue, L, D., Wybo, D, M. and Kirsch, J. L. (1992). The impact of data integration on the costs
and benefits of information systems. MIS Quarterly, 16 (3), 293.
Dale Goodhue, D., Lewis, W., and Thompson, R. (2007). Statistical power in analyzing
interaction effects: Questioning the advantage of PLS with product indicators.
Information Systems Research, 18 (2), 211-227.
Gorry, G. A., Scott Morton, M. S. (1971). A framework for management information systems.
Sloan Management Review, 13 (1), 55-72.
Graham, P. (2008, August). Data quality: You don’t just need a dashboard! Strategy execution.
DM Review Magazine. Retrieved from
http://www.dmreview.com/issues/2007_50/10001727-1.html?portal=data_quality.
157
Guimaraes, T., Igbaria, M. and Lu, M.T. (1992). The determinants of DSS success: An integrated
model. Decision Sciences, 23 (2), 409-432.
Hair, J.F, Anderson, R.L., Tatham, R., and Black, W. (1998). Multivariate data analysis (5
th
ed).
New York: Prentice Hall.
Hannula, M. and Pirttimaki V. (2003). Business intelligence empirical study on the top 50
Finnish companies. Journal of American Academy of Business, 2 (2), 593-599.
Harding, W. (2003). BI crucial to making the right decision. Financial Executive, 19 (2), 49-50.
Hartono, E., Santhanam, R. and Holsapple, C.W. (2007). Factors that contribute to management
support system success: An analysis of field studies. Decision Support Systems, 43 (1),
256-268.
Havenstein, H. (2006, October). QlikTech looks to broaden access to BI data. ComputerWorld,
Retrieved from
http://www.computerworld.com/action/article.do?command=viewArticleBasic&articleI
d=9004369.
Henderson, J. C, and Venkatraman, N. (1993). Strategic alignment: Leveraging information
technology for transforming organizations. IBM Systems Journal, 32 (1), 4-19.
Herring, J. (1996). Measuring the Value of Competitive Intelligence: Accessing &
Communicating CI’s Value to Your Organization. Alexandria, VA: SCIP Monograph Series.
Hong, K and Kim, Y. (2002). The critical success factors for ERP implementation: An
organizational fit perspective. Information and Management, 40, 25-40.
Hostmann, B.,Herschel, G. and Rayner, N. (2007). The evolution of business intelligence: The
four worlds. Retrieved from Gartner database.
158
Howson, C. (2004, 2
nd
quarter). Ten mistakes to avoid when selecting and deploying BI tools.
TDWI Quarterly Ten Mistakes to Avoid Series. Retrieved from http://www.bi-
bestpractices.com/view-articles/4741.
Howson, C. (2006, September). The seven pillars of BI success. Intelligent Enterprise. Retrieved
from http://www.intelligententerprise.com/showArticle.jhtml?articleID=191902420.
Howson, C. (2008). Successful business intelligence: Secrets to making BI a killer app. New York,
NY: McGraw-Hill.
Huck, S. W. (2004). Reading statistics and research, (4
th
ed). Boston, MA: Pearson Education.
Hung, S.Y., Ku, Y. C., Liang, T. P. and Lee, C. J. (2007). Regret avoidance as a measure of DSS
success: An exploratory study. Decision Support Systems, 42 (4), 2093-2106.
Imhoff, C. (2005, August). Risky business! Using business intelligence to mitigate operational
risk. DM Review Magazine. Retrieved from
http://www.dmreview.com/issues/20050801/1033577-1.html.
Jain, H., Vitharana, P. and Zahedi, F. (2003). An assessment model for requirements
identification in component-based software development. Database for Advances in
Information Systems, 34 (4), 48-63.
Jarvenpaa, L. S. and Ives, B. (1993). Organizing for global competition: The fit of information
technology. Decision Sciences, 24 (3), 547-580.
Jourdan, Z., Rainer, R. K. and Marshall, T. E. (2008). Business intelligence: An analysis of the
literature. Information Systems Management, 25 (2), 121–131.
Kanuk, L. and Berenson, C. (1975). Mail surveys and response rates: A literature review. Journal
of Marketing Research, 12 (4), 440-453.
159
Karahanna, E., Straub, D.W., and Chervany, N.L. (1999). Information technology adoption across
time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS
Quarterly, 23 (2), 183-213.
Karimi, J., Somers, T.M. and Gupta, Y. P. (2004). Impact of environmental uncertainty and task
characteristics on user satisfaction with data. Information Systems Research, 15 (2), 175-
195.
Kearns, G. S. and Lederer, A. L. (2003). A resource-based view of strategic IT alignment: How
knowledge sharing creates competitive advantage. Decision Sciences, 34 (1), 1-29.
Keen, P. G. W. and Scott Morton, M. S. (1978). Decision support systems: An organizational
perspective. Reading, MA: Addison-Wesley.
Keith, T. Z. (2006). Multiple regression and beyond. Boston, MA: Allyn & Bacon.
Keller, R. T. (1994). Technology-information processing fit and the performance of R&D project
groups: A test of contingency theory. Academy of Management Journal, 37 (1), 167-179.
Kerlinger, F.N. and Lee, H.B. (2000). Foundations of behavioral research (4
th
ed). New York:
Thomson Learning.
Kirs, P. J., Sanders, G. L., Cerveny, R. P., and Robey, D. (1989). Experimental validation of the
Gorry and Scott Morton framework. MIS Quarterly, 13 (2), 183-197.
Klein, G., Jiang, J. J. and Balloun, J. (1997). Information system evaluation by system typology.
Journal of Systems and Software, 37 (3), 181-186.
Kulkarni, U. R., Ravindran, S. and Freeze, R. (2006). A knowledge management success model:
Theoretical development and empirical validation. Journal of Management Information
Systems, 23 (3), 309-347.
160
Kviz, F. J. (1977). Toward a standard definition of response rate. The Public Opinion Quarterly,
41 (2), 265-267.
Lakshminarayan, K., Harp, S.A., Goldman, R. and Samad, T. (1996). Imputation of missing data
using machine learning techniques. Proceedings of the Second International Conference
on Knowledge Discovery and Data Mining. Portland, OR. Retrieved from
http://www.aaai.org/Papers/KDD/1996/KDD96-023.pdf.
Lewis, G. J. (2004). Uncertainty and equivocality in the commercial and natural environments:
The implications for organizational design. Corporate Social Responsibility and
Environmental Management, 11 (3), 167-177.
Loftis, L. (2008, November). Getting data in, getting information out. DM Review Magazine,
2008. Retrieved from http://www.dmreview.com/issues/2007_53/10002146-1.html.
Lonnqvist, A., and Pirttimaki, V. (2006). The measurement of business intelligence. Business
Intelligence, 23 (1), 32-40.
Lovelace, K. and Rosen, B. (1996). Differences in achieving person-organization fit among
diverse groups of managers. Journal of Management, 22 (5), 703-722.
Malone, R. (2005). Data warehousing: Information under control. Forbes. Retrieved from
http://www.forbes.com/logistics/2005/12/23/cardinal-data-warehouse-
cx_rm_1222cardinal.html.
Mangione, T. W. (1995). Mail surveys: Improving the quality. Thousand Oaks, CA: Sage
Publications.
161
Manglik, A. (2006). Increasing BI adoption: An enterprise approach. Business Intelligence
Journal, 11 (2). Retrieved from
http://www.tdwi.org/Publications/BIJournal/display.aspx?ID=8038.
Manglik, A. and Mehra, V. (2005). Extending enterprise BI capabilities: New patterns for data
integration. Business Intelligence Journal, 10 (1). Retrieved from:
http://www.tdwi.org/research/display.aspx?ID=7486.
Marcoulides, G. A. and Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30 (2), iii-x.
Martinich, L. (2002). Managing innovations, standards and organizational capabilities. IEEE
International Engineering Management Conference, 1, 58- 63.
McMurchy, N. (2008). Take these steps to develop successful BI business cases. Retrieved from
Gartner database.
McKnight, W. (2004, April). Business intelligence return on investment issues. DM Review, 62.
Retrieved from: www.dmreview.com.
Miller, D. (2007, October). Measuring BI success: business goals and business requirements. DM
Review. Retrieved from: http://www.dmreview.com/news/10000100-1.html.
Millet, I. and Gogan, J. L. (2006). A dialectical framework for problem structuring and
information technology. The Journal of the Operational Research Society, 57 (4), 434-
442.
Moss, L. T. and Atre, S. (2003). Business intelligence roadmap: The complete project lifecycle for
decision-support applications. Boston, MA: Addison-Wesley.
Munro, M. C. and Davis, G. B. (1977). Determining management information needs: A
comparison of methods. MIS Quarterly, 1 (2), 55-67.
162
Negash, S. (2004). Business intelligence. Communications of the Association for Information
Systems, 13, 177-195.
Nelson, R. R., Todd, P. A., and Wixom, B. H. (2005). Antecedents of information and system
quality: Within the context of data warehousing. Journal of Management Information
Systems, 21 (4), 199-235.
Neumann, S. (1994). Strategic information systems: Competition through information
technologies. New York, NY: Macmillan College Publishing.
Nunnally, J.C. and Bernstein, I.H. (1998). Psychometric theory, New York: McGraw-Hill.
O'Leary-Kelly, S.W. and Vokurka, R.J. (1998). The empirical assessment of construct validity.
Journal of Operations Management, 16 (4), 387-405.
Olszak, C. M. and Ziemba, E. (2003). Business intelligence as a key to management of an
enterprise. Proceedings of Informing Science And IT Education. Santa Rosa, CA.
Retrieved from
http://proceedings.informingscience.org/IS2003Proceedings/docs/109Olsza.pdf.
Oltra, V. (2005). Knowledge management effectiveness factors: The role of HRM. Journal of
Knowledge Management, 9 (4), 70-86.
Parikh, A. A. and Haddad, J. (2008, October). Right-Time information for the real-time
enterprise timely information drives business. DM Direct. Retrieved from
http://www.dmreview.com/dmdirect/2008_92/10002003-1.html?portal=data_quality.
Pirttimaki, V., Lonnqvist, A., and Karjaluoto, A. (2006). Measurement of business intelligence in
a Finnish telecommunications company. Electronic Journal of Knowledge Management,
4 (1), 83-90.
163
Power, D. J. (2002). Decision support systems: Concepts and resources for managers. Westport,
CT: Quorum Books.
Power, D. J. (2003, May). A brief history of decision support systems [Web log post]. Retrieved
from http://dssresources.com/history/dsshistory.html.
Premkumar, G., Ramamurthy, K., and Saunders, C. S. (2005). Information processing view of
organizations: An exploratory examination of fit in the context of interorganizational
relationships. Journal of Management Information Systems, 22 (1), 257-294.
Rai, A., Lang, S. S., and Welker, R. B. (2002). Assessing the validity of IS success models: An
empirical test and theoretical analysis. Information Systems Research, 13 (1), 50-69.
Ray, G., Muharma W. A., and Barney J. B. (2005). Information technology and the performance
of the customer service process: A resource-based analysis. MIS Quarterly 29 (4), 625-
652.
Raymond L. (2003). Globalization, the knowledge economy, and competitiveness: A business
intelligence framework for the development SMES. Journal of American Academy of
Business, 3 (1/2), 260-269.
Ross, J. W., Beath C. M., and Goodhue D. L. (1996). Develop long-term competitiveness through
IT assets. Sloan Management Review, 38 (1), 31-44.
Rouibah, K., and Ould-ali, S. (2002). Puzzle: A concept and prototype for linking business
intelligence to business strategy. Journal of Strategic Information Systems, 11 (2), 133-
152.
Rud, O. P. (2009). Business intelligence success factors: Tools for aligning your business in the
global economy. Hoboken, NJ: John Wiley and Sons.
164
Ryan, A. M. and Schmit, M. J. (1996). An assessment of organizational climate and P-E fit: A tool
for organizational change. International Journal of Organizational Analysis, 4 (1), 75-95.
Ryan, S.D., Harrison, D.W., and Schkade, L.L. (2002). Information-technology investment
decisions: When do costs and benefits in the social subsystem matter?” Journal of
Management Information Systems, 19 (2), 85-127.
Saaty, T. L. and Kearns, K. P. (1985). Analytical planning: The organization of systems. Oxford:
Pergamon Press.
Sabherwal, R. and Kirs, P. (1994). The alignment between organizational critical success factors
and information technology capability in academic institutions. Decision Sciences, 25 (2),
301-331.
Sabherwal, R. (2007). Succeeding with business intelligence: Some insights and
recommendations. Cutter Benchmark Review, 7 (9), 5-15.
Sabherwal, R. (2008). KM and BI: From mutual isolation to complementarity and synergy. Cutter
Consortium Executive Report, 8 (8), 1-18.
Sabherwal, R. and Becerra-Fernandez, I. (2010). Business intelligence: Practices, technologies,
and management. Hoboken, NJ: John Wiley & Sons.
Sambamurthy, V., and Zmud, R. W. (1992). Managing IT for success: The empowering business
partnership. Morristown, NJ: Financial Executives Research Foundation.
Sanders, G. L. and Courtney, J. F. (1985). A field study of organizational factors influencing DSS
success. MIS Quarterly, 9 (1), 77-95.
Sanders, N. R., Premus, R. (2005). Modeling the relationship between firm IT capability,
collaboration, and performance. Journal of Business Logistics, 26 (1), 1-25.
165
Sauer, C. and Willcocks, L. (2003). Establishing the business of the future: The role of
organizational architecture and information technologies. European Management
Journal, 21 (4), 497–508.
Sawka, K. (2000). Are we valuable? Competitive Intelligence Magazine, 3 (2). Retrieved from
http://www.scip.org/Publications/CIMArticleDetail.cfm?ItemNumber=1191.
Schuman, H., and Pressor, S. (1981). Questions and answers in attitude survey. New York, NY:
Academic Press.
Schwab, D. P. (1980). Construct validity in organizational behavior. In L. L. Cummings & B. M.
Staw (Eds.), Research in Organizational Behavior (pp. 3-43). Greenwich, CT: JAI Press.
Scott Morton, M. S. (1984). The state of the art of research. In F. W. McFarlan (Ed.), The
Information Research Challenge (pp. 13-41). Boston, MA: Harvard University Press.
Seeley, C.P. and Davenport, T.H. (2006). KM meets business intelligence. Knowledge
Management Review, 8 (6), 10-15.
Setia, P., Sambamurthy, V., and Closs, D. J. (2008). Realizing business value of agile IT
applications: Antecedents in the supply chain networks. Information Technology and
Management, 9 (1), 5-19.
Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R. and Carlsson, C. (2002). Past,
present, and future of decision support technology. Decision Support Systems, 33 (2),
111–126.
Silver, M. S. (1991). Systems that support decision makers: Description and analysis. Chichester,
United Kingdom: Wiley & Sons.
Simon, H. A. (1960). The new science of management decision. New York: Harper and Row.
166
Soelberg, P. O. (1967). Unprogrammed decision making. Industrial Management Review, 8 (2),
19-29.
Solomon, M.D. (2005). Ensuring a successful data warehouse initiative. Information Systems
Management, 22 (1), 26-36.
Sommer, D. (2008). Report highlight for market trends: Business intelligence, worldwide, 2008.
Retrieved from Gartner database.
Srinivasan, A. (1985). Alternative measures of system effectiveness: Associations and
implications. MIS Quarterly, 9 (3), 243-253.
Srivastava, J. and Cooley, R. (2003). Web business intelligence: Mining the web for actionable
knowledge. INFORMS Journal on Computing, 15 (2), 191-207.
Stock, G. N. and Tatikonda, M. V. (2008). The joint influence of technology uncertainty and
interorganizational interaction on external technology integration success. Journal of
Operations Management, 26 (1), 65-80.
Swafford, P. M., Ghosh, S. and Murthy, N. (2008). Achieving supply chain agility through IT
integration and flexibility. International Journal of Production Economics, 116 (2), 288-
297.
Swoyer, S. (2008, September 24). Lyza empowers new class of BI consumers. TDWI. Retrieved
from http://www.tdwi.org/News/display.aspx?id=9129.
Tatikonda, M. V. and Rosenthal, S. R. (2000). Technology novelty, project complexity, and
product development project execution success: A deeper look at task uncertainty in
product innovation. IEEE Transactions on Engineering Management, 47 (1), 74-87.
167
Tatikonda, M. V. and Montoya-Weiss, M. M. (2001). Integrating operations and marketing
perspectives of product innovation: The influence of organizational process factors and
capabilities on development performance. Management Science, 47 (1), 151-172.
Teo, T. S. H. and King, W. R. (1997). Integration between business planning and information
systems planning: An evolutionary-contingency perspective. Journal of Management
Information Systems, 14 (1), 185-216.
Tsai, C.H. and Chen, H. Y. (2007). Assessing knowledge management system success: An
empirical study in Taiwan's high-tech industry. Journal of American Academy of
Business, 10 (2), 257-264.
Tuggle, F. D, and Gerwin, D. (1980). An information processing model of organizational
perception, strategy and choice. Management Science, 26 (6), 575-592.
Tushman, M. L. and Nadler, D. A. (1978). Information processing as an integrating concept in
organizational design. The Academy of Management Review, 3 (3), 613-624.
Vitt, E., Luckevich, M, and Misner, S. (2002). Business intelligence: Making better decisions
faster, Redmond, WA: Microsoft Corporation.
Wang, E.T.G. (2003). Effect of the fit between information processing requirements and
capacity on organizational performance. International Journal of Information
Management, 23 (3), 239-247.
Watson, H. J. (2008). Why some firms’ BI efforts lag. Business Intelligence Journal, 13 (3), 4-7.
Watson, H. J. (2005). Are data warehouses prone to failure? Business Intelligence Journal, 10
(4), 4-7.
168
Watson, H. J., Annino, D. A., Wixom, B. H., Avery, K. L., and Rutherford, M. (2001). Current
practices in data warehousing. Information Systems Management, 18 (1), 47-55.
Watson, H.J. and Donkin, D. (2005). Editorial preface: Outstanding BI and data warehousing
practice exists around the world: The Absa Bank in South Africa. Journal of Global
Information Technology Management, 8 (4), 1-6.
Watson, H. J., Goodhue, D. L., and Wixom, B. H. (2002). The benefits of data warehousing: Why
some organizations realize exceptional payoffs. Information and Management, 39 (6),
491-502.
Watson, H.J., Abraham, D.L., Chen, D., Preston, D., and Thomas, D. (2004). Data warehousing
ROI: Justifying and assessing a data warehouse. Business Intelligence Journal, 9 (2), 6-17.
Watson, H. J., Fuller, C., and Ariyachandra, T. (2004). Data warehouse governance: Best
practices at Blue Cross and Blue Shield of North Carolina. Decision Support Systems, 38
(3), 435-450.
Watson, H.J., Wixom, B.H., Hoffer, J.A., Anderson-Lehman, R., and Reynolds, A. M. (2006). Real-
time business intelligence: Best practices in Continental Airlines. Business Intelligence,
23 (1), 7-18.
Watson, H. J., and Wixom, B. H. (2007). The current state of business intelligence. Computer, 40
(9), 96-99.
Watson, H. J. and Wixom, B. H. (2007). Enterprise agility and mature BI capabilities. Business
Intelligence Journal, 12 (3), 13-28.
Weier, M.H. (2007). QUERY: What's next in BI? Information Week, 1128, 27-29.
169
Weill, P., M. Subramani, and Broadbent, M. (2002). Building IT infrastructure for strategic
agility. MIT Sloan Management Review, 44 (1), 57-65.
Wells, D. (2003, April). Ten best practices in business intelligence and data warehousing. TDWI
FlashPoint. Retrieved from
https://www.tdwi.org/Publications/display.aspx?id=6638&t=y.
White, C. (2005, May). The next generation of business intelligence: Operational BI. Information
Management Magazine. Retrieved from http://www.dmreview.com.
White, C. (2004, September). Now is the right time for real-time BI. Information Management
Magazine. Retrieved from http://www.dmreview.com.
Williams, S. and Williams, N. (2007). The profit impact of business intelligence, San Francisco,
CA: Morgan Kaufmann.
Wixom, H. and Watson, H. J. (2001). An empirical investigation of the factors affecting data
warehousing success. The Journal of Business Strategy, 25 (1), 17-41.
Wu, J. H. and Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and
McLean's model. Information & Management, 43 (6), 728-739.
Wunsch, D. (1986). Survey research: Determining sample size and representative response.
Business Education Forum, 40 (5), 31-34.
Yoon, Y., Guimaraes, T. and O’Neal, Q. (1995). Exploring the factors associating with expert
systems success. MIS Quarterly, 19 (1), 83-106.
Zack, M. H. (2007). The role of decision support systems in an indeterminate world. Decision
Support Systems, 43 (4), 1664-1674.
170
Zaltman, G., Duncan, R. and Holbek, J. (1973). Innovation and organizations. New York: John
Wiley and Sons.
Zhang, M. and Tansuhaj, P. (2007). Organizational culture, information technology capability,
and performance: The case of born global firms. Multinational Business Review, 15 (3),
43-77.
doc_542063161.pdf