THE EFFECTS OF TOP MANAGEMENT SUPPORT ON STRATEGIC INFORMATION SYSTEMS PLANNING SUCCESS
THE EFFECTS OF TOP MANAGEMENT SUPPORT ON STRATEGIC
INFORMATION SYSTEMS PLANNING SUCCESS
by
Gerald Elysee
APIWAN D. BORN, Ph.D., Faculty Mentor and Chair
H. PERRIN GARSOMBKE, Ph.D., Committee Member
TAMIR BECHOR, Ph.D., Committee Member
William Reed, Ph.D., Dean, School of Business and Technology
A Dissertation Presented in Partial Fulfillment
Of the Requirements for the Degree
Doctor of Philosophy
Capella University
April 2012
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© Gerald Elysee, 2012
Abstract
The success of strategic information systems planning (SISP) is of paramount importance
to academics as well as practitioners. SISP is a management process that enables
organizations to successfully harness the power of current- and next-generation
information systems (IS) applications to fulfill their business goals. Hence, by capturing
the major constructs that can influence SISP success, the SISP theory has made
significant contribution to research and practice. However, the literature shows that,
despite this important contribution, the SISP theory has only been used to a limited
extent. Aiming to address this scarcity of SISP theory utilization, this study developed a
research model that integrates four constructs derived in large part from the SISP theory.
The four constructs are: (a) the extent of top management support, (b) the usefulness of
the information systems plan, (c) the degree of Information technology infrastructure
flexibility, and (d) the degree of SISP success. Based on the contingency theory, the
usefulness of the information systems plan (ISP) and the IT infrastructure flexibility
(ITIF) were examined as two conditions that can mediate the effects of top management
support on SISP success. Data analysis was conducted using Partial Least Squares, which
is a type of structural equation modeling that accommodates small sample sizes. From a
sample of 57 information systems executives from US organizations, support was found
for the hypothesis that top management support has a direct, positive, and significant
effect on SISP success. Also supported are the hypotheses that top management support
has a significant effect on the usefulness of the ISP, which in turn has a similar effect on
SISP success. By contrast, support was not found for the hypotheses that top management
support has a significant effect on ITIF, which in turn is significantly related to SISP
success. Top management support was shown to influence the degree of SISP success
both directly and indirectly through the usefulness of the organizations’ ISP, but not
through their IT infrastructure flexibility (ITIF). Unlike ITIF, the usefulness of the ISP
was confirmed to be a significant mediator of the effects of top management support on
SISP success.
iv
Dedication
I wish to dedicate this dissertation to my wife, Evelyn, for the love, patience, and support
she provided me during the entire Ph.D. program; to my daughter, Victoria, who
provided me with a sense of purpose and responsibility; to my parents, Sainsalio and
Marie, for instilling in me the value of hard work and the importance of education; to my
uncle, Frank, for being a great role model.
v
Acknowledgments
I wish to acknowledge several individuals who have contributed to the successful
completion of this dissertation. First of all, I am forever grateful to Dr. Apiwan D. Born,
my dissertation advisor, for the expert guidance, support, and encouragement she
provided me throughout all the phases of my dissertation. I would like to thank my other
dissertation committee members, Dr. Henry Garsombke and Dr. Tamir Bechor, whose
feedback and inputs helped improve the quality of this dissertation in a significant way. I
wish to acknowledge Dr. Nurul Aman for his valuable comments and suggestions on
earlier draft of my dissertation proposal.
vi
Table of Contents
Acknowledgments............................................................................................................... v
List of Tables ...................................................................................................................... x
List of Figures ................................................................................................................... xii
CHAPTER 1. INTRODUCTION ....................................................................................... 1
Introduction to the Problem ................................................................................................ 6
Background of the Study .................................................................................................... 8
Statement of the Problem .................................................................................................. 12
Purpose of the Study ......................................................................................................... 12
Rationale ........................................................................................................................... 13
Research Questions/Hypotheses ....................................................................................... 14
Significance of the Study .................................................................................................. 15
Definition of Terms........................................................................................................... 16
Assumptions and Limitations ........................................................................................... 18
Nature of the Study ........................................................................................................... 19
Organization of the Remainder of the Study .................................................................... 20
CHAPTER 2. LITERATURE REVIEW .......................................................................... 22
Introduction ....................................................................................................................... 22
SISP Definitions................................................................................................................ 23
SISP Theory ...................................................................................................................... 25
Selection of Constructs for the Research Model........................................................... 28
Top management support construct (TPORT). ......................................................... 30
Information systems plan construct (ISP). ................................................................ 35
vii
IT infrastructure flexibility construct (ITIF).. ........................................................... 36
SISP success construct (SES). .................................................................................. 44
Conclusion ........................................................................................................................ 48
CHAPTER 3. METHODOLOGY .................................................................................... 53
Examination of Research Model and Development of Hypotheses ................................. 53
Hypotheses Related to Top Management Support (TPORT) Construct ....................... 55
Hypothesis Related to Information Systems Plan (ISP) Construct ............................... 61
Hypothesis Related to IT Infrastructure Flexibility (ITIF) Construct .......................... 63
Research Design................................................................................................................ 66
Sample............................................................................................................................... 68
Sampling Frame ............................................................................................................ 68
Sample Size ................................................................................................................... 69
Operationalization of Research Constructs ....................................................................... 72
Extent of Top Management Support (TPORT) ............................................................ 73
Usefulness of Information Systems Plan (ISP) ............................................................. 74
Degree of IT Infrastructure Flexibility (ITIF) .............................................................. 74
Degree of SISP success (SES) ...................................................................................... 74
Data Collection ................................................................................................................. 76
Non-respondent Bias ..................................................................................................... 77
Data Analysis .................................................................................................................... 77
Validity and Reliability ..................................................................................................... 79
Ethical Considerations ...................................................................................................... 81
viii
Summary ........................................................................................................................... 82
CHAPTER 4. RESULTS .................................................................................................. 83
Survey questionnaire ......................................................................................................... 84
Field Test ...................................................................................................................... 85
Data collection .................................................................................................................. 87
Response Rate ............................................................................................................... 90
Sample Size Adequacy ................................................................................................. 90
Demographic Data ............................................................................................................ 91
Profile of Respondents .................................................................................................. 91
Non-respondent Bias Analysis ...................................................................................... 98
Research Model Assessment............................................................................................. 99
Measurement Model ..................................................................................................... 99
Initial PLS Analysis - Reliability and Validity ........................................................... 101
Preparation for Final PLS Analysis - Indicator Removal and Retention .................... 106
Final PLS Analysis - Model Fit with the Data ............................................................ 109
Structural Model and Hypothesis Testing................................................................... 110
Direct Effect ................................................................................................................ 110
Mediating Effects ........................................................................................................ 111
Path Analysis .............................................................................................................. 112
Significance of Mediating Effects .............................................................................. 115
Effect Sizes ................................................................................................................. 116
Presence of Full or Partial Mediation ......................................................................... 119
ix
Summary ......................................................................................................................... 121
CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS ................. 123
Findings and Implications ............................................................................................... 123
Contributions................................................................................................................... 125
Limitations ...................................................................................................................... 127
Recommendations ........................................................................................................... 128
REFERENCES ............................................................................................................... 130
APPENDIX. SURVEY OF STRATEGIC INFORMATION SYSTEMS PLANNING
SUCCESS ....................................................................................................................... 141
x
List of Tables
Table 1. IT Infrastructure Management Actions for Different Types of Events .............. 38
Table 2. Summary of Study Hypotheses........................................................................... 67
Table 3. Description of SISP Constructs and Variables ................................................... 75
Table 4. Respondents’ Job Titles (N = 57) ....................................................................... 92
Table 5. Respondents’ Years of Experience with Current Organization (N = 57) ........... 93
Table 6. Respondents’ Years of Experience in Industry (N = 57) .................................... 94
Table 7. Respondents’ Industry (N = 57).......................................................................... 95
Table 8. Annual Information Technology (IT) budget (N = 57) ...................................... 96
Table 9. Number of Employees (N = 57) ......................................................................... 97
Table 10. Number of Information Technology (IT) Employees (N = 57) ........................ 98
Table 11. Initial PLS Analysis - Reliability and discriminant validity coefficients ....... 103
Table 12. Initial PLS Analysis - Latent variable correlations and square root of AVE . 104
Table 13. Initial PLS Analysis - Combined loadings and cross-loadings....................... 105
Table 14. Final PLS Analysis - Reliability and Discriminant Validity Coefficients ...... 106
Table 15. Final PLS Analysis - Latent Variable Correlations and Square Root of AVE 107
Table 16. Final PLS Analysis - Combined Loadings and Cross-loadings ...................... 108
Table 17. Final PLS Analysis - Variance Inflation Factors ............................................ 109
Table 18. Final PLS Analysis - Model fit indices and P values ..................................... 110
Table 19. Summary of Hypothesis Testing Results ........................................................ 112
xi
Table 20. Final PLS Analysis - Path Coefficients .......................................................... 115
Table 21. Final PLS Analysis - P Values for Path Coefficients ..................................... 115
Table 22. Final PLS Analysis - Standard errors for path coefficients ............................ 116
Table 23. Test Result Showing Significance of ISP Mediating Effect (N = 57) ............ 117
Table 24. Test Result Showing Significance of ITIF Mediating Effect (N = 57) .......... 118
xii
List of Figures
Figure 1. Conceptual model. ............................................................................................... 6
Figure 2. SISP theory. ....................................................................................................... 26
Figure 3. Research model and hypotheses. ....................................................................... 56
Figure 4. Direct model. ................................................................................................... 111
Figure 5. Mediating model. ............................................................................................. 114
1
CHAPTER 1. INTRODUCTION
Since 1980, information systems executives have consistently ranked strategic
information systems planning (SISP) improvement as one of their top ten concerns
(Brancheau, Janz, & Wetherbe, 1996; Hevner, Berndt, & Studnicki, 2000; Luftman &
Ben-Zvi, 2010; Moynihan, 1990). The SISP improvement concern has resonated with
many researchers who have become interested in identifying and investigating the
constructs that influence the success of SISP.
SISP is defined in the literature as "the process of identifying a portfolio of
computer-based applications that will assist an organization in executing its business
plans and realizing its business goals" (Hartono, Lederer, Sethi, & Zhuang, 2003; Lederer
& Sethi, 1988, 1992; Phillip, 2007). This definition has been widely used in SISP
research. In order to embed this study within the existing SISP literature, this widely
accepted SISP definition is adopted in this study. SISP also includes exploiting and
exploring current- and next-generation information systems (IS) applications and
opportunities to enable the company to gain a competitive advantage, outperform its
competition, and achieve sustainable profitability.
This study involved developing a SISP research model to investigate the direct
and indirect effects of top management support and SISP success. This study is
important because, according to a 2009 survey of IT executives by the Society for
Information Management, the strategic planning of information technology and systems
2
remains a top concern of IS executives. Explaining this finding, Lufman and Ben-Zvi
(2010) pointed out that:
Unlike previous recessions, when IT was the first place that business executives
looked to reduce costs, during this recession, many business and IT leaders have
been working closer together to identify strategic opportunities for leveraging IT
to reduce costs and improve productivity throughout the organization. (p. 52)
In today’s marketplace, SISP is vital to a firm’s competiveness because it
provides “the strategic thinking that identifies the most desirable IS on which the firm can
implement and enforce its long-term IT activities and policies” (Bechor, Neumann,
Zviran, & Glezer, 2010, p. 17). Consequently, SISP continues to be an important area for
both research and practice.
To advance research in this area, Lederer and Salmela (1996) developed the SISP
theory to express SISP as a system whose success or failure can be influenced by the
direct linkages among seven planning constructs: internal environment, external
environment, planning resources, planning process, information plan, plan
implementation, and plan alignment. In 2004, Brown extended on the SISP theory
through extensive literature review and meta-analysis, and found other linkages and
hypotheses beyond those identified by Lederer and Salmela.
The continuous application of the SISP theory and its derivatives is vital to the
success of SISP research and practice because it enables the relationships between the
constructs to be investigated and presented in a rigorous, coherent, parsimonious, and
comprehensive manner (Lederer & Salmela, 1996). However, the SISP literature reveals
that very little effort has been made to use the SISP theory to provide a comprehensive
3
representation of the constructs that can influence SISP success. For instance, Brown
(2004) found the application of the SISP theory to be very sparse.
To address this sparsity, the SISP theory was used in large part to specify a
comprehensive model of SISP. Model specification is a significant part of this study.
Schumacker and Lomax (2010) suggested that the extant literature in terms of theory,
research, and published studies can be used to determine which constructs to include in
the model and which ones not to include. Based on the literature review, the four
constructs selected for inclusion in the model are (a) top management support, (b)
information systems plan, (c) information technology (IT) infrastructure flexibility, and
(d) SISP success.
The model was used to investigate the direct and indirect effects of top
management support on SISP success. Top management support is the independent
variable concerned with the level of senior management’s interest, understanding, and
participation in SISP and information systems-related efforts. Whereas, SISP success is
the dependent variable focusing on the impact the SISP exercise has on the overall
effectiveness of the firm. Following a contingency approach, this study also examined the
usefulness of the information systems plan and the flexibility of the Information
Technology (IT) infrastructure as two conditions under which the direct relationship
between top management support and SISP success can change. Consistent with this
approach, the information systems plan and the IT infrastructure flexibility were
considered potential mediating constructs in this study. The information systems plan
documents the selected applications needed to be implemented to enable firms to achieve
their business goals. Whereas, the IT infrastructure flexibility provides the capabilities to
4
enable firms to quickly and economically implement the recommended applications in
order to be able to adapt to an ever changing business environment.
The flexibility of IT infrastructure has been heralded as the new competitive
weapon that is needed in the contemporary business environment to enable quick and
easy implementation of business strategies (Byrd & Turner, 2001). To represent a
dynamic aspect of contemporary business environment, IT infrastructure flexibility was
selected as one of two mediating constructs that can affect the relationship between top
management support (independent variable) and SISP success (dependent variable).
This study hypothesized that top management support (the independent variable)
directly influences the degree of SISP success (the dependent variable). Adopting a
contingency perspective, this study also hypothesized that top management support
indirectly influences SISP success through the mediating variables of information
systems plan and IT infrastructure flexibility. A conceptual model is presented in Figure
1 to start framing the research. The full research model and hypotheses are depicted in
Figure 3.
Survey research methodology was used to collect the data, which were analyzed
using Partial Least Squares (PLS). According to Chin, Marcolin, and Newsted (2003),
“The use of PLS has been gaining interest and use among IS researchers in recent
years…because of its ability to model latent constructs under conditions of nonnormality
and with small to medium sample sizes” (p. 197). Because of these features, Partial least
squares (PLS) was selected to conduct a Structural Equation Modeling (SEM) based
analysis for this study. According to the guidelines provided by Cohen (1992), Chin
(1998), Gefen, Straub, and Boudreau (2000), and Newkirk, Lederer, and Johnson (2008),
5
the minimum sample required to analyze this study’s research model should be at least
34.
The sample size of 57 resulted from the data collection was found to be adequate
to conduct the partial least squares (PLS) analysis on this study’s research model. This is
because the sample size 57 exceeds the minimum sample size requirement of 34. Chou
(2010), Khanlarian (2010), Tomaszewski (2010), and Garza (2011) employed similar
guidelines to determine the sample size requirement for the specific models in their
dissertation research. Likewise, Jung, Chow, and Wu (2003) used PLS in their article
because they had a small sample size of 32.
The results of the PLS analysis show support for the hypothesis that top
management support directly influences the degree of SISP success. Also supported are
the hypotheses that the usefulness of the information systems plan (ISP) is influenced by
top management support, which in turn influences the degree of SISP success. By
contrast, PLS analysis did not reveal support for the hypotheses that top management
support influences the IT infrastructure flexibility (ITIF), which in turn influences the
degree of SISP success.
The findings of this study have important implications for business and IS
executives who conduct SISP exercises as well as researchers who study SISP
phenomena. Theoretically, the findings of this study confirm prior research about the
critical role of top management support in influencing the degree of SISP success both
directly and indirectly. The usefulness of the organizations’ information systems plan
was found to be a significant, but partial mediator of the relationship between top
management support and SISP success. To achieve an even deeper understanding of
6
SISP success, additional research is needed to identify other possible mediators such as
top management leadership styles, quality of ISP process, organizational culture under
which the relationship between top management support and SISP success can change.
As for the IT infrastructure flexibility (ITIF), it was found to be an insignificant
mediator of the effect of top management support on SISP success. Taken together, the
findings of this study practically mean that training of senior executives in top
management support should emphasize the importance of ITIF. Also, efforts to improve
ITIF should be exerted through the information systems plan (ISP) because the latter was
found to be a significant vehicle with which top management support can help the
organization achieve SISP success.
Figure 1. Conceptual model.
Introduction to the Problem
Achieving strategic information systems planning (SISP) success is of paramount
importance to practitioners and researchers. This is because SISP success enables
corporations to successfully utilize IS applications to gain organizational effectiveness
and efficiency and achieve a competitive advantage. However, SISP success is a difficult
and elusive concept that can be influenced by a variety of constructs (Basu, Hartono,
Lederer, & Sethi, 2002; Bechor, Neumann, Zviran, & Glezer, 2010; Doherty, Marples, &
MEDIATORS
Top Management Support of SISP
SISP Success
7
Suhaimi, 1999; Raghunathan and Raghunathan, 1994; Segars & Grover, 1998). Referring
to Raghunathan and Raghunathan’s (1994) planning success measurement model, Segars
and Grover (1998) asserted that “Within the context of general IS planning, this work
demonstrates that planning success seems to be a complex system of interrelated
constructs” (p. 140). Consistent with this assertion, Lederer and Salmela (1996)
developed the SISP theory to depict the predominant SISP constructs that can influence
SISP success, and explain the relationships between them as primarily a system of input-
process-output. However, a closer examination reveals that this system of constructs
conceived by the SISP theory also extends to other boundaries such as the
implementation and outcome domains, as illustrated in Figure 2.
The SISP theory represents a significant contribution to research and practice
because it provides a comprehensive representation of the entire SISP process as well as
key insights into the complexity of the relationships between the constructs. However,
despite this very significant contribution, the use of the SISP theory is very scarce. To
address this scarcity, this study built upon the work of Bhatt, Emdad, Roberts, and Grover
(2010), Brown (2004), Chung, Rainer, and Lewis (2003), Doherty, Marples, and Suhaimi
(1999), Hann and Weber, (1996), Lederer & Salmela (1996), Premkumar and King
(1994a, 1994b), Ragu-Nathan, Apigian, Ragu-Nathan, and Tu (2004), and many others
by developing a comprehensive model of SISP for investigating the direct effects of top
management support on SISP success derived largely from the SISP theory.
Following the contingency theory, the usefulness of IS plan, and the IT
infrastructure flexibility were viewed as potential mediating variables that can alter the
relationship between top management support (independent variable) and SISP success
8
(dependent variable). The direct and mediated (indirect) relationships between top
management support and SISP success were analyzed following the Partial Least Squares
(PLS) structural equation modeling (SEM) approach. Chou (2010), Khanlarian (2010),
Tomaszewski (2010), and Garza (2011) also employed PLS to conduct SEM analysis in
their dissertation research. Several other investigators such as Thong, Yap, and Raman
(1996) and Newkirk, Lederer, and Johnson (2008) also used PLS in their research.
Furthermore, Khalifa, Yu, and Shen, (2008), and Neufeld, Dong and Higgins (2007)
applied PLS to test mediating models that are similar in structure to the one presented in
this study.
Background of the Study
This study was concerned with developing a SISP model to investigate the direct
and indirect effects of top management support on SISP success. The model employed
the strategic information systems planning (SISP) theory developed by Lederer and
Salmela (1996), and the extension to the SISP theory provided by Brown (2004), the
information systems plan studied by Hann and Weber (1996), and the information
technology (IT) infrastructure flexibility researched by Bhatt, Emdad, Roberts, and
Grover (2010). The original SISP theory identified seven constructs associated with
SISP. They are (a) internal environment, (b) external environment, (c) planning
resources, (d) planning process, (e) information plan, (f) plan implementation, and (g)
plan alignment (Lederer & Salmela, 1996). This study developed a four-construct model
mainly from the seven constructs suggested by the SISP theory to provide a
9
comprehensive representation of SISP with which to examine the complexity of direct
and indirect relationships between top management support on SISP success.
With regards to model specification, Schumacker and Lomax (2010) argued that
researchers should use the relevant theory and literature to select the constructs to be
included in the model. Following this suggestion, the internal, external environment, and
the planning process constructs suggested by the SISP theory were not selected for
inclusion in this study’s research model. This is because the detailed analysis of the
internal and external environments performed to identify the firm’s strengths,
weaknesses, opportunities, and threats is already addressed in the planning process
construct (Premkumar & King, 1994a). In other words, since the assessment of their
effects is already accounted for in the planning process construct, they did not have to be
treated as separate constructs. Based on this argument, they were not considered for the
research model. However, the planning process construct, which addresses the internal
and external environments, was not selected either for inclusion in the mediating model.
Consistent with Tallon (2008b), it was decided rather than measuring the process
and its numerous activities, it is simpler and more manageable to assess the output or the
outcome of the process to get a sense whether related activities produce the expected
benefits. Thus, measuring the output and outcome, which in this study are the information
systems plan and the degree of SISP success, is akin to measuring the degree of the
effectiveness of SISP process and its activities. As a result, the planning process was
omitted from the research model.
The plan implementation construct suggested in the SISP theory was replaced
with the construct of IT infrastructure flexibility. This is because the IT infrastructure
10
flexibility represents a dynamic aspect of the contemporary business environment. It
facilitates the quick and inexpensive implementation of applications, which is expected to
lead to greater SISP success.
The constructs that were selected in this study are: (a) top management support,
(b) information systems plan, (c) IT infrastructure flexibility, and (d) SISP success. It is
widely recognized in the literature that top management support is an organizational
factor that has been found to impact all phases of SISP, and by extension SISP success.
Several studies have determined that among the organizational factors most germane to
the planning process, top management support is the most salient problem associated
with IS planning effort (Kearns, 2006; Teo & Ang; 2001). Furthermore, top management
support has also been identified as the most critical predictor of optimal IS
implementation (Bradford & Florin, 2003; Lemon, Liebowitz, Burn, & Hackney, 2002;
Somers & Nelson, 2001).
Teo and Ang (2001) performed a key study in which they articulated IS planning
as comprising three phases: “the launching phase, the plan development phase, and the
implementation phase”. Teo and Ang indicated that “The main reason for doing this is
that different problems may be more important in different phases of IS planning.” Using
a more comprehensive approach, they investigated the variables causing IS planning
problems over the three phases of SISP. This represents a significant contribution to this
stream of research because Teo and Ang found, among the other problems, the most
grievous problem associated with IS planning effort is the failure to seek top management
support in all phases of the IS planning. These study results suggest that top management
support has a critical role to play in all of the phases of SISP process. The SISP theory
11
offers a comprehensive framework within which to study these issues by taking into
account their interrelationships. However, the literature also reveals that very few studies
have used the SISP theory in their investigations.
Top management support is necessary to secure funding, and provide strategic
direction in addition to supporting the whole SISP effort and its implementation to ensure
realization of objectives. Top management support can reduce organizational problems,
influence and encourage organizational involvement, which can minimize resistance, and
lead to the acceptance the implementation of plan’s specification (Lederer & Sethi,
1992). Olorunniwo and Udo (2002) support this position by arguing that “Research and
experience support the fact that the degree of management support of a project will lead
to significant variations in the degree of acceptance or resistance to the project, and by
extension, to the degree of success…” (p. 28). This observation also implies that top
management support is critical to SISP success. This is because the support of top
management helps improve the level of organizational involvement, participation, and
cooperation which in turn reduce resistance to the implementation of applications
recommended in the information systems plan. Resistance reduction will lead to greater
stakeholders’ satisfaction, which is an indicator of SISP success.
In keeping with the relationships explained in the SISP theory by Lederer and
Salmela (1996) and extension by Brown (2004), top management support was
hypothesized to directly influence SISP success. To get a deeper understanding, a
contingency approach was used to examine the role of the information systems plan and
IT infrastructure flexibility as two circumstances that can mediate the relationship
between top management support on SISP success.
12
Statement of the Problem
The SISP theory represents a significant contribution to research and practice
because it provides a rigorous, coherent, parsimonious, and comprehensive way of
investigating SISP. However, the problem is, despite the importance of SISP theory to the
success of SISP research and practice, very few empirical studies have applied it (Brown,
2004). Besides Gottschalk (1999a, 1999b, 1999c) who used it to study the relationship
between the content characteristics of the information systems plan document and the
plan implementation, it was hard to find any explicit reference to the Lederer and Salmela
(1996) SISP theory in the SISP literature (Brown, 2004), particularly in SISP studies on
top management support and SISP success. These problems represent excellent
opportunities to contribute to the SISP body of knowledge.
Purpose of the Study
Seeking to address the scarcity of SISP theory utilization, the purpose of this
study was three-fold:
1. To build upon the work of Lederer and Salmela (1996), and Brown (2004) on
SISP theory to derive a set of four constructs that gives a comprehensive
representation of SISP.
2. To develop a research model linking the four constructs based on direct
relationship between top management support and SISP success as well as top
management support indirect relationship with SISP success through the potential
mediating constructs of information systems plan, and IT infrastructure flexibility.
3. To empirically test the research hypotheses and validate the research model.
13
This study is different than previous research because it adapted the SISP theory to
derive a comprehensive set of constructs that provides a holistic perspective on how top
management support affects SISP success and demonstrated the mediating role of the
constructs of information systems plan, and IT infrastructure flexibility in influencing
that relationship.
Rationale
The increasing importance of SISP as a strategic instrument, the correspondingly
large expenditures on IT, the scarcity of empirical research using the SISP theory and its
derivatives justified this investigation. This study involved establishing a research model
that integrates four constructs derived in large part from the SISP theory. The four
constructs are: (a) the extent of top management support, (b) the usefulness of the
information systems plan, (c) the quality of IT infrastructure flexibility, and (d) the
degree of SISP success.
The research model is shown in Figure 3. It depicts the direct effect of top
management support on SISP success as well as top management support’s indirect effect
on SISP success via the mediating constructs of information systems plan, and IT
infrastructure flexibility. The model explores these mediating constructs through which
top management support can help the organization achieve SISP success and meet its
business goals.
Understanding the complexity of the direct and indirect relationships between top
management support and SISP success and the mediating role of information systems
plan and IT infrastructure flexibility is desirable to both theory and practice. Because
14
SISP success or failure impacts the successful implementation of business strategies, it
continues to be a major concern for both information systems and business executives
(Lufman & Ben-Zvi, 2010). Consequently, the findings of the study are expected to
provide the SISP researchers and practitioners with a better understanding of how the
support of top management support can help improve SISP success–both directly and
indirectly.
Research Questions/Hypotheses
The research questions that this study addressed are:
1. What is the direct relationship between the constructs of top management support
and SISP success?
2. From a contingency perspective, to what extent do the constructs of information
system plan usefulness, and IT infrastructure flexibility mediate the relationship
between the top management support and SISP success?
To help answer these questions, this study established a four-construct model
derived largely from the SISP theory. The four constructs are: (a) top management
support, (b) information systems plan, (c) IT infrastructure flexibility, and (d) SISP
success. The study was guided by the hypotheses listed below, which are based on the
direct and indirect (mediated) relationships between top management support and SISP
success.
H1. The higher the degree of top management support, the higher the usefulness
of the information systems plan.
15
H2. The higher the degree of top management support, the higher the degree of IT
infrastructure flexibility.
H3. The higher the degree of top management support, the higher the degree of
SISP success.
H4. The higher the usefulness of the information systems plan, the higher the
degree of SISP success.
H5. The higher the degree of IT infrastructure flexibility, the higher the degree of
SISP success.
Further explanation and justification for the above hypotheses are provided in
chapter 3.
Significance of the Study
This study is significant because it deals with the following four major SISP
shortcomings:
1. The use of the SISP theory is very sparse in published research studies on top
management support and SISP success.
2. Failure in securing and sustaining top management support as key planning
resource input to the planning process persists.
3. Only 24% of applications recommended by SISP are developed (Lederer & Sethi,
1988).
4. The role of the mediators (information systems plan, and Information Technology
(IT) infrastructure flexibility) in influencing the relationship between top
management support and SISP success needs to be better understood.
16
In addition, this study is significant because it addresses a topic that continues to
be of great importance to researchers and practitioners even in current economic
condition. During periods of economic contraction, SISP can provide the long-run
thinking needed to successfully identify the applications necessary for cost reduction and
productivity improvements. Lufman and Ben-Zvi (2010) predicted that during periods of
economic recovery and expansion “IT strategic planning will continue to be important
but will focus on helping the organization increase revenues and profits” (p. 53). These
views suggest that SISP is critical to a firm’s competiveness, thus its success remains a
key concern for both researchers and practitioners in today’s marketplace and beyond.
Failed SISP and IT/IS implementations typically lead to a number of hard and soft
losses for the firms. The hard losses can include money, time, and other resources. On the
other hand, the soft losses can involve reductions in employee morale, and a growing lack
of trust and faith in senior executives to deliver successful changes (Heracleous, 2000).
These failures tend to put future SISP undertakings in doubt and jeopardy. Against this
backdrop, this study contributes to the body of knowledge by integrating four constructs
derived largely from the SISP theory to establish a comprehensive SISP model to provide
key insights into the complexity of the direct and indirect relationships between top
management support and SISP success.
Definition of Terms
A brief description of each of the four constructs is provided below.
Information systems plan is a document that contains the information system (IS)
strategy. It is a key SISP deliverable. In order for it to be considered useful, it must be
17
based on top management’s perspective and reflects the business goals. It must also be
implemented in order to ensure successful utilization of IT to help realize the business
goals (Hann & Weber, 1996; Lederer & Salmela, 1996; Lederer & Sethi, 1992; Lederer
& Sethi, 1996).
Information technology (IT) infrastructure flexibility refers to the degree to which
the existing IT infrastructure possesses properties such as scalability, compatibility, and
modularity. These properties enable the applications specified in the information systems
plan to be implemented quickly and economically in order to meet the business goals
(Bhatt, Emdad, Roberts, & Grover, 2010; Byrd & Turner, 2001; Chung, Rainer, & Lewis,
2003; Kumar, 2004).
SISP success refers to the impact the SISP exercise has on the overall
effectiveness of the firm in terms of satisfaction of the planner and user community,
alignment between IS plans and corporate plans, implementation of the IS plans, increase
in understanding of firm’s operations, improvement in planning capability and
cooperation among cross-functional departments (Bechor, Neumann, Zviran, & Glezer,
2010; Doherty, Marples, & Suhaimi, 1999; Lee & Pai, 2003; Raghunathan &
Raghunathan, 1994; Wang & Tai, 2003; Warr, 2005).
Top Management Support refers to the degree to which chief executive officers
(CEOs), chief operation officers (COOs), chief financial officers (CFOs), chief
technology officers (CTOs), or the vice presidents of a firm are interested in, participate
in, comprehend, and support the IS function, including SISP and other information
systems-related efforts (Bajwa, Rai, & Brennan, 1998; Raghunathan & Raghunathan,
1988; Ragu-Nathan, Apigian, Ragu-Nathan, & Tu, 2004).
18
Assumptions and Limitations
The subjects for this study comprised the information systems (IS) executives in
US organizations, including both government and private sectors that conduct SISP. This
study was based on the assumption that the subjects are very involved in SISP, thus are
the most knowledgeable about SISP-related efforts in their organizations (Gottschalk,
2002). This study also assumed that their responses reflect their knowledge and
perceptions regarding this study’s constructs, which are: (a) the extent of top
management support, (b) the usefulness of the information systems plan, (c) the quality
of IT infrastructure flexibility, and (d) the degree of SISP success.
This study employed survey research methodology to measure the information
systems (IS) executives’ perceptions, and assumed that this measurement is a good
representation of reality. One of the limitations this study has is the use of single
informants per organization to collect survey data. This approach may result in potential
bias in IS executives’ responses. Another limitation is that the use of survey research
methodology only involved taking a snapshot of SISP life. Because SISP life is very
dynamic, taking a snapshot does not capture all of the particularities, complexities, and
diversity of meanings involved with the constructs under study in the SISP context.
However, one of the benefits of using the survey research approach is that reliabilities
and validities are easier to assess as well as generalizability and replicability (Cooper &
Schindler, 2008; Creswell, 2009; Holt, Armenakis, Field, & Harris, 2007).
19
An additional limitation is that the research model does not include the various
other constructs and mediators that can influence the relationships between top
management support and SISP success. However, one of the benefits of using a limited
number of constructs is that, from a resource standpoint, the complexity of the model is
manageable.
Nature of the Study
This study developed a four-construct model, employed a cross-sectional survey
design to collect data, and examined SISP success in terms of top management support
and the effects that the mediating constructs of information systems plan and IT
infrastructure flexibility have on the relationship between top management support and
SISP success. Because this study tested a fairly new research model, it is characterized as
exploratory in nature, even though the SISP and contingency theories that underlie it are
well established (Malhotra & Grover, 1998; Pinsonneault & Kraemer, 1993).
According to Bergeron, Raymond, and Rivard (2001), when mediation is
involved, the appropriate analytical technique to be used is path analytic technique.
Consistent with this suggestion, this study used the path coefficients produced by the PLS
software to test the research hypotheses and validate the research model. This path
analysis approach has been employed by researchers such as Neufeld, Dong and Higgins
(2007), Khalifa, Yu, and Shen, (2008), Chou (2010), Khanlarian (2010), Tomaszewski
(2010), and Garza (2011).
20
Organization of the Remainder of the Study
The remaining four chapters of this research are summarized herein. Chapter 2
contains an extensive review of the SISP literature pertinent to this study. After defining
SISP, the importance of SISP success is discussed in relation to top management support.
Then, the research model of SISP success is presented depicting the direct relationship
between top management support and SISP success as well as the indirect influence of
top management support on SISP success through the information systems plan and the
Information Technology infrastructure flexibility. This chapter provides an overview of
the key independent, mediating, and dependent SISP constructs that concern this study,
and a summary of the hypotheses identified from the literature review through the lens of
the SISP and contingency theories.
Chapter 3 introduces the selected research design and method and provides
justification for the appropriateness of this study. The four constructs in the research
model are discussed along with their measurement scales, associated reliability, validity
measures, and other properties. The resulting data instrument, population, and procedures
are also be covered in this chapter.
Chapter 4 reports on the data collected and presents the results of the Partial Least
Squares statistical analysis performed in relation to each hypothesis. In addition, this
chapter reports on whether or not the hypotheses are supported. Moreover, this chapter
answers the research question based on the results of the data analysis.
Chapter 5 concludes the dissertation with a summary of the findings, and how
they contribute to the SISP body of knowledge from a practitioner and researcher
21
perspective. Research limitations are also discussed along with recommendations for
future studies.
22
CHAPTER 2. LITERATURE REVIEW
This chapter presents a comprehensive review of the strategic information systems
planning (SISP) research literature. It focuses on the SISP theory to identify a set of
constructs for developing the research model. Information relevant to SISP in previous
research was used for the analysis of constructs and their relationships, the development
of research hypotheses, and the hypothesis testing. To facilitate a review of the literature,
this chapter is divided into nine sections: Introduction, SISP Definitions, SISP Theory,
Top Management Support, Information Systems Plan, IT infrastructure flexibility, SISP
Success, and Conclusion.
Introduction
In this information-intensive business environment and fast-paced technological
and demographic changes, firm success depends not only on well-crafted corporate
strategies but also on its ability to harness the power of current- and next-generation of
information systems (IS) applications to help implement those strategies. SISP has been
widely recognized in the literature as a critical process by which IS investments can be
used to exploit and explore the power of IS applications to equip corporations with
systematic capabilities. McFarlan (1984) suggested that altering the Porter’s (2008)
competitive forces may lead to a lasting competitive advantage for companies. In
concurrence with this suggestion, Lederer and Sethi (1988) argued that the systematic
capabilities enabled by SISP can be used to “build barriers against new entrants, change
the basis of competition, generate new products, build in switching costs, or change the
balance of power in supplier relationship” (p. 526).
23
SISP undertakings require substantial investments for an organization in terms of
people, time, and money to use IS as a strategic instrument to provide above capabilities.
Because of IS potential strategic impact on firms’ success and levels of investments, SISP
success has been a key concern for information systems (IS) executives as well as
business executives. This is because if SISP fails, it can lead to a lack of plan
implementation, which would render the organization incapable of achieving its business
goals, taking advantage of current- and next-generation opportunities, and implementing
future applications timely and economically (Lederer & Sethi, 1988). The following sub-
section discusses many variations associated with SISP definitions.
SISP Definitions
In terms of SISP definitions, the literature shows numerous variations. For
instance, SISP has been conceptualized as "the process of identifying a portfolio of
computer-based applications that will assist an organization in executing its business
plans and realizing its business goals" (Hartono, Lederer, Sethi, & Zhuang, 2003; Lederer
& Sethi, 1992; Phillip, 2007). In a similar vein, Earl (1993) argued that SISP should
incorporate information systems strategy along with information management strategy
and information technology strategy. Based on this reasoning, Earl suggested that SISP
should focus on the following objectives:
·Aligning investment in IS with business goals
· Exploiting IT for competitive advantage
· Directing efficient and effective management of IS resources
· Developing technology policies and architectures. (p. 1)
24
War (2005) captured this view of SISP by defining SISP as “the process of
deciding upon the direction, development and policies for an organization’s use and
management of information and networking technologies” (p. 1). Likewise, in his study
of organizational memory systems, Atwood (2002, p. 4) conceptualized the convergence
of information systems (IS) applications, databases, and telecommunication networks as a
“superstructure” with knowledge capturing, processing, and distributing capabilities. On
the other hand, Lederer and Sethi (1988) viewed SISP as a process of discovering that
superstructure by carefully choosing what architecture, computer hardware and software
applications needed to support successful implementation of business plans so that the
corporate goals can be met. SISP was also viewed as involving the identification of high
pay-back applications, databases, and communications networks. In addition, SISP is
seen as a process that assists organizations in using IS creatively and innovatively to
achieve an enduring competitive advantage.
Consistent with prior definitions, Remenyi (1991) argued that “SISP involves
matching the computer applications with the objectives of organizations so as to
maximize the return on the efforts of the Information Systems Department (ISD), as well
as the return earned by the organization as a whole” (p. 13). Among these
conceptualizations, Earl’s (1993) view of SISP appears to be the most comprehensive
because it contains elements of both exploitative and explorative uses of IS to fulfill
business goals.
To reflect these multidimensional views, SISP can thus be portrayed as a process
of determining how IS investments will be used to define and implement a
“superstructure” capable of helping the firm achieve its desired future state, and
25
ultimately gain and maintain a lasting competitive advantage to outperform its
competition. As discussed in previous section, SISP includes exploiting and exploring
current- and next-generation applications and opportunities by digitizing and globalizing
value chain activities in order to gain (a) organizational efficiency, (b) organizational
effectiveness, and (c) competitive advantage. SISP also involves creating a blueprint by
which IS strategies in alignment with business strategies can be successfully executed
taking into account change management considerations.
Discussing the Raghunathan and Raghunathan’s (1994) planning success
measurement model, Segars and Grover (1998) pointed out that “Within the context of
general IS planning, this work demonstrates that planning success seems to be a complex
system of interrelated constructs” (p. 140). Recognizing the importance of SISP to
successful utilization of IT, Lederer and Salmela (1996) crafted the SISP theory to help
researchers as well as practitioners visualize SISP as a system of interrelated constructs.
SISP Theory
In 1996, Lederer and Salmela developed the theory of SISP, which depicted SISP as a
system that is composed of the following constructs:
(1) the external environment, (2) the internal environment, (3) planning
resources, (4) the planning process, (5) the information plan, (6) the
implementation of the information plan, and (7) the alignment of the information
plan with the organization’s business plan. (p. 241)
26
Figure 2. SISP theory capturing the major SISP constructs that can influence alignment or SISP success and relating them as a system in a Y-structure. Adapted from “Toward a Theory of Strategic Information Systems Planning,” by A. L. Lederer and H. Salmela, 1996, Journal of Strategic Information Systems, 5, p. 240.
As depicted in Figure 2, the SISP theory explains SISP as a system of
input-process-output-implementation-outcome in which the constructs of the input
domain influence the process domain, which impacts the output domain whose
effectiveness affects the implementation domain, which in turn influences the outcome
domain. For instance, the SISP theory shows that the constructs of the external
environment, the internal environment, planning resources serve as inputs to the planning
process. In the SISP literature, the construct of top management support is viewed as one
Internal Environment
External Environment
Planning Resources
Planning Process
Information Plan
Plan Implementation
Alignment
27
of the key resources needed to ensure that the proper level of investments are made to the
SISP efforts in terms of information, people, time, and money. The informational inputs
from top management include the business goals that SISP should be aiming for. This
view is consistent with Premkumar and King (1991) who indicated that “The business
mission, objectives, strategies, and plans of the organization provide the necessary
background information to guide the IS strategic planning process” (p. 2).
When engaged with the planning process, these inputs are then converted in
outputs. The outputs consist of the planning deliverables which are represented in the
SISP theory as information systems plan construct. It is a document that specifies the IS
applications needed to meet the goals of the business. However, in order to realize the
business goals, the recommended applications must be implemented. The plan
implementation construct refers to the execution of the plan, which in this study is also
concerned with how flexible the existing IT infrastructure is to facilitate successful
implementation of IS plan. In the SISP theory, the alignment construct is concerned with
the outcomes of SISP. Lederer and Salmela (1996) viewed alignment between
information systems plan, plan implementation, and the business goals as an indicator of
SISP effectiveness and the fulfillment of SISP goals.
Recognizing the SISP complexity, Lederer and Salmela (1996) challenged other
researchers to expand on the theory and “test the relationships among constructs whose
effects take place through other constructs” (p. 248). In 2004, Brown responded to this
challenge and identified other relationships among the constructs suggested by the SISP
theory. Lederer and Salmela’s SISP theory used “alignment” construct to represent the
final dependent variable of SISP. However, Brown (2004, p. 23) replaced the
28
“alignment” construct with “planning outcomes” because the latter captures the broad
range of approaches researchers employed to measure the effectiveness of the SISP
system. One of the outcomes this study is concerned with is SISP success. Thus, this
study used SISP success as the dependent variable instead of “alignment” or “planning
outcomes”. SISP success construct is shown as SES in the research model that is depicted
in Figure 3.
Selection of Constructs for the Research Model
In terms of selecting the constructs for the research model, Schumacker and Lomax
(2010) suggested that:
Model specification involves using all of the available relevant theory, research,
and information to develop a theoretical model. Thus, prior to any data collection
or analysis, the researcher specifies a particular model that should be confirmed
using variance-covariance data. In other words, available information is used to
decide which variables to include in the theoretical model (which implicitly also
involves which variables not to include in the model) and how these variables are
related. (p. 55)
Following the above suggestion, this study initially selected four constructs for
the research model from the seven constructs suggested above by the SISP theory. These
four constructs are: (a) planning resources, (b) information systems plan, (c) plan
implementation, and (d) alignment.
The planning resources construct was replaced with top management support.
This is because the literature shows that one of the critical human resources that is needed
29
to achieve SISP objectives is top management support. The plan implementation
construct was replaced with the IT infrastructure flexibility construct to represent an
aspect of the dynamic environment that contemporary organizations have to deal with. In
this study, IT infrastructure flexibility is viewed as one of the two potential mediators of
the relationship between top management support and SISP success. In the contemporary
business environment, flexibility in IT infrastructure is needed to facilitate the
implementation of the applications recommended by the information systems plan, which
is needed to meet the business goals.
The alignment construct was replaced with SISP success following Brown’s
(2004) replacement of alignment with planning outcomes. The planning process construct
was not selected because measuring the degree of SISP success is akin to measuring the
degree of the effectiveness of SISP process. This omission is consistent with Tallon
(2008b) who argued that, rather than measuring the process and its numerous activities, it
is simpler and more manageable to assess the output or the outcome of the process to get
a sense whether related activities produce the expected benefits.
The resulting constructs that are relevant to this study include: (a) top
management support, (b) information systems plan, (c) IT infrastructure flexibility, and
(d) SISP success. In concordance with the SISP theory (Brown, 2004; Lederer &
Salmela, 1996) and the contingency theory, this study expected the level of support of top
management to directly influence SISP success and indirectly via the mediating
constructs of the information systems plan and IT infrastructure flexibility. All of the four
constructs are discussed below in the sub-sections that follow.
30
Top management support construct (TPORT). The literature suggests that top
management is one of the key human resources that is critical to the success of SISP. For
instance, Premkumar and King (1991) asserted that “human resources are the primary
resource input to IS planning” (p. 2). In a survey of 246 information systems (IS) top
managers, Premkumar and King (1994b) conceptualized human resources in terms of the
quantity and quality of top management’s participation and information inputs and also in
terms of the involvement of the user community in the SISP activities and the planning
skills of IS employees.
The support of top management has also been cited in the literature as one of the
most critical success factors affecting nearly all facets of the SISP system (Kearns, 2000,
2006; Lederer & Sethi, 1992; Raghunathan & Raghunathan, 1988; Ragu-Nathan,
Apigian, Ragu-Nathan, & Tu, 2004). The general information systems (IS) literature
provides a similar understanding that top management support is a meta-factor (Young &
Jordan, 2008) or “the most important critical success factor for project success and is not
simply one of the many factors” (p. 713). Likewise, through a comprehensive review of
the literature review on top management support and the enterprise information systems
(EIS) implementation, Loonam and McDonagh (2005) discovered that
EIS implementation literature, particularly studies on critical success factors,
point to top management support as a fundamental prerequisite for
implementation success. Indeed, more generally, studies on information systems
(IS) management equally endorse top management support as the most important
factor for ensuring the effective introduction of EIS in organizations. (p. 164)
31
Similarly, in a survey of 69 firms, Bajwa, Rai, and Brennan (1998) found that
“high levels of top management support indirectly influence EIS success by creating a
supportive context for the IS organization” (p. 31). In the same vein, Carbonell and
Rodriguez-Escudero (2009) reported that “Findings from 183 new product projects
indicate that top management support has a more positive effect on innovation speed
under conditions of high technology novelty and high technological turbulence” (p. 28).
Top managers are expected to have extensive business knowledge. Because of
this business background, they can (a) identify threats and opportunities in the external
environment as well as strengths and weaknesses in the internal environment, (b) provide
valuable inputs to the planning process, (c) help to reduce environmental uncertainty, and
(d) allocate the proper level of resources to the planning process (Teo & Ang, 2000). The
support of top management can result in high organizational commitment (Newman &
Sabherwal, 1996). Basu, Hartono, Lederer, and Sethi (2002) suggested that
organizational commitment is present when (a) adequate resources are provided, (b)
management initiates and controls SISP, (c) the SISP goals are realistic and reasonable,
(d) SISP champions and sponsors show visible support for SISP, and (e) key people are
retained to ensure continuity throughout the SISP study. These aforementioned studies
suggest that the support of top management is needed to encourage the presence of
organizational commitment for SISP efforts.
In a survey of 80 firms, Lederer and Sethi (1988) found lack of top management
support to be the most intense resource problem affecting SISP. A subsequent study
performed by Flynn and Goleniewska (1993) confirmed these findings. Teo and Ang
(2001) provided further evidence of the importance of top management support to SISP
32
success. They performed a key study in which they articulated information systems (IS)
planning as comprising three phases: “the launching phase, the plan development phase,
and the implementation phase”. They investigated the variables causing IS planning
problems over the three phases of SISP. This represents a significant contribution to this
stream of research because Teo and Ang found among the other problems, the most
grievous problem associated with IS planning effort is the failure to seek top management
support in all phases of the IS planning.
Lederer and Sethi (1992) found that problems affecting SISP success revolve
around six related, however, distinct dimensions: (a) organization, (b) implementation,
(c) database, (d) hardware, (e) software, and (f) cost. These researchers examined how an
organizational issue can become a technical issue, a cost issue, an alignment issue, an
impact issue, and vice versa. They explained that if top management support, as an
organizational factor, a central planning resource, a major input to the planning process,
is inadequate, the planning efforts will not be taken seriously by the other SISP
stakeholders. As a result, organizational commitment and involvement will be anemic.
Without adequate organizational commitment and involvement, the planners not
receiving requirements from the user community might end up specifying hardware and
software applications that do not meet the needs of the user community.
From a top management perspective, if the applications specified by the planners
are not in congruence with the business goals, then the IS plan would be considered not
useful and top managers may not support its implementation. Or, the user community
might resist implementation all together or attempt to implement their own applications.
A phenomenon Keen (1981) referred to as counterimplementation. Reworking of the
33
information systems plan to resolve the aforementioned problems would increase the
costs of the whole effort in terms of time, people, and money. That is why researchers
such as Basu, Hartono, Lederer, and Sethi (2002), Hann and Weber (1996), Newman and
Sabherwal (1996), and Teo and Ang 2001 suggested that, to avert these problems, top
management support should be provided at the initiation of the planning activities and
across all phases to increase the likelihood of SISP success.
In sum, Lederer and Sethi (1992) discovered that SISP success depends, not only
on addressing technical dimension of the IS plans, but also on providing adequate
consideration of the variables for the organizational dimension. One of these
organizational variables that needs further attention to enable SISP success is top
management support (Lederer & Sethi, 1992; Teo & Ang, 2001). Consistent with this
view, Raghunathan and Raghunathan (1988) found in a survey of 128 information
systems (IS) executives that top management support affects the emphasis given by
organizations to SISP system as a strategic instrument.
Similarly, Ragu-Nathan, Apigian, Ragu-Nathan, and Tu (2004) conducted a
quantitative survey research methodology to determine to what extent top management
support impacts the information systems in an organization. They presented a conceptual
model, dubbed “two-tiered framework”, to portray and test the direct and indirect
relationship between top management support and information system performance. In
the Ragu-Nathan et al. (2004) study, top management support was operationalized in
terms of the degree of top management’s (a) involvement with IS function, (b) interest in
IS function, (c) understanding of the importance of IS, (d) support of IS function, (e)
34
perception of IS as a strategic resource, (f) understanding of IS opportunities, and (g)
pressure on departments to work with IS.
The model portrays “the direct relationship between top management support and
IS performance as well as top management’s indirect effect on IS performance through
the intervening variables of structure, integration, control, and current and future
portfolios” (Ragu-Nathan et al., 2004, p. 461). Those intervening variables are viewed as
influencing the interactions between the independent and dependent variables.
Self-rating questionnaire was employed to collect data from a sample frame of
3000 prospective participants drawn from a directory of top information systems (IS)
executives (Ragu-Nathan et al., 2004). Structural Equation Modeling of the data collected
using LISREL software package supports the main hypothesis that top management
support is positively related with IS performance both directly and indirectly (Ragu-
Nathan et al., 2004). Subsequent to Ragu-Nathan et al., Kearns (2006) performed a study
of top management support of SISP in electric and non-electric-industries. They
conceptualized top management support as a multidimensional construct, which they
operationalized in terms of three dimensions: top management perception of IS function;
CEO participation in SISP; and business plan alignment with IS plan.
This study will adopt the Ragu-Nathan et al. (2004) conceptualization and
operationalization of top management support construct. They are very comprehensive,
and employ seven variables that capture the top management support construct in a
holistic way. The top management support construct is shown as TPORT in the research
model depicted in Figure 3. This study expected the extent of top management support to
35
affect the influence the information systems plan, which is discussed in sub-section
below as one of the four constructs in this study.
Information systems plan construct (ISP). The information systems plan is one
of major outputs of the planning process. It is a written document that articulates IS
application projects that need to be developed to help the corporations attain their goals.
Consequently, it is critical that top management clearly communicate the business goals
of the firm as they will be used as the basis for information systems plan (Hann & Weber,
1996). Gottschalk (1999a, 1999b, 1999c) characterized the information systems plan as
“a plan comprised of projects for application of information technology to assist an
organization in realizing its goals.”
Consistent with this characterization, Hann and Weber (1996) argued that the
information systems plan document should focus on the business. Otherwise, it will not
reflect the corporate goals. And, if it does not reflect the goals of the corporation, top
management will not support the plan implementation. The usefulness of the information
systems plan document is a very important construct in this study. It is conceptualized as
the extent to which the plan document reflects the business goals (Hann & Weber, 1996).
The information systems plan is more useful if it focuses on the business (Brown, 2004;
Byrd, Sambamurthy, & Zmud, 1995; Hann and Weber, 1996). The business goals need to
be reflected in the plan document so that support and resources needed for successful
implementation can be allocated.
Consistent with the contingency theory, this study looked at two conditions under
which the direct relationship between top management support and SISP success can
36
change. One of the conditions is the usefulness of the information systems plan (ISP)
construct, which is considered in this study as a potential mediator that can alter the
relationship between top management support and SISP success. The other condition is
the IT infrastructure flexibility (ITIF) construct, which is also considered in this study a
potential mediator capable of influencing the relationship between top management
support and SISP success. The ITIF construct is discussed in sub-section below.
IT infrastructure flexibility construct (ITIF). The IT infrastructure flexibility
(ITIF) construct is concerned with the capabilities an existing infrastructure in a firm
must possess to enable recommended applications to be implemented quickly and
economically. The rapidity of applications implementation allows firms to be agile in
order to respond quickly and adapt to changes in the business environment (Byrd &
turner, 2001; Chung, Rainer, & Lewis, 2003; Kumar, 2004; Ray, Muhanna, & Barney,
2005). By facilitating the alignment of IT-business and the quick and inexpensive
implementation of business applications, IT infrastructure flexibility is considered a
significant source of IT business value enhancement, business agility, IT effectiveness,
and competitive advantage (Bhatt, Emdad, Roberts, & Grover, 2010; Byrd & Turner,
2001; Chung, Rainer, Lewis, 2003; Duncan, 1995; Kumar, 2004; Ness, 2005;
Sambamurthy, Bharadwaj, & Grover, 2003).
Ray, Muhanna, and Barney (2005) defined a flexible IT infrastructure as “a
complex set of technological resources planned for and developed over time” (p. 631). If
an existing IT infrastructure has many elements of legacy systems still in its base, it can
be said that such an infrastructure is inflexible. This is because, according to Bhatt,
37
Emdad, Roberts, and Grover (2010), “Legacy systems are often rigid, which also limits
an organization’s ability to respond to external opportunities” (p. 341). Such an inflexible
infrastructure is expected to have adverse impact on the business value of the IT
infrastructure. However, a flexible IT infrastructure should add to the IT infrastructure
business value. For instance, in 2004, Kumar performed a key study that looked at the
extent to which flexibility contributes to the business value of IT infrastructure. Based on
the extant literature, Kumar identified in Table 1 the actions that should be taken by
organizations to improve IT infrastructure flexibility to support planned initiatives from
business strategies, new regulations or unplanned changes from major price fluctuations
(Kumar, 2004).
For the purposes of this study, another way of interpreting Table 1 is that, if a
business strategy of a firm wants to gain access to new technologies and markets through
mergers and acquisitions, then its IT infrastructure must be sufficiently flexible to enable
the heterogeneous systems to be integrated quickly and economically. Table 1 suggests
the type of technologies that would be appropriate for this particular event. For instance,
for mergers and acquisitions, Table 1 recommends the use of open systems architecture.
Kumar (2004) explained that an open systems-based infrastructure would reduce the cost
and time associated with integrating heterogeneous systems. However, an open systems
architecture is just one dimension of flexibility. Another technology that can enable the
IT infrastructure to be more flexible is the messaging bus.
38
Table 1
IT Infrastructure Management Actions for Different Types of Events
Events Actions
Changes in system demand volume (number of transactions)
• Use infrastructure with redundant processors, software for load balancing;
• Use architecture with integrated components, setup, and administrative toots;
• Use clustered servers in a scalable architecture; • Use infrastructure with extra capacity; • Use groups of low-end and medium-sized servers; • Maintain mirror sites in multiple locations.
Price changes, poor vendor service/financials Changes in usage patterns resulting in need to modify content or applications without interruptions in performance
• Use open architecture to accommodate multiple vendor products.
• Use intelligent middleware for easy data/ application segment reconfiguration;
• Use an architecture that supports multiple software standards;
• Use an architecture that provides modular prebuilt components;
• Design the network to support gateways to other systems;
• Use multiple Web servers for different application tiers;
• Use a component-based architecture. Availability of new technology or application that needs to be integrated into the infrastructure
• Use off-the-shelf products integrated into the existing infrastructure;
• Use mix of generic/custom components for easy customization;
• Use third-party solutions that can be easily and quickly integrated;
• Use existing infrastructure to reduce the cost of implementing a new application.
Events that result in system downtime (bugs, viruses, power outages)
• Use clusters of servers and redundant architectures;
• Use firewalls, virus detection software, and other security tools; Use multipath network design and fault-tolerant backbone components.
Mergers and acquisitions
• Use an open systems architecture; • Use an architecture that is scalable.
Note. From Ram L. Kumar, "A Framework for Assessing the Business Value of Information Technology Infrastructures", Journal of Management Information Systems, 21, No. 2 (Fall 2004). Copyright © 2004 by M.E. Sharpe, Inc. Used by permission.
39
An infrastructure that is equipped with a messaging bus can complete integration
projects quickly and inexpensively. Kumar explained that “This is because the effort
required to complete each system integration project without the messaging bus would be
significantly larger and lead to a lesser number of completed projects in a given time with
fixed development resources” (p. 20).
Similarly, an infrastructure that uses component-based architecture can respond to
contents and applications modification without the need to disrupt performance (Haag &
Cummings, 2009; Kumar, 2004). Likewise, an infrastructure that is equipped with
redundant software or hardware can survive a negative event such as a component failure.
Because of these properties, Kumar (2004) argued and proved that flexibility of
IT infrastructure is a significant source of IT infrastructure business value.
Explaining how business and IT executive support can impact flexibility, Duncan (1995)
pointed out that “infrastructure flexibility may be affected by a kind of support from
business in which the need for infrastructure is recognized and IT leadership in planning
for and managing those resources is supported” (p. 50). Similarly, Broadbent, Weill, and
Neo (1999) found that planning has a positive impact on IT infrastructure capability.
According to Ray, Muhanna and Barney (2005), “A flexible IT infrastructure facilitates
rapid development and implementation of IT applications that enhance…process
performance by enabling the organization to respond swiftly to take advantage of
emerging opportunities or to neutralize competitive threats” p. 631.
In addition to requiring the support of top management, building flexibility in the
IT infrastructure also needs considerable investment. The investment can be justified if it
brings about higher flexibility, which will add to the business value of the IT
40
infrastructure. Consistent with this view, Kumar (2004) suggested that “IT infrastructure
investments, such as investments in connectivity, systems integration, and data storage
and retrieval (storage area networks, data warehousing, and data mining) often increase
the value of the infrastructure by enhancing flexibility” (p. 17).
In addition to enhancing the business value of information technology (IT), a
flexible IT infrastructure can also help organizations achieve business agility
(Sambamurthy, Bharadwaj, & Grover, 2003). In a 2009 survey of IT executives, Luftman
and Ben-Zvi (2010) found business agility and speed to market to be one of the top five
issues IS and business executives are concerned about. Consistent with this finding,
Patten, Fjermestad, Whitworth, and Mahinda (2005) suggested earlier that “Business
agility is rapidly becoming the focus of managers trying to be more competitive in a
global economy” (p. 1). Focusing on the contemporary business environment, Tallon
(2008a) asserted that agility is essential to the survival of “firms operating in turbulent
markets marked by rapid product obsolescence, short product lifecycles, high customer
turnover, and price volatility” (p. 22).
The literature shows that a flexible IT infrastructure can equip firms with the
capabilities needed to achieve business agility (Sambamurthy, Bharadwaj, & Grover,
2003). For instance, according to Byrd and Turner (2001), “…if an organization supports
a wide variety of hardware and software, that organization can easily cope with changes
in industry standards” (p. 43). Conversely, an inflexible infrastructure can hinder a firm’s
ability to identify and respond to market threats and opportunities (Bhatt, Emdad,
Roberts, & Grover, 2010; Tallon, 2008a). Consequently, the flexibility of a firm’s IT
infrastructure is an important construct in helping it adapt to the changes in its business
41
environment (Byrd & turner, 2001; Chung, Rainer, & Lewis, 2003; Kumar, 2004; Ray,
Muhanna, & Barney, 2005). In line with this thinking, Chung, Rainer, and Lewis (2003)
indicated that “IT infrastructure flexibility is now being viewed as an organizational core
competency that is necessary for organizations to survive and prosper in rapidly-
changing, competitive, business environments” (p.191).
In her seminal work on the measurement of IT infrastructure flexibility, Duncan
(1995) described IT infrastructure as a set of sharable and reusable “IT resources that
provide a foundation to enable present and future business application” (p. 39).
Sharability refers to the extent to which IT infrastructure enables the information to be
generated, disseminated, and shared across the organizations and external partners.
Whereas, reusability refers to the degree to which IS components can be reused in
different application developments. Supporting the same reusability argument, Haag and
Cummings (2009) mentioned that “Component-based development is a general approach
to systems development that focuses on building small self-contained blocks of code
(components) that can be reused across a variety of applications within an organization”
(p. 167).
Rockart, Earl, and Ross (1996) offered a more expansive definition of IT
infrastructure than Duncan’s (1995) by indicating that:
IT is currently charged with creating an “IT infrastructure” of
telecommunications, computers, software, and data that is integrated and
interconnected so that all types of information can be expeditiously — and
effortlessly, from the users' viewpoint — routed through the network and
redesigned processes. Because it involves fewer manual or complex computer-
42
based interventions, a "seamless" infrastructure is cheaper to operate than
independent, divisional infrastructures. In addition, an effective infrastructure is a
prerequisite for doing business globally, where the sharing of information and
knowledge throughout the organization is increasingly vital. (p. 49)
Due to rapid advances in technology, hardware and software vendors are
upgrading their products faster than they did before, leading to rapid technology
obsolescence and declined IT product life cycles. Lee and Xia (2005) pointed out that
“As a result, organizations must continuously adopt and integrate new technologies into
their existing portfolios of business applications and IT infrastructure” (p. 76). These
forces heighten the need for flexibility in IT infrastructure. In other words, IT
infrastructure must provide the foundation on which advanced business applications can
be developed and implemented quickly and economically.
In light of the dynamism and complexities that exist in contemporary business
environment, the flexibility of the IT infrastructure has become an important capability
for a firm to have in order for it to be able to cope with uncertainty and ambiguity. Some
researchers refer to IT infrastructure flexibility as “the new competitive weapon” and see
it as critical to achieving and maintaining a lasting competitive success (Byrd & Turner,
2001, p. 41).
From a process perspective, Hitt, Ireland, Sirmon, and Trahms (2011) argued that
strategic management is one of the key processes that organizations can engage in to help
them minimize and/or capitalize on the uncertainty and ambiguity in the environment.
Within the IS field, Battagalia (1991) suggested that SISP has become a necessity for
corporations wanting to use their information infrastructure as a central resource to
43
reduce uncertainty and achieve breakthroughs in organizational effectiveness and
efficiency. As stated previously, SISP involves the identification of computer-based
applications that need to be implemented in order for the firms to realize their business
goals, strategic opportunities, return on investment and other benefits.
Consistent with this view, this study suggests that IT infrastructure flexibility as a
business goal should be included in the SISP process so that the right applications and the
proper level of support and investment can be allocated to make it a reality. As discussed
in the SISP theory, one of the outputs of the planning process, which is the core of SISP,
is the information systems plan (Lederer & Salmela, 1996). The latter is composed of
written documents that describe the IS strategy, and a blueprint for executing it.
However, implementation of the information systems plan is difficult and elusive
because most implementation efforts involve extensive software development projects to
produce the IS applications necessary to meet desired business goals. For instance,
Madanayake, Gregor, Hayes, and Fraser (2009) found these types of applications to be
high risk since “they involve changing requirements, a variety of business domains, a
variety of technical platforms and large amounts of monetary investments” (p. 2).
Another reason the IT strategic projects are considered high risk is because they must be
implemented in the existing infrastructure, which may not be flexible “to support the
design, development, and implementation of heterogeneity of business applications”
(Byrd & Turner, 2001, p. 43).
Consistent with this view, Lee and Xia (2005) suggested that “the contemporary
IS organizations seem to be far from being flexible” (p. 76). The absence of flexibility
makes it difficult to implement the business applications quickly and inexpensively.
44
Therefore, it can be argued that IT infrastructure flexibility is a critical factor of SISP
success because it facilitates the implementation of recommended applications so that the
business goals can be realized (Byrd & turner, 2001; Chung, Rainer, & Lewis, 2003;
Kumar, 2004; Ray, Muhanna, & Barney, 2005).
Thus, a high degree of flexibility in existing IT infrastructure is expected to lead
to a higher degree of SISP success, which is discussed in sub-section below as one of the
four constructs in this study.
SISP success construct (SES). SISP success is regarded as the ultimate outcome
of the SISP efforts. SISP failure results in wasted time, money, and resources, and
incompatible applications. Whereas, the success of SISP helps organizations to
successfully apply information systems (IS) technology to fulfill their business goals.
However, the literature has shown that SISP success is a difficult and elusive concept
because it can be influenced by a variety of constructs (Basu, Hartono, Lederer, & Sethi,
2002; Bechor, Neumann, Zviran, & Glezer, 2010; Doherty, Marples, & Suhaimi, 1999;
Raghunathan and Raghunathan, 1994; Segars & Grover, 1998). Hence, SISP success is
an area of paramount importance to practitioners and academics, who have become
interested in identifying the factors and problems that can impact SISP success.
The flexibility of IT infrastructure is a major source of IT business value
enhancement, business agility, IT effectiveness, and competitive advantage (Bhatt,
Emdad, Roberts, & Grover, 2010; Byrd & Turner, 2001; Chung, Rainer, & Lewis, 2003;
Duncan, 1995; Kumar, 2004; Ness, 2005; Sambamurthy, Bharadwaj, & Grover, 2003).
This study argues that IT infrastructure flexibility is also a catalyst that can help
45
implement the business applications quickly and economically such that SISP success
can be achieved.
According to Raghunathan and Raghunathan (1994), SISP is a managerial process
that is complex and multi-faceted. Raghunathan and Raghunathan suggested that its
success be measured using two interrelated dimensions: enhancement in SISP capability
and fulfillment of SISP objectives. They argued that the former dimension emphasizes
the ability of the SISP system to cultivate both creativity and management control by
focusing on the means or process by which SISP activities will deliver success.
Creativity helps deal with complexity and uncertainty, which is defined as lack of
sufficient information about certain aspects of the environment that inhibit management
ability to predict the impact of certain decisions on the organization (Daft & Marcic,
2009). However, creativity, if left alone, can get out of control. Consequently, control
mechanisms are also needed to balance creativity so that it does not become
unmanageable during periods of frequent adaptations. With respect to the second
dimension (fulfillment of SISP objectives), Raghunathan and Raghunathan posited that it
focuses on the ends or goals or outcomes that the planning process is aiming for.
Likewise, Wang and Tai (2003) conceptualized the capabilities dimension as process-
centric as it concentrates on the process, and the objectives dimension as goal-centric as it
focuses on the SISP objectives.
Bechor, Neumann, Zviran, and Glezer (2010) also employed the model proposed
by Raghunathan and Raghunathan (1994) to measure SISP success as a function of two
dimensions. One dimension relates to the capabilities that can be gained during or after
46
the plan is crafted. The other dimension relates to the achievement of objectives that can
occur during and after the plan’s recommendations are implemented.
Similarly, in a survey of 70 organizations in UK, Warr (2005) used the two-
dimensional model developed by Raghunathan and Raghunathan (1994) to account for
the planning capabilities improvement (means) and planning goals achievement (ends)
resulted from SISP. However, Segars and Grover (1999) studied SISP effectiveness in
terms of four dimensions. They are planning alignment, planning analysis, planning
cooperation, and planning capabilities. In 2006, Newkirk and Lederer employed similar
dimensions to measure SISP success in relation to the different SISP phases and the level
of uncertainty in the environment. However, in 1999, Doherty, Marples, and Suhaimi
surveyed 267 IT directors to examine the connection between the approach used to
conduct SISP and the resulting SISP success. Their measure of SISP success includes
seven variables, which are different from the ones used in aforementioned SISP studies.
These variables are: satisfaction, alignment, contribution, implementation, analysis,
capability, and cooperation. The variables are defined below.
1. Satisfaction variable refers to participants’ perception that time spent on SISP
exercise was worth it.
2. Alignment variable refers whether IS strategies reflect the business strategies.
3. Contribution variable refers to the overall impact SISP has on the firm in terms of
improved abilities to make better decisions, and identify emerging opportunities.
4. Implementation variable refers to the degree to which the Information Systems
plans have been, or are scheduled and likely to be, implemented.
47
5. Cooperation refers to general agreement reach cross-functional departments
regarding the schedules and trade-offs for the IS plans implementation.
6. Capabilities refer to improvement that can be achieved over time in the capability
of the planning process.
7. Analysis refers to studies that firms conduct to analyze and understand their
business and IT environments.
This set of variables used by Doherty, Marples, and Suhaimi (1999) to measure
SISP success is broad and very comprehensive. This is because they address the two
dimensions that Raghunathan and Raghunathan (1994) proposed to measure SISP success
in a comprehensive way. These two dimensions are: SISP capability improvement and
goals achievement.
In the Doherty, Marples, and Suhaimi’s (1999) SISP success measure, SISP
capability improvement is represented by the “capabilities” variable whose definition is
provided above. Goals achievement, on the other hand, is represented by the “alignment”,
“implementation”, and “contribution” variables. Beyond these variables, the Doherty et
al.’s (1999) SISP success measure also includes “analysis”, “cooperation”, and
“satisfaction” variables. “Analysis” variable corresponds to the detailed analysis involved
in the planning process construct. “Cooperation” and “satisfaction” variables are other
indicators of SISP success, relating to the social dimension of SISP. In sum, the seven
variables account for (a) the SISP capabilities improvement dimension, (b) the planning
goals achievement dimension, (c) the social dimension of SISP. Doherty et al. suggested
that “it is best to assess SISP success using “multiple, interrelated success measures” (p.
39). Following this suggestion, this study will adapt the above variables to represent SISP
48
success because they capture the SISP success construct in a more complete way than the
other measures discussed above.
Conclusion
The literature review focused on the SISP definitions, SISP theory and extension,
and the four constructs that are relevant to this study: (a) top management support, (b)
information systems plan, (c) IT infrastructure flexibility, and (d) SISP success.
Brown (2004) extended the SISP theory and captured additional hypotheses
beyond those identified by Lederer and Salmela. The additional hypotheses captured by
Brown that are relevant to this study are:
1. “· More extensive and better quality planning resources results in more planning
outcomes” (p. 37).
2. “·A more extensive and useful information plan results in more positive planning
outcomes” (p. 38).
Lederer and Salmela’s (1996) SISP theory used “alignment” construct to
represent the final dependent variable of SISP. However, Brown (2004) replaced the
alignment construct with outcomes and pointed out that the reason is “to accommodate
the alternative outcome measures that have been used in SISP” (p. 23). Following Brown,
it should also be noted that planning outcomes have been assessed variously as the
fulfillment of SISP objectives, the attainment of SISP capabilities, the improvement of
SISP effectiveness, and the achievement of SISP success (Basu, Hartono, Lederer, &
Sethi, 2002; Bechor, Neumann, Zviran, & Glezer, 2010; Doherty, Marples, & Suhaimi,
1999; Raghunathan and Raghunathan, 1994; Segars & Grover, 1998). Instead of
49
alignment or planning outcomes, this study will use SISP success as the final dependent
variable, to assess SISP performance as reflected in Figure 3. Based on the above
argument, the above links identified by Brown imply that:
1. A high degree of top management support as a key planning resource will lead to
a high degree of SISP success.
2. A more useful information systems plan will lead to a high degree of SISP
success.
These links are combined with the SISP theory links and others to develop the research
model and five hypotheses for this study, which will be presented in the next chapter.
The literature reveals that the impact of top management support on SISP success
can be examined using the strategic information systems planning (SISP) theory, which
was developed by Lederer and Salmela (1996) and extended by Brown (2004). In effect,
top management support influences nearly all aspects of the SISP system. Hence, it is not
surprising that top management support has been found to be critical to SISP success
(Basu, Hartono, Lederer, and Sethi, 2002; Kearns, 2006; Lederer & Mendelow, 1988;
Raghunathan & Raghunathan, 1988). Besides, Gottschalk (1999a, 1999b, 1999c) who
used the SISP theory to examine the linkage between two of the constructs (information
systems plan and implementation), little empirical research exists that applies the SISP
theory to investigate the SISP phenomena that can influence SISP success.
From a SISP theory perspective, top management support belongs to the input
domain. If deficient, it can adversely impact the planning process by discouraging
organizational commitment, involvement, and support for planning process in terms of
50
equipping it with adequate resources, with key department representatives, users, IS
planners to serve as members of the process project team.
Consequently, the resultant deficiency in organizational involvement and support
will lead to an inferior information systems plan, which is a major output of the planning
process. This is because, without adequate organizational involvement, the planners
might end up specifying software hardware and databases in the information systems plan
document that do not meet the evolving needs of the organization. These specifications
will result in users’ resistance during plan implementation, and might even lead to
counterimplementation. Counterimplementation is a phenomenon that occurs when users
attempt to implement their own applications instead of the ones recommended (Keen,
1981).
These conditions will result in misalignment between information systems plan,
implemented systems, and the business needs. Furthermore, counterimplemented
applications will lead to a proliferation of incompatible systems within the organization.
In this context, the SISP would be regarded a failure. To prevent failure, sustained top
management support is needed throughout the SISP system to encourage organizational
support at the onset of the planning process, which can increase organizational
commitment, minimize resistance, and lead to the acceptance of the implementation of
plan’s specifications (Lederer & Sethi, 1992).
Referring to the Raghunathan and Raghunathan’s (1994) planning success
measurement model, Segars and Grover (1998) pointed that “Within the context of
general IS planning, this work demonstrates that planning success seems to be a complex
system of interrelated constructs” (p. 140). Lederer and Salmela (1996) supported this
51
view by capturing the major SISP constructs that can influence SISP success, and relates
them as a system in the SISP theory. However, despite the SISP theory contribution to
research and practice, its use in SISP inquiries has been very sparse.
To address this dearth of SISP theory usage, this study suggested the adaptation of
the SISP theory to develop a comprehensive framework with which to study the
connection between top management support and SISP success. Specifically, this study
integrated four constructs mainly from SISP theory to comprehensively explain the direct
and indirect impact that top management support has on SISP success via the mediating
constructs of information systems plan, and IT infrastructure flexibility.
The planning process is viewed as obtaining inputs about the internal and external
environments from the top managers to ensure that a comprehensive analysis takes place
during the planning process. In this regard, the continued support and involvement of
management becomes even more critical throughout all of the SISP phases in order to
allow the timely incorporation and adaptation to changes in the environment. In a similar
vein, Pun, Sankat, and Yiu (2007), paraphrasing Pun and Lee (2000), argued that
“Management involvement has to be a continuous process because of continual changes
in the internal environments that affect the information requirements and the external
environments that influence technological, competitive and manpower availability
parameters” (p. 131).
The literature on the constructs of the information systems plan (ISP) and the
Information Technology infrastructure flexibility (ITIF) was reviewed. Following the
contingency theory, ISP and ITIF are investigated as two conditions under which the
effects of top management support on SIS success can change. Venkatraman (1989)
52
conceptualized contingency approaches as (a) moderation, (b) mediation, (c) matching,
(d) covariation, (e) profile deviation, and (f) gestalts. Bergeron, Raymond, and Rivard
(2001) studied these six conceptualizations to show the differences among them and to
illustrate how to appropriately use them when applying contingency theory in IT strategic
management research.
Among the above six conceptualizations, this study selected mediation as a
contingency approach with which to investigate the information systems plan (ISP) and
the Information Technology infrastructure flexibility (ITIF) as two conditions that can
alter the relationship between top management support and SISP success. In this study,
top management support is the independent variable that is expected to influence the
usefulness of ISP (mediating variable), and the ITIF (mediating variable), which in turn
are expected to influence SISP success (dependent variable).
Based on the aforementioned studies and suggested theoretical linkages, this
study principally hypothesized that: The degree of top management support has a
positive effect on the mediating constructs (usefulness of the information systems plan
and the degree of information technology infrastructure flexibility), which in turn are
positively related to the degree of SISP success. With this review of the literature, the
groundwork has been established for the significance and relevance of this research. The
following chapter presents the methodology for the research.
53
CHAPTER 3. METHODOLOGY
This chapter describes the development of the research model and hypotheses. In
addition, it explains the methodology to be applied to the research model to test
hypotheses developed. The constructs in the research model are also discussed along with
their measurement scales, associated reliability, validity measures, and other properties.
Data collection and analysis procedures are also covered in this chapter.
Examination of Research Model and Development of Hypotheses
SISP success is of great importance to researchers and practitioners because it is
necessary to ensure the realization of business goals. The major contribution that the
SISP theory made to research and practice is that it captures the major SISP constructs
that can influence SISP success, and relates them as a system. However, despite the
importance of SISP theory to the success of SISP research and practice, there are very
few empirical studies that employ it (Brown, 2004). This scarcity of SISP theory
application motivates this study to develop a comprehensive model of SISP based largely
on the SISP theory. The model was employed to investigate the effects that top
management support has on SISP success both directly and indirectly.
With regards to model specification, Schumacker and Lomax’s (2010) argued that
researchers should use the relevant theory and published studies to identify the constructs
to be included in the model. Using insights gained from the SISP literature review, in
particular the SISP theory, top management support, Information Technology (IT)
infrastructure flexibility, and SISP success, this study identified four constructs for the
research model (Bhatt, Emdad, Roberts, & Grover, 2010; Brown, 2004; Doherty,
54
Marples, & Suhaimi, 1999; Hann & Weber, 1996; Lederer & Salmela, 1996; Premkumar
& King, 1994a, 1994b); Ragu-Nathan, Apigian, Ragu-Nathan, & Tu, 2004).
IT infrastructure flexibility (ITIF) construct was incorporated in the model to
represent an aspect of the dynamic environment since the flexibility of IT infrastructure
has been heralded as the new competitive weapon that is needed to implement strategies
quickly and easily (Byrd & Turner, 2001). Chung, Rainer, and Lewis (2003), Kumar
(2004), and Ray, Muhanna, and Barney (2005) also viewed the flexibility of IT
infrastructure as critical to achieving rapid development and implementation of business
applications. Quick and inexpensive strategy implementation enabled by ITIF in turn
facilitates SISP success. Overall, the four constructs that make up the model are: (a) top
management support (TPORT), (b) information systems plan (ISP), (c) information
technology infrastructure flexibility (ITIF), and (d) SISP success (SES).
Using this set of constructs, the model provides a comprehensive representation of
the major SISP constructs that can affect its success. As shown in Figure 3, the model
depicts the direct impact of top management support on SISP success as well as top
management support indirect influence on SISP success through the constructs of
information systems plan, and IT infrastructure flexibility. The constructs are shown
connected based on the hypothesized relationships drawn from literature review in the
previous chapter as well as the SISP theory by Lederer and Salmela (1996) and Brown
(2004). This study used the model to answer the following research questions:
1. What is the direct relationship between the constructs of top management support
and SISP success?
55
2. To what extent do the constructs of information system plan usefulness, and IT
infrastructure flexibility mediate the relationship between the top management
support and SISP success?
Relying on the contingency theory, this study examined the direct and indirect
effects of top management support on SISP success. The research model is depicted in
Figure 3, which relates top management support directly to SISP success and indirectly
via the mediating constructs of planning process, information systems plan, and IT
infrastructure flexibility. The hypothesized relationships identified in this study are
discussed below.
Hypotheses Related to Top Management Support (TPORT) Construct
The first construct in the research model is top management support, which is
labeled as TPORT in Figure 3. Many researchers have characterized top management
support as a central human resource that is essential to the success of SISP effort as it can
ensure the adequacy of informational, human, technical, and financial support allocated
to the SISP study (Bajwa, Rai, & Brennan, 1998; Brown, 2004; Flynn & Goleniewska,
1993; Kearns, 2004, 2006; Lederer & Sethi, 1988, 1992; Premkumar & King, 1991;
Raghunathan & Raghunathan, 1988; Ragu-Nathan, Apigian, Ragu-Nathan, & Tu, 2004;
Teo & Ang, 2001; Wang & Chen, 2006; Young & Jordan, 2008).
56
Figure 3. Research model and hypotheses relating top management support (TPORT) directly to SISP success (SES) and indirectly via the mediating constructs of information systems plan (ISP), and information technology infrastructure flexibility (ITIF).
In general IS literature, top management support is regarded as the most
significant critical success factor for project success and not just one of the factors
(Young & Jordan, 2008). Similarly, the SISP literature suggests that top management
support has a critical role to play in all of the phases of SISP process (Teo & Ang, 2001).
Top management support is necessary to secure funding, and provide strategic direction
in addition to supporting the SISP effort and its implementation to ensure realization of
objectives. Top management support can reduce organizational problems, influence, and
encourage organizational involvement, which can minimize resistance and lead to the
acceptance of the implementation of plan’s specification (Lederer & Sethi, 1992).
This study identified a total of five hypotheses (H1-H5). They are discussed in
sub-sections below starting with hypothesis 1. In the hypotheses, top management
IS plan (ISP)
Top Management Support
IT Infrastructure
Flexibility
SISP Success (SES)
H1
H2
H4
H5
H3
57
support is the independent variable. The usefulness of the information systems plan and
the degree of IT infrastructure flexibility are the mediating variables. The degree of SISP
success is the dependent variable.
Justification for hypothesis 1 - top management support and usefulness of the
information systems plan. The information systems plan is a major output of the
planning process. It is a document that reflects the choices the SISP participants made
during the planning process focusing on detailed analysis of the business and information
systems environments. The usefulness of the information systems plan is dependent on
whether it reflects the business goals. Top management support has been found to be a
major factor in producing useful information systems plan. For instance, Hann and Weber
(1996), in a multi-method study, demonstrated that when top management controls the
planning process, their business goals and subsequent changes are reflected in the
information systems plan, which makes the plan document more useful. By contrast,
when IS management controls the planning process, their goals are reflected in the plan
document not top management’s, resulting in a less useful plan (Brown 2004).
These studies suggest that one of the benefits of top management support is it
enables the planning process to focus on the business rather than solely on IT. When the
focus is on the business, the usefulness of the information systems plan is higher (Brown,
2004; Byrd, Sambamurthy, & Zmud, 1995; Hann & Weber, 1996). Hence, the usefulness
of the plan document depends on the level of top management support, which explicitly
provides the business goals to be incorporated in the plan document. Knowing what goals
to focus on reduces environmental uncertainty for the participants, improves the quality
58
and the level of environmental and technology analyses, and also helps to achieve faster
conflict resolution among the different participants and stakeholders. Hence, it is
hypothesized that
H1. The higher the degree of top management support, the higher the usefulness
of the information systems plan.
Justification for hypothesis 2 - top management support and degree of IT
infrastructure flexibility. Information technology (IT) infrastructure flexibility (ITIF) is
important because it enables the quick implementation of recommended business
applications, which in turn leads to SISP success (Byrd & turner, 2001; Chung, Rainer, &
Lewis, 2003; Kumar, 2004; Ray, Muhanna, & Barney, 2005). However, one of the
factors that can influence the degree of IT infrastructure flexibility is top management
support. There is a rich body of literature highlighting the importance of top management
support to the success of technology-related activities. For instance, in a study of 183 new
product development projects, Carbonell and Rodriguez-Escudero (2009) found top
management support had a significant impact on innovation speed in environments
characterized by high technological turbulences.
The IT infrastructure is composed of a myriad of IT/IS elements interconnected
together. Among the elements that make up the IT infrastructure are the IT architecture,
hardware and software applications, network and telecommunications, and IT personnel.
As discussed previously, a flexible IT infrastructure is one that possesses properties of
scalability, compatibility, and modularity. Focusing on the planning domain, Palanisamy
(2005) studied the relationship between user involvement in IS planning, IS flexibility,
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organizational flexibility, and IS success. The results of the study show that “IS success
and organizational flexibility could be achieved through IS flexibility, which could be
generated by involving users in IS planning” (Palanisamy, 2005, p. 63). Users’
involvement was found to be a significant factor in achieving IS flexibility.
Based on this finding, this study argues that top management should also have a
positive impact on IS infrastructure flexibility by encouraging the involvement of the
users and provide an adequate level of resources and follow-ups to ensure the successful
implementation of recommended applications. This argument suggests that the degree of
top management support can influence the degree of IT infrastructure flexibility. Hence,
it is hypothesized that
H2. The higher the degree of top management support, the higher the degree of IT
infrastructure flexibility.
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Justification for hypothesis 3 - top management support and degree of SISP
success. In this study, SISP success is a function of seven variables termed alignment,
analysis, capability, contribution, cooperation, implementation, and satisfaction (Doherty,
Marples, & Suhaimi, 1999). While researchers still argue about various SISP constructs
that can affect SISP success, nearly all of them agree that top management support is
essential for the success of SISP and other IS-related activities. For example, among the
problems associated with SISP planning effort, Lederer and Sethi (1988) and Teo and
Ang (2001) found that the most grievous one is the failure to seek top management
support in all phases of the IS planning. Kearns (2006) pointed out that “Without this
support, business strategies may not be implemented optimally and returns on IS
investments can be restricted” (p. 236).
The literature provides compelling evidence to support the contention that top
management support is one of the factors that is considered critical to SISP success
(Lederer & Sethi, 1992; Teo & Ang, 2001). This finding is supported by Raghunathan
and Raghunathan (1988) who surveyed information systems (IS) executives in 128
organizations and found that top management support affects the emphasis given by
organizations to SISP system as a strategic instrument. Furthermore, Brown (2004)
hypothesized that “More extensive and better quality planning resources results in more
planning outcomes” (p. 37). This is consistent with the view that greater top management
support will lead to greater allocation of planning resources, which will result in higher
degree of SISP success. The aforementioned studies provide support for the following
hypothesis proposed by this study:
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H3. The higher the degree of top management support, the higher the degree of
SISP success.
Hypothesis Related to Information Systems Plan (ISP) Construct
The information systems plan construct (ISP), displayed in the research model in
Figure 3, is a major output of the planning process. It is a written document that
articulates IS application projects that need to be developed to help corporations attain
their goals. The evidence in the literature suggests that the goals of the corporation and
related changes need to be conveyed by top management throughout the planning
process. These goals in turn should be used as a basis for preparing the information
systems plan (Gottschalk, 1999a, 1999b, 1999c; Hann & Weber, 1996; Pavri & Ang,
1995). This study identifies one hypothesis in which the usefulness of the information
system plan influences the degree of SISP success. The hypothesis (H4) is presented and
discussed in sub-section below.
Justification for hypothesis 4 - usefulness of the information systems plan
and the degree of SISP success. The information systems plan (ISP) document is one of
the key deliverables of the planning process. The IS plan is considered less useful when
IS management controls the planning process, and more useful when top management
controls the planning process (Hann & Weber, 1996). This is because, in the latter
situation, with sufficient top management support present, business or corporate goals are
likely to be the focus of the information systems plan document, not just the IS
management goals. When the focus is on the business, the usefulness of IT plan is higher
(Brown, 2004; Byrd, Sambarmurthy, & Zmud, 1995). Lederer and Salmela (1996)
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suggested a direct link between information plan and plan implementation, and
hypothesized that “A more useful information plan produces greater plan
implementation” (p. 247).
The implementation of the IS plan is defined by Gottschalk (1999c) as “the
process of completing the projects for application of information technology to assist an
organization in realising its goals” (p. 111). In this definition, the implementation of the
IS plan is tied to the business goals. Thus, the extent of IS plan implementation is critical
to SISP success. Consistent with this view, Raghunathan and King (1988) found
significant relationship between the extent of IS plan implementation and user
satisfaction. Likewise, the quality of plan implementation mechanisms was one of the
organizational factors that were found to be positively associated with SISP effectiveness
(Premkumar& King, 1994b). Based on the above work by Lederer and Salmela (1996),
Brown (2004) suggested a direct link between the information systems plan and planning
outcomes. Consistent with this link, he hypothesized that “A more extensive and useful
information plan results in more positive planning outcomes” (p. 38).
As discussed previously, in addition to alignment, the SISP outcomes have been
assessed variously as SISP objectives, SISP capabilities, SISP effectiveness, and SISP
success. In similar vein, Gupta and Raghunathan (1989) indicated that “the ultimate
success of systems planning depends on the success of the individual projects covered by
the plan” (p. 786). The above links imply that a more useful plan will lead to greater plan
implementation, which will result in a higher degree of SISP success.
Thus, the aforementioned studies provide compelling evidence to support the
following hypothesis proposed by this study:
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H4. The higher the usefulness of the information systems plan, the higher the
degree of SISP success.
Hypothesis Related to IT Infrastructure Flexibility (ITIF) Construct
Displayed in Figure 3, the IT infrastructure flexibility (ITIF) is concerned with the
properties such as scalability, compatibility, modularity that an existing IT infrastructure
should possess in order to facilitate the timely and successful implementation of the
business applications (Byrd & turner, 2001; Chung, Rainer, & Lewis, 2003; Kumar,
2004; Ray, Muhanna, & Barney, 2005). Implementation of these applications is critical to
achieving SISP success. The literature shows that one of the variables that can impact IT-
business alignment and the successful implementation of business applications is the
flexibility of existing IT infrastructure. Focusing on applications development and
implementation, Duncan (1995) argued that “While individual systems are generally
identified as a direct source of IT's business value, IS executives are gradually
recognizing that IT infrastructure is key to the development time, the cost—essentially
the feasibility—of implementing an innovative system” (p. 38).
Byrd and Turner (2001) identified eight dimensions of IT infrastructure
flexibility. They are “(1) data transparency, (2) compatibility, (3) application
functionality, (4) connectivity, (5) technical skills, (6) boundary skills, (7) functional
skills, and (8) technology management” (p. 44). These researchers studied the impact of
IT infrastructure flexibility using above dimensions and found that it is positively related
to competitive advantage. Likewise, in a survey of 105 top executives, Bhatt, Emdad,
Roberts, and Grover (2010) investigated the impact of IT infrastructure flexibility on
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firm’s information generation and dissemination capabilities, its organizational
responsiveness, and competitive advantage in dynamic environments. They measured IT
infrastructure flexibility in terms of the degree to which the IT infrastructure is scalable,
compatible, modular, and can handle multiple business application (Bhatt et al., 2010, p.
342).
Scalability refers to the extent to which the “storage, processing, and
communication capacities can be expanded in response to environmental change” (Bhatt,
Emdad, Roberts, & Grover, 2010, p. 344). Compatibility relates to degree to which new
systems can be easily integrated and connected with existing systems without major
work. Modularity refers to the ability “to modify, upgrade, and reconfigure existing IT
components quickly in response to evolving business requirements” (Bhatt et al., 2010, p.
344).
As shown in the research model in Figure 3, this study identifies one hypothesis
in which the degree of the IT infrastructure flexibility influences the degree of success.
The hypothesis (H5) is presented and discussed in sub-section below.
Justification for hypothesis 5 - IT infrastructure flexibility (ITIF) and the
degree of SISP success. The literature revealed that the existence of a flexible IT
infrastructure enhances the likelihood of the business applications implementation, which
is needed for SISP success. Consistent with Byrd and Turner (2001), Bhatt, Emdad,
Roberts, and Grover (2010) found that IT infrastructure flexibility indirectly influenced
competitive advantage through the constructs of organizational information generation
and dissemination and organizational responsiveness. In a survey of 200 U.S. and
Canadian firms, Chung, Rainer, and Lewis (2000) examined the impact of the flexibility
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of Information technology infrastructure on strategic alignment between IT and business
and the degree of applications implementation. Both, alignment and applications
implementation, are aspects of SISP success.
The business applications Chung et al. (2003) used to investigate the concept of
applications implementation were represented by: “transaction processing systems,
management information systems, executive information systems, decision support
systems, expert systems, data warehousing, data mining, inter-organizational information
systems, knowledge management, network management, and disaster recovery” (p. 196).
IT infrastructure flexibility, on the other hand, was represented in terms of four
dimensions: compatibility, connectivity, modularity, and IT personnel. Whereas, the
strategic alignment was conceptualized as the degree of congruence between IT strategic
plan and the business strategic plan. Chung et al. found that IT infrastructure flexibility
was significantly and positively related to strategic alignment and the degree of
applications implementation. In this study, the latter variables are considered to be
indicators of SISP success. This finding provides further evidence that IT infrastructure
flexibility is a major source of competitive advantage, and is critical to SISP success.
This finding has led Chung et al. (2003) to argue that “IT infrastructure flexibility
enables organizations to build or modify business applications quickly and easily. As a
result, a flexible IT infrastructure plays an important role in the extent of implementation
of various applications in a firm” (p. 195). In other words, Chung et al. revealed that there
is a link between IT infrastructure flexibility and applications implementation. From a
SISP perspective, this linkage suggests that IT infrastructure flexibility will enable the
recommended applications to be successfully implemented, which are needed to achieve
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SISP success. Similarly, Ness (2005) investigated the relationship between IT flexibility,
strategic alignment, and IT effectiveness. He conceptualized IT flexibility in terms of the
degree of connectivity, modularity, and compatibility that a firm’s IT infrastructure
possesses. In a survey of 85 CIOs and IT senior executives, he found that IT flexibility,
strategic alignment, and IT effectiveness are positively related.
Drawing on the above literature, this study suggests that the level of SISP success
is dependent on the existence of high IT infrastructure flexibility needed to support the
implementation of the recommended. The aforementioned studies and suggested linkages
support the following hypothesis proposed by this study:
H5. The higher the IT infrastructure flexibility, the higher the degree of SISP
success.
The five hypotheses presented above are also displayed in Table 2 below. In sum,
this study proposed that higher level of the top management support has both direct and
indirect effects on SISP success. The constructs of information systems plan and IT
infrastructure flexibility are viewed as mediators of the effects of top management
support on SISP success. The research model portrayed in Figure 3 shows the paths that
connect the research constructs illustrating the hypothesized relationships discussed
above.
Research Design
The selection of methodology for empirical investigations is influenced by a
variety of factors such as study scope, purpose, topic maturity (at early stages or at
mature stages), level of detail, the availability of resources, and cost. Qualitative case
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study and quantitative survey research approaches are the two methodologies that were
considered for this study. According to Premkumar and King (1994b, p. 90) and
Benbasat, Goldstein, and Mead (1987), case study research is appropriate when “research
and theory are at their early, formative stages” (p. 369). Conversely, when research and
theory are beyond the formative stages, then quantitative survey research methodology is
more appropriate.
Table 2
Summary of Study Hypotheses
Study Hypotheses
H1 The higher the degree of top management support, the higher the usefulness of the information systems plan.
H2 The higher the degree of top management support, the higher the degree of IT infrastructure flexibility.
H3 The higher the degree of top management support, the higher the degree of SISP success.
H4 The higher the usefulness of the information systems plan, the higher the degree of SISP success.
H5 The higher the degree of IT infrastructure flexibility, the higher the degree of SISP success.
There is plenty of evidence in the SISP literature to support the contention that
research and theory in the SISP planning domain are beyond early stages. However, since
a new model is involved in this study, its purpose was characterized as exploratory.
Quantitative survey research methodology enables the developed research model to be
tested along with associated hypotheses. It also enables randomization to be used to
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increase study validity (Malhotra & Grover, 1998). It enables the results to be generalized
from a sample to a larger population using rigorous statistical analysis techniques
(Gottschalk, 1999c, p. 111). In light of the above considerations, quantitative survey
research methodology was selected as the most appropriate for this study.
Sample
The target population consisted of information systems (IS) executives in US
organizations including both government and private sectors that conduct SISP. The IS
executives include senior vice presidents, vice presidents, chief information officers
(CIOs), or directors. They tend to be knowledgeable of the SISP efforts undertaken by
their firms. Previous researchers such as Gottschalk (1999a, 1999b, 1999c), Kearns
(2006), Lee and Pai (2003), Premkumar and King (1994a, 1994b), Sabherwal and King
(1995) have also used IS executives as informants.
Sampling Frame
A sampling frame was developed with the Directory of Top Computer Executives
along with the directory of 500 most innovative U.S. users of information technology
(IT) listed in the InformationWeek 500 and the directory of the top 1000 US public
companies published in the Fortune 1000. Only the corporations that are in the Directory
of Top Computer Executives with their full addresses listed will be included. Then, a
sample of information systems (IS) executives was pulled from the frame, employing
systematic probability sampling to reduce bias while increasing generalizability of the
findings. Chi, Jones, Lederer, Li, Newkirk, and Sethi (2005), and Hartono, Lederer, Sethi,
and Zhuang (2003) also employed a similar approach.
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Applied Computer Research, Inc. (ACR) is the publisher of the Directory of Top
Executives, which was used as a sampling frame source by numerous IS researchers
including Ness (2005). In this study, the participants were asked whether their firms
employ SISP. The firms that do not conduct SISP were not included in the study.
Sample Size
Validation of the model and testing of the hypotheses associated with the four
constructs was conducted using the Structural Equation Modeling (SEM). Partial least
squares (PLS) and Linear Structural Relationships (LISREL) are the two widely known
tools that can be used to conduct SEM. Fornell and Bookstein (1982) argued that “LIREL
requires relatively large samples for accurate estimation and relatively few variables and
constructs for convergence; PLS is applicable to small samples in estimation as well as in
testing…” (p. 450).
Partial least squares (PLS) is a components-based Structural Equation Modeling
(SEM) technique that uses a least squares estimation algorithm to estimate latent variable
scores, including path coefficients, weights and loadings (Chin, 1998). Because PLS is
less sensitive to sample size than LISREL (Chin, 1998; Haenlein & Kaplan, 2004;
Newkirk, Lederer, & Johnson, 2008), it was selected as the appropriate data analysis tool.
The appropriateness of PLS over LISREL is discussed in greater detail below in the Data
Analysis section. Following Khalifa, Yu, and Shen (2008), PLS analysis of this study’s
research model involved the following methods:
1. Squared multiple correlation (R²) – percentage of variance explained by the
independent variables and the overall model.
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2. Path coefficients – strengths of the relationships between the constructs.
3. Jacknifing resampling procedure for statistical significance.
4. Testing the significance of mediating effects using Excel spreadsheet from
ScriptWarp Systems (n.d.) at http://www.scriptwarp.com/warppls/#Resources.
Selection of the appropriate sample size should be based on the statistical tool
employed in this study. Because this study used the Partial Least Squares (PLS) statistical
tool, the sample size was based on guidelines provided by Cohen (1992), Chin (1998),
Gefen, Straub, and Boudreau (2000), and Newkirk, Lederer, and Johnson (2008), which
take into account the complexity of the model.
Gefen et al. (2000) suggested in Table 2 of their article that the minimum sample
size requirement for PLS should be “At least 10 times the number of items in the most
complex construct” (p. 9). Likewise, Chin (1998), a widely recognized expert of PLS and
developer of PLS software, suggested that, the minimum sample size should be 10 times
either “(a) the block with the largest number of formative indicators (i.e., largest
measurement equation) or (b) the dependent LV with the largest number of independent
LVs impacting it (i.e., largest structural equation)” (p. 311). In this study, condition (a) is
ruled out because there are no formative indicators in the model. All constructs in the
model are operationalized using only reflective indicators. Thus, only condition (b) is
possible in this study.
The model in Figure 3 shows one main dependent latent variable (SISP success)
being impacted by three independent latent variables (LVs) (top management support,
information systems plan, and IT infrastructure flexibility). Thus, dependent variable
SISP success abbreviated as SES is the most complex construct since it has the greatest
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number (3) of independent variables or predictors impacting it. According to above
approach suggested by Gefen et al. (2000) and Chin (1998), the PLS minimum sample
size requirement for this study model is 30 or 10 times 3 independent variables or
predictors. Similarly, Newkirk et al. (2008) pointed out “A strong rule of
thumb…suggests sample size be 10 times the largest number of structural paths directed
at a particular construct in the structural model” (p. 208). A sample size of 30 was
obtained for this study using the above Chin, Gefen et al., and Newkirk et al. approach.
Henseler, Ringle, and Sinkovics (2009) suggested that the selection of sample size
should also depend on desired statistical power level, not just on the generally accepted
10-times-rule of thumb. Statistical power is also an important element in the data
analysis. Cohen (1992) suggested that “In research planning, the investigator needs to
know the N necessary to attain the desired power for the specified α and hypothesized
ES” (p. 156). Cohen referred to N as the sample size, α as the significance level, and ES
as the effect size of the population.
As mentioned previously, partial least squares (PLS) analysis of the study’s
research model used squared multiple correlation (R 2 ) to examine the amount of variance
in the dependent variable that is explained by the independent variables and the overall
model. Hence, the R 2 provided by PLS software was employed as part of the statistical
analysis. Correspondingly, the effect size and sample suggested by Cohen for statistical
test involving multiple R or squared multiple correlation (R 2 ) was used in determining the
required effect size and minimum sample size for the model.
For multiple R or the squared multiple correlation (R 2 ), Cohen (1992) in his Table
1 on page 157 of his article regarded 0.02 to be a small effect, 0.15 to be a medium effect,
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and 0.35 to be a large effect. In addition, Cohen provided in his article Table 2 (page 158)
that specifies the minimum sample size for the above effect sizes. To use this table, the
independent variables, the desired power level, effect size, and significance level α must
be known. As mentioned previously, the number of independent variables in this study is
three. The desired power level, effect size, and significance level α are 0.80, 0.35, and
0.05, respectively. Taken together, Cohen showed a minimum sample size of 34 is
necessary to achieve a large sized effect of 0.35. Chou (2010), Khanlarian (2010),
Tomaszewski (2010), and Garza (2011) also employed the Cohen’s approach to
determine the sample size requirement for the model they used in their PLS dissertation
research. In summary, to achieve a large effect of 0.35, a sample size of at least 34 is
required for a power of 0.80 at the 0.05 significance level.
Operationalization of Research Constructs
The measures used to assess each of the constructs (top management support,
information systems plan, IT infrastructure flexibility, and SISP success) have been
adopted from previous empirical research in order to maximize the validity and reliability
of the survey instrument. Specifically, the work of Bhatt, Emdad, Roberts, and Grover
(2010), Doherty, Marples, and Suhaimi (1999), Hann and Weber (1996), Ragu-Nathan,
Apigian, Ragu-Nathan, and Tu (2004) played a key role in the operationalization of the
study constructs. This approach is consistent with Straub (1989) suggestion to use
measures previously used by previous researchers. All variables were operationalized
using a 7-point Likert scale. Measures selected from previous studies to assess each of
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the constructs in this study are discussed below in greater detail. The operationalization
of the constructs is presented in the Appendix.
Extent of Top Management Support (TPORT)
As depicted in the Appendix, the top management support construct (TPORT)
was measured using seven variables from a previous study performed by Ragu-Nathan,
Apigian, Ragu-Nathan, and Tu (2004, p. 469). They are (a) top management
involvement with IS function, (b) top management interest in IS function, (c) top
management understanding of the importance of IS, (d) top management support of IS
function, (e) top management perception of IS as a strategic resource, (f) top management
understanding of IS opportunities, and (g) top management pressure on departments to
work with IS.
As noted above, the operationalization of top management support takes into
account the whole IS function. SISP is a fundamental part of the IS function, this
operationalization also applies to the SISP context. Consequently, the aforementioned
Ragu-Nathan et al. (2004) seven-item measure is recast in this study to assess the extent
of top management support. The seven indicators are shown in the Appendix as variables
(TPORT1 – TPORT7) in the questionnaire. Also, a description of the study constructs
and variables is provided in Table 3. The Cronbach’s alpha reliability coefficient reported
for this construct measure was 0.91 (Ragu-Nathan et al., 2004, p. 469). The measures
selected for the remaining constructs are discussed below, starting with the usefulness of
the information systems plan (ISP).
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Usefulness of Information Systems Plan (ISP)
The usefulness of the information systems plan (ISP) was measured using the
scale from previous work performed by Hann and Weber (1996). The Cronbach’s alpha
reliability coefficient that Hann and Weber (1996) reported for the final scale was 0.77.
The information systems plan usefulness scale consists of five indicators, which are
detailed in the Appendix as variables (ISP1 – ISP5) in the questionnaire. The information
systems plan construct and related variables are described in Table 3.
Degree of IT Infrastructure Flexibility (ITIF)
The degree of IT infrastructure flexibility (ITIF) was measured using a five-item
scale employed by Bhatt, Emdad, Roberts, and Grover (2010) in their study of the role of
ITIF in dynamic environments. The Cronbach’s alpha reliability coefficient reported for
this construct measure was 0.84 (Bhatt et al., 2010, p. 345). The five indicators that
measure the degree of IT infrastructure flexibility are detailed in the Appendix as
variables (ITIF1-ITIF5). The construct of IT infrastructure flexibility and related
variables are described in Table 3.
Degree of SISP success (SES)
The SISP success (SES) construct is the main dependent variable. It was assessed
using seven variables from the SISP success study performed by Doherty, Marples, and
Suhaimi (1999). The seven variables are: satisfaction, alignment, contribution,
implementation, analysis, capability, and cooperation. In addition, the Doherty et al.
(1999) scale also measures the extent of implementation which is a principal objective of
SISP.
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Table 3
Description of SISP Constructs and Variables
Constructs Description of SISP Constructs and Variables
Primary Sources
Top Management Support (TPORT)
The breadth of interest, support, and involvement of top or corporate management in SISP and other information systems-related activities, and perception they hold about the IS ability to help achieve a competitive advantage, and their understanding of IS opportunities.
Kearns (2006); Bajwa, et al. (1998); Raghunathan and Raghunathan (1988); Ragu-Nathan et al. (2004)
Information systems plan (ISP)
The usefulness of information systems plan or the degree to which the information systems plan reflects or aligns with the corporate goals.
Hann and Weber (1996); Lederer and Salmela (1996); Lederer and Sethi (1992); Lederer and Sethi (1996)
IT infrastructure flexibility (ITIF)
The degree to which existing infrastructure possesses properties such as scalability, compatibility, modularity to enable applications recommended in the information systems plan to be implemented successfully, quickly and economically.
Bhatt, Emdad, Roberts, and Grover (2010); Byrd and Turner (2001); Chung, Rainer, and Lewis (2003); Kumar (2004)
SISP success (SES)
The degree to which the SISP exercise has resulted in greater satisfaction on the part of the participants, better alignment with business plans, higher contribution to the overall organizational performance, greater implementation of information systems plans, greater cooperation among departments with competing priorities, better SISP capabilities over time, and better analysis of business operations
Doherty, Marples, and Suhaimi (1999); Segars and Grover (1998)
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In all, SISP success measure is composed of seven indicators, which are shown in
the Appendix as variables (SES1 – SES7). Also, a description of the study constructs and
variables is provided in Table 3. The Cronbach’s alpha reliability coefficient was not
available for the Doherty et al. SISP success measure.
Data Collection
Based on previous construct measures, a survey instrument, shown in the
Appendix, was used to collect data from study participants. The survey instrument was
prepared for the information systems (IS) executives, and contained indicators to measure
the extent of top management support, the usefulness of the information systems plan, the
degree of IT infrastructure flexibility, and SISP success. The survey instrument requested
for a variety of demographic data pertaining to the participants and their organizations
such as the title of the participant, tenure, organizational level, total number of
employees, total number of IT employees, annual revenues from all sources, annual IT
budget, and industry type (adapted from Teo & Ang, 2001). In addition, the instrument
asked whether SISP is conducted in respondents’ firms. No personally identifiable data
such as gender, name, and education were requested.
E-mail and web-based surveys were not used because they would introduce a web
of insecure communications between computers, which would make it difficult to protect
respondents’ anonymity and confidentiality. Because of this vulnerability, traditional
postal mail survey was used instead of e-mail or web-based survey. This approach was
approved by the Institutional Review Board (IRB).
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Non-respondent Bias
Non-respondent bias was assessed using the late return approach by classifying
the group that replied prior to the reminder(s) as early respondents (Bechor, Neumann,
Zviran, & Glezer, 2010; Sabherwal, 1999). Conversely, the group that replied after the
reminders was treated as late respondents. This approach is consistent with Armstrong
and Overton (1977) who mentioned that “Persons who respond in later waves are
assumed to have responded because of the increased stimulus and are expected to be
similar to nonrespondents” (p. 397). Statistical technique such as t test was performed to
determine whether there is significant difference between the early respondents and late
respondents.
Data Analysis
As suggested by Bergeron, Raymond, and Rivard (2001), when mediation is
adopted as a contingency approach, the appropriate analytical approach to be used is path
analytic technique. Consistent with this suggestion, testing of the research hypotheses as
well as validation of the research model was conducted using path analysis with
Structural Equation Modeling (SEM).
SEM is an analytic technique designed to validate a model with many latent
constructs and numerous observed variables. SEM and multiple regression techniques
have similar purposes. However, the key difference between those two approaches is that
“SEM has a unique ability to simultaneously examine a series of dependence
relationships (where a dependent variable becomes an independent variable in subsequent
analysis) while also simultaneously analyzing multiple dependent variables” (Shook,
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Ketchen, Hult, & Kacmar, 2004, p. 397). When compared with regression and factor
analysis techniques, SEM possesses a distinctive ability for assessing the measurement
model within the theoretical structural model (Thong, Yap, & Raman, 1996). According
to Cheung (2007), “Structural Equation Modeling (SEM) is widely used to test models
with mediating effects” (p. 227). Because of these unique features over traditional data
analysis approaches, SEM was selected as the most appropriate data analysis for testing
of the research hypotheses as well as validation of the mediated model in Figure 3.
Several analytical tools can be used to implement SEM. Among them, the most
well-known are Linear Structural Relationships (LISREL) and Partial Least Squares
(PLS). LISREL was introduced in 1973 by Joreskog, and PLS was introduced by Wold in
1975 (Haenlein & Kaplan, 2004). LISREL and PLS are considerably different. According
to Fornell and Bookstein (1982), “LISREL attempts to account for observed covariances,
whereas PLS aims at explaining variances (of variables observed and/or unobserved)” (p.
450). LISREL requires strict assumptions about the data, including normal distributions,
interval scales, and large sample sizes. In contrast, PLS has less restrictions as it can work
without normal data distributions, with nominal, ordinal, and interval scales, and with
small sample sizes (Fornell & Bookstein, 1982). Consistent with these observations,
Haenlein & Kaplan (2004) emphasized that “One more area in which PLS is typically
recommended is that of situations in which the sample size is small” (p. 295).
Because of these attributes, partial least squares (PLS) was selected over LISREL
as the most appropriate data analysis tool. PLS was employed by Chou (2010),
Khanlarian (2010), Tomaszewski (2010), and Garza (2011) in their dissertation research.
Several other investigators such as Newkirk, Lederer, and Johnson (2008), Thong, Yap,
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and Raman (1996), and many others also used PLS in their research. Furthermore,
Khalifa, Yu, and Shen, (2008), and Neufeld, Dong and Higgins (2007) applied PLS to
test mediating models that are similar in structure to the one presented in this study.
Validity and Reliability
In order to maximize the validity and reliability of the survey instrument, this
study combines the scales that have been used by previous researchers to measure the
study’s four constructs, which are (a) top management support, (b) information systems
plan, (c) IT infrastructure flexibility, and (d) SISP success.
Reliability refers to the extent to which the instrument produces consistent results.
Validity refers to the degree the instrument measures what it purports to measure. First,
the scale for the top management support (TPORT) construct has been validated by
Ragu-Nathan, Apigian, Ragu-Nathan, and Tu (2004). On page 469, these authors have
demonstrated scale reliability by calculating a Cronbach’s alpha of 0.91, which is well
above the acceptable level of 0.70 suggested by Ragu-Nathan et al. (2004), Norusis
(2008), and many other researchers. With regards to the information systems plan (ISP)
construct, Hann and Weber (1996, p. 1054) calculated a Cronbach’s alpha value of 0.77.
For the IT infrastructure flexibility (ITIF) scale, Bhatt, Emdad, Roberts, and Grover
(2010, p. 345) measured a composite reliability of 0.84, indicating high reliability value.
With respect to the reliability of the SISP success scale, its value is unknown as it was not
reported in the Doherty, Marples, and Suhaimi (1999) study. These researchers, however,
noted that “To maximise the reliability and validity of the research instrument, and to
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strongly embed the research within the existing literature, the questions used were, where
possible, adapted from published research” (p. 267).
All of the above researchers are affiliated with reputable universities and their
studies are published in peer-reviewed journals. This suggests that they have considerable
credibility in the research community. The survey questionnaire used in this study is
shown in the Appendix. It is based on the above scales that have been used by
aforementioned researchers.
Consistent with Straub (1989), Creswell (2009) suggested that “When
one…combines instruments in a study, the original validity and reliability may not hold
for the new instrument, and it becomes important to reestablish validity and reliability
during data analysis” (p. 150). Thus, in addition to conducting field testing procedure
discussed previously, this study assessed the reliability and validity of the constructs prior
to testing the study hypotheses to ensure the credibility of subsequent data analysis
results. Specifically, reliability of the constructs was examined via the Cronbach’s alpha
method and the Internal Composite Reliability (ICR) method. Recommended level of
reliability is 0.70 (Khalifa, Yu, & Shen, 2008). Convergent validity was assessed via
factor loading method, which involved examining the degree to which each of the survey
indicators loads on its respective construct. Thong, Yap, and Raman (1996) pointed out
that “Some researchers suggest 0.55 as the minimum factor loading to explain at least
30% of the variance in the construct” (p. 259). Discriminant validity was assessed via the
average variance extracted (AVE) method, which involved examining the degree to
which the indicators measured different constructs, and not the same thing. To
demonstrate discriminant validity, AVE should be at least 0.50 (Fornell & Lacker, 1981).
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Ethical Considerations
Ethics is an integral part of the design of this research (Steenbarger &
Manchester, 1996). It enables the protection of the privacy, confidentiality, and
anonymity of participants. Steenbarger and Manchester indicated that “Participation in a
project should always be voluntary, with experimenters informing subjects in advance of
the general intent of the study, the nature of research procedures, and the risks and
benefits of participation.” Since this study used external organizations as the data source
and only posed minimum risk to participants, the Institutional Review Board (IRB)
approved a waiver of documentation of informed consent. IRB approval was secured
before data collection began.
The survey questionnaire packages were distributed via traditional postal mail to
the participants. The packages included a cover letter, the survey questionnaire
(Appendix), adult informed consent form, and a prepaid-postage self-addressed envelope.
The cover letter explained the purpose of the study, expressing that participation is
voluntary, and also assuring anonymity and confidentiality of participants. The informed
consent form was not required to be signed by the participants since IRB has waived the
documentation. The informed consent form was provided to the participants to help them
decide whether they want to participate in this study. These measures were intended to
improve the participants’ response rate.
The data collected are maintained in a non-networked, and password-protected
computer. In addition, hardcopies of data are kept in a locked file cabinet, which will be
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discarded using an electric shredder. Electronic copies of data will be discarded using a
data destruction service so that destroyed data cannot be recovered afterward.
Summary
This chapter explains the development of a research model depicting the
hypothesized relationships among the constructs of top management support, information
systems plan, Information Technology (IT) infrastructure flexibility, and SISP success.
This chapter also discusses the quantitative research methodology that was applied to the
research model to test the formulated hypotheses. This includes explanation of the
sampling, data collection and analysis approaches, including instrument reliability and
validity assessment methods. Partial least squares, which is a type of structural equation
modeling suitable for small sample sizes, was used to assess the measurement and
structural models. The results of the PLS analyses of data are presented in the next
chapter.
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CHAPTER 4. RESULTS
This study developed a four-construct model to investigate the direct and indirect
relationships between top management support and strategic information systems
planning (SISP) success. The four constructs are (a) top management support, (b)
information systems plan, (c) information technology (IT) infrastructure flexibility, and
(d) SISP success. The model is illustrated diagrammatically in Figure 3. Top management
support (TPORT) is the independent variable. The information systems plan (ISP)
usefulness and the information technology (IT) infrastructure flexibility (ITIF) represent
two circumstances under which the relationship between top management support and
SISP success can vary, thus were considered mediating constructs. SISP success is the
dependent variable, which is concerned with whether:
1. The SISP participants are satisfied with the process.
2. The information systems plans are aligned with business plans.
3. Organizational performance is improved.
4. The information systems plans are implemented.
5. Departments are cooperating.
6. SISP capabilities are improved.
7. Analysis of business operations is improved.
This chapter explains the research methodology used to collect data, test the
hypotheses, and validate the study model. It consists of six sections: (a) survey
questionnaire, (b) field test, (c) data collection, (d) descriptive statistics, (e) testing of
constructs’ validity and reliability, (f) data analysis.
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The first section describes the survey questionnaire used to collect data for this
research. The field test, data collection, and demographic data are then discussed in
sections 2, 3, and 4, respectively. The fifth section explains the results of the constructs’
validity and reliability testing. The last section discusses the results of the data analysis
performed using partial least squares (PLS), a structural equation modeling (SEM) tool,
to test the hypotheses and validate the model.
Survey questionnaire
Due to security concerns associated with web-based survey, traditional postal
mail survey was determined to be the most appropriate methodology for this research.
Consequently, a survey questionnaire was used to collect data from practicing
information systems (IS) executives. The survey questionnaire was developed using
established scales. All constructs in the model were measured using multiple items on a
seven-point Likert scale ranging from Strongly Disagree (SD = 1) to Strongly Agree (SA
= 7). The Appendix shows the survey items.
The beginning of the survey questionnaire provided potential respondents with the
following definition of SISP that is widely used in the published SISP research: “the
process of identifying a portfolio of computer-based applications that will assist an
organization in executing its business plans and realizing its business goals" (Lederer &
Sethi, 1988, p. 446). The survey then asked potential respondents whether SISP is
conducted in their organization. If they answered yes, they could proceed to answer the
rest of the questions. The questionnaire also explained the meaning of the scale and gave
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instructions on how to use it to indicate their responses to the questions asked. From
there, the survey questionnaire is organized into five sections.
As reflected in the Appendix, the first section contained a seven-item scale to
measure the extent of top management support based on the work of Ragu-Nathan,
Apigian, Ragu-Nathan, and Tu (2004). An example of items in this scale is “Top
management involvement with IS function is strong.” The second section contained a
seven-item scale to measure the degree of SISP success based on the work of Doherty,
Marples, and Suhaimi (1999). An example of items in this scale is “The SISP process in
my organization has resulted in greater implementation (i.e., the extent to which the IS
plans have been or are thought to be implemented).”
The third section contained a five-item scale to measure the usefulness of the
information systems plan (ISP) based on the work of Hann and Weber (1996). An
example of items in this scale is “The goals and objectives of the most recent
information systems plan prepared for my organization are linked to the overall goals and
objectives of my organization.” The fourth section contained a five-item scale to
measure the degree of the IT infrastructure flexibility (ITIF) based on the work of Bhatt,
Emdad, Roberts, and Grover (2010). An example of items in this scale is “our
information systems are scalable.” The fifth section requested for a variety of
demographic data concerning the potential respondents and their organizations.
Field Test
Three experts in the Information Systems and Management fields were consulted
and requested to comment on how to improve the survey instrument, if there is a better
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way to ask the questions, or if there are questions that are simply not needed, and whether
the instructions given in the instrument are clear and easy to follow.
The above approach was initially referred to as pretesting. However, subsequent
investigation revealed that the procedure employed resembles the field test approach
described as follows by Capella University Research Center (2011): “Experts in the field
review the questions and offer feedback to the researcher about whether the questions are
appropriate for the population, whether they will make sense to the population, and
whether they represent the perspectives of the field. Experts in the field typically include
faculty, practitioners, or respected researchers.” Hence, the above instrument
improvement approach hereafter will be referred to as field test.
The field test conducted by the three experts resulted in several minor
modifications to the survey instrument that included the consistent use of relevant
terminology, addition of a citation, rewording of an opening statement, clarification of a
couple of sentences, removal of redundant instrument description, removal of construct
abbreviations, corrections of typographical errors associated with MA (Moderately
Agree), and addition of verbal navigational path for beginning instructions. Consistent
with Fanning (2005), a front cover page and a back cover page have also been added to
make instrument look more official and to also provide space in the back for additional
comments and thank-you note. These changes improved the instrument significantly by
making the instructions clearer and questions easier to understand, follow, and complete.
Capella University Research Center (2011) suggested the use of pilot study “when
a researcher has modified a valid instrument to the point that new validity information is
necessary.” Reviewing this suggestion, it is concluded that these minor modifications
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resulted from the field test do not affect the instrument validity to the point that a pilot
study is warranted.
Based on the following, it is concluded that a pilot study was not needed:
1. The scales used in this study are from published research in order to maximize the
instrument validity and reliability.
2. The field test has been conducted and only resulted in minor changes, which do
not warrant content re-validation.
3. In addition to conducting the field test, the instrument validity and reliability will
be demonstrated as part of the main study before conducting data analysis.
The final survey questionnaire is shown in the Appendix.
Data collection
The respondents selected to be part of the study included Information Systems
(IS) executives across the United States of America. They were chosen because they are
considered knowledgeable of the strategic information systems planning (SISP) efforts
undertaken in their firms. Using systematic probability sampling, a sample of Information
Systems (IS) executives was drawn from the Directory of Top Computer Executives
published by Applied Computer Research, Inc. (ACR).
The data collection process lasted 10 weeks, starting on November 25, 2011 and
finishing on January 28, 2012. The first batch of the survey questionnaire package was
mailed to 547 information systems (IS) executives between November 25, 2011 and
November 28, 2011. Four items were included in the package: a cover letter, the survey
questionnaire (Appendix), adult informed consent form, and a prepaid-postage self-
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addressed envelope. The cover letter explained the objectives of the study, invited the
selected the IS executives to participate, and informed them that their anonymity and
confidentiality will be strictly maintained. The letter also highlighted that this study is
part of the researcher’s Ph.D. dissertation. In addition, a summary of the final results was
promised in the letter to entice them to participate in the study. The adult informed
consent form highlighted the risks and benefits of the study, and explained that
participation is voluntary.
The first reply was received on November 28, 2011. In light of the approaching
busy Holiday season, follow-up letters were mailed out a week after the invitation to
remind potential participants to complete the survey. Two weeks after the follow-up
letters, 111 reminder calls were made between December 14, 2011 and December 22,
2011. In many cases, the calls were answered by the companies’ voicemail automated
attendants, which explained how to navigate the companies’ voicemail systems. The
automated attendants specified what number to press on the telephone to access the
companies’ directory for the Information Systems (IS) executives’ extension numbers.
However, in most of these cases, only the Information Systems (IS) executives’ voice
mails were reached.
In other cases, the calls reached a human operator who then forwarded the calls to
the executive assistants or the information systems (IS) executives’ voice mails. These
calls revealed the reason that 26 surveys were returned as undeliverable is because the IS
executives had been either separated from subject firms, or changed positions, or the
companies went out of business. In a few cases, new addresses were obtained either from
the receptionist or the executive assistants. And, the new addresses were used to re-mail
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nine and fax two of the returned surveys to respective IS executives. In other cases, it was
discovered also that some companies had a policy prohibiting participation in external
surveys.
Five weeks after the 547 questionnaires were mailed, 26 of them were returned as
undeliverable, and 53 responses were received. However, of the 53 responses, only 47
were completely filled out, thus considered usable. Six respondents indicated that their
firms do not conduct SISP and only provided demographic data. On December 28, 2011,
the researcher then decided to mail a second batch consisting of 100 questionnaires to a
random sample of new information systems (IS) executives from the sampling frame.
However, for the second batch mailing, only the title, company, and address were
included on the envelope so that the person in the current position would receive the
surveys.
Between January 11, 2012 to January 20, 2012, 64 reminder calls were made to
follow up on the second batch recipients with an additional 66 reminder calls made to
continue following up on the first batch recipients. As a result of these calls, 35 e-mail
addresses and 10 fax numbers were obtained, which were used to send follow-up letters
and fax the survey packages to those who mentioned that they did not receive the
surveys. Three weeks after mailing the second batch, three questionnaires were returned
as undeliverable, six responses were received. Of the six responses, two indicated they do
not perform SISP. However, one of two still completely filled out the survey. As a result,
the researcher decided to include it as a usable response. This inclusion resulted in five
usable responses from the 100 questionnaires mailed in the second batch.
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Overall, as of January 28, 2012, 65 responses were received from the grand total
of 647 survey questionnaires (first batch and second batch) mailed. Including recent
returns, a total of eight out of the 65 responses did not completely fill out the surveys and
indicated that their companies do not conduct SISP. Only 57 completely filled out the
surveys, thus are considered usable responses representing an 8.8% or (57/647)*100
unadjusted response rate. However, when the 31 undeliverable survey questionnaires (28
from first and 3 from second batch) minus the 11 re-mailed or faxed surveys plus at least
14 non-participants due to company policy as found out during follow-up calls (11 from
first batch and 3 from second batch) are omitted, the adjusted response rate is 9.3% or
(57/613)*100.
Response Rate
After adjusting for the undeliverable and resent surveys, and non-participants
based on company policy, the usable response rate is 9.3%, which was similar to several
other studies involving information systems (IS) executives as participants. For instance,
in previous IS studies, the response rates were 9% in Bechor, Neumann, Zviran, and
Glezer’s (2010) study, and also 9% in Gattiker and Goodhue’s (2005) study. Consistent
with these observations, Kearns and Lederer (1999) pointed out that “Low response rates
are not uncommon for surveys sent to senior officers” (p. 47). Similarly, Bechor et al.
(2010) indicated that “Low response rates are typical of American CIOs” (p. 20).
Sample Size Adequacy
Because this study selected the partial least squares (PLS) statistical tool,
determining whether the sample size of 57 is adequate was based on guidelines provided
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by Cohen (1992), Chin (1998), Gefen, Straub, and Boudreau (2000), and Newkirk,
Lederer, and Johnson (2008). Using these PLS-related guidelines, the sample required to
analyze this study’s research model depicted in Figure 3 should be at a minimum 34 in
order to achieve a large effect size of 0.35, and a statistical power of 0.80 at the 0.05
significance level. Chou (2010), Khanlarian (2010), Tomaszewski (2010), and Garza
(2011) used similar PLS-based guidelines to determine the minimum sample required to
analyze the specific models in their dissertation research. Likewise, Jung, Chow, and Wu
(2003) used PLS in their article because they had a small sample size of 32. Therefore,
the 57 usable cases received from data collection are considered adequate for PLS
analysis for this research model.
Demographic Data
The WarpPLS 2.0 software was used for the data analysis. This software was
developed by Kock (2011) to conduct structural equation modeling (SEM) using Partial
Least Squares (PLS). Since this software can read raw data directly from Excel files
(Kock, 2011), data collected for this study were entered in a spreadsheet. Then, the
spreadsheet was divided into two datasets. The first dataset contains the data to study the
profile of the respondents, and the second dataset contains the data to validate the model
and test the hypotheses. The profile of the respondents is presented below.
Profile of Respondents
As shown in Table 4, the majority (98%) of the respondents identified themselves
as information systems (IS) executives, including senior vice presidents, vice presidents,
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chief information officers (CIOs), or directors. On average, the respondents had worked
for their current organization for 12 years.
Table 4
Respondents’ Job Titles (N = 57)
Job Title Frequency Percentage
Senior vice president CIO/IT/R&D-
IT/operations director
9 16
Vice president CIO/IT, vice chancellor
CIO
10 18
CIO/CTO/CISO/CPO 27 47
Deputy CIO 2 3
Director IT/CIO/IR/GI 8 14
ISO 1 2
Total 57 100
As shown in Table 5 below, only 10 respondents had less than five years with
their current organization while 56% had between 5 and 15 years. However, the majority
(82%) had been with their current organization for more than five years. In terms of
industry, they had been in their industry for an average of 24 years.
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Table 5
Respondents’ Years of Experience with Current Organization (N = 57)
Years Frequency Percentage
Less than 5 10 18
5-15 32 56
16-25 8 14
26-35 5 9
Greater than 35 2 3
Total 57 100
As reflected in Table 6, only two had been in their industry for less than five years
while 23 (40%) had between 16 and 25 years with their industry. However, the vast
majority (97%) had been in their industry for more than five years. Their executive job
titles coupled with years of experience in current organization and industry qualify them
as knowledgeable respondents.
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Table 6
Respondents’ Years of Experience in Industry (N = 57)
Years Frequency Percentage
Less than 5 2 3
5-15 10 18
16-25 23 40
26-35 16 28
Greater than 35 4 7
Missing 2 4
Total 57 100
Table 7 below shows that respondents worked in a variety of organizations with
7% of them in manufacturing, 16% in healthcare, 9% in financial services, 14% in
education, 19% in government, 5% in transportation, 10% in insurance, and 18% in other
organizations. This result means that SISP is conducted in a wide variety of
organizations, including the government.
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Table 7
Respondents’ Industry (N = 57)
Primary Industry Frequency Percentage
Manufacturing 4 7
Healthcare 9 16
Financial services 5 9
Education 8 14
Government 11 19
Transportation 3 5
Insurance 6 10
Other 10 18
Missing 1 2
Total 57 100
The analysis of respondents’ IT budget is summarized in Table 8. Only nine
respondents (16%) had fewer than $7 million for their IT budget while 40 (70 %) had
greater than $10 million, which is an indication that these are major organizations. Table
8 shows that 56% of the respondents had an IT budget greater than 20 million.
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Table 8
Annual Information Technology (IT) budget (N = 57)
Budget ($) Frequency Percentage
1 – 6 million 9 16
7 – 10 million 3 5
11 - 20 million 8 14
21 – 30 million 8 14
31 – 50 million 3 5
51 - 100 million 7 12
Greater than 100
million
14 25
Missing 5 9
Total 57 100
The assessment of the size of the respondents’ organizations is summarized in
Tables 9 and 10. Table 9 shows only seven organizations (12%) had less than 1100 full-
time equivalent (FTE) employees while the majority 49 (86%) had between 1100 FTE
employees and 58,000 employees.
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Table 9
Number of Employees (N = 57)
Number of employees
Frequency Percentage
Less than 1100 7 12
1100-4999 18 32
5000-9999 12 21
10,000-19,999 7 12
20,000-29,999 7 12
30,000 – 58,000 5 9
Missing 1 2
Total 57 100
Regarding the size of their Information Technology organization, it is depicted in
Table 10. Only one (2%) had less than 11 full time IT employees while 23 (40%) had
more than 200 FTE IT employees. However, the great majority 56 respondents (98%)
had more than 11 FTE IT employees, which is another indication that the majority of
respondents’ organizations are large.
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Table 10
Number of Information Technology (IT) Employees (N = 57)
Number of IT employees Frequency Percentage
Less than 10 1 2
11-79 13 23
80-150 16 28
151-200 4 7
Greater than 200 23 40
Total 57 100
Non-respondent Bias Analysis
Non-respondent bias was analyzed using the late return approach by classifying
the returns into two groups (Bechor, Neumann, Zviran, & Glezer, 2010; Sabherwal,
1999). The usable returns received within five weeks of first mailing totaling 44 were
classified as early respondents group. By contrast, the usable returns received after five
weeks of first mailing totaling 13 were classified as the late respondents group. This is
consistent with Armstrong and Overton (1977) who mentioned that “Persons who
respond in later waves are assumed to have responded because of the increased stimulus
and are expected to be similar to nonrespondents” (p. 397). A t-test was conducted and
showed that there was no significant difference between the early respondents group and
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late respondents group. Hence, non-response bias was not an issue. The research model
assessment is discussed in the next section.
Research Model Assessment
The assessment of the research model was performed using the measurement
model and the structural model (Gebauer, Kline, & He, 2011). First, the model fit indices
and p values obtained from partial least squares (PLS) analysis along with the instrument
reliabilities, and convergent and discriminant validities were used to evaluate the
characteristics of the measurement model. Second, in the structural model, the path
coefficients, their p values (significance level), R 2 statistics, and the mediating effects'
significance tests were used to test the hypotheses. Path coefficients represent the
strengths of the relationships between the latent variables. The p values represent the
significance level of the path coefficients. R 2 statistics represent the magnitude of the
variance in the dependent variables that is explained by their respective independent
variables. The initial PLS analysis results associated with the measurement model are
discussed next.
Measurement Model
Prior to validating the model and testing the study hypotheses, the reliabilities and
validities of the instrument scales were assessed using an initial partial least squares
(PLS) analysis. Chin, Marcolin, and Newsted (2003) indicated that PLS “simultaneously
models the structural paths (i.e., theoretical relationships among latent variables) and
measurement paths (i.e., relationships between a latent variable and its indicators)” (p.
197). In addition, PLS is able to perform such modeling and measuring with small sample
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sizes, and does not require the data to be normally distributed (Chin, 1998; Chin,
Marcolin, & Newsted, 2003; Newkirk, Lederer, and Johnson, 2008). Because of these
features, PLS was selected as the appropriate SEM tool to use for the data analysis.
This study employed the WarpPLS 2.0 software for the PLS analyses. Among the
various algorithm options provided in the WarpPLS 2.0 software, the PLS regression
algorithm was selected to validate the model and test the hypotheses. The PLS regression
algorithm first uses conventional PLS regression algorithm to calculate the parameter
estimates associated with the latent variables and their indicators. Then, it applies a
robust path analysis algorithm to compute the path coefficients describing the links
between the latent variables (Kock, 2011).
Following Schumacker and Lomax (2010), and Kock (2011), the model
constructs were referred to as latent variables. The items measuring the model constructs
were referred as indicators that are expected to be highly correlated with their respective
latent variables (Fornell & Bookstein, 1982; Kock, 2011). There were no formative
indicators in this study model. All four model constructs were defined as reflective (R)
latent variables (Chin, 1998; Kock, 2011), and abbreviated as follows:
1. TPORT, which refers to extent of top management support.
2. SES, which refers to the degree of strategic information systems planning (SISP)
success.
3. ISP, which refers to the usefulness of information systems plan.
4. ITIF, which refers to the degree of IT infrastructure flexibility.
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Regarding the assessment of the statistical significance, Kock (2011) pointed out
that “jackknifing tends to generate more stable resample path coefficients (and thus more
reliable P values) with small sample sizes (lower than 100), and with samples containing
outliers” (p. 9). Following this suggestion, the statistical significance was assessed using
the jackknifing method with 500 resamples, given that the sample size in this study is
below 100. The results of the reliability and validity assessment are discussed next.
Initial PLS Analysis - Reliability and Validity
An initial PLS analysis was run with all of the latent variable indicators to
evaluate the reliabilities and validities of the instrument scales, and determine which
indicators should be removed and which ones should be retained. Reliability refers to the
extent to which the instrument produces consistent results. Validity refers to the degree
the instrument measures what it purports to measure.
For reliability to be considered adequate, both the composite scale reliability also
known as internal consistency reliability (ICR), and the Cronbach’s alpha coefficient for
each latent variable should be at least 0.70 (Fornell & Lacker, 1981; Kock, 2011;
Newkirk, Lederer, & Johnson, 2008). Referring to the scales’ reliability, Kock (2011)
suggested that “If a latent variable does not satisfy any of these criteria, the reason will
often be one or a few indicators that load weakly on the latent variable. These indicators
should be considered for removal” (p. 33).
In terms of validity, this study is concerned with two types of validity: convergent
validity and discriminant validity. Convergent validity refers to the extent to which each
of the indicators loads on its respective latent variable. For convergent validity to be
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considered adequate, the factor loadings of items associated with each latent variable
should be 0.50 or above (Kock, 2011; Thong, Yap, & Raman, 1996). In addition, the p
values associated with the loadings should be below 0.05 (Kock, 2011). Discriminant
validity refers to the extent to which the indicators measure different latent variables, and
not the same latent variable. For a satisfactory discriminant validity, the values of average
variances extracted (AVE) should be 0.50 or higher (Chin, 1998; Fornell & Lacker, 1981)
or the square roots of the Ave should be equal to or greater than 0.70. The AVE refers to
the amount of variance that is shared between a latent variable and its indicators. Citing
Fornell and Lacker (1981), Kock (2011) suggested that “for each latent variable, the
square root of the average variance extracted should be higher than any of the
correlations involving that latent variable” (p. 34).
The results of the initial PLS analysis depicted in Table 11 show the composite
reliability to be adequate for all of the constructs since they achieved scores between
0.721 and 0.941. The Cronback alpha coefficients are also acceptable, but only for top
management support (TPORT), strategic information systems planning success (SES),
and IT infrastructure flexibility (ITIF). As for the information systems plan (ISP)
construct, the Cronback alpha coefficient score is 0.542, which failed to meet the
recommended minimum of 0.70. As shown in Table 11, discriminant validity was found
to be adequate for all of the constructs, except for ISP that had an average variance
extracted (AVE) score of 0.461, which is below the recommended threshold of 0.50.
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Table 11
Initial PLS Analysis - Reliability and discriminant validity coefficients
Construct Composite Reliability
Cronback Alpha Average Variance Extracted (AVE)
TPORT 0.941 0.926 0.694
SES 0.890 0.855 0.538
ISP 0.721 0.542 0.461
ITIF 0.901 0.863 0.647
Note: TPORT = Extent of Top Management Support; SES = Degree of SISP Success; ISP = Usefulness of Information Systems Plan; ITIF = Degree of IT infrastructure flexibility
In addition, as shown in Table 12, the square root of the average variance
extracted (AVE) at 0.679 for ISP is lower than the 0.732 correlation coefficient with SES.
Thus, for the ISP construct, the square root of AVE did not meet the Kock (2011)
guideline that “the square root of the average variance extracted should be higher than
any of the correlations involving that latent variable” (p. 34). Based on the AVE and
square root AVE analyses, ISP construct was found to have very weak discriminant
validity because it did not meet the AVE and square root AVE requirements. As
suggested by Kock (2011), a close examination of the indicators’ loadings showed that
the ISP4 indicator (plans are based on a short-term view, operational focus) was the
underlying problem that prevented the ISP construct from meeting the recommended
thresholds for reliability and discriminant validity.
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Table 12
Initial PLS Analysis - Latent variable correlations and square root of AVE
Construct TPORT SES ISP ITIF
TPORT 0.833 0.632 0.548 0.197
SES 0.632 0.733 0.732 0.368
ISP 0.548 0.732 0.679 0.311
ITIF 0.197 0.368 0.311 0.804
Note: Square roots of average variances extracted (AVE's) shown on diagonal and are in bold.
The examination revealed that ISP4 indicator loaded higher on the other
constructs than on the ISP construct. Specifically, the ISP4 indicator shown in Table 13
achieved a factor loading of -0.314 with the ISP construct, and a p value of 0.092. These
values suggest that ISP4 had a very low convergent validity because its factor loading did
not meet the recommended threshold of at least 0.50 (Kock, 2011). In addition, its
associated p value was not less than 0.05, as recommended by Kock.
Because of these unacceptable results, ISP4 indicator was found to be an
inappropriate measure of the ISP construct. The ISP4 indicator reflects a more
operational focus, which is incongruent with the ISP construct, which has a more
strategic focus. Based on these findings, ISP4 was considered to be unsuitable for
measuring the ISP construct.
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Table 13
Initial PLS Analysis - Combined loadings and cross-loadings
INDICATORS TPORT SES ISP ITIF P value
TPORT1 0.818 -0.197 0.305 -0.141 <0.001
TPORT2 0.827 -0.144 0.209 -0.032 <0.001
TPORT3 0.866 0.051 -0.108 0.072 <0.001
TPORT4 0.846 0.121 -0.158 0.119 <0.001
TPORT5 0.854 -0.081 0.127 -0.023 <0.001
TPORT6 0.815 -0.070 -0.063 0.073 <0.001
TPORT7 0.804 0.324 -0.314 -0.075 <0.001
SES1 0.017 0.810 0.220 -0.085 <0.001
SES2 -0.042 0.768 -0.048 -0.051 <0.001
SES3 0.039 0.801 0.317 -0.242 <0.001
SES4 -0.183 0.681 0.076 0.078 <0.001
SES5 0.323 0.750 0.176 0.030 <0.001
SES6 -0.101 0.703 -0.531 0.071 <0.001
SES7 -0.100 0.598 -0.343 0.296 <0.001
ISP1 -0.277 -0.014 0.678 0.021 <0.001
ISP2 0.381 -0.453 0.689 0.088 0.002
ISP3 -0.027 0.278 0.845 0.073 <0.001
ISP4 0.073 0.262 -0.314 0.372 0.092
ISP5 -0.040 0.227 0.746 -0.027 <0.001
ITIF1 -0.151 0.040 0.226 0.833 <0.001
ITIF2 0.161 -0.018 -0.365 0.800 <0.001
ITIF3 0.252 -0.069 -0.197 0.814 <0.001
ITIF4 -0.111 -0.010 0.076 0.837 <0.001
ITIF5 -0.158 0.062 0.274 0.733 <0.001
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Preparation for Final PLS Analysis - Indicator Removal and Retention
Consistent with Kock’s (2011) suggestion, the ISP4 indicator was removed from
the data analysis. In summary, the initial PLS analysis discussed above resulted in the
removal of IS4 and the retention of seven indicators for top management support
(TPORT), seven indicators for strategic information systems planning success (SES),
four indicators for information systems plan (ISP), and five indicators for IT
infrastructure flexibility (ITIF).
Table 14
Final PLS Analysis - Reliability and Discriminant Validity Coefficients
Construct Composite Reliability
Cronback Alpha Average Variance Extracted (AVE)
TPORT 0.941 0.926 0.694
SES 0.890 0.855 0.538
ISP 0.836 0.736 0.562
ITIF 0.901 0.863 0.647
Note: TPORT = Extent of Top Management Support; SES = Degree of SISP Success; ISP = Usefulness of Information Systems Plan; ITIF = Degree of IT infrastructure flexibility
As shown in Tables 14, 15, and 16, the final PLS analysis shows the reliabilities,
average variance extracted, latent variable correlations, and factor loadings and cross-
loadings of the retained indicators met or exceeded the minimum requirements discussed
above. Particularly, after removing the ISP4 indicator, the Cronbach alpha reliability
coefficient for ISP construct increased to 0.736, as reflected in Table 14; its AVE
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increased to 0.562, as reflected in Table 14; and, as shown in Table 15, its square root
AVE increased to 0.749, which is higher than all other correlations involving the ISP
construct. These results are now above the suggested thresholds of 0.70, 0.50, and 0.70
for reliability and discriminant validity.
Table 15
Final PLS Analysis - Latent Variable Correlations and Square Root of AVE
Construct TPORT SES ISP ITIF
TPORT 0.833 0.632 0.553 0.197
SES 0.632 0.733 0.743 0.368
ISP 0.553 0.743 0.749 0.349
ITIF 0.197 0.368 0.349 0.804
Note: Square roots of average variances extracted (AVE's) shown on diagonal and are in bold.
As shown in Table 16, with the removal of the ISP4 indicator, the information
systems plan (ISP) construct achieved factor loadings between 0.683 and 0.862 with all
associated p values below 0.001. These results show that the ISP construct has achieved
adequate convergent validity because the indicators’ factor loadings are above the
minimum threshold of 0.50 (Kock, 2011; Thong, Yap, & Raman, 1996). Taken together,
the ISP construct after removing the ISP4 indicator exhibited acceptable reliabilities,
convergent, and discriminant validities.
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Table 16
Final PLS Analysis - Combined Loadings and Cross-loadings
INDICATORS TPORT SES ISP ITIF P value
TPORT1 0.818 -0.180 0.290 -0.153 <0.001
TPORT2 0.827 -0.170 0.252 -0.049 <0.001
TPORT3 0.866 0.100 -0.180 0.089 <0.001
TPORT4 0.846 0.152 -0.207 0.136 <0.001
TPORT5 0.854 -0.078 0.125 -0.028 <0.001
TPORT6 0.815 -0.131 0.022 0.061 <0.001
TPORT7 0.804 0.307 -0.297 -0.065 <0.001
SES1 0.016 0.810 0.203 -0.096 <0.001
SES2 -0.038 0.768 -0.091 -0.043 <0.001
SES3 0.040 0.801 0.275 -0.250 <0.001
SES4 -0.187 0.681 0.128 0.069 <0.001
SES5 0.311 0.750 0.289 0.004 <0.001
SES6 -0.095 0.703 -0.538 0.097 <0.001
SES7 -0.091 0.598 -0.402 0.322 <0.001
ISP1 -0.291 -0.036 0.683 -0.030 <0.001
ISP2 0.357 -0.557 0.721 0.023 <0.001
ISP3 -0.039 0.233 0.862 0.035 <0.001
ISP5 -0.036 0.315 0.718 -0.037 <0.001
ITIF1 -0.155 0.024 0.252 0.833 <0.001
ITIF2 0.159 -0.065 -0.307 0.800 <0.001
ITIF3 0.257 -0.051 -0.228 0.814 <0.001
ITIF4 -0.111 -0.004 0.070 0.837 <0.001
ITIF5 -0.156 0.105 0.222 0.733 <0.001
As displayed in Table 17 below, the variance inflation factors for the three
independent variables or predictors (TPORT, ISP, and ITIF) are 1.667, 1.714, and 1.093,
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respectively. These values are less than 10, thus suggesting that multicollinearity is not an
issue (Petter, Straub, & Rai, 2007).
Table 17
Final PLS Analysis - Variance Inflation Factors
Construct TPORT ISP ITIF
SES 1.441 1.577 1.139
Note: The VIFs are for the latent variables on each column (predictors), with reference to the latent variables on each row (criteria). VIFs only exist for rows referring to latent variables with more than one predictor.
Final PLS Analysis - Model Fit with the Data
Reflecting the retained indicators, the final PLS analysis was also used to assess
the model fit with the data. The WarpPLS 2.0 software provided three model fit indices:
“average path coefficient (APC), average R-squared (ARS), and average variance inflator
factor (AVIF)” (Kock, 2011, p. 23). To determine whether the model fits with the data,
Kock suggested that the p values for both the APC and the ARS should be less than 0.05
at the 0.05 significance level, and the AVIF be less than 5. As depicted in Table 18, the
model met all three criteria. This finding suggests that the predictive and explanatory
quality of the model is adequate.
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Table 18
Final PLS Analysis - Model fit indices and P values
Indices Mediating Model P Values
Average Path Coefficient (APC)
0.343 <0.001
Average R- Squared (ARS)
0.327 <0.001
Average Variance Inflator Factor (AVIF)
1.386*
*Good if < 5
Structural Model and Hypothesis Testing
The final PLS analysis was also used to examine the structural model in order to
test the hypotheses. The structural model is a combination of direct and mediating effects.
Hence, two PLS analyses were conducted: one for the direct effect and one for the
mediating effects. The results of these analyses are shown in Figures 4 and 5 with
hypothesis testing results summarized in Table 24. The analysis of the direct effect is
discussed in next section.
Direct Effect
The direct model only includes top management support as the independent
variable and SISP success (SES) as the dependent variable. As shown in Figure 4, top
management support (TPORT) was positively and significantly associated with SISP
success (SES) (β = 0.63, p < 0.01). This finding is consistent with hypothesis 3, which
predicted a positive relationship between the extent of top management support and the
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degree of SISP success. The analysis also shows that in the direct model 40% of the
variance in SES is explained by TPORT.
Figure 4. Direct model.
Mediating Effects
According to Cheung (2007), “Mediators are variables that explain the association
between an independent variable and a dependent variable” (p. 227). The mediating
model was formed after adding the two potential mediators to the direct model. As shown
in Figure 5, the mediating model includes top management support (TPORT) as the
independent variable, information systems plan (ISP) and the IT infrastructure flexibility
(ITIF) as the mediating variables, and SISP success (SES) as the main dependent
variable.
As displayed in Figure 5, top management support (TPORT) was positively and
significantly associated with the usefulness of ISP (β = 0.55, p < 0.01), providing support
for hypothesis 1. With respect to IT infrastructure flexibility, top management support
was found to be positively associated with it (β = 0.20, p = 0.09). However, that
association was insignificant because the 0.09 p value was greater than 0.05 significance
level, so it failed to support hypothesis 2. Consistent with hypothesis 4, the usefulness of
the information systems plan (ISP) was significantly and positively associated with the
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degree of SISP success (β = 0.52, p < 0.01). On the other hand, while IT infrastructure
flexibility (ITIF) was positively associated with SISP success (β = 0.12, p = 0.13), that
association was insignificant, so it failed to support hypothesis 5. Table 19 summarizes
the hypothesis testing results.
Table 19
Summary of Hypothesis Testing Results
Hypothesi s
Description Path Coefficient
P Value Supported (Yes/No)
H1 The higher the degree of top management support, the higher the usefulness of the information systems plan.
0.55 <0.01 Yes
H2 The higher the degree of top management support, the higher the degree of IT infrastructure flexibility.
0.20 0.09 No
H3 The higher the degree of top management support, the higher the degree of SISP success.
0.32 <0.01 Yes
H4 The higher the usefulness of the information systems plan, the higher the degree of SISP success.
0.52 <0.01 Yes
H5 The higher the degree of IT infrastructure flexibility, the higher the degree of SISP success.
0.12 0.13 No
Path Analysis
This study also employed the mediating model to examine the usefulness of the
information systems plan (ISP) and information technology (IT) Infrastructure Flexibility
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(ITIF) as two conditions under which the relationship between top management support
and SISP success can vary.
Consequently, this study posed the following question: From a contingency
perspective, to what extent do the constructs of information system plan (ISP) usefulness,
and IT infrastructure flexibility (ITIF) mediate the relationship between the top
management support and SISP success? To help answer this question, two approaches
were used. First, an analysis of the two mediating paths, ISP and ITIF, was performed, as
suggested by Bergeron, Raymond, and Rivard (2001). Second, a test of how significant
the mediating effects of ISP and ITIF are was conducted based on ScriptWarp Systems
(n.d.), which incorporates the Preacher and Hayes (2004) approaches as well as the Sobel
test using the coefficients produced by the WarpPLS 2.0 software program.
The path shown in Figure 5 between top management support (TPORT) and
information systems plan (ISP) is positive and significant (β = 0.55, p < 0.01). Similarly,
the path between information systems plan (ISP) and strategic information systems
planning success (SES) is positive and significant (β = 0.52, p < 0.01). The analysis of
these two paths revealed that ISP is a strong mediator.
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Figure 5. Mediating model.
By contrast, the path shown in Figure 5 between top management support
(TPORT) and IT infrastructure flexibility (ITIF), though positive, is not significant at the
0.05 level. Similarly, the path between ITIF and SES, though positive, is not significant.
The analysis of the ITIF paths shows that the ITIF construct is not a significant mediator.
These path analyses revealed that, among the two potential mediators (ISP and
ITIF), only ISP was found to be a significant mediator. Even though the above path
analysis helped determining the presence or absence of significant mediating effect,
Preacher and Hayes (2004) suggested that, a formal test should be performed to directly
involve the paths associated with the mediating model and help prevent type I and type II
errors.
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Significance of Mediating Effects
Following Preacher and Hayes (2004) suggestion, in addition to the path analysis,
a formal test was also conducted to confirm the above mediation findings.
Table 20
Final PLS Analysis - Path Coefficients
Construct TPORT ISP ITIF
SES 0.317 0.525 0.122
ISP 0.553
ITIF 0.197
Table 21
Final PLS Analysis - P Values for Path Coefficients
Construct TPORT ISP ITIF
SES 0.003* <0.001* 0.133**
ISP <0.001*
ITIF 0.092**
Note: * = significant, ** = not significant
Using the path coefficients, p values, and standard errors (Tables 20, 21, and 22)
produced by the WarpPLS software for the mediating model, this study employed the
Excel spreadsheet provided by ScriptWarp Systems (n.d.) at
http://www.scriptwarp.com/warppls/#Resources to determine the significance of
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mediating effects based on Preacher and Hayes (2004) approaches which also incorporate
the Sobel test.
Table 22
Final PLS Analysis - Standard errors for path coefficients
Construct TPORT ISP ITIF
SES 0.106 0.116 0.139
ISP 0.130
ITIF 0.165
The test result shown below in Table 23 confirms the previous finding that ISP
(Tab = 2.99, p < 0.01) was a significant mediator. By contrast, the test result in Table 24
reveals that ITIF (Tab = 0.66, p > 0.05) was not a significant mediator, confirming
previous finding.
Effect Sizes
The effect sizes for the three dependent variables (information systems plan, IT
infrastructure flexibility, and SISP success) were examined using their R 2 values. A rule
of thumb from Cohen (1992) is that the effect size f 2
is small, medium, and large when
R 2 s are 0.0196, 0.1304, and 0.2592, respectively.
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Table 23
Test Result Showing Significance of ISP Mediating Effect (N = 57)
Coefficient Value Description
a 0.5330 (Path coefficient calculated by WarpPLS)
b 0.5250 (Path coefficient calculated by WarpPLS)
Sa 0.1300 (Standard error calculated by WarpPLS)
Sb 0.1160 (Standard error calculated by WarpPLS)
Sab 0.0933 (Sobel's standard error for mediating effect)
ab 0.2798 (Product path coefficient for mediating effect)
Tab 2.9986 (T value for mediating effect)
Pab 0.0020 (P value for mediating effect, one-tailed)
Pab' 0.0041 (P value for mediating effect, two-tailed)
Note: This mediating test used ScriptWarp Systems (n.d.) spreadsheet available at http://www.scriptwarp.com/warppls/#Resources.
Examining Figure 5, the final PLS analysis shows R 2 s
for information systems
plan (ISP) and strategic information systems planning success (SES) to be 0.31 and 0.64
respectively. Both of these R 2 values represent a large size effect per Cohen’s rule of
thumb. By contrast, the R 2
for the IT infrastructure flexibility (ITIF) was 0.04, suggesting
a small to medium size effect.
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Table 24
Test Result Showing Significance of ITIF Mediating Effect (N = 57)
Coefficient Value Description
a 0.1970 (Path coefficient calculated by WarpPLS)
b 0.1220 (Path coefficient calculated by WarpPLS)
Sa 0.1300 (Standard error calculated by WarpPLS)
Sb 0.1390 (Standard error calculated by WarpPLS)
Sab 0.0364 (Sobel's standard error for mediating effect)
ab 0.0240 (Product path coefficient for mediating effect)
Tab 0.6595 (T value for mediating effect)
Pab 0.2561 (P value for mediating effect, one-tailed)
Pab' 0.5123 (P value for mediating effect, two-tailed)
Note: This mediating test used the ScriptWarp Systems (n.d.) spreadsheet available at http://www.scriptwarp.com/warppls/#Resources.
While it is desirable to evaluate the effect sizes of the individual dependent
variables (ISP, ITIF, and SES), it is also important to look at the main dependent variable
(SES) and assess the size of the mediating effect between the direct model in Figure 4
and the mediating model in Figure 5. This study conducted such an examination using an
f 2
formula that focuses solely on the main dependent variable (SES), taking into account
the differences between R 2
obtained for SES in the direct model, and the one obtained for
SES in the mediating model. This assessment also used different guidelines than the one
employed above for the three individual dependent variables.
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According to Cohen (1992), Chin (1998), Chin, Marcolin, and Newsted (2003),
the mediating effect is considered small if f 2 is 0.02, medium if f
2 is 0.15, and large if f
2 is
0.35 for the squared multiple correlation (R 2 ) analysis. The f
2 for the main dependent
variable (SES) in the direct and mediating models was calculated using the following
formula based on Chin (1998) and Chin, Marcolin and Newsted (2003):
f 2
= (R 2 mediating model – R
2 direct model)/(1 - R
2 direct model)
Looking at Figure 4, the R 2 for SISP success (SES) in the direct model is 0.40.
This suggests that the direct model had a large f 2
since the R 2 of 0.40 exceeded Cohen’s
(1992) R 2 value of 0.2592 for large effect size. However, it is also important to assess the
size of the mediating effect of adding the two mediators in the model. Using, the R 2 of
0.40 for the direct model in Figure 4 and the R 2
of 0.64 for the mediating model, effect
size f 2 of adding the two mediators in the model is computed as follows:
f 2 = (0.64-0.40)/1-0.40) = 0.24/0.60 = 0.40
The result shows that the effect size f 2 of adding the two mediators in the model is
0.40, exceeding the 0.35 value suggested for large mediating effect by Cohen (1992),
Chin (1998), and Chin, Marcolin, and Newsted (2003. This result suggests that the model
experienced a very large mediating effect after adding the two mediators. Most of the
mediating effect came from the ISP and very little from ITIF as the latter is not a
significant mediator.
Presence of Full or Partial Mediation
The results of the mediating effect analyses (Figure 5) confirmed study’s
prediction that the information systems plan (ISP) is a significant mediator of the
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relationship between top management support (TPORT) and strategic information
systems planning success (SES). By contrast, the analyses show that the IT infrastructure
flexibility (ITIF) is not a significant mediator. To generate prescriptions for future
research, it is important to also know whether ISP is a full or partial mediator.
Presenting some guidelines on how to make this determination, Preacher and
Hayes (2004) suggested that “When the effect of X on Y decreases to zero with the
inclusion of M, perfect mediation is said to have occurred (James & Brett, 1984, call this
situation complete mediation). When the effect of X on Y decreases by a nontrivial
amount, but not to zero, partial mediation is said to have occurred” (p. 717). In this
study, X is represented by top management support (TPORT), Y by strategic information
systems planning success (SES), and M by information systems plan (ISP). The IT
infrastructure flexibility (ITIF) was not included in this analysis because earlier test
results indicated that it was not a significant mediator.
Looking at direct model in Figure 4, the path coefficient between TPORT (X) and
SES (Y) is 0.63 and the p value is < 0.01. By contrast, the mediating model in Figure 5
that includes the ISP mediator (M) showed the path coefficient between TPORT (X) and
SES (Y) to be 0.32 and the p value to be < 0.01.
As reflected in Figures 4 and 5, the addition of ISP as a mediator in the model
substantially decreased the direct effect of top management support on strategic
information systems planning success (SES) from (β =0.63, p <0.01) to (β =0.32, p
<0.01). Applying the Preacher and Hayes (2004) guidelines, ISP cannot be considered a
full mediator because when it was added to the direct model, the direct effect of TPORT
on SES did not decrease all the way to zero. However, the decrease, though substantial,
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suggests that ISP is only a partial mediator, not a full mediator. From a contingency
perspective, this partial mediation implies that there are other possible conditions such as
top management leadership styles, quality of ISP process, organizational culture under
which the relationship between top management support and SISP success can change.
Summary
This chapter presents the results of the initial and final PLS analyses conducted to
assess the measurement model, validate the structural model, and test the hypothesized
relationships between the constructs of top management support (TPORT), information
systems plan (ISP), information technology (IT) infrastructure flexibility (ITIF), and
strategic information systems planning (SISP) success (SES). As shown in Table 19,
supported are hypotheses (H1 and H4) suggesting that the degree of top management
support (TPORT) has a significant and positive effect on the mediating construct of
information systems plan (ISP), which in turn is positively related to the degree of SISP
success (SES). Also, supported is H3 that argued that top management support (TPORT)
is significantly and positively related SISP success (SES).
However, not supported are hypotheses (H2 and H5) which proposed that top
management support is positively and significantly related to IT infrastructure flexibility
(ITIF), which in turn is positively related to the degree of SISP success (SES). The results
of the PLS path analyses also showed, among the two potential mediators, only the
information Systems plan (ISP) was confirmed to be a significant mediator of the
relationship between top management support (TPORT) and SISP success (SES).
Furthermore, the R 2 analysis showed that 31% of the variance in information systems
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plan (ISP) is explained by top management support (TPORT). This finding suggests that
top management support (TPORT) is a good predictor of the information Systems plan
(ISP) usefulness.
By contrast, the IT infrastructure flexibility (ITIF) was found to be an
insignificant mediator. The R 2 analysis indicated that only 4% of its variance is explained
by top management support (TPORT). Hence, top management support (TPORT) is not a
good predictor of IT infrastructure flexibility (ITIF). Discussions of results, their
theoretical and practical implications, limitations, and recommendations for future
research are presented in the next chapter.
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CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS
This chapter discusses the findings of this study and its implications.
Additionally, theoretical and practical contributions of this study are presented. Finally,
the limitations of this study and suggestions for future research are discussed.
Findings and Implications
Building upon the work of Lederer and Salmela (1996), and Brown (2004) on
SISP theory, this study developed a four-construct SISP model to investigate the direct
and indirect effects of top management support on SISP success. The four constructs are:
(a) the extent of top management support (TPORT), (b) the usefulness of the information
systems plan (ISP), (c) the degree of IT infrastructure flexibility (ITIF), and (d) the
degree of SISP success (SES).
This study investigated the direct effects of top management support (TPORT) on
SISP success (SES). Using contingency approach, it also examined the usefulness of the
information systems plan (ISP) and the flexibility of the IT infrastructure flexibility
(ITIF) as two conditions under which the relationships between top management support
and SISP success can change. The usefulness of the information systems plan (ISP) is a
reflection of the extent to which the plan reflects or aligns with the business goals (Hann
& Weber, 1996). The IT infrastructure flexibility (ITIF) is a function of how much IT
infrastructure is scalable, compatible, modular, and can handle multiple business
applications (Bhatt, Emdad, Roberts, & Grover, 2010).
Scalability refers to the extent to which the “storage, processing, and
communication capacities can be expanded in response to environmental change” (Bhatt,
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Emdad, Roberts, & Grover, 2010, p. 344). Compatibility refers to the degree to which
new systems can be easily integrated and connected with existing systems without major
work. On the other hand, modularity refers to the ability “to modify, upgrade, and
reconfigure existing IT components quickly in response to evolving business
requirements” (Bhatt et al., 2010, p. 344). A flexible IT infrastructure enables quick and
inexpensive completion of integration projects, which can lead to a higher degree of SISP
success.
Prior studies have indicated that top management support is one of the most
critical success factors influencing all facets of the SISP system, and by extension SISP
success (Kearns, 2000, 2006; Lederer & Sethi, 1992; Raghunathan & Raghunathan,
1988; Ragu-Nathan, Apigian, Ragu-Nathan, & Tu, 2004; Teo & Ang, 2001). Top
management support is needed to secure funding, and provide strategic direction in
addition to supporting the whole SISP effort and its implementation to ensure successful
utilization of IT toward realizing the business goals. Top management support reduces
organizational problems, influences, and encourages organizational involvement, which
minimizes resistance, and leads to the acceptance the implementation of plan’s
specification (Lederer & Sethi, 1992).
On the basis of prior research, five hypotheses were advanced about how the
support of top management support directly influences the degree of SISP success, and
indirectly through the mediating constructs of information systems plan (ISP) usefulness
and the IT infrastructure flexibility (ITIF). Based on a survey of IS executives from 57
U.S. organizations, this study found, as predicted, that there was a direct, positive, and
significant relationship between top management support and SISP success. In addition,
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the degree of SISP success was found, as predicted, to be positively and significantly
related to the companies’ information systems plan (ISP). By contrast, the relationship
between top management support (TPORT) and IT infrastructure flexibility (ITIF) was
found to be positive, but insignificant. Similarly, the relationship between IT
infrastructure flexibility (ITIF) and SISP success (SES) was found to be positive, but
insignificant. These findings show that the top management support influenced SISP
success both directly and indirectly through the information systems plan (ISP), and not
through the IT infrastructure flexibility (ITIF). The information systems plan (ISP) was
found, as predicted, to be a significant mediator of relationship between top management
support (TPORT) and SISP success (SES). The IT infrastructure flexibility (ITIF), on the
other hand, was found to be an insignificant mediator of that relationship.
Contributions
This study presents several important theoretical and practical contributions.
First, by using the SISP theory to provide theoretical underpinnings for the research
model, this study addressed the scarcity of SISP theory application in empirical studies.
Second, it incorporated in the model a new construct, IT infrastructure flexibility (ITIF),
representative of a dynamic aspect of the business environment. Third, it established and
empirically tested a new model with top management support (TPORT) as the
independent variable, SISP success (SES) as the main dependent variable, and the
usefulness of information systems plan (ISP) and IT infrastructure flexibility as
mediating variables. This model has not been tested before, thus this study provides a
framework for future research.
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Fourth, it confirmed prior research about the critical role of top management
support in influencing the outcomes of SISP, both directly and indirectly. For instance,
one of the most important outcomes of the SISP is an IS plan that reflects the business
plan and recommends applications that will provide a competitive advantage. This study
finding confirms the important role of top management support in facilitating the
usefulness of the IS plan.
Fifth, this study confirmed the role of the information systems plan as a vehicle
by which top management support influences SISP success. Among the two potential
mediators (ISP and ITIF) examined by this study, only ISP was found to be a significant
mediator. The IT infrastructure flexibility (ITIF) was found not to be a significant
mediator.
Regarding the practical contributions of this research, the results provide
practitioners with a better understanding of (a) how top managers can directly influence
SISP success, (b) how top managers can engage the information systems plan as vehicle
with which to indirectly influence SISP success, and (c) where to focus training of senior
executives in top management support.
Top management support was found to have a direct significant positive effect on
SISP success (SES) (β = 0.63, p < 0.01). And, SISP success was found to be significantly
and positively impacted by the usefulness of the information systems plan (ISP) (β =
0.52, p < 0.01) and top management support (TPORT) (β = 0.32, p < 0.01), but not by the
IT infrastructure flexibility (ITIF) (β = 0.12, p = 0.13). The impact of top management
support was found to be positive and significant on ISP (β = 0.55, p < 0.01). By contrast,
it was found to be insignificant on ITIF (β = 0.20, p = 0.09).
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This unexpected finding can be explained in two ways. First, current
organizational efforts to improve ITIF in the future are not reflected in the existing IT
infrastructure. As one of the respondents indicated “Comments related to systems
flexibility are lower, as current IS plans are strategically designed to improve flexibility.
Current systems are not as flexible or integrated as they will be in 3 years.” Second, the
concept of IT infrastructure flexibility (ITIF) as a separate construct in SISP is recent.
Thus, it may be possible that many organizations do not yet understand the importance of
ITIF in providing the foundation on which advanced business applications can be
developed and implemented quickly and economically in this dynamic environment, and
lead to higher business agility and competiveness.
From a practical standpoint, the failure of top management support to influence
the flexibility of IT infrastructure in a significant way means that training of senior
executives in top management support should emphasize the importance of ITIF. In
addition, it means that efforts to improve ITIF should be exerted through the information
systems plan (ISP) because the latter has been found to be a significant vehicle with
which top management support can help the organization achieve SISP success.
Limitations
Although several important results have been found, this research like any study
is not without limitations. First, the use of single informants to collect data may result in
possible bias from respondents. Second, the study examined two potential mediating
constructs, i.e. information systems plan (ISP) and IT infrastructure flexibility (ITIF).
However, only ISP was confirmed as a significant mediator. ITIF was not a significant
128
mediator of the relationship between top management support (TPORT) and SISP
success (SES). Given that the direct relationship between TPORT and SES was still
significant after adding ISP, ISP is considered a partial mediator, and not a full mediator.
This partial mediation implies that there are other possible factors such as top
management leadership styles, quality of ISP process, and organizational culture that can
alter the relationship between top management support and SISP success. A third
limitation is that this study used a SISP concept that was developed in the 1980’s, which
reflects a formal approach to strategic planning of information systems using established
business plans. In today’s contemporary business environment, this approach may not
accommodate the need to change quickly to adapt to the dynamism in the environment.
Overall, the model is limited as it does not capture the various other constructs
and mediators that can influence the relationships between top management support and
SISP success. Further studies are needed to identify other possible contingent factors
under which the relationship between top management support and SISP success can
change. Nevertheless, this study shows that 64% of the variance in the main dependent
variable, SISP success (SES), is explained by top management support (TPORT),
information systems plan (ISP), and IT infrastructure flexibility (ITIF).
Recommendations
The results of this survey research highlight the importance of top management
support and its ability to directly and indirectly SISP success. Only 57 usable surveys
were received from data collection. Even though this sample size is adequate for PLS
analysis of this study’s research model, obtaining a larger sample size is desirable in
129
future research, particularly if a more complex model is involved. Future research should
also use case study analysis to qualitatively investigate the relationships between the
constructs in the research model. In-depth interviews of selected information systems
(IS) executives, CEOs as well as planners may help to explore the quantitative results in
more depth and to gain a richer and a more elaborate understanding of how top
management support contributes to greater SISP success. The use of case study analysis
technique may also yield a different set of contingent factors than the ones employed in
this study. Future studies should examine the extent to which the direct and indirect
effects of top management support on SISP success are different in public and private
sectors as well as in large and small organizations. Future research should pursue the
development of a new concept of SISP that accommodates the need to quickly identify
the applications necessary for adaptation to the constant changes in contemporary
business environment.
130
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APPENDIX. SURVEY OF STRATEGIC INFORMATION SYSTEMS PLANNING
SUCCESS
Your responses are anonymous and will be treated in the strictest confidence. For the purpose of this questionnaire, Strategic Information Systems Planning (SISP) is defined by Lederer and Sethi (1988) as “the process of identifying a portfolio of computer-based applications that will assist an organization in executing its business
plans and consequently realizing its business goals”.
Please indicate below whether SISP is conducted in your company: � Yes GO TO section 1 and proceed with the rest of the questionnaire. Complete
all sections. � No What are the reasons for not conducting SISP? Please pick all that apply. � SISP is too costly. � SISP takes too much time. � We used to conduct SISP, but it was not successful. � Other___________________________________________ And, complete only section 5 and return the questionnaire using the pre-
paid self-addressed envelope. Based on your recent SISP experience, please circle the extent to which you agree or disagree with the statements/questions outlined below using the following 7-point scale:
SCALE SD =1 D=2 MD=3 N=4 MA=5 A=6 SA =7
Strongly
Disagree
Disagree Moderately Disagree
Neutral Moderately Agree
Agree Strongly Agree
Section 1 – Extent of Top Management Support
(TPORT) Adopted from Ragu-Nathan et al. (2004)
SD D MD N MA A SA
In my organization, …
TPORT1. top management involvement with the information systems (IS) function is strong. TPORT2. top management is interested in the IS function. TPORT3. top management understands the importance of IS. TPORT4. top management supports the IS function. TPORT5. top management considers IS as a strategic resource.
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Section 1 – Extent of Top Management Support
(TPORT) Adopted from Ragu-Nathan et al. (2004)
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TPORT6. top management understands IS opportunities. TPORT7. top management keeps the pressure on operating units to work with IS.
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Section 2 – Degree of SISP Success (SES) Adopted from Doherty et al. (1999)
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The SISP process in my organization has resulted in
greater…
SES1. satisfaction (e.g., the participants feel that effort expended on the IS planning exercise has been time well spent). SES2. alignment between information systems and business strategies SES3. contribution to the overall effectiveness (e.g. better managerial decisions and discovery of new IT-based opportunities). SES4. implementation (i.e., the extent to which the IS plans have been or are thought to be implemented.) SES5. cooperation (e.g., the achievement of a general agreement concerning development priorities, implementation schedules and managerial responsibility among the key personnel involved in the IS planning process). SES6. capabilities(e.g., an improvement over time in the capability of the planning process to support effective IS planning). SES7. analysis (i.e., the ability to understand the internal operations of the organization in terms of its processes, procedures and technologies).
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Section 3 – Usefulness of Information Systems Plan
(ISP) Adopted from Hann and Weber (1996)
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Each of the following statements refers to your
organization's most recent information systems plans. Please
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Section 3 – Usefulness of Information Systems Plan
(ISP) Adopted from Hann and Weber (1996)
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circle the extent to which you agree or disagree with these
statements.
ISP1. The plans focus on the end users of the services and products provided by the information systems function (e.g., application systems requested by users). ISP2. Their goals and objectives are linked to the overall goals and objectives of my organization. ISP3. Their contents will be or have been implemented in practice. ISP4. The plans are based on a short-term view, operational focus. ISP5. The plans are based on a long-term view, strategic focus.
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Section 4 – Degree of IT infrastructure flexibility
(ITIF) Adopted from Bhatt et al. (2010)
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In my organization, …
ITIF1. our information systems are scalable. ITIF2. our information systems are compatible. ITIF3. our information systems are adapted to share information. ITIF4. our information systems are modular. ITIF5. our information systems can handle multiple business applications.
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Section 5 – Company Information 1. My job title is ______________________________________________________ 2. I have worked _______ years in this company and _______ years in this industry. 3. Firm’s principal industry: _________________________________________________ 4. Total Number of Employees (full time equivalents)______________________________
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Section 5 – Company Information 5. Total Number of Information Technology (IT) Employees (full time equivalents): � less than 10 � 151 – 200 � 11 - 79 � above 200 � 80 – 150 6. Approximate annual Information Technology (IT) budget (US Dollar)________________________
Thank you for taking the time to complete this questionnaire. Please check to ensure that you have answered all questions. Your insights are very valuable in helping us understand how top management support influences SISP success. Use the space below to provide additional comments.
Please return the questionnaire using the pre-paid self-addressed envelope.
Should you have any questions, please do not hesitate to contact me via e-mail at
gelysee@capellauniversity.edu or by phone at 401-353-4717.
If you wish to obtain a summary of the results after research completion, please provide your information below or include your business card. Your anonymity and confidentiality will be strictly maintained. Thank you for your assistance.
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Your Name_____________________________________________________ Company Name__________________________________________________ Address __________________________________________________ __________________________________________________ City_________________________State____________Zip_________________ E-mail __________________________________________________________
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