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

iii

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.

iv

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.

v

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.

vi

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

vii

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

viii

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

ix

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

x

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

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.

1

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

2

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).

3

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.

4

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

5

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

6

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

7

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

8

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

9

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

10

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.

11

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.

12

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[+]

13

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

14

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.

15

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

16

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,

17

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).

18

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

19

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.,

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

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

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).

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).

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

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

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

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

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

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.

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).

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

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

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

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,

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).

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. .

37

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

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

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

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

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,

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

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.

44

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