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MANAGEMENT OF CHANGE TO ENSURE IS SUCCESS:
A LONGITUDINAL STUDY
A Dissertation
Presented to the Faculty of the College of Business
of Trident University International
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy in Business Administration
by
Pauline Ash Ray
Cypress, California 90630
2011
Defended September 2, 2011
Approved by:
Office of Academic Affairs
Date: October 10, 2011
Dean: Dr. Scott Amundsen
Director, PhD Program: Dr. Joshua Shackman
Committee Chair: Dr. Wenli Wang
Committee Member: Dr. Jerry Cha-Jan Chang
Committee Member: Dr. Roger McHaney
Pauline Ash Ray
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2011 Pauline Ash Ray
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BIOGRAPHICAL SKETCH
Pauline Ash Ray is an assistant professor of business at Thomas University, Thomasville,
Georgia. Prior to her doctoral studies at Trident University, she earned her B.S. in Chemical Engineering
from Mississippi State University. During her career in industry, she earned her M.S. in Business and her
B.S. in Accounting at Mississippi University for Women. She entered an academic career in 2003
teaching as an adjunct at Southwest Georgia Technical College and Thomas University. She earned her
Master's Certificate in Accounting from Brenau University. In further study she completed her 18
graduate hours in Management and at TUI in Information Systems. Pauline served as the Blackboard
Coordinator for four years. She taught as an adjunct for Brenau University and Trident University in
finance and accounting.
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I dedicate this work to the memory of my loving and supportive husband Albert Ash, who
believed and encouraged me in this pursuit. I would also like to thank my mother Zella Mathews
for her wonderful work ethic along with my family who has given moral support and
encouragement throughout the program. And I thank the new love of my life and husband
Richard Ray, who has given support and encouragement to complete the task so long in the
making. To God be the glory for His wondrous grace and sustaining guidance.
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ACKNOWLEDGMENTS
Foremost I want to thank my dissertation committee members Dr. Jerry Cha-Jan Chang,
Dr. Roger McHaney, and particularly my mentor and chair Dr. Wenli Wang, for their continuous
encouragement and efforts on my behalf. Throughout the years of this endeavor, I have learned
to appreciate patience, perseverance, and new friendships thanks to Dr. Wangs genuine
mentorship. I also want to thank the Directior of the PhD program Dr. Joshua Shackman for his
expertise and suggestions for improving this dissertation.
There are many supporters from both universities who helped me throughout my studies
and I want to thank them for their work, especially: Jenny Swearingen, Theresa Reese, Carolyn
Treadon, Robin Ouzts, Gary Bonvillian, Ann Landis, Denae Johnson, Crissie Grove and all of
those who made this possible by participating in my research study. In addition, I want to thank
Dr. Geoffrey Hubona for his assistance and teaching in SmartPLS.
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MANAGEMENT OF CHANGE TO ENSURE IS SUCCESS:
A LONGTUDINAL STUDY
TABLE OF CONTENTS
Page
Table of Contents i
List of Tables ii
List of Figures iv
Abstract V
Chapter I: INTRODUCTION 1
Section 1.1 Problem Statement and Gap of Knowledge 5
Section 1.2 Research Questions 9
Section 1.3 Significance of Study 9
Chapter II: LITERATURE REVIEW AND THEORETICAL FRAMEWORK 12
Section 2.1 Literature Review 13
Section 2.1.1 Users Perception of Management of Change Effectiveness 16
Section 2.1.2 Readiness for Change and Resistance to Change 19
Section 2.1.3 End-user Computing Satisfaction 25
Section 2.2 Theoretical Development 27
Section 2.3 Hypotheses 31
CHAPTER III. METHODOLOGY 37
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Section 3.1 Research Design 37
Section 3.2 Data Collection 39
Section 3.2.1 Human Subject Concerns 44
Section 3.2.2 Population and Sample. 44
Section 3.3 Measurement Development 46
Section 3.4 Method of Analysis 51
Chapter IV: Data Analysis and Research Findings 54
Section 4.1 Measurement Validation 54
Section 4.2 Data Analysis and Results 59
Section 4.3 Hypotheses Testing 62
Chapter V: Implications and Conclusions 78
Section 5.1 Discussion 78
Section 5.2 Implications for Research 81
Section 5.3 Implications for Practice 83
Section 5.4 Limitations and Future Research 84
Section 5.5 Conclusion 86
References 989
APPENDICES
97
Appendix A Detailed Instrument Items 97
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Appendix B Descriptive Statistics by Item 105
Appendix C Cross Loadings 109
Appendix D Survey Invitation Emails 111
Appendix E Timeline-Qualitative Data 117
Appendix F Interviews 129
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LIST OF TABLES
Table 1. Timeline on Implementation... 40
Table 2. Sample Sizes
45
Table 3. Descriptive Statistics (n=145). 55
Table 4. Assessment of the Measurement Model.. 57
Table 5. Discriminant Validity (Inter-correlations) of Latent Variable
Constructs 57
Table 6. Sample Processing.. 59
Table 7. Descriptive Statistics (n= 56)... 61
Table 8. Question Order.. 65
Table 9. Combined PLS Sample Results .......
67
Table 10. Comparison of PLS Sample Results.. 72
Table 11. Best Item Scores on EUCS/RES/MOC/REA. 74
Table 12. Lowest Item Scores on MOC/REA 75
Table 13. Comment Summary.... .......Summary.......................................................................................
77
Table 14. Descriptive Statistics by Item.. 105
Table 15. Cross-Loadings ....Loadings...............................................................................................
109
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LIST OF FIGURES
Figure 1. Management of Change Research Model.
Model
12
Figure 2. Model for Testing Longitudinal Effects 28
Figure 3. Results: at Time 1 (n=145).......
Observations..
63
Figure 4. Results: at Time 2 (n=145)....... 63
Figure 5. Results: at Time 3 (n=145) ........................................................... 64
Figure 6. PLS Results of Full Model Testing for n=145...
..Sample.........
66
Figure 7. PLS Results of Full Model Testing for n=56 (Matched Respondent Cases) .. 70
Figure 8. Modified Management of Change Research Model. 82
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MANAGEMENT OF CHANGE TO ENSURE IS SUCCESS:
A LONGITUDINAL STUDY
Pauline Ash Ray
Trident University International 2011
This dissertation aims to understand the effect of management of change on the success
of information system (IS) implementation. A research model is developed drawing on change
management research. Data collected from a longitudinal field survey before, during, and after
an enterprise-wide IS implementation are analyzed to test the proposed hypotheses. The results
indicate that management of change can be used to increase readiness for change and end-user
computing satisfaction during and after the implementation. Readiness for change positively
affects satisfaction during an implementation but not after. Contrary to the literature, no
significant relationship between resistance to change and satisfaction exists. The paper
contributes to IS research and practice by drawing attention to the importance of management of
change and readiness for change for IS success.
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CHAPTER I
Introduction
Enterprise-wide information systems support and integrate multiple
organizational business functional areas. They achieve greater efficiency in the transfer
and use of information preventing the entry of redundant data and duplication of effort.
This type of technology enhances business performance in support of the organization's
business strategy by improving the efficiency of information use and controlling its
access. Organizations have made significant investment into these systems. In order to
realize a return on investment, it is necessary to functionally integrate the technology
into workflow and job routines (Xue et al. 2009), support effective system use, and
satisfy users (Nelson, 2003). This study seeks to understand the relationships among
users perceptions of management of change strategies, readiness for change, resistance
to change, and end-user computing satisfaction before, during and after an enterprise-
wide information system (IS) implementation and how such an understanding facilitates
a successful system implementation.
Successful implementation practices cannot be overlooked when investing in a
new enterprise-wide information system. Kwahk and Lee (2008, p.474) cite that new
enterprise resource planning (ERP) implementations have a 60-90% failure rate due to
resistance to change while Vollman (1996) attributes the high failure rate to
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managements failure to understand skills necessary to manage the change. Self and
Schraeder (2009) agree that many contributing factors add to these poor results but
suggest a primary reason for the failure could be organizational managers inability to
fully understand what is necessary to guide their organizations through a change
initiative. The system implementation must be managed both as a technological
innovation and an organizational change. Proper planning of an implementation process
can reduce the likelihood of failure and help prevent other undesirable consequences
such as reduced employee morale (Self and Schraeder, 2009).
A variety of change management strategies have been reported in the literature.
One of the key models put forward to help managers and leaders successfully
implement change is Lewins three steps to change unfreeze, moving and
refreezing (Lewin, 1951). In the first stage, emphasis is placed on efforts to minimize
obstacles to change and maximize the change effort. The next stage seeks recognition
that the change is needed and acceptance of the proposed change. In the final stage, the
new system has to be reinforced and consolidated (Lewin, 1951). Refreezing implies
stasis with innovation ending with improved organizational routines. However, research
in IS suggests that while state of the art technology rapidly advances there is no end to
the implementation process (Bikson, 1987; Bikson et al., 1985). Another key model is
Kotters 8 steps to transforming your organization, which emphasizes the need for
continual communication and having a shared vision (Kotter, 1995).
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A critical user attitude for a successful implementation is "change orientation" --
the extent to which participants in an innovation process view the change as a positive,
problem-solving, and achievable goal that benefits the entire organization. This attitude
was a highly significant predictor of success in a cross-sectional study of organizations
introducing computerized information tools (Bikson et al., 1987). Hence, in addition to
communication and a shared vision, the organizations need for and ability to
implement the change must be imparted to users.
Bikson et al. (1987) suggest that important aspects of user attitudes toward the
new system include affective assessment (user satisfaction), cognitive assessment
(discrepancy between old and new system), and user resistance to the change. Davis et
al. (1989) addresses the ability to predict users' computer acceptance from a
measurement of their intentions, and the ability to explain their intentions in terms of
their attitudes, subjective norms, perceived usefulness, perceived ease of use, etc. Davis
et al., (1989, p. 982) stated that These results suggest the possibility of useful models
of the determinants of user acceptance, with practical value for evaluating systems and
guiding managerial interventions aimed at reducing problems. Venkatesh and Davis
(2000) conducted a longitudinal study before, during, and after an implementation, an
idea which can create the opportunity for managerial interventions from data analysis.
Taken holistically, these studies imply that for a successful implementation changes
must be managed to reduce resistance and increase readiness for the change in a
dynamic manner with interventions if necessary.
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Complementary research on management of change implementation exists in the
Information Technology field for antecedents of IS acceptance (Capaldo and Rippa
2009, Joshi 1991, Shang and Su 2004). Common threads exist in different areas of
research that address overcoming resistance, creating readiness, and enhancing
acceptance. Organizational change and IS acceptance research builds upon
organizational behavior research citing many of the same studies (Bikson et al. 1987;
Davis et al., 1989; Judson 1991; Kotter, 1995; Lewin, 1951). In the IS acceptance area,
Capaldo and Rippa (2009) propose the evaluation of organizational capabilities when
selecting appropriate implementation strategies and change management interventions
during the implementation. Some of their example strategies include communication,
management support, modification, and training. Joshi (1991) posits that individuals
evaluate change for the expected outcome and then decide to either react favorably or
resist. A pre-implementation analysis of the potential impact of a new system for
identified user groups and an attempt to address their concerns in training and
communication programs as part of the implementation strategy in the change
management process is recommended (Joshi, 1991). Change strategies that can
overcome resistance and create readiness assist in successful implementation (Shang
and Su, 2004). Other research in the IS acceptance area has also been conducted on
how to prevent, reduce, or overcome resistance to change (Bhattacherjee and Hikmet,
2007; Hirscheim and Newman, 1988). Other research addresses how to prepare the
organization for change through strategies to increase readiness for change (Kwahk and
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Kim; 2007; Kwahk and Lee, 2008). Change management strategies in these studies
include communication, training, management support, and technical resource
availability. The conclusion from this brief research review is that the precursors for IS
success must involve the users attitudes toward the change management process as
well as toward the change itself.
Section 1.1 Problem Statement and Gap of Knowledge
Realizing the importance of this area, even after reviewing the prior research, it
is still not clear how change should be managed during the change process and how
change management strategies can enhance the implementation success. There is a lack
of longitudinal studies in change management. It is also unclear that between reducing
resistance and creating readiness which is more effective to ensure a successful
implementation. After much research of the areas the question still persists whether
readiness and resistance are opposite ends of a continuum or separate states of attitude.
Although common threads exist in organizational behavior, change
management, and IS acceptance research (that addresses overcoming resistance,
creating readiness, and enhancing acceptance), no study combines these particular
constructs with users perception of management of change effectiveness in a
comprehensive model to explain their relationships. Further, no clear indication exists
on whether it is more important to overcome resistance or build readiness for change.
Research has not determined how early in the change process management strategies
should be introduced or how effective they are throughout the implementation. It is
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important then, and a goal of this study, to research a model representing the
relationships between the key constructs of readiness, resistance, users perception of
management of change effectiveness, and end-user satisfaction; to explore the relative
importance of resistance and readiness to creating user satisfaction; and to develop an
instrument that gives an early indication of the management of change effectiveness as
it surfaces issues from feedback in the dynamic change process.
A search was conducted in organizational change, human behavior, IS
acceptance, and other literature for factors that influence successful IS implementations.
While studies that reduce resistance and build readiness to accept change provide a
basis for this study, no combined model that also includes users perception of
management of change effectiveness and an appropriate acceptance measure in
mandatory situations was discovered. The literature search did not reveal any existing
study that addresses early detection of issues including readiness, resistance, and users
perception of management of change effectiveness as antecedents of user satisfaction.
A plethora of research regarding readiness and resistance, and their relationships exists.
However, the literature is inconclusive about which one has more impact or if they
interact as opposite ends of a continuum on IS implementation success.
Change is a process (Orlikowski and Hofman, 1997). Hence, change
management and its impacts should be studied along with the change and preferably
pre-, during, and after a change. Although much research has been conducted on
management of change, readiness, resistance, end-user computing satisfaction, and their
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respective relationships with one another, no research has looked closely inside the
change management process and explicitly examined the relationship among all of them
longitudinally. Venkatesh and Davis (2000) is the most relevant longitudinal study;
however, their study extending the Technology Acceptance Model (TAM2) mainly
captured snapshots of use characteristics at three time frames and did not introduce any
process measures for the change. They tested technology acceptance in both mandatory
and voluntary settings over a period spanning three months during which they measured
the effects of perceived usefulness and ease of use on usage intention and actual usage.
This research, however, studies only the mandatory use of technology, and
argues that end-user computing satisfaction is a better measure for true technology
acceptance in mandatory settings rather than use intention and actual use. In mandatory
settings, use intention can be influenced by compliance requirements (Xue et al., 2009)
and the actual use depends on the role, needs, and the proficiency of the user. Therefore,
user satisfaction with the system is a better indication of the system success than use
intention and actual use.
Venkatesh and Davis (2000) recommended further research to determine how
early in system development one can reliably measure key user reactions as indicators
of post-implementation success of the system. Venkatesh et al. (2003) studied
antecedents of acceptance of new systems as indicated by usage intention and usage
behavior. They recommended additional research to understand the drivers of
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acceptance in order to proactively design interventions targeted at populations of users
that may be less inclined to adopt and use new systems.
This research investigates the causal relationships among users perception of
management of change effectiveness (MOC), readiness to change (REA), resistance to
change (RST), and end-user computing satisfaction (EUCS) before, during and after an
IS implementation. Data are collected at the three points of an IS implementation: after
a decision is made about a new IS implementation but before its initiation, during the
implementation after the first major modules are implemented, and after the entire
implementation is complete with the new system in use for a while.
This study also represents an effort to understand the relative importance of
resistance and readiness in creating user satisfaction and if these relationships change
over the course of the implementation. This research studies only the mandatory use of
technology and argues that EUCS is a better measure for true technology acceptance in
mandatory settings rather than intention and the actual use in IS acceptance studies
under voluntary IT settings. It also expands the tools available for management of
change during a new IS implementation, particularly those for early detection,
intervention, and the prediction of success. The example case is based on longitudinal
data and observations taken at three points in time as the Comprehensive Academic
Management System (CAMS Enterprise) -- an integrated web-based Academic
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Enterprise Resource Planning System for higher education -- is introduced replacing
several separate un-integrated legacy systems.
Section 1.2 Research Questions
The following research questions are being investigated in this study:
1. How does end-user satisfaction with an existing system affect management of
change to a new IS implementation?
2. How does management of change in a new IS implementation affect readiness
for the new IS?
3. How does management of change in a new IS implementation affect resistance
to the new IS?
4. How does readiness for change affect the success of the new IS implementation
as evidenced by end-user computing satisfaction?
5. How does resistance to change affect the success of the new IS implementation
as evidenced by end-user computing satisfaction?
6. How does management of change in a new IS implementation affect the
success of the new IS implementation as evidenced by end-user computing
satisfaction?
Section 1.3 Significance of the Study
This study draws from the streams of literature from change management,
organizational behavior, and information technology acceptance, and intends to
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contribute value to these areas. The findings of this study can help to understand how
management of change effectiveness can foster increased user satisfaction, an indicator
of IS implementation success. Training, communication, and management support are
some of the strategies used in management of change that are expected to change
resistance, readiness, and end-user satisfaction over the course of an IS implementation.
This study adds to the body of knowledge by introducing an explanatory model
of how management of change effectiveness during an IS implementation can promote
user satisfaction in mandatory IT settings. The research model includes both readiness
and resistance, exploring how they are affected by management of change strategies
and, in turn, how they may affect IS implementation success as indicated by end-user
computing satisfaction. For the theorists, this study contributes to the understanding of
the relationships between the readiness and resistance constructs longitudinally during
an implementation and the relative importance of them. No known prior study has
combined these constructs to evaluate their interactions, their relative importance to the
implementation success, or if any of these relationships change during an
implementation.
Managers may believe that they are being supportive in communication but are
unaware of the perceptions and attitudes of their employees at the operational level
(Bonvillian, 1997). Managers need tools to identify implementation issues early on and
to adapt the management of change strategies so as to better achieve a successful
implementation by reducing resistance, increasing readiness to accept system changes.
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For the practitioners, this study contributes an early indicator to capture issues and to
provide feedback to enable management to adapt their strategies and direct their
resources during the implementation.
The rest of the paper is organized as follows. Chapter II introduces the literature
review, the research model, and the hypotheses. Chapter III describes the survey
research process and Chapter IV reports the results of the data analysis and research
findings. Chapter V discusses the implications for research and practice and concludes.
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CHAPTER II
Literature Review and Theoretical Framework
This chapter presents the process model in Figure 1 then the literature overview.
The model was derived from the more specific literature review beginning with the
users perception of management of change effectiveness (MOC) followed by resistance
to change (RST) and readiness for change (REA), and ending with end-user computing
satisfaction (EUCS).
Figure 1 Management of Change Research Model
Readiness for Change
End-User Satisfaction of
New System
End-User Satisfaction of
Old SystemUsers
Perception of
Management of Change
Effectiveness
Resistance to Change
H1[-]
H4[+]
H7[+]
H3[-] H5
[-]
H6[+]
H2[+]
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Figure 1 depicts the process model for a longitudinal study. The feedback from
data collected at a survey point serves as input to management to adapt management of
change strategies for greater resulting implementation success, (whether they entail
behavior modification of the user, technical support, or modification of a technological
application, etc.). The longitudinal model portraying the three survey points for full
model testing is pictured in Figure 2 in section 2.2.
Section 2.1 Literature Review
In general, management change strategies that (1) enhance perceptions of ease
of use and perceived usefulness; (2) provide sufficient information to enable
comparison of the before and after processes; (3) introduce new interfaces; and (4)
create an empowering vision of the desired end will illuminate the need for change
(Davis, et al., 1989, Kotter 1995). Additionally, adjustment of cognitive assessment of
the change can be important and should include both clear descriptions of advantages
offered by the changes and the expected system improvements to be gained. Cognitive
adjustment prepares current system users by communicating the need for change prior
to change process initiation (Armendakis, 1993). These strategies must include
elements of communication, training, and management support.
Self-Determination Theory (SDT) is a relevant organizational behavior research
stream that addresses change in the workplace, and its acceptance (Deci 1972, Deci and
Ryan 1985, Deci and Ryan 1989). SDT research explores the consequences of work
climates that enhance intrinsic motivation as well as the integration of extrinsic
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motivation, which contributes to important work outcomes (Gagne and Deci, 2005).
Baard, et al. (2004) focused on workplace factors that support autonomy and facilitate
internalization of extrinsic motivation. A workplace exhibiting such factors with
timely, effective communications and training can serve to internalize the external
motivation for IS implementation (Baard et al., 2004; Kirner, 2006). Understanding and
applying this theory of motivation helps the manager assess and use strategies to assist
in the implementation of change (Armendakis, 1993).
In other related organizational change research, Holt et al. (2003, p. 262) posits
that "the extent to which the organization achieves the benefits at the end of the process
is affected by the influence strategies used by organizational leaders to encourage
adoption and implementation of the change." Such change management helps achieve
the success of an IS implementation indicated by user satisfaction with the system, the
information generated by the system, and its ease of use (Venkatesh and Davis 1996).
Examples of research include: (1)the organizational change area on how to prevent,
reduce, or overcome resistance to change (Armendakis, et al. 1993, 1999; Folger and
Skarlicki, 1999; Henry, 1994; Holt, Self, Thal, Lo, 2003; Judson, 1991; Self, 2007; Self
and Schraeder, 2009); and (2) motivations place in preparing the organization for
change through the application and measurement of strategies to increase readiness for
change (REA) (Armendakis, 1993; Self, 2007; Self and Schraeder, 2009). These
studies, for the most part, explored how to create readiness or reduce resistance as the
dependent variable, but this study seeks to determine their relative effects on IS success.
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To quantify and measure the effects of change management, it is necessary to
have an indicator of success. IS benefits are sometimes intangible and the literature
contains many examples of user satisfaction serving as a surrogate measure for IS
success (Ives, Olson, Baroudi, 1984; Baroudi and Orlikowski, 1988; Straub 1989;
DeLone and McLean 2003). Even in a mandatory system, the preparation of an
organization for change by enhancing readiness and reducing resistance is important to
achieve not only usage but also user satisfaction.
Orlikowski and Barley(2001) introduces the importance of the organizational
behavior theories in the information technology research. Further, Orlikowski and
Hofman (1997) stresses that change is a dynamic process which cannot be pre-
determined without adaptation during implementation. Yet, no model was given on
how to study such a dynamic change process. Hence, this study suggests that change
should be studied along with the change, and proposes a process model to enable the
longitudinal study of a change process.
A process model should address the dynamic nature of a change and contain a
feedback loop which allows for adaptation. Feedback should be measured scientifically
so that meaningful inputs are injected into the adaptation in the change process.
Usefulness and ease of use are the two precursors for IS success in studies by
Venkatesh and Davis (2000). Nelson (2003) avers the advantages of using the End-User
Computing Satisfaction instrument (EUCS) to measure the success of an IS
implementation operationalized with subscales in content, accuracy, format, and
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timeliness to measure usefulness of an IS and a subscale called ease of use. The
model for this study is derived from and combines the theories in Orlikowski and
Hofman (1997) and Nelson (2003) using EUCS as the determinant of a successful IS
implementation during and after a change and measuring EUCS during an
implementation to provide feedback for adaptation during the change process. Such a
longitudinal process model (with measurements before, during and after
implementation) provides a solution to what Orlikowski and Hofman (1997) proposed.
This completes the process overview and the process framework model pictured
in Figure 1 is derived and expanded from the extensive literature review for the four
major constructs which follows.
Section 2.1.1 Users perception of management of change effectiveness
From the organizational behavior area, a person acts to achieve, or to avoid, a
desired or an undesired consequence (Baard et al., 2004). In order to manage change
effectively information must be shared with employees, and their concerns must be
addressed as they surface (Parker, 2009). Management of change must motivate
employees by creating a work climate that satisfies basic psychological needs to
enhance intrinsic motivation. A mandatory system can apply introjection, which entails
taking in a value or regulatory process but not accepting it as ones own (Deci, et al.,
1994). Research findings imply that when people are coerced into doing something
without a clear rationale, they generally become less interested in the task and will
perform it only as long as some form of surveillance is in place. On the other hand,
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when people are provided with reasons and choices for doing the task, they generally
become more interested in it and are more likely to continue engaging it, even after
external demands are removed (Koestner, Ryan, Bernieri, and Holt, 1984). Thus,
management of change strategies can encourage integration, through which the
regulation is assimilated internally resulting in self-determination and intrinsic
motivation (Deci et al., 1994; Armenakis et al., 1993; Gagne el al., 2000; Gagne and
Deci, 2005; Self, 2007; Self and Schraeder, 2009).
According to Orlikowski and Hofman, changes associated with technology
implementations are an ongoing process and cannot all be anticipated ahead of time
(1997): Management of change strategies such as training that increase self-efficacy
and commitment to the change increase in importance as the amount of simultaneous
and overlapping change in the surroundings increase (Herold, Fedor, and Caldwell,
2007). Examples of related management of change strategies include communication to
share information with employees while addressing their concerns, and provision of
additional training when needed. Armenakis, et al., (1999) proposes that the
communication introducing the change should address key questions to set the stage for
the change and to create readiness in the change participants: Providing a meaningful
rationale for doing the task, acknowledging that people might not find the activity
interesting, and emphasizing choice rather than control are change management
strategies that promote internalization and satisfaction (Deci et al., 1994; Gagne and
Deci, 2005).
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Top management support, business involvement, communication, and training
are important factors in managing these changes successfully in enterprise systems
(Shang and Su, 2004). Many researchers have been interested in how to promote user
satisfaction for successful implementations (Chau, 1996; Davis, 1989; Igbaria et al.,
1997). The level of satisfaction depends on the motivation and ability to change
(Judson, 1991; Kotter, 1995; Lewin, 1951). Empathy and concern, two elements of
communication, are also conducive to satisfaction of organizational change and apply to
management of change during IS implementations (Kirkpatrick, 1985; Gagne el al.,
2000; Gagne and Deci, 2005). Published research has studied these elements and their
influence on the users resistance/readiness for change to the system (Herold, et al.,
2007). Users who did not perceive a positive outcome would not express acceptance
through satisfaction with the new system.
Objective measures for the number or extent of activities executed that
demonstrate management of change strategies are prohibitive. This research defines
users perception of management of change effectiveness (MOC) as the users
evaluative opinion of the dynamic use of those strategies and techniques practiced by
management to introduce and facilitate an organizational change. Our research explores
the users perception of management of change effectiveness and whether the strategies
employed have persuaded users that the change is beneficial and that they should act to
achieve desired consequences. Specifically, in this research the goal is to enhance
readiness and overcome resistance, resulting in greater end-user computing satisfaction
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during and post-implementation of an integrated information system (e.g., an academic
enterprise resource planning (ERP). Feedback from users is included in the model to
surface the concerns and allow adaptation of management of change strategies to
modify user behavior, strengthen needed support, or modify the IS technological
application if needed. This concept is discussed in research although not formally
modelled in literature (Benn and Baker, 2009; Folger and Skarlicki 1999; Orlikowski
and Hofman, 1997; Parker, 2009). This research intends to fill in the gap.
Section 2.1.2 Readiness for change and resistance to change
Readiness for change is related to ones attitude toward change, and the
respondent's belief of how others view their attitude toward change (Kwahk and Kim,
2007). This study adopts the definition of readiness collectively reflects the extent to
which an individual or individuals are cognitively and emotionally inclined to accept,
embrace, and adopt a particular plan to purposefully alter the status quo (Holt et al.,
2007, p. 235). Readiness is reflected in organizational members' beliefs, attitudes, and
intentions regarding the need and the organization's capacity to implement changes.
Strategies of the management of change, change agent credibility, and interpersonal and
social dynamics are important in the readiness creation process (Armenakis, et al.,
1993). Readiness creation is often discussed in conjunction with prescriptions for
resistance reduction (Piderit, 2000). Other research has been conducted on overcoming
resistance to change by creating readiness with management strategies matched to the
sources of resistance. The most influential readiness factors are (a) discrepancy (i.e.,
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20
the belief that a change was necessary), (b) efficacy (i.e., the belief that the change
could be implemented), (c) organizational valence (i.e., the belief that the change would
be organizationally beneficial), (d) management support (i.e., the belief that the
organizational leaders were committed to the change), and (e) personal valence (i.e., the
belief that the change would be personally beneficial) (Holt, et al., 2003; Self, 2007;
Self and Schraeder, 2009). The underlying assumption is that organizations will move
through the stages of readiness, adoption, and institutionalization of change when
organizational members recognize that the change is appropriate, beneficial, and
supported (Holt, et al., 2003).
Similarly, Armenakis et al. (1999) proposed that the communication introducing
the change should address five key questions to set the stage for the change and to
create readiness in the change participants:
(1) Is the change necessary?
(2) Is the change being introduced the right change to make?
(3) Are key organizational members supportive of the change?
(4) Do I or we (the organizational members) have the ability to
successfully implement the change?
(5) What is in it for me if we change(Self and Schraeder, 2009, p.172)?
Holt et al. (2007, p. 235) observed that readiness to change scales usually assess
four dimensions: (1) the content of the change; (2) the context (environment); (3) the
process of the change; and (4) the factors related to individuals involved in the changes.
Strategies for the communication elements in each dimension were reported to create
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21
readiness and prevent resistance (Self and Scraeder, 2009). Piderit (2000) proposed that
the first step in implementation of change is to create readiness for the change rather
than merely overcoming resistance. Management of change is also applied to overcome
resistance that develops during the implementation as issues arise resulting from the
change. Self and Schraeder (2009) emphasize the continuing management of readiness
efforts across all stages of implementation, not just at the beginning, to increase the
likelihood of success. Therefore, management of change is a dynamic process during
the implementation (Orlikowski and Hofman, 1997). In this study the users perception
of management of change effectiveness reflects how well they believe that the change
process has been managed to achieve these goals: whether the elements of
communication, management support, technical availability, and training needed to
create readiness and/or reduce resistance have led to the subsequent end user computing
satisfaction.
Dent and Goldberg (1999) credit Kurt Lewin with the concept of resistance to
change. Lewin believed that the status quo was equilibrium between barriers to change
and forces driving change. He believed it was more effective to weaken the barriers
than to strengthen the drivers to bring about the change. Kwahk and Kim (2008) cited
resistance to change as a contributing factor to high failure rates of new IS
implementations. Resistance has been defined as any conduct that tries to keep the
status quo, i.e. resistance is equivalent to inertia, as the persistence to avoid change
(Maurer, 1996). Oreg defines it as an individuals tendency to resist or avoid making
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22
changes, to devalue change generally, and to find change aversive across diverse
contexts and types of change (Oreg, 2003). This study adopts the definition of
resistance as a generalized opposition to change engendered by the expected adverse
consequences of change (Bhattacherjee and Hikmet, 2007). Whether a user is satisfied
or dissatisfied with the system leads to either positive or negative behaviors. Hultman
(1995) argue that resistance consists of two dimensions: active and passive. Active
resistance includes behaviors such as being critical, selective use of facts, sabotaging,
and starting rumors. Passive resistance is displayed by behaviors such as public
support, but failure to implement the change, procrastinating, and withholding
information or support. Jiang, Muhanna, and Klein (2000) summarized the seven
reasons employees resist new technology:
Change in job content.
Loss of status.
Interpersonal relationship altered.
Loss of power.
Change in decision-making approach.
Uncertainty/unfamiliarity/misinformation.
Job insecurity.
Factors identified as causing resistance include innate resistance to change, lack
of involvement in the change process, lack of management support, poor system
quality, and the lack of designer-user interaction (Hirscheim and Newman, 1988).
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23
Harvey's 16 resistance factors for which he develops antidotes indicate the importance
of management support and communication, two elements of the management of
change actions that increase readiness and prevent/reduce resistance (Harvey, 1995).
Henry (1995) states that, "Researchers have found that resistance can be
categorized according to whether or not end users attribute their problems to specific
features of the technology, are computer anxious, or have a negative attitude toward
computers" (p 20). Specific features of the technology causing the end user's resistance
can be identified and assessed for validity. If the complaint is valid, one approach is to
modify the technology to increase acceptance/satisfaction. If the complaint is based in
anxiety, and the end user cannot be reassigned, special training to reduce anxiety can be
conducted. Involvement in the design or early training can provide the end user with a
sense of participation and a feeling of vested interest.
The Jiang, et al. study (2000) further explores strategies used to reduce
resistance to change through five key activities such as: involving employees,
addressing concerns about IS development using open communication, sharing change
information, showing sympathy, and retraining. Negative behaviors are related to
resistance which can occur at any stage in implementation (Cooper and Zmud, 1990).
Change managers, therefore, need to delve into the reasons for user resistance and to
learn effective strategies for managing different states of changes. A complete model of
user resistance would lead to better implementation strategies and desired
implementation outcomes (Joshi, 1991).
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24
Folger and Skarlicki (1999) claim that resistance to the change may result from
some legitimate issues that need to be addressed. Benn and Baker (2009) examine a
model that incorporates input from resisting employees and channels conflict into
innovative outcomes to modify change. This co-evolutionary perspective fosters
institutional change to integrate with the human systems of the organization. The
change is then more easily integrated into the processes, procedures, and norms of the
organization. This perspective indicates that change management is a dynamic process
requiring recognition, evaluation, and reconciliation of issues throughout the change
implementation not only to lower resistance but also to benefit the organization. This
research strengthens the concept of the feedback loop in the model for this study to
allow issues to be surfaced and examined for corrective action as the co-evolutionary
perspective mentioned by Benn and Baker (2009).
Research on the acceptance and resistance to change follows two predominant
approaches. One approach views acceptance and resistance to change as opposite ends
of a continuum. By this view, low scores on acceptance instrument items indicate
resistance (Venkatesh and Davis, 2000). Self and Schraeder incorporate readiness
measurements in the resistance scale continuum (2009). However, the other approach
considers acceptance separately from resistance to change. Self avers that resistance
and readiness are not polar opposites on a linear continuum. Instead, resistance and
readiness represent complex states, impacted by numerous individual and organizational
factors (2007, p. 11). Lauer and Rajagopalan ( 2003) treat resistance and acceptance
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25
as separate constructs but analyzed cases post hoc by identified behaviors using a
framework rather than a measurement instrument. Holt, et al. (2003) adds empirical
support to previous anecdotal recommendations for implementing change, still without
measuring resistance. This study regards and measures readiness, resistance, and
acceptance/satisfaction separately using the resistance measurement instrument
developed by Bhattacherjee and Hikmet (2007). An analysis of the data determines
whether readiness and resistance to change are separate constructs as pictured in the
research model and if so, which one has more prominent effects.
Section 2.1.3 End-user computing satisfaction
In the literature on finite measures of IS performance, IS benefits are sometimes
intangible, and hence, user satisfaction is utilized as a surrogate measure (Ives, Olson,
Baroudi, 1984; Baroudi and Orlikowski, 1988; Straub, 1989; DeLone and McLean,
2003). A survey of the sensitivities to user needs, participation, and communication
was used to examine satisfaction as a measure of how well the change was being
managed (Davis et al., 1989). Chen and Lee (2000, p. 554) define end-user satisfaction
with an information system as "the overall affective evaluation an end-user has
regarding his or her experience related with the information system. This study
defines success of an information system as the extent to which users are satisfied with
the system; the information generated; and its ease of use. Some of the research
addressing how to increase user acceptance/satisfaction includes the Technology
Acceptance Model (TAM), which posits that user acceptance/satisfaction is predicted
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26
by user perceptions regarding the ease of use and usefulness of the new system (Chau,
1996; Davis, 1989; Igbaria, et al., 1997; Szajna, 1996; Taylor and Todd, 1995;
Venkatesh and Davis, 2000). However, earlier studies (Judson, 1991; Kotter, 1995;
Lewin, 1951) suggest the level of acceptance/satisfaction depends on the motivation and
ability to change. Martins and Kellermann (2004) focus on motivating factors and
enabling factors, which influence user acceptance/satisfaction. In their study, change
motivators, such as the explanation of realized benefits, positively influence perceived
usefulness. Change enablers, such as training, positively influence perceived ease of
use of the system. Accordingly, it can be acknowledged that management of change
strategies regarding communication and training promote change
acceptance/satisfaction.
Of the different instruments to measure user satisfaction, the primary measure
used in this study was the well-known instrument, the End-User Computing Satisfaction
instrument, in part because it has been validated for overall correlations (Doll and
Torkzadeh, 1988, 1989; McHaney, Hightower, and Pearson, 2002). The EUCS
instrument has been used extensively in a variety of workplace settings and continues to
be tested to extend its use in current practice (internet web services: Abdinnour-Helm,
Chaparro, and Farmer, 2005; public sector: Aladwani, 2002; Doll and Torkzadeh,
1989; Harper, Slaughter, and Norman, 1997; Taiwanese business: McHaney,
Hightower, and Pearson, 2002; On-line banking: Pikkarainen, Pikkarainen, and Pahnila,
2006; and ERP application: Somers, Nelson, and Karimi, 2003). This instrument has
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27
been validated for measurement across subgroups using invariance analysis (to verify
that the 5 first-order factors have equivalent item-factor loadings across populations
subgroups). Researchers have used EUCS as a standardized measure of advanced
information technologies and propose it to practitioners for evaluating ERP
implementations (Nelson, 2003).
Section 2.2 Theoretical development
Figure 1 presents the management of change research model. Management of
change is critical to the success of enterprise-wide IS implementations. It is important
to understand the effects of change management on creating readiness and overcoming
resistance in order to improve end-user satisfaction, which is often used as the surrogate
measure of IS success.
Research has been conducted on the impacts of both resistance and readiness on
satisfaction from the self-determination theory research (Deci et al., 1994; Gagne and
Deci, 2005; Self and Schraeder, 2009), from change management research (Piderit,
2000), and from information systems research (Chin and Lee, 2000; Kwahk and Lee,
2008), but with inconclusive results. It is unclear whether readiness and resistance are
simply the reverse of each other. This study seeks to examine if they are both important
antecedents of user satisfaction, and if not, which one plays a more prominent role. As
discussed in the significance of the study, the research model is a proposed explanation
of how management of change can enhance and support information systems during
implementation. The longitudinal samples and instrument wording (REA future
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28
oriented, MOC evaluates past action, and EUCS evaluates current state) are used to
establish time sequence and allow testing of a causal model of some of the constructs.
Results and qualitative comments from each survey point serve as feedback input to
management to adapt the change process strategies. Our combined model to test
longitudinally for causation is presented in Figure 2.
MOC2
REA3
EUCS3
RST2
MOC3EUCS1
REA2
RST3
EUCS2 T1
Time 2Time 1 Time 3
Figure 2 Model for Testing Longitudinal Effects
H1 (-)
H6 (+) H6 (+)
H2 (+) H2 (+)H4 (+) H4 (+)
H3 (-)
H7 (+)
H5 (-)H5 (-) H3 (-)
Data and comments collected at Time 1, Time 2, and Time 3 were collated,
analyzed, summarized, and forwarded to the management, and the management did
improve its strategies accordingly. By providing a feedback loop the survey actually
changed MOC in reality in later periods and that may have consequently affected REA
and RST.
Armenakis, et al. (1993) posits a model separating resistance and readiness and
discusses methods to reduce resistance and build readiness. Although that study
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29
considers readiness as a precursor for the user to decide to resist or support the change,
it was the first model found that separates the constructs and proposes that readiness
could be managed. This is an important contribution to constructing a model to test
how management of change relates to resistance and readiness and they in turn relate to
end user satisfaction. The techniques recommended by Armenakis, et al., to increase
readiness aligns with increasing perceived ease of use and perceived usefulness to
increase acceptance posited by Davis, et al. (1989). Orlikowski and Hofman (1997)
contributes the dynamic aspect of management of change requiring adaptation during
the implementation. The concept of using feedback is reinforced by the
recommendation that members' concerns should be acknowledged exploring strategy
effectiveness further to identify when managers should embrace resistance rather than
try to avoid it (Holt, et al. 2003). From Venkatesh and Davis (2000) we draw the idea
of a longitudinal approach testing before, during and after implementation, but we use
satisfaction as an indication of success in the mandatory IT setting rather than time
usage in the voluntary IT setting.
Nelson (2003) contributes the validation of using the End-User Computing
Satisfaction instrument (EUCS) as a measure of the success of newly implemented ERP
applications. Although there is no one model that this study builds upon, these concepts
do contribute and synthesize to the proposed model that management of change
strategies could affect IS successful implementations by creating readiness, reducing
resistance, as well as directly affecting the end-user satisfaction.
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30
With the feedback loop and longitudinal application this research design meets
the criteria recommended by Holt, et al., (2007, p.253)
It would be useful to change agents to know how the employees feel about
proposed changes. Knowing whether the employees (a) felt the change was
appropriate, (b) believed management supported the change, (c) felt capable to
making the change successful, and (d) believed the change was personally
beneficial would alert them to needed attention about the change. Periodic
assessment of these sentiments may provide the necessary information to take
whatever actions may be needed to make the change successful.
The research model depicted in Figure 1 seeks to understand the relationship of
the users perception of management of change effectiveness on readiness, resistance,
and directly on end-user satisfaction. It integrates user feedback of satisfaction or
concerns to surface issues for evaluation of relevance and importance used in decision
making of whether to adapt the management of change processes or to improve an
element of the IT itself. The longitudinal testing before, during, and after
implementation allow testing of the variables and comparison of their relationships over
the implementation period. Resistance and readiness are studied for their effects on
user satisfaction and to evaluate which is a better precursor. This process model offers
periodic assessment of the sentiments which may provide the necessary information to
take whatever actions needed to make the change successful, as proposed by Holt
(2007).
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31
Section 2.3 Hypotheses
This research explores the relationship among users perception of management
of change effectiveness, readiness for change, resistance to change, and end-user
computing satisfaction before, during, and after a new IS implementation. Satisfaction
with the old system is seen as decreasing the subject's readiness for change to a new
system. Doubtful attitudes inhibit favorable reactions and promote resistance to IS
change (Joshi, 1991). It is assumed that users who are satisfied with the old existing
information system are not motivated to use a different information system. Those
users do not see the discrepancy of a new desired endstate or the efficacy to achieve it
will not have a positive attitude toward the change (Armenakis, et al., 1993). These
users are less collaborative in the change process. Those users who are very dissatisfied
with the old system should welcome the change resulting in a more favorable
perception of management of change effectiveness of the new system. Hence, change
managers should assess users attitudes towards the replaced information system and
adjust their strategies accordingly (Armenakis, et al., 1993).
H1. End-user computing satisfaction with the old system negatively affects the users
perception of management of change effectiveness for the new implementation.
Management of change includes: 1) communication of the need for change; 2)
promoting the expected benefits of the new system; 3) management support for the
planned change; and 4) training to promote ease of use and to diminish uncertainty
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32
(Deci et al., 1994; Gagne and Deci, 2005). The readiness message should incorporate
two issues: (a) the need for change, that is, the discrepancy between the desired end-
state (which must be appropriate for the organization) and the present state; and, (b) the
individual and collective efficacy (i.e., the perceived ability to change) of parties
affected by the change effort. (Martins and Kellerman, 2004). These strategies aim to
inform users of the benefits of the change and encourage them to favorably respond to
the change. Bentleys (2005) seventh prerequisite for successful implementation called
Education is defined as the ability to understand the solution (technology), why the
business needs it, how the technology works, what one can expect from it, and what
changes are required. These objectives are attained through communication and training
to establish realistic users expectations. Creating discrepancy in the users mind
between the old system and the new increases the users readiness to accept the change.
High ratings on MOC should result from effective efforts to prepare users to accept the
change.
H2. Users perception of management of change effectiveness positively affects
Readiness for Change.
IS researchers also recognize users' acceptance of a system as a major objective
of system implementation and the organizational change it entails. Understanding and
effectively managing resistance are, therefore, important determinants of the system
success (Jiang et al., 2000). Resistance to change can be managed by communicating
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33
the rationale for the change (Deci et al., 1994; Gagne and Deci, 2005). Resistance is
reduced as the ease of using the new system and the expected utilization benefits are
enhanced.
If a users perception of the management of change effectiveness is high, then it
is expected that the users resistance to change decreases. Low MOC measurements
would indicate a negative opinion of change management effectiveness, which
increases user resistance.
H3. Users perception of management of change effectiveness negatively affects
Resistance to Change.
Kwahk and Lee (2008) found that readiness for change had an indirect, positive
effect on behavioral intention to use an enterprise-wide system through the influences of
perceived usefulness and perceived ease of use; both are important causal antecedents
of acceptance/ satisfaction according to Venkatesh and Davis (1996). Venkatesh and
Davis (2000) suggested that interventions to increase the comparative effectiveness
between the new and old systems may produce increased leverage to promote user
acceptance/satisfaction. Training represents an obvious opportunity and is one of the
major elements of management of change. Training impacts the user's belief regarding
both ease of use and usefulness and is one management strategy to create readiness to
prepare users to accept the change (Venkatesh and Davis, 1996). If creating readiness
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34
has a positive effect on perceived usefulness and ease of use then it should increase user
satisfaction which indicates a successful implementation.
H4. Readiness for change positively affects end-user computing satisfaction of the new
system.
Changes that are considered favorable are not resisted and may even be sought
after and welcomed while changes considered unfavorable are likely to be resisted.
More resistance deters internalization of the benefits of change and reduces satisfaction
with the change. MIS researchers recognize that better theories or models of user
resistance would lead to better implementation strategies and desired implementation
outcomes (Joshi, 1991). Overcoming resistance should lead to greater acceptance or
EUCS. Readiness for change is expected to positively impact satisfaction with the new
system, whereas resistance to change is expected to lower satisfaction (Piderit, 2000).
H5. Resistance to change negatively affects end-user computing satisfaction of the new
system.
Change management is critical to successful IS implementation. Top
management support, business involvement, communication, and training are important
factors in managing these IS changes successfully (Shang and Su 2004). Many
researchers have been interested in how to promote user satisfaction for successful
implementations (Chau, 1996; Davis, 1989; Igbaria et al., 1997; Venkatesh and Davis,
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35
2000). The level of satisfaction depends on the motivation and ability to change
(Judson, 1991; Kotter, 1995; Lewin, 1951). Motivating factors and enabling factors
influence user satisfaction. Change motivators, such as the explanation of realized
benefits, positively influenced perceived usefulness. Change enablers, such as training,
positively influenced perceived ease of use of the system (Martins and Kellerman,
2004; Venkatesh et al., 2000). It is recognized that satisfaction can be enhanced by
giving managers a tool to proactively design interventions targeted at populations of
users that may be less inclined to adopt and use new systems (Doll, 2004, p. 426). An
instrument that helps managers to identify weak areas in change strategies can supply
feedback to adapt the change process during the implementation to promote end-user
satisfaction. This expected affect is indicated by the longitudinal model Figure 2. It is
expected that as the perception of the effectiveness of the change management increases
so does the users satisfaction with the system.
H6. Users perception of management of change effectiveness positively affects end-
user computing satisfaction of the new system.
If feedback is collected on the users concerns about the change or technology
and acted upon by adapting management of change strategies in order to address those
concerns then the users perceptions of how well the change is managed should improve
(Holt, et al., 2003; Holt, et al., 2007; Jiang, et al., 2000).
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36
H7. Feedback from end-user computing satisfaction of the new system positively affects
users perception of management of change effectiveness.
The theoretical model in Figure 1 and the longitudinal testing model in Figure 2
depict the hypotheses of proposed relationships of the users perception of the
management of change, readiness for change, resistance to change, and end-user
computing satisfaction. Although literature streams of management of change and IS
acceptance contain research of these constructs, no study was found with a model that
contained them all. This research investigates them together longitudinally in a process
model. .
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CHAPTER III
Methodology
Section 3.1 Research Design
This study is of non-experimental quantitative explanatory longitudinal design
since independent variables are not manipulated. To establish causation, variables must
be correlated, independent variable must precede dependent variable in time order, and
the observed relationship must not be due to a third confounding variable. A
longitudinal design rather than a single cross-sectional design can be used to establish
time order. Techniques to establish time order in this study include collection of
samples at three sequential points in time and wording of constructs such as MOC
referring to change actions already occurred and EUCS referring to the current level of
satisfaction (Johnson and Christensen, 2006).
The purpose of this research is to analyze causal relationships between the four
main variables. The research design is a longitudinal study with surveys at three points
in time, and at each point it is a cross-sectional observational study using a web-based
survey. The research setting is in a small university replacing multiple separated
systems with a new, integrated mandatory use student information system.
This study differs from the longitudinal study of Venkatesh and Davis (2000) by
using feedback from two of the three survey points spanning 15 months to surface
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38
issues as input for management of the change process. It measures end-user satisfaction
in this mandatory setting rather than usage time in a voluntary setting. The concepts
applied in this studys model are prevalent in literature but no previous study could be
found that investigates this combination of variables longitudinally in a process model.
Although studies were found that treated resistance and readiness as separate variables
conceptually, only one study was found with a measurement instrument for resistance
separate from readiness (Bhattacherjee and Hikmet, 2007). This study treats readiness
and resistance separately and searches for the answer which one plays a more prominent
role in the change process.
This study is based on data and observations taken as Comprehensive Academic
Management System (CAMS Enterprise) an integrated, web-based Academic
Enterprise Resource Planning System for higher education is introduced.
CAMS, marketed as an academic Enterprise Resource Planning system, is
similar to an Enterprise Resource Planning (ERP) system for a business. First, it
provides a student (i.e., customer) portal, allowing students to access email, financial
data, grade reports, and the course management system similar to how customers
remote access to a business. Secondly, the online testing in CAMS is parallel to what is
typically used for employee training by a business human resource department.
Thirdly, CAMS faculty and staff portals offer functionalities similar to those a business
offers to its employees. Faculty conduct classes in an online environment interfaced
with the backend data management system. They can access to appropriate student
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39
records, advise students, complete course registration and post grades. Staff, depending
on the department where they work and their job titles, interfaces with email, accounts
payable, admissions, financial aid, registar and the student databases. Both faculty and
staff can conduct their respective functions serving students in CAMS. CAMS, as a
campus (enterprise)-wide management system, interfaces and integrates academic
(business) functions and eliminates unnecessary duplicated data entries and inconsistent
data management in the old legacy information systems that only served a specific
business function or audience. CAMS, just as it has been marketed, indeed functions as
an ERP system in a general business. Therefore, results of this study may be
generalizable to other enterprise-wide integrated software implementations facing
similar integration and change management challenges.
Section 3.2 Data Collection
The university organization in target groups from administration/staff, faculty,
and students responded to the emails soliciting their participations in the survey that
was placed on a controlled access web site. Follow-up emails were sent to maximize
the response rate and enable comparison of late respondents to earlier ones. A note at
the beginning of the survey explained the purpose of the study and the procedure for
handling the data. It was emphasized that the data would be kept confidential and used
only for research purposes. All constructs were measured using the survey. (See
Appendix D for email invitations). The data were collected with the survey instrument
contained in Appendix A using SurveyGold software (www.surveygold.com). Several
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40
techniques were used to encourage participation. First, it was explicitly stated in the
instructions that participation was voluntary and that no identifying information would
be shared. Additionally, in order to encourage participation, upon completion of the
survey, respondents were directed to a password protected site that collected their
information for an incentive drawing for $100 held at the end of each survey collection
period. A final drawing was held for $100 for those who had participated in all three
surveys.
See Table 1, Time-line on implementation, for the dates and phases of
implementation at each sample point. Qualitative data was collected to establish the
timeline (Appendix E).
Harvey recommended that users complaints during change implementation
about technology should be examined. If the need was revealed then the technology
should be modified (1994). Accordingly, when faculty complained about the
inadequacy of the proposed course management module included in the CAMS ERP,
Table 1 Time-line on Implementation
Milestones Date Survey Date
Employee training CAMS and Blackboard
CAMS Faculty training
Feb. 17-20
March 20
Feb.26 Mar. 10
Mar./10 New hosted Blackboard implemented less than 1
week before Fall classes
Aug. 14
Student Portal open Oct. 29
Faculty portal open Dec. 10 Nov. 25 Dec. 24
Technical help desk/ Student g-mail Jan. 13
Implementation complete/ register and submit
grades online
March Apr. 15 May 3
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41
the decision was announced to employ an updated hosted integrated Blackboard instead.
This decision was made and announced prior to the Time 1 survey.
Data were collected at three points: in March 2009, which are referred to as
Time 1, at the initiation of the new system, in November 2009, which are referred to as
Time 2, after the registrars module and upgraded course management system was
implemented, and in April 2010, which are referred to as Time 3, after the
implementation of all systems was complete and in operation for a month. As new
modules were implemented the parallel older system modules were completely
displaced and taken offline except the old student information database, which was
read accessible for a short period before being taken offline. By Time 3 all modules
and integration were complete and the old student records database, email, and un-
integrated Blackboard were completely displaced.
The survey instrument was modified slightly at each time to reflect some
specific needs at that time. Issues identified by the survey comments were forwarded to
management as input for adapting change management strategies. Communications
from the management, comments on improved workflow enabled by the new IS system,
priority changes, or other issues indicated in the survey comments were collected as
qualitative data and are useful in interpreting data results.
Issues identified by the survey comments were collated and forwarded to
management as input for change management strategies. Management
communications, comments on improved work methods enabled by the new IS system,
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42
and shifting priorities or issues indicated in survey comments were collected as
qualitative data. Management communications announced a compromise for upgrade
of the existing course management platform (Blackboard) and integration to the new
information system in lieu of using the CAMS module considered inferior by faculty.
Emails announced expected benefits, time-lines for the implementation and periodic
updates as modules were implemented. Instruction, training schedules, and technical
support structure were announced. Clarifying emails were sent to address rumors and
unrest.
Control variables are chosen to account for variance in the dependent variables
that might be explained by factors other than the hypothesized variables. Agarwal and
Prasas (1990) posit individual difference factors affect beliefs in usefulness and ease of
use in IT acceptance. Individual differences in that study are defined as user factors
that include traits such as personality and demographic variables, as well as situational
variables that account for differences attributable to circumstances such as experience
and training (1990, p. 2). Gender was examined as a control variable in a study of how
specific change messages and change facilitation strategies relate to perceptions of the
change benefits (Holt, et al., 2003). Vankatesh and Davis, (2000) take into account
certain variables that might determine acceptance factors tied to social context and
individual characteristics (such as age, level of income or education). A number of
demographic variables including age and education have been studied and shown to
influence system use. Dillion and Morris (1996) aver that it is not surprising that age
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43
influences the use of technology within broad parameters but not in a strong
relationship. Demographic factors of age and gender are therefore collected in this study
to test if age or gender plays a role in relationships under study and to help examine if
there is bias in the sample or not.
In addition, Nelson (1990) suggests that investigations of individual adjustment
to technological innovations should include job characteristics as potential influences on
attitudes and behavior and should do so in a longitudinal multiple measures design.
Objective job content along with perceived job characteristics should be studied. Palm,
Colombet, Sicotte, and Deqoulet, (2006) investigate the effect of functional group
(medical secretaries, nurses and physicians) on acceptance and user satisfaction of a
clinical IS. They focused on user characteristics, user satisfaction, and perceived
usefulness and concluded that satisfaction is higher in the group of medical secretaries
who are the most frequent users of the computer IS functions and the only users of the
appointment and scheduling functions. Laerum, Karlsen, and Faxvaag (2004) in a
separate study reached the similar result that secretaries generally use hospital IS
functionalities more frequently in their daily tasks and are more satisfied than nurses or
doctors. Therefore, based on the literature, the group factor of students, staff, and
faculty is also considered for a control variable in this study due to their differences in
work assignments, computer modules used, training, and function, It is expected that the
different university groups of students, staff, and faculty may also react differently to
the implemented change.
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Section 3.2.1 Human subject concerns
It was emphasized that the data would be kept confidential and used only for
research purposes. All constructs were measured using the survey. To track
respondents, each survey was assigned a unique code and respondents did not need to
provide their identity on the survey. A list of codes that matched the email addresses of
respondents was created from the incentive drawing survey link to which only the
researcher had access.
Section 3.2.2 Population and sample
A small, private university was the setting for sampling during implementation
of a new integrated student information system. Permission was granted by the
President and Vice-President of Academic Affairs to conduct the study on user
satisfaction as it relates to users perception of management of change effectiveness and
the impact on resistance/readiness to change. Initial interviews were approved and
conducted to explore the proposed model. Employees names and email addresses were
obtained by functional group of the school. The university community in target groups
from administration, faculty, students, and staff (advisers, registration, financial aid,
admissions, advancement, academic support, and business office) were sent emails
soliciting their participation in the survey with an information link to access the survey
web site.
Despite the limited population size in this small university (approximately 100
faculty, 50 staff, and 1000 students), the response rates for the survey across all three
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points are consistently satisfying. Initially, 181, 325, and 207 surveys were completed at
points Time 1, Time 2 and Time 3, respectively. However, after pre-processing for
missing data, each data set was reduced to 145 records (All surveys with greater than
10% N/A (Not Applicable) responses or missing data were eliminated. Those with 10%
or less were replaced with the average value. The Partial Least Squares (PLS) testing
required the same number of cases at each point. Time 1 retained 145 cases, which
determined the number for the other two points. After stringent elimination, Time 2
still exceeded 145 cases, so random number generation was used to eliminate cases to
the required level.)
Table 2 indicates the sample size in each group with a total of 145 at points
Time 1, Time 2, and Time 3 for data analysis.
Table 2 Sample Sizes
Group TIME 1 TIME 2 TIME 3
Students 86 102 87
Faculty 31 23 28
Staff 28 20 30
Total (n) 145 145 145
The descriptive statistics of each group show that samples of each group at each
point are representative of the respective population (Table 6), indicating no sample
bias. The sample size of 145 at each point satisfies the minimum sample size
requirements in this particular study with the desired effect size and power.
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To determine the minimum sample size, the following factors were considered:
the power analysis with power of 0.8 at the 95% confidence level and 0.5 effect size
requires a sample size of 102. Additionally, SmartPLS requires ten times the number of
items measuring a latent variable, which are 120 for this study.
In this study, not all individuals use all applications or perform exactly the same
tasks, so although the unit of measure is the individual, the unit of analysis is the
aggregated experience, which represents the organizational level. The goal of using the
organization as the unit of analysis is to provide findings that are useful to organizations
assessing their current state of readiness, resistance, users perception of management of
change effectiveness, and end-user satisfaction to manipulate their management of
change strategies to affect a successful enterprise implementation.
Fifty-six subjects responded to the surveys at all three points in time spanning
15 months. The data from these longitudinally matched respondent records was
analyzed for comparison to the results from the larger sample of 145 respondents. The
students were not as heavily represented in the smaller matched respondent group since
students in the population changed with one class graduating and another entering
between sample points. The staff and faculty were more stable groups and a larger
portion of these two groups participated at all three survey points.
Section 3.3 Measurement Development
After conducting a literature review and developing a tentative research model,
administrative personnel were interviewed to finalize the appropriate research model
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and necessary instruments for assessment. A total of seven interviews were conducted
with key administrative individuals having titles such as Vice President Academic
Affairs," "Assistant Dean," Vice President of Finance" also responsible for technical
services , "Executive Director of Enrollment Management and Student Life," "Director
of Institutional Assessment," "Registrar," and "Assistant Registrar." Each interview
was recorded using a digital recorder. The transcriptions contain a total of 18,104
words (Appendix F).
Exploratory questions were asked concerning why the change was being made,
expected benefits, expected resistance, participation in selection, communications to the
organization, and important elements for successful implementation. Communication,
training, management support, and technical support were all listed by interviewees as
important. They each expressed trust in the new system with benefits of integration and
improved accuracy. Change agent credibility and good data migration were also listed
as important to the success of the new system. The interviews also served as a reminder
of critical elements for successful change to the interviewees. No new elements
surfaced that would not fit into the existing constructs and model.
At the end of one interview, the Vice President of Finance offered to list
improvements in the task procedures after implementation for the accounting area
employees. This type of data was recommended for Information System research in the
literature review materials and was deemed valuable. From these conversations the idea
developed to add a comment area on the survey (Please comment on any job tasks that
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have improved or worsened with the change). The comments from the initial survey
surfaced issues that needed addressing and were collated and sent to management. At
the second survey point, the email invitation included a statement: "Your comments
will be anonymous but your concerns will be passed on to administration."
Four constructs are measured in this study. All instrument items are detailed in
Appendix A. Some instrument items are modified at the different data collection points
to specifically refer to the information system under examination at that point. For
instance, at the pre-implementation of the new IS system, CAMS, the EUCS
measurements specifically refer to the old information system composed of fx Scholar,
ACT, and Response Plus, etc. Questions are carefully worded with proper tense. For
instance, MOC measures the users perception of how well change has been managed
before the date of the survey, and EUCS measures users present satisfaction on the date
of the survey with the current information system. All four construct's items were
measured using a five-point Likert scale, anchored at 1 = strongly disagree and 5 =
strongly agree. Appendix A contains the measurement items.
The operationalization of users perception of management of ch