Technology Acceptance Within Organizations Improving Understanding of Failing Technology Projects Using Social Network Analysis and the Technology Acceptance Model

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Technology Acceptance within Organizations: Improving Understanding of Failing Technology Projects Using

Social Network Analysis and the Technology Acceptance Model

By

Benjamin Leonard MBA – Marketing & MIS, Carlson School of Management, University of Minnesota

Bachelors of Science - Mechanical Engineering, Institute of Technology, University of

Minnesota

Graduate Diploma in Management Research, University of South Australia

A thesis submitted for the degree of

DOCTOR OF PHILOSOPHY

Division of Business

International School of Business

February 2012

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Contents

Table of Contents

Contents ........................................................................................................................................... 2

List of Figures .................................................................................................................................... 8

List of Tables ................................................................................................................................... 10

Glossary/Abbreviations .................................................................................................................. 12

Abstract .......................................................................................................................................... 13

Declaration ..................................................................................................................................... 14

Acknowledgments .......................................................................................................................... 15

CHAPTER 1 - OVERVIEW OF THE RESEARCH .................................................................................... 16

1.1 Background of the Problem ................................................................................................. 16

Innovation and technology acceptance is important for success and ROI. ............................... 16

New technology acceptance initiatives are continuing to fail or be underutilized .................... 17

Organizational Adoption is complex with many phases ........................................................... 17

Diffusion of innovation & Technology Acceptance Models have matured ................................ 18

Early phases of contingent technology adoption/acceptance is complex ................................. 18

Social Networks Measures are gaining momentum as antecedents and direct impacts on BI and

Usage. ..................................................................................................................................... 19

1.2 Research Questions ............................................................................................................... 19

1.3 Study Rationale ..................................................................................................................... 21

1.3.1 Contribution to knowledge ............................................................................................. 21

1.3.2 Contribution to practice ................................................................................................. 23

1.4 Structure of Dissertation........................................................................................................ 24

1.5 Summary ............................................................................................................................... 25

CHAPTER 2 – LITERATURE REVIEW .................................................................................................. 26

2.1 Introduction .......................................................................................................................... 26

2.2 Organizational Innovation...................................................................................................... 29

2.2.1 Organizational Innovation & Adoption ............................................................................ 29

2.2.2 Strength of Weak Ties .................................................................................................... 34

2.3 Diffusion of Innovations and Technology Acceptance ............................................................ 36

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2.3.1 Technology Acceptance Model ....................................................................................... 36

2.3.2 Diffusion of Innovations.................................................................................................. 37

2.3.3 Personal Innovativeness ................................................................................................. 38

2.4 Social Network Analysis ......................................................................................................... 38

2.4.1 Organizational Sciences – Hawthorne Studies ................................................................. 39

2.4.2 Network Theory ............................................................................................................. 40

2.4.4 Structural Hole Theory.................................................................................................... 40

2.4.5 Social Capital .................................................................................................................. 42

2.4.6 Simmelian Ties ............................................................................................................... 43

2.4.7 Centrality ....................................................................................................................... 45

2.4.8 Agent-Based Modeling ................................................................................................... 46

2.5 Technology Acceptance Integrating Social Network Analysis.................................................. 46

2.5.1 Model of Acceptance with Peer Support ......................................................................... 46

2.5.2 Social Information Processing Model (SIPM) ................................................................... 48

2.5.3 Tie Strength .................................................................................................................... 51

2.6 Gaps in the current knowledge or theory ............................................................................... 52

2.6.1 Evaluate data and insight on failed technology acceptance ............................................. 52

2.6.2 Evaluate data and insight on contingent acceptance within the earlier stages of

adoption/acceptance. ............................................................................................................. 53

2.6.3 Explanatory Models: ....................................................................................................... 54

2.6.4 Account for Heterogeneity: ............................................................................................ 54

2.6.5 Account for Network Dynamics: ..................................................................................... 54

2.6.6 Take Advantage of Richer Data: ...................................................................................... 54

2.6.7 Network Dynamics over Time: ........................................................................................ 54

2.7 Summary ............................................................................................................................... 55

CHAPTER 3 – RESEARCH DESIGN AND METHODS ............................................................................ 56

3.1 Introduction .......................................................................................................................... 56

3.2 Hypothesis Development and Key Variables .......................................................................... 56

3.2.1 Key variables (dependent, moderating, intervening) and a conceptual framework ......... 56

3.2.2 Definitions of the Constructs and Measures of Constructs .............................................. 59

3.2.3 Hypothesis - how will they help answer the Research Questions .................................... 62

3.3 Research Design .................................................................................................................... 71

3.3.1 Type of Sample and sample size determination .............................................................. 71

3.3.2 Survey Instrument and Measures ................................................................................... 75

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3.3.3 Data Collection Process .................................................................................................. 77

3.3.4 Data Analysis .................................................................................................................. 78

3.3.5 Validity and Reliability of the Study ................................................................................ 83

3.4 The longitudinal Study ........................................................................................................... 88

3.4.1 Location of the Study ...................................................................................................... 88

3.4.2 Subjects or Participants .................................................................................................. 89

3.4.3 Technologies used for the study ..................................................................................... 89

3.5 Summary ............................................................................................................................... 93

CHAPTER 4 - DATA ANALYSIS .......................................................................................................... 94

4.1 Introduction .......................................................................................................................... 94

4.2 Data Management prior to entry ........................................................................................... 94

4.3 Initial data analysis for suitability ........................................................................................... 94

4.4 Reliability / Loadings (Pooled for all projects & times)............................................................ 95

4.4.1 Internal Consistency Reliability ....................................................................................... 95

4.4.1 Covariance between Independent Variables and Behavioral Intention ............................ 96

4.5 Descriptive Statistics .............................................................................................................. 97

4.5.1 Frequencies .................................................................................................................... 97

4.5.2 Descriptive Statistics for Factors ..................................................................................... 99

4.5.3 Normality, Linearity, and Homoscedasticity .................................................................. 103

4.6 Inter-Correlations among Study Variables ............................................................................ 110

4.7 Multiple Regression ............................................................................................................. 113

4.7.1 Hierarchical Regression Results for Behavioral Intention............................................... 115

4.7.3 Hierarchical Regression Results for Performance Expectancy ........................................ 119

4.7.4 Hierarchical Regression Results for Effort Expectancy ................................................... 121

4.7.5 Hierarchical Regression Results for Facilitating Conditions ............................................ 123

4.7.6 Overall Regression Model Results ................................................................................. 125

4.8 Social Network Graphs ........................................................................................................ 128

4.8.1 Event Management System .......................................................................................... 128

4.8.2 Enterprise Project Management ................................................................................... 156

4.10 Social Network Mapping Survey Results to Networks......................................................... 165

4.10.1 Effort Expectancy ....................................................................................................... 166

4.10.2 Social Influence .......................................................................................................... 167

4.10.3 Facilitating Conditions ................................................................................................ 169

4.10.4 Behavioral Intention ................................................................................................... 170

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4.10.5 Implicit social influence .............................................................................................. 172

4.11 Hypothesis Testing ............................................................................................................ 173

RQ1-3: Coping and Influencing Network impact on the traditional UTAUT model factors? .... 173

RQ4: Influencing Network Effects on Project Success ............................................................ 175

4.13 Summary ........................................................................................................................... 176

CHAPTER 5 - DISCUSSION .............................................................................................................. 177

5.1 Introduction ........................................................................................................................ 177

5.2 What the overall study found .............................................................................................. 177

Social Networks are relevant to Technology Acceptance ....................................................... 177

The performance expectancy of an individual’s network neighbors impacts their performance

expectancy ............................................................................................................................ 178

Social Network Analysis gives a new visibility of Technology Acceptance at both the macro and

micro levels. .......................................................................................................................... 178

Inhibitors or structural problems of the Coping and Influencing Network that could negatively

impact the organizational adoption or individual technology acceptance become visible in the

network graphs ..................................................................................................................... 179

Social Network Analysis of the Influencing and Coping Networks show ties between the

organizational adoption and individual acceptance in contingent technology acceptance. .... 180

5.3 What do these results mean to organizations ...................................................................... 180

How the theory is extended .................................................................................................. 180

What aspects are falsified ..................................................................................................... 180

False Leads ............................................................................................................................ 181

How does the organizational context restrict you and may create false possibilities for future

researchers? ......................................................................................................................... 181

How practitioners can translate these findings directly to innovations and processes that you

targeted, and what analogous tasks the findings can be extended to .................................... 181

Which technology characteristics were of critical importance ............................................... 183

The sustaining and disruptive nature of the innovation studied ............................................. 183

5.4 How these results fit with theories and results underlying or relevant to them .................... 184

Diffusion of Innovation & Technology Acceptance Models .................................................... 184

Model of Acceptance with Peer Support (MAPS) ................................................................... 184

5.5 How the results fit with or not fit with past evidence in IS, and why .................................... 185

Organizational Adoption is complex with many phases ......................................................... 185

Early phases of contingent technology adoption/acceptance is complex ............................... 186

Innovation and technology acceptance is important for success and ROI. ............................. 187

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5.6 Summary ............................................................................................................................. 187

CHAPTER 6 - CONCLUSIONS .......................................................................................................... 189

6.1 Introduction ........................................................................................................................ 189

6.2 Summary of Research .......................................................................................................... 189

6.3 Contribution to Knowledge .................................................................................................. 189

Expand the Understanding of Influencing and Coping Network on Technology Acceptance ... 190

Combine Social Network Analysis Perspective with UTAUT Perspective ................................. 190

Provide insight on the early stages of contingent acceptance initiatives that eventually failed or

where significantly delayed ................................................................................................... 191

Provide a new technology context researched in technology acceptance .............................. 191

Use of snowball surveys to bridge hierarchies and organizations ........................................... 191

6.4 Contribution to practice ...................................................................................................... 191

Provide an additional real-world example on using Social Network Analysis within an

Organization to Support Technology Acceptance. ................................................................. 192

Insight for Better Technology Acceptance Planning ............................................................... 192

Improve Likelihood of Successful Technology Acceptance ..................................................... 192

Better Mechanism to Identify Change Inhibitors ................................................................... 193

6.5 Study Limitations ................................................................................................................. 193

Context limitations ................................................................................................................ 193

Data gathering limitations ..................................................................................................... 194

Variables selection limitations ............................................................................................... 194

Analytical limitations ............................................................................................................. 195

Technology and innovation feature constraints limitations.................................................... 195

Theoretical and practical limitations...................................................................................... 195

6.6 Future Research .................................................................................................................. 195

Future research that addresses limitations of the research ................................................... 195

New project contexts such as successful projects or non-contingent TAM ............................. 196

More Data Longitudinal Studies of the Coping and Influencing Network................................ 196

Action Research while leveraging the Social Network Modeling and measuring TAM perceptions

............................................................................................................................................. 196

References .................................................................................................................................... 197

Appendices ................................................................................................................................... 207

Appendix A – Survey Questionnaire ........................................................................................... 208

Acceptance of New Technology Survey .......................................................................... 208

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Appendix B – Information Sheet ................................................................................................ 215

Appendix C – Second Survey Correspondence ........................................................................... 217

Appendix D – Consent Form ...................................................................................................... 219

Appendix E – Ethics Approval .................................................................................................... 220

Appendix F – Statistical Syntax from SPSS to calculate results .................................................... 221

Appendix G - Addressing procedures for ensuring sound ethical practices ................................. 227

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List of Figures

Figure 1 - Basic model of the different conceptual areas; leveraging constructs from technology

acceptance, social networks, and organizational innovation. The light green boxes make up the

foundation of this research. The red circle shows the key constructs .............................................. 27

Figure 2 - Timeline of significant research & theories for technology adoption/acceptance and

organizational/social networks ........................................................................................................ 28

Figure 3 Organizational Level Acceptance Factors from Frambach and Schillewaert’s 2002............. 32

Figure 4 Individual Acceptance Factors from Frambach and Schillewaert 2002 ............................... 33

Figure 5 - Conceptual Model proposed for Traditional Unified Theory of Acceptance and Use of

Technology ...................................................................................................................................... 37

Figure 6 Social Capital Framework Propose by Adler and Know 2002 .............................................. 43

Figure 7 - Model used in Sykes et al. 2009 ....................................................................................... 47

Figure 8 - Combined TAM and SIPM model from Lee et al. 2003 ...................................................... 50

Figure 9 - Venkatesh conceptual model for the Unified theory of acceptance and use of technology

(UTAUT)(Venkatesh, Morris et al. 2003) .......................................................................................... 57

Figure 10 - Extension of the Unified Theory and Use of Technology incorporating the actual network

connections of the population’s dyads to represent the social influence factor ............................... 58

Figure 11 Longitudinal Approach to Data Collection......................................................................... 78

Figure 12 - Example of Organizational Social Network constructed from asking respondents "List 1-3

people that you interact with or will interact with in a typical week that you get help from for

effective use of the Event Registration System". .............................................................................. 82

Figure 13 Plot of Linear Curve Fit of Performance Expectancy and Behavioral Intention ................ 105

Figure 14 Plot of Linear Curve Fit of an individual’s Influence Network Neighbor’s Performance

Expectancy and their own Performance ........................................................................................ 106


Expectancy and their own Use Behavior ........................................................................................ 107

Figure 16 Plot of Linear Curve Fit of an individual’s Coping Network Neighbor’s Performance

Expectancy and their own Performance Expectancy ...................................................................... 108

Figure 17 Plot of Linear Curve Fit Normalized Coping Centrality and their own Effort Expectancy .. 109

Figure 18 - Statistically Significant R2 Regression Results ................................................................ 127

Figure 19 EMS Pre Pilot Coping Network........................................................................................ 136

Figure 20 EMS Pre-pilot Coping Network Grouping ........................................................................ 136

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Figure 21 EMS Post-Pilot Coping Network ...................................................................................... 142

Figure 22 EMS Post Pilot Coping Network Grouping ....................................................................... 143

Figure 23 EMS Pre Pilot Influencing Network ................................................................................. 148

Figure 24 EMS Pre-Pilot Influencing Groups ................................................................................... 149

Figure 25 EMS Post Pilot Influencing Network ............................................................................... 154

Figure 26 EMS Post-Pilot Influencing Groups ................................................................................. 155

Figure 27 EPM Pre Pilot Coping Network ....................................................................................... 159

Figure 28 EPM Pre-Pilot Coping Groups ......................................................................................... 160

Figure 29 EPM Post Pilot Coping Network ...................................................................................... 161

Figure 30 EPM Pre Pilot Influencing Network ................................................................................. 163

Figure 31 EPM Pre-Pilot Influencing Groups .................................................................................. 164

Figure 32 EPM Post Pilot Influencing Network ............................................................................... 165

Figure 33 Pre-Pilot Event Management System Coping Network with Effort Expectancy Mapping . 166

Figure 34 Post-Pilot Event Management System Coping Network with Effort Expectancy Mapping 167

Figure 35 Pre-Pilot Event Management System Coping Network with Social Influence Mapping .... 168

Figure 36 Post-Pilot Event Management System Coping Network with Social Influence Mapping .. 169

Figure 37 Pre-Pilot Event Management System Coping Network with Facilitating Conditions Mapping

...................................................................................................................................................... 169

Figure 38 Post-Pilot Event Management System Coping Network with Facilitating Conditions

Mapping ........................................................................................................................................ 170

Figure 39 Pre-Pilot Event Management System Coping Network with Behavioral Intention Mapping

...................................................................................................................................................... 171

Figure 40 Post-Pilot Event Management System Coping Network with Behavioral Intention Mapping

...................................................................................................................................................... 172

Figure 41 Pre-Pilot Event Management System Coping Network with Implicit Social Influence

Mapping ........................................................................................................................................ 172

Figure 42 Post-Pilot Event Management System Coping Network with Implicit Social Influence

Mapping ........................................................................................................................................ 173

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List of Tables

Table 1 List of the two conceptual models used to determining their predictability in this research. 21

Table 2 Research Hypotheses .......................................................................................................... 69

Table 3 - Summary of unique surveys for different technologies, time, and individuals .................... 72

Table 4 - Coded attributes for participants not collected in survey ................................................... 79

Table 5 - Survey Instrument Question Sources and Types of Participants ......................................... 84

Table 6 - Summary of Technologies Used in Study ........................................................................... 93

Table 7 - Reliability Statistics............................................................................................................ 95

Table 8 - Estimates of Fixed Effects for Covariance between Company and Behavioral Intention ..... 96

Table 9 - Estimates of Covariance Parameters for Covariance between Company and Behavioral

Intention ......................................................................................................................................... 96

Table 10 Estimates of Fixed Effects for Covariance between Role and Behavioral Intention ............. 96

Table 11 Estimates of Covariance Parameters for Covariance between Role and Behavioral Intention

........................................................................................................................................................ 96

Table 12 Department Frequencies of Participants ............................................................................ 97

Table 13 Company Frequencies of Participants ................................................................................ 97

Table 14 Location Frequencies for Participants ............................................................................... 98

Table 15 Technology Frequency for Participants ............................................................................. 99

Table 16 Age Frequencies for Participants ....................................................................................... 99

Table 17 Experience Frequencies for Participants ........................................................................... 99

Table 18 - Descriptive Statistics for Factors (Pooled) ...................................................................... 100

Table 19 Descriptive Statistics for Factors (EMS Pre-Pilot) .............................................................. 101

Table 20 Descriptive Statistics for Factors (EMS Post-Pilot) ............................................................ 101

Table 21 Descriptive Statistics for Factors (EPM Pre-Pilot) ............................................................. 102

Table 22 Descriptive Statistics for Factors (EPM Post-Pilot) ............................................................ 103

Table 23 Correlation Matrix ........................................................................................................... 113

Table 24 - Hierarchical Regression Results for Behavioral Intention ............................................... 116

Table 25 Hierarchical Regressions Results for System Use .............................................................. 118

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Table 26 Hierarchical Regression Results for Performance Expectancy........................................... 120

Table 27 Hierarchical Regression Results for Effort Expectancy ...................................................... 122

Table 28 Hierarchical Regression Results for Facilitating Conditions ............................................... 124

Table 29 - Hypothesis Results ........................................................................................................ 174

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Glossary/Abbreviations

BI – Behavioral Intention

CSR – Customer Service Representative

EE – Effort Expectancy

EMS – Event Management System, also referred to as Event Registration System

EPM – Enterprise Project Management

FC – Facilitating Conditions

IS – Information Systems

ISI – Implicit Social Influence

Ops - operations

PE – Performance Expectancy

PM – Project Manager

SI – Social Influence

SU – System Usage

UTAUT - Unified Theory of Acceptance and Use of Technology

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Abstract

Innovation and technology acceptance is important for the success of organizations, yet new

technology projects continue to fail or be underutilized. Although technology acceptance has

matured, organizational adoption at its earlier phases and the contingent relationship between

individual technology acceptance of users and organization adoption is complex and not well

understood or researched. This research longitudinally explored the early phases of two contingent

new technology projects that eventually failed after pilot phases. This exploration was done through

the lens of Social Network Analysis of the Influencing and Coping Social Networks the factors of the

Unified Theory of Acceptance and Use of Technology (UTAUT). Social Network Analysis measures are

gaining momentum as antecedents and direct impacts on Behavioral Intention and System Usage in

addition to their ability to bridge the macro and micro level understanding of technology acceptance

through understanding how actors cope with new technologies and influence each other over time.

This study sought to further understand the organizational technology acceptance in the early

phases of a contingent acceptance of a new technology, by understanding if the influencing or

coping networks add additional explanatory power of the behavioral intention and system usage

over the traditional UTAUT model. In addition to the direct impacts on behavioral intention and

system usage, this study also looked at the informal influencing and coping network factors as

antecedents of traditional technology acceptance factors of performance expectancy, effort

expectancy, and facilitating conditions from the traditional UTAUT model. This traditional regression

analysis of this study showed that an actor’s coping centrality and the performance expectancy of

that actor’s neighbors significantly impact their own performance expectancy. This further

supported earlier work on the importance of network position on technology acceptance.

This more traditional research approach was then augmented by a longitudinal evaluation of the

dynamics of the actors within the social networks before and after their respective pilots to

understand if using a social network graphs of the influencing and coping networks with the UTUAT

perception factors could improve the likelihood of success of the projects for practitioners.

The longitudinal evaluation of the actors within the social network graphs supported the traditional

research approach finding, but more importantly gave significant additional insight into the

dynamics of the macro and micro level understanding of technology acceptance through

understanding how actors coped with new technologies across organizational boundaries over time.

The longitudinal evaluation also showed how the central influential actors changed over time which

resulted in the breakdown of a high density coping and influencing network centered on individuals

who have positive performance expectancy into a disjoint and less dense coping and influencing

network centered on individuals with negative performance expectancy. These dramatic influencing

and coping network changes during the early phases of these contingent technology acceptance

efforts coincided with the eventual cancelation or de-scoping of the projects. This demonstrated

that social network graphs have the potential to bring visibility to acceptance inhibitors and bridge

the organizational and individual decisions, which has significant impacts for practitioners and

suggestions for future research.

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Declaration

This thesis presents work carried out by myself and does not incorporate without acknowledgment

any material previously submitted for a degree or diploma in any university; to the best of my

knowledge it does not contain any materials previously published or written by another person

except where due reference is made in the text; and all substantive contributions by others to the

work presented, including jointly authored publications, are clearly acknowledged.

Benjamin P Leonard

July 21, 2011

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Acknowledgments

In this study, I was the recipient of generous help and support of many people. Without their

support and advice, this study would have been difficult to accomplish. For their help, I am grateful

to all of them. Each day I am blessed with my wife, Yvonne, and my four sons: Bearach, Patrick,

Ronan, & Cormac for their support and patience allowing me to follow a dream. I am also fortunate

to have supportive parents who impressed on me their passion for education and gaining knowledge

at any level.

I wish to express my deepest appreciation and gratitude to my supervisor Prof. Doug Vogel who

provided the steering, resources, and feedback I needed to become a researcher. I would also like to

thank my friends and colleagues Tim Boileau, Matt Googins, and Scott Heintzeman who supported

me in many ways in the dual life of being a practitioner and researcher in a demanding job during

this study. The generosity of time from the leaders and study participants in the organization that

was the subject of this study was invaluable. There are countless family, friends, colleagues, and

fellow students that provided me encouragement or support. To all of these individuals, I am

deeply indebted.

Benjamin Leonard Page 16

SNA and UTAUT modeling of Contingent Projects

CHAPTER 1 - OVERVIEW OF THE RESEARCH

The research problem for this thesis is the lack of understanding of how to better leverage social

network analysis of the informal coping and influencing networks within an organization to better

understand, monitor, predict, and improve the success and acceptance of new innovation or

technology projects.

To that end, practitioners or researchers trying to improve the success of new technology or

innovation projects could significantly benefit from understanding how the informal networks

influence the perceptions and usage of new innovations or technologies. Examples of insights that a

practitioner or researcher could gain from addressing this problem could include:

Who are the central actors helping an organization cope with a new innovation?

Who has the most influence over an innovation’s acceptance, how much influence do they

have, and what are their perceptions of the innovation or new technology?

Are there informal groups of users that are tightly connected or are they disjoint and less

dense with many isolated individuals?

Do these influencing and coping networks change during the early phases of projects

contingent on both organizational and individual acceptance?

Or simply, do the social network graphs have the potential to bring visibility to acceptance

inhibitors and bridge the organizational and individual decisions?

1.1 Background of the Problem

The importance of this research problem is underscored by understanding six areas; how important

innovation and technology acceptance is for organizational success and ROI, the high failure rate of

new technology acceptance initiatives, the complexities of organizational adoption, the maturing

models of technology acceptance, the complexities of early phases of contingent technology

acceptance, and the increasing focus of social network metrics on behavioral intention and usage of

new technologies. These areas are further explained in the following six sections.

Innovation and technology acceptance is important for success and ROI.

New innovations often in the form of new systems or software has become more critical to almost

every facet of modern day life (Mann 2002). Innovation has been shown to be key driver of

corporate success, this is especially true for younger companies whose growth comes from finding

new markets with innovative new products (Cardozo R, K et al. 1993). The acceptance of new

technologies such as information systems is also becoming more critical in the competitive global

environment (Lee, Kim et al. 2006).

These new innovations create several issues for organizations who need to successfully implement

them to gain the potential benefits or return on investment. Within organizations, older

technologies are continually being replaced by new innovations to drive improved productivity or



deliver new services, but they are only successful when the employees or users accept and

effectively use the new technology (Lee, Kim et al. 2006).

New technology acceptance initiatives are continuing to fail or be

underutilized

Devaraj and Kohli also remind us that it’s not the investment in the technology that is the key to

success, it’s the usage. Many innovations also fail due to the lack of acceptance by employees, so

understanding the factors of user acceptance and improving the acceptance of new technologies is

important for the success organizations (Talukder 2008).

Even though research has been able to tie impacts of technology on organizational performance

(Devaraj and Kohli 2003), it does not address the IT Payoff; specifically the dollars invested in IT is

not a good measure of the effectiveness of IT, it’s the usage that’s critical (Devaraj and Kohli 2003).

There are many reasons for failed technical projects but lack of acceptance or non-involvement of

users is a common source of failure. Non-adoption or acceptance could mean many different things

to individual non-adopters because they might have actively or passively decided to reject the

innovation; alternatively it could be that non-adopters have not reached that point in the adoption

process yet (Frambach and Schillewaert 2002). What is important is that when a new technology

project outright fails, or fails to be utilized adequately, it has a significant financial impact. Emam

and Kuru also found that 26% to 34% of new technology projects were considered failures due to

performance or cancellation (Emam and Koru 2008). Overall, there is a continued call for more

research on failed technology acceptance initiatives (Frambach and Schillewaert 2002) due to their

complexities.

Organizational Adoption is complex with many phases

Adoption is often used to describe to the decision of an organization to make use of an innovation.

If acceptance of individuals in that organization is contingent upon a prior organization adoption, its

referred to as contingent innovation decision or forced adoption (Ram and Jung 1991; Rogers 2003).

Organizational adoption is significantly different then user acceptance and the decision process

could vary from organization to organization because different factors or technology attributes could

be important to each.

Frambach and Schillewaert developed a widely cited multi-level framework of determinants which

provided two levels of acceptance factors to address both the individual and organizational

acceptance (Frambach and Schillewaert 2002). The factors at the organizational level included both

perceived innovation characteristics and adopter characteristics. The perceived innovation

characteristics of compatibility, complexity, observability, and trial ability are leveraged from the

diffusion of innovation research thread (Rogers 2003). The organization adopter characteristics that

impact organizational adoption are organization size (Kennedy 1983), organization structure

(Zaltman, Duncan et al. 1973), and organizational innovativeness (Morrison 1996).

The longitudinal nature and process of organizational and individual adoption is also not widely

understood, which adds further layers of complexity. Most studies focus on dichotomous

adoption/non-adoption process but we know little about effect of different factors at the various



stages of that process (Olshavsky and Spreng 1996). Further research has been suggested on

organizational adoption in several areas, such as why some technologies do not become adopted

(Frambach and Schillewaert 2002), understanding the pre-adoption stage, influence of supplier

activities on innovation adoption within the organization, critical mass on innovation adoption and

the impact of network externalities (Frambach and Schillewaert 2002).

Diffusion of innovation & Technology Acceptance Models have matured

The individual technology acceptance research threads have been extensively researched and

matured to the level were the significant factors can now explain the majority of the behavioral

intention and usage variance (Venkatesh, Morris et al. 2003). Some of the earlier research by Rogers

(Rogers 2003) used factors that were more relevant to organizational adoption, but Davis’s

technology acceptance model has become the foundation for most of the individual technology

acceptance models for the last two decades (Davis 1986).

In the evolutionary variations based on the technology acceptance model, perceived usability and

perceived ease of use continued to show up as the most common factors of technology acceptance,

while a host of other factors and moderators have been evaluated since the introduction of the

Technology Acceptance Model. Eight of these model variations were evaluated and consolidated

into the United Theory of Acceptance and Use of Technology (UTAUT) in 2003. This model used four

core determinants of performance expectancy, effort expectancy, social norm, and facilitating

conditions to explain up to 70% of the variance in user intention (Venkatesh, Morris et al. 2003),

with performance expectancy continuing to be the largest factor.

Knowing the high explanatory power of the UTAUT, it could be assumed that this is a mature

research thread as stated by Venkatesh, Morris et al. “Given that UTAUT explains as much as 70

percent of the variance in intention, it is possible that we may be approaching the practical limits of

our ability to explain individual acceptance and usage decisions in organizations”.

In reality there are other challenges; such as addressing the impacts of the social impacts and

acceptance in earlier stages of a contingent technology acceptance initiatives (Venkatesh, Morris et

al. 2003; Sykes, Venkatesh et al. 2009). These might not yield significantly higher explanation of the

variance of usage, but they could be the antecedents of the core factors for technology acceptance,

yield significant understanding at the micro-level, and provide practical knowledge for managers.

Early phases of contingent technology adoption/acceptance is complex

Organizational adoption of innovations is more complex than individual adoption because of such a

higher number of stages involved in the adoption decision, and the effects of organizational

structure (Ram and Jung 1991). The longitudinal nature and process of organizational and individual

adoption is not widely understood, which adds additional layers of complexity. Most studies focus

on dichotomous adoption/non-adoption process but we know little about effect of different factors

at the various stages(Olshavsky and Spreng 1996).

In an organizations there are significant dependencies between the two decision processes at both

the organizational and individual level, for example the managers central to the organizational

decision process could have significant influence on the individual decision process by categorizing it,



setting some of the evaluation criteria when comparing it to a previous technology, and clearly

communicating some of its benefits and appropriate use (Olshavsky and Spreng 1996).

Social Networks Measures are gaining momentum as antecedents and direct

impacts on BI and Usage.

A more recent research thread is now bringing together user acceptance and the social network

analysis to address the quantitative impacts and understanding of the impacts of social networks

within organizations. A recent example of this is the Model of Acceptance with Peer Support (MAPS)

that enhances the constructs of the belief-based Technology Acceptance Model, organizational

innovation, and Social Network tools. This was introduced in the 2009 work of Tracy Sykes,

Viswanath Venkatesh, and Sanjay Gosain (Sykes, Venkatesh et al. 2009).

This type of a model distinguishes between social network perspective and individual intention

models. Two key constructs used are network density and network centrality. Network density

describes the connectedness of a network which is the actual number of ties in a network as a

proportion of the maximum possible number of ties. Network centrality was defined as the extent

of an individual involvement in assistance exchanges with coworkers (Sykes, Venkatesh et al. 2009).

This is consistent with other research which has shown that for successful diffusion of a new

technology where it is important to form positive impressions to central actors of a social network

(Burt 1987; Papa 1988; Lee, Cho et al. 2003). Based on the research, the behavioral intention or

system-use was shown to increase with network centrality, so “actors who are centrally located

within organizational networks enjoy a broad array of benefits and opportunities” (Sykes, Venkatesh

et al. 2009).

There are three social network concepts involved in this research that are helpful in understanding

the dynamics of technology acceptance within an organization when it comes to new technologies

that help provide understanding of the actions of some actors. The first two are the concepts of

coping (knowledge sharing) and influencing (changing system) pathways within the network which

are typically unavailable to those on the periphery with longer pathways or isolated from the

network altogether (Ibarra and Andrews 1993; Sykes, Venkatesh et al. 2009). The third is co-

adoption; this is relevant because people in central structural positions in a network will have more

influence over system design and can adjust it to their needs which is often labeled as co-adoption,

thus driving up their usage.

This merging of social network analysis and technology acceptance opens up several avenues for

future research that relate to conceptualizations and constructs related to social networks (Sykes,

Venkatesh et al. 2009). Some of these could include changing social networks over time, the role of

Simmilian ties on technology acceptance, the networks interactions of organizational decision

makers and individual users, and the role of social networks on the failure of new technologies.

1.2 Research Questions

Given the increased understanding of blending technology acceptance model and social network

analysis constructs, it’s important to see if they provide explanatory benefits in various scenarios of

technology acceptance that have proven to be challenging in previous research but important to IS



leaders. These include failed new technology implementations or struggling contingent acceptance

at its early stages which could benefit by looking at them through the lens of social network analysis

and UTAUT.

The first research question is to further expand the work from MAPS (Sykes, Venkatesh et al. 2009)

on our understanding of the influencing network on Behavioral Intention and System Use.

RQ1: For organizational technology acceptance in the early phases of a contingent

acceptance of a new technology, does the informal influencing network add additional

explanatory power of the behavioral intention and system usage over the traditional UTAUT

model?

The second research question is to look at the influencing network connections and position as an

antecedent of the traditional factor, Performance Expectancy, in the UTAUT model.


acceptance of a new technology, do the informal influencing networks impact the

performance expectancy of the traditional UTAUT model?

The third research question is to look at the coping network connections and position as an

antecedent of the traditional factors, Effort Expectancy and Facilitating Conditions, in the UTAUT

model.


acceptance of a new technology, do the informal coping networks impact the Effort

Expectancy or Facilitating Conditions of the traditional UTAUT model?

To answer this first three research questions, the study needed to determine the amount of

explained variance from the mean that is accountable for the factors (Adjusted R2) in each

conceptual model for of these factors. The adjusted R2 value was chosen to adjust the explained

variance based on the number of variables or degrees of freedom.



Table 1 List of the two conceptual models used to determining their predictability in this research.

Competing Conceptual Models

MODEL 1: Traditional Model of Unified Theory of Acceptance and Use of Technology

(UTUAT)

As shown in Figure 9 - Venkatesh conceptual model for the Unified theory of acceptance and use of technology

(UTAUT)(Venkatesh, Morris et al. 2003)

MODEL 2: Extended Unified Theory of Acceptance and Use of Technology (UTUAT)

incorporating the Coping and Influencing Networks Centrality and Neighbors Performance

Expectancy within these networks.

As shown in Figure 10 - Extension of the Unified Theory and Use of Technology incorporating the actual

network connections of the population’s dyads to represent the social influence factor

The fourth research question is more qualitative, then quantitative, but is used uncover if using

social network graphs merged with technology acceptance perceptions would add valuable insight

to managers in the process of planning, executing, and monitoring a technology acceptance project

and potentially increase the likelihood of identifying potential problems, opportunities, or key

resources for specific roles to help in their day-to-day decision making.

R4: For organizational technology acceptance in the early phases of a contingent acceptance

of a new technology, would using a social network graphs of the influencing and coping

networks with the UTUAT perception data improve the likelihood of success of the project?

1.3 Study Rationale

The need for this research is to improve both research and practitioner understanding and

effectiveness regarding technology acceptance.

1.3.1 Contribution to knowledge

This research will contribute to information system research by expanding our understanding of

organizational technology acceptance in five ways which include evaluating if there is increased

explanatory power of the UTAUT model when including social network constructs, provide insight on

the early stages of contingent acceptance initiatives that eventually failed or where significantly

delayed, provide data on event management systems and enterprise project management systems,

show the usage of snowball surveys to bridge hierarchies and organizations. The following sections

discuss these in more detail.

Evaluating if there is increased explanatory power of the UTAUT model when

including social network constructs



Explanatory power of technology acceptance necessitates both a micro and macro level

understanding of the decision making process to accept a technology. Rather than taking the more

common aggregate view of modeling technology acceptance as is common with the Bass Model or

its descendants for diffusion of innovations as described in Meade’s 2006 meta-analysis (Meade and

Islam 2006), I will focus on individual social network analysis to create more understanding of both

the micro and macro-levels of technology acceptance from a single approach and model.

Similar to the original MAPs research (Sykes, Venkatesh et al. 2009), one objective of this research is

to see if social network constructs could potentially add significant explanatory power of technology

acceptance. The UTAUT study showed adjusted R2 values of up to .70, the MAPS (Venkatesh, Brown

et al. 2008) research wasn’t able to demonstrate that level of adjusted R2, but it did show that the

social constructs are a significant factor on system use. Zhang and Sun’s meta-analysis has found

relatively low explanatory power in technology acceptance through a meta-analysis of 8 models

which typically were accounting for 40% overall of our understanding of Behavioral Intention. Zhang

and Sun’s meta-analysis has also shown the significance of subjective norm or social influence on

technology acceptance within mandatory environments such as organizations but it decreases over

time (Sun and Zhang 2005), which is one of the reasons why this research is focused on the social

network impacts on organizational technology acceptance with longitudinal surveys.

Expand the Understanding of Influencing and Coping Network on Technology

Acceptance

Earlier research has shown that the behavioral intention or system-use was shown to increase with

network centrality, so “actors who are centrally located within organizational networks enjoy a

broad array of benefits and opportunities” (Sykes, Venkatesh et al. 2009). This research is focused

on validating this finding and expanding it further to understand if the influencing or coping network

centrality and the impact of neighbors perceptions on behavioral intention and some of it

antecedents.

Combine Social Network Analysis Perspective with UTAUT Perspective

Being part of a coping or influencing network has been shown to influence behavioral intention and

system use (Sykes, Venkatesh et al. 2009) and this research will look at these social network graphs

overlaid with the factors from the Unified Theory of Acceptance and Use of Technology(Venkatesh,

Morris et al. 2003) to see how the two could be leveraged together.

Provide insight on the early stages of contingent acceptance initiatives that

eventually failed or where significantly delayed

The two technology acceptance projects used in this research were in the early stages of a

contingent technology acceptance where both the organizational decision was overlapped with a

user pilot. The main focus is on the users, but since these are in the early stages of a contingent

acceptance, it provides very unique data. One project was cancelled after the pilot and the other

met significant delays.



A social network view could help show how the help networks and resource controlling mechanisms

are supporting or hindering the system use (Sykes 2009). In this case, does it shed additional light

on the failure of the technology initiatives? Emam and Kuru also found that 26 to 34% of new

technology projects were considered failures due to performance or cancellation(Emam and Koru

2008). There is a continued call for more research on failed technology acceptance initiatives

((Frambach and Schillewaert 2002) due to their complexities.

Provide a new technology context researched in technology acceptance

In researching the literature for this study, there were many technologies that had been researched

previously, especially when it comes to technologies that are used by larger user-bases, such as

laptops, mobile phones. For the two technologies used in the study, Event Management System

and Enterprise Project Management System, the researcher was unable to find examples with these

two technologies.

Use of snowball surveys to bridge hierarchies and organizations

One of the areas mentioned for future research is the idea of hierarchy and boundary spanning for

social network analysis within organizations (Sykes, Venkatesh et al. 2009). In this research, a

“snow-ball” survey approach was taken where the connection survey questions were open-ended

and additional surveys were sent to newly identified users to attempt to observe the non-obvious

hierarchy and boundary spanning. This is significantly different than using a fixed list of users and

having the users identify which ones they interact with, which can be limiting because it might not

identify all of the network connections that are not obvious to management or the researcher.

1.3.2 Contribution to practice

For practitioners this research will provide an example of showing on how to perform social network

analysis within an organization to support technology acceptance, provide better insight for better

technology acceptance planning, improve the likelihood of successful technology acceptance, and

Provide an additional real-world example on using Social Network Analysis within

an Organization to Support Technology Acceptance.

Social Network Analysis is still an emerging skillset and the toolsets are highly limited to academic

researchers, graduate students, and a small set of companies and government entities that are now

focusing on the relationships between individuals, not just their attributes such as marketing and

social media companies. These are generally not exposed to the larger technology industry and

there is little commercial training in their use. Thus for a knowledgeable technology practitioner,

there is little available to help provide the steps necessary to develop an actionable social network

model to better support a technology acceptance initiative. This is clearly not training material, but

tackling real-world problems with new approaches and exploring the challenges and opportunities in

the results helps close the academia-practitioner gap for this domain.

Insight for Better Technology Acceptance Planning

The significance of this research is that it could provide a more deterministic modeling approach for

the technology acceptance within a network. This could benefit marketers or change agents in



creating their new technology change management, roll-out planning, or communication plans.

Operationally, a combination of user perceptions and appropriate social network graphs could be

used in the planning and testing of specific communication plans, training plans, and efficient

deployment of change agents. The coping and influencing networks (Sykes, Venkatesh et al. 2009)

would provide valuable insight while assigning roles and choosing resources for a pilot.

Communications and training plans could be more effectively targeted based on network and

perception roles. For example an isolated resource would require more hands-on training then

someone who already has a high usage and is very central in the coping network. Usage of

technology acceptance perception values will also help with the planning; this general approach of

using pre implementation surveys on factors such as perceived usability to better plan and predict

usage is valuable and was shown to be a strong predictor six months later in (Davis and Venkatesh

2004)

Improve Likelihood of Successful Technology Acceptance

Recent literature has emphasized the financial costs of inaccurate innovation adoption modeling.

First, misreading the adoption of a new innovation in the early stages is costly; over-optimism could

yield wasted investments and misreading slow adoption could yield managerial decision to

discontinue a potentially successful product. To better read the diffusion, we must understand the

tipping point, which in early stage adoption is an outcome of the social networks which are non-

uniform (Dattee and Weil 2007). Second, the importance of diffusion of innovations or technology

acceptance into organizations is growing in popularity as the ability for a company to quickly

implement new technology innovations is becoming more fundamental to their competitiveness

(Tether and Tajar 2008 ).

Better Mechanism to Identify Change Inhibitors

Social networks have been shown to a reveal ties that channel or bottle-neck information in the

organization. These identified bottlenecks are often central nodes that provide the only connection

between separate groups of the network (Abrahamson and Rosenkopf 1997) (Sykes, Venkatesh et al.

2009).

There is frustration on the lack of explanatory feedback from innovation adoption or acceptance and

the inhibitors to adoption from the aggregate models. This requires better disaggregate models

which can account for the “non-smooth life cycle shapes” that can result from lack of reference or

opinion leaders or substitution innovations (Dattee and Weil 2007). There is also a demand for

deeper insight into the inhibitors and stimulators of diffusion of innovations into organizations at

both an aggregate and individual level to help suppliers market their products more effectively

(Framback 2002).

The work of Devaraj and Kohli suggest that failure systems to show productivity improvements are

due to a lack of system use(Devaraj and Kohli 2003). A social network view could help show how the

help networks and resource controlling mechanisms are supporting or hindering the system use

(Sykes 2009). In this case, does it shed additional light on the failure of the technology initiatives?

1.4 Structure of Dissertation



This dissertation is divided into six chapters. Chapter 1, the Introduction provides an introduction

the background to this research, the need, research questions, and structure of the dissertation.

Chapter 2, the Literature Review, provides an overview of the related theories, key constructs and

significant prior research that provides the foundation that this research builds upon. The chapter

has five sections which provide an overview of prior research on organizational innovation, diffusion

of innovation and technology acceptance, social network theories, and the integration of technology

acceptance and social network analysis. These sections are followed a description of how this

research contributes to the prior research and a summary of the chapter.

Chapter 3, Design and Methodology chapter describes the architecture of the Design and

Methodology that was used for the study, with the goal of providing enough guidance to duplicate

this study by a knowledgeable researcher. This chapter starts by describing the hypothesis

development and key variables and how they help answer the research questions. This is then

followed by the research design section which addresses the types of sample and sample size, survey

instrument and measures, data collection process, data analysis, validity, and reliability issues for

this type of this research. Next, the longitudinal study section describes the location of the study,

subjects, and technologies used for the study. The chapter is then concluded with a summary.

Chapter 4, Data Analysis, provides a systematic analysis of the data collected during the research.

The data analysis starts with three sections to look at the data management prior to entry, the initial

data analysis for suitability, and reliability for the different loadings. These sections are followed by

the descriptive statistics and correlations. Following the correlation analysis, the regression analysis

and hypothesis testing are reviewed and analyzed. The next three sections focus on providing both

a quantitative and qualitative analysis of the social network data. These include looking at the social

network graphs for the pre-pilot event management project, and a mapping of the UTAUT responses

to the social network graphs, and a review of the cluster analysis. The last section is a summary of

the chapter.

Chapter 5, Discussion, provides a discussion of the research results. First, the chapter discusses the

overall study findings, which is followed by a discussion on what the results means to organizations.

The next two sections discuss how the results fit with the theories underlying them as well as past IS

research.

Chapter 6, Conclusions, wraps up the study by providing a short summary of the research, its

contribution to knowledge and practice, its limitations, and suggestions for future research.

1.5 Summary

This introduction chapter provided the background of the problem for this study, presented the

research questions, discuss the study rationale, and provided an overview of the structure of the

dissertation. In chapter 2, which is the literature review, it provides overview of the related

theories, key constructs and significant prior research that provides the foundation that this

research builds upon.



CHAPTER 2 – LITERATURE REVIEW

2.1 Introduction

The purpose of this chapter is to provide an overview and analysis of the related theories, key

constructs and significant prior research that provides the context for the research problem which is

the lack of understanding of how to better leverage social network analysis of the informal coping

and influencing networks within an organization to better understand, monitor, predict, and

improve the success and acceptance of new innovation or technology projects.

The chapter has five sections. This introduction section provides an overview of the chapter and a

brief overview of the significant constructs as well as timeline of the major research tracks that

inform this research. Section 2.2 provides an overview of the significant literature around

organizational innovation and the strength of weak ties on organizational innovation. This is then

followed by section 2.3 which covers the individual diffusion of innovation and technology

acceptance. Section 2.4 then provides an overview of the significant social network theories and

constructs which include: the Hawthorne Studies, Structural Hole Theory, Social Capital, Simmelian

Ties, Agent Based Modeling, and Social Network Analysis. Section 2.5 then focuses on the models

that combine social network analysis and individual technology acceptance which include Model of

Acceptance with Peer Support (MAPs) and Social Information Processing Model (SIP). Section 2.6

then reviews seven gaps in the prior literature that this research hopes to help start closing that

relate to a lack of understanding of informal networks. This chapter is then concluded with a

summary of the chapter.

The scope of this research spans three macro areas of study, specifically social networks,

organizational innovation, and individual technology acceptance. These and the surrounding key

constructs are shown in figure 1, where the light green colored boxes show the scope relevant to

this research, and the grey boxes are related constructs. The red circle shows the key constructs

most used in this research.

Innovation adoption and technology acceptance literature has evolved fairly independently of

organizational innovation or social networks for much of its history, but the three have started to

have significant overlap in the last 10 years. Figure 2 - Timeline of significant research & theories for

technology adoption/acceptance and organizational/social networks.

Organizational innovation and social networks have had significant overlap since the early

Hawthorne studies (Roethlisberger and Dickson 1939). Social networks research evolved around

graph theory and then gained deep understanding of these network structures such as the

importance of weak ties, structural-hole positions, and the benefits of centrality. Organizational

innovation or organizational culture research recognized the importance of social networks and

leveraged constructs such as how the weak connections helped drive their own innovativeness.

Innovation adoption and technology acceptance evolved in a more serial approach with significant

milestones along the way which included Roger’s Diffusion of Innovations research in 1957, Davis’s

Technology Acceptance Model in 1986, and Venkatesh’s Unified Theory of Technology Acceptance in

2003. The streams of research have started to overlap and leverage each other in more recent



research such as Model of Acceptance with Peer Support (Sykes, Venkatesh et al. 2009). These three

streams of research are the focus of the majority of this literature review and the contributions of

this research is focused on addressing gaps in bringing these two research streams together.

In reviewing these three streams of research in this chapter, seven gaps are uncovered. These

include gaining better understanding the dynamics of failed technology acceptance projects, looking

at contingency acceptance of organizations and individuals in early stages of the initiatives, providing

better explanatory models, understanding network heterogeneity, accounting for network

dynamics, taking advantage of the richness in individual data, and understanding network dynamics

over time. These seven groups are revisited and explained together in more detail in section 2.6

Gaps in the current knowledge or theory.

Out of

ScopeIn Scope

Communication

Proximity

Innovativeness

and Adoptor

Categories

Radial personal

network

(innovativeness

factor – Valente &

Forman 1998)

Simmilian Ties/

Social Distance

(Georg Simmel)

Social Learning

Theory

(Bandura 1986)

Heterophilous

Connections

(innovation

factor)

Theory of Planned

BehaviorTheory of

Reasoned Action

Actor Network

Theory Aggregate

Modeling (ie

Bass)

Disaggregat

e Modeling

(individual)

Network

Theory

Innovation

Decision

Process

Individual

Innovation &

Technology

Acceptance

Social /

Personal

NetworksUnified Theory of

User Acceptance

of Technology

(UTUAT)

Organizational

Innovation

Legend

Diffusion of

Innovations

(Rogers)

Technology

Acceptance

Model (TAM)

Social

Capital

Social Information

Processing Model

(SIMP)

Conditional

Acceptance

(organization &

indivdual)

Density

Model of

Acceptance with

Peer Support

(MAPS)

Centrality

Social

Network

Analysis

Tie

StrengthStructural

Hole Theory

Interlocking

Networks

Strength of

Weak Ties

Theory

Critical Mass

Agent Based

Modeling (Delre)

Viral

Marketing

Figure 1 - Basic model of the different conceptual areas; leveraging constructs from technology acceptance, social

networks, and organizational innovation. The light green boxes make up the foundation of this research. The red circle

shows the key constructs



Figure 2 - Timeline of significant research & theories for technology adoption/acceptance and organizational/social

networks



2.2 Organizational Innovation

2.2.1 Organizational Innovation & Adoption

The duality of organizational adoption and individual acceptance.

New system acceptance requires input at both the managerial or organizational level and the

individual level. It is important to understand not only the end users beliefs, attitudes and

intentions, but the management strategies, policies, and actions which have significant effect on the

successful acceptance of a technology (Bhattacherjee 1998).

For a technology supplier or driver of an innovation, the innovation acceptance is only successful

when both the organization and the individuals accept the innovation and the targeted adopters

demonstrate commitment by continuing to use the product over times (Bhattacherjee 1998; Rogers

2003). This is also widely researched from the product success perspective such as the research on

factors of new product success by Montoya-Weiss and Calantone (Montoya-Weiss and Calantone

1994) which has shown that the factors that lead to product success are quite varied as well.

Within the organization, the adoption is referred to as intra-organizational acceptance. If

acceptance of individuals is contingent upon a prior organization adoption, its referred to as

contingent innovation decision or forced adoption (Ram and Jung 1991; Rogers 2003). Forced

adoption carries the added assumption of mandatory usage to the users after the company has

made a decision to adopt a technology which has significant impacts on the individual adoption such

as forcing the creation of a coping network to help cope with the new technology (Ram and Jung

1991).

With Roger’s terminology for diffusion of innovations, it is important to clarify that adoption refers

to the decision of any individual or organization to make use of an innovation, whereas diffusion

refers to the accumulated level of users of an innovation in a market (Rogers 2003). Researchers

such as Bass focused on the larger aggregate modeling of diffusion, whereas Rogers focused more

on innovation adoption (Wright, Upritchard et al. 1997).

Overall, both organizational adoption and individual acceptance are heavily researched, and it is

likely they overlap in the early phase of introducing a new technology in an organization such as

pilot. When they overlap their interdependencies are not heavily researched even though key

individuals in the organization might have influence on both the organizational adoption and

individual acceptance of the larger user base.

Stages of organizational adoption

At a macro level, stages of organizational adoption are often referred to as Initiation and

Implementation (Frambach and Schillewaert 2002). During initiation, the organization becomes

aware of the solution, forms an attitude towards it, and evaluates it. The decision for the

organization to adopt the technology happens between these two stages. During implementation,



the organization has ideally made the decision to use the new technology and the focus turns to

making it available and accepted by the end users (Zaltman, Duncan et al. 1973; Frambach and

Schillewaert 2002).

Frambach and Schillewaert’s two stages for organizational adoption do not offer as much

understanding of the process as one might gain from a more detailed process at the user acceptance

level. For example, William McGuire suggests five stages for the individual levels as part of the

Hierarchy of Effects, where a user must pass through five stages to get from knowledge to a

behavior change; knowledge, persuasion, decision, implementation, and confirmation (McGuire

1989; Rogers 2003). James Prochaska also suggested five stages of change for an innovation

decision process which map fairly closely to McGuire’s which include pre-contemplation,

contemplation, preparations, action, and maintenance (Prochaska, DiClemente et al. 1992; Rogers

2003). These individual adoption or acceptance stages are not usable at the organizational level

because organizations have a different motivations or factors or objectives of using a technology.

Meyer & Goes (Meyer and Goes 1988) proposed a 9 step process of organization decision-making

stages in the assimilation of Medical Innovations which are grouped as Knowledge Awareness,

Evaluation-Choice, and Adoption-Implementation stages(Meyer and Goes 1988).

Knowledge-Awareness Stage

1. Apprehension. Individual organization members learn of an innovation's

existence.

2. Consideration. Individuals consider the innovation's suitability for their

organization.

3. Discussion. Individuals engage in conversations concerning adoption.

Evaluation-Choice Stage

4. Acquisition proposal. Adoption of equipment embodying the innovation is

proposed formally.

5. Medical-fiscal evaluation. The proposed investment is evaluated according to

medical and financial criteria.

6. Political-strategic evaluation. The proposed investment is evaluated according to

political and strategic criteria.

Adoption-Implementation Stage

7. Trial. The equipment is purchased but still under trial evaluation.

8. Acceptance. The equipment becomes well accepted and frequently used.

9. Expansion. The equipment is expanded, upgraded, or replaced with a second-

generation model.

It is important to understand which stage a new technology is in because each stage could have

different moderating effects of acceptance factors or organizational challanges. For example, Meyer

and Goes showed that in later stages of innovation process, innovations were found to be lower risk,

but more complex (Meyer and Goes 1988).

Overall, these different phased frameworks for organizational adoption are helpful in understanding

the serial process that an organization goes through, but they do not address that different decision



makers in the organization could be at different stages or explain the impacts of the connections and

interactions between these decision makers.

Complexity of adoption at the organizational and individual level

Given the wide range of research, individual technology acceptance can be complex by itself, but

then combining the complexities of organizational adoption makes it a quite daunting at an

aggregate level. In Ram and Jung’s research on forced adoption, they suggest that the organizational

adoption of innovations is more complex than individual adoption because of factors such as the

higher number of stages involved in the adoption decision, and the effects of organizational

structure (Ram and Jung 1991). This complexity of combining organization and individual decisions

often does not account for the longitudinal dynamics at different stages, non-adoption and

dependencies between the two decisions, although Frambach and Schillewaert’s multi-level

framework is attempts to address some of these shortcomings.

The longitudinal nature and process of organizational and individual adoption is not widely

understood, which adds additional layers of complexity. Most studies focus on dichotomous

adoption/non-adoption process but we know little about the effect of different factors at the various

stages (Olshavsky and Spreng 1996). In addition these studies also only include a few indirect factors

and direct factors (Frambach and Schillewaert 2002) and limit the study to either the organizational

adoption or individual acceptance level.

Non-adoption within either of the organizational decision and individual acceptance is also a

shortcoming in research. Research on both decisions primarily focus on adoption which avoids

dealing with the complexities of non-adoption, with some exceptions such as Stevens et al.’s

research on the consumer voluntary setting on the non-adoption of ATMS (Stevens, Warren et al.

1989).

In an organization there are significant dependencies between the two decision processes at both

the organizational and individual level. For example the managers involved to the organizational



communicating some of its benefits and appropriate use (Olshavsky and Spreng 1996)

Frambach and Schillewaert’s 2002 research on organizational innovation adoption: a multi-level

framework of determinants and opportunities for future research provided two levels of acceptance

factors to address both the individual and organizational acceptance which are shown in Figure 3

Organizational Level Acceptance Factors from Frambach and Schillewaert’s 2002 and Figure 4

Individual Acceptance Factors from Frambach and Schillewaert 2002 (Frambach and Schillewaert

2002). This is a significant step that could help in the critical early phases where the organizational

decision and individual decisions could still be in flux. One shortcoming of this model is that these

two frameworks do not address the interdependencies between the two decision processes when

they overlap or how the individual perceptions are directly impacted by their social connections to

others.



Figure 3 Organizational Level Acceptance Factors from Frambach and Schillewaert’s 2002



Figure 4 Individual Acceptance Factors from Frambach and Schillewaert 2002

Important factors for organizational adoption

Organizational adoption is significantly different then user acceptance and the decision process

could vary from organization to organization because different factors or technology attributes could

be important to each. Anderson and Narus indicated that the factors that an organization considers

when adopting an innovation are the perceived benefits and economic incentives and if they exceed

that of alternatives (Anderson and Narus 1999; Frambach and Schillewaert 2002).

The perceived net benefit affects the adoption rate for organizations (Robinson 1990). These

perceived benefits can be interpreted differently for each organization. An example of this can be

seen in the research by Mansfield on the diffusion of flexible manufacturing systems in Japan,

Europe and the United States. In this research it was discovered that the United States adopted

flexible manufacturing systems at a slower rate than other regions and one of the reasons cited was

that often the US companies did not account for the improvements in quality as a significant benefit,

whereas other regions did (Mansfield 1993).

Organization characteristics also impact organizational adoption, such as organization size (Kennedy

1983), organization structure (Zaltman, Duncan et al. 1973), and organizational innovativeness

(Morrison 1996). Organizational structure can either help or hinder innovation adoption. For

example in a more formal and centralized organization, they are less likely to adopt an innovation,

but they are better equipped to manage it if it is adopted (Zaltman, Duncan et al. 1973). The

opposite holds true for specialized or highly complex organizations. Organizational Dispositional

Innovativeness (ODI) and Leading Edge Status (LES) influence time to adoption. Supplier marketing



activity also drives adoption by organizations (Frambach, Barkema et al. 1998) as do the marketer’s

choices on targeting the innovation, its communication, and activities to reduce the perceived risk

(Easingwood and Beard 1989). Reducing the risk might include trial without purchase or the supplier

absorbing the risk. By reducing the Implementation risk, financial risk and operational risk, the

innovation adoption can be improved (Frambach and Schillewaert 2002)

Several other important factors could also impact the organizational adoption. The higher the

degree of interconnectedness and information sharing, the more likely organizations will be exposed

to new ideas and products. Organizations also look for network externalities or critical mass (Markus

1990; Rogers 2003). Environmental or influences such as business partners in their network have

used a system or competitive pressures. Some research has indicated that high levels of

competition stimulate innovation adoption for organizations, which was operationalized by

measuring the communication openness of industries (Gatignon and Robertson 1989).

Clearly, there are many factors that drive organizational adoption and how an innovation is

perceived, but the research of Markus, Gatignon, and Robertson start to suggest that one’s network

connections or communication openness help drive adoption of new innovations but are unable to

show it directly.

2.2.2 Strength of Weak Ties

The strength of weak ties plays an important role as a construct in organizational innovation.

Granovetter’s (Granovetter 1973) work focused on the importance of weak ties rather than the

more researched strong ties to understand their cohesive power, their unique role of tying groups

together across organizational boundaries. This underlying theory also focuses on using micro level

network analysis with these weak ties to show their impact on macro level. This concept of a weak

tie network brings a very hidden dimension of the network that is more sparse, non-visible, but plays

a more important role when it comes to diffusion, communication, mobility, and general social

cohesion that holds the overall structure together (Granovetter 1973).

The verity of this research does ring true and does a good job using prior research to qualitatively

check the assertions of the strength of weak ties by providing a sound background on what weak ties

mean, prove some of the underlying concepts and assertions of weak ties, then bringing those forth

and validating them in different contexts, and holds together well as foundational construct to add

significant clarity at a broad scale to the structure of social graphs, which one would not see through

strong ties. The following nine insights and concepts within the strength of weak ties help address

this particular research study.

First, sociometry is a quantitative method for measuring social relationships which is the precursor

of network analysis with roots to researchers such as Jacob L. Moreno who used early social network

graphs in his work as early as 1934 (Moreno 1934). One criticism for sociometry was its primary

focus on smaller groups and lack of focus on structural issues with any theoretical detail

(Granovetter 1973).

Second, Granovetter defines the strength of a tie as “quantitative method for measuring social

relationships”(Granovetter 1973). Ties can be strong, weak or absent, but there can be multiple



contexts or modes of a relationship as show in Sykes et al.’s work on influencing and coping modes

of networks (Granovetter 1973; Sykes, Venkatesh et al. 2009)

Third, a strong or weak ties not only tells you something about the compatibility of two actors, A &

B, it also indicates a likelihood of compatibility of others who might already have a connection to B,

that they would more likely to be compatible if they met (Granovetter 1973).

Fourth, Cognitive balance (Newcomb 1961) suggests that if strong ties exist between A & B and A &

C, it would be expected that if B&C started interacting, it would be expected that it would be a

positive interaction, or else it would introduce a "psychological strain" into the triad (Newcomb

1961; Granovetter 1973), but this is less crucial for weak ties, so the constraints on their neighbors

are reduced based on the relations.

Fifth, bridges are weak ties between two networks and happen less frequently, essentially it’s when

A&B have strong ties, A&C have strong ties, but B&C do not have a tie. This happens rarely in larger

networks. As people know each other longer, there is an increased occurrence of triads with three

strong reciprocated ties. When bridges are removed, they impact the diffusion of information more

than other nodes(Granovetter 1973).

Sixth, there is a critical distance along a network where it is not feasible to communicate due to

distortion within each transmission (Harary 1965; Granovetter 1973) So transmission are limited to

n-degrees, so connections or "local bridges" of shorter n-degrees play an important role in

communication processing although they are not a "bridge" to the earlier definition by being the

only path.

Seventh, when collecting the dyad or network connections from individuals, a researcher can

provide a list of individuals or take an open question approach. By taking an open question

approach to the naming of other individuals in the social network questions in trying to identify the

coping and influencing networks, it is hoped to identify more of the weak connections between the

network nodes which often are not identified with using a more limited list of participants which are

often strong ties (Granovetter 1973).

Eighth, by monitoring the time of adoption or acceptance by those individuals within a network, the

path of acceptance can be inferred (Granovetter 1973).

Lastly, often early innovators "under-conform" to social norms so they are often marginal not central

to the network. In Coleman et al’s study on publish health innovations, it was found that central

figures lead in the adoption more often when it was safe and uncontroversial, otherwise non-

central actors might champion or lead the initial adoption of a higher risk technology(Coleman, Katz

et al. 1966). In this study its suggested that central figures desire to protect their

reputation(Granovetter 1973) This would suggest that a controversial innovation will start with the

innovative marginal resources with few weak ties and likely stay within a few cliques, but those that

are less risky will expand and diffuse thought the central figures quickly.

Overall, weak ties are the lattice that ties together the larger network, which was quantitatively

shown based on a study by Raporort and Horvath (Rapoport and Horvath 1961; Granovetter 1973)

of high school students, more people could be reached through the weak ties then the strong ties in

terms of friendships. Weak ties hold together the larger social structure.



2.3 Diffusion of Innovations and Technology Acceptance

Individual acceptance or adoption of technology has also become a more important research area

after organizational adoption. As users are enabled to accomplish more through technology then

before, which Kantor refers to as the democrazation of IT, this new functionality to accomplish more

tasks has added complexity (Kanter 2000). It’s quite common for companies to spend many times

as much on implementing software as they do in buying it (Kanter 2000).

2.3.1 Technology Acceptance Model

The Technology Acceptance Model (TAM) is the more predominant theory that is currently used for

the adoption or acceptance of new technologies. Its origin can be traced back to Davis’s 1986 thesis

work (Davis 1986) where he looked to find out “What are the major motivational variables that

mediate between system characteristics and actual use of computer-based systems end-users in

organizational settings?" (Davis 1986). Davis heavily relied on the earlier works of Fishbein’s in the

1960s and 1970s on attitudes and motivational aspects of behaviors.

TAMs conceptual models vary substantially but usually are based on the independent variables of

subjective norm, perceived ease of use, and perceived usability, which drive the behavioral intention

of the potential technology users.

As the TAM models mature and evolve, some more recent research is bringing in other independent

variables and giving more direct managerial guidelines such as Limayem’s work on “How Habit Limits

the Predictive Power of Intention: The Case of Information Systems Continuance” (Limayem, Hirt et

al. 2007)

Another common area of evolution and research of the Technology Acceptance Model is around the

explanatory power of technology acceptance. Sun and Zhang’s research on “The role of moderating

factors in user technology acceptance” (Sun and Zhang 2005) exposes the limitations of explanatory

powers and the inconsistencies between prior studies as well as highlights the moderating factors

that account for both the limitations of the explanatory power and the inconsistencies. Sun

highlights that existing studies account for less than 60% of variance explained, especially those

using field studies with professional users. The explanatory power of field studies is less than 40%.

In this research subjective norm is a network variable.

The impact of the social influence on behavioral intentions has several significant findings. Prior

research by Davis and Bagozzi (Davis and Bagozzi 1989) and Mathieson(Mathieson 1991) found no

significant direct effects, but Venkatesh and Davis(Venkatesh and Davis 2000) showed both direct

and indirect effects of social norms. For example the subjective norm factor increases dramatically

from .05-.06 to .20-.25 from voluntary to mandatory settings, which is the more common situation in

organizational technology acceptance(Venkatesh, Morris et al. 2003). This is also consistent with

previous research on critical mass. Critical mass has long been a driver of technologies where the

utility increases with increased users, such as communications technologies (Markus 1990; Katz and

Shapiro 1994; Frambach and Schillewaert 2002). It’s important for a critical mass within a person’s

reference or work group which motivates the user to imitate the action (Kraut, Rice et al. 1998).



A more recent milestone in the evolution of TAM was Venkatesh’s research on “User Acceptance of

Information Technology: Toward a Unified View” where he reviews and combines 8 models into the

Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris et al. 2003). This

is a significant step in technology acceptance research due to its attempt at unifying existing models,

reviewing the scales for each dependent variable, and leveraging two rounds of empirical data to

show the variations in the explanatory power of the moderating variables. The UTAUT was used as a

foundation in this research because of its maturity and acceptance.

In addition to the more common factors of Performance Expectancy and Effort Expectancy which

has been a common thread in most user acceptance and innovation adoption, the UTUAT has put

additional focus on the Facilitating Conditions and Social Norm. The facilitating conditions could

include such as training and education (Igbaria 1993), organizational technical support (Davis and

Bagozzi 1989) and incentives and control structures (Bhattacherjee 1998)

Figure 5 - Conceptual Model proposed for Traditional Unified Theory of Acceptance and Use of Technology

TAM has been the predominant model for technology acceptance in the last 10-20 years, yet the

social influence still has mixed results in terms of its significance. This is why this research attempts

to use TAM and Social Network Analysis of the Influencing and Coping network to triangulate and

better understand technology acceptance.

2.3.2 Diffusion of Innovations

Prior to the gradual emergence of TAM in the 90s, Diffusion of Innovations had been the

predominant research framework for the acceptance and adoption of technologies or innovations.

The most widely known expert is Everett Rogers who has made substantial contributions including

books such as Attributes of Innovations and Their Rate of Adoption (Rogers 2003) and the Diffusion

of Innovations originally published in 1962 which is now in its fifth edition (Rogers 2003). The



Diffusion of Innovations has provided a foundation as a predecessor and alternative model to the

Technology Acceptance Model as well and is still a widely used framework in its initial form in the

adoption of new innovations. For example Ko and Kim has recently used it to identify the status of

Customer Relationship Management (CRM) adoption and explore the influence of organizational

characteristics on the CRM adoption process in the Korean fashion industry (Ko, Kim et al. 2007).

With Roger’s terminology for diffusion of innovations, adoption refers to the decision of any

individual or organization to make use of an innovation, whereas diffusion refers to the accumulated

level of users of an innovation in a market (Rogers 2003)

The Diffusion of Innovations conceptual models have slight variations, but primarily focus on various

moderating factors and the independent variables of observability, trialability, compatibility,

complexity, and relative advantage. Overall this is a nice complement to TAM in situations where a

researcher might be looking to understand the organizational adoption or where the innovation is

not technology specific, such as “wellness” or “organic farming”. A significant limitation to the

Diffusion of Innovation is its lack of understanding or recognizing network impacts or how individuals

or organizations could be influenced by their colleagues or other connections.

2.3.3 Personal Innovativeness

Personal Innovativeness is an important area of research by itself, as it varies between people.

There are several key findings around Personal Innovativeness that could have network contexts or

add to the understanding of the coping or influencing networks within an organization.

When implementing new technologies to smaller groups of individuals, organizations want to target

the most innovative people. This criticality of personal innovativeness on the success of new

innovation adoption supports the importance of creating an environment that rewards

innovativeness and risk taking (Agarwal and Prasad 1998). For systems that need to be

implemented by the entire organization, it’s important to spend more time at the earlier stages to

understand the patterns and flows that are compatible with the less innovative individuals, which

would suggest joint application development or prototyping to better elicit preferences (Agarwal

and Prasad 1998). This is essentially a socio-technical approach to system design (Mumford and

Henshall 1979)

When investing time to improve the acceptance of technologies to those who are less innovative,

you should focus on developing approaches to improve relative advantage first since it is a

significantly larger factor then ease of use (Agarwal and Prasad 1998).

Overall, Personal Innovativeness could help add to our understanding of why certain people

frequently tend to have central network roles in bringing in new technologies. Ibarra suggests that

network centrality mediates the involvement in innovations (Ibarra 1993), meaning that more

central figures more often have roles in innovation projects in companies.

2.4 Social Network Analysis

In this section we cover the significant research areas of social networks and social network analysis

that are relevant to this research. For this discussion, the significant research areas are divided into



8 areas; the origins of organizational science, network theory, structural hole theory, social capital,

Simmilian ties, centrality, and agent-based modeling.

Before discussing the research, it’s important to understand potential limitations. First, networks

themselves are embedded in larger contexts (Granovetter 1985; Brass, Galaskiewicz et al. 2004). For

example, mergers and acquisitions are environmental jolts that can have significant impacts on the

informal coping networks (Danowski and Edison-Swift 1985; Brass, Galaskiewicz et al. 2004), this is

also true for downsizing (Shah 2000). Second, Networks have been shown to be made up with

actors that have significant similarity or homophily as individuals tend to interact with others

depending on how similar they are to themselves (Mehra, Kilduff et al. 1998; Brass, Galaskiewicz et

al. 2004). This might be an underlying cause of clustering of perceptions by neighboring actors.

Third, networks often shadow the physical proximity to others (Borgatti and Cross 2003). Lastly,

attitude formation and change occur primarily through interaction with others, so as people cope or

try to make sense of something they compare their perceptions with those they interact with

(Erickson 1988).

2.4.1 Organizational Sciences – Hawthorne Studies

Social Networks had their origin within organizational science dating back to the 1920s when

Harvard Business School started a 10 year series of anthropological investigations at the Hawthorne

Works Factory of Western Electric in Chicago. This was the first time Sociographs were used to

diagram the social interactions (Roethlisberger and Dickson 1939; Kilduff and Tsai 2003). This is

essentially a great analysis of the significance of cliques which was observed through the game-

playing and friendship networks of 14 workers.

These 14 workers in a bank wiring room essentially were aligned to either the "front of the room" or

"back of the room" groups or cliques. The groups appeared to be organized by their location, which

cut across job titles, but it was found that each group established their own social norms which they

conformed to such as snack preferences, work behavior, and productivity (Roethlisberger and

Dickson 1939; Kilduff and Tsai 2003).

The Hawthorne studies were also a good example on Cognitive Network Theory, which is the

perception of how one perceives the network. Cognitive Accuracy is how close that individual's

perception is close to reality (Kilduff and Tsai 2003). This was also seen in the early Hawthorne

studies where management would act incorrectly without knowing the true workgroup positions

which would create negative sentiments and unexpected consequences (Roethlisberger and Dickson

1939; Kilduff and Tsai 2003).

The Hawthorne studies opened up many questions for organizational researchers in terms of

culture, norms, and workplace behaviors. This is essentially a post-structuralism approach where

the goal is not to find the statistical significance or search for the ideal quantifiable model, but

instead to find meaning and open up new questions (Borgatti, Everett et al. 2002; Kilduff and Tsai

2003). In the case of technology acceptance studies using social network analysis, we should

consider this post-structuralism approach to help uncover new knowledge about how perceptions

and networks are formed in this very dynamic and confusing environment of conditional acceptance

of technologies within organizations



The early foundation of organizational science and the importance of groups in understanding the

norms and behaviors of employees as far back as the Hawthorne studies have significant relevance

and insight to introducing new innovations or technologies into organizations today.

2.4.2 Network Theory

Network Theory is often challenged debating if it’s a theory at all, but rather a set of descriptive

approaches or methodologies (Borgatti, Mehra et al. 2009). Regardless of one’s perspective,

networks and their structural analysis continue to bring more insight to managerial and research

contexts. This can be seen in organizational innovation or how new products or innovations are

promoted through these informal networks.

Social Network Theory brings in the concept of social capital and the impact of social networks on

innovations both within and between organizations. Petrakos and Olson’s research on “How inter-

firm collaboration benefits IT innovation” highlights that performance and behavior of a firm due to

innovation can be better understood through analyzing the network. This research is an important

tie between the impacts of innovation and the social network of individuals in an organization

(Patrakosol and Olson 2007).

Social network theory also overlaps into the idea of viral marketing as an effective channel for

marketing innovations and new technologies. Leskovec presents a model that successfully identifies

communities, product, and pricing categories for which viral marketing seems to show how the

networks involved in viral marketing create a “recommendation” network. This research also

discusses the structure of viral network which can be driven by an internet community for a

profession or geography more than some of the basic demographics (Leskovec, Adamic et al. 2007).

Overall, social network analysis or network theory provides a means to understand the structure and

channels of communications that enable individuals to cope and be influenced by their colleagues

and other connections regarding new innovations or technologies.

2.4.4 Structural Hole Theory

Structural Hole Theory effectively originated in 1992 from the work of Ronald S. Burt with his book

on Structural Holes: The Social Structure of Competition (Burt 1992). The theory is based on industry

research on supply chains where it was observed that individuals with many connections in a

competitive supplier/distributor/consumer network gain a financial competitive advantage through

their network and the position of his contacts. This advantage is manifested through better

information and control of the interactions (Burt 1992).

From a social network view, structural-hole positions in a network are the individuals (or players)

that connect two different groups. This allows them to potentially benefit from a more

heterogeneous flow of information from multiple network groups and potentially have more control

of information or influence within the larger network (Burt 1992).

In Burt’s work, the competitive advantage from information and control allowed the individual to

have insight into more business opportunities than others and are able to exert more influence or

control of the network through the connections and positions of their contacts. This competitive

advantage then allowed them to enjoy a higher rate of return on their investments (Burt 1992).



As the influence and control on the network shifts from producers to these suppliers and customers

that fill structural-hole positions, the profit also shifted to them. Leveraging these structural hole

positions was not done for the majority of the transactions, but instead was used for the big

transactions because the bulk of business is within a few transactions (Burt 1992). This could have

relevance on individuals during either the organizational or individual acceptance of a technology,

where someone filling a structural-hole position might only leverage this position for a select few

decisions.

For the benefits of the Structural Hole Theory to hold as an autonomous model, three points need to

hold true. First control is via exclusive access to ties that other players do not have similar access to

the networks and information. Second, the structural autonomy is non-linear function of constraint

that decreases most rapidly with the loss of structural holes, so these are the relationships that are

most important. Third, it should be difficult for competition to get inside of the boundary, or gain

access to these positions.

This can also be translated to Technology Acceptance because organizations can be highly political

and often do not offer the same level of benefits to all users within an organization; for example a

new system to ease time reporting for a call center could add work for the finance group. Knowing

this, if a finance employee was occupying a structural-hole position they use have significant

influence on the organizational or individual acceptance.

The structural hole theory and the impact of structural holes is similar at both the micro (individual)

& macro (organizational) levels (Burt 1992). This parallels the duality of technology acceptance

which has challenges at both the organizational and individual level (Frambach and Schillewaert

2002).

This research also makes many other steps forward in social network positions that are relevant to

general organizational and individual performance could help interpret the coping and influencing

networks within an organization. Managers filling many structural-hole positions get promoted

faster. Structural-hole effects are most evident working on social frontiers where two worlds meet.

This could be where entrepreneurial wits are needed as opposed to homogeneous worlds (Burt

1992). The benefits of occupying structural holes are more important for remote offices and to new

mangers that have been at the company for less than 3-4 years (Burt 1992). Higher level positions in

an organization mean that leaders will often be between the frontier boundary of an employer

protected by the firm and a leader responsible for the firm. The social frontier into the top

leadership is the most significant boundary to bridge in an organization.

This research also supports the importance of a strong mentor or strategic partner in the

organization for individual success. The earliest promotions come to those who have a strong

strategic partner who is a non-boss. This strategic partner allows the individual to leverage the

network and structural hole positions of the strategic partner as well, essentially they are using a

borrowed network (Burt 1992). It is good if the strategic partner is not a boss which reduces the

possibility of the relationship going sour. This relationship can also be leveraged for negotiation if

there's a disagreement with the individual’s boss and the strategic partner is a more compelling

reference for the individual (Burt 1992). This also potentially parallels technology acceptance within

an organization because if a new technology does not have a good strategic partner or sponsor, it

has less likelihood of being successful.



Overall, structural hole theory has a rich set of ideas when it comes to performing a structural

analysis of informal networks within an organization, especially when it comes to new innovations or

technologies that are somewhat controversial within an organization.

2.4.5 Social Capital

Social Capital is a broad concept that means many things to many people (Narayan and L. 1997) and

the research has been growing over the last three decades in the fields of organizations, families,

youth behavior, education, public health, community, governance, economic development,

intellectual capital, and collective action in general (Adler and Kwon 2002). Within Organizations,

social capital has been researched for career success, finding jobs, product, team effectiveness,

turnover rates, and supplier relationships innovation (Gabbay and Zuckerman 1998; Tsai and

Ghoshal 1998).

Adler and Kwon reviewed the existing social capital research from different fields to propose a

conceptual framework that identifies the sources, benefits, risks and contingencies to help clarify

and improve the utility of social capital. This conceptual framework contributes to the

understanding of social capital by bridging domain fields and many different perspectives of social

capital research which is shown in Figure 6 Social Capital Framework Propose by Adler and Know

2002.

One challenge is defining social capital; Adler and Know reviewed 23 definitions of Social Capital

from previous research that spanned from internally focused, neutral, or externally focused sources

of social capital. Given these various perspectives from previous research and the characteristics of

Social Capital, Adler and Know proposed the following definition: "Social Capital is the goodwill

available to individuals or groups. Its source lies in the structure and content of the actor's social

relations. Its effects flow from the information, influence, and solidarity it makes available to the

actor."(Adler and Kwon 2002)

Relative to technology acceptance, this study explains the solidarity side of social capital that could

impact acceptance of new technologies due to intergroup loyalty, strong bonds, and the

development of shared norms. It fits within the context of other research in how it ties into social

network constructs such as strong or weak ties.

Social Capital is categorized within three dimensions of social structure; market relationships where

products & services are exchanged, hierarchical relations where authority is exchanged for materials

or spiritual security, and social relations where favors and gifts are exchanged (Adler and Kwon

2002).

According to Adler and Know, Social Capital is characterized by seven attributes. First it’s a long-

lived asset which resources can be invested either externally or internally and has the expectation of

a future flow of benefits. These benefits could be access to information, power, solidarity, collective

identity, or capacity for collective action. Second social capital can also be converted, such as using

friendship capital to get a job. Third, it can compensate or replace other resources, such as

minimizing transaction costs. Forth, it requires maintenance. Fifth, it can be a collective good, which

benefits everyone or a group such as collective bargaining. Sixth, it is located within relationships



are not owned by only one individual. Lastly, its investments and benefits are difficult to measure

(Adler and Kwon 2002).

Group solidarity as an outcome of social capital could have a significant impact on technology

acceptance networks. If networks where highly connected it facilitates the emergence of effective

norms and maintains trustworthiness of others, and in turn increasing social capital as a group

(Coleman 1988). These strong ties and solidarity could also limit the inflow of new ideas into the

group (Gargiulo and Bernassi 1999; Adler and Kwon 2002). Alternatively if the network was less

connected it would be a less trustworthy and violations of norms will be less visible and not

punished (Coleman 1988), thus weakening social capital.

There is also a risk that those individuals who have roles of bridging groups as a focal actor could

limit the information flow into the group even though they individually have significant social capital

(Gabbay and Zuckerman 1998; Adler and Kwon 2002). A group with strong internal bonds and weak

external bonds will have high social capital, but will inhibit new ideas. This is suggesting that in an

organizational change or technology acceptance initiative, a group with high solidarity could hinder

the acceptance if it doesn’t benefit them (Krackhardt and Stern 1988; Adler and Kwon 2002).

Alternatively a group with weak internal bonds, but strong external bonds, there is little solidarity or

social capital and minimizing the likelihood of collective norms (Adler 02).

Figure 6 Social Capital Framework Propose by Adler and Know 2002

Related to our research problem, where we are trying to gain understanding of the influencing and

coping networks within an organization, the construct of social capital is important to understand

when understanding how tightly connected groups either help or discourage a new innovation at

either the organizational or individual level.

2.4.6 Simmelian Ties



Simmelian ties, similar to structural holes, isolates, or cliques are roles that are identified through

social network analysis. A Simmelian tie is defined as two people who are reciprocally and strongly

tie to each other and both people are reciprocally and strongly tie to at least one third party in

common (Krackhardt 1999). This definition might look like a 3-person clique. Krackhardt’s study

suggests that occupying the bridging role is not always advantageous and can be more constraining

because of Simmelian Ties.

Simmel suggested that social triads are fundamentally different then dyads (Krackhardt 1999). First,

dyads preserve the individuality of both players and can't be out-voted. Second, within a dyad an

individual has more bargaining power, they can leave, but it doesn't dissolve the group. Third,

conflict is better managed in a triad, unlike in a dyad where the positions could become very polar;

the presence of the third party is very moderating.

There is little difference between a triad and larger cliques, but there is a dramatic difference

between a dyad and triad (Burt 1992; Krackhardt 1999). Within the context of innovation adoption

or acceptance of technologies within an organization, if there is conflict on whether to accept new

technology, these triads could create significant influence on the organizational and individual

decision process.

People are constrained not just by their role but also the rules that govern how a role set should act

(Krackhardt 1999), these would be the structurally equivalent people. A bridging person between

two cliques is then constrained by the sets of norms in both groups. If these two cliques have

different perceptions or benefits/impacts of using a new technology, this puts the bridging person in

a difficult situation. Essentially the more cliques a person is embedded into, the more constrained

they are. A tertius is someone who takes advantage of what two different actors know and can

make deals independently with each group without the other knowing. If a person in a Simmilian tie

changes his/her behavior to a private behavior, they are less constrained (Burt 1992; Krackhardt

1999), which could explain why a person might not speak up against an innovation during a large

meeting, but will in one on one conversations.

Simmilian ties are an important structural role when it comes to organizations and innovations,

although not strongly researched for technology acceptance or innovation adoption. Krackhardt

introduces several key learning’s within the context of a union vote within an organization in

researching Simmilian Ties in his work; the Ties that Torture (Krackhardt 1999). One could consider

a union vote as an important innovation within an organization that requires organizational

acceptance and individual acceptance similar to new technologies.

The strength of this research is that it describes an organizational situation using the union vote

context and the difficulties of trying to satisfy the norms of multiple groups. For an individual this

situation stressful and close to impossible in satisfy with the individual’s public behaviors (Frambach

and Schillewaert 2002). In the union vote one of the most central figures, Chris, was tightly tied to

cliques that had polar opposite opinions of the union vote; the management and employees. The

difficult situation left him no choice but to quit 10 days before the vote and rejoined 2 days later. He

had power, but was constrained publically to use it (Burt 1992). It is important to address this role

and discuss the importance of the private/public behavior differences in organizations going through

change such as new innovations or technologies.



2.4.7 Centrality

The concept of centrality is a frequently used social network metric on individuals or networks

relative to their position or network structure. Informal structure has been shown to be more

important than the organizational formal hierarchy, especially when multiple network boundaries

need to be spanned and innovations are a source of power (Ibarra 1993). It is most commonly used

to understand the most important actors and distribution of power or influence in a network. For

innovations or new technologies, there are several areas of centrality research that would benefit

the structural analysis of the informal coping and influencing networks.

A network itself that is centralized has a highly central actor around which peripheral actors collect

around (Borgatti 2002). This centralization is essentially a network-level measure that reflects the

variability scores among unit (Marsden 1990).

Three common measures of centrality are betweenness centrality which measures the control or the

capacity to interrupt communication, closeness centrality measures reflect freedom from control of

others, and degree centrality which is a measure that reflects a network size focus on the level of

communication activity (Freeman 1979). Innovations propagate in networks as information so

betweenness centrality is most commonly used for control of information in networks regarding

change or new innovations.

Centrality of individuals in networks has been shown to have many benefits. With higher levels of

centrality an individual has a larger base of potential power which they can use to affect outcomes if

they choose (Salancik and Pfeffer 1977; Mintzberg 1983; Ibarra 1993). These individuals also have

an increased sense of a systems power distribution and political landscape (Freeman, Romney et al.

1987; Ibarra 1993). Similar to social capital and occupying structural holes, one's power also

depends on who you are linked to (Brass 1984), and ties beyond your own workgroups has shown to

increase one's influence as well.

Looking at innovation adoption specifically, Ibarra also suggests that network centrality mediates the

involvement in innovations (Ibarra 1993), meaning that central figures more often have roles in

innovation projects in companies. This centrality helps get innovations approved and implemented.

Kanter uses a term called “innovation power tools” which refers to three sets of resources that help

drive innovations; information, resources, and support (Kanter 1988).

Overall centrality has been used to help understand many research areas such as innovation,

control, power, or influence. It has significant overlap with the other network constructs of social

capital, Simmilian ties, and structural-hole theory because they all focus on central or bridging

positions within a network. Its role with innovation is also discussed in the later section on Model of

Acceptance and Peer Support.

As a quantifiable measure, centrality is important and tempting to focus our resources on new

innovations only to those with high centrality, but there are limitations to heavily relying on

centrality to draw conclusions regarding innovation or technology acceptance without context. The

largest concern is that researchers need to understand the underlying meaning or actors perceptions

regarding the innovation or new technology. If a researcher cannot clearly see the network

structural inhibitors to a new technology or convert results in to actionable recommendations how



to improve the acceptance of a new technology within a network, then they might not be close

enough to the details and relying too heavily on centrality or other quantifiable metric.

2.4.8 Agent-Based Modeling

A related approach to social network modeling is agent-based modeling. Janssen and Ostrom

describe Agent-based modeling as the computational study of social agents as evolving systems of

autonomous interacting agents. It also is frequently been used as a quantitative methodology for

social science research (Janssen and Ostrom 2006) .

ABM has been used to simulate the efficacy of different promotional strategies that support the

launch of products. It could be used as an alternative or in conjunction with to social network

analysis to operationalize the interactions of a network via its mathematical structure of its utility

equations, and potential of providing explanatory power at both the micro and macro level from a

single model.

Delre’s research on “Targeting and timing promotional activities: An agent-based model for the take-

off of new products” (Delre, Jager et al. 2007), effectively utilizes agent-based-modeling in

promotional planning, optimal targeting strategy, and the study of both micro and macro dynamics

systematically. It provides a basic framework for simulation of take-off strategies for new products

into a network of consumers and their individual decisions. This is in contrast to more traditional

research approaches that tend to focus less on market determinants, such as consumers'

preferences and social factors because they are more difficult to measure (Delre, Jager et al. 2007).

The combination of these consumer attributes and ABM approach further enable the explanatory

power of comparing different technology acceptance take-off strategies into networks of

interconnected groups. In this research it demonstrated the benefits of targeting small cohesive

groups of consumers in distant areas of the market as the optimal strategy which was referred to as

“throwing rocks rather than gravel” (Delre, Jager et al. 2007).

ABM is a promising approach to simulate a promotional approach and the technology acceptance of

a new technology into a network if we know how the end users are connected and how they are

influenced by the network. Alternatively Social Network Analysis or modeling would be a more

promising approach if we are trying to gain understanding how individual are influenced by the

network.

2.5 Technology Acceptance Integrating Social Network Analysis

2.5.1 Model of Acceptance with Peer Support

The Model of Acceptance with Peer Support (MAPS) was a significant recent research step that

combined belief-based Technology Acceptance Model, organizational innovation, and Social

Network tools. This was introduced in the 2009 work of Tracy Sykes, Viswanath Venkatesh, and

Sanjay Gosain described in the Journal article titled Model of Acceptance with Peer Support: A Social

Network Perspective to Understand Employee System Use. Its purpose was to develop a model of

system use, accounting for the coping network, influencing network, and the individual-level

technology adoption research as shown in Figure 7 - Model used in Sykes et al. 2009. It



incorporated these key social network constructs then empirically validated the proposed model in a

field study where it benchmarked the model against an intention-based model of individual-level

adoption and use.

Figure 7 - Model used in Sykes et al. 2009

Two key calculations used are network density and network centrality. Network density describes

the connectedness of a network which is the actual number of ties in a network as a proportion of

the maximum possible number of ties. Network centrality was defined as the extent of an individual

involvement in assistance exchanges with coworkers (Sykes, Venkatesh et al. 2009). This focus on

the central resources is consistent with other research which has shown that for successful diffusion

of a new technology, it is important to form positive impressions to central actors of a social network

(Burt 1987; Papa 1988; Lee, Cho et al. 2003)

Network Density was further evolved to Valued Network Density which is the connectedness of an

employee to others, weighted by the perceived strength of the tie and the neighbors control of

system-related information, knowledge, and other resources needed for effective system use (Sykes,

Venkatesh et al. 2009).

The two concepts of coping (knowledge sharing) and influencing (changing system) network modes

can be viewed via social network tools as two independent sets of connections and networks. The

resources and help within the networks are typically unavailable to those on the periphery with

longer pathways or isolated from the network altogether (Ibarra and Andrews 1993; Sykes,

Venkatesh et al. 2009).

This influencing network is generally used to access individuals that have information, knowledge,

and other resources regarding the new technology or innovation. Those that are perceived to have

access to these resources are in central structural positions in the influencing network and will have

more influence over system design to meet their needs. This process of influencing a technology to



meet your needs is often labeled as co-adoption, which also drives up the usage of those involved in

the co-adoption.

Alternatively, the coping network is used to help users to cope with the change. This could include

all thoughts and behaviors performed by a person in order to manage (minimize, reduce, master,

tolerate) demands emanating from a specific change in the person-environment relationship

(Lazarus 1966). Coping theory is used to predict the behavior of individuals facing disruptive events

(Lazarus 1966) and has two major functions: the management of the problem and regulation of

emotion (Beaudry and Pinsonneault 2001). Problem-focused acts are oriented at changing the

environment that is impacting the person (Lazarus and Folkman 1984; Beaudry and Pinsonneault

2001). Emotion-focused acts are directed at internally changing aspiration, learning new skills,

adjusting behaviors, and others (Beaudry and Pinsonneault 2001). People use both types of copying

in the most stressful situations, although men might use more problem focused coping in a work

environment (Folkman and Lazarus 1980) (Folkman and Lazarus 1985; Sykes, Venkatesh et al. 2009).

The sample size of this research was 87, where the social network data was collected using a roster-

based sociometric approach employing a fixed contact roster and asks respondents to describe their

relationships. It employed a "help network matrix" to determine extent of help-seeking and help-

giving, which is important because the different network modes. The different network modes

allowed the research to uncover the different central roles in the network. A participant can be

central to a help network, but have little access to system resources or control information. The five

modes used here are get help, give help, knowledge, resources, and information. This approach has

the benefit of capturing more of the potential connections between the identified participants, but

also does not allow for identifying potential connections that are not part of the study.

The leadership that interacted with the researchers was not included in the study in order to avoid

bias, but removing the leadership from the network does remove a potentially significant sector of

individuals that could have significant influence over facilitating conditions or could have central

roles as well. (Sykes, Venkatesh et al. 2009)

The research showed that the social network constructs are a significant factor in the variance of

acceptance in organizations and helps get to a higher R^2 then just facilitating conditions. Similar to

many studies using social networks, the sample size was relatively modest with 87 employees in one

business unit in an organization. Based on the research, the behavioral intention or system-use was

shown to increase with network centrality, so “actors who are centrally located within organizational

networks enjoy a broad array of benefits and opportunities” (Sykes, Venkatesh et al. 2009).

New technologies systems can also be viewed a disruptive event (Beaudry and Pinsonneault 2001)

and MAPS attempts to understand the influencing and coping network impacts on system usage

with some success. Since this is a relatively new research area with limited examples of the

dynamics within the influencing and coping networks within the context of a new technology

acceptance initiative, this research would benefit from an exploratory look into a more detailed

structural analysis review of the connections and the context of the connections.

2.5.2 Social Information Processing Model (SIPM)



Similar to MAPS, Social Information Processing (SIPM) was a model developed to help address the

social influences in technology acceptance. SIPM was originally developed because attitudes and

use of technology was being shown to be influenced by much more then objective characteristics of

the system or extent of use and user preferences; instead it was being influenced by culture, norms,

and social context (Lee, Cho et al. 2003).

This model was originally proposed by Salancik and Pfeffer in 1978. In this earlier research the

individual perceptions were shown to be influenced by the people they interacted with via opinions,

information, and behaviors (Salancik and Pfeffer 1978; Hossain and Silva 2009). A more recent

example of its use was by Lee et al. on Technology Acceptance and Social Networking in Distance

Learning (Lee, Cho et al. 2003). A combined model from the research is shown in Figure 8 -

Combined TAM and SIPM model from Lee et al. 2003.

In this study the researchers used TAM to determine attitude foundation for use of integrated

communication and engineering design tools in a distributed learning environment, then used SIPM

with social network analysis to see how attitudes changed over time. Using the social network

analysis via UCINET (Borgatti, Everett et al. 2002), the researchers found that the student’s attitude

change was significantly influence by other students’ attitude changes.

This model used performance expectation (individual’s performance gains) and social expectation

(social and entertainment experiences) as antecedents of perceived usability and perceived ease of

use. The social influence was calculated as the product of the neighboring nodes sum (attitude of

change) and the communication frequency. (Lee, Cho et al. 2003)

Unlike the use of betweenness centrality as the best measure of information flow in technology

acceptance (Brandes, Kenis et al. 2006; Lerner 2009) , this research used degree centrality which is a

measure of the frequencies of (incoming/outgoing) communications with others. This was used

assuming that when an actor has a high degree centrality, the actor is an opinion leader or other

significant role in the network (Lee, Cho et al. 2003).



Figure 8 - Combined TAM and SIPM model from Lee et al. 2003

This research also used UCINET to the calculate Quadratic Assignment Procedure (QAP) and other

correlations of inter-network comparisons, which is an appropriate alternative to parametric

modeling knowing that network data does not hold up to the independent measurement

assumptions. A more detailed explanation of this usage can be found in the research (Lee, Cho et al.

2003)

QAP calculates Pearson's correlation coefficient and simple matching coefficient between two

corresponding matrices many times over to determine if an association between the two networks is

statistically significant. In this case they compared social networks at different times to see how

attitudes changed over time. The ability of social network tools like UCINET to run an

autocorrelation to determine if a network can be divided by attributes is valuable for understanding

technology acceptances when it comes to understanding social networks with smaller sample sizes.

The sample size of the research was relatively modest with 31 students in two universities.

Interestingly, the research used two classes at different universities as the context, which originally

looked like two large sub-networks, but they appeared to start to merge as the students started to

work together as the semester continued. The QAP showed that their correlation was small (.18) so

many of their independent characteristics stayed the same, even though it looked like their

networks were merging.

This research and its attempt at accounting for the social network calculated values as an

antecedent of Perceive Ease of Use as well as longitudinally watching the network overtime showed

that the rigorous analysis using social network tools over time is a has significant utility for

researchers and practitioners on technology acceptance in the future.



2.5.3 Tie Strength

Strong and weak ties are both important, but play different roles regarding innovation and new

technologies. Strong ties represent more frequent interaction and extensive interaction between

two individuals that are reciprocated, and have emotional closeness. Weak ties represent

infrequent contact, lack of emotional closeness, and not necessarily reciprocal. In addition to the tie

strength research related to SIPM in the previous section, tie strength and technology acceptance

have been combined in research problems by evaluating both strong and weak ties, personal

consequences, and conformity.

Hossain and Silva recently evaluated strong and weak ties the possibility for extending TAM to

incorporate the influence of the different types of social ties as a new theoretical construct. The

research looked at the adoption of a virtual community (Community Capacity Building Network), in a

mandatory context with independent factors of both strong and weak ties. Their research showed

that strong ties influence attitude towards use, behavioral intention, and actual usage, but it was

unclear on the magnitude of these relationships and their impact on improving the adjusted R2 for

explaining the variance of behavioral intention or actual usage (Hossain and Silva 2009).

Wejnert has also suggested that different tie strengths are more appropriate depending on the

consequences of an innovation. Innovations that have public consequences such as a new

management style would be propagated through weak connections such as professional

organizations. Alternatively, an innovation with private consequences such as fertility control

methods would likely propagates through strong ties and requires frequent face-to-face h

interaction. (Wejnert 2002; Hossain and Silva 2009)

Lastly, strong ties can also drive conformity which includes conformity of adoption of technologies or

innovations. Strong ties in a network impose great demand for conformity and individuals are

expected to heed the advice of these strong tie neighbors. This is also influenced by the content of

these relationships, essentially if one individual adopts a new technology, then it is more likely that

others will. It also means that conformity to not adopt is an individual’s neighbors are not

conforming (Ruef 2002; Hossain and Silva 2009).

Structural equivalence & similar social ties has also been shown to put pressure to conform or adopt

an innovation because structural equivalent actors experience the same social structure and

standards and model their behavior after their neighbors and those that have similar social

structures. These interactions are essentially their strong ties (Hossain and Silva 2009).

Overall, ties and tie-strength is becoming more important as direct influencers of behavioral

intention and usage in technology acceptance or as antecedents to the drivers of behavioral

intention. As we understand the social networks better in contingent acceptance, this large body of

earlier findings could become clearer.



2.6 Gaps in the current knowledge or theory

There is a theme in the literature that demands more informal network understanding from the

diffusion and technology acceptance models. This lack of understanding the social network can be

partially filled by bringing visibility to the informal coping and influencing networks of these

organizations as a new technology is being introduced into an organization in order to help

triangulate the current understanding of technology acceptance.

In the prior research, this lack of understanding the informal network can be grouped in seven areas

to help solve key issues in technology acceptance. These include gaining better understanding the

dynamics of failed technology acceptance projects, looking at contingency acceptance of

organizations and individuals in early stages of the initiatives, providing better explanatory models,

understanding network heterogeneity, accounting for network dynamics, taking advantage of the

richness in individual data, and understanding network dynamics over time. These seven groups are

expanded on in the following subsections.

2.6.1 Evaluate data and insight on failed technology acceptance

Most technology acceptance research within organizations focuses on successful new technologies

acceptance initiatives that have been adopted by the organization, and then accepted by the

individuals within the organizations, but very little focus on those that fail at some stage in this

process or are significantly underused. This research puts the early phases of failed two failed

technology projects under a microscope via the combination of social network analysis and

technology acceptance. The two target technology acceptance initiatives have failed at some stage

or have been dramatically reduced in scope and a social network analysis could shed light on the

failure of the technology initiatives.

This need for research on failed technology acceptance has been highlighted in several recent

research studies. Devaraj and Kohli suggest that failure of systems to show productivity

improvements are often due to a lack of system use (Devaraj and Kohli 2003) which could mean that

the technology was implemented, but large portions of the users might underutilize it. Davis and

Venkatesh as well as Jasperson et al. also concur that new technologies are not being

utilized to their full functional potential and new implementations continue to have a high rate of

failure, so the study of these causes are important (Davis and Venkatesh 2004). In the case of larger

expenditures such as ERP systems, the failure is often inadequate training or change management,

but there is a need to look at these failures both during the implementation and post-acceptance

phases (Davis and Venkatesh 2004; Jasperson, Carter et al. 2005). These assertions would benefit

from more explanatory power of the underutilization. Sykes et al. states this gap more directly in

suggesting that a social network view could also help show how the help networks and resource

controlling mechanisms are supporting or hindering the system use (Sykes, Venkatesh et al. 2009).

Frambach and Schillewaert also suggest future research on the non-adoption of innovations. They

suggest more research on the continued use of innovations and the role of critical mass on

innovation adoption and the impact of network externalities (Frambach and Schillewaert 2002).

An interesting related gap in the literature suggests the need for technology suppliers to understand

why some new technology implementations fail. Bhattacherjee explains that for a new technology

supplier, the innovation acceptance is only successful when both the organization and the



individuals accept the innovation. In addition the targeted adopters must demonstrate commitment

by continuing to use the product over time (Bhattacherjee 1998). For this reason, a longitudinal

social network approach should include gathering research from technology suppliers who interact

with the target company employees which could also shed light on actions they can take to avoid

failures.

Lastly, there is also a call for further understanding of the reasons for non-adoption, suggesting that

we might understand adoption, but non-adoption and its dynamics. Gatignon and Robertson point

out that we cannot assume that non-adoption is the opposite of adoption (Gatignon and Robertson

1989). Their research on laptop computers suggest that the dynamics will likely be more complex

because the non-adoption causes can be within either the initiation or implementation phases.

Non-adoption or acceptance could also mean many different things to individual non-adopters

because they might have actively or passively decided to reject the innovation, alternatively it could

be that they have not reached that point in the adoption process yet (Frambach and Schillewaert

2002). These assertions suggest that alternative research approaches such as social network analysis

could help shed light on why non-adoption happens.

2.6.2 Evaluate data and insight on contingent acceptance within the earlier

stages of adoption/acceptance.

The second gap is a lack of technology acceptance understanding and the social network at the early

stages of a new technology implementation, such as a pilot where both the individual and overall

organizational acceptance is still undecided. Usage of technology acceptance perception values will

also help with the planning; this general approach of using pre implementation surveys on factors

such as perceived usability to better, understand and predict usage is valuable. Perceived usability

was shown to be a strong predictor six months later in related situations (Davis and Venkatesh

2004), but should be explored more.

There is significant discussion suggesting that there are many stages in contingent acceptance, but

little understanding of these early stages. Most technology acceptance research is done when the

technology is well into the implementation stage and primarily focus on the dichotomous

adoption/non-adoption process but we know little about effect of different factors at the various

stages (Olshavsky and Spreng 1996; Frambach and Schillewaert 2002). Stages of organizational

adoption have been described as the initiation stage where the company becomes aware of the

solution, form an attitude towards it, evaluates it, and the implementation stage (Zaltman, Duncan

et al. 1973). The decision for the organization to adopt the technology usually happens between

these two stages.

A combination of understanding the social networks and TAM is also beneficial at this early stage

knowing that the various researchers have suggested that the factors for each stage could be

different. There has been research on the factors of adoption at different stages such as Meyer &

Goes that found that later stages of innovation process, innovations were found to be lower risk, but

more complex. In addition, Labay and Kinnear found that relative advantage is more important later

in acceptance for the organization (Labay and Kinnear 1981; Meyer and Goes 1988; Frambach and

Schillewaert 2002).



2.6.3 Explanatory Models:

Much of the quantitative literature on acceptance is primarily focused on aggregate models such as

the Bass Model or its related descendants that incorporate hazard factors. Aggregate Bass model

lacks explanatory power because it oversimplified the decision process and doesn’t account for

market heterogeneity (Dattee and Weil 2007). The concept of an innovation system lack attention

at the micro-level, which often means you can’t see what is blocking the diffusion of an innovation

(Hekkert, Suurs et al. 2007) The work of Devaraj and Kohli suggest that failure systems to show

productivity improvements are due to a lack of system use (Devaraj and Kohli 2003). A social

network view could help show how the help networks and resource controlling mechanisms are

supporting or hindering the system use (Sykes, Venkatesh et al. 2009). In this research hopes to

shed additional light on the failure of the technology initiatives in a more explanatory fashion then

the aggregate quantitative analysis.

2.6.4 Account for Heterogeneity:

There is a gap in providing more complete framework that is grounded in the principle of social

heterogeneity, realizing that people are influence and are influenced differently in social networks,

which is not available within the traditional Diffusion models. Dattee and Weil suggest that

researchers focus on the critical initial adoption of an innovation which is driven by the social

interactions within the network of the opinion leaders and change agents (Dattee and Weil 2007).

2.6.5 Account for Network Dynamics:

There is also a gap to under Within organizations, there is a need to better understand the

organizational dynamics, network dynamics, and social processes with respect for innovation

acceptance at a personal level (Ruud T. Framback 2002). There was also a demand for deeper insight

into the inhibitors and stimulators of diffusion of innovations into organizations at both an aggregate

and individual level to help suppliers market their products more effectively (Framback 2002).

2.6.6 Take Advantage of Richer Data:

The data that is often used in understanding technology acceptance is based on the individual

attributes and the TAM factors that have been heavily researched such as perceived usability, but

there has not been significant research incorporating the vastly larger set of data available once you

incorporate social network connections, such as the network centrality or the average perceived

usability of the direct connections a user has in the coping network. Emmanouilides and Davies

proposed a methodology for modeling social interaction effects on the adoption of innovations, but

also recognize that it does have several limitations such as individual variations and interaction over

time and the need to incorporate richer data such as distances between households (Emmanouilides

and Davies 2007).

2.6.7 Network Dynamics over Time:



The last gap is the need for understanding the changing social network dynamics over time and

understanding how the structure of the social network might change at the critical points early in

the technology acceptance that could be good predictors of wider acceptance. In Meade and Islam’s

25 year review of Modeling and forecasting of diffusion of innovations, they highlight the

opportunity in future research to tap into the growing availability of cross-sectional and time series

data describing consumers, which could be use d to provide more accuracy (Meade and Islam 2006).

Dattee and Weil underscore this point suggesting that to better understand the acceptance across a

large organization, we must understand the tipping point, which in early stage adoption is an

outcome of the social networks which are non-uniform. (Dattee and Weil 2007)

2.7 Summary

The scope of this research and literature review spanned three macro areas of study, specifically

social networks, organizational innovation, and individual technology acceptance.

This literature review provided an overview and analysis of the related theories, key constructs and

significant prior research that provides the foundation that this research builds upon. The chapter

had five sections. This first section, 2.1, provided an overview of the chapter and timeline of

research streams. Section 2.2 provided an overview of the significant literature around

organizational innovation and the strength of weak ties on organizational innovation. This was then

followed by section 2.3 which covered the individual diffusion of innovation and technology

acceptance. Section 2.4 then provided an overview of the significant social network theories and

constructs which included: the Hawthorne Studies, Structural Hole Theory, Social Capital, Simmelian

Ties, Agent Based Modeling, and Social Network Analysis. Section 2.5 then focused on the models

that combine social network analysis and individual technology acceptance which include Model of

Acceptance with Peer Support (MAPs) and Social Information Processing Model (SIP). Section 2.6

then reviewed the seven gaps in the prior literature that this research hopes to help start closing.

The chapter then concluded with a summary of the chapter.



CHAPTER 3 – RESEARCH DESIGN AND METHODS

3.1 Introduction

This chapter will describe the Design and Methodology that was used to adequately answer the four

research questions and provide enough guidance to duplicate this study by a knowledgeable

researcher.

This chapter starts by describing the hypothesis development, key variables and how they help

answer the research questions. This is then followed by the research design section which addresses

the types of samples and sample size, survey instrument and measures, data collection process, data

analysis, validity issues, and reliability issues for this type of this research. Next, the longitudinal

study section describes the location, subjects and technologies used for the study. The chapter is

then concluded with a summary.

3.2 Hypothesis Development and Key Variables

3.2.1 Key variables (dependent, moderating, intervening) and a conceptual

framework

For this research, there are two variations of technology acceptance conceptual models that this

research is interested in. The goal is compare the adjusted R2 values of Behavior Intention between

the two. The first model which also serves as the foundation of the conceptual model is the highly

cited Unified Theory of Acceptance and Use of Technology (UTAUT) from Venkatesh which is show in

the figure below (Venkatesh, Morris et al. 2003).



Performance

Expectancy

Effort Expectancy

Social Influence

H1

H2

H3

Use

BehaviorBehavior

Intention

H4

Facilitating

Conditions

Experience

AgeGender

Voluntariness of

Use

Figure 9 - Venkatesh conceptual model for the Unified theory of acceptance and use of technology (UTAUT)(Venkatesh,

Morris et al. 2003)



The second model is based on extending the UTAUT model to incorporate the actual network

connections of other individuals such as opinion leaders which could result from proximity, and team

assignments. This is shown in Figure 10 - Extension of the Unified Theory and Use of Technology

incorporating the actual network connections of the population’s dyads to represent the social

influence factor

Performance

Expectancy

Effort Expectancy

Social Influence

H1

H2

H3

Use

BehaviorBehavior

Intention

Facilitating

Conditions

Experience

AgeGender

Voluntariness

of Use

H7

Influence Centrality H4

Coping Centrality

H9

Influence

Centrality

H10 H12Coping Alter’s

Performance

Expectancy

Influence Alter’s

Performance Expectancy

Influence Alter’s

Performance

Expectancy

H5

H8

H6

H11 H13

Figure 10 - Extension of the Unified Theory and Use of Technology incorporating the actual network connections of the

population’s dyads to represent the social influence factor



3.2.2 Definitions of the Constructs and Measures of Constructs

The key variables for UTAUT and it’s the network constructs used in this study are further defined in

the following sections. First, this section defines the standard UTAUT variables of Performance

Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. This is then followed by

the network variables and constructs used which includes Power and Centrality, Neighbors, Groups,

Cliques, Clusters, Graphs, Matrices, Modes, Influencing and Coping Network Data, and Average

Expectancy of Neighbors.


Performance Expectancy can be summarized as measuring the perceptions if the person feels that

the new technology is going to help them be more productive in their job or task they are trying to

accomplish. Below are the survey questions used from the UTAUT study (Venkatesh, Morris et al.

2003)

I would find the system useful in my job.

Using the system enables me to accomplish tasks more quickly.

Using the system increases my productivity.

If I use the system, I will increase my chances of getting a raise.

Effort Expectancy

Effort Expectancy can be summarized as measuring the perceptions if the person feels that the new

technology is going to be easy to use. Below are the survey questions used from the UTAUT study

(Venkatesh, Morris et al. 2003)

My interaction with the system would be clear and understandable.

It would be easy for me to become skillful at using the system.

I would find the system easy to use.

Learning to operate the system is easy for me.

Social Influence

Social Influence can be summarized as measuring the perceptions if the person feels that the people

they interact with encourage them to use the system. Below are the survey questions used from the

UTAUT study (Venkatesh, Morris et al. 2003)

People who influence my behavior think that I should use the system.

People who are important to me think that I should use the system.

The senior management of this business has been helpful in the use of the system.

In general, the organization has supported the use of the system.



Facilitating Conditions

Facilitating Conditions can be summarized as measuring the perceptions if the person feels that they

have support, knowledge and resources to use the new technology. Below are the survey questions

used from the UTAUT study (Venkatesh, Morris et al. 2003).

I have the resources necessary to use the system.

I have the knowledge necessary to use the system.

The system is not compatible with other systems I use.

A specific person (or group) is available for assistance with system difficulties.

Power & Centrality

Power is an important concept when it comes to social or political networks. It essentially means

who is able to get their own way. One of the more widely used definitions is Max Weber’s: “Power

is the probability that one actor within a social relationship will be in a position to carry out his will

despite resistance, regardless of the basis on which this probability rests." (Raab 2009) When it

comes to network analysis within organizations it is important to recognize that power is not just

from the traditional hierarchical structure, but also from the informal network structures where

information and access to resources are often controlled. A key metric for social network power is

centrality.

Centrality in networks is a calculated value that takes many forms but its general objective is to help

identify the most important actors in a social network. The more common types of and calculations

for centrality are closeness, betweenness, status, eigenvector, status, contra-status, and backbone

centrality (Brandes 2009). When it comes to technology acceptance, we will be primarily using

“betweenness centrality” which is a function of how many other pairs of nodes a node is positioned

between. We will be using this centrality measure because it is recommended when you are trying

to understand the flow of information which generally flows across the shortest routes in a network.

This is what we need as we are trying to understand perceptions and information around technology

acceptance.(Lerner 2009)

Neighbor’s, Groups, Cliques, & Clusters

Within social network analysis one of the research goals is often to identify actors that naturally

belong together because of their attributes, relationships, perceptions, or behaviors. The number

of steps between two nodes in a network is referred to as the distance.

A common categorization or sub-graph of an overall network graph is a clique where all the nodes

are connected with each other. These are further classified as k-cliques, k-clans, k-plexes, and k-

cores (Lerner 2009). For this research we will be primarily focused on k-cliques which are less

restrictive where some of the nodes might be 2 or 3 steps away, where k is 2 or 3.

Partitioning network graphs is done by either breaking it into its connected components or finding

good clustering within components. Components are sub-graphs where every node is connected to

the larger component. There are several manual and systematic clustering approaches for

clustering and finding natural groups within a graph (Lerner 2009). In this study the groups are

analyzed using the Girvan-Newman clustering approach within Netdraw (Jin, Girvan et al. 2001).



Positional equivalence, pendants, and isolates are also important to understand in organizational

networks. Two actors are considered to have positional equivalence when they have similar or

equivalent roles in the network based on their position. Pendants are nodes that are connected to

the larger networks with only one connection or edge and isolates have no tie to the larger network

groups.

Graphs, Matrices, & Modes

Social network are generally viewed as graphs of actors (nodes) and their relationships (edges) which

is a good visualization for organizational networks because the number of nodes can often fit on one

view which is the case for this research. Figure 12 - Example of Organizational Social Network

constructed from asking respondents "List 1-3 people that you interact with or will interact with in a

typical week that you get help from for effective use of the Event Registration System".is an example

of a network graph. The network connections for these are often stored as adjacency matrices

which are two dimensional arrays where each row i and column j represent the different nodes in

the graph and each entry Xij represents the presence of a relationship. The relationship can simply

be binary to represent existence of a relationship, or it can also be directional and convey

relationship strength (Lerner 2009). There might be several types of relationships between the

different actors. In this research we are interested in where people go for help, which is a different

relationship then where they might go to get resources for effective use. Each of these relationships

is considered different modes which are stored in separate arrays.

Influencing and Coping Network Data

The relational data needed for social network analysis generally comes from four sources; surveys,

statistics, documentary analysis, and observations. For this research the relationship data is

collected from survey’s by asking the following five questions (Schneider 2009; Sykes, Venkatesh et

al. 2009). The first two are used to understand an individual’s coping network and the last three are

to understand their influencing network.

List 1-3 people that you interact with or will interact with in a typical week that you get help from for effective

use of the <system>.

List 1-3 people that you interact with or will interact with in a typical week that you give help too for effective use

of the <system>.

List 1-3 people that you interact with or will interact with in a typical week that have access to information

needed for effective use of the <system> (e.g., system features, upcoming releases, demo dates, etc.).

List 1-3 people that you interact with or will interact with in a typical week that you believe have strong

domain/functional knowledge for effective use of the <system> (e.g., such as tips and tricks, short-cuts, process

sequence, etc.).

List 1-3 people that you interact with or will interact with in a typical week that have access to resources for

effective use of the <system> (e.g., training resources, manuals, tutorials, etc.).



Average Performance Expectancy of Neighbors

The Average Performance Expectancy of the Network Neighbors construct is also labeled as the

Influence Network Alter’s PE (Performance Expectancy) and Coping Networks Alter’s PE. This is an

ego-based measure for every actor in the network which is essentially the average of the continuous

Performance Expectancy variable of all the neighbors that an individual is connected to and is

generated using the Ucinet Ego Network Composition routine for a continuous variable (Borgatti,

Everett et al. 2002). This can also be calculated using matrix algebra to transform an NxN adjacency

matrix of the out-degree connections and a 1xN attribute matrix which contains the Performance

Expectancy for each participant. The matrix algebra is shown in equation 1, which yields a 5xN

matrix of the neighborhood means.

Equation 1 - Calculating Neighborhood PE means

[ ] [ ] [ ]

3.2.3 Hypothesis - how will they help answer the Research Questions

This section will describe the 13 hypothesis listed Table 2 Research Hypotheses, where are used

within this causal research to help answer the research questions.

The hypotheses are based on three quantitative driven research questions:


acceptance of a new technology, does the informal influencing network add additional

explanatory power of the behavioral intention and system usage over the traditional UTAUT

model?

RQ1a. Does the Influence Network Centrality add additional explanatory power of

the behavioral intention over the traditional UTAUT model?

RQ1b. Does the Influence Network Centrality add additional explanatory power of

the system usage over the traditional UTAUT model?

RQ1c. Does the Neighbors Performance Expectancy in Influencing Network add

additional explanatory power of the behavioral intention over the traditional UTAUT

model?

RQ1d. Does the Neighbors Performance Expectancy in the Influencing Network add

additional explanatory power of the system usage over the traditional UTAUT

model?


acceptance of a new technology, do the informal influencing networks impact the

performance expectancy of the traditional UTAUT model?

RQ2a. Does the Influence Network Centrality add additional explanatory power of

the Performance Expectancy over the traditional UTAUT model?



RQ2b. Does the Neighbors Performance Expectancy in the Influencing Network add

additional explanatory power of the Performance Expectancy over the traditional

UTAUT model?


acceptance of a new technology, do the informal coping networks impact the Effort

Expectancy or Facilitating Conditions of the traditional UTAUT model?

RQ3a. Does the Coping Network Centrality add additional explanatory power of the

Effort Expectancy over the traditional UTAUT model?

RQ3b. Does the Neighbors Performance Expectancy in the Coping Network add

additional explanatory power of the Effort Expectancy over the traditional UTAUT

model?

RQ3c. Does the Coping Network Centrality add additional explanatory power of the

Facilitating Conditions over the traditional UTAUT model?

RQ3d. Does the Neighbors Performance Expectancy in the Coping Network add

additional explanatory power of the Facilitating Conditions over the traditional

UTAUT model?

To answer these first three research questions, the study needed to determine the amount of

explained variance from the mean that is accountable for the factors (Adjusted R2) in each

conceptual model for these factors. The adjusted R2 value was chosen to adjust the explained

variance based on the number of variables or degrees of freedom.

The first three hypotheses are based on the conceptual model and hypothesis from the earlier

Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris et al. 2003). The

purpose of these three hypotheses is twofold. First they enable the comparability of this research

data back to the earlier UTUAT research (Venkatesh, Morris et al. 2003). This is essentially a check

of its verity; does the work ring true and is it consistent with accepted knowledge in the field? If it

departs; why? Second, this provides a base to build on with additional constructs.

Hypothesis 1

Performance Expectancy will positively impact Behavioral Intention.

The 1st hypothesis is similar to the first hypothesis within the original UTAUT study which was to

validate the influence of performance expectancy on behavioral intention, but the moderating

factors are not included. When looking at the UTAUT factors, this study only focuses on the direct

impacts in order to validate that they are consistent with prior research, but not replicate the more

extensive and rigorous analysis on moderating factors that are involved in UTAUT (Venkatesh, Morris

et al. 2003)

It is important to note that the influence of performance expectancy has been shown to be

moderated by gender and age, such that the effect will be stronger for men and particularly for

younger men. The gender difference is theorized that men tend to be more task-oriented and

performance oriented based on socialization and gender roles. The age moderator is theorized by



the tendency for younger workers being more motivated by extrinsic rewards and younger women

placing different levels importance on job related factors when they are younger without children,

then later when they have families (Venkatesh, Morris et al. 2003).

Hypothesis 2

Effort Expectancy will positively impact Behavioral Intention.

The 2nd hypothesis is similar to the second hypothesis within the original UTAUT study which is to

validate the influence effort expectancy on behavioral intention, but the moderating factors are not

included. When looking at the UTAUT factors, this study only focuses on the direct impacts in order

to validate that they are consistent with prior research, but not replicate the more extensive analysis

on moderating factors that are involved in UTAUT (Venkatesh, Morris et al. 2003)

It is important to understand that in prior research, the influence of Effort Expectancy on Behavioral

Intention will be moderated by gender, age, and experience, such that the effect will be stronger for

women, particularly younger women, and particularly at early stages of experience. The experience

moderator is expected to be more significant in earlier stages of using a new technology as they

present a barrier to usage, but become less in later stages. It is also expected that effort expectancy

is more important to women than men based on gender roles. Age is also expected to play a role, as

increased age has been associated with increased difficulty in processing complex stimuli.

(Venkatesh, Morris et al. 2003)

Hypothesis 3

Social Influence will positively impact Behavioral Intention.

The 3rd hypothesis is similar to the third hypothesis within the original UTAUT study which is to

validate the impact of social influence on behavioral intention, but without the moderating factors.

Similar to the first two hypothesis, it’s important to know that the influence of social influence on

behavioral intention has been shown to be moderated by gender, age, voluntariness, and

experience, such that the effect will be stronger for women, particularly older women in mandatory

settings and in the early stages of experience (Venkatesh, Morris et al. 2003).



Hypothesis 4

Facilitating Conditions will positively impact System Use.

Similar to Hypothesis 1-3, Hypothesis 4 is also a validation of the data to understand if it reaches

similar conclusions and relationships to the prior research in the original UTAUT study (Venkatesh,

Morris et al. 2003).

Hypothesis 5

Influence Network Centrality will positively impact System Use.

Based on the research by Sykes et al. the behavioral intention and system-use was shown to increase

with network centrality, so “actors who are centrally located within organizational networks enjoy a

broad array of benefits and opportunities” (Sykes, Venkatesh et al. 2009). This would likely be more

impactful for the Influencing network. This influencing indicates that people in central structural

positions in a network will have more influence over system design to their needs which is often

labeled as co-adoption, thus driving up their usage.

This is also consistent that central individuals within an organization will likely have more assistance

and resources to use new systems. Mossholder et al. makes the observation that “Individuals with

high centrality are more connected with others in their organizations, implying greater involvement

in assistance exchanges with coworkers, whether the purpose is work-related or more

personal”(Mossholder, Settoon et al. 2005)

Prior research has also shown that for use of new technologies, central individuals seek to increase

their own performance. As Sparrowe et al observed, “individual job performance was positively

related to centrality in advice networks and negatively related to centrality in hindrance

networks”(Sparrowe, Linden et al. 2001), so one would expect those central in advice networks to

have higher behavioral intention and system use then those on the periphery of the networks.

Leskovec presents a model that successfully identifies communities, product, and pricing categories

for which viral marketing seems to show how the networks involved in viral marketing create a

“recommendation” network (Leskovec, Adamic et al. 2007). For technology acceptance within an

organization, one could expect that depending on what group a person is in, they will be influenced

by the values and recommendations within that group. For example an internal functional group

might be more influenced by the social norm then a group that is primarily driven by numbers, such

as a client revenue-generating group. One would also expect that the more involved individuals are

in the group, or how central they are, they would also be impacted more by the social norm. An

isolate, or someone who is not central, might not be influenced by social norms as much.

This hypothesis could also be viewed as a validation that users who are central to the network have

significantly more access to resources and people to help them with a new technology as opposed to

someone who isn’t as central. Betweenness centrality is considered the mode at which information

flows within networks, so access to facilitating conditions for a technology would be expected to

correlate to centrality (Raab 2009). The Betweenness Centrality is normalized via Ucinet based on

Freeman’s work (Borgatti, Everett et al. 2002)



Hypothesis 6

The Neighbors Performance Expectancy in the Influencing Network will positively impact the

Behavioral Intention.

Performance Expectancy of a person’s Neighbor’s will have a significant impact on the person’s own

Behavioral Intention and System Use. This is also supported by the 2006 study in the adoption of

mobile technologies where network neighbors (those consumers linked to a prior customer) adopt

the service at a rate 3–5 times greater than baseline groups selected by the best practices of the

firm’s marketing team. From this, we would also expect the Performance Expectancy and Effort

Expectancy perceptions to be influenced by the neighbors (Hill, Provost et al. 2006). Also, an

increase in interactions a user has with others who have access to information, domain knowledge

or can offer help with a technology, the more likely they will have a positive behavioral intention.

Essentially, Hill et al. found that predictive power increased based on the number and duration of

communications (Hill, Provost et al. 2006).

As noted in the Hill research for mobile phones, adoption is significantly driven by the neighbors, and

the groups in this research on organizations are relatively small, so this study would expect

neighbors inside of these groups to impact the behavioral intention as a moderator (Hill, Provost et

al. 2006).

Hypothesis 7

Neighbors Performance Expectancy in the Influencing Network will positively impact System Use.

This hypothesis is supported because people in central structural positions in a network will have

more influence over system design to meet their needs. Those in the Influencing network are

expected to have more domain knowledge, access to information and resources related to the

technology, thus increasing their ability to co-adopt the technology. This drives up their usage which

would make those central in the influencing network more critical on system use then those central

in the coping network.

Suggesting that Performance Expectancy of a person’s Neighbor’s will have a significant impact on

the person’s own System Use is also supported by the 2006 study in the adoption of mobile

technologies where network neighbors (those consumers linked to a prior customer) adopt the

service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s

marketing team. From this, we would expect the Performance Expectancy and Effort Expectancy

perceptions to also be influenced by the neighbors (Hill, Provost et al. 2006).

Hypothesis 8

The Influence Network Centrality will positively impact Performance Expectancy

This hypothesis is supported by understanding the dynamics of co-adoption, opportunities of being

central, job performance, and betweenness centrality.

First, Influencing Network Centrality suggests that people in central structural positions in a network

will have more influence on the co-adoption of the system design, which should also increase their



Performance Expectancy of the new technology. The Influence Network Centrality would also

suggest that these individuals have more opportunities to try new technologies.

Second, these resources have more opportunities to use new technologies as suggested by Sykes et

al., where the behavioral intention or system-use was shown to increase with network centrality

because “actors who are centrally located within organizational networks enjoy a broad array of

benefits and opportunities” (Sykes, Venkatesh et al. 2009). This increased use of new technologies

would increase their perceptions of new technologies or personal innovativeness.

Third, job performance increases when an individual is central in networks. Sparrowe et al observed

that “individual job performance was positively related to centrality in advice networks and

negatively related to centrality in hindrance networks”(Sparrowe, Linden et al. 2001), so one would

expect those central in advice networks to have higher Performance Expectancy then those on the

periphery of the networks because innovative technologies help them increase their job

performance.

Lastly, users with high betweenness centrality have significantly more access to resources and

people to help them with a new technology which will help with their perceptions of the technology.

Betweenness centrality is considered the mode at which information flows within networks, so

access to facilitating conditions for a technology would be expected to correlate to centrality (Raab

2009). Central individuals within an organization are also more likely to have more assistance and

resources to use new systems. Mossholder et al. makes the observation that “Individuals with high

centrality are more connected with others in their organizations, implying greater involvement in

assistance exchanges with coworkers, whether the purpose is work-related or more

personal”(Mossholder, Settoon et al. 2005).

Hypothesis 9

Neighbors Performance Expectancy in the Influencing Network will positively impact Performance

Expectancy.

Performance Expectancy of a person’s Neighbors will have a significant impact on the person’s own

Behavioral Intention and System Use. This is supported by the 2006 study in the adoption of mobile

technologies where network neighbors (those consumers linked to a prior customer) adopt the same

mobile service at a rate 3–5 times greater than baseline groups selected by the best practices of the

firm’s marketing team. From this, we would expect the Performance Expectancy and Effort

Expectancy perceptions to also be influenced by the neighbors (Hill, Provost et al. 2006).

Hypothesis 10

The Coping Network Centrality will positively impact the Effort Expectancy.

Coping theory is an important concept within the mapping of social networks and technology

acceptance. Coping is defined as all thoughts and behaviors performed by a person in order to

manage (minimize, reduce, master, tolerate) demands emanating from a specific change in the

person-environment relationship (Lazarus 1966). It would be expected that those more central in

the coping network would use the available resources help cope with a new technologies. If they



have more resources by being central in a coping network, it would be expected that they have

higher Effort Expectancy or in other words, perceive that it would be easier to use.

Mossholder et al. further supports that centrality gives more opportunities for help by making the

observation that “Individuals with high centrality are more connected with others in their

organizations, implying greater involvement in assistance exchanges with coworkers, whether the

purpose is work-related or more personal” (Mossholder, Settoon et al. 2005).

Hypothesis 11

Neighbors Performance Expectancy in the Coping Network will positively impact the Effort

Expectancy.

As mentioned for Hypothesis 10, coping theory is an important concept within the mapping of social

networks and technology acceptance and encompasses all the thoughts and behaviors performed by

a person in order to manage (minimize, reduce, master, tolerate) demands emanating from a

specific change in the person-environment relationship (Lazarus 1966).

Research has shown that Performance Expectancy of a person’s Neighbors will have a significant

impact on the person’s own Behavioral Intention and System Use. For example, this is supported by

the 2006 study in the adoption of mobile technologies where network neighbors (those consumers

linked to a prior customer) adopt the service at a rate 3–5 times greater than baseline groups (Hill,

Provost et al. 2006). Building upon this line of reasoning; this hypothesis suggests that the

antecedents of Behavioral Intention, such as Effort Expectancy, are the more important factors that

individuals discuss with their network neighbors. Thus, one could expect that the Performance

Expectancy and Effort Expectancy perceptions are the mediating factors that are directly influenced

by the neighbors own perceptions, as opposed to direct use.

Hypothesis 12

The Coping Network Centrality will positively impact the Facilitating Conditions

The specific survey questions that have been validated to measure Facilitating conditions are as

follows (Venkatesh, Morris et al. 2003):

• I have the resources necessary to use the system.

• I have the knowledge necessary to use the system.

• The system is not compatible with other systems I use.

• A specific person (or group) is available for assistance with system difficulties.

With the exception of the “compatibility” question, these indicate closeness or access to information

or people. For this reason we would expect closeness, specifically Betweenness Centrality which is a

good information flowing measure would have a relationship with Facilitating Conditions (Raab

2009). One challenge, which is not directly addressed in this research, is to determine if the Coping

Network Centrality moderating or mediating factor between Facilitating Conditions and Behavioral

Intention.



Hypothesis 13

Neighbors Performance Expectancy in the Coping Network will positively impact the Facilitating

Conditions

As mentioned earlier, coping theory is defined as all thoughts and behaviors performed by a person

in order to manage (minimize, reduce, master, tolerate) demands emanating from a specific change

in the person-environment relationship (Lazarus 1966). One might expect that if an individual whose

neighbor’s that help them cope with a new technology have higher Performance Expectancy

measures that could increase that individuals own Facilitating Conditions score because those

neighbors would be a better resource to help cope or provide assistance with using the new

technology.

Once again, reviewing the survey questions used to measure Facilitating Conditions are (Venkatesh,

Morris et al. 2003) in the previous section indicate closeness or access to information or people who

could help them. For this reason we would expect that if someone was predominately surrounded

by individuals in their Coping Network who had a high Performance Expectancy of the new

technology, it would indicate that they are in a location of the network that also has access to more

Facilitating Conditions.

Summary of the Hypothesis

As a summary, Table 2 provides a listing of these 13 hypotheses, their related research questions,

the UTAUT model used, their dependent variable, and their independent variable.

Table 2 Research Hypotheses

Hyp. RQ Model Hypothesis, Research Question Model

Number

Dependent

Variable

Independent

Variable

H1 Traditional

UTAUT

Performance Expectancy will Positively

Impact Behavioral Intention

Behavioral

Intention

Performance

Expectancy

H2 Traditional

UTAUT

Effort Expectancy will Positively Impact

Behavioral Intention

Behavioral

Intention

Effort Expectancy

H3 Traditional

UTAUT

Social Influence will Positively Impact


Behavioral

Intention

Social Influence

H4 Traditional

UTAUT

Facilitating Conditions will positively

impact System Use

System Use Facilitating

Conditions

H4 RQ1a Extended

UTAUT

Influence Network Centrality will

positively impact Behavioral Intention

Behavioral

Intention

Influence Network

Centrality

H5 RQ1b Extended

UTAUT

The Influence Network Centrality will

positively impact System Use

System Use Influence Network

Centrality

H6 RQ1c Extended The Neighbors Performance Expectancy

in the Influencing Network will

Behavioral Neighbors

Performance



UTAUT positively impact the Behavioral

Intention

Intention Expectancy in the

Influencing

Network

H7 RQ1d Extended

UTAUT

The Neighbors Performance Expectancy



System Use

Neighbors

Performance

Expectancy in the

Influencing

Network

H8 RQ2a Extended

UTAUT

The Influence Network Centrality will

positively impact Performance

Expectancy

Performance

Expectancy

Influence Network

Centrality

H9 RQ2b Extended

UTAUT




Expectancy

Performance

Expectancy

Neighbors

Performance

Expectancy in the

Influencing

Network

H10 RQ3a Extended

UTAUT

The Coping Network Centrality will

Positively Impact the Effort Expectancy

Effort

Expectancy

Coping Network

Centrality

H11 RQ3b Extended

UTAUT


in the Coping Network will Positively

Impact the Effort

Effort

Expectancy

Neighbors

Performance

Expectancy in the

Coping Network

H12 RQ3c Extended

UTAUT


Positively Impact the Facilitating

Conditions

Facilitating

Conditions

Coping Network

Centrality

H13 RQ3d Extended

UTAUT



Impact the Facilitating Conditions

Facilitating

Conditions

Neighbors

Performance

Expectancy in the

Coping Network

The fourth research question was more qualitative then quantitative, but is used to uncover if using

social network graphs merged with technology acceptance perceptions would add valuable insight

to managers in the process of planning, executing, and monitoring a technology acceptance project

and potentially increase the likelihood of identifying potential problems, opportunities, or key

resources for specific roles to help in their day-to-day decision making.




Similar to the Hawthorne studies, this requires a post-structuralism approach where the goal is not

to find the statistical significance or search for the ideal quantifiable model, but instead to find



meaning and open up new questions (Borgatti, Everett et al. 2002; Kilduff and Tsai 2003). In the

case of technology acceptance studies using social network analysis, we should consider this post-

structuralism approach to help uncover new knowledge about how perceptions and networks are

formed in this very dynamic and confusing environment of conditional acceptance of technologies

within organizations

3.3 Research Design

This research design for the first three research questions is based on a positivist research

knowledge paradigm to help uncover the behavioral intention of using new technologies and

provide a quantitative model of the acceptance of technology at the macro level and also

understanding the user interactions using social network analysis at the micro level. The positivist

approach is taken because the research is assuming that there are quantifiable models of both

technology acceptance and social networks that will determine people’s behavior towards new

technologies or innovations. To do this, the research looks at the acceptance of two technologies

within a large organization and attempts to be an objective viewer focusing on the facts, looking for

causality, and operationalizing concepts.

This quantitative causal study used hypothesis testing investigating the correlation of multiple

technology acceptance factors and social network measures on the acceptance of new technologies

in this organization. The primary instruments are a questionnaire with scaling to determine

perceptions of the new technologies and to identify social network connections (dyads). In addition

supporting company documents and observations were used to help determine participant

attributes and provide context of the technology acceptance pilots.

The data analysis used primarily statistical descriptive results such as Pearson’s correlation

coefficient and factor analysis. This research used cluster sampling where the natural group (cluster)

was the population within an organization that was adopting a new technology. This helped ensure

we can create a representative social network model of the connections of individuals and their

interactions regarding the target technology.

This non-contrived study will include a longitudinal questionnaire which is given to the same

individuals prior to a new technology pilot, then a few months after the pilot starts which will add to

our understanding of how the influencing and coping networks could change in parallel with the

success or failure of a new technology or innovation.

The research was longitudinal to also gain further insight over time of the change in the social

network and the user’s scores on Performance Expectancy, Effort Expectancy, Social influence,

Facilitating Conditions, Behavioral Intention, and the moderating variables of gender, department,

age, experience, and voluntariness of use over time.

3.3.1 Type of Sample and sample size determination



Technology pilots or proof-of-concepts within organizations are often limited to small groups of

people; to support a smaller sample size of less than 100 surveys, we needed to understand the

characteristics of the sample, the sample size relative to the number of variables, and the

importance of sample size within the context of social network analysis. We discuss these in the

next three subsections and then provide our conclusions on the number samples needed and if two

pilots are adequate.

Characteristics of Sample

There were approximately 81 surveys completed for two separate pilots completed which was

relatively close to what was expected. In looking at the sample size, it is important to understand

the potential autocorrelation or dependencies within the data. This research was targeted at two

technologies with surveys before and after a pilot within a single organization; most users completed

two surveys and three users were involved in both technologies pilots given their roles in the

organizations at the time (one of which was the researcher) which is shown in Table 3.

Table 3 - Summary of unique surveys for different technologies, time, and individuals

Unique Surveys Pre-Pilot Post-Pilot Total

Event Registration System 35 25 61

Enterprise Project Management 11 9 20

Total 46 34 81

Unique Users

Event Registration Systems Only 32

Enterprise Project Management Systems Only 8

Both Systems 3

Total 42

Sample Size Relative to Number of Variables

For this analysis one of the main data analysis techniques planned was multiple regressions. There

are several suggested sample size guidelines for this type of analysis that this study could use. First,

Hair, Anderson, et al. suggest you should have 20 samples for each cell for ANOVA. They also

suggest that and for each independent variable you should have a minimum of 5 observations for

each independent variable (Hair, Anderson et al. 1998). They also suggest a more desirable level is

15-20 observations for each independent and dependent variable to make the sample generalizable.



One other way to evaluate the sample size for the factor analysis is from Guadoganoli & Velicer, who

indicated that the research would need five to ten times the number of respondents per item

(Guadagnoli and Velicer 1988); this would indicate that the research could need 24 questionnaire

items multiplied by 10, which yields 240 respondents. Thankfully, these 21 questions that are part

of the regression are reduced to six measures, which results in a required sample size of around 60

observations. For this study, this indicates that 80 observations could support this study with up to 8

variables.

Sample Size within the Context of Social Network Analysis

This smaller sample size of 42 unique user is typical for social network analysis (Kilduff and Tsai

2003), but not for factor analysis resulting from power analysis (MacCallum, Browne et al. 1996).

Thus, determining the sample size for this research was one of the more challenging areas of the

research because we are bringing in a relatively new unit of observation, the dyad, which also means

we cannot assume that each individual sample is independent. The survey and other measures in

this research are intended to provide a comparison across two modeling approaches for the impact

of social norm, so we need to consider the different models in evaluating the correct number of

samples. The following sections discuss the sample size based on the two models being presented

and in light of the research questions.


(UTUAT)

For the basic factor analysis of the User Acceptance of Information Technology, this research is

essentially duplicating the same factors and measures that were used by Venkatesh in his earlier

work of User Acceptance of Information Technology; Toward a Unified View (Venkatesh, Morris et

al. 2003). So it’s expected that the results are in line with the earlier work and the primary research

questions are not to validate the factor analysis for all the different variables in that study, which

would likely require a larger sample size,

Alternatively, this research is only focused on getting a further understanding of the effect of the

social norm factor uses by Venkatesh in comparison to other social norm modeling approaches,

which will give the research more flexibility in its sample size.

One strategy that was used to proving the impacts of several social network variables on technology

acceptance with a smaller sample size was to minimize the degrees of freedom of this research to

allow the reduction of samples needed while still providing an adequate confidence in the results. A

second strategy was to combine the results of both research projects (Enterprise Project

Management and the Event Management System. A third strategy was including the second set of

longitudinal surveys for each of the participants, which brings the sample size up to 81, which is

often a strategy to increase the power (Maxwell 2004). This required the research to address the

statistical challenges and potential risk of these results not being independent.


(UTUAT) with Actual Network Connections



For the second model which replaces the survey questions on social influence with a richer measure

that will be determined based on the behavioral intention of the subject’s actual network

connections. By incorporating the actual network connection data, the research gathers richer data

that includes who the participants look to as opinion leaders and coping support around this

technology. More importantly it gains visibility of the opinion leader’s perceptions of this new

technology. In this case we are not just looking for large sample sizes, but trying gain further insight

into the interactions between the individuals which does not require such large sample sizes.

The tradition of analyzing the network connections can yield meaningful results for groups as small

as 14 as was done in the Hawthorn research that studied the motivation and interaction of 6

networks within a bank wiring room (Roethlisberger and Dickson 1939). In determining the

confidence of a social network where the unit of observation is a dyad, there are inherent problems

such as each dyad being strongly dependent on each other. This could lead to statistical

autocorrelation, which could yield positive bias in an ordinary-least-squares (OLS) tests and

significant relationships between dependent and independent variables that don’t exist. To simplify

this model in terms of degrees of freedom, the research used all of an individual’s connections in

either of the coping or influencing networks as a single factor for group membership and centrality

rather than using each network mode as a separate factor.

Conclusion on Sample Size for this Research

With limiting the number of variables to 8-9 in the finalized model, using a longitudinal study, using

hierarchical or multi-level regression to validate that the research is not overlooking any missing

variables due to the potential of autocorrelation or dependence amongst the results, and

understanding that social network analysis of organizations often have smaller sample sizes, this

indicates that a sample size of 80 should be adequate for this research. This is consistent with the

precedent of having richer data, but smaller samples, such as the Hawthorn study of 14

(Roethlisberger and Dickson 1939; Sykes, Venkatesh et al. 2009) , the research on MAPS of 81 (Sykes,

Venkatesh et al. 2009), and the research on TAM/SIPM of 31 (Lee, Cho et al. 2003).



3.3.2 Survey Instrument and Measures

Data collection methods: questionnaire design, question design

This study will primarily be using instruments that have been used before in order to allow

comparisons to previous research and focus more energy on the factors around social influence

through different ways of modeling it.

For the bulk of the questionnaire it leveraged questions from earlier works from Venkatesh on

UTAUT. (Venkatesh, Morris et al. 2003) For Performance Expectancy, Effort Expectancy, Social

Influence, Facilitating Conditions, and Behavioral Intention, the survey questions were based on

earlier established work and had acceptable loadings of greater the 0.7 showing adequate content

validity (Venkatesh, Morris et al. 2003)


I would find the system useful in my job.

Using the system enables me to accomplish tasks more quickly.

Using the system increases my productivity.

If I use the system, I will increase my chances of getting a raise.

Effort expectancy

My interaction with the system would be clear and understandable.

It would be easy for me to become skillful at using the system.

I would find the system easy to use.

Learning to operate the system is easy for me.

Social influence





Facilitating conditions





Behavioral intention to use the system

I intend to use the system in the next <n> months.

I predict I would use the system in the next <n> months.

I plan to use the system in the next <n> months

.



For the implicit social influence questions, they were based on Kim, Jahng et al’s research on “An

empirical investigation into the utilization-based information technology success model: integrating

task performance and social influence perspective” (Kim, Jahng et al. 2007)

Implicit social influence

My colleagues are frequently using the software for their job-related tasks.

My co-workers are frequently using the software for their job-related tasks.

My supervisor is frequently using the software for his or her job-related tasks.

My subordinates are frequently using the software for their job-related tasks.

My company’s executives are frequently using the software for their job-related tasks.

In the second technology acceptance project, the Event Management System, there was not an

effective way to gather system usage through system logs or specific knowledge of usage, so four

questions were added to the post-pilot survey to provide a secondary validation of the actual system

usage which were also used in earlier work in the study titled “Prediction of Internet and World

Wide Web usage at work: a test of an extended Triandis model” published in 2002 by Cheung,

Chang, and Lai (Cheung, Chang et al. 2000). These questions had a loading of .75 or greater. It is

important to note that when looking at self-reported or system reported usage, Agarwal and Prasad

(Agarwal and Prasad 1998) concluded that current usage was not a significant predictor of future use

intention.

I use the <system> very intensively (many hours per day, at work)

I use the <system> very frequently (many times per day, at work)

I use the <system> for variety of applications (reports, projects, decision making, etc..)

Overall, I use the <system> a lot

To enable the social network modeling, five other questions were added to identify the interactions

between the individuals in a social network which were also leveraged from earlier works to help

identify the coping and influence networks, but were modified to allow 1-3 answers from the

respondents. (Sykes, Venkatesh et al. 2009)


needed for effective use of the Event Registration System (e.g., system features, upcoming releases, demo dates,

etc.).


use of the Event Registration System.


of the Event Registration System.


domain/functional knowledge for effective use of the Event Registration System (e.g., such as tips and tricks,

short-cuts, process sequence, etc.).


effective use of the Event Registration System (e.g., training resources, manuals, tutorials, etc.).



3.3.3 Data Collection Process

Method of contacting respondents

The research involves participants within the same company of which the researcher has established

relationships with most departments in its headquarters and one of its larger satellite offices. For

the respondents, the researcher initially contacted them individually via phone, individually in-

person, or as part of a regularly scheduled staff meeting to explain the purpose, outcomes, level of

anonymity prior to sending them an hardcopy survey, participant consent, and information sheet.

Enterprise Project Management (EPM) Technology Pilot

For the MS Project Server subjects, the researcher attended the project kick off and training session

which consisted of a team of 11 people who were going to participate in the pilot. The meeting

included eight employees of the marketing company and three consultants who were hired to help

configure and conduct the pilot on a sampling of projects within the IT department. The researcher

received permission from the trainer to use 15 minutes on the agenda right before a break on the

first day. During this first 15 minutes where the researcher gave an overview of the research, then

provided the attendees with the information sheet, participant consent form, and hardcopy surveys

which were all returned by the end of the break. The researcher emphasized the confidentiality of

the surveys and that they were completely optional because the researcher was a stakeholder in the

initial decision to evaluate this particular technology, and several of the individuals participating in

the pilot reported into the researcher’s department that he headed as the IT Director. The

researcher was also included as a participant because he was a stakeholder and potential user of the

new technology and would likely use it in his department if the company moves forward with a

rollout after the pilot. The CIO was also asked to complete a survey because he would likely be using

reports or project portfolio views from the technology, was providing the funding, was the final

decision maker for the project, and frequently inquired onto its progress (approximately 1-2 times

per week).

The pre-pilot survey was distributed in a hardcopy format primarily because the researcher had face-

to-face access to the subjects and needed them to sign the participant consent form as well. The

second survey was delayed and conducted eight months later for two reasons. First, it was

concluded based on the pilot and two months of informal discussions that there was not enough ROI

to continue after the pilot so an immediate rollout was not pursued. Second, the majority of the IT

organization was overwhelmed with work because the company was sold to a new owner which

required moving and rebuilding the datacenter and applications. The original 11 subjects in the MS

Project Server pilot were significantly dispersed by the time the second survey was conducted. Two

of the three consultants were on assignment at other clients and two the employees no longer

worked for the marketing company. For those who were no longer at the marketing company, an

online survey was sent to their current email addresses and responses were collected from 9 of the

original 11 respondents.

Event Management System Pilot

For the Event Management System pilot, the 36 initial hardcopy surveys were distributed

individually to the resources that were close to the pilot or parts of the operations groups that would



be significantly impacted by a new events registration technology. The initial selection of

participants was done by the researcher and the department Vice President where most of the

events staff worked. Originally they picked 30 individuals who would be impacted or were part of

the network of individuals working on this pilot. After distributing the first 30 surveys and receiving

some back, there were several others that were also given surveys because they were identified as

part of the network of individuals who might give help or receive help on the usage of the Event

Management System from the others who already completed the surveys.

To distribute the surveys, the researcher visited each person’s desk, gave them a 2-3 minute

overview of the research and gave them the survey, participant consent form, information letter,

and self-addressed stamped envelope to mail the survey in a confidential fashion to the researcher’s

residence. One group which sat in the same cube row put all of their survey results in a single

intercompany envelope as they finished them on the desk of their team lead. Some of these were

put into sealed envelopes before they were combined with the others in the larger envelope. The

researcher collected these twice daily so they were not left on the team lead’s desk for an extended

period. The researcher was also included as a participant in this study, as well as the CIO because

they were involved in the business case, functionality, and training discussions around the pilot.

Thus, they were part of the network of individuals affecting the technology acceptance.

The data collection approach included both survey data and observational data collected at the

beginning of the pilots and then after the initial pilots were complete as shown in Figure 11

Longitudinal Approach to Data Collection.

Pre-PIlot (month 1)

Survey Data (all participants)

Control/Moderator Variables (age, etc)

Influence Network Connections

Coping Network Connections


Effort Expectancy

Social Influence


Behavioral Intention to us the system

Implicit Social Influence

Observational Data

Proximity Network

Role / Title / Company / Department

Organization Hierarchical

Longitudinal Approach to Data Collection

Post Pilot (3 months later for EMS, 8 months

later for EPM)

Survey Data (all participants)

Control/Moderator Variables (age, etc)

Influence Network Connections

Coping Network Connections


Effort Expectancy

Social Influence


Behavioral Intention to us the system

Implicit Social Influence

Self-Reported Usage (pre & post pilot)

Figure 11 Longitudinal Approach to Data Collection

3.3.4 Data Analysis

Overview and statistical tools used



The data analysis for this research consisted of collecting the pre and post-pilot survey data and

identifying any significant missing network nodes. This survey data was then combined with data

from organizational documents and the names encoded for confidentiality into statistical analysis

software. Social network models were then created to calculate the network perception values,

centrality, and groups. Some of the network measures, such as betweenness centrality and the

neighbor’s average performance expectation were then fed back into the statistical software as

attributes enabling pooled and un-pooled statistical analysis of descriptive statistics, correlations,

loadings, hierarchical regressions, and Analysis of Variance to calculate adjusted R2 values. These

steps enabled the research to address the hypothesis and research questions.

Two additional steps were completed in order to provide further insight into the data. First, there

were two qualitative interviews done to provide context of the event registration technology

acceptance. Second, the standard UTUAT perceptions attributes for each participant were mapped

back into the network graphs to allow visualization of the perceptions in combination with groups

and centralities. The statistical analysis tool used for this quantitative study was SPSS Version 19

(Statistical Package for Social Sciences). UCINET Network Analysis Software (Borgatti, Everett et al.

2002) was used for matrix algebra to transform perception attributes and adjacency matrices into

neighborhood means for each actor and consolidate network modes, and calculate centralities and

groups. NetDraw (Borgatti 2002) was used to render network graphs and their related attributes.

Collection of Pre and Post-Pilot Data, Encode Names, and Identify Missing Nodes

For both the pre and post pilot surveys, the technology acceptance attributes, basic demographics,

and social network connections were collected through confidential online or hardcopy surveys. The

remaining attributes that were collected and coded outside of the survey based on organizational

documentation as described in table 6.

Table 4 - Coded attributes for participants not collected in survey

Attribute Source Coding Notes

Gender Researcher Entered The researcher already knew the participant or met them

when the survey has presented to the participant

Office

Location

Office Layout & location

noted on organizational

hierarchy diagram

Researcher segmented headquarters using office layout

and noted their location on a hierarchy view of the

organization during distribution of surveys.

Company Company affiliation of

participant, if not part of

target company

Non-marketing company individuals were labeled by their

specific company, such as “Event Management System

Vendor” or “Client A”

Role Organizational hierarchy

diagram, established

consulting role, or published

on company website

Roles were generalized to allow pooling across both

initiatives and protect confidentiality if only one person

has a specific title.



Title Organizational hierarchy

diagram, established

consulting role, or published

on company website

The titles are closely mapped to the roles and not as

generic, so they are not used in the analysis.

Experience Coded based on pilot

timeline

Pre-pilot: 0

Post-pilot: 1

This was generalized, but not perfect because some

individuals had prior experience with the technologies

before the pilots through other companies or some

exposure for a specific client.

Voluntariness Coded taking into account

the status of pilot, role, or

company

Exec: voluntary for their role

Director: voluntary for their role, unless they were

assigned to the pilot initiative as a user

Technology Vendor: Mandatory for both pre and

post pilot surveys

Technology Consulting Vendor: mandatory for

both pre and post-pilot surveys

Resources assigned to pilots: mandatory for pre-

pilot survey

Voluntary for marketing company resources on

EPM system post-pilot survey because it was

organizationally decided not to implement the

system

Actual Usage Surveyed for most individuals

on EMS project, but coded

for EPM project based on

role, and company

As reported in second EMS survey, if not reported,

then actual usage entered based on following

rules, or left blank if questionable

Marketing Company Exec: No usage, since they

didn’t have hands-on experience

Technology Vendor: Yes

Technology Consulting Vendor: Yes

For the social network connections, the survey asked for 1-3 names of resources that the participant

interacted with on a weekly basis for the five network modes; give help, get help, knowledge,

resources, and information. This was done because of the large size of the departments that could

influence the acceptance of the technologies and the difficulty of accurately identifying all of the

possible relevant connection nodes in the five network modes. For the Enterprise Project

Management pre-pilot survey, there were a significant number of connections outside of the initial

targeted list of survey participants; many of which were outside of the marketing company. Based



on this learning, when the Event Management Surveys were sent out, a “snowball” approach was

taken where additional surveys were also sent out to connections that were identified by 2 or more

of the initial targeted participant list as connections.

By not providing a list of the possible connections, there was a risk of missing significant connections

resulting in a lower density network, or having a larger number of isolates because the survey

participant might not be able to think of a relevant connection at the time they took the survey.

Initial data analysis for suitability

The initial data analysis focused on checking the quality of the data look for intervening or

controlling variables such as gender using the basic statistical descriptive values and looking for

outliers.

Correlations

The relationship strength and direction between the continuous variables were calculated using the

Pearson’s correlation coefficient r. This can also be squared to determine the shared variance.

When looking at the relationship strength, the study followed the rule of thumb that r=.10 indicates

a small effect, .30 indicates a medium effect, .50 indicates a large effect size (Tharenou, Donohue et

al. 2007).

To avoid computational and interpretational problems with multiple regressions, the study also

validated the assumption of absence of multi-collinearity through the check of the bivariate

correlations using Pearson’s correlation coefficients. The study didn’t need to drop any variables,

which would be the solution if two independent variables were correlated higher than .70. If the

coefficient was greater than .70 it would indicate that they may have collinearity and the least

theoretically defensible one would be dropped (Wilkinson and (TFSI) 1999).

Adjacency Matrices

For each independent survey set, adjacency matrices were created for each of the five modes of

connections; give help, get help, information, knowledge, and resources. These were created to be

used in Ucinet (Borgatti, Everett et al. 2002) and Netdraw (Borgatti 2002) to perform social network

analysis. There are two survey sets for each new technology initiative; one for the pre-pilot surveys

and one for the post pilot surveys. All of the strength of the connections was 1 for an identified

connection and 0 for non-connection. An additional adjacency matrix was created for each survey

set which represented the influence and coping networks. The coping network was the sum of the

get help and give help connection modes. The influence network was the sum of the access to

information, resources, and information modes. Thus if a participant indicated they frequently

interact with a colleague whom they gave help to on the technology and received help on the

technology, their aggregate connection strength is 2 for the coping network.

Social Network Graphs

Network graphs were created in Ucinet and Netdraw to help visualize and analyze the network

dyads identified from the surveys. Below is an example of an organizational social network of



individuals for illustrative purposes which were constructed by asking survey respondents to list 1-3

people that they interact with or will interact with in a typical week to get help from for effective use

of a particular new technology. In this case, each node represents a person, each directed link

represents identified relationships, the node size represents the betweenness centrality of the

nodes, and the color-coding represents particular factions of the network.

Figure 12 - Example of Organizational Social Network constructed from asking respondents "List 1-3 people that you

interact with or will interact with in a typical week that you get help from for effective use of the Event Registration

System".

Betweenness Centrality

This study needed to calculate the betweenness centrality using Ucinet and Netdraw for the coping

and influence networks. The calculation from Ucinet was imported back into the statistical analysis

tool (SPSS) to be used for the regression analysis. The centrality measure that was used to

determine the relative node size in the graphs was betweenness centrality within Netdraw. For the

regression analysis, the betweenness centrality was then normalized to allow comparison across

networks, then copied back into SPSS for factor analysis.

Hierarchical Regressions

Multiple regressions are the most common way in business research to understand the extent of

multiple independent variables on the dependent variable of Behavioral Intention, Use Behavior,

and others (Tharenou, Donohue et al. 2007). In this research, where the participants are from the

same workgroups, there is a significant risk in drawing inaccurate conclusions caused by

autocorrelation of data that resulted from the potential dependence between respondents. To

mitigate this risk, this study used a hierarchical regression to validate that the potential second or



third level fixed or random factors are not significant drivers of variance by initially entering the

demographic variables, then entering the work environment factors. Specifically, study will initially

be looking at time (experience), gender, department, technology, voluntariness, company, and role.

After these are accounted for, the variance from relevant UTAUT variables will be evaluated, and

only then the study evaluates the impact of the social network variables of coping network

centrality, influence network centrality, coping neighbor’s average performance expectancy, and

influencing neighbor’s average performance expectancy.

Alternatively, if these initial multiple regressions that include demographic factors are not

conclusive of negating autocorrelation, then a researcher could use a multi-level mixed model to

analyze the data at multiple levels to ensure the study is uncovering any homophily within the

sample set so it can be generalized beyond this sample (Painter ; Singer 1998; Tabachnick and Fidell

2001).

3.3.5 Validity and Reliability of the Study

Reliability – is the data free of random errors?

Reliability, one goal of any research, is to ensure that it is replicable and will produce similar results if

repeated. This can be also viewed as the extent to which a measure is free of random measurement

error (Smithson 2005). Poor reliability weakens the effect size and limits our ability to detect

relationships between variables.

For this research the reliability was addressed in three ways. First, the study used existing scales

from prior established research for the survey questions which were shown to be reliable. All of the

survey questions from the UTAUT were based on earlier established work and had acceptable

loadings of greater the 0.7 (Venkatesh, Morris et al. 2003) and the Implicit Social Influence loading

was .868 (Kim, Jahng et al. 2007) showing adequate content validity.

Second, the study calculated the Cronbach's alpha coefficient for the pooled data, which is an

internal consistency reliability to validate that items are consistent with each other. (Nullally, 1978)

Third, the researcher had three experts review the survey instrument to validate that the questions

where understandable. For a pre-pilot of the survey instruments, the research asked these three

employees at the target company to review the surveys and identify any areas that might be

confusing to answer. The three experts were an IT Director and two Business Consultants. All three

were also targeted users for the research as well. There was only one significant issues identified

which was in the Event Management System survey where question 20 could be confusing.

Question 20 was the first of the implicit social influence questions based on Kim, Jahng et al’s

research on “An empirical investigation into the utilization-based information technology success

model: integrating task performance and social influence perspective” (Kim, Jahng et al. 2007). The

possible confusion was from the fact that in the previous 19 questions from the UTAUT research

(Venkatesh, Morris et al. 2003), the word “system” was replaced by the specific technology in

question for this study. In the end, the implicit social influence questions where not modified from

the source study which only referred to the “software”. After discussing this with the IT Director,

the researcher agreed this was a possible source of confusion, but it was decided to keep the



wording unchanged to stay consistent with the earlier Enterprise Project Management survey data.

The confusion risk was also mitigated because the technology name is stated on the title, the

communications of the survey, and was discussed when the researcher handed the survey to the

participant.

Validity of Survey Instrument and Measures

To help ensure this validity of this research, all of the survey questions were based on previous

published research and did not require localization. The research sources, any modifications to the

questions, and types of participants used are in Table 5 - Survey Instrument Question Sources and

Types of Participants. Generally, the questions where used as is or with only minor modifications to

improve clarity.

Table 5 - Survey Instrument Question Sources and Types of Participants

Survey

Questions

Previous

Research

Source

Modifications Types of Participants

Perceived

Usability

Questions

(Venkatesh,

Morris et al.

2003)

Replaced “system”

with specific

technology name to

clarify question

80 voluntary research analysts using financial

research software in financial services

industry analysts and 53 mandatory call-

center representatives in retail electronics

using contract document management

software

Perceived

Ease of Use

Questions

(Venkatesh,

Morris et al.

2003)


with specific

technology name to

clarify question






software

Social

Influence

(Venkatesh,

Morris et al.

2003)


with specific

technology name to

clarify question






software

Facilitating

Conditions

(Venkatesh,

Morris et al.

2003)


with specific

technology name to

clarify question






software



Behavioral

Intention

(Venkatesh,

Morris et al.

2003)


with specific

technology name

and replaced “<n>”

with 3 months to

clarify question






software

Implicit Social

Influence

(Kim, Jahng

et al. 2007)

No change 136 corporate IT users of word-processing

software and 126 corporate IT users of

computer-mediated communication

software in 15 firms

Network

Identification

(Sykes,

Venkatesh

et al. 2009)

Created 5 questions

using wording for 5

modes from

reference to identify

1-3 people the user

interacts with on a

typical basis

87 employees in one business unit

Internal Validity Threats

Validity is the extent that a measure actually measures what it intended to measure. Cook &

Campbell define internal validity as “the approximate validity with which we can infer that a

relationship between two variables is causal or that the absence of a relationship implies the

absence of cause” (Cook and Campbll 1979). There are seven major threats to internal validity

which could lead to incorrect conclusions on the cause and effect of the dependent and independent

variables which include history, maturation, testing, instrumentation, selection, statistical

regression, and mortality. These are evaluated in more detail in the following seven sections. Of

these seven threats, there are some minor concerns of the internal validity due to the history effects

of the high-stress and changing environment.

History

History effects are when certain events could be occurring during the research that could impact the

relationship between the variables. In the case of these two new technology pilots there was

significant reorganization resulting from a merger happening in parallel, but these reorganization

activities did not have formal or stated direct impacts on the contingent or individual acceptance of

the Enterprise Project Management System or Event Management System. For example, the

funding or expectations of either project were not directly tied to any merger initiatives or decisions.

Regardless, this type of a high-stress and rapidly changing environment could impact the perceptions

or innovativeness of individuals or the organization.



There was also a continual flow of events and projects in various stages throughout these pilots,

which is the business as usual state and it would be expected that these new technology pilots

would experience the same challenges regardless of the time of the year.



Maturation

Maturation effects would only be a concern if the results are affected by the passage of time. For

example if the change in the behavioral intention of the Event Management System over time is

caused by the employees being new and becoming better at managing client events overtime. This

effect is not expected because almost all the individuals managing the events and projects in the two

systems have been working in their professions for 5+ years as project managers, event planners, or

operations staff.

Testing

The testing effects are not a concern in this research because this field experiment did not include a

treatment between the first and second surveys. The longitudinal nature was focused on

understanding the social networks overtime, not to test the effectiveness of a particular treatment.

The testing effects only become significant when the respondents use their experience in filling out

the surveys the first time to become more proficient or take a different approach on completing the

surveys the second time.

Instrumentation

Instrumentation effects result from a change in the measuring instrument between pre-testing and

post-test. In this research the survey questions stayed the same across the surveys. The significant

difference is that most of the post-pilot surveys were online rather than hardcopy surveys. This

should not be a significant issue since the formatting, order, and wording where similar. Also, the

pre and post-pilot surveys were not statistically intended to measure the effects of a treatment that

happens between the tests. Instead each survey is intended to understand the relationship of the

antecedents on different dependent variables at that specific time.

Selection

The selection bias effects are complicated due to the cluster sampling and snowball survey

approach, but not concern for this type of social network research as suggested on previous

research.

Regarding the cluster sampling; essentially the research participants were not randomly selected,

but instead were selected using a cluster sampling where the natural group (cluster) was the

population within an organization that were participating in a pilot project. This cluster sampling

helped ensure the ability to create a representative social network model of the connections of

individuals and their interactions regarding the target technology. Selection effects could result

from this cluster sampling approach if more employees with higher personal-innovativeness or

having roles as formal process owners were being selected more often for technology pilots.

In addition to the cluster sampling, understanding the selection bias is further complicated by the

use of a “snowball survey”. For the Event Management System, the self-reported social network

connection questions were modified to ask an open ended question in a format that asks for a set

number of people who the user interacts with such as “List 1-3 people that you interact with or will

interact with in a typical week that have access to information needed for effective use of the Event

Registration System (e.g., system features, upcoming releases, demo dates, etc.).” This enabled a



snowball survey approach that lets the original respondents identify other important users that were

not part of the initial target sample. As respondents identified additional people they might go to

for help or knowledge on the technology, additional surveys were sent to these new respondents.

This snowball approach showed that the networks for use of technologies reached outside of the

business units; which included technical vendors, clients, colleagues in consulting companies, and

even spouses.

Self-reporting of social ties is the most common approach to gather network data and survey

methods have been shown to be reliable in collecting network data. Since this is a self-reported

survey, these are the cognitive networks that are the perceived social relations of an individual,

which could be different than the actual social relations (Marsden 1990).

Thus, this approach has also been shown to add to the validity because omitting or deletion of

boundaries can lead to misleading results (Barnes 1979; Marsden 1990). Erickson also supported

this use of snow-ball surveys to uncover the boundaries with self-reported social relations (Erickson

1988).

Statistical regression

Statistical regression effects result from having participants who are at one extreme of a continuum

and do not represent the true cause and effect relationship. To understand the impacts of statistical

regression effects, the normality, linearity, and homoscedasticity of the results were reviewed to

ensure the validity of the data within the hierarchical regression.

Mortality

Mortality effects could impact the validity in situations where a significant portion of individuals

drop from the test between a pre and post-treatment survey. In this case, this was not an issue

because there was not a specific treatment between the pre and post-pilot surveys and very few

participants dropped from the study between the surveys. The mortality was also kept low because

the majority of the participants, who left the company before the post-pilot surveys, completed the

post-pilot surveys online.

External Validity Threats

External validity is focused on ensuring that the research conditions are as close and compatible as

possible to a real-world situation. Since this is a field experiment, its external validity is high and can

be generalized to other similar organizational settings running pilots for new technologies.

3.4 The longitudinal Study

3.4.1 Location of the Study

The technology acceptance initiatives for this study occurred in a 1200-1500 person company

headquarters. The company was privately-held, global marketing company providing sales,

marketing and advertising programs for Fortune 1000 clients. It was acquired by a larger publically

held company 6 months prior to the first pilot, so these pilots were occurring within the first 18



months of this acquisition, so there were significant merger activities going on in during these

timeframes. The acquiring company was a larger global marketing and loyalty organization, but for

this research the focus was only on the US region.

As a full-service marketing company, they are heavy users of new technologies for internal business

processes as well as developing and maintaining technology solutions for 50+ clients. The client

solutions included consumer loyalty systems, event registration system, travel booking systems,

sales/channel incentive programs, and employee engagement systems. Ibarra’s 1993 research, also

in an Marketing / Ad company, describes the dynamic nature of the clients which results in a high

pressure culture where a client can end their relationship abruptly and the assurance of employment

is weak especially for those working on the client that left (Ibarra 1993).

This company was chosen primarily because it was the researchers employer, which gave the

researcher both support and additional access to the subjects. The CIO and the Executive

Committee at the company have been supportive and open to the idea of research being conducted

within the company even after the researcher left the company in January 2011 as an outcome of

reorganization resulting from the merger. The role of the researcher in the company was that of an

IT Director for client-facing systems in the company and a large part of the researcher’s role is to

continue to improve the ability of the company to roll out new technologies.

3.4.2 Subjects or Participants

The subjects for this research are essentially a cluster sampling of individuals who are adopting the

same technology in roughly the same timeline to ensure that the researcher would be able to map

the actual network connections of teams and departments. These were longitudinal studies where

each participant completed the same survey at an early point in the introduction of the new

technology into the organization and at a later point where the subjects and organization have used

the technology for a few months. This research was done on actual projects inside of a global

marketing company, so it is non-contrived. The unit of analysis was the individuals with additional

understanding of dyad or network connections.

3.4.3 Technologies used for the study

Criteria used to select technology pilots used for this study

When looking for new technology acceptance pilot projects that would work within the target

organization, two were chosen that met the following criteria:

The new technologies would require significant changes in processes

These technologies where group technologies, not individual technologies, which meant that

the new technologies success is dependent on groups of a user using them, not just a single

individual.

These technologies were used to accomplish a business objective, not a personal objective.

There was a pilot involved prior to a larger roll-out.

The groups that would be the subjects are easily accessible by the researcher, which

generally meant the users were predominantly in the company headquarters.



The timing for the pilot projects coincided when the researcher’s availability to conduct the

research amongst other work demands.

The executive contacts in the company approved of using the technologies as part of the

research (CIO, VP HR, relevant department heads).

They were centralized initiative with a definite start date so the research and approvals can

be planned for and approved by the company executive committee.

Two pilot technologies were chosen for the study that met these criteria; an Enterprise Project

Management System and an Event Management System which are summarized in Table 6 -

Summary of Technologies Used in Study and described in the following two sections.

To help categorize these two new technologies or innovations, Damanpour provided a

categorization of innovations by classifying them as Administrative Innovations and Technical

Innovations. In the case of the Event Management System, it is a technical innovation because it

directly related to new products, services and the production process technologies (Damanpour

1987). Alternatively, administrative innovations are more internally focused on internal

management, which better describes the Enterprise Project Management System. Damanpour also

pointed out that often those with formal authority tend to be more involved in administrative

innovations like the Enterprise Project Management, whereas technical innovations such as the

Event Management System tend to require more informal network power and centrality.

Enterprise Project Management (EPM) System

The first project that used to collect technology acceptance and social network data was the

implementation of an enterprise project management tool, which was piloted by a small team of

individuals from the IT organization within the large marketing company in 2010.

An Enterprise Project Management System is essentially a consolidated repository for a portfolio of project plans, change records, issues, and risks from many different project managers within an organization. The company wanted the project managers to use Microsoft Project Professional to manage their project plans and store them within Microsoft Project Server where they were consolidated and leveraged a single resource list. Microsoft describes MS Project Server as follows

“Microsoft Project Server 2010 provides innovative capabilities across the entire lifecycle to help organizations

effectively initiate, select, plan and deliver projects on time and within budget. Enhance productivity and realize

results with Project Server 2010.”

There was an initial set of 11 users that were participating in the two month pilot. For technology

acceptance research, this became an even more intriguing set of data because at the completion of

the pilot, it was decided that the Enterprise Project Management tool would not implemented based

on the consensus of the pilot group, costs, and the other external factors such as a company merger

that put the follow-on phase on hold then eventually cancelled. The fact that this technology was

not accepted by the organization makes this an interesting set of data and case study for this

research. A second set of survey data was still collected eight months after the first survey from the

pilot group to complete the longitudinal data for this relatively small sample size.

Event Management System (EMS)



The second and larger of the two technology acceptance projects was an Event Management

System. This Software-As-A-Service Event Management System was targeted to replace the current

9-year old internally built Event Management System (EMS). Event Management Systems are used

by meeting planners for meetings, events, user conferences, and trade-show to manage the

registration of participants and their subsequent travel arrangements, activities, sessions, billing, and

other event activities.

Below is a more detailed description of an Event Management System from Capterra. In January of

2011, there were 193 different commercially available event management systems listed on

Capterra.

“Event Management software automates marketing, registration, coordination and scheduling of events. See More Planners use Event Management applications to manage the planning and process of meetings and events, including marketing, attendee registration, badge creation, calendar generation and venue space management. These applications also offer a variety of self-service functions for event attendees such as online registration, content delivery and fee payment. Event Management software is related to Registration software and Ticketing software” (Capterra 2011)

The system being piloted, lists the following functionality for their Event Registration System:

1. Event Registration

2. Event Email Marketing

3. Secure Online Payment Processing

4. Housing and Travel Management

5. Contact Management

6. Event Budget Management

7. Custom Event Websites

8. Event Workflow Management

9. Event Calendar

10. On-Site Functionality

11. Event Reporting

12. Event Data Security

13. 508 Compliance

14. Event Support & Training

15. Professional Services

The current proprietary registration system has been in place for 9 years and supports more than

500 meetings and events annually. There are approximately 150 users of the system that include

finance, event planners, call center representatives, onsite staff, attendee data specialists, IT staff,

and others. The current custom system and the various user groups have co-adopted over the 9-

years where the processes, people, and technology have evolved together closely. This resulted in

many features or ways of accomplishing tasks of the Event Management System which are designed

specifically for the target marketing company and would likely not be found in commercially

available systems.

The company was comfortable with their current system but was looking into what they should do

differently for their registration services to overcome three obstacles and exploit three trends to set

http://www.capterra.com/event-management-software




http://www.capterra.com/registration-software

http://www.capterra.com/ticketing-software



themselves up for growth in the future. The obstacles with their current registration system was

that its costs were no longer competitive, it was lacking competitive services, and switching to a new

event registration system would have significant impacts on the staff, clients, and processes. They

also recognized that there were three opportunities that presented themselves if they switched to

one of the commercially available registration system. First, there were more competitive event

registration options available in the market. Second, commercially available EMS’s had standard

Application Program Interfaces (APIs), which would help them reduce costs and speed up service

delivery for their clients that required integrations. Third, the market leading event registration

systems had other services that they currently didn’t offer their clients, which could also help grow

their business.

Overcoming these obstacles and taking advantage of these opportunities is what lead to the decision

to assemble a cross-functional team and conduct a two month pilot using 2-3 of the upcoming live

events to better prepare for an eventual change from their proprietary registration system.

It’s important to note that there was some exposure inside the company to four commercially

available EMS systems within the marketing company staff because some clients mandated that the

marketing company use the client’s instance of an EMS that the client had previously used or had

already paid for a license.



Table 6 - Summary of Technologies Used in Study

Technology

Acceptance

Projects

Typical Participants Population

Size for pilot

Pilot Project Description

Event

Management

System

(EMS)

Event Planners, IT Staff,

Contact Center Reps,

Financial Staff,

40-60 Replace 9 year old internal

proprietary system with

commercially available system

Enterprise

Project

Management

Project Teams, Program

Managers, Project

Manager Consultants

10-15 Implement centralized

enterprise portfolio

management services for project

managers

3.5 Summary

This chapter described the Design and Methodology that was used to adequately answer the four

research questions and provided enough guidance to duplicate this study by a knowledgeable

researcher.

This chapter started by describing the hypothesis development and key variables and how they help

answer the research questions. This was then followed by the research design section which

addressed the types of sample and sample size, survey instrument and measures, data collection

process, data analysis, validity issues, and reliability issues. Next, the longitudinal study section

described the location of the study, subjects, and technologies used for the study.

Chapter 4 presents the results of the two technology acceptance initiatives based on this design and

methodology.



CHAPTER 4 - DATA ANALYSIS

4.1 Introduction

The purpose of this chapter is to provide a systematic analysis of the data collected during the

research. The data analysis chapter starts with three sections to look at the data management prior

to entry, the initial data analysis for suitability, and reliability for the different loadings. These

sections are followed by the descriptive statistics and correlations. Following the correlation

analysis is the regression analysis to determine the explanatory power of the different models via

hierarchical regressions. The core statistical analysis is focused on testing the research questions

and hypothesis using exploratory factor analysis to provide an understanding of the loading and

determine if any factors need to be combined to their underlying dimensions, then using the

Pearson Correlation Coefficient to understand the linear association between the variables. The

statistical analysis is then completed with the analysis using multiple regression analysis to

determine the adjusted R2 of each model to understand the predictability of each model.

In addition to the core statistical analysis, this chapter also reviews the social networking and social

network analysis results to provide more insight into the data and answer the research questions.

These include analyzing the coping and influencing social network graphs for the two technologies as

well as mapping of the UTAUT responses to the social network graphs. This chapter then has a

section devoted to the conclusions of the research hypothesis. The last section is a summary of the

chapter.

4.2 Data Management prior to entry

For the data management prior to data entry, there was a validation of the questionnaire data for

missing data. There were not any missing answers in the paper or electronic surveys for the scaled

questions, but there were significant gaps in the social network data which is expected in responses

for questions such as “List 1-3 people that you interact with or will interact with in a typical week

that you get help from for effective use of the Event Registration System”. The pre-pilot surveys

were distributed in paper format along with the participant consent forms, but the post-pilot surveys

were online surveys which forced entries for all non-social network data. Upon data entry, a check

of a sampling of the data was done to ensure the raw data is categorized correctly and entered into

the statistical program correctly.

4.3 Initial data analysis for suitability

The initial data analysis focuses on checking the quality of the data look for intervening or controlling

variables such as gender using the basic statistical descriptive values and looking for outliers.

For the statistics, this research uses a pairwise deletion rather than Listwise so as to not eliminate

cases that do not have a full set of data, which happens in about a third of the cases for various

reasons such as an individual who completed a pre-pilot survey, but did not complete a post-pilot

survey. This individual might still have data for the post-pilot case such as a centrality which is

calculated based on how the other network nodes identified them with coping and influencing

connections.



4.4 Reliability / Loadings (Pooled for all projects & times)

4.4.1 Internal Consistency Reliability

Reliability is often defined as the extent to which a measure is free of random measurement error

(Smithson 2005; Tharenou, Donohue et al. 2007). These 24 questions were coded as interval scales

of measurement rather than ordinal. This is commonly done provided there are five or more

response categories and the underlying construct is conceptualized as theoretically continuous

(Tabachnick and Fidell 2001; Tharenou, Donohue et al. 2007). The internal consistency reliability

validates that items are consistent with each other and typically is measured by Cronbach's alpha

coefficient. Table 7 contains the Cronbach’s Alphas for this data. The results show that the

behavioral intention and implicit social influence of .90 or higher which is considered highly reliable.

Performance Expectancies are higher than .70, showing a fair reliability, but the Facilitating

Conditions at .625 is below the .70 threshold indicating a fair reliability (Nunnally 1978; Tharenou,

Donohue et al. 2007). Poor reliability weakens the effect size and limits our ability to detect

relationships between variables, which make the Cronbach’s Alpha of .625 for Facilitating Conditions

more vulnerable to this risk. In this research there werre not significant relationships between the

network measures and the facilitating conditions, so this does not impact any significant results.

Table 7 - Reliability Statistics

Cronbach's Alpha N of Items

Performance Expectancy .860 4

Effort Expectancy .840 4

Social Influence .790 4

Facilitating Conditions .625 4

Behavioral Intention .987 3

Implicit Social Influence .907 5



4.4.1 Covariance between Independent Variables and Behavioral Intention

Validity is simply the extent that a question measures what it’s supposed to. For this research, the

reliability and validity was also checked for the organizational or individual parameters that could be

the hidden source of variance using a Type III test of Fixed Effects and Estimate of Covariance within

SPSS to ensure we were measuring the correct measure. This included department, technology,

company, and role which are shown in table 8 through table 11.

From Table 8 - Estimates of Fixed Effects for Covariance between Company and Behavioral Intention,

the average Behavioral Intention was 5.66 and is statistically significant. From the Estimates of

Covariance Parameters, the estimated variance of the intercept is .20 and the estimated residual is

3.95, with only the residual as significant. This indicates that there is little discernible or statistically

significant variation between companies.

In running this same scenario for role as a nesting variables yielded similar results.

Table 8 - Estimates of Fixed Effects for Covariance between Company and Behavioral Intention

Parameter Estimate Std. Error Df t Sig. 95% Confidence Interval

Lower Bound Upper Bound

Intercept 5.655556 .740691 1.670 7.636 .027 1.780931 9.530182

a. Dependent Variable: Behavioral Intention.

Table 9 - Estimates of Covariance Parameters for Covariance between Company and Behavioral Intention

Parameter Estimate Std. Error Wald Z Sig. 95% Confidence Interval

Lower

Bound

Upper

Bound Residual 3.951923 .630707 6.266 .000 2.890420 5.403261 Intercept [subject =

Company]

Variance 1.016137 1.544468 .658 .511 .051663 19.986118


Table 10 Estimates of Fixed Effects for Covariance between Role and Behavioral Intention

Parameter Estimate Std. Error Df t Sig. 95% Confidence Interval

Lower Bound Upper Bound

Intercept 4.887560 .269596 4.776 18.129 .000 4.184654 5.590466

a. Dependent Variable: Behavioral Intention .

Table 11 Estimates of Covariance Parameters for Covariance between Role and Behavioral Intention

Parameter Estimate Std. Error Wald Z Sig. 95% Confidence Interval

Lower

Bound

Upper

Bound Residual 3.984840 .682346 5.840 .000 2.848755 5.573997



Intercept [subject =

Role]

Variance .193221 .421033 .459 .646 .002699 13.830490


4.5 Descriptive Statistics

4.5.1 Frequencies

Table 12 through 17 provides an overview of the frequencies of participants in terms of department,

company, location, technologies, and experience with the technology. The largest groups of

participants in the research are from the marketing company IT department and the business

operations groups.

Table 12 Department Frequencies of Participants

Frequency Percent Valid Percent Cumulative Percent

Valid EPM Vendor 10 7.6 8.2 8.2

IT E&E 25 18.9 20.5 28.7

IT Loyalty 10 7.6 8.2 36.9

IT Exec 2 1.5 1.6 38.5

IT 19 14.4 15.6 54.1

Participation - Reg/Data 14 10.6 11.5 65.6

Participation – Trainer 2 1.5 1.6 67.2

Participation - Air/Cust 12 9.1 9.8 77.0

Program Management 5 3.8 4.1 81.1

Registration System Vendor 9 6.8 7.4 88.5

Client A 1 .8 .8 89.3

Participant Management – Transportation 2 1.5 1.6 91.0

Client Services 2 1.5 1.6 92.6

Exec 7 5.3 5.7 98.4

Finance 2 1.5 1.6 100.0

Total 122 92.4 100.0

Missing System 10 7.6

Total 132 100.0

Table 13 Company Frequencies of Participants


Valid Marketing Company 102 77.3 79.7 79.7

Event Management System Vendor 9 6.8 7.0 86.7

EPM Consulting Vendor 10 7.6 7.8 94.5

Client of Marketing Company 1 .8 .8 95.3

Other 6 4.5 4.7 100.0

Total 128 97.0 100.0





Valid Marketing Company 102 77.3 79.7 79.7

Event Management System Vendor 9 6.8 7.0 86.7

EPM Consulting Vendor 10 7.6 7.8 94.5

Client of Marketing Company 1 .8 .8 95.3

Other 6 4.5 4.7 100.0

Total 128 97.0 100.0


Total 132 100.0

Table 14 Location Frequencies for Participants


Valid Detroit 3 2.3 2.5 2.5

Minneapolis - Suite E North 20 15.2 16.4 18.9

Minneapolis - Suite E South 18 13.6 14.8 33.6

Minneapolis - Suite H 55 41.7 45.1 78.7

Minneapolis - Suite C 4 3.0 3.3 82.0

Minneapolis - Suite F 2 1.5 1.6 83.6

EPM Vendor Office Minneapolis 9 6.8 7.4 91.0

Event Registration System Vendor Office DC 9 6.8 7.4 98.4

Other 2 1.5 1.6 100.0

Total 122 92.4 100.0


Total 132 100.0



Table 15 Technology Frequency for Participants


Valid Event Registration System 81 61.4 61.4 61.4

Enterprise Project Management 51 38.6 38.6 100.0

Total 132 100.0 100.0

Table 16 Age Frequencies for Participants


Valid 25 or less 2 1.5 2.3 2.3

26-35 15 11.4 17.2 19.5

36-45 22 16.7 25.3 44.8

46-55 40 30.3 46.0 90.8

56-65 8 6.1 9.2 100.0

Total 87 65.9 100.0


Total 132 100.0

Table 17 Experience Frequencies for Participants


Valid Pre-Pilot 67 50.8 51.1 51.1

Post Pilot (2+ months after start) 64 48.5 48.9 100.0

Total 131 99.2 100.0

Missing System 1 .8

Total 132 100.0

4.5.2 Descriptive Statistics for Factors

Table 18 shows the overall pooled statistics for this study. 41% of the respondents were male, with

the remaining female and the average age was 45 years old. The original UTAUT factors (Venkatesh,

Morris et al. 2003), the coping Network Neighbor’s PE, and the influence coping Network Neighbor’s

PE are presented in their 7-point Likert scales, but the voluntariness, normalized actual use,

normalized coping centrality and normalized influence centrality are normalized so their values are

between 0 and 1.



Table 18 - Descriptive Statistics for Factors (Pooled)

N Minimum Maximum Mean Std.

Deviation

Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std.

Error

Statistic Std.

Error Age 89 21 61 44.77 9.521 -.473 .255 -.355 .506 Male or Female 123 0 1 .41 .495 .351 .218 -1.908 .433

Voluntariness of Use 81 0 1 .31 .465 .844 .267 -1.321 .529

Performance

Expectancy

81 1 7 4.17 1.432 -.363 .267 -.355 .529

Social Influence 81 2 7 4.72 1.248 .023 .267 -.470 .529

Effort Expectancy 81 3 7 5.31 1.066 -.293 .267 -.594 .529

Facilitating

Conditions

81 2 7 4.72 1.111 -.032 .267 -.469 .529

Behavioral Intention 81 1 7 4.89 2.036 -.534 .267 -.934 .529

Implicit Social

Influence

81 1.00 7.00 3.1432 1.54765 .295 .267 -.788 .529

Normalized Actual

Use

70 .000 1.000 .35114 .415412 .534 .287 -1.503 .566

Normalized Coping

Centrality

111 .000 .708 .03918 .096567 3.947 .229 21.102 .455

Normalized Influence

Centrality

108 .000 .683 .03817 .097439 4.087 .233 20.255 .461

Coping Network

Neighbor’s PE

66 1.50 6.50 4.4713 .95729 -.393 .295 .759 .582

Influence Network

Neighbor’s PE

68 1.50 6.25 4.5014 1.02687 -.562 .291 .285 .574

Valid N (listwise) 45

Tables 19 and 20 provide the descriptive statistics of both the pre-pilot results and post-pilot results

for the Event Management System pilot. For this technology pilot the majority of the respondents

were women, 63%, and the average age was 45. This project was unable to reach its initial goals

and can’t be considered a success, which is also shown in table 19 and 20 in the form of dropping

results. The performance expectancy dropped from 4.13 to 3.74, which swayed it from an overall

positive impression to a negative impression that this technology will help the participants do their

job. The behavioral intention also dropped from 5.15 to 4.70 between the pre and post pilot results.

In conjunction to these drops the Normalized Actual Use also dropped from .31 to .262 and the

Average Coping and Influencing Centralities dropped as well which is further explored in the Social

Networks later in this chapter due to the fragmentation of the coping network. Alternatively and

somewhat surprisingly, the Effort Expectancy, Social Influence, Implicit Social Influence, and

Facilitating Conditions rose significantly. Lastly, the average performance expectancies of the

neighbors also dropped in accordance with the drops in the performance expectancies.



Table 19 Descriptive Statistics for Factors (EMS Pre-Pilot)

N Minimum Maximum Mean Std. Deviation

Age 35 31 61 44.50 9.762 Male or Female (0:Female, 1 Male) 41 0 1 .32 .471

Voluntariness of Use ( 35 0 1 .29 .458

Performance Expectancy 35 1 7 4.13 1.477

Social Influence 35 2 7 4.52 1.099

Effort Expectancy 35 3 7 5.11 .963

Facilitating Conditions 35 2 7 4.39 1.104

Behavioral Intention 35 1 7 5.15 1.898

Implicit Social Influence 35 1.00 7.00 3.0000 1.46408

Normalized Actual Use 25 .000 1.000 .31040 .360491

Normalized Coping Centrality 43 .000 .280 .03147 .064726

Normalized Influence Centrality 42 .000 .352 .03619 .080405

Coping Network Neighbor’s PE 32 2.50 5.75 4.5424 .77592

Influence Network Neighbor’s PE 32 2.50 6.25 4.4506 .86391


Table 20 Descriptive Statistics for Factors (EMS Post-Pilot)


Age 27 31 61 44.57 8.884 Male or Female 35 0 1 .37 .490

Voluntariness of Use 26 0 1 .23 .430



Effort Expectancy 26 3 7 5.17 1.079

Facilitating Conditions 26 3 7 4.70 1.070





Normalized Influence Centrality 29 .000 .175 .01845 .037551

Coping Network Neighbor’s PE 23 1.50 6.25 3.9910 1.05439

Influence Network Neighbor’s PE 17 1.50 5.63 3.8085 1.10612


Tables 21 and 22 provide the descriptive statistics of both the pre-pilot results and post-pilot results

for the Enterprise Project Management pilot. For this technology pilot 52% of the respondents were

male, and the average respondent age was 46. This project also was canceled shortly after the pilot,

which should show a drop in many measures. In this case, the post-pilot surveys were

approximately 6-8 months after the initial survey, so the post-pilot results are several months after



the completion of the initial pilot. The performance expectancy dropped from 4.95 to 4.61, which

was not as significant of a drop as the Event Management System between the pre and post-pilot

surveys. The behavioral intention also dropped from 5.97 to 2.78 between the pre and post pilot

results which would be expected as the project was cancelled after the pilot. In parallel with the

drops in Performance Expectancy and Behavioral Intention, the Normalized Actual Use also dropped

from .818 to .125, which was expected because the resources working on the pilot used the

Enterprise Project Management System during the pilot, but most didn’t have access to it after the

pilot was cancelled. Unlike the Event Management System project, the Effort Expectancy, Social

Influence, Implicit Social Influence, and Facilitating Conditions dropped in parallel with the other

factors. Lastly, the average performance expectancies of one’s neighbors appeared to rise, but this

was a false result because there were so few connections left in the network and the only remaining

connected neighbors where the EPM consulting vendor resources that likely were still using EPM

tools with their other clients.

Table 21 Descriptive Statistics for Factors (EPM Pre-Pilot)




Performance Expectancy 11 3 6 4.95 .886

Social Influence 11 5 7 5.77 .918


Facilitating Conditions 11 4 7 5.48 .997






Centrality

22 .000 .400 .05641 .111304

Coping Network Neighbor’s

PE

8 4.50 5.42 4.9896 .35558

Influence Network

Neighbor’s PE

12 4.50 5.75 5.1790 .43728




Table 22 Descriptive Statistics for Factors (EPM Post-Pilot)







Facilitating Conditions 9 4 7 5.11 .961






Centrality

15 .000 .683 .05507 .174876

Coping Network Neighbor’s

PE

3 5.29 6.50 6.0120 .64104

Influence Network Neighbor’

s PE

7 3.25 6.25 5.2544 1.16264


4.5.3 Normality, Linearity, and Homoscedasticity

Three assumptions used in analysis of variances are the Normality, Linearity, and Homoscedasticity.

This study validates the normality assumption of univariate normality using Kurtosis and skewness

which are shown in table 18 for the pooled data (Wilkinson and (TFSI) 1999). For normality, we

expect the absolute value of Kurtosis should be less than 5, and the absolute value of skewness

should be less than 2. In reviewing the Kurtosis and Skewness of the pooled data in



Table 18 - Descriptive Statistics for Factors (Pooled), the assumption of normality is validated for all

the factors with the exception of the Normalized Influence Centrality and Normalized Coping

Centrality.

To validate the linearity assumption of the data, this research used a manual inspection of data the

plot to validate that it’s generally linear and not obviously non-linear. Violation of the linearity

assumption weakens power of the statistical test to detect an effect, but does not invalidate the

results (Wilkinson and (TFSI) 1999; Tharenou, Donohue et al. 2007). Linearity can be seen in the

significant relationships of this study as shown in the following plots: Figure 13 Plot of Linear Curve

Fit of Performance Expectancy and Behavioral Intention, Figure 14 Plot of Linear Curve Fit of an

individual’s Influence Network Neighbor’s Performance Expectancy and their own Performance,


Expectancy and their own Use Behavior, Figure 16 Plot of Linear Curve Fit of an individual’s Coping

Network Neighbor’s Performance Expectancy and their own Performance Expectancy, and Figure 17

Plot of Linear Curve Fit Normalized Coping Centrality and their own Effort Expectancy.

The final validation is the assumption of homoscedasticity which is checking the variability of

different scores is roughly the same at all values. Pearson's correlation coefficient and multiple

regressions assume homoscedasticity of data because they compare means of independent groups.

This validation was also done by a manual check based on the generated bivariate scatter plots of

variable combinations and check to see that scores do not disperse as they move up or down each

scale.

If homoscedasticity is significantly violated, a researcher can adopt a more conservative alpha

criterion such as .025 for moderate heteroscedasticity and .01 for severe heteroscedasticity in tests

of statistical significance (Wilkinson and (TFSI) 1999; Tharenou, Donohue et al. 2007). The factors

that are concerning in terms of homoscedasticity is the Normalized Coping Centrality as shown in

Figure 17 Plot of Linear Curve Fit Normalized Coping Centrality and their own Effort Expectancy. This

is consistent with the centrality data also violating the assumptions of normality and should be taken

into account in any hypothesis relating to centrality.

The remaining factors appear to validate the assumption as homoscedasticity as was checked in the

significant relationships of this study using the plots in Figure 13 Plot of Linear Curve Fit of

Performance Expectancy and Behavioral Intention, Figure 14 Plot of Linear Curve Fit of an

individual’s Influence Network Neighbor’s Performance Expectancy and their own Performance,


Expectancy and their own Use Behavior, and Figure 16 Plot of Linear Curve Fit of an individual’s

Coping Network Neighbor’s Performance Expectancy and their own Performance Expectancy.



Figure 13 Plot of Linear Curve Fit of Performance Expectancy and Behavioral Intention



Figure 14 Plot of Linear Curve Fit of an individual’s Influence Network Neighbor’s Performance Expectancy and their own

Performance



Figure 15 Plot of Linear Curve Fit of an individual’s Influence Network Neighbor’s Performance Expectancy and their own

Use Behavior



Figure 16 Plot of Linear Curve Fit of an individual’s Coping Network Neighbor’s Performance Expectancy and their own




Figure 17 Plot of Linear Curve Fit Normalized Coping Centrality and their own Effort Expectancy



4.6 Inter-Correlations among Study Variables

The relationship strength and direction between the continuous variables were calculated using the

Pearson’s correlation coefficient which is shown in Table 23. These are squared to determine the

shared variance. When looking at the relationship strength between the variables, this study

followed the rule of thumb that r=.10 indicates a small effect, .30 indicates a medium effect, and .50

indicates a large effect size. (Tharenou, Donohue et al. 2007)

To avoid computational and interpretational problems with multiple regressions, this study also

validated the assumption of absence of multi-collinearity through the check of the bivariate

correlations using Pearson’s correlation coefficients. Based on the correlations in table 23, there

were three correlations that exceed .70 that were suspect of multiple-collinearity; specifically the

Influencing Network Centrality correlated highly with the Coping Network Centrality, the Coping

Network Neighbor’s PE correlated with the Influencing Network Neighbor’s PE, and the Facilitating

Conditions with Effort Expectancy. The correlations between the two centrality measures and two

“Average PE of an individual’s Neighbor’s” could be an indication that the Influencing and Coping

Networks are likely made up of many of the same individuals, but in prior research they were

treated separately. The correlation between the Facilitating Conditions and Effort Expectancy was

only .707, yet these are well-researched factors, so no action was taken to avoid multi-collinearity.

Based on these results, it was decided to not drop any variables, which often is the solution if two

independent variables were correlated higher than .70. More specifically, it would indicate that they

may have collinearity and the least theoretically defensible one would be dropped (Wilkinson and

(TFSI) 1999).

There were nine notable observations from this correlation analysis that are discussed in the

following sections which either confirmed existing UTAUT findings or brought new insight from the

combination of UTAUT variables with social network variables.

Network centrality correlated to Effort Expectancy, Social Influence, & Facilitating

Conditions

Both the influencing and coping network centrality measures are significantly correlated to Effort

Expectancy, Social Influence, and Facilitating Conditions. These were small-effect correlations

between .250-.280 with a significance of .05. These five factors share a common theme of relating

to an individual’s access to other resources and people which enable ease of use and remove

obstacles to using a technology.

An individual’s Performance Expectancy is correlated with their Neighbor’s

Performance Expectancy.

An individual’s Performance Expectancy is correlated with their Neighbor’s Performance Expectancy.

These were large-effect correlations at the .01 significance level. The importance of this correlation

is at the center of this research because it bridges the most important factor in UTAUT, Performance

Expectancy, to other individuals within their informal networks. This could indicate significant

influence by others in the networks, but it needs to be evaluated within the later regression analysis



to determine if there are any underlying reasons for this correlation such as particular departments

being very homogenous.

An individual’s Facilitating Conditions is correlated to the average Performance

Expectancy of their neighbors.

The neighbor’s average performance expectancy for both coping and influencing networks are highly

correlated positively to their responses on Facilitating Conditions. This is a medium effect

correlation at the .05 confidence level for the coping network and the .01 confidence level for the

influencing network. This could suggest that with higher Performance Expectancy of one’s

neighbors, the more we feel that we have access to Facilitating Conditions to help us use the new

technology or innovation.

An individual’s Behavioral Intention and Usage is correlated to their neighbor’s

average Performance Expectancy.

The neighbor’s average performance expectancy is positively correlated to the Behavioural Intention

and Usage Behaviour as a medium effect at the .05 significance level or better. This could just be the

result of an antecedent chain of Neighbor Performance Expectancy to Individual Performance

Expectancy to Behavior Intention to Usage.

Implicit Social Influence or Social Influence measures are NOT correlated to the

Average Performance Expectancy of one’s neighbors.

This lack of a significant correlation of either the Implicit Social Influence or Social Influence

measures with the Average Performance Expectancy of an individual’s Neighbors is surprising. One

might expect that if an individual’s colleagues, subordinates, supervisors, and other important

individuals are frequently using a new technology, they might also expect these to be connections in

their informal networks and have positive Performance Expectancy. This is not the case, which could

indicate that an individual’s Performance Expectancy is not influenced by their Neighbor’s usage of a

system, but instead is only influenced if their neighbors feel the system has utility (PE) and helps

them do their job better.

This could be a factor in why Social Influence and Implicit Social influence measures have

traditionally not been a large contributor to Behavioral Intention because the social measures are

focused on usage, not perceptions of utility.

The Influence and Coping Networks are highly correlated

The Influencing and Coping Networks are highly collinear as shown by their large-effect correlations

at the .01 significance level for both the Centrality and Average Neighbor’s Performance Expectancy

measures. As discussed earlier, a researcher might drop one of the networks if this was only a

quantitative study, but there are significant differences in reviewing the social network graphs in

terms of power within these two networks, so they are not combined.



Average Neighbor Performance Expectancy is more correlated to TAM factors then

Centrality Measures

When looking at the correlations between the Network measures and the UTAUT measures, the

Average Neighbor Performance Expectancy measures have more significant and larger effect

correlations then the Centrality measures. This could suggest that network neighborhoods play a

larger role in UTAUT then network position.

Neighbor’s Performance Expectancy is correlated to a user’s Department

Another noticeable correlation is between the user’s Department and the Average Neighbor’s

Performance Expectancy. This is a medium effect correlation at a .01 significance level. This could

have resulted from resources within a department being more likely to be connected within a

network and having tendency to be homogenous in their perceptions.

Performance Expectancy, Effort Expectancy, and Facilitating Conditions are

correlated with Department

Performance Expectancy, Effort Expectancy, Facilitating Conditions have medium-effect correlations

with the individual’s Department. This can be expected because the individuals within each

department could be impacted by a new technology similarly, have common experience and

comfort levels with the previous technology, or have similar access to resources to help the use a

new technology. The most important result from this correlation is that the analysis needs to

account for the Department impacts early in the hierarchical regression before we can draw any

conclusions on the other variables.



Table 23 Correlation Matrix

Vln

tr

Dep

t

Ag

e

PE

EE

SI

FC

BI

ISI

Use

Inf

Cen

t

Cp

Cen

t

Cp

Nei

gh

bo

r’s

PE

Inf

Nei

gh

bo

r’s

PE

Voluntari-

ness of Use

1

81

Department

0.105 1

0.351

81 124

Age

0.001 0.028 1

0.99 0.798

81 89 89

Performanc

e

Expectancy

-0.127 -.326**

-0.185 1

0.263 0.003 0.098

80 81 81 81

Effort

Expectancy

-0.003 -.382**

-0.186 .663**

1

0.978 0 0.097 0

80 81 81 81 81

Social

Influence

-.268* -0.182 0.037 .551

** .475

** 1

0.016 0.104 0.746 0 0

80 81 81 81 81 81

Facilitating

Conditions

-0.056 -.324**

-.236* .649

** .707

** .523

** 1

0.623 0.003 0.034 0 0 0

80 81 81 81 81 81 81

Behavioral

Intention

-.426**

-0.049 -0.095 .567**

.425**

.638**

.456**

1

0 0.666 0.4 0 0 0 0

80 81 81 81 81 81 81 81

Implicit

Social

Influence

-0.163 -0.063 -0.016 .463**

0.204 .530**

.388**

.369**

1

0.148 0.575 0.886 0 0.068 0 0 0.001

80 81 81 81 81 81 81 81 81

Normalized

Actual Use

-.509**

-0.209 -.301* .463

** .417

** .462

** .438

** .613

** .367

** 1

0 0.082 0.011 0 0 0 0 0 0.002

69 70 70 70 70 70 70 70 70 70

Normalized

Influence

Centrality

-0.168 -0.164 -0.061 0.161 .279* .273

* .271

* 0.151 0.021 0.181 1

0.137 0.099 0.581 0.155 0.012 0.014 0.015 0.181 0.851 0.137

80 103 84 80 80 80 80 80 80 69 108

Normalized

Coping

Centrality

-0.194 -0.144 0.005 0.14 .255* .263

* .264

* 0.167 0.05 0.15 .887

** 1

0.085 0.144 0.963 0.216 0.022 0.018 0.018 0.138 0.661 0.219 0

80 105 84 80 80 80 80 80 80 69 99 111

Coping

Neighbor’s

PE

-0.082 -.422**

-0.21 .470**

.400**

0.21 .289* .370

** 0.144 .344

* 0.108 0.007 1

0.532 0 0.105 0 0.002 0.108 0.025 0.004 0.273 0.014 0.403 0.955

60 66 61 60 60 60 60 60 60 50 62 66 66

Influence

Neighbor’s

PE

0.021 -.469**

-0.146 .592**

.455**

0.125 .370**

.244* 0.139 .279

* 0.024 -0.034 .865

** 1

0.87 0 0.242 0 0 0.318 0.002 0.049 0.264 0.037 0.848 0.782 0

65 68 66 66 66 66 66 66 66 56 67 67 57 68

Contents of each cell contains Pearson Correlation, Sig. (2-tailed), N *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

4.7 Multiple Regression



This section presents the results the five hierarchical regressions to evaluate the significance of the

main research factors and the traditional UTAUT factors on behavioral intention, usage behavior,

performance expectancy, effort expectancy, and facilitating conditions.

When looking at the UTAUT factors, the regressions focus only on the direct impacts in order to

validate that they are consistent with prior research, but not recreate all the analysis on moderating

factors that are involved in UTAUT (Venkatesh, Morris et al. 2003)

Multiple regressions are the most common way in business research to understand the extent of

multiple independent variables on the dependent variable of Behavioral Intention, Use Behavior,

and others (Tharenou, Donohue et al. 2007). In this research, where the participants are from the

same workgroups, there is a significant risk drawing inaccurate conclusion resulting from

autocorrelation of data due to the dependence between respondents. To mitigate this risk, this

study used a hierarchical regression to validate that the potential second or third level fixed or

random factors are not significant drivers of variance by initially entering the demographic variables

and work environment factors. Specifically, the study will initially be looking at time (experience),

gender, department, technology, voluntariness, company, and role. After these, the variance from

relevant UTAUT variables will be evaluated, and only then the study evaluates the impact of the

social network variables of coping network centrality, influence network centrality, coping neighbors

average PE, and influencing neighbors average PE.

The hierarchical regression uses pairwise deletion as opposed to listwise within SPSS to not eliminate

cases that have incomplete data. The definition of pairwise deletion within SPSS is as follows:

“Delete cases with missing values pairwise. Each correlation coefficient is computed using cases with

complete data for the pair of variables correlated. If INCLUDE is also specified, only cases with

system-missing values are deleted pairwise” (IBM 2009)

The methods used are not stepwise but add via forced entry, where the variables specified are

entered in order in which variables are entered by specifying the variables on multiple

METHOD=ENTER subcommands within SPSS 19. A stepwise method is also possible here, but could

yield better but potentially misleading results based on chance because it eliminates any predictors

that do not significantly explain the dependent variable. As suggested by Thompson “Stepwise

regression is essentially a ‘fishing expedition’, which capitalizes too much on chance and therefore

should not be used for hypothesis testing (Thompson 1995)



4.7.1 Hierarchical Regression Results for Behavioral Intention

Table 24 presents the analysis of variance results for the pooled data from both the Event

Management System study and Enterprise Project Management study, including both the pre and

post pilot data. The hierarchical regression uses pairwise deletion as opposed to listwise within SPSS

to not eliminate cases that have incomplete data.

The dependent variable is Behavioral Intention of the users. This regression initially looks at the

demographical and organizational factors of Department, Gender, Technology (EMS or EPM),

Experience (pre or post-pilot), Age, and voluntariness. This is done first to eliminate the variance

from these predictors so as to not overstate the explained variance from the remaining UTAUT and

social network predictors.

Next this analysis evaluates the explained variance for Performance Expectancy, Effort Expectancy

and Social Influence since they are the traditional UTAUT predictors of Behavioral intention. After

the explained variance from the UTAUT variables are accounted for, then the study evaluates the

impact of the social network variables of a user’s Coping Network Neighbor’s PE, Influence Network

Neighbor’s PE, Coping Network Centrality, and Influence Network Centrality,

The table presents the R (Pearson’s correlation coefficient), R2, Adjusted R2, Standard Error and the

change statistics which include the R2 and the statistical significance of the change using an f-test.

For this study we are primarily focused on the Adjusted R2 because it accounts for the addition of so

many variables by decreasing the degrees of freedom as to incorrectly gain explanatory power by

combining more and more predictors.

In reviewing the results of the demographical and organizational factors of Department, Gender,

Technology (EMS or EPM), Experience (pre or post-pilot), Age, and voluntariness there are two

factors that could have a significant role. Experience explained 6.5% change in Behavioral Intention

based on its change in R2, but it was only significant at a .062 level. Voluntariness explained 13.1%

change in Behavioral Intention based on its change in R2 which was significant at a .005 level.

In reviewing the results of the UTAUT factors of Performance Expectancy, Effort Expectancy and

Social Influence there are two factors that could have a significant role. Performance Expectancy

explained 25.8% change in Behavioral Intention with a .000 significance level. Social Influence also

explained 8.3% change in Behavioral Intention with a .003 significance level.

In reviewing the results of the social network factors of Coping Network Neighbor’s PE, Influence

Network Neighbor’s PE, Coping Network Centrality, and Influence Network Centrality, there are two

factors that could have a significant role. Coping Network Neighbor’s PE explained 3.2% change in

Behavioral Intention with a .048 significance level. Influence Network Neighbor’s PE, also explained

2.4% change in Behavioral Intention but with only a .080 significance level.



Table 24 - Hierarchical Regression Results for Behavioral Intention

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

Change Statistics R Square

Change

F Change df1 df2 Sig. F Change 1 .049

a .002 -.016 2.052 .002 .130 1 55 .719

2 .049b .002 -.035 2.071 .000 .000 1 54 .993 3 .211c .044 -.010 2.046 .042 2.327 1 53 .133 4 .327d .107 .038 1.997 .062 3.636 1 52 .062 5 .337e .113 .026 2.009 .007 .374 1 51 .543 6 .495f .245 .154 1.872 .131 8.700 1 50 .005 7 .709g .503 .432 1.534 .258 25.456 1 49 .000 8 .728h .530 .451 1.508 .027 2.738 1 48 .105 9 .783

i .613 .539 1.382 .083 10.118 1 47 .003

10 .803j .645 .568 1.338 .032 4.140 1 46 .048 11 .818k .669 .588 1.307 .024 3.210 1 45 .080 12 .818l .669 .579 1.321 .000 .039 1 44 .845 13 .826m .682 .585 1.311 .013 1.704 1 43 .199

a. Predictors: (Constant), Department

b. Predictors: (Constant), Department, Male or Female

c. Predictors: (Constant), Department, Male or Female, Technology

d. Predictors: (Constant), Department, Male or Female, Technology, Experience

e. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age

f. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use

g. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Performance

Expectancy

h. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Performance

Expectancy, Effort Expectancy

i. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Performance

Expectancy, Effort Expectancy , Social Influence

j. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Performance

Expectancy, Effort Expectancy , Social Influence , Coping Network Neighbor’s PE

k. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Performance

Expectancy, Effort Expectancy , Social Influence , Coping Network Neighbor’s PE, Influence Network Neighbor’s PE

l. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Performance

Expectancy, Effort Expectancy , Social Influence , Coping Network Neighbor’s PE, Influence Network Neighbor’s PE, Normalized

Coping Centrality

m. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Performance

Expectancy, Effort Expectancy , Social Influence , Coping Network Neighbor’s PE, Influence Network Neighbor’s PE, Normalized

Coping Centrality, Normalized Influence Centrality



4.7.2 Hierarchical Regressions Results for System Use





The dependent variable is System Use. This regression initially looks at the demographical and

organizational factors of Department, Gender, Technology (EMS or EPM), Experience (pre or post-

pilot), Age, and voluntariness. This is done first to eliminate the variance from these predictors so as

to not overstate the explained variance from the remaining UTAUT and social network predictors.

Next this analysis evaluates the explained variance for Behavioral Intention and Facilitating

Conditions because they are the traditional UTAUT predictors of Behavioral intention. After the

explained variance from the UTAUT variables are accounted for, then the study evaluates the impact

of the social network variables of a user’s Coping Network Neighbor’s PE, Influence Network

Neighbor’s PE, Coping Network Centrality, and Influence Network Centrality, ,



For this study we are primarily focused on the Adjusted R2 because it accounts the addition of so




Technology (EMS or EPM), Experience (pre or post-pilot), Age, and voluntariness there was three

factors that could have a significant role. Experience explained 8.9% change in System Use based on

its change in R2, with a .034 significance level. Age explained 9.9% change in System Use based on

its change in R2, with a .020 significance level. Voluntariness explained 34.7% change in System Use

based on its change in R2, with a .000 significance level.

In reviewing the results of the UTAUT factors of Behavioral Intention and Facilitating Conditions

there was only factor that could have a significant role. Behavioral Intention explained 13.6%

change in System Use with a .000 significance level.


Network Neighbor’s PE, Coping Network Centrality, and Influence Network Centrality, none of these

factors had a significant role on System Use.



Table 25 Hierarchical Regressions Results for System Use

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change F Change df1 df2 Sig. F Change

1 .209a .044 .024 .410410 .044 2.202 1 48 .144

2 .209b .044 .003 .414753 .000 .000 1 47 .993

3 .261c .068 .007 .413909 .024 1.192 1 46 .281

4 .397d .157 .082 .397932 .089 4.768 1 45 .034

5 .506e .256 .172 .378071 .099 5.852 1 44 .020

6 .777f .603 .548 .279369 .347 37.583 1 43 .000

7 .860g .739 .696 .229116 .136 21.931 1 42 .000

8 .862h .743 .693 .230244 .004 .590 1 41 .447

9 .863i .745 .688 .232206 .002 .310 1 40 .581

10 .863j .745 .680 .234945 .000 .073 1 39 .789

11 .864k .747 .674 .237221 .002 .255 1 38 .616

12 .865l .748 .667 .239844 .001 .173 1 37 .680







g. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Behavioral

Intention

h. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Behavioral

Intention , Facilitating Conditions

i. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Behavioral

Intention , Facilitating Conditions , Coping Ego Performance Expectancy

j. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Behavioral

Intention , Facilitating Conditions , Coping Ego Performance Expectancy, Influence Ego Performance Expectancy

k. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Behavioral

Intention , Facilitating Conditions , Coping Ego Performance Expectancy, Influence Ego Performance Expectancy,

Normalized Coping Centrality

l. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Behavioral

Intention , Facilitating Conditions , Coping Ego Performance Expectancy, Influence Ego Performance Expectancy,

Normalized Coping Centrality, Normalized Influence Centrality



4.7.3 Hierarchical Regression Results for Performance Expectancy





The dependent variable is Performance Expectancy of the users. This regression initially looks at the



from these predictors so as to not overstate the explained variance from the remaining social

network predictors.

The study then evaluates the impact of the social network variables of a user’s Coping Network

Neighbor’s PE, Influence Network Neighbor’s PE, Coping Network Centrality, and Influence Network

Centrality, ,



This study is primarily focused on the Adjusted R2 because it accounts the addition of so many

variables by decreasing the degrees of freedom as to incorrectly gain explanatory power by



Technology (EMS or EPM), Experience (pre or post-pilot), Age, and voluntariness there was only one

factor that could have a significant role. Department explained 10.6% change in Performance

Expectancy based on its change in R2, with a .013 significance level.


Network Neighbor’s PE, Coping Network Centrality, and Influence Network Centrality, only two of

these factors had a significant role on Performance Expectancy. Coping Network Neighbor’s PE

explained 10.1% change in Performance Expectancy based on its change in R2, with a .011

significance level. Influence Network Neighbor’s PE explained 11.1% change in Performance

Expectancy based on its change in R2, with a .004



Table 26 Hierarchical Regression Results for Performance Expectancy


Square

Std. Error of

the

Estimate

Change Statistics

R Square

Change

F Change df1 df2 Sig. F

Change 1 .326a .106 .090 1.366 .106 6.519 1 55 .013 2 .350b .122 .090 1.366 .016 1.000 1 54 .322

3 .350c .122 .073 1.379 .000 .009 1 53 .924

4 .377d .142 .076 1.377 .020 1.205 1 52 .277

5 .425e .181 .100 1.359 .038 2.382 1 51 .129

6 .444f .197 .101 1.358 .017 1.034 1 50 .314

7 .546g .298 .198 1.283 .101 7.038 1 49 .011

8 .640h .409 .310 1.189 .111 9.015 1 48 .004

9 .653i .427 .317 1.184 .018 1.460 1 47 .233

10 .655j .429 .305 1.194 .002 .163 1 46 .688







g. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Coping Network

Neighbor’s PE

h. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Coping Network

Neighbor’s PE, Influence Network Neighbor’s PE

i. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Coping Network

Neighbor’s PE, Influence Network Neighbor’s PE, Normalized Coping Centrality

j. Predictors: (Constant), Department, Male or Female, Technology, Experience, Age, Voluntariness of Use, Coping Network

Neighbor’s PE, Influence Network Neighbor’s PE, Normalized Coping Centrality, Normalized Influence Centrality



4.7.4 Hierarchical Regression Results for Effort Expectancy





The dependent variable is Effort Expectancy of the users. This regression initially looks at the




network predictors.



Centrality, ,



This study is primarily focused on the Adjusted R2 because it accounts the addition of so many

variables by decreasing the degrees of freedom as to incorrectly gain explanatory power by




factor that could have a significant role. Department explained 14.6% change in Effort Expectancy

based on its change in R2, with a .003 significance level.


Network Neighbor’s PE, Coping Network Centrality, and Influence Network Centrality, only two of

these factors had a significant role on Effort Expectancy. Coping Network Neighbor’s PE explained

5.7% change in Effort Expectancy based on its change in R2, but with only a .061 significance level.

Coping Network Centrality explained 6.2% change Effort Expectancy based on its change in R2, with a

.042 significance level.



Table 27 Hierarchical Regression Results for Effort Expectancy


Square

Std. Error of

the

Estimate

Change Statistics R Square

Change


Change 1 .382a .146 .130 .994 .146 9.397 1 55 .003 2 .383b .147 .115 1.003 .001 .038 1 54 .846 3 .383c .147 .098 1.012 .000 .003 1 53 .959 4 .383d .147 .081 1.022 .000 .001 1 52 .974 5 .421e .177 .097 1.013 .031 1.898 1 51 .174 6 .422f .178 .080 1.023 .001 .069 1 50 .795 7 .486g .236 .127 .996 .057 3.681 1 49 .061 8 .517h .267 .145 .986 .032 2.064 1 48 .157 9 .574i .329 .201 .953 .062 4.351 1 47 .042 10 .576j .332 .186 .961 .002 .167 1 46 .685 a. Predictors: (Constant), Department







Neighbor’s PE









4.7.5 Hierarchical Regression Results for Facilitating Conditions Table 28 presents the analysis of variance results for the pooled data from both the Event




The dependent variable is Facilitating Conditions of the users. This regression initially looks at the




network predictors.



Centrality, ,



For this study we are primarily focused on the Adjusted R2 because it accounts the addition of so





factor that could have a significant role. Department explained 10.5% change in Facilitating

Conditions based on its change in R2, with a .014 significance level.


Network Neighbor’s PE, Coping Network Centrality, and Influence Network Centrality, only one of

these factors had a significant role on Facilitating Conditions. Coping Network Centrality explained

5.4% change Facilitating Conditions based on its change in R2, but with a .055 significance level.



Table 28 Hierarchical Regression Results for Facilitating Conditions


Square

Std. Error of

the

Estimate

Change Statistics

R Square

Change


Change 1 .324a .105 .089 1.061 .105 6.468 1 55 .014 2 .386b .149 .117 1.044 .043 2.753 1 54 .103

3 .396c .157 .109 1.049 .008 .504 1 53 .481

4 .399d .159 .094 1.058 .002 .139 1 52 .710

5 .477e .227 .152 1.024 .068 4.515 1 51 .038

6 .492f .242 .151 1.024 .015 .972 1 50 .329

7 .511g .261 .156 1.021 .019 1.272 1 49 .265

8 .534h .285 .166 1.015 .024 1.603 1 48 .212

9 .583i .340 .213 .986 .054 3.878 1 47 .055

10 .590j .349 .207 .990 .009 .637 1 46 .429








Neighbor’s PE









4.7.6 Overall Regression Model Results

Figure 18 - Statistically Significant R2 Regression Results is a visual summary of the different

regression models analyzed in this study. The non-statistically significant results are omitted, but the

figure includes the statistically significant relationships at the .01, .05, and .10 levels. Moderating

variables were not evaluated in this study, but the potentially intervening variables (technology,

department, voluntariness, experience, and age) where evaluated as part of the regression model.

There were ten notable observations in the overall regression model which are discussed below.

Results are consistent with UTAUT

In comparing these results to the conceptual model from the Unified Theory of Acceptance and Use

of Technology (Venkatesh, Morris et al. 2003), the results show that Behavioral Intention is largely

impacted by the Performance Expectancy with smaller impacts from Voluntariness and Social

Influence. A hierarchical regression also yielded a .54 adjusted R2 for direct effects on Behavioral

Intention before including any of the influencing or coping network measures which is also

consistent with similar research on UTAUT.

Voluntariness has unusually high impacts on Usage while Behavioral Intention has

unusually low impact on Usage

A result that differs from UTUAT is the low explanatory power of Behavioral Intention on Usage,

0.136, and the high explanatory power of Voluntariness on Usage which is not consistent with

UTAUT. This result is likely for two reasons. First, the Voluntariness was not self-reported, but

coded based on the situation for the particular project group, so the voluntariness measure validity

is a concern. Second, the voluntariness of using the Event Management System was not simple for

the users because the larger organizational decision was still being vetted which is one of the

challenges in a contingent acceptance. For example, if a savvy and influential user on a client event

project did not want to use the new Event Management System yet was told it was mandatory, they

might consider lobbying their customer contact to request to not use the new event system. This

was suggested as a commonly used tactic in a follow-up interview as a method to use one’s

preferred Event Management System or influence the larger organizational decision.

Department is a significant antecedent to Effort Expectancy, Performance

Expectancy, and Facilitating Conditions

In this study the users’ Department was also shown to have a significant relationship as an

antecedent of Performance Expectancy, Effort Expectancy, and Facilitating Conditions. A mixed

model could provide more clarity of this potentially intervening variable, but this was out of scope

for this study. This can be expected because the individuals within each department could be

impacted by a new technology similarly, have common experience and comfort level with the

previous technology, or have similar access to resources to help them use a new technology.

Influence Network Centrality was not a significant antecedent to UTAUT variables

The Influence Network Centrality was tested in all the hierarchical regression models but was not

shown to have any significant relationships on the dependent variables of Behavioral Intention, Use



Behavior, Performance Expectancy, Effort Expectancy, and Facilitating Conditions. This means that

the participant’s measure of centrality or lack of centrality within the influencing network does not

seem to have a statistically significant impact on any of the key UTAUT factors. The significance of

this is that an individual could be viewed as influential with access to information, resources, and

knowledge of the new technology, but this itself is not a significant driver of their perceptions,

intentions, or usage of the system.

Coping Network Centrality was a significant antecedent to Facilitating Conditions &

Effort Expectancy

The Coping Centrality did explain 6.2% of the variance in Effort Expectancy at a .05 confidence level.

It also explained 5.4% of the variance in Facilitating conditions, but only at a .10 confidence level.

Both of these relationships were hypothesized because it would be expected that the centrality in a

coping network would enable closer access to Facilitating Conditions such as help or resources which

in turn could avoid ease of use problems of the new technology.

Neighbor’s Performance Expectancy in the influencing network was a significant

antecedent to Performance Expectancy & Behavioral Intention

The Neighbors Performance Expectancy in the Influence Network is a measure of the average

performance expectancy of a user’s close neighbors in the Influencing Network and it also had

significant relationships to the users own Performance Expectancy as well as Behavioral Intention.

The Influence Network Neighbor’s PE explained 11% of the variance of Performance Expectancy at a

.01 confidence level. This was also hypothesized because Performance Expectancy has continually

been the largest driver of Behavioral Intention and if a worker is going to influence a colleague about

a technology, one could expect that they would express their satisfaction/dissatisfaction in how well

a new technology helps them accomplish their job, which is the core context of the relationship

between the two.

Neighbor’s Performance Expectancy in the Coping Network was a significant

antecedent to Performance Expectancy, Effort Expectancy, and Behavioral Intention

The Neighbors Performance Expectancy in the Coping Network had significant relationships to the

users own Performance Expectancy, Effort Expectancy, as well as Behavioral Intention.

Surprisingly, the Neighbors Performance Expectancy in the Coping Network explains 15% of the

Performance Expectancy at a .01 confidence level. It also explains 10.5% of the variance in the

Behavioral Intention at a .05 confidence level. Not a significant, but still important it also explains

about 5% of the variance in Effort Expectancy at a .10 confidence level. The significance of these

impacts is that it brings visibility to the social influence between individuals and their perceptions of

a new technology or innovation.

Age is has a significant impact on Usage and Facilitating Conditions

The average age of participants in this study was about 45 with many of the participants being with

the company for many years. Age also have significant relationships with Use Behavior, explaining

10% of variance at .05 confidence level, as well as Facilitating Conditions, explaining 7% of the



variance at a .05 confidence level. This relationship suggests that younger users feel that they have

more access to resources, knowledge, or assistance for effective use of the new technology.

The Performance Expectancy is driven by the Neighbor Performance Expectancy

and department.

Performance expectancy has traditionally been the most significant driver of behavioral intention, so

its role in UTAUT is important. In this hierarchical regression analysis, the Performance expectancy

had an adjusted R2 of .32 with the variance being explained by its Neighbor Performance Expectancy

in the influencing and coping networks as well as the individuals department. Once again, this

highlights that individuals cannot be considered independent in their attitudes and perceptions of

new technologies.

Neighbor Performance Expectancy is more important to Technology Acceptance

then Network Centrality

The influencing and coping network measures were evaluated as antecedents to the common

factors in UTAUT as well as Behavioral Intention and Use Behavior. Similar to the results in the

correlation analysis, the Neighbor Performance Expectancy had larger and more significant impacts

than the network centrality measures. This further suggests that perceptions and intentions of a

new technology are driven more by the perceptions in one’s immediate network neighborhood then

their structural position in the network.

Performance

Expectancy

Effort Expectancy

Social Influence

.258**

.083**

Use

BehaviorBehavior

Intention.136**

Facilitating

Conditions

Experience

Age

Gender

Voluntariness

of Use

.068*

.131**.062*

Influence

Centrality

Coping

Centrality

.054^

Influence

Centrality

Coping Network

Alter’s PE

Influence

Network Alter’s

PE

Influence

Network

Alter’s PE

.111**

.05^

.024^, H11

Dept

.106*

.146**.146**

.105**

.032*

.374**

.089*

.099*

^. Correlation is significant at the 0.10 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Figure 18 - Statistically Significant R2 Regression Results



4.8 Social Network Graphs

In reviewing the social network graphs, insight on organizational and individual technology

acceptance can be gained in addition to just bringing social network quantifiable factors, such as

centrality, group membership, and other quantitative measures back into a statistical analysis. In

this section, we will review the coping and influencing modes of social networks for the pre-pilot

data for the Event Registration System and Enterprise Project Management System. To provide

better understanding, the graphs are color-coded based on the performance expectation as well as

groups in the organization using Girvan-Newman clustering. The nodes are also sized to reflect the

betweenness centrality of the network mode.

4.8.1 Event Management System

The pre-pilot and post-pilot coping networks for the Event Management System are shown in Figure

19 EMS Pre Pilot Coping Network and Figure 21 EMS Post-Pilot Coping Network, which represent the

relationships identified when asking the participants the following two questions:

List 1-3 people that you interact with or will interact with in a typical week that you get help

from for effective use of the Event Registration System.

List 1-3 people that you interact with or will interact with in a typical week that you give help

too for effective use of the Event Registration System.

The graphs are color-coded based on performance expectancy, which gives a good indication of the

participant’s perception if the new Event Management System will help them do their job. Green

means that they gave a positive response, yellow indicates neutrality, red indicates a negative

response, and grey means that there was not a response to the performance expectancy question.

The response rate for the pre-pilot surveys was very high for the two projects, 46 out of 49, which is

likely attributable to the researcher being an employee and hand-delivering each hardcopy survey,

which some follow-up. The post-pilot surveys had high response rates, but not as good as the first

surveys; 34 out of 49 which could be a concern when assembling the social network graphs. In

reviewing the post-pilot results based on the role of the participants in the first survey, it appears

that most, if not all of the central figures responded, although in many cases they might have moved

out of central roles. Examples of these central position changes can be seen where the central

resources in the EMS pre-pilot coping network shown in Figure 19 EMS Pre Pilot Coping Network

(Kim, Dayna, Linda, Josh, & Jayden), also appear in the post-pilot coping network shown in Figure 21

EMS Post-Pilot Coping Network, but they are no longer occupying the central roles.

It’s also important to note that the target of the post-pilot surveys also only included the participants

who completed a pre-pilot survey, which could miss some new actors that joined the project since

the initial survey. This risk is mitigated because the new actors would likely be identified by the

others in the coping network.

In the case of the Event Management System, it was a controversial initiative and had been

evaluated almost annually for the previous 3-4 years. Past negative experiences with two other



commercially available Event Management Systems used for particular clients in the previous 3 years

based on specific client needs had generated significant negative experiences as well.

At the beginning of the pilot for the Event Registration there was approval to move ahead with a

pilot for the new event registration system, but it was a deeply political issue that would drive

significant change, so it is possible that system use is highly related to the political opinions within

the network of weather to use the system for the new clients or to continue to use the legacy

system for event management. Essentially it was also a very distributed decision as well. Any of the

40+ marketing clients could mandate the use of a particular event registration system for their

events and many had previous experience or even a license to a commercially available event

registration system, so there is 4-5 different event registration systems used in any given year. Using

these other registration systems was not the norm and usually the client was satisfied with using the

marketing company’s legacy registration system. This also means that a client account management

staff could have some flexibility into what solution they should propose to a client. Essentially if any

one client mandated to use a particular Event Management System, then it would automatically be

the solution for that client.

This opens an interesting opportunity that an opinionated marketing company employee who could

sway the direction of what Event Registration System to use by steering a client in one direction or

another during presentations or day to day communications with the client. Close to the beginning

of the pilot, the researcher asked two separate employees who were both opinion leaders on their

impressions of a large client presentation that occurred a few days earlier. This was a client

presentation to propose the new Event Management System and other event options for a large

user conference to be held later that year.

The one employee from IT, Josh, who was presenting the new Event Management System options

and recommending the usage of the new registration system, described the meeting as this. "The

client was excited about the lower cost and new functionality of the new system and appeared to be

in agreement with that direction until the client asked about how comfortable the marketing

company was with the direction. Other marketing employees responded by bringing up risks,

issues, and concerns about the new direction and quickly brought the clients focus back to the

legacy system, which was the final outcome of the meeting." Josh suggested that the other

employees were casting fear into the client with the goal of swaying the decision.

That same day the researcher asked another opinion leader, Kim, who was not in the meeting and

has been skeptical on the potential change. She was not present in the meeting but offered a recap

of the meeting which was very different than that of Josh. She described the outcome of the

meeting as follows: The client was just fine with the legacy system and it was only one person at the

client who had an issue with the legacy system because it couldn’t use radio buttons. " She then

went on to explain that the reviews of the new system showed that there was significant gaps in the

functionality and that “it’s just not the tool for us right now and maybe sometime later we will find a

better one”. This was a surprising statement because it inferred that the pilot that was just about to

start was not appropriate right now.

This makes this organizational technology decision a hybrid of voluntariness. There was a company

direction decision made and communicated by the two executive team members who had ultimate

responsibility of this business and the technology solutions involved. Although many resources did



not believe that this was a good direction and could be using their role as an opinion leader or

influence over a client to reverse or delay this direction. So it seems that the initial technology

acceptance of new systems within companies are very politically driven and when it’s a client facing

technology, there are many people who will continue to push their personal opinion that they

believe is correct long after an enterprise decision is made. This makes the modeling for technology

acceptance complex because of the politics within a contingent acceptance. The system use might

not follow a more predictable technology acceptance in this dynamic situation, which suggests

looking to other dimensions that would provide more insight on this political dynamic, which is why

a social network analysis would be enlightening.

It’s also important to note that since this initial data was collected prior to most of the users having

hands-on experience, it would likely mean that their perceptions are based more on the perceptions

from those around them, not their own experiences.



4.8.1.1 Event Management System Pre-Pilot Coping Network (EMS)

Description:

Figure 19 EMS Pre Pilot Coping Network, which was generated in NetDraw, shows the coping

network at the beginning of the Event Management System Pilot. This coping network shows who

the actors interact with in order to help manage (minimize, reduce, master, tolerate) demands

emanating from a specific change in the person-environment relationship (Lazarus 1966). The

connections are directional based originating at the actor who identified others as someone they get

or give help to. The nodes are color-coded based on Performance Expectancy responses for the

Event Management System; red for a negative, yellow for neutral, and green for a positive response.

The nodes are sized relative to each other based on betweenness centrality which represents how

many pairs of nodes they are between(Freeman 1977).

Groups

In reviewing this network for groups that naturally belong together, the network participants

naturally fall into three groups when evaluated using the Girvan-Newman analysis within

Netdraw(Borgatti 2002), which can be generalized based on their current roles into three classes;

the isolates, client support team, and the pilot project.

There are no networks that are disjoint, but there are several individuals that are not part of the

coping network. The isolates are essentially those participants where did not identify any person

that they interact with that they give or receive help from regarding the Event Management System.

The group is generally made up of IT resources and an account team lead that will be impacted by

the current or future use of this technology but are not integrated into the coping network yet.

The second natural group or class of individuals is located on the lower left side of Figure 19 EMS Pre

Pilot Coping Network, can be generalized as the client facing team who currently works directly or

indirectly with the one client who uses the new Event Management System or the second client who

is evaluating using it. Generally with their client facing roles, this group is continually under

significant pressure to manage events in the US and globally. The Marketing Company primarily

focuses on high-touch differentiated events for customers and VIPs, so clients expect a flawless and

differentiated event where the attendees do not experience any challenges in the event registration

process or onsite. The clients will have very specific requests, so hand-holding is normal for the VIPs

and other attendees. This does make these client-delivery teams very risk-adverse to new

technologies so as to not upset a demanding client that has no appetite for any mistakes.

The third group or class can be generalized as the new technology pilot group, which is shown on the

right side of Figure 19 EMS Pre Pilot Coping Network. This group is primarily made up of the IT and

Data/Operations resources that have been tied into the pilot or earlier evaluations of the new Event

Management System to replace the previous legacy system. Six of the eleven people are IT

resources which have had responsibility for the most of the pre-pilot deliverables, such as a

comparison of functionality, business case, gap list, and others. The central figures in this pilot group

are Dayna, the experienced IT Analyst who has focused on client event delivery for many years and

has many social ties to the individuals in the Data/Operations groups. Another central figure in this

group is Linda, an IT Analyst who previously worked in the Data/Ops group, but was brought back to



the company as a Subject Matter Expert to work on bridging the pilot group and the events delivery

teams based on her past background.

It is important to note that when looking at these groups that have been together for a long time

that group solidarity as an outcome of social capital could have a significant impact technology

acceptance networks. If networks where highly connected it facilitates the emergence of effective

norms and maintains trustworthiness of others, and in turn increasing social capital as a group

(Coleman 1988). These strong ties and solidarity could also limit the inflow of new ideas into the

group (Gargiulo and Bernassi 1999; Adler and Kwon 2002).

The clusters have moderate intra-cluster sparsity, where the two groups have multiple connections

between each other. Jayden, Dayna, London, Emma, and Santiago help bridge the two groups

together. These individuals have been working together on client events for many years and have

likely built up significant social capital or trust with each other over the years. Alternatively, none of

the newer IT resources or contractors brought in for this particular new Event Management System

pilot project such as Patrick, Jennifer, Linda or Josh are playing a role in intra-cluster connections.

This network does have high intra-cluster density in terms of connections with the two groups or

clusters. There are several instances of cliques within the network group where everyone in the

small groups is connected to all of the others in the group such as the clique that is made up of

Isabelle, Kim, Dayna, and Linda. In looking at the groups for the more relaxed k-clique definition, a

clique where everyone is still connected or within a path distance of k away from others, almost all

of the pilot group and client-facing groups would be part of 2-cliques.

Individual Power and Positions

“One of the primary purposes of graph theory in social network analysis is the identification of the

most important actors in the social network”(Wasserman and Faust 1994). The five most central

resources within this this network is Santiago, Dayna, Julie, Josh, and Ava.

The most central person in Figure is Santiago, who is a data ops analyst who has had experience

using the new Event Management System while working on the one client that currently uses it for

their events. He was not identified as a key resource when the VP in charge of the area helped

identify the group of resources to survey. As a technical leader in the organization at the beginning

of this pilot, surprisingly the researcher had never met Santiago in 2 years of working in partnership

with this group. If this group was disjoint from the larger network, it would be a good example of a

highly centralized graph, where it has a highly central actor, Santiago, around which the peripheral

collect around (Borgatti 2002).

The second notable individual within the pilot cluster in Figure 19 EMS Pre Pilot Coping Network was

Josh the IT Analyst. At the time of the survey, Josh was business analyst and a solution architect

whose responsibilities included identifying solutions that met client needs and helping identify

future technologies that the company could use to differentiate its service offerings. He had a high

personal innovativeness, and was a strong supporter of moving from the legacy Event Management

System to the new Event Management System. His opinions put himself in conflict with others like

Kim, the Data Operations manager in terms of opinions on if and how to transition to a new

technology. He described an internal client solution meeting with a client facing team, himself, and



Kim where there was general agreement on the direction, but at the conclusion of the meeting, he

felt Kim undermined the solution by immediately expressing concerns to the client account lead on

the decision.

Another central person in the pilot group was Dayna, who acted as an intra-cluster bridging role

between the two network clusters in Figure 19 EMS Pre Pilot Coping Network. As mentioned earlier,

she was an individual with significant domain experience in the events business as well as managing

and configuring the previous legacy system as both a business consultant and front-end

programmer. She also was socially tied to many of the resources within the Data/Operations and IT

organization and was respected for her knowledge on managing many of the recurring events over

the years which made her an efficient analyst. It’s possible that her strong ties into both the

Data/Ops and IT organization could start feeling like a Simmilian tie, where she would be significantly

limited in expressing her opinion without causing friction in her relationships in one or both groups

(Krackhardt 1999).

For many years, she also was a very vocal supporter of the previous legacy system. This also put her

opinions in conflict (albeit a good working relationship) with many resources who felt that moving to

the new technology would benefit the company; which included Josh and Joseph the IT Director (this

researcher’s role prior to the pilot). To provide context on this conflict, 2 months before the pilot,

there was a meeting with Maria the IT Director, Joseph the IT Director, and Josh to discuss who

should be involved in the pilot of this new Event Management System. As the higher ranking

technical resource, Joseph suggested that Dayna should not be on the new Event Management

System pilot because of her vocal history stating that there is not a new Event Management System

that would satisfy the needs of the Marketing Company’s clients as well as the legacy system does.

It was felt that the team would spend too much time justifying any process change to Dayna and she

would potentially undermine the pilot.

In hindsight, the decision to not include Dayna, was ill-informed decision for three reasons; her

substantial domain experience, her centrality and bridging role in the coping network, and her

positive performance expectancy. The coping network graph validates her central in the coping of

individuals using the new technology from many different groups. At the beginning of the pilot in

January of 2011, it was eventually decided that she would be part of pilot because she was so

familiar with the capabilities of the legacy system and previously documented gaps of the new

system. So she was included in many of the discussions and meetings regarding the new Event

Management System at the beginning of the initial pilot.

There is also one notable figure, which was not showing up as high coping centrality, but plays an

important part of this group. Kim was the Data/Ops Lead Manager who is the supervisor to most of

the Data/Ops resources and works on client deliverables as well. She is very involved in the

evaluation of the new Event Management System, likely because of her experience in the domain as

well as the impact it could have on her team, which she felt were significant. She was the only

person in this smaller pilot group that had negative performance expectancy. This was a point of

contention with some of the IT resources because they felt that Kim’s perception of the new Event

Management System was making their jobs much more difficult because they continued to have to

go to increasing levels of details to address the differences or perceived gaps between the internally

developed legacy Event Management System and the newer vendor supplied Event Management



System. The previous system was internally developed in 2000 and through co-adoption over the

next 10 years, the system, the Data/Ops team, client teams, IT teams, and the processes all evolved

overtime together. So it would be expected that there would be differences between a vendor

system and an internally developed system which everyone was comfortable with. If any of these

gaps has a negative impact on any of the long-standing clients with recurrent events, it could yield a

significant issue within the company, so Kim’s concerns for the gaps were generally considered valid

from most other stakeholders, especially the functional VP she reported to and the delivery teams

from other departments.

There were not any individuals who filled structural holes because of the intra-cluster sparsity where

there were multiple connections between the groups. From a social network view, structural holes

would be the individuals (or players) that occupy a network position that connects two different

groups. This allows them to potentially benefit from a more heterogeneous flow of information

from multiple network groups and potentially have more control of information or influence within

the larger network.

It could be assumed that most of the connections were strong ties because of the wording of the

survey questions which asked only for those individuals who were they had frequent interaction

with. One might speculate if some of the vendor connections such as Josh and Lucas the Vendor

executive were closer to a weak tie relationship.

Generally the networks had small diameters within and between the groups, meaning it didn’t take

to many steps along a path between any two individuals within the connected network.


The performance expectancy of many of the executive or director-level actors was not surprising.

The two functional executives responsible for larger departments, Dawn and Richard, do not believe

it will help them do their job, likely because they are fairly removed from the day to day client event

support. The client focused executive, Lily, believes it will help her do her job which could relate to

the high level of responsibility she has for the one client who uses the new technology, although the

Event Management System is licensed directly by the client and is primarily managed by that client

who is quite happy with it. Pat, the Account Team VP/Director is also responsible for one of the

larger client who has expressed significant frustration over the older legacy system has been in

discussions with this client on the potential usage of the new Event Management System, but only

gives it a neutral response for her performance expectancy. Lucas, the last executive in the network

is the Event Management System Vendor executive responsible for growing a relationship and long

term contract with the marketing company, so it is not surprising that he has positive performance

expectancy.

The client facing teams have a mostly positive or neutral performance expectancy for the new Event

Management System, this is likely because the two larger clients currently already use this new

Event Management System or are evaluating using it at the time of this first survey. The pilot group

in general has very positive performance expectancy with the exception of the two functional

executives and Kim the Data Ops Lead/Manager

Context at time of survey



The state of organizational or contingent decision at the time of the surveys in late January 2011 was

generally positive. A business case was developed by a cross-functional team including Joseph,

Julie, Patrick, Kim, Josh, Dayna, and others that was then approved by the functional execs and

president of the company early in January and communicated out to most of the resources in the

network. The business case was part of a larger plan to substantially increase sales over the next 3-4

years. The proposal was to invest a substantial amount into a 2 month pilot. The future return on

investment in the pilot required new functionality to generate new revenue opportunities and

reduction of costs by retiring the legacy system within a few quarters. There were five tactics for the

pilot. First, establish a cross-functional team including resources from the marketing company and

the technology vendor. Second, conduct the pilot with a real client. Third, if the pilot is successful,

launch the platform across the larger client base. Fourth, establish a product management function

with SMEs & trainers to manage the product line over time with the vendor and facilitate adoption

and usage across the client base. Lastly, identify key metrics for monitoring and controlling the new

services.

The state of the organization was in flux, a merger had just finished with a new owner of the

company and there was a significant business evaluation to determine the strategic direction of the

marketing company. There was a general sense of anxiety of the employees as they waited for the

larger direction and what any impacts would be on their group. At the end of January there was a

small layoff of many of the middle management in the company including Joseph the IT Director as

part of this larger strategic direction.

The state of the resources on the pilot was positive as the resources worked on a more detailed plan

and was in discussions with different client teams to figure out which client would be the best

candidate for the new technology pilot.



a. Color coding relative surveyed Performance Expectancy mean measured from 1-7 Likert scale, where 1 is strongly disagree, 4 is neutral, and 7

is strongly agree

b. Represents “Give Help” and “Get Help” connections identified as part of survey

c. Connection originate from person identifying connection regardless of whether its “Give Help” or “Get Help”

d. Relative node sizes based on betweenness centrality, calculated within Netdraw

e. Red 1-3.49, Yellow 3.50-4.50, Green 4.51-7.00, Grey N/A

f. Generated from Netdraw

Figure 19 EMS Pre Pilot Coping Network

a. Color coding based on most efficient grouping using Gervin-Newman Clustering (.418)

b. Connections represents “Give Help” and “Get Help” connections identified as part of survey



e. Generated from Netdraw

Figure 20 EMS Pre-pilot Coping Network Grouping



4.8.1.2 Event Management System Post-Pilot Coping Network

Description:

Similar to the Pre-Pilot, Figure 21 EMS Post-Pilot Coping Network was generated in NetDraw and

shows the coping network at the expected end of the Event Management System Pilot. This coping

network shows who the actors interact with in order to help manage (minimize, reduce, master,

tolerate) demands emanating from a specific change in the person-environment relationship

(Lazarus 1966). The connections are directional based originating at the actor who identified others

as someone they get or give help to. The nodes are color-coded based on Performance Expectancy

responses for the Event Management System; red for a negative, yellow for neutral, and green for a

positive response. The nodes are sized relative to each other based on betweenness centrality

which represents how many pairs of nodes they are between(Freeman 1977).

Groups

The coping network has fragmented significantly since the pre-pilot survey. In reviewing this

network for groups that naturally belong together, the network participants naturally fall into six

groups when evaluated using the Girvan-Newman analysis within Netdraw (Borgatti 2002) as shown

in Figure 22 EMS Post Pilot Coping Network Grouping. The isolates are considered a group for this

study. The groups can then generalized based on their current roles into six classes, the first three

have some semblance to pre-pilot groups but have changed members; the isolates (black), client

support team (grey), the pilot project (pink). The other three can be generalized as the cross-

functional operations leadership (blue), the vendor (green), and the data/ops leadership (red).

Not only did the network reduce in density, but it also became more disjoint. There are now seven

isolates rather than four at the beginning of the pilot. The isolates are essentially those participants

who did not identify any person that they interact with that they give or receive help from regarding

the Event Management System. The group is still made up of IT resources and an account team lead

that will be impacted by the current or future use of this technology but are not integrated into the

coping network yet, but now one of the functional execs as well as Dayna have become isolates.

Dayna was very central in pre-pilot coping network, but has been taken off the project because they

wanted to reduce the cost of the pilot.

The second natural group or class of individuals is color coded as grey in Figure 22 EMS Post Pilot

Coping Network Grouping, was generalized as the client facing team who currently works directly or

indirectly with the one client who uses the Event Management System or the second client who is

evaluating using it. With their client facing roles, this group is continually under significant pressure

to manage events in the US and globally. This group also reduced in size and the most probable

client that was expected to switch over to use the new Event Management System decided to keep

moving forward with the legacy system, which was not surprising because the client-delivery team is

very risk-adverse to new technologies so as to not upset a demanding client that has no appetite for

any mistakes.

The third group or class was generalized as the new technology pilot group, which is color-coded as

pink in Figure 22 EMS Post Pilot Coping Network Grouping. This group changed significantly as it was



separated from the any connections to the client facing group, most of the IT resources have been

taken off the project or left the company, and two strategy-level executives became involved in the

future pilot or direction of the Event Management Capabilities. The only central figure is Josh, the IT

Analyst who is only involved on the periphery of the project. He was removed from a full-time role

in the Event Management area shortly after voicing some concerns over the direction of the Events

Technologies, and his only connection to the larger coping network is a non-reciprocated connection

to the Functional Exec who is the primary sponsor of the Event Management System pilot.

In summary, the pre-pilot cluster that was classified as the pilot group consisted of mostly IT staff

with very few remaining. Linda and Patrick were contractors who were let go, Josh and Dayna were

reassigned, and Joseph left the company in a post-merger layoff.

The cross-functional operations leadership (blue) is also a new cluster. In the pre-pilot coping

network they were part of other groups, but now it seems the central decision maker, Dawn, is the

central figure in a five person group of cross-functional managers when it comes to coping with the

new Events Management System. Dawn ego coping network also includes Kim the Data-Ops Lead

Manager, who is separated into the data/ops leadership (red) group. This group centered on Kim is

small, but she is now central to the larger network and has a controlling position in the coping

network to any information coming from the pilot group and the cross-functional leadership group.

The most dramatic change to this network is the separation of the new Events Management System

vendor from the larger coping network. In the pre-pilot, most of their ties in the coping network

were through the IT group, but with most of those individuals removed, they are no longer part of

the coping network and the other groups did not identify the vendor as part of their coping network.

The only group of individuals that stayed relatively the same is the client-focused group which is

primarily made up of the data/ops group and different account team member, many of which report

to Kim. These individuals have worked together for many years and would be the most impacted by

a change in Event Management Technologies. There is a certain amount of transient membership in

the other groups, but this group likely has a significant amount of solidarity and norms. As

mentioned earlier, group solidarity as an outcome of social capital could have a significant impact

technology acceptance networks. If networks where highly connected it facilitates the emergence

of effective norms and maintains trustworthiness of others, and in turn increasing social capital as a

group (Coleman 1988). These strong ties and solidarity could also limit the inflow of new ideas into

the group (Gargiulo and Bernassi 1999; Adler and Kwon 2002).

The clusters in the connected network have very low intra-cluster sparsity, where the two groups

have generally one connection between each other. At the point of the pre-pilot, Josh, Dawn, and

Kim are playing bridging roles between the different groups, with Kim controlling the information

flow into the delivery teams.

This network also significantly reduced in intra-cluster density in terms of connections with the two

groups or clusters. Only the client facing group (grey) in Figure 22 EMS Post Pilot Coping Network

Grouping, has most everyone connected to all of the others in the group such as the 2-clique that is

made up of Santiago, Ava, Patty, Diego, and others.




The power has shifted substantially from Figure 19 EMS Pre Pilot Coping Network to the Figure 21

EMS Post-Pilot Coping Network . The three most central resources within this this are now Josh,

Dawn, and Kim.

Santiago was one of the more central figures in the pre-pilot coping network, but with the

fragmentation of the coping network, he is now isolated in terms of pathways to others outside of

his immediate group with only one pathway through Kim and Dawn.

Josh is still a central person, but after being removed from the project full-time and taken off much

of the solution support for the Events area, his connections are now also limited to long-pathways

through Dawn and Kim. As mentioned earlier, he had high personal innovativeness, and was a

strong supporter of moving from the legacy Event Management System to the new Event

Management System, but it could be that his opinions put himself in conflict with others like Kim,

the Data Ops manager in terms of opinions on if and how to transition to a new technology. This

could have been a factor in being separated from the Events Management Groups and has voiced

significant frustration over this situation.

Another central person in the earlier pre-pilot group was Dayna, who earlier acted as an intra-cluster

bridging role between the two network clusters in Figure 19 EMS Pre Pilot Coping Network, but now

she is completely isolated from the coping network for the new Event Management System. She

also was an individual with significant domain experience in the events business as well as managing

and configuring the previous legacy system as both a business consultant and front-end programmer

and at the time of the post-pilot survey, she is focusing on supporting events on the legacy Event

Management System.

She also was socially tied to many of the resources within the Data/Operations and IT organization

and was respected for her knowledge on managing many of the recurring events over the years

which made her an efficient analyst. For many years, she also was a very vocal supporter of the

previous legacy system. She is likely a central figure in the coping network around the legacy Event

Management System.

The most central figure in the Figure 21 EMS Post-Pilot Coping Network is Kim, the Data/Ops Lead

Manager who is the supervisor to most of the Data/Ops resources and works on client deliverables

as well. Looking back at the pre-pilot coping network where she was a non-central figure in the

coping network. She was one of the few people in this smaller pilot group that had negative

performance expectancy in Figure 19 EMS Pre Pilot Coping Network. She was very involved in the


well as the impact it could have on her team, which she felt were significant.

In the pre-pilot, there was significant contention with some of the IT resources because they felt that

Kim’s perception of the new Event Management System was making their jobs much more difficult.

Several felt they needed to go to increasing levels of details to address the differences or perceived

gaps between the internally developed legacy Event Management System and the newer vendor

supplied Event Management System. This list of gaps was central issue in the pre and post pilot

timeframes and it was suggested at the time of the post-pilot survey to wait until the vendor was

able to close more of the perceived gaps before the group moved forward with a larger pilot or using

other key clients.



Unlike the pre-pilot coping network, were there were not any individuals who filled structural holes,

now Josh, Dawn and Kim all fill structural-hole positions. From a social network view, structural

holes would be the individuals (or players) that occupy a network position that connects two

different groups. This allows them to potentially benefit from a more heterogeneous flow of

information from multiple network groups and potentially have more control of information or

influence within the larger network.



with. As mentioned earlier, one might wonder if some of the vendor connections such as Josh and

Lucas the Vendor executive were closer to a weak tie relationship.

Generally the groups within networks had small diameters, meaning it didn’t take to many steps

along a path between any two individuals within the connected network, but the pathways for the

vendor and new isolates no longer existed and the pilot group was pushed out to the periphery of

the coping network.


The performance expectancy of individuals changed is several cases, but the larger change was the

positive performance expectation resources moving out of central roles into periphery or out of the

network all-together.

The directors or above stayed roughly the same as a group. They are the ones who are very involved

at the organizational decision of using the new Event Management System for all of the Event clients

supported by the Marketing Company. In the pre-pilot survey, the two functional executives

responsible for largest departments, Dawn and Richard, do not believe it will help them do their job,

likely, because they are fairly removed from the day to day client event support. This was consistent

with the post-pilot survey, although Dawn was now neutral on performance expectancy.

Kim’s Performance Expectancy remained negative from the pre-pilot to the post-pilot timeframes

even though much of her team’s Performance Expectancy is positive or neutral. If Performance

Expectancy of an individual impacts the perceptions of neighbors, this makes Kim’s Performance

Expectancy more important because now only Kim and Isabelle were identified as part of the coping

network for Dawn who is the key decision maker and sponsor for the Events Management System.

There were other connections with Josh and Leah, but Josh’s connection to Dawn was not

reciprocated and Leah didn’t fill out a post-pilot survey.

Pat, the Account Team VP/Director is also responsible for one of the larger client who has expressed

significant frustration over the older legacy system has been in discussions with this client on the

potential usage of the new Event Management System, was neutral for her performance expectancy

in the pre-pilot, which dropped to a negative performance expectancy in the post-pilot survey. This

could be because the client issues around the legacy system reduced or new issues or concerns

decreased her perception of the new system. Her client was originally targeted to use the new

Event Management System as part of the pilot, but this was changed around the time the pilot

started. Pat also surprisingly became an isolate as well in post-pilot coping network.



Lucas, the last executive in the network is the Event Management System Vendor executive

responsible for growing a relationship and long term contract with the Marketing Company, so it is

not surprising that he has positive performance expectancy again.

The client facing teams still have a mostly positive or neutral performance expectancy for the new

Event Management System, this is likely because the client that currently uses this new Event

Management System is still satisfied and a proponent of its use.

The remaining people in the pilot cluster still has a positive performance expectancy, with the

exception of Josh who had reduced his performance expectancy from positive to neutral, which

might be a reaction to his frustration over the progress of the pilot between the pre and post-pilot

timeframes.

Visually, you can see that the Performance Expectancy of individuals within Figure 19 EMS Pre Pilot

Coping Network and Figure 21 EMS Post-Pilot Coping Network generally tend to have similar

Performance Expectancies as their neighbors, which is important in evaluating the hypothesis that a

neighbors Performance Expectancy will impact an individual’s behavioral intention.


The state of organizational or contingent decision at the time of the surveys in April was significantly

different then at the pre-pilot. At the pre-pilot timeframe, a business case was developed by a

cross-functional team including Joseph, Julie, Patrick, Kim, Josh, Dayna, and others that was then

approved by the functional execs and president of the company early in January and communicated

out to most of the resources in the network. At the time of the post-pilot, the pilot did not reach a

conclusion although they tried it for a new client and a new pilot would need to be executed again

and a new business case put together with a potential rollout in the next year. In reviewing the

plans of the previously approved plan, the pilot can be considered a failure.

The original business plan was to invest a substantial amount into a 2 month pilot, but during this

timeframe most of the individuals were removed from the project, although a contract was

established with the Event Management System Vendor. In the original plan, the future return on


reduction of costs by retiring the legacy system within a few quarters. At the time of the post pilot

survey, it was changed to only partially use the new Event Management System during any

subsequent pilots and continuing using the legacy system for the back-office processing which

eliminated the possibility of cost savings of retiring the legacy system.

The state of the organization at the time of the post-pilot survey was still in flux as the company was

settling into a clearer strategic direction of the marketing company. At the point of the pre-pilot

survey, there was a general sense of anxiety of the employees as they waited for the larger direction

and what any impacts would be on their group.

As mentioned earlier parts of the pilot were completed, such as vendor on-sight training and further

understanding of the potential and perceived functional gaps that would need to be addressed with

the Event Management System vendor, but in general the pilot was going to go through a restart

and a larger rollout was being discussed for the next year.




is strongly agree

b. Represents “Give Help” and “Get Help” connections identified as part of survey





Figure 21 EMS Post-Pilot Coping Network



f. Color coding based on most efficient grouping using Gervin-Newman Clustering (.550)

g. Connections represents “Give Help” and “Get Help” connections identified as part of survey

h. Connection originate from person identifying connection regardless of whether its “Give Help” or “Get Help”

i. Relative node sizes based on betweenness centrality, calculated within Netdraw

j. Generated from Netdraw

Figure 22 EMS Post Pilot Coping Network Grouping

4.8.1.3 Event Management System Pre-Pilot Influencing Network

Description

Figure 23 EMS Pre Pilot Influencing Network, was generated in NetDraw and shows the influencing

network at the beginning of the Event Management System Pilot. This influencing network shows

who the actors interact with along pathways within the network that have access to information,

resources, and knowledge which are typically unavailable to those on the periphery with longer

pathways or isolated from the network altogether (Ibarra and Andrews 1993; Sykes, Venkatesh et al.

2009). This influencing indicates that people in central structural positions in a network will have

more influence over system design to their needs which is often labeled as co-adoption, thus driving

up their usage.

The connections are directional based originating at the actor who identified others as someone

they get or give help to. The nodes are color-coded based on Performance Expectancy responses for

the Event Management System; red for a negative, yellow for neutral, and green for a positive

response. The nodes are sized relative to each other based on betweenness centrality which

represents how many pairs of nodes they are between(Freeman 1977).

Groups



In reviewing this network for groups that naturally belong together, the network participants

naturally fall into four groups when evaluated using the Girvan-Newman analysis within

Netdraw(Borgatti 2002), which are color-coded in Figure 24 EMS Pre-Pilot Influencing Groups. These

groups are significantly different then the coping network.

One significant difference from the Figure 19 EMS Pre Pilot Coping Network is that the IT staff and

vendor contacts are clustered together in their group which is shaded black in Figure 24 EMS Pre-

Pilot Influencing Groups. It is appropriate and promising to see that employees in an organization

do interact with important actors outside of their organization that might help them understand

how to use a new technology, but they might not be able to identify them by name. The challenge is

that without a name, it’s not possible to follow-up with a secondary survey to these individuals via a

snowball survey approach.

The second group which is the pilot group and functional leads, which is shown in Figure 24 EMS Pre-

Pilot Influencing Groups on the right and color-coded as red. To generalize the members, it’s the

division execs, operations staff, and leads who are involved in the decision to use the new Events

Management System, and the other members who are participating in the pilot. Although it not

easy to generalize this cluster because there are several Data/Ops and CSR resources in the group

that are not part of the pilot.

The group on the lower left in Figure 23 EMS Pre Pilot Influencing Network is a fairly centralized

group, similar to the coping network; we will continue to refer to this group as the client group. It

can be generalized as those individuals who have had some exposure to the new Event Registration

System while working with the one client who mandated the usage of the new Event Registration

System due to compatibility needs within the client’s organization. All of the account team

members are part of this group and the few individuals who support this client.

One interesting actor in this group is that it is heavily centralized around a data ops analyst who was

not originally identified as a key user of the Event Registration System by the events technical

director (the researcher) or the division exec responsible for the events area that put together list of

survey respondents and the pilot group. Instead this central individual was identified via the

snowball survey method to build the social network and its boundaries. This actor occupies a

structural hole and helps link the two networks together in terms of access to information in the

effective use of the Event Registration System. A second identified actor in this group that was

unexpected was a client executive who was a customer of the marketing company. Lily, the

VP/Director identified this person as someone that they interact with frequently who has access to

information for the effective use of the system. A survey was not sent to this newly identified actor

because the actor was a customer outside of the organization and client communications are highly

sensitive and critical. This is another case where the boundary the network extends beyond the

organization.

There are no networks that are disjoint, but there are several individuals that are not part of the

coping network. The isolates are essentially those participants where did not identify any person

that they interact with that they believe have access to information, knowledge, or resources

regarding the new Event Management System.



The clusters have lower intra-cluster sparsity, where the two larger groups only have a single

connection between each other and connected via the Data Ops Analysts. Alternatively, none of the

newer IT resources or contractors brought in for this particular new Event Management System pilot

project such as Patrick, Jennifer, Linda or Josh are playing a role in intra-cluster connections.

This network does have high intra-cluster density in terms of connections with the two groups or

clusters. There are several instances of cliques within the network group where everyone in the

small groups is connected to all of the others in the group such as the clique that is made up of

Linda, Kim, Dayna, and Linda. In looking at the groups for the more relaxed k-clique definition, a

clique where everyone is still connected or within a path distance of k away from others, almost all

of the pilot group (red & black) and client-facing groups (blue) would be part of 2-cliques.

The group of isolates who were unable to identify anyone they interacted with that had access to

information for the effective use of the Event Registration System. The IT Analyst that was part of

the isolated group also works in the events area, but is heavily dedicated to managing the previous

legacy event system with little time to interact with others on the new system due to a major client

program running at this time on the legacy event system. The two call-center reps are likely in this

same situation focused on the client program using the legacy technology.


“One of the primary purposes of graph theory in social network analysis is the identification of the

most important actors in the social network”(Wasserman and Faust 1994). The five most central

resources within this this network is Santiago, Julie, Dayna, Kim, Josh, and Linda. The most central

person in Figure is Santiago similar to the coping network. Santiago is a data ops analyst who has

had experience using the new Event Management System while working on the one client that

currently uses it for their events.

The second notable individual within the pilot cluster in Figure 24 EMS Pre-Pilot Influencing Groups,

was Josh the IT Analyst. He is the central person is the IT/Vendor cluster that is color-coded as black.

At the time of the survey, Josh was business analyst and a solution architect whose responsibilities

included identifying solutions that met client needs and helping identify future technologies that the

company could use to differentiate its service offerings. It had high personal innovativeness, and

was a strong supporter of moving from the legacy Event Management System to the new Event

Management System. His opinions put himself in conflict with others like Kim, the Data Ops

manager in terms of opinions on if and how to transition to a new technology.

Another central person in the pilot group was Dayna was an IT Analyst by closer and more integrated

into the Data Operations group. As mentioned earlier, she was an individual with significant domain

experience in the events business as well as managing and configuring the previous legacy system as

both a business consultant and front-end programmer. She also was socially tied to many of the

resources within the Data/Operations and IT organization and was respected for her knowledge on

managing many of the recurring events over the years which made her an efficient analyst. For

many years, she also was a very vocal supporter of the previous legacy system.

Similar to the pre-pilot copping network, Kim was not showing up as high coping centrality, but plays

an important part of this group. Kim was the Data/Ops Lead Manager who is the supervisor to most



of the Data/Ops resources and works on client deliverables as well. She is very involved in the


well as the impact it could have on her team, which she felt were significant. She had negative

performance expectancy, which was a point of contention with some of the IT resources because

they felt that Kim’s perception of the new Event Management System was making their jobs much

more difficult because they continued to have to go to increasing levels of details to address the

differences or perceived gaps between the internally developed legacy Event Management System

and the newer vendor supplied Event Management System.

Julie is another data ops analyst who was central to this network and identified several people that

she frequently interacts with that has significant knowledge, information, or resources regarding the

new technology. Here central role between the groups is likely because she was at the center of the

discussion of whether to use the new Events Management System on the client that she is dedicated

to. She was also extremely busy with the client’s current demands and which had several existing

event registrations starting soon that required a decision on which Events Management System to

use, there were existing issues brought up by the client on the legacy Events Management System,

and the client was looking for proposals on new opportunities from the marketing company. These

dynamics are what likely put her in the center of interacting with others who had access to

resources, knowledge, and information regarding the new Events Management System.

Both Julie and Santiago filled structural holes where they were the only connections between the

groups. From a social network view, structural holes would be the individuals (or players) that

occupy a network position that connects two different groups. This allows them to potentially

benefit from a more heterogeneous flow of information from multiple network groups and

potentially have more control of information or influence within the larger network.



with. One might wonder if some of the vendor connections such as Josh and Lucas the Vendor


Generally the networks had slightly larger diameters within and between the groups then the coping

network, meaning it took more steps along a path between any two individuals within the

connected network for information to travel.


Overall the performance expectancy was neutral or positive with the central individuals within the

influencing network as shown in Figure 23 EMS Pre Pilot Influencing Network with Santiago, Dayna,

Julie, and Josh all having positive or neutral Performance Expectancy. Kim was the fifth most central

individual in the diagram and was the first person to have a negative Performance Expectancy

amongst the central resources.

The VP/Executive level varied significantly in terms of Performance Expectancy. The two functional

executives responsible for larger departments, Dawn and Richard, do not believe it will help them do

their job, likely, because they are fairly removed from the day to day client event support. The client

focused executive, Lily, who believes it will help her do her job, has high level responsibility for the



one client who uses the new technology, although the Event Management System is licensed

directly by the client and is primarily managed by that client who is quite happy with it. Pat, the

Account Team VP/Director is also responsible for one of the larger client who has expressed


potential usage of the new Event Management System, but only gives it a neutral response for her

performance expectancy. Lucas, the last executive in the network is the Event Management System

Vendor executive responsible for growing a relationship and long term contract with the Marketing

Company, so it is not surprising that he has positive performance expectancy.

The client facing teams in the lower left of Figure 23 EMS Pre Pilot Influencing Network have a

mostly positive or neutral performance expectancy for the new Event Management System, this is

likely because the two larger clients are currently already use this new Event Management System or

are evaluating using it at the time of this first survey. The grouping of Data/Ops analysts and CSRs in

the upper right of Figure 23 EMS Pre Pilot Influencing Network, many of whom were also integrated

into the pilot group, varied in terms of performance expectancy. There were also two functional

execs tied into the network.


The state of organizational or contingent decision at the time of the surveys in late January was

generally positive. A business case was developed by a cross-functional team including Joseph,

Julie, Patrick, Kim, Josh, Dayna, and others that was approved by the functional execs and president

of the company early in January and communicated out to most of the resources in the network.

The business case was part of a larger plan to substantially increase sales over the next 3-4 years.

The proposal was to invest a substantial amount into a 2 month pilot. The future return on


reduction of costs by retiring the legacy system within a few quarters. There were five tactics for the

pilot. First, establish a cross-functional team including resources from the marketing company and

the technology vendor. Second, conduct the pilot with a real client. Third, if the pilot is successful,

launch the platform across the larger client base. Fourth, establish a product management function

with SMEs & trainers to manage the product line over time with the vendor and facilitate adoption

and usage across the client base. Lastly, identify key metrics for monitoring and controlling the new

services.

The state of the organization was in flux, a merger had just finished with a new owner of the

company and there was a significant business evaluation to determine the strategic direction of the

marketing company. There was a general sense of anxiety of the employees as they waited for the

larger direction and what any impacts would be on their groups. At the end of January there was a

small but significant layoff of many of the middle management in the company including Joseph the

IT Director as part of this larger strategic direction.

The state of the pilot was positive prior to the pilot; the resources worked on a more detailed plan

and were in discussions with different client teams to determine which client would be the best

candidate for the new technology pilot.




is strongly agree

b. Represents Access to Information, Resources, or Strong Domain Knowledge connections identified as part of survey

c. Connection originate from person identifying connection regardless of mode




Figure 23 EMS Pre Pilot Influencing Network



a. Color coding based on most efficient grouping using Gervin-Newman Clustering





Figure 24 EMS Pre-Pilot Influencing Groups

4.8.1.4 Event Management System Post-Pilot Influencing Network

Description:

Figure 32 EPM Post Pilot Influencing Network, was generated in NetDraw and shows the influencing

network 3 months after the beginning of the Event Management System Pilot.

This influencing network shows who the actors interact with along pathways within the network that

have access to information, resources, and knowledge which are typically unavailable to those on

the periphery with longer pathways or isolated from the network altogether (Ibarra and Andrews

1993; Sykes, Venkatesh et al. 2009). This influencing indicates that people in central structural

positions in a network will have more influence over system design to meet their needs which is

often labeled as co-adoption, thus driving up their usage. The connections are directional based

originating at the actor who identified others as someone they interact with whom have access to

resources, knowledge and information. The nodes are color-coded based on Performance

Expectancy responses for the Event Management System; red for a negative, yellow for neutral, and

green for a positive response. The nodes are sized relative to each other based on betweenness

centrality which represents how many pairs of nodes they are between (Freeman 1977).

Groups



The coping network has fragmented significantly since the pre-pilot survey. In reviewing this

network for groups that naturally belong together, the network participants naturally fall into five

groups when evaluated using the Girvan-Newman analysis within Netdraw (Borgatti 2002) as shown

Figure 26 EMS Post-Pilot Influencing Groups. The isolates are considered a group for this study. The

post-pilot influencing groups are more difficult to generalize into classes then the coping networks

and the pre-pilot influencing groups.

Not only did the network reduce in density, but it also became more disjoint. There are now nine

isolates rather than three at the beginning of the pilot. The isolates are essentially those

participants who did not identify any person that they interact with that they give or receive help

from regarding the Event Management System. The group is still made up of IT resources and an

account team lead that will be impacted by the current or future use of this technology but are not

integrated into the coping network yet, but now one of the functional execs as well as Dayna have

become isolates. Dayna was very central in pre-pilot influencing network, but has been taken off the

project because they wanted to reduce the cost of the pilot.

The disjoint group shown in Figure 26 EMS Post-Pilot Influencing Groups which is color-coded as

purple is made up of the IT staff and vendor contacts that now have reduced roles in the pilot. This

group changed significantly as it was separated from the any connections from the larger network.

Many of the IT resources have been taken off the project or left the company. The only remaining

central figure is Josh, the IT Analyst who is now only involved on the periphery of the project. He

was removed from a full-time role in the Event Management area after voicing some concerns over

the direction of the Events Technologies, and his only connection to the larger coping network is a

non-reciprocated connection to the Functional Exec who is the primary sponsor of the Event

Management System pilot. Linda and Patrick were contractors who were let go, Josh and Dayna

were reassigned, and Joseph left the company in a post-merger layoff.

The cross-functional operations leadership (blue) is an ego centric group centered on Dawn the

functional exec. In the pre-pilot coping network they were part of other groups, but now it seems

the central decision maker, Dawn, is the central figure in a six person group of cross-functional

managers when it comes to coping with the new Events Management System. Dawn’s ego

influencing network also includes Kim the Data-Ops Lead Manager, who is also part of the data/ops

leadership (red) group. This group centered on Kim is central to the larger network and has the most

controlling position in the coping network to any information coming from the pilot group and the

cross-functional leadership group.

Similar to the coping network, one of the most dramatic changes to this network is the separation of

the new Events Management System vendor from the larger influencing network. In the pre-pilot,

most of their ties in the influencing network were through the IT group, but with most of those

individuals removed, they are no longer part of the influencing network and the other groups did not

identify the vendor as part of their influencing network. Maria the IT Director is still connected to

Lucas the Vendor Executive which gives a pathway to Dawn and Kim for the Vendor, but Maria’s

connection to Dawn is not reciprocated.

The only group of individuals that has some semblance to the pre-pilot group is the client-focused

group which is primarily made up of the data ops group and different account team, many of which

report to Kim. These individuals have worked together for many years and would be the most



impacted by a change in Event Management Technologies. There is a certain amount of transient

membership in the other groups, but this group likely has a significant amount of solidarity and

norms.

The clusters in the connected network have very low intra-cluster sparsity, where the two groups

have generally one connection between each other. At the point of the post-pilot surveys, Kim is the

only one playing significant bridging roles between the different groups, with Kim controlling the

information flow into the delivery teams.

This network also significantly reduced in intra-cluster density in terms of connections with the two

groups or clusters. Only the client facing group (grey) in Figure 26 EMS Post-Pilot Influencing

Groups, has most everyone connected to all of the others in the group such as the 2-clique that is

made up of Santiago, Ava, Patty, Diego, Emma, and Jean


Similar to the coping network, the power has shifted substantially from Figure 23 EMS Pre Pilot

Influencing Network to Figure 25 EMS Post Pilot Influencing Network. The three most central

resources within this network are now Kim, Dawn, and Ava.

Santiago was one of the more central figures in the pre-pilot influencing network, but with the

fragmentation of the social network, he is now isolated in terms of pathways to others outside of his

immediate group with only one pathway through Kim.

Josh is no longer central or even connected to the main influencing network, after being removed

from the project full-time and taken off much of the solution support for the Events area, his

connections are only to others who also have very little influence. As mentioned earlier, he had

high personal innovativeness, and was a strong supporter of moving from the legacy Event

Management System to the new Event Management System, but it could be that his opinions put

himself in conflict with others like Kim, the Data Ops manager on if and how to transition to a new

technology. This could have been a factor in being separated from the Events Management Groups

and has voiced significant frustration over this situation.

Another central person in the earlier pre-pilot group was Dayna, who earlier acted as an intra-cluster

bridging role between the two network clusters in Figure 19 EMS Pre Pilot Coping Network, but now

she is completely isolated from the influencing network for the new Event Management System as

well. She also was an individual with significant domain experience in the events business as well as

managing and configuring the previous legacy system as both a business consultant and front-end

programmer and at the time of the post-pilot survey, she is focusing on supporting events on the

legacy Event Management System.

She also was socially tied to many of the resources within the Data/Operations and IT organization

and was respected for her knowledge on managing many of the recurring events over the years

which made her an efficient analyst. For many years, she also was a very vocal supporter of the

previous legacy system.

The most central figure in the Figure 25 EMS Post Pilot Influencing Network is Kim, the Data/Ops

Lead/Manager who is the supervisor to most of the Data/Ops resources and works on client



deliverables as well. Looking back at the pre-pilot influencing network where she was a non-central

figure in the coping network. She was one of the few people in this smaller pilot group that had

negative performance expectancy in Figure 23 EMS Pre Pilot Influencing Network. She was very

involved in the evaluation of the new Event Management System, likely because of her experience in

the domain as well as the impact it could have on her team, which she felt were significant.

As discussed earlier in the pre-pilot, there was significant contention with some of the IT resources

and Kim because they felt that Kim’s perception of the new Event Management System was making

their jobs much more difficult. Likely because they continued to have to go to increasing levels of

details to address the differences or perceived gaps between the internally developed legacy Event

Management System and the newer vendor supplied Event Management System. This list of gaps

was central issue in the pre and post pilot timeframes and it was suggested at the time of the post-

pilot survey to wait until the vendor was able to close more of the perceived gaps before the group

moved forward with a larger pilot or using other key clients.

In the pre-pilot influencing network, both Santiago and Julie filled structural holes, only Kim filled a

structural-hole position. From a social network view, structural holes would be the individuals (or

players) that occupy a network position that connects two different groups. This allows them to

potentially benefit from a more heterogeneous flow of information from multiple network groups

and potentially have more control of information or influence within the larger network.



with. One might wonder if some of the vendor connections such as Josh and Lucas the Vendor


Generally the smaller groups within networks had very small diameters, meaning it didn’t take to

many steps along a path between any two individuals within the connected network, but the

pathways for the vendor and new isolates no longer existed and the pilot group was pushed out to

the periphery of the coping network.


As was shown in the coping network, the Performance Expectancy of individuals changed in several

cases, but the larger change was with the actors with high Performance Expectancy moving out of

central roles into periphery or out of the network all-together.

The directors or above stayed roughly the same as a group. They are the ones who are very involved

at the organizational decision of using the new Event Management System for all of the Event clients

supported by the Marketing Company. In the pre-pilot survey, the two functional executives

responsible for largest departments, Dawn and Richard, do not believe it will help them do their job,

likely, because they are fairly removed from the day to day client event support. This was consistent

with the post-pilot survey, although Dawn was now neutral on performance expectancy.

Kim’s Performance Expectancy remained negative from the pre-pilot to the post-pilot timeframes

even though much of her team’s Performance Expectancy is positive or neutral. If Performance

Expectancy of an individual impacts the perceptions of neighbors, this makes Kim’s Performance



Expectancy more important because now only Kim and Isabelle were identified as part of the

influencing network for Dawn who is the key decision maker and sponsor for the Events

Management System. There was another connection to Dawn from Maria, but Maria’s connection

was not reciprocated. This is similar to what happened on the Coping Network.

Pat, the Account Team VP/Director is also responsible for one of the larger client who has expressed


potential usage of the new Event Management System, was neutral for her performance expectancy

in the pre-pilot, which dropped to a negative performance expectancy in the post-pilot survey. This

could be because the client issues around the legacy system reduced or new issues or concerns

decreased her perception of the new system. Her client was originally targeted to use the new

Event Management System as part of the pilot, but this was changed around the time the pilot

started. Pat also surprisingly became an isolate as well in post-pilot coping network.

Lucas, the last executive is the network is the Event Management System Vendor executive

responsible for growing a relationship and long term contract with the Marketing Company, so it is

not surprising that he has positive performance expectancy again.

The client facing teams still have a mostly positive or neutral performance expectancy for the new

Event Management System, this is likely because the client that currently uses this new Event

Management System is still satisfied and a proponent of its use.

The remaining people in the pilot cluster still has a positive performance expectancy, with the

expectation of Josh who had reduced his performance expectancy from positive to neutral, which

might be a reaction to his frustration over the progress of the pilot between the pre and post-pilot

timeframes.

Visually, you can see that the Performance Expectancy of individuals within Figure 23 EMS Pre Pilot

Influencing Network and Figure 25 EMS Post Pilot Influencing Network generally tend to have similar

Performance Expectancies as their neighbors, which is important in evaluating the hypothesis that a

neighbors Performance Expectancy will impact an individual’s behavioral intention.


The state of organizational or contingent decision at the time of the surveys in April was significantly

different then at the pre-pilot. At the pre-pilot timeframe, a business case was developed by a

cross-functional team including Joseph, Julie, Patrick, Kim, Josh, Dayna, and others that was then

approved by the functional execs and president of the company early in January and communicated

out to most of the resources in the network. At the time of the post-pilot, the pilot did not reach a

conclusion although they had used the new Event Management System for some of the functionality

for a new client in conjunction with using the legacy Event Management System, but it was decided

that a new pilot would need to be executed again and a new business case put together with a

potential rollout in the next year. In reviewing the plans of the previously approved plan, the

original pilot can be considered a failure.

The original business plan was to invest a substantial amount into a 2 month pilot, but during this

timeframe most of the individuals were removed from the project, although a contract was

established with the Event Management System Vendor. In the original plan, the future return on




reduction of costs by retiring the legacy system within a few quarters. At the time of the post pilot

survey, it was changed to only partially use the new Event Management System during any

subsequent pilots and continuing using the legacy system for the back-office processing which

eliminated the possibility of cost savings of retiring the legacy system.

The state of the organization was still in flux as the company was settling into a clearer strategic

direction of the marketing company. At the point of the pre-pilot survey, there was a general sense

of anxiety of the employees as they waited for the larger direction and what any impacts would be

on their groups, but at the time of the post-pilot survey, it was becoming clearer, but there was still

anxiety.

As mentioned earlier, parts of the pilot were completed, such as vendor on-site training and a

further understanding of the potential and perceived functional gaps that would need to be

addressed with the new Event Management System vendor, but in general the pilot was going to go

through a restart and a larger rollout was being delayed until the next year.


is strongly agree






Figure 25 EMS Post Pilot Influencing Network




b. Represents Access to Information, Resources, or Strong Domain Knowledge connections identified as part of survey Connection originate from

person identifying connection regardless of whether its “Give Help” or “Get Help”

c. Relative node sizes based on betweenness centrality, calculated within Netdraw

d. Generated from Netdraw

Figure 26 EMS Post-Pilot Influencing Groups



4.8.2 Enterprise Project Management

The smaller of the two new technology projects in this study was a 2-3 month project to determine if

the marketing company should use an Enterprise Project Management tool, which was piloted by a

small team of individuals from the IT organization within the large marketing company in 2010.

An Enterprise Project Management tool is essentially a consolidated repository for a portfolio of project plans, change records, issues, and risks across from many different project managers. In this case the company was looking to have the project managers using Microsoft Project Professional to manage project plans in combination with Microsoft Project Server where they are consolidated and leverage a consolidated resource list for their project plans. Microsoft describes MS Project as

“Microsoft Project Server 2010 provides innovative capabilities across the entire lifecycle to help organizations

effectively initiate, select, plan and deliver projects on time and within budget. Enhance productivity and realize

results with Project Server 2010.”(Microsoft 2010)

This Enterprise Project Management initiative had been proposed in the two previous years by

Joseph the IT director and proposals had been presented by a 3rd party Microsoft implementation

consulting vendor twice before, but it wasn’t until 2010 that it was able to fit into the budget. A plan

was then developed by Maria and Abby from the Marketing Company in conjunction with Bruno and

Ester from the implementation consulting vendor.

The implementation roadmap consisted of a three phase implementation. Phase 1 was for three

months and focused on implementing basic controls and enable project and portfolio visibility for

key stakeholders. This did not include any integration to the Enterprise Resource Planning (ERP)

system which meant there would be significant manual dual entry of financial data. Phase 2 was for

the following four months and included integration to the ERP system in a proof-of-concept level as

well as an increased focused on the PMO Processes. Phase 3 was a full integration between the ERP

systems. All three phases had specific plans on the extent of EPM tool usage, process updates, and

training which were focused on hitting specific business outcomes.

For Phase 1, there was an initial set of 11 users that were performing the two month pilot.

For technology acceptance research, this became an even more intriguing set of data because at the

completion of the pilot, it was decided that the Enterprise Project Management tool would not

implemented based on the consensus of the pilot group, costs, and the other external factors such

as a company merger that put the follow-on phase on hold then eventually cancelled. The fact that

this technology was not accepted by the organization makes this an interesting set of data and case

study for this research. A second set of survey data was still collected eight months after the first

survey from the pilot group to complete the longitudinal data for this relatively small sample size.

For this research the coping and influence relationship data using a the survey by asking the

following five questions (Schneider 2009; Sykes, Venkatesh et al. 2009).

Influencing


needed for effective use of the MS Project Server (e.g., system features, upcoming releases, demo dates, etc.).




domain/functional knowledge for effective use of the MS Project Server (e.g., such as tips and tricks, short-cuts,

process sequence, etc.).


effective use of the MS Project Server (e.g., training resources, manuals, tutorials, etc.).

Coping


use of the <system>.


of the <system>.

The pre-pilot data was collected at the kick-off training session for Phase 1 of the pilot. As

mentioned earlier, phase 1 was completed, but the project was cancelled before Phase 2 started.

The post-pilot data was not collected until several months after the project was cancelled due to

work demands of the researcher.

4.8.2.1 Enterprise Project Management System Pre and Post Pilot Coping Network

The Coping Network for the Enterprise Project Management system pilot is smaller than the Event

Management System and essentially includes those individuals who are part of the pilot to evaluate

if it would benefit the company to operate from a consolidated repository for a portfolio of project

plans, change records, issues, and risks across from many different project managers. In this case

the company was looking to have the project managers using Microsoft Project Professional to

manage project plans in combination with Microsoft Project Server where they are consolidated and

leverage a consolidated resource list for their project plans.

There was an initial set of 11 users that were performing the two month pilot which are shown in

the in Figure 30 EPM Pre Pilot Influencing Network and Figure 27 EPM Pre Pilot Coping Network

. A second set of survey data was still collected eight months after the first survey from the pilot

group to complete the longitudinal data for this relatively small sample size which are in Figure 29

EPM Post Pilot Coping and in Figure 32 EPM Post Pilot Influencing Network.

Description:

The pre and post pilot Coping Networks were generated in NetDraw and shows the coping network

Enterprise Project Management pilot project. This coping network shows who the actors interact

with in order to help manage (minimize, reduce, master, tolerate) demands emanating from a

specific change in the person-environment relationship (Lazarus 1966). The connections are

directional based originating at the actor who identified others as someone they get or give help to.

The nodes are color-coded based on Performance Expectancy responses for the EPM system; red for

a negative, yellow for neutral, and green for a positive response. The nodes are sized relative to

each other based on betweenness centrality which represents how many pairs of nodes they are

between(Freeman 1977).

Groups



Figure 28 EPM Pre-Pilot Coping Groups shows the four groups in the EPM pilot that were all tied

together by Maria who was the main project manager for pilot at the marketing company. Joseph

the IT Director was Maria’s supervisor and was the one who had proposed this project in previous

years, but was not directly involved in the pilot and showing up as an isolate here. The four groups

relate to different departments within IT and at the vendor. The vendor’s coping network are shown

in black, the Event’s IT PMs are shown in gray, the loyalty IT PMs are shown in purple.

Figure 29 EPM Post Pilot Coping Network shows that the coping network fragmented after the

project was cancelled and the only group that remained was the vendor group. All of the Marketing

Company employees no longer needed a coping network for Enterprise Project Management, so it

dissolved.


Figure 27 EPM Pre Pilot Coping Network shows that Maria was the most central resource in the

coping network which is intuitive because she is holding all four groups together. Bruno, Susan,

Jason, and Ella are central in their smaller clusters but do not have any direct intra-cluster

connections other than Maria.

Maria clearly filled a structural hole. Some of the others might have filled structural holes for parts

of the network that were not included in this survey, as can been seen in Figure 27 EPM Pre Pilot

Coping Network where there are grey actors who were identified as part of the coping network, but

were not surveyed since a snowball survey approach was not used for the EMP study. From a social

network view, structural holes would be the individuals (or players) that occupy a network position

that connects two different groups. This allows them to potentially benefit from a more


of information or influence within the larger network.



with. One might wonder if some of the vendor connections such as Maria’s connection to Alma,

Ester, and Bruno were closer to a weak tie relationship.


along a path between any two individuals within the connected network, but the intra-cluster

pathways were fairly long which could be a limiting factor for this coping network.

Figure 29 EPM Post Pilot Coping Network shows that with the cancellation of the project, the only

central person in the network was Bruno.


In looking at Figure 27 EPM Pre Pilot Coping Network the Performance Expectancy was generally

positive in all the groups with the exception of the Ella and Owen who were in the IT Loyalty group

who were neutral. Unlike the other groups in the study, this group reported up to a different

Director within the marketing company.



Figure 29 EPM Post Pilot Coping Network shows the Performance Expectancy after the project was

cancelled. The vendor resources were no longer at the marketing company, but were still positive

towards an EPM system, which would be expected because they have a practice in the area of

Enterprise Project Management with many clients. The Performance Expectancy of most of the

individuals within the marketing company now has a negative or neutral view of whether an EPM

system will help them do their job. The only exception is Abby, who has used an EPM system at a

previous employer and was a significant supporter of starting the initial pilot project. Owen had also

left the company before the second survey.


is strongly agree






Figure 27 EPM Pre Pilot Coping Network



a. Color coding based on most efficient grouping using Gervin-Newman Clustering (.550)





Figure 28 EPM Pre-Pilot Coping Groups




is strongly agree






Figure 29 EPM Post Pilot Coping Network

4.8.2.3 Enterprise Project Management System Pre and Post Pilot Influencing

Network

There was an initial set of 11 users that were performing the two month pilot which are shown in

Figure 30 EPM Pre Pilot Influencing Network. A second set of survey data was still collected 7-8

months after the first survey from the pilot group to complete the longitudinal data for this relatively

small sample size which is shown in Figure 32 EPM Post Pilot Influencing Network.

Description:

Figure 30 EPM Pre Pilot Influencing Network, was generated in NetDraw and shows the influencing

network at the beginning of the Enterprise Project Management Pilot. The influence network is

based on asking the respondents to list 1-3 people they interact with frequently that have access to

information, knowledge and resources related to EPM.

This influencing network shows who the actors interact with along pathways within the network that

have access to information, resources, and knowledge which are typically unavailable to those on

the periphery with longer pathways or isolated from the network altogether (Ibarra and Andrews

1993; Sykes, Venkatesh et al. 2009). This influencing indicates that people in central structural

positions in a network will have more influence over system design to their needs which is often

labeled as co-adoption, thus driving up their usage. The connections are directional based

originating at the actor who identified others as someone they identified. The nodes are color-



coded based on Performance Expectancy responses for the Enterprise Project Management System;

red for a negative, yellow for neutral, and green for a positive response. The nodes are sized relative

to each other based on betweenness centrality which represents how many pairs of nodes they are

between(Freeman 1977).

Groups

Figure 31 EPM Pre-Pilot Influencing Groups shows the three groups in the EPM centered on Maria

who was the main project manager for pilot at the marketing company. The three groups relate to

different departments within IT and at the vendor. The vendor’s influencing network is shown in red

which also includes Abby and Maria who have been working with them. The Engagement & Event’s

IT PMs are shown in blue and the loyalty IT PMs are shown in purple.

Figure 32 EPM Post Pilot Influencing Network shows that the influencing network fragmented after

the project was cancelled. Unlike the coping networks, which completely dissolved in the marketing

company, the influencing network clusters still existed after the project was cancelled.


Figure 30 EPM Pre Pilot Influencing Network shows that Maria was the most central resource in the

coping network which is intuitive. Ester, Susan, and Ella are central in their smaller clusters.

Maria clearly filled a structural hole. Some of the others might have filled structural holes for parts

of the network that were not included in this survey, as can been seen in Figure 30 EPM Pre Pilot

Influencing Network where there are grey actors who were identified as part of the coping network,

but were not surveyed since a snowball survey approach was not used for the EMP study. From a

social network view, structural holes would be the individuals (or players) that occupy a network

position that connects two different groups. This allows them to potentially benefit from a more


of information or influence within the larger network.


survey questions which asked only for those connections they had frequent interaction with. One

might wonder if some of the vendor connections such as Maria’s connection to Alma, Ester, and

Bruno were closer to a weak tie relationship.


along a path between any two individuals within the connected network, but the intra-cluster

pathways were fairly long which could be a limiting factor for this coping network especially from

resources within the black color-coded group shown in Figure 31 EPM Pre-Pilot Influencing Groups.

Figure 32 EPM Post Pilot Influencing Network shows that with the cancellation of the project, the

only central people in the disjoint network were Bruno from the vendors and Ella for the IT Loyalty

PMs.




In looking at Figure 30 EPM Pre Pilot Influencing Network the Performance Expectancy was generally

positive in all the groups with the exception of the Ella and Owen who were in the IT Loyalty group

who were neutral. Unlike the other groups in the study, this group reported up to a different

Director within the marketing company.

Figure 32 EPM Post Pilot Influencing Network shows the Performance Expectancy after the project

was cancelled. The vendor resources were no longer at the marketing company, but were still

positive towards an EPM system, which would be expected because they have a practice in the area

of Enterprise Project Management with many clients. The Performance Expectancy of most of the

individuals within the marketing company now has a negative or neutral view of whether an EPM

system will help them do their job. The only exception is Abby, who has used an EPM system at a

previous employer and was a significant supporter of the starting the project. Owen had also left

the company before the second survey and he was in a new role that wouldn’t need an EPM system.


is strongly agree






Figure 30 EPM Pre Pilot Influencing Network




b. Represents Access to Information, Resources, or Strong Domain Knowledge connections identified as part of survey Connection originate from

person identifying connection regardless of whether its “Give Help” or “Get Help”

c. Relative node sizes based on betweenness centrality, calculated within Netdraw

d. Generated from Netdraw

Figure 31 EPM Pre-Pilot Influencing Groups


is strongly agree








Figure 32 EPM Post Pilot Influencing Network

4.10 Social Network Mapping Survey Results to Networks

The purpose of this section is to present and look for insight by mapping the UTAUT factors of Effort

Expectancy, Social Influence, Facilitating Conditions, Behavioral Intention, and Implicit Social

Influence to the pre and post-pilot coping networks for the Event Management System.

Performance Expectancy is not mapped to the coping networks in this section because it is covered

in the previously presented social network graphs as the default color coding for the actors. The

graphs for the Enterprise Project Management System are also not presented in this section, but

instead presented in the Appendix for reference. Also, the coping and influencing networks are

highly correlated as shown in Table 23 Correlation Matrix so only the coping network is used for this

part of the study for parsimony.



4.10.1 Effort Expectancy

The Effort Expectancy measures are based on the following survey questions.

• My interaction with the system would be clear and understandable.

• It would be easy for me to become skillful at using the system.

• I would find the system easy to use.

• Learning to operate the system is easy for me.

The Effort Expectancy of the Events Management Technology has started fairly positive and stayed

positive from the pre to the post pilot surveys as shown in Figure 33 Pre-Pilot Event Management

System Coping Network with Effort Expectancy Mapping and Figure 34 Post-Pilot Event

Management System Coping Network with Effort Expectancy Mapping. It can be seen in the EMS

pre-pilot network that Effort Expectancy is high in the core of the network, and those that are on the

periphery have lower Effort Expectancy, which supports the research question that the centrality in

the coping network impacts the Effort Expectancy.

Color coding based on factor mean measured from 1-7 Likert scale, where 1 is strongly disagree, 4 is neutral, and 7 is strongly agree. Red 1-

3.49, Yellow 3.50-4.50, Green 4.51-7.00, Grey N/A

Connections represents “Give Help” and “Get Help” connections identified as part of survey

Connection originate from person identifying connection regardless of mode

Relative node sizes based on betweenness centrality, calculated within Netdraw

Generated from Netdraw

Figure 33 Pre-Pilot Event Management System Coping Network with Effort Expectancy Mapping









Figure 34 Post-Pilot Event Management System Coping Network with Effort Expectancy Mapping

4.10.2 Social Influence

The Social Influence measures are based on the following survey questions.





The Social Influence of the Events Management Technology has started fairly positive and stayed

positive from the pre to the post pilot surveys with the exception of some of the IT staff who had

reduced involvement in the project such as Josh and the actors on the account team such as Pat.

This is shown in Figure 35 Pre-Pilot Event Management System Coping Network with Social Influence

Mapping and Figure 36 Post-Pilot Event Management System Coping Network with Social Influence

Mapping.









Figure 35 Pre-Pilot Event Management System Coping Network with Social Influence Mapping









Figure 36 Post-Pilot Event Management System Coping Network with Social Influence Mapping

4.10.3 Facilitating Conditions

The Facilitating Conditions measures are based on the following survey questions.





The third question was reverse-coded to allow aggregation with the other three questions. The

Facilitating Conditions of the Events Management Technology has started fairly positive and stayed

positive from the pre to the post pilot surveys with the exception of Kim who increased her

facilitating conditions from negative to positive in the post-pilot survey which also correlates to her

moving into a more central role in the coping network. This is shown in Figure 37 Pre-Pilot Event

Management System Coping Network with Facilitating Conditions Mapping and Figure 38 Post-Pilot

Event Management System Coping Network with Facilitating Conditions Mapping.







Figure 37 Pre-Pilot Event Management System Coping Network with Facilitating Conditions Mapping









Figure 38 Post-Pilot Event Management System Coping Network with Facilitating Conditions Mapping

4.10.4 Behavioral Intention

The Behavioral Intention measures are based on the following survey questions.

I intend to use the system in the next <n> months.

I predict I would use the system in the next <n> months.

I plan to use the system in the next <n> months

The Behavioral Intention of the Events Management Technology has started fairly positive and

stayed positive from the pre to the post pilot surveys with the exception of IT staff that had

dramatically reduced their role in the pilot project such as Josh, Dayna, and Joseph as well as

account team resources who are now disconnected from the coping network such as Pat. This is

shown in Figure 39 Pre-Pilot Event Management System Coping Network with Behavioral Intention

Mapping and Figure 40 Post-Pilot Event Management System Coping Network with Behavioral

Intention Mapping.









Figure 39 Pre-Pilot Event Management System Coping Network with Behavioral Intention Mapping









Figure 40 Post-Pilot Event Management System Coping Network with Behavioral Intention Mapping

4.10.5 Implicit social influence

The Implicit Social Influence measures are based on the following survey questions.

My colleagues are frequently using the software for their job-related tasks.

My co-workers are frequently using the software for their job-related tasks.

My supervisor is frequently using the software for his or her job-related tasks.

My subordinates are frequently using the software for their job-related tasks.

My company’s executives are frequently using the software for their job-related tasks.

The Implicit Social Influence of the Events Management Technology has started fairly negative in the

core of the network and actually showed signs in improving in the core including such core people as

Dawn the functional executive. This is shown in Figure 41 Pre-Pilot Event Management System

Coping Network with Implicit Social Influence Mapping and Figure 38 Post-Pilot Event Management

System Coping Network with Facilitating Conditions Mapping.







Figure 41 Pre-Pilot Event Management System Coping Network with Implicit Social Influence Mapping









Figure 42 Post-Pilot Event Management System Coping Network with Implicit Social Influence Mapping

4.11 Hypothesis Testing

RQ1-3: Coping and Influencing Network impact on the traditional UTAUT

model factors?

Table 29 - Hypothesis Results presents the results of the hypothesis presented in the design of this

study to address the research questions. A summary of the constructs and their significant

relationships can also been seen in Figure 18 - Statistically Significant R2 Regression Results.

The first four hypothesis are based on a traditional UTAUT model (Venkatesh, Morris et al. 2003)

which is also shown in Figure 9 - Venkatesh conceptual model for the Unified theory of acceptance

and use of technology (UTAUT)(Venkatesh, Morris et al. 2003) . Of these four hypothesis, it would

be expected that Performance Expectancy will impact Behavioral Intention (H1) was accepted

because with a large positive impact and a high significance level. This was validated and the

hypothesis is accepted. The Effort Expectancy’s positive impact on Behavioral Intention (H2) is

historically smaller as was the case in this study and there was not a significant enough relationship

so it was rejected. The Social Influence’s positive impact on Behavior Intention (H3) was accepted

with a satisfactory confidence level, so it was also accepted. Lastly, the Facilitating Condition’s

impact on System Use (H4) was also rejected with a unsatisfactory confidence level. These four

hypotheses are consistent with results from studies of TAM or UTAUT where Performance



Expectancy will have a significant and large impact on Behavioral Intention, but the remaining three

factors; Effort Expectancy, Social Influence, and Facilitating conditions would be more susceptible to

not having significant impacts on behavioral intentions or system use.

Most of the other hypotheses were rejected as there were not significant relationships between the

social network predictor factors and the UTAUT dependent variables with the exception of two

hypotheses. The Neighbors Performance Expectancy in the Influencing Network did positively

impact Performance Expectancy, where it explained 11.1% of the variance with a correlation that

was significant at the 0.01 level (2-tailed). The other accepted hypothesis is that the Coping Network

Centrality did positively Impact the Effort Expectancy where it explained 6.2% of the variance with at

correlation that was significant at the 0.05 level (2-tailed).

There were a few other significant relationships that were revealed in the hierarchical regressions

that are important, but not part of the hypothesis. The most significant is that the neighbor’s

average performance expectancy in the coping network explained 14.6% of an actor’s own

performance expectancy.

Table 29 - Hypothesis Results

Hyp. RQ Model Hypothesis, Research Question Accepted / Rejected Impact

H1 Traditional

UTAUT

Performance Expectancy will Positively

Impact Behavioral Intention

Accepted .258 **

H2 Traditional

UTAUT

Effort Expectancy will Positively Impact


Rejected

H3 Traditional

UTAUT

Social Influence will Positively Impact


Accepted .083**

H4 Traditional

UTAUT

Facilitating Conditions will positively

impact System Use

Rejected

H4 RQ1a Extended

UTAUT


positively impact Behavioral Intention

Rejected

H5 RQ1b Extended

UTAUT



Rejected

H6 RQ1c Extended

UTAUT

Neighbor’s Average Performance

Expectancy in the Influencing Network

will positively impact the Behavioral

Intention

Rejected

H7 RQ1d Extended

UTAUT

The Neighbor’s Average Performance


will positively impact System Use

Rejected

H8 RQ2a Extended The Influence Network Centrality will


Rejected



UTAUT Expectancy

H9 RQ2b Extended

UTAUT

The Neighbors Average Performance


will positively impact Performance

Expectancy

Accepted .111**

H10 RQ3a Extended

UTAUT


Positively Impact the Effort Expectancy

Accepted .062*

H11 RQ3b Extended

UTAUT



Impact the Effort Expectancy

Rejected

H12 RQ3c Extended

UTAUT


Positively Impact the Facilitating

Conditions

Rejected

H13 RQ3d Extended

UTAUT



Impact the Facilitating Conditions

Rejected

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

RQ4: Influencing Network Effects on Project Success

The fourth research question was more qualitative then quantitative, but was used uncover the

potential of using social network graphs merged with technology acceptance perceptions. The

question focuses on determining if the combination of network graphs and TAM would add valuable

insight to managers in the process of planning, executing, and monitoring a technology acceptance

project and potentially increase the likelihood of identifying potential problems, opportunities, or

key resources for specific roles to help in their day-to-day decision making.

This was summarized as:




The visual validation of mapping the UTAUT measures such as Performance Expectancy onto the

Influencing and Coping Network graphs does provide additional insight which could be used to

improve the likelihood of success of a technology acceptance project. Having an understanding of

perceptions is valuable for managers in planning for communications and change. If these same

managers also have a better understanding of Influencing and Coping networks, this also gives them

additional insight via a structural analysis looking for central figures, groups, isolates, network

density and other network metrics. So combining the technology acceptance and the social network

measures has yielded additional insight that is greater than the sum of its parts.



4.13 Summary

The purpose of this chapter was to provide the systematic analysis of the data collected during the

research. The data analysis started with three sections to look at the data management prior to

entry, the initial data analysis for suitability, and reliability for the different loadings. These sections

were then followed by the descriptive statistics and correlations. Following the correlation analysis

was the regression analysis which determined the explanatory power of the different factors via

hierarchical regressions. The statistical analysis was then completed using multiple regression

analysis to determine the adjusted R^2 of each model to understand the predictability of each

model.

In addition to the core statistical analysis, this chapter also reviewed the social network analysis

results to provide more insight into the data and answer the research questions. These included

analyzing the coping and influencing social network graphs for the two technologies as well as

mapping of the UTAUT responses to the social network graphs.



CHAPTER 5 - DISCUSSION

5.1 Introduction

The purpose of this chapter is to provide a discussion of the research results. First, the chapter

discusses the overall study findings, which is followed by a discussion on what the results means to

organizations. The next two sections discuss how the results fit with the theories underlying them as

well as past IS research. The chapter is concluded with a summary.

5.2 What the overall study found

The goal of this study was to see if social network analysis on the informal influence and coping

networks provide explanatory benefits in various scenarios of technology acceptance. These

scenarios include studying the perceptions and social networks of struggling or failing contingent

technology acceptance projects at their early stages which could benefit by looking at them through

the lens of social network analysis and UTAUT.

The projects chosen for this study both had a pilot phase where the user acceptance was in process

in parallel with a larger organizational decision to adopt the technology. The first project that was

used to collect technology acceptance data was the implementation of an enterprise project

management tool, which was piloted by a small team of individuals from the IT organization within

the larger marketing company in 2010. This project was cancelled after the pilot. The second and

larger of the two technology acceptance projects was to replace the current 9-year old internally

built Event Management System with a commercially available Event Management System. Event

Management or Registration Systems are used by meeting planners for meetings, events, user

conferences, and tradeshows to manage the registration of participants and their subsequent travel

arrangements, activities, sessions, billing etc. The planned implementation and timeline were

dramatically cut back after the initial pilot timeframe.

The results of the longitudinal evaluation of the Coping and Influencing Networks merged with

measuring the UTAUT factors yielded five key findings which highlight how the two models

complement each other to yield more insight when used together.

Social Networks are relevant to Technology Acceptance

The measures from the Social Network modeling of the Influencing and Coping networks were

shown to provide significant explanatory power on UTAUT factors such as Performance Expectancy

and Behavioral Intention. In addition, the UTAUT measures were also able to add valuable context

to the social network graphs demonstrating how central individual’s perceptions influenced the

overall perceptions and information flowing regarding a new technology. Together the two showed

how technology acceptance is not an individual activity, but is a social activity which includes central

figures attempting to control information and influence their networks based on their own

perceptions of the new technology.



The performance expectancy of an individual’s network neighbors impacts

their performance expectancy

The Average Performance Expectancy of an individual in the Influencing and Coping Network

explained 26% of that individual’s variance in Performance Expectancy. This was also hypothesized

because Performance Expectancy has continually been the largest driver of Behavioral Intention and

if a worker is going to influence a colleague about a technology, one could expect that they would

express their satisfaction/dissatisfaction in how well a new technology helps them accomplish their

job.

Once again, stepping back and looking at the Performance Expectancy through the lens of the pre

and post-pilot Influencing Social Network graphs provides an additional view that supports this

finding.

This is visually shown in the pre and post- pilot views of the Event Management System in Figure 23

EMS Pre Pilot Influencing Network and Figure 25 EMS Post Pilot Influencing Network. In these

figures, the Performance Expectancy values are visually clustered together. In the pre-pilot view,

there are grouping of positive Performance Expectancy Groups such as Dayna, Julie, Josh, Justin, and

Lucas. There are also negative groups such as Richard, James, Dawn, Kim, James, Linda, Isabelle and

Jen.

Social Network Analysis gives a new visibility of Technology Acceptance at

both the macro and micro levels.

Explanatory power of technology acceptance necessitates both a micro and macro-level

understanding of the decision making process to accept a technology. Rather than taking the more

common aggregate view of modeling technology acceptance, as is common with the Bass Model or

its descendants for diffusion of innovations as described in Meade’s 2006 meta-analysis (Meade and

Islam 2006), this study focused on individual social network analysis to create more understanding of

both the micro and macro-levels of technology acceptance from a single approach and model. At

the macro- level this study can compare the organizational adoption or aggregate individual

acceptance to Behavioral Intention, Performance Expectancy, and others, but it can also show the

dynamics at the micro-level. We can also see how particular individuals are separated from the

coping network, or how central actors such as Kim in Figure 19 EMS Pre Pilot Coping Network, can

influence the two decision makers in the organizational adoption through her connections as well as

eventually influencing the technology acceptance of individuals by controlling much of the

information in Figure 21 EMS Post-Pilot Coping Network.



Inhibitors or structural problems of the Coping and Influencing Network that

could negatively impact the organizational adoption or individual technology

acceptance become visible in the network graphs

The combination of the network graphs and the UTAUT perceptions showed three inhibiting

conditions for technology acceptance and organizational adoption in the Event Management

System. First, the main decision makers at the organizational level were primarily connected to

individuals who had a negative performance expectancy of the system. Second, the central

individuals who had positive performance expectancy were removed from the project. Third, the

network became fragmented and the groups became disconnected from the technology vendor and

other resources to help understand how to effectively use the system. These three changes

coincided with the reduced scope and postponement of the pilot.

The first of these inhibitors is visible in Figure 21 EMS Post-Pilot Coping Network where we can see

that Dawn, who is the main decision maker for the organizational adoption, only identified Isabelle,

Kim, and Leah, each having primarily negative performance expectancy, as part of her coping

network. Richard, the second decision maker for the organizational adoption, originally was

connected to the coping network through Dawn and James (Figure 19 EMS Pre Pilot Coping

Network) whom also had negative or neutral connections at the time of the pre-pilot. Based on this,

Dawn and Richard would likely be getting a negative impression of the New Event Management

System because of their immediate connections to the coping network, even though the larger

majority of the coping network was positive in terms of performance expectancy. This is likely a

factor for the organizational decision makers to delay and scale-back usage of the technology at the

organizational level.

The second and third inhibitors became apparent when two of the central resources, that had

positive performance expectancy, were removed from the pilot to save money at the request of Kim

who had negative performance expectancy. This change likely reduced the friction on the project

significantly between those central individuals that had positive and negative performance

expectancy, but it fragmented the coping network and left it centered on individuals with a negative

Performance Expectancy. This fragmentation also separated the vendor from the influencing

network.

For the Event Management System, the future was at risk; with the vendor now separated from the

network, more resources isolated, and the central person who controls the majority of the

information having a negative Performance Expectancy, it would be expected that this technology

would have a higher risk of failure.



Social Network Analysis of the Influencing and Coping Networks show ties

between the organizational adoption and individual acceptance in

contingent technology acceptance.

In the case of these two pilots, both the organizational adoption decision and the individual

acceptance of the individuals in the pilot were happening in parallel. On the same Coping Network

graph, the organizational decision makers and their ties to the individuals within the pilot were

visible. Some individuals, such as Kim in the Event Management System, had ties to both groups and

could use them to influence either the individual acceptance or the organizational adoption.

5.3 What do these results mean to organizations

To the target organization, these results provide a new view of the individual and organizational

perspective on these two technologies. This insight could have changed the outcome of the two

pilots or led to better decision making. For example, the organization could have been more

focused on the differing needs of the users, understood the true perceptions of the technology, and

understood change inhibitors early in the project.

For organizations in general, the following section discusses how the theory is extended, what

aspects of theory are falsified, what were the false leads, how practitioners can benefit from this

type of analysis, and the characteristics of the technologies this could apply to.

How the theory is extended

There are three areas where this study is extending our understanding of technology acceptance.

First, this combination of social network modeling and UTAUT factors show that individual

technology acceptance is impacted at the micro-level by an individual’s neighbors. Second, this

study also shows that the structural analysis of the overall influencing or coping network show that

technology acceptance can be impacted by the overall structure of the network such as how

fragmented it is at the macro-level. Third, social influence can potentially be measured more

effectively via social network analysis of the Influencing and Coping Network rather than the

traditional Social Influence measures in UTAUT or the Implicit Social Influence measures from (Kim,

Jahng et al. 2007).

What aspects are falsified

This study challenges the traditional Social Influence measures. As mentioned in the previous

section, social influence can potentially be measured more effectively via social network analysis of

the Influencing and Coping Network rather than the traditional Social Influence measures in UTAUT

or the Implicit Social Influence measures from (Kim, Jahng et al. 2007). The underlying reasons could

be in how social influence happens in technology acceptance. In the traditional UTAUT Social

Influence measures or Implicit Social Influence Measures suggest that system usage of those around

an individual influences their Behavioral Intention. This study shows that the Performance

Expectancy of those around an individual influences their Performance Expectancy. The significance

of this is that an individual will use their Coping and Influencing Networks to determine if they think



a new technology will help them do their jobs more effectively, but not directly determine if they are

going to use the system.

False Leads

There were two other modeling approaches evaluated as a way to model the interactions between

the individuals prior to selecting social network analysis. Agent-based-modeling was explored where

each individual would be modeled with utility equations which would be dependent on the

individual that they are connected to. Dynamic engineering modeling tools such as finite element

tools were also explored where each individual would be structurally connected as elements with

connections sized to reflect the connection strength. Both of these approaches were prototyped,

but later eliminated because of their previously limited use in understanding organizational

networks.

How does the organizational context restrict you and may create false

possibilities for future researchers?

There are three aspects of the context that could make these results not directly apply to other

environments. These include the high pressure environment, the high personal innovativeness of

some individuals involved, and the complex nature of voluntariness in this contingent acceptance.

Ibarra best described the stressful environment in his 1993 research which was also in an Marketing

/ Ad company as an environment where the dynamic nature of the clients causes a high pressure

culture where a client can end their relationship abruptly and the assurance of employment is weak

especially for those working on the client that left (Ibarra 1993).

This context also has individuals whose roles requires a high personal innovativeness in developing,

integrating and launching customized consumer loyalty systems, sales/channel incentive programs,

and employee engagement systems within a few months for clients. These individuals are

accustomed to using new technologies which could cause friction with operations staff that could

have a lower personal innovativeness.

Lastly, the context has a complex understanding of voluntariness. The IT and functional leadership

can communicate that a new technology is mandatory, but there is a understanding in Marketing

companies that the client is always right and if they request a different technology, that supersedes

any internal mandate. This does allow client-facing teams to influence their clients on what

technology to request.

How practitioners can translate these findings directly to innovations and

processes that you targeted, and what analogous tasks the findings can be

extended to

The exciting aspect of using social network analysis and UTAUT measures for practitioners is that the

survey questions are simple and non-threatening which gives the approach utility for practitioners.

Eight immediate ways to translate these findings include involving the central actors, identifying

subject matter experts, the best people to help co-adopt the technology, identify isolates,



understand how to better use vendors, identify training groups, and customize training and

communications programs.

Involve, train, incentivize central people

Once a manager knows who the central individuals are and what their perceptions of the new

technology are, they can focus limited resources on involving and training these individuals on the

new technology. Also if these individuals have a negative Performance Expectancy, then the

manager can get help to understand the reasons for the negative Performance Expectancy, provide

additional training, or even incentivize the individual or make it part of their annual objectives to

help the organization transition to the new technology.

Central people in coping network are best subject matter experts for others

The central individuals in the Coping Network have already been identified by their coworkers as

where the others go for help, so a manager should involve the individual in the new technology

project and free up some of their workload so they have the time to help others with using the new

technology.

Central people in influencing network should steer co-adoption of the innovation

Those central individuals in the Influencing Network are viewed by their coworkers as individuals

who have significant domain knowledge and access to resources and information. A manager can

take advantage of this by involving these individuals in the new technology project to help steer the

co-adoption of the technology to match the needs of the organization.

Connect the isolates with the central coping people

The Coping Network graphs will also identify those individuals who were not able to name anyone

they interact frequently that they get help from in using the new technology. In this case, a manager

could improve the coping network by pairing these isolates to central individuals in the coping

network with the expectation that the central individual checks in with the isolated resource on a

regular frequency.

Vendors need to connect to multiple groups in coping network

The new technology vendors are a valuable resource when it comes to finding way to help

individuals understand how to do their job in a new system. Often these vendors only have a few

connections to the network which is visible in both the Event Management and Enterprise Project

Management System social network graphs. A manager could finds ways to make sure the vendor

has connections to every natural group so the individuals have short network paths to access the

vendor for questions.

Train the natural groups together

The social network graphs show the natural groups of the organizations that interact frequently and

have been shown to have similar Performance Expectancy values. A manager should consider these

groups as logical training groups which allow groups to adopt technologies together. This follows

the suggestion by Delre in his research where he demonstrated the benefits of targeting small



cohesive groups of consumers in distant areas of the market as the optimal strategy for gaining

acceptance of an innovation which was referred to as “throwing rocks rather than gravel” (Delre,

Jager et al. 2007).

Customize training & communications based on users perceptions and usage

An organization can also provide better planning for new technologies in terms of technology

change management, roll-out planning, or communication plans. Operationally, a combination of

user perceptions and appropriate social network graphs could be used in the planning and testing of

specific communication plans, training plans, and efficient deployment of change agents.

Communications and training plans could be more effectively targeted based on network and

perception roles. For example the isolated resources would require more hands-on training then

someone who already has a high usage and is very central in the coping network.

Which technology characteristics were of critical importance

The Event Management System and the Enterprise Project Management System are both

commercially available systems. Both are also considered group technologies that require a critical

mass of organizational users to make them valuable as opposed to individual productivity tools.

The Enterprise Project Management System is essentially a consolidated repository for a portfolio of

project plans, change records, issues, and risks from many different project managers within an

organization. For the pilot, the team was using a Software-As-A-Service instance from a hosting

vendor.

The Event Management System was also a Software-As-A-Service that was targeted to replace the

current 9-year old internally built Event Management System (EMS). Event Management System

systems are used by meeting planners for meetings, events, user conferences, and tradeshows to

manage the registration of participants and their subsequent travel arrangements, activities,

sessions, billing, and other event activities.

Damanpour provided a categorization of innovations by classifying them as Administrative

Innovations and Technical Innovations. In the case of the Event Management System, it is a

Technical Innovation because it directly related to new products, services and the production

process technologies (Damanpour 1987). Alternatively, Administrative Innovations are more

internally focused on internal management, which better describes the Enterprise Project

Management System. Damanpour also pointed out that often those with formal authority tend to

be more involved in Administrative Innovations like the Enterprise Project Management, whereas

Technical Innovations such as the Event Management System tend to require more informal

network power and centrality.

The sustaining and disruptive nature of the innovation studied

Both the Event Management System and the Enterprise Project Management System required

changes to established enterprise projects with significant integrations to other enterprise systems.

The Event Management System would be more disruptive to the organization due to its central role

in the large Meetings and Events line of business. It would require changes in the finance



department, call center, event operations, travel operations, IT, and other groups as well as

potentially eliminate jobs.

5.4 How these results fit with theories and results underlying or

relevant to them

Diffusion of Innovation & Technology Acceptance Models

In the evolutionary variations based on the Technology Acceptance Model, perceived usability and

perceived ease of use continued to show up as the most common factors of technology acceptance,

while a host of other factors and moderators have been evaluated since the introduction of the

Technology Acceptance Model. Eight of these model variations were evaluated and consolidated

into the United Theory of Acceptance and Use of Technology (UTAUT) in 2003. This model used four

core determinants of Performance Expectancy, Effort Expectancy, Social Norm, and Facilitating

Conditions to explain up to 70% of the variance in user intention (Venkatesh, Morris et al. 2003),

with performance expectancy continuing to be the largest factor. This research showed similar

results, where the UTAUT factors without any moderators were able to explain 58.5% of the

variance in behavioral intention with performance expectancy being the largest factor. The research

was not able to show a significant relationship with effort expectancy, but did show social influence

driving 8.3% of the variance of behavioral intention, thus further validating the earlier UTAUT model

factors.

One thing that was interesting was likely caused by the contingent nature of the organizational and

individual decisions, there were significant effects of Voluntariness of Use on Behavioral Intention as

well as Department as an antecedent to Performance Expectancy, Effort Expectancy, and Facilitating

Conditions. At the time of these pilots, these different departments were grappling with the larger

organizational decisions and it was unclear if using these technologies would be mandatory in the

long-run.

Some of these differences could be a result of the early stage of these technology acceptance

initiatives which has been a known area of interest as pointed out in the need to address the

impacts of the social impacts and acceptance in earlier stages of a contingent technology acceptance

initiatives (Venkatesh, Morris et al. 2003; Sykes, Venkatesh et al. 2009).

Model of Acceptance with Peer Support (MAPS)

The Influence Network Centrality was tested in all the hierarchical regression models but was not

shown to have any significant relationships on the dependent variables of Behavioral Intention, Use

Behavior, Performance Expectancy, Effort Expectancy, and Facilitating Conditions. This means that

the participant’s centrality or lack of centrality within the influencing network does not seem to have

an impact on any of the constructs.

The Coping Network Centrality did have statistically significant relationships with Effort Expectancy

and Facilitating Conditions. The Coping Centrality did explain 6.2% of the variance in Effort

Expectancy at a .05 confidence level, but this is somewhat diffused in this study because Effort

Expectancy was not shown to have a statistically significant relationship with other key variables



such as behavioral intention. The Coping Centrality also explained 5.4% of the variance in Facilitating

Conditions, but only at a .10 confidence level. Both of these relationships were hypothesized

because it would be expected that the centrality in a coping network would give a user closer access

to Facilitating Conditions such as help or resources which in turn could avoid Ease of Use problems

with a system that could reduce a user’s Effort Expectancy.

These are different results from the recent example of this Model of Acceptance with Peer Support

(MAPS) which showed that network centrality explained 15 to 17% of the system use (Sykes,

Venkatesh et al. 2009). This does not mean that the results are in conflict because the studies have

significant differences, such as the survey technique, the other factors used in the regressions, and

the conceptual model used.

5.5 How the results fit with or not fit with past evidence in IS, and why

There has been significant research around organizational adoption and individual technology

acceptance. Overall, the context and results of this study are not only consistent with the underlying

theories as shown in previous sections, it is also fits with past research for the IS industry. This

includes the research showing that organizational adoption is complex with many phases, early

phases of contingent technology acceptance increases the complexity, new technology initiatives

continue to fail or be underutilized, and innovation and technology acceptance is important for

success and ROI.

Organizational Adoption is complex with many phases

The organizational adoption of these two technologies included multiple phases of rollouts after the

original pilot. Both started with organizational agreement and funding to move forward with the

pilots and assumed that these would continue into the later phases as well. Originally the

technology suppliers or implementers were significantly involved in the decision at the

organizational and individual coping networks, but this changed during the pilot or soon after, which

change the dynamics significantly. The reversal or dramatic scope reduction of these projects

emphasize that external factors or internal dynamics can dramatically alter what looks like a very

straightforward phased decision process at the organizational level.

As mentioned earlier, adoption is often used to describe to the decision of an organization to make

use of an innovation. If acceptance of individuals in that organization is contingent upon a prior

organization adoption, its referred to as contingent innovation decision or forced adoption (Ram and

Jung 1991; Rogers 2003). This study confirmed and provided an example how organizational

adoption is significantly different then user acceptance and the decision process could vary from

organization to organization because different factors or technology attributes could be important

to each.

This study also aligns well to the results to Frambach and Schillewaert’s study, which proposed a

widely cited multi-level framework of determinants which provided two levels of acceptance factors

to address both the individual and organizational acceptance (Frambach and Schillewaert 2002). The

factors at the organizational level included both perceived innovation characteristics and adopter

characteristics. The perceived innovation characteristics of compatibility, complexity, observability,

and trial ability are likely what the functional gap analysis within the Event Management System was



evaluating or the goal of the hands-on pilot in the Enterprise Project Management pilot. The

organization adopter characteristics that impact organizational adoption are organization size

(Kennedy 1983), organization structure (Zaltman, Duncan et al. 1973), and organizational

innovativeness (Morrison 1996). With only two projects, the organizational adopter characteristics

were not evaluated in this study.

These results also fit with the research on the longitudinal nature and process of organizational and

individual adoption which is not widely understood, which adds additional layers of complexity.

Most studies focus on dichotomous adoption/non-adoption process but we know little about effect

of different factors at the various stages (Olshavsky and Spreng 1996). Further research had been

suggested on organizational adoption in several areas, such as why some technologies do not

become adopted (Frambach and Schillewaert 2002), pre-adoption stage, influence of supplier

activities on innovation adoption within the organization, critical mass on innovation adoption and

the impact of network externalities (Frambach and Schillewaert 2002). This study provided an

example where the micro network dynamics and change in power likely impacted the overall success

of these pilots.

Early phases of contingent technology adoption/acceptance is complex

The early phases of these two new technology initiatives started out positively, but the

organizational and individual dynamics showed dramatic changes in coping and influencing networks

as well as significant changes in control and perceptions of the projects between the beginning and

end of the pilots. There was evidence in the social networks of the cross-dependencies between the

individual acceptance in pilots and the larger organizational decision. This is consistent with past

research in this area.

Organizational adoption of innovations is more complex than individual adoption because of such as

the higher number of stages involved in the adoption decision, and the effects of organizational

structure (Ram and Jung 1991). The Event Management System pilot had a plan for the pilot and

subsequent phases, but external dynamics and changes within the project by the time the pilot was

complete called for a reevaluation the rollout at a later date that invalidated what was thought to be

a straightforward plan.

In an organization there are significant dependencies between the two decision processes at both

the organizational and individual level, for example the managers central to the organizational



communicating some of its benefits and appropriate use (Olshavsky and Spreng 1996). With the

power changes in the Event Management Systems and who the organizational decisions makers

were connected to in the coping and influencing networks showed that the individuals going through

acceptance can influence the larger organizational decision.

New technology acceptance initiatives are continuing to fail or be

underutilized

These two new technology projects support the challenge that prior research had shown on the

failure of new technology initiatives or the underutilization of new technologies. For the Event



Management System, it was dramatically reduced in scope leaving much of the functionality

demands on the older legacy system, which eliminates the ability to get a positive ROI by bringing in

a new technology. Essentially the underutilization will likely cost more to support then just having

the legacy system. Devaraj and Kohli also remind us that it’s not the investment in the technology,

it’s the usage. Research has been able to tie impacts of technology on organizational performance

(Devaraj and Kohli 2003), but when looking at IT Payoff, the dollars invested in IT is not a good

measure of the effectiveness of IT, it’s the usage that’s critical (Devaraj and Kohli 2003) which was

shown to be limited after both of these pilots.

There are many reasons for a failed technical projects but lack of acceptance or non-involvement of

users is a common source of failure. Non-adoption or acceptance could mean many different things

to individual non-adopters because they might have actively or passively decided to reject the

innovation, alternatively it could be that they have not reached that point in the adoption process

yet (Frambach and Schillewaert 2002). What is important is that when a new technology project

outright fails or fails to be utilized adequately, it has a significant financial impact. Emam and Kuru

also found that 26% to 34% of new technology projects were considered failures due to

performance or cancellation (Emam and Koru 2008). This has the objective for practitioners to

provide an additional tool and approach to help understand and reduce the likelihood of failure of

these projects that do fail.

Innovation and technology acceptance is important for success and ROI.

This marketing company, like most others is under constant pressure to find new ways to improve

productivity, reduce costs, or enable new revenue generating services. When the existing Event

Management System was developed by the marketing company, there were few alternatives. Now

there are 193 systems categorized as Event Management Systems (Capterra 2011). The company

does not have a budget to increase the functionality to keep up with the competitors so their

survival requires them to eventually look at other alternatives. This is consistent with the previous

research on the importance of new innovations and technology acceptance to continue to generate

a return on investment and be competitive in the marketplace.

This need to find a more competitive way to manage events is consistent with previous research.

First, new innovations, often in the form of new systems or software, have become more critical to

almost every facet of modern day life (Mann 2002). Second, innovation has been shown to be a key

driver of corporate success, this is especially true for younger companies whose growth comes from

finding new markets with innovative new products (Cardozo R, K et al. 1993). Lastly, the acceptance

of new technologies, such as information systems, is becoming more critical in the competitive

global environment (Lee, Kim et al. 2006).

These two innovations created several issues for the organization that needed be able to

successfully implement them to gain the potential benefits or return on investment. Within

organizations older technologies are continually being replaced by new innovations to drive

improved productivity or deliver new services, but they are only successful when the employees or

users accept and effectively use the new technology (Lee, Kim et al. 2006).

5.6 Summary



The purpose of this chapter was to provide a discussion of the research results. First, the chapter

discussed the overall study findings, which was followed by a discussion on what the results means

to organizations. The next two sections discussed how the results fit with the theories underlying

them as well as past IS research. This discussion of the results and their relevance leads into the

conclusions presented in chapter 6.



CHAPTER 6 - CONCLUSIONS

6.1 Introduction

This chapter concludes the study by providing a short summary of the research, its contribution to

knowledge and practice, its limitations, and suggestions for future research.

6.2 Summary of Research

This research longitudinally explored the early phases of two contingent new technology projects

that eventually failed after pilot phases. This research was done through the lens of Social Network

Analysis of the Influencing and Coping Social Networks and measuring the factors of the Unified

Theory of Acceptance and Use of Technology (UTAUT).

This study extended our understanding of the organizational technology acceptance in the early

phases of a contingent acceptance of a new technology, by understanding how the influencing or

coping networks add additional explanatory power of the Behavioral Intention and system usage

over the traditional UTAUT model. In addition to the direct impacts on Behavioral Intention and

system usage, this study also looked at the informal influencing and coping network factors as

antecedents of traditional technology acceptance factors of Performance Expectancy, Effort

Expectancy, and Facilitating Conditions from the traditional UTAUT model. A hierarchical regression

analysis of this study showed that an actor’s Coping Centrality and the Performance Expectancy of

that actor’s neighbors significantly impact their own Performance Expectancy. This further

supported earlier work on the importance of network position and neighbors on technology

acceptance.

This more traditional research approach was then augmented by a longitudinal evaluation of the

dynamics of the actors within the social networks before and after their respective pilots to

understand if using a social network graphs of the influencing and coping networks with the UTUAT

perception factors as a diagnosis tool could improve the likelihood of success of the projects for

practitioners.

The longitudinal evaluation of the actors within the social network graphs supported the traditional

research approach findings, but more importantly gave significant additional insight into the

dynamics of the macro and micro-level understanding of technology acceptance through

understanding how actors coped with new technologies across organizational boundaries over time.

The longitudinal evaluation also showed how the central influential actors changed over time which

resulted in the breakdown of a high density networks centered on individuals who have positive

performance expectancy into a disjoint and less dense networks centered on individuals with

negative performance expectancy. These dramatic influencing and coping network changes during

the early phases of these contingent technology acceptance efforts coincided with the eventual

cancelation or de-scoping of the projects. This demonstrated that social network graphs have the

potential to bring visibility to acceptance inhibitors and bridge the organizational and individual

decisions, which has significant impacts for practitioners and suggestions for future research.

6.3 Contribution to Knowledge



This research contributed to information system research by expanding our understanding of

organizational technology acceptance in five ways. These include expanding our understanding of

influencing and coping networks, increasing explanatory power of the UTAUT model when including

social network constructs, provided insight on the early stages of contingent acceptance initiatives

that eventually failed or where significantly delayed, provided new data on acceptance of event

management systems and enterprise project management systems, and through snowball surveys

showed the bridging of hierarchies and organizations in technology acceptance.

Evaluating if there is increased explanatory power of the UTAUT model when

including social network constructs

Explanatory power of technology acceptance necessitates both a micro and macro-level

understanding of the decision making process to accept a technology. Rather than just taking the

more common aggregate view of modeling technology acceptance as is common with the Bass

Model or its descendants for diffusion of innovations as described in Meade’s 2006 meta-analysis

(Meade and Islam 2006), this research focused on individual social network analysis to create more

understanding of both the micro and macro-levels of technology acceptance from a single approach

and model.

Similar to the original MAPs research (Sykes, Venkatesh et al. 2009), one objective of this research is

to see if social network constructs could potentially add significant explanatory power of technology

acceptance. This research showed that the influencing and coping social networks play a significant

factor as an antecedent of Performance Expectancy and Effort Expectancy.

Expand the Understanding of Influencing and Coping Network on Technology

Acceptance

Earlier research has shown that the behavioral intention or system-use was shown to increase with

network centrality, so “actors who are centrally located within organizational networks enjoy a

broad array of benefits and opportunities” (Sykes, Venkatesh et al. 2009). This research was able to

validate that the coping network neighbors play a significant direct role in an actor’s behavioral

intention, but the coping network measures played a much larger role as an antecedent of

performance expectancy and effort expectancy.

Combine Social Network Analysis Perspective with UTAUT Perspective

Being part of a coping or influencing network has been shown to influence behavioral intention and

system use (Sykes, Venkatesh et al. 2009) and this research looked at these social network graphs

overlaid with the key factors from the Unified Theory of Acceptance and Use of Technology

(Venkatesh, Morris et al. 2003) to see how the two could be leveraged together. The research

showed that in these two failed projects the coping network became fragmented and sparse, and in

the case of the Event Management System, the coping network information became controlled by

actors with negative Performance Expectancy of the new technology. The social network also

showed that the main organizational decision makers responsible for the organizational decision

connections within the coping network were only to actors with a negative Performance Expectancy



in the post pilot, which likely influenced the organizational decision to dramatically reduce and delay

the project.

Provide insight on the early stages of contingent acceptance initiatives that

eventually failed or where significantly delayed

The two technology acceptance projects used in this research were in the early stages of a

contingent acceptance where both the organizational decision which was overlapped with a user

pilot where employees were in the process of accepting the technology. The Enterprise Project

Management project was cancelled soon after the pilot and the Event Management System met

significant delays and reductions in scope, thus making it a failed project when it was no longer

feasible to gain an ROI. The main focus of the research was on these employees, but since these are

in the early stages of a contingent acceptance, it provided very unique data of both.

This research showed some of the coping and influencing dynamics of the two failed projects which

included the dramatic reduction and fragmentation of the coping and influencing network and the

disconnecting of the technology vendors from the coping networks. As pointed out in earlier

research, a social network view could help show how the help networks and resource controlling

mechanisms are supporting or hindering the system use (Sykes 2009). Emam and Kuru also found

that 26 to 34% of new technology projects were considered failures due to performance or

cancellation (Emam and Koru 2008). This research contributed to the continued call for more

research on failed technology acceptance initiatives (Frambach and Schillewaert 2002) due to their

complexities. In this study, it did shed additional light on the failure of the technology initiatives.

Provide a new technology context researched in technology acceptance

In researching the literature for this study, there were many technologies that had been researched

previously, especially when it comes to technologies that are used by larger user-bases, such as

laptops or mobile phones. This research adds two more examples to the growing list of systems

studied in technology acceptance that have little historical exposure in the technology acceptance

research; specifically Event Management Systems and Enterprise Project Management Systems.

Use of snowball surveys to bridge hierarchies and organizations

As mentioned as future research is the idea of spanning hierarchies and boundaries for social

network analysis within organizations (Sykes, Venkatesh et al. 2009). In this research, a “snow-ball”

survey approach was taken where the connections were open-ended and additional surveys were

sent to newly identified network actors to attempt to observe non-obvious hierarchy and boundary

spanning. This is a different approach then using a fixed roster list of users and having the users

identify which ones they interact which can be limiting because it might not identify all of the

network connections that are not obvious to management or the researcher. This research showed

that the coping and influencing networks for these two technologies spanned to technology vendors,

clients, past colleagues, spouses, help-desks, and others.

6.4 Contribution to practice



The managerial implications of this research span four areas. First this research provides an example

of how to use Social Network Analysis and Technology Acceptance factors within an organization.

This study then leverages this insight from the graphs and results from using Social Network Analysis

and Technology Acceptance factors to enable better planning, improve the likelihood of success, and

allow manager to identify inhibitors to technology acceptance.

Provide an additional real-world example on using Social Network Analysis

within an Organization to Support Technology Acceptance.

There is significant benefits that a company could gain by the use social network analysis and graphs

to improve their innovativeness and ability to adopt new innovations. The additional insight in the

decision process could ensure that the most central coping and influencing network individuals are

involved in the acceptance and co-adoption of the technology. It is also important to ensure that

the expertise from the technology vendors are well connected into the coping network further

enable knowledge sharing and avoid frustration of employees.

Social Network Analysis is still an emerging skillset and toolsets are highly limited to academic

researchers, graduate students, and a small set of companies and government entities that are now

focusing on the relationships between individuals, not just their attributes such as marketing and

social media companies; these are generally not exposed to the larger technology industry and there

is little commercial training in their use. Thus for a knowledgeable technology practitioner, there is

little available to help provide the steps necessary to develop an actionable social network model to

better support a technology acceptance initiative. This is clearly not training material, but tackling

real-world problems with new approaches and exploring the challenges and opportunities in the

results helps close the academia-practitioner gap for this domain.

Insight for Better Technology Acceptance Planning

The significance of this research is that it could provide a more deterministic planning for the

technology acceptance within the different individuals and groups within a network. This could

benefit marketers or change agents in creating their new technology change management, roll-out

planning, or communication plans. Operationally, a combination of user perceptions and

appropriate social network graphs could be used in the planning and testing of specific

communication plans, training plans, and efficient deployment of change agents. The coping and

influencing networks would provide valuable insight while assigning roles and choosing resources for

a pilot. Communications and training plans could be more effectively targeted based on network

and perception roles. For example an isolated resource would require more hands-on training then

someone who already has a high usage and is very central in the coping network. Usage of

technology acceptance perception values will also help with the planning; this general approach of

using pre implementation surveys on factors such as perceived usability to better plan and predict

usage is valuable and was shown to be a strong predictor six months later in related situations (Davis

and Venkatesh 2004)

Improve Likelihood of Successful Technology Acceptance



Corporations invest significant amount into new innovations and technologies, their long-term

survival requires them to get a return on this investment. This research suggests that we need to

actively monitor and support our informal coping and influencing networks to improve the likelihood

of a successful technology acceptance and make more informed decisions at the organizational

adoption-level.

This need was consistent with recent literature which has emphasized the financial costs of

inaccurate innovation adoption modeling. First, misreading the adoption or acceptance of a new

innovation in the early stages is costly; over-optimism could yield wasted investments. Alternatively,

a misread slow adoption or acceptance could yield managerial decision to discontinue a potentially

successful product or innovation within a large organization. To better read the acceptance across a

large organization, we must understand the tipping point, which in early stage adoption is an

outcome of the social networks which are non-uniform (Dattee and Weil 2007). Second, the

importance of diffusion of innovations or technology acceptance into organizations is growing in

popularity as the ability for a company to quickly implement new technology innovations is

becoming more fundamental to their competitiveness (Tether and Tajar 2008 ).

Better Mechanism to Identify Change Inhibitors

Obstacles for the acceptance of the Event Management System could be identified better, such as

knowing that there is little access within the organization to the vendor expertise. This micro-view

of the social networks would help identify this and other types of change inhibitors. It is also

important to overlay the technology acceptance factors onto the social networks to further

understand factors such as the Performance Expectancy or Behavioral Intention of the central

figures in the coping and influencing networks.

This need for identifying change inhibitors is also consistent with recent literature where there is

frustration on the lack of explanatory feedback from innovation adoption or acceptance and the

inhibitors to adoption from the aggregate models. This requires better disaggregate models which

can account for the “non-smooth life cycle shapes” that can result from lack of reference or opinion

leaders or substitution innovations (Dattee and Weil 2007). There was also a demand for deeper

insight into the inhibitors and stimulators of diffusion of innovations into organizations at both an

aggregate and individual level to help suppliers market their products more effectively (Framback

2002).

6.5 Study Limitations

This study focused on a critical early phase for new technology acceptance and organization

adoption. Within this research there are potential limitations that could include context, data

gathering, variable selection, analytical, technology -features, theoretical and practical limitations.

These limitations are important to understand and help in suggesting topics for future research.

Context limitations

There are three aspects of the context that were already discussed in section 5.2 that could make

these results not directly applicable to other environments. These include the high pressure



environment, the high personal innovativeness of some individuals involved, and the complex nature

of voluntariness in this contingent acceptance.

In addition to these three limitations, this research also limited its focus to the early phases of a

contingent technology acceptance of failed pilots. This could mean that the UTAUT factors might

not be applicable to situations where the organizational decision has been firmly decided or later

phases of technology acceptance after a pilot is complete and a larger organizational roll-out is in

process.

Data gathering limitations

The snowball survey approach worked well for this research and was effective in getting a good

understanding of the network boundaries of the influencing and coping network; so there were not

significant limitations in getting a good sample for network analysis.

Alternatively, there are limitations on the voluntariness measure. Rather than using self-reported

survey questions, the voluntariness was based on observation, executive communications, and

several project factors that could yield autocorrelation. For example, if a client project team was

expected to use the Event Management System as part of the pilot, it was assumed it was

mandatory for those individuals involved if they were in a supporting role for the client.

Also, this research uses self-reported usage for the Event Management System which could be

improved upon with actual usage data from log files. For the Enterprise Project Management

System, the actual usage was assigned based on researcher observation of their involvement of

hands-on working sessions in a pilot that used the Enterprise Project Management system.

Variables selection limitations

For this research, the UTAUT factors were chosen as a more recent and validated version of the

earlier TAM models. This choice is appropriate for the individual acceptance, but it could have

limitations within the organizational adoption. To more appropriately understand the

organizational adoption in combination with the network analysis, it could be beneficial to survey

the key resources for the organizational decision with additional measures for organizational

adoption or use a multilevel framework as is identify by Frambach and Schillewaert framework

shown in Figure 3 Organizational Level Acceptance Factors from Frambach and Schillewaert’s 2002

and Figure 4 Individual Acceptance Factors from Frambach and Schillewaert 2002 (Frambach and

Schillewaert 2002).



Analytical limitations

Analytically the hierarchical regression used in this study focused on direct and mediating effects of

variables. It is likely that higher adjusted R2 values for Behavioral Intention and System Usage and

more significant relationships between factors could be realized if the analytics included the

mediating effects of voluntariness, age, gender, or experience. Some of the social network

measures could also be theoretically supported as moderating factors.

Technology and innovation feature constraints limitations

The technologies evaluated in this research were group technologies within organizations that

required a critical mass of users for them to add value. This approach could yield different results if

applied to consumer technologies or individual productivity tools that do not require multiple users

to yield benefits to the single user.

Theoretical and practical limitations

Lastly, there is the practical limitation that this research primarily focused on new technologies or

systems, which limits the potential of leveraging the Coping and Influencing Networks within an

organization to just new technologies. There are many examples of change within organizations that

would benefit from understanding the perceptions of the employees as well as how they use the

Coping and Influencing networks within organizations. These could include wellness programs,

mergers, new business process, or other innovations that are not technology specific.

These context, data gathering, variable selection, analytics, technology, and practical limitations

offer many areas for future research.

6.6 Future Research

There are several areas of possible future research that include those that address the limitations of

the study, bring in new contexts, increasing the sets of actual data, and taking an action research

approach to new technologies.

Future research that addresses limitations of the research

The limitations of this research described in section 6.5 surfaced many potential areas for future

research. First, future research could include scenarios in later phases where the organizational

decision has been decided and a larger organizational roll-out is in process which would expand the

context. Data gathering could be improved by using self-reported voluntariness as well as

systematic system usage. Variable selection could bring in multi-level frameworks to incorporate

more appropriate variables for organizational adoption. The analytics could focus more attention on

the moderating factors of UTAUT as well bring in network measures as moderating factors.

Extending this type of research to consumer or individual productivity tools could validate its

applicability to other technologies. Lastly, future research can also extend this type of research to

non-technology changes and innovations to address the practical limitation of only focusing on new

technologies.



New project contexts such as successful projects or non-contingent TAM

Since this research was limited to the early phases of a contingent technology acceptance of failed

pilots. Future research could address scenarios where the organizational decision has been firmly

decided or later phases of technology acceptance after a pilot is complete and a larger

organizational roll-out is in process. At later phases, it could be expected that there would be

higher levels of Behavioral Intention and more stability in the central positions of the Coping and

Influencing networks.

More Data Longitudinal Studies of the Coping and Influencing Network

Second, this study showed some of the fascinating dynamics of the changing structure and central

actors in a contingent acceptance. Future researcher could benefit by taking a similar longitudinal

approach to mapping out the Coping and Influencing Networks in parallel with gathering the UTAUT

or TAM perceptions at more points in time for successful and unsuccessful new technologies. With

more longitudinal sets of network data and UTAUT measures, the research community will have

more scenarios to look for patterns and understanding of the true social acceptance of new

technologies. In addition, the more we understand how individuals are impacted by their

neighboring nodes, the closer we are to be able to create simulations of TAM across networks and

time.

Action Research while leveraging the Social Network Modeling and

measuring TAM perceptions

Third, studying a new technology acceptance projects while leveraging the influencing and coping

network graphs and UTAUT/TAM perceptions as an observer is valuable, but participating as an

action researcher where the researcher uses the insights to deliberately suggest changes to the

project to improve its success would help understand the true utility of this type of insight to

practitioners. A researcher could apply similar approaches in addition apply actions such as the

seven outlined in Section 5.3 on how practitioners could use these findings directly to innovations or

processes.



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Appendices



Appendix A – Survey Questionnaire

Acceptance of New Technology Survey

Instructions Answer questions as they relate to you. For most answers, select the one answer that is most

applicable to you or fill in the blanks.

Please provide the following

First Name

Last Name

Check here if you would like a copy of the final report

Your Age (Select only one.) 25 or less

26-35 36-45

46-55

56-65

66-75 76 or more

About the system

1. I would find the system useful in my job. (Select only one.)

1 = strongly disagree

2 = moderately disagree 3 = somewhat disagree

4 = neutral (neither disagree nor agree)

5 = somewhat agree 6 = moderately agree

7 = strongly agree

2. Using the system enables me to accomplish tasks more quickly. (Select only one.) 1 = strongly disagree



5 = somewhat agree

6 = moderately agree 7 = strongly agree

3. Using the system increases my productivity. (Select only one.)



1 = strongly disagree



5 = somewhat agree


4. If I use the system, I will increase my chances of getting a raise. (Select only one.) 1 = strongly disagree



5 = somewhat agree


5. My interaction with the system would be clear and understandable. (Select only one.) 1 = strongly disagree



5 = somewhat agree


6. It would be easy for me to become skillful at using the system. (Select only one.) 1 = strongly disagree




7 = strongly agree

7. I would find the system easy to use. (Select only one.) 1 = strongly disagree




7 = strongly agree

8. Learning to operate the system is easy for me. (Select only one.) 1 = strongly disagree



5 = somewhat agree



6 = moderately agree

7 = strongly agree

9. People who influence my behavior think that I should use the system. (Select only one.) 1 = strongly disagree

2 = moderately disagree

3 = somewhat disagree 4 = neutral (neither disagree nor agree)

5 = somewhat agree


10. People who are important to me think that I should use the system. (Select only one.) 1 = strongly disagree



5 = somewhat agree


11. The senior management of this business has been helpful in the use of the system. (Select only one.) 1 = strongly disagree



5 = somewhat agree


7 = strongly agree

12. In general, the organization has supported the use of the system. (Select only one.) 1 = strongly disagree



5 = somewhat agree


13. I have the resources necessary to use the system. (Select only one.) 1 = strongly disagree



5 = somewhat agree




14. I have the knowledge necessary to use the system. (Select only one.) 1 = strongly disagree


3 = somewhat disagree

4 = neutral (neither disagree nor agree) 5 = somewhat agree


7 = strongly agree

15. The system is not compatible with other systems I use. (Select only one.) 1 = strongly disagree



5 = somewhat agree


16. A specific person (or group) is available for assistance with system difficulties. (Select only one.) 1 = strongly disagree



5 = somewhat agree


17. I intend to use the system in the next 3 months. (Select only one.) 1 = strongly disagree



5 = somewhat agree


18. I predict I would use the system in the next 3 months. (Select only one.) 1 = strongly disagree



5 = somewhat agree


19. I plan to use the system in the next 3 months (Select only one.) 1 = strongly disagree







7 = strongly agree

20. My colleagues are frequently using the software for their job-related tasks. (Select only one.) 1 = strongly disagree 2 = moderately disagree




7 = strongly agree

21. My coworkers are frequently using the software for their job-related tasks. (Select only one.) 1 = strongly disagree



5 = somewhat agree


7 = strongly agree

22. My supervisor is frequently using the software for his or her job-related tasks. (Select only one.) 1 = strongly disagree





7 = strongly agree

23. My subordinates are frequently using the software for their job-related tasks. (Select only one.) 1 = strongly disagree





7 = strongly agree

24. My company’s executives are frequently using the software for their job-related tasks. (Select only one.) 1 = strongly disagree



5 = somewhat agree




7 = strongly agree

Organizational Network

The following questions will ask about other persons whom you interact with in using <System

X>. Names will be used to correlate your <System X> usage with theirs, but the names will be

coded and removed in our data file in order to preserve the confidentiality and privacy of the

participants.

25. List 1-3 people that you interact with or will interact with in a typical week that have access to information needed for effective use of <System X> (e.g., system features, upcoming releases, demo dates, etc.).

1.

2.

3.

26. List 1-3 people that you interact with or will interact with in a typical week that you get help from for effective use of <System X>.”<System X>.

1.

2.

3.

27. List 1-3 people that you interact with or will interact with in a typical week that you give help to for effective use of <System X>.

1.

2.

3.



28. List 1-3 people that you interact with or will interact with in a typical week that you believe have strong domain/functional knowledge for effective use of <System X> (e.g., such as tips and tricks, short-cuts, process sequence, etc.).

1.

2.

3.

29. List 1-3 people that you interact with or will interact with in a typical week that have access to resources for effective use of <System X> (e.g., training resources, manuals, tutorials, etc.).

1.

2.

3.



Appendix B – Information Sheet

Ben Leonard

MBA, PhD Candidate

International Graduate School of Business

Division of Business, University of South Australia

[email protected] .

763-458-0140

Doug Vogel (PhD supervisor)

Professor and Chair of Information Systems

City University of Hong Kong

[email protected]

May 8, 2009

[Recipient Name]

[Street Address]

[City, ST ZIP Code]

Dear [Recipient Name]:

As a <target company> colleague, I am extending this invitation and asking for your help

with an important study that I am conducting as part of my PhD research to understand the

impacts of individual networks on the acceptance of new technologies within organizations.

The best way we have of learning about these issues is by asking individuals in organizations

about their experiences and expectations of using certain new technologies and how those

around them impact their decisions to accept a new technology. You have been selected for

this research because you are a current or potential user of <new system being

implemented>.

The survey questions should only take about 10-15 minutes to complete and will be asked

twice, now and in three months. Your responses are voluntary and will be kept confidential.

The surveys do request names, but these are immediately coded to a unique number prior to

analysis of the data and are only used to understand the cross-impacts of individual networks

on the perceptions and expectations of technology acceptance within an organization. Only

myself and my PhD supervisor, Professor Doug Vogel, will have access to individual

responses, and no identifiable data collected will be released to <target company> or any

other entity. The information collected as part of the study will be retained for five years in a

combination of hardcopies and electronic format in a locked safe at my residence and another

copy at the university.

mailto:[email protected]




You can withdraw at any time and if you have any questions about this survey, please contact

myself by telephone at 763-458-0140 or by email at

[email protected] .

The study has been reviewed and approved by <target company>’s Executive Leadership.

The University of South Australia’s Human Research Ethics Committee has reviewed this

study. Should you wish to discuss the project with someone not directly involved, in

particular in relation to matters concerning policies, information about the conduct of the

study, or your rights as a participant, please contact

Ms Vicki Allen, Executive Officer

Human Research Ethics Committee

University of South Australia

GPO BOX 2471

Adelaide SA 5000

Email: [email protected] Tel: +61 8 8302 3118

By taking a few minutes to share your thoughts and opinions, you will be helping this

research out a great deal. For participating you can also receive a copy of the final reports;

there is a check box on the survey if you would like a copy.

I hope you enjoy completing the questionnaire and look forward to receiving your responses.

Many thanks

Ben Leonard

PhD Candidate




Appendix C – Second Survey Correspondence

To: [Email]

From: [email protected]

Subject:

Second Survey for Ben's PhD Research

Body: [FirstName], Three months ago, you completed an initial questionnaire on the XXXX Registration System for an important study that I am conducting as part of my PhD research. This is the second and final survey and your help is contributing knowledge to the fields of organizational social networks and their influence on new technologies. I understand that due to client demands and pilot timing you might not have used the system directly as part of your role right now, but your perceptions of the system are critical for the research to be completed. Attached is a link to the survey. Thank you in advance for your continued support. Here is a link to the survey: http://www.surveymonkey.com/s.aspx This link is uniquely tied to this survey and your email address. Please do not forward this message. The survey questions should only take about 10-15 minutes to complete. Your responses are voluntary and will be kept confidential. You can withdraw at any time and if you have any questions about this survey, please contact myself by telephone at 612-454-8031 or by email at [email protected]. By taking a few minutes to share your thoughts and opinions, you will be helping this research out a great deal. I hope you enjoy completing the questionnaire and look forward to receiving your responses. Thanks for your participation in this research and enabling me to complete my PhD research which has been in progress for 5 years! Ben Leonard MBA, PhD Candidate International Graduate School of Business Division of Business, University of South Australia [email protected] 612-454-8031 Doug Vogel (PhD supervisor) Professor and Chair of Information Systems City University of Hong Kong [email protected] Please note: This is the final survey, but if you do not wish to receive any further communications on this research, please click here. http://www.surveymonkey.com/optout.aspx



To: [Email] From: [email protected]

Subject: Follow Up Research Survey on MS Project Server at <target company> Body: [FirstName]

I hope this email finds you well. Eight months ago, you completed an initial questionnaire for an important study that I am conducting as part of my PhD research to understand the impacts of individual networks on the acceptance of new technologies within organizations. I am now focused full time on completing my data analysis and dissertation writing for my doctorate by this spring. Below is a link to the follow-up questionnaire that I need your help completing to gain insight into the expectations and opinions of the users of MS Project Server after eight months. Although we didn't implement MS Project Server for <target company>, you current perceptions of MS Project Server are very relevent to my research. Thank you in advance for your continued support. http://www.surveymonkey.com/s.aspx The survey questions should only take about 10-15 minutes to complete. Your responses are voluntary and will be kept confidential. You can withdraw at any time and if you have any questions about this survey, please contact myself by telephone at 612-454-8031 or by email at [email protected] By taking a few minutes to share your thoughts and opinions, you will be helping this research out a great deal. For participating you will also receive a copy of the final reports. I hope you enjoy completing the questionnaire and look forward to receiving your responses. Many thanks Ben Leonard MBA, PhD Candidate International Graduate School of Business Division of Business, University of South Australia [email protected] . 612-454-8031 Doug Vogel (PhD supervisor) Professor and Chair of Information Systems City University of Hong Kong [email protected] http://www.surveymonkey.com/optout.aspx



Appendix D – Consent Form

PARTICIPANT CONSENT FORM

Research Project: Understanding the impacts of personal networks on the acceptance of new

technologies within organizations.

Researcher: Benjamin Leonard, PhD Candidate

Research Supervisor: Professor Doug Vogel

I have read the Participant Information Sheet and the nature and purpose of the

research project has been explained to me. I understand and agree to take part.

I understand the purpose of the research project and my involvement in it.

I understand that I may withdraw from the research project at any stage and that this

will not affect my status now or in the future.

I understand that while information gained during the study may be published, I will

not be identified and my personal results will remain confidential.

Name of participant……………………………………………………………….......

Signed…………………………………………………….Date……………………….

I have provided information about the research to the research participant and believe that

he/she understands what is involved.

Researcher’s signature and date……………………………………………………...



Appendix E – Ethics Approval

On Wed, Oct 14, 2009 at 11:20 AM, Vicki Allen <[email protected] wrote: Dear Benjamin Re: Ethics protocol P244/09GSB "Technology Acceptance within Organizations: Improving understanding using agent-based modeling with rich data" Thank you for providing the amendments and/or additional information requested by the Chairperson of the University's Human Research Ethics Committee (email dated 4 September 2009). I am pleased to advise that your protocol has been granted ethics approval and meets the requirements of the National Statement on Ethical Conduct in Human Research. Please regard this email as formal notification of approval. Ethics approval is always made on the basis of a number of conditions detailed in the attachment; it is important that you are familiar with, and abide by, these conditions. It is also essential that you conduct all research

according to UniSA guidelines, which can be found at http://www.unisa.edu.au/res/ethics/default.asp

Clinical Trials The National Statement on Ethical Conduct in Human Research defines a clinical trial as a form of human research designed to find out the effects of an intervention. Health-related interventions can include drugs, surgical procedures, devices, behavioural treatments, dietary interventions or process-of-care changes. If your project is a clinical trial you are required to register it in a publicly accessible trials registry prior to enrolment of the first participant (eg Australian New Zealand Clinical Trials Registry

http://www.anzctr.org.au/Survey/UserQuestion.aspx) as a condition of ethics approval. Best wishes for your research. Regards, Vicki Vicki Allen Ethics and Compliance Officer & Executive Officer, Uni SA Human Research Ethics Committee University of South Australia Research and Innovation Services Mawson Lakes Campus Mawson Lakes Boulevard Mawson Lakes SA 5095 Tel: +61 8 8302 3118 Fax: +61 8 8302 3921

Email: [email protected]

CRIS Provider No. #00121B


http://www.unisa.edu.au/res/ethics/default.asp

http://www.anzctr.org.au/Survey/UserQuestion.aspx




Appendix F – Statistical Syntax from SPSS to calculate results

* OPEN FILE GET FILE='/Users/benjamin_leonard/Documents/PhD/Research Data Files/PhD Research Data 16.sav'. DATASET NAME DataSet2 WINDOW=FRONT. {}... * PREPARE DATA * Recode Question 15 to reverse coding to parallel the scale with the other 3 Facilitating Conditions Questions RECODE PBC5_Q15 (1=7) (2=6) (3=5) (4=4) (5=3) (6=2) (7=1) INTO PBC5_Q15_Reverse_Coded. VARIABLE LABELS PBC5_Q15_Reverse_Coded '<the system> is not compatible with other systems I use. (reverse coded)'. EXECUTE. * CHECK RELIABILITY AND VALIDITY FOR SIX MAIN CONSTRUCTS * RELIABILITY - Extent to which a measure is free of random measurement error (Smithson, 2005 - reference from management research methods). * Poor reliablity weakens the effect size and limits our ability to detect relationships between variables. * VALIDITY - Extent that a measure measures what its supposed to. * Internal consistency reliability - items are consistent with each other and typically measured by Cronbach's alpha coefficient (.90 highly reliablity, .70 fair reliablity - Nullally, 1978 - pg 153 Management research methods. * These 24 questions were coded as interval scales of measurement rather then ordinal. This is commonly done provided there are five or more response catagories and the underlying construct is * conceptualized as theoretically continuous (Tabachnick, B.g. & Fidell, L.S. (2001). Using multivariate statistis (4th ed.). New York: Allyn and Bacon RELIABILITY /VARIABLES=U6_Q1 RA1_Q2 RA5_Q3 OE7_Q4 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE. RELIABILITY /VARIABLES=EOU3_Q5 EOU5_Q6 EOU6_Q7 EU4_Q8 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE. RELIABILITY /VARIABLES=SN1_Q9 SN2_Q10 SF2_Q11 SF4_Q12 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE. RELIABILITY /VARIABLES=PBC2_Q13 PBC3_Q14 PBC5_Q15_Reverse_Coded FC3_Q16 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE. RELIABILITY /VARIABLES=BI1_Q17 BI2_Q18 BI3_Q19 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE. RELIABILITY /VARIABLES=ISI1_Q20 ISI2_Q21 ISI3_Q22 ISI4_Q23 ISI5_Q24 /SCALE('ALL VARIABLES') ALL



/MODEL=ALPHA /STATISTICS=DESCRIPTIVE. * REDUCE 24 QUESTIONS TO SIX MAIN CONSTRUCTS BY CALCULATING MEANS FOR EACH MAIN CONSTRUCTS COMPUTE Performance_Expectancy_Mean=MEAN(U6_Q1,RA1_Q2,RA5_Q3,OE7_Q4). EXECUTE. COMPUTE Effort_Expectancy_Mean=MEAN(EOU3_Q5,EOU5_Q6,EOU6_Q7,EU4_Q8). EXECUTE. COMPUTE Social_Influence_Mean=MEAN(SN1_Q9,SN2_Q10,SF2_Q11,SF4_Q12). EXECUTE. COMPUTE Facilitating_Conditions_Mean=MEAN(PBC2_Q13,PBC3_Q14,PBC5_Q15_Reverse_Coded, FC3_Q16). EXECUTE. COMPUTE Behavioral_Intention_Mean=MEAN(BI2_Q18,BI3_Q19,BI1_Q17). EXECUTE. COMPUTE Implicit_Social_Influence_Mean=MEAN(ISI1_Q20,ISI2_Q21,ISI3_Q22,ISI4_Q23,ISI5_Q24). EXECUTE. * Calculate Use Behaviors from comprehensiveness and freqeuncy survey questions COMPUTE Postpilot_Self_Reported_Use_Behavior=(Postpilot_use_frequency+ Postpilot_use_comprehensiveness-2)/12. EXECUTE. COMPUTE Prepilot_Self_Reported_Use_Behavior=(Prepilot_use_frequency+ Prepilot_use_comprehensiveness-2)/12. EXECUTE. * DESCRIPTIVE STATISTICS FOR RAW DATA DESCRIPTIVES VARIABLES=U6_Q1 RA1_Q2 RA5_Q3 OE7_Q4 EOU3_Q5 EOU5_Q6 EOU6_Q7 EU4_Q8 SN1_Q9 SN2_Q10 SF2_Q11 SF4_Q12 PBC2_Q13 PBC3_Q14 PBC5_Q15_Reverse_Coded FC3_Q16 BI1_Q17 BI2_Q18 BI3_Q19 ISI1_Q20 ISI2_Q21 ISI3_Q22 ISI4_Q23 ISI5_Q24 /STATISTICS=MEAN STDDEV VARIANCE MIN MAX . * CALCULATE DESCRIPTIVE STATS FOR OVERALL MEANS and check for normality of data * Use pairwise deletion for missing values, removes cases only when the missing data is needed for the calculation of a relationship * VALIDATE NORMALITY ASSUMPTION of univariate nomality using Kurtosis and skewness. * Reference: Wilkinson, L. & The Task Force on Statistical Inference (TFSI) (1999). Statistical methods in psychology journals: Guidelines and explanation. American Psychologist, 54, 594-604) * - Absolute value of Kurtosis should be less then 5 * - Absolute value of skewness should be less then 2 * DESCRIPTIVES VARIABLES=Age Gender Voluntariness Performance_Expectancy_Mean Social_Influence_Mean Effort_Expectancy_Mean Facilitating_Conditions_Mean Behavioral_Intention_Mean Implicit_Social_Influence_Mean Use_Behavior Coping_Centrality Influence_Centrality Coping_Ego_PE Influencing_Ego_PE /STATISTICS=MEAN STDDEV MIN MAX KURTOSIS SKEWNESS. * VALIDATE LINEARITY ASSUMPTION OF DATA * Manual inspection of data plot to vaidate that its generally linear and not obviously non-linear. * Violation of the linearity assumption weakens power of the statistical test to detect an effect, but does not invalidate the results.



* Reference: Wilkinson, L. & The Task Force on Statistical Inference (TFSI) (1999). Statistical methods in psychology journals: Guidelines and explanation. American Psychologist, 54, 594-604) * VALIDATE HOMOSCEDASTICITY ASSUMPTION OF DATA * Variability of different scores are roughly the same at all values * Generate bivariate scatter plots of variable combinations * Check to see that scores do not disperse as they move up or down each scale * Pearson's correlation coeficient and mulitpl regression assume homoscedasticity of data because they compare means of independent groups. * Reference: Phyllis Tharenou, Ross Donohue, Brian Cooper, Management Research Methods, Cambridge Univeristy Press, 2007, page 202 * If homoscedasticity is significantly violated, you can adopt a more conservative alpha criterion (.025 for moderate heteroscedasticy and .01 for severe heteroscedasticity * in tests of statistical significance. * Reference: Wilkinson, L. & The Task Force on Statistical Inference (TFSI) (1999). * Statistical methods in psychology journals: Guidelines and explanation. American Psychologist, 54, 594-604) * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES=Behavioral_Intention_Mean WITH Performance_Expectancy_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES=Behavioral_Intention_Mean WITH Effort_Expectancy_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES=Behavioral_Intention_Mean WITH Social_Influence_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES=Behavioral_Intention_Mean WITH Implicit_Social_Influence_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES=Behavioral_Intention_Mean WITH Facilitating_Conditions_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES= Coping_Centrality WITH Use_Behavior /CONSTANT



/MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES= Coping_Centrality WITH Effort_Expectancy_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES= Influencing_Ego_PE WITH Performance_Expectancy_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES= Coping_Ego_PE WITH Performance_Expectancy_Mean /CONSTANT /MODEL=LINEAR /PLOT FIT. * Curve Estimation. TSET NEWVAR=NONE. CURVEFIT /VARIABLES= Influencing_Ego_PE WITH Use_Behavior /CONSTANT /MODEL=LINEAR /PLOT FIT. * VALIDATE ASSUMPTION OF ABSENCE OF MULTICOLLINEARITY * Two or more independent variables are highly correlated * Causes interpretational and/or computational problems in multiple regression * Validated by checking bivariate correlations * If two independent variables are correlated higher then .70, they may have collinearity * Solution: the least theorectially defensible one should be dropped * Reference: Wilkinson, L. & The Task Force on Statistical Inference (TFSI) (1999). * Statistical methods in psychology journals: Guidelines and explanation. American Psychologist, 54, 594-604) * CORRELATIONS: * CALCULATE THE RELATIONSHIP STRENGTH AND DIRECTION BETWEEN TWO CONTINOUS VARIABLES * Pearsons corrlation coefficient "r" * Researcher can square r to dermine shared variance * Rule of thumb: r=.10 small effect, .30 medium effect, .50 large effect size * Reference: Phyllis Tharenou, Ross Donohue, Brian Cooper, Management Research Methods, Cambridge Univeristy Press, 2007, page 209 CORRELATIONS /VARIABLES= Voluntariness Department Age Performance_Expectancy_Mean Effort_Expectancy_Mean Social_Influence_Mean Facilitating_Conditions_Mean Behavioral_Intention_Mean Implicit_Social_Influence_Mean Use_Behavior Influence_Centrality Coping_Centrality Coping_Ego_PE Influencing_Ego_PE /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=pairwise. * CALCULATE REGRESSIONS TO MATCH STANDARD UTAUT MODEL REGRESSION



/MISSING pairwise /STATISTICS COEFF OUTS R ANOVA CHANGE ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Behavioral_Intention_Mean /METHOD=ENTER Department /METHOD=ENTER Gender /METHOD=ENTER Technology /METHOD=ENTER Experience /METHOD=ENTER Age /METHOD=ENTER Voluntariness /METHOD=ENTER Performance_Expectancy_Mean /METHOD=ENTER Effort_Expectancy_Mean /METHOD=ENTER Social_Influence_Mean /METHOD=ENTER Influencing_Ego_PE /METHOD=ENTER Coping_Ego_PE /METHOD=ENTER Coping_Centrality /METHOD=ENTER Influence_Centrality. REGRESSION /MISSING pairwise /STATISTICS COEFF OUTS R ANOVA CHANGE ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Use_Behavior /METHOD=ENTER Department /METHOD=ENTER Gender /METHOD=ENTER Technology /METHOD=ENTER Experience /METHOD=ENTER Age /METHOD=ENTER Voluntariness /METHOD=ENTER Behavioral_Intention_Mean /METHOD=ENTER Facilitating_Conditions_Mean /METHOD=ENTER Coping_Ego_PE /METHOD=ENTER Influencing_Ego_PE /METHOD=ENTER Coping_Centrality /METHOD=ENTER Influence_Centrality. REGRESSION /MISSING pairwise /STATISTICS COEFF OUTS R ANOVA CHANGE ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Performance_Expectancy_Mean /METHOD=ENTER Department /METHOD=ENTER Gender /METHOD=ENTER Technology /METHOD=ENTER Experience /METHOD=ENTER Age /METHOD=ENTER Voluntariness /METHOD=ENTER Coping_Ego_PE /METHOD=ENTER Influencing_Ego_PE /METHOD=ENTER Coping_Centrality /METHOD=ENTER Influence_Centrality. REGRESSION /MISSING pairwise /STATISTICS COEFF OUTS R ANOVA CHANGE ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Effort_Expectancy_Mean /METHOD=ENTER Department



/METHOD=ENTER Gender /METHOD=ENTER Technology /METHOD=ENTER Experience /METHOD=ENTER Age /METHOD=ENTER Voluntariness /METHOD=ENTER Coping_Ego_PE /METHOD=ENTER Influencing_Ego_PE /METHOD=ENTER Coping_Centrality /METHOD=ENTER Influence_Centrality. REGRESSION /MISSING pairwise /STATISTICS COEFF OUTS R ANOVA CHANGE ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Facilitating_Conditions_Mean /METHOD=ENTER Department /METHOD=ENTER Gender /METHOD=ENTER Technology /METHOD=ENTER Experience /METHOD=ENTER Age /METHOD=ENTER Voluntariness /METHOD=ENTER Coping_Ego_PE /METHOD=ENTER Influencing_Ego_PE /METHOD=ENTER Coping_Centrality /METHOD=ENTER Influence_Centrality. DATASET ACTIVATE DataSet1. DESCRIPTIVES VARIABLES=PE EE /STATISTICS=MEAN STDDEV MIN MAX KURTOSIS SKEWNESS. DESCRIPTIVES VARIABLES=Performance_Expectancy_Mean Effort_Expectancy_Mean Social_Influence_Mean Facilitating_Conditions_Mean Behavioral_Intention_Mean Use_Behavior /STATISTICS=MEAN STDDEV MIN MAX. USE ALL. COMPUTE filter_$=(Experience = 0). VARIABLE LABELS filter_$ 'Experience = 0 (FILTER)'. VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMATS filter_$ (f1.0). FILTER BY filter_$. EXECUTE. USE ALL. COMPUTE filter_$=(Experience = 1). VARIABLE LABELS filter_$ 'Experience = 1 (FILTER)'. VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMATS filter_$ (f1.0). FILTER BY filter_$. EXECUTE. COMPUTE Postpilot_Self_Reported_Use_Behavior=(Postpilot_use_frequency+ Postpilot_use_comprehensiveness-2)/12. EXECUTE.



Appendix G - Addressing procedures for ensuring sound ethical

practices

There are two significant areas that had ethical challenges to ensure the details of every respondent

and their responses are treated as strictly confidential:

First, the researcher was an IT Director in the company where the research was conducted and part

of his role as an IT Director is to leverage technology to further business objectives. This could have

either biased or indirectly put pressure on respondents to adopt the particular technology. To

minimize this risk, it was made clear in the information sheet and during any initial verbal overview

of the research to the participants on the anonymity of the data back to the company. The

researcher also kept a journal of his own intentions and experiences and was not either the business

sponsor or the direct project manager of the technology acceptance initiatives that are part of this

study.

Second, to perform the social networking, the researcher needed to identify and model the network,

both identifying who works together on formal teams and who they look to as opinion leaders from

their questionnaire. To accomplish confidentiality while still maintaining anonymity, the researcher

assigned a unique ID to each person to not disclose any names in the research.