Informations systems theory explaning and predicting our digital society

In fact, some of my more candid colleagues have labeled me as their favorite “a-theoretic author.” This hesitancy is perhaps all the more difficult to understand because the very first pap

Integrated Series in Information Systems

University of Hamburg, Hamburg, Germany

For further volumes:

http://www.springer.com/series/6157

wwwwwwwwwwwwwwww

Yogesh K Dwivedi L Michael R Wade

Scott L Schneberger

Editors

Information Systems Theory

Explaining and Predicting

Our Digital Society, Vol 1

ISSN 1571-0270

ISBN 978-1-4419-6107-5 e-ISBN 978-1-4419-6108-2

DOI 10.1007/978-1-4419-6108-2

Springer New York Dordrecht Heidelberg London

Library of Congress Control Number: 2011936384

© Springer Science+Business Media, LLC 2012

All rights reserved This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,

NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis Use in connection with any form of information storage and retrieval, electronic adaptation, computer software,

or by similar or dissimilar methodology now known or hereafter developed is forbidden.

The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

Printed on acid-free paper

Springer is part of Springer Science+Business Media (www.springer.com)

To my adorable daughter, Saanvi, on her first birthday, for brightening my each day with her smile and touchingly mischievous

wwwwwwwwwwwwwwww

Foreword

I hesitated when asked to provide a foreword to this two-volume treatise on theories relevant to the information systems field for two reasons One, I claim no special expertise in the many theoretical frameworks and constructs that have been devel-oped in our field or brought into it from other disciplines that are described in this book And two, I have not been particularly adept at incorporating these theories into my own research and publications In fact, some of my more candid colleagues have labeled me as their favorite “a-theoretic author.”

This hesitancy is perhaps all the more difficult to understand because the very first paper in Volume One is “DeLone and McLean IS Success Model,” a “theory” paper that Bill DeLone, a doctoral student of mine at UCLA, and I published in

Information Systems Research in 1992; and which, in a recent survey published in

the Communications of the AIS (2009), was recognized as the most cited IS research

paper published in the world in the last 15 years

The path from first submission to final publication of this paper was one fraught with minefields and critiques, chief among which was the question: “But where is

the theory?” John King, the editor-in-chief of ISR at that time, although fully aware

of the criticism about the apparent lack of theory in the paper, decided to take a chance and publish it anyway As indicated above, his judgment appears to have been vindicated, if citations are any indication

But the question of what constitutes good theory and the role that it can – and should – play in information systems research is still, in my view, an essential question this book can help researchers answer The aforementioned DeLone and McLean Success paper, and their several follow up papers, still suffer from the criticism of a lack of strong theoretical grounding And they are not alone; there are two more examples

In the 1970s, Peter Drucker had occasion to relocate from New York to Los Angeles and made inquiries at the business school at UCLA to see if it were possible

to obtain a faculty appointment in the school A vote of the faculty was held and his application was turned down “He’s not a scholar; he’s just an ‘arm-chair’ philoso-pher.” “There is no theory base to any of his writings.” “He’s just a glorified consul-tant.” So instead, he went to the Claremont Graduate University, where they named the school after him!

viii Foreword

Also in the 1970s, Dick Nolan published his famous “Stages of Growth” papers,

first in the Communications of the ACM (1971) and the following year in the

Harvard Business Review (1972) They too were soundly criticized as having no

theory base; and shortly thereafter, he left the Harvard Business School to form Nolan Norton & Co which proved wildly successful in providing Stage-Assessment consulting to numerous companies who seemed to exhibit no concern about its lack

of a theoretical base

So what are we to make of the 22 theories presented in Volume One and the 21 theories in Volume Two?

We should study them carefully; and, where they fit the research question that we

wish to address, use them; and where possible, refine and extend them For readers

like myself, these two volumes can serve as a graduate course in the exposition of theories of potential relevance to information systems research They bring together

in an eminently accessible form the theories that form the basis of much – nay, most – of the published IS research in the last 30 years

Ignore them at your peril – but use them with discretion

Preface

To advance our understanding of information systems (IS), it is necessary to conduct relevant and rigorous IS research IS research, in turn, is built on a foundation of strong and robust theory Indeed, the IS field has a long and rich tradition of devel-oping and appropriating theories to examine central disciplinary themes, such as the

IS life cycle and IS business value, along with a host of social and political factors The ISWorld wiki “Theories Used in IS Research1” (TUISR) lists 87 such theories and models While this site is a valuable resource for the field, much more could be assembled to aid IS researchers in using theories to explain and predict how infor-mation systems can be used within today’s digital society

In our own careers, we have found it to be a major challenge to identify ate theories for our work, and even harder to fully understand the theories that we encounter We would encounter theories we find interesting, but the papers where

appropri-we found them provide an incomplete account or a superficial explanation of what the theory was about, or how it could be used It was this problem of theory identi-fication and comprehension that led us to create this book We wanted to produce a collection of papers about theories that could be used by IS researchers as a starting point for their work This collection would act like a one-stop-shop for IS theory

We already had the TUISR wiki that provided basic information on theory; but with this book, we wanted to provide more depth and insight into the theories that popu-lated our field

We believe the lack of a comprehensive source of information on theory poses special problems for researchers Due to a deficiency of experience within a new area, it may not be easy to fully comprehend and use a new theory in an appropriate manner Furthermore, it is sometimes difficult for researchers to determine which particular theory, out of the vast number available, may be appropriate in a research context

We felt a literary and meta-analytic collection of IS theories would not only vide a significant contribution to IS knowledge, but would also be a valuable aid to

pro-IS researchers, practitioners and students

x Preface

The overall mission of this book is to provide a comprehensive understanding and coverage of the various theories and models used in IS research Specifically, it aims to focus on the following key objectives:

To describe the various theories and models applicable to studying IS/IT

To provide a critical review/meta-analysis of IS/IT management articles that

‡

have used a particular theory/model

To discuss how a theory can be used to better understand how information

sys-‡

tems can be effectively deployed in today’s digital world

This book contributes to our understanding of a number of theories and models The theoretical contribution of this book is that it analyzes and synthesizes the rel-evant literature in order to enhance knowledge of IS theories and models from vari-ous perspectives To cater to the information needs of a diverse spectrum of readers, this book is structured into two volumes, with each volume further broken down into two sections

The first section of Volume 1 presents detailed descriptions of a set of theories centred around the IS life cycle, including:

DeLone and McLean’s Success Model

xi Preface

Discrepancy Theory Models

Considering the breadth and depth of the content, we hope this book will become

a trusted resource for readers wishing to learn more about the various theories and models applicable to IS research, as well as those interested in finding out when and how to apply these theories and models to investigate diverse research issues

xii Preface

We sincerely hope this book will provide a positive contribution to the area of Information Systems To make further research progress and improvement in the understanding of theories and models, we welcome all feedback and comments about this book from readers Comments and constructive suggestions can be sent to the Editors care of Springer, USA, at the address provided at the beginning of the book

Acknowledgments

While our names alone appear on the cover, this book would not have been possible without the material assistance of a great many people We would like to take this opportunity to convey our thanks to the efforts of those people who helped and supported us at various stages in the completion of this work

First of all, we would like to thank the series editors – Professor Stefan Voß and

Professor Ramesh Sharda – for including this title under Springer’s Integrated Series in Information Systems We would also like to thank the dedicated people at

Springer, USA, namely: Mr Neil Levine (Editor, Operations Research &

Management) for handling the book proposal and finalizing the contract, and

Mr Matthew Amboy for skillfully managing the project, and keeping the book

(and us) on schedule

A book like this would not be possible without the tireless efforts of a legion of volunteer reviewers The developmental and constructive comments provided by these reviewers dramatically improved the quality of each submission In addition,

we would like to express our gratitude to the chapter authors for contributing

inter-esting and relevant material to this project We are also highly grateful to Prof

Ephraim R McLean and Professor Michael D Myers for providing the forewords.

Last but not least, we bestow our unbounded gratitude and deepest sense of respect to our families whose blessing, concerted efforts, constant encouragement and wholehearted co-operation enabled us to reach this milestone

Happy theorizing!

Yogesh, Mike, and Scott

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Contents

1 The Updated DeLone and McLean Model of Information

Systems Success 1

Nils Urbach and Benjamin Müller 1.1 Introduction 2

1.2 Development of the D&M IS Success Model 3

1.3 Constructs and Measures 4

1.3.1 System Quality 4

1.3.2 Information Quality 5

1.3.3 Service Quality 5

1.3.4 Intention to Use/Use 6

1.3.5 User Satisfaction 7

1.3.6 Net Benefits 7

1.4 Construct Interrelations 8

1.4.1 System Use 9

1.4.2 User Satisfaction 10

1.4.3 Net Benefits 11

1.5 Existing Research on IS Success 11

1.6 Conclusion 13

References 14

2 If We Build It They Will Come? The Technology Acceptance Model 19

Joseph Bradley 2.1 Introduction 20

2.2 Literature Review 21

2.2.1 Expectancy-Value Theory 21

2.2.2 Theory of Reasoned Action 22

2.2.3 Technology Acceptance Model 23

2.2.3.1 TAM Variables 24

2.2.3.2 Impact of TAM 25

xvi Contents

2.2.3.3 Types of Information Systems Examined 25

2.2.3.4 External Variables Tested 25

2.2.3.5 TAM Publications 26

2.2.3.6 Characteristics of Research Subjects 26

2.2.3.7 Major Limitations of the Model 27

2.2.3.8 Most Published Authors 27

2.2.3.9 Recent TAM Research 27

2.2.4 TAM Model Elaborations 28

2.2.4.1 TAM2 28

2.2.4.2 Unified Theory of Acceptance and Use of Technology (UTAUT) 29

2.2.4.3 TAM and Task-Technology Fit Model 30

2.2.4.4 TAM3 31

2.3 Future of the Technology Acceptance Model 32

2.4 Conclusions 33

References 34

3 A Bibliometric Analysis of Articles Citing the Unified Theory of Acceptance and Use of Technology 37

Michael D Williams, Nripendra P Rana, and Yogesh K Dwivedi 3.1 Introduction 38

3.2 Methodology 39

3.3 Findings 40

3.3.1 Demographic Data: Citations by Year 40

3.3.2 Demographic Data: Citations by Journal/Source 40

3.3.3 Demographic Data: Most Cited Citations 41

3.3.4 Analysis and Systematic Review of Articles Citing the UTAUT Originating Article 42

3.3.4.1 Citations with No Use of UTAUT 42

3.3.4.2 Citations with Use of UTAUT with Different Research Methods 43

3.3.4.3 Citations with Partial Use of UTAUT 43

3.3.4.4 Citations with Complete Use of UTAUT 43

3.3.5 IS Research Topics and Types of IS Examined 46

3.3.5.1 Keyword Analysis 46

3.3.5.2 Types of IS Investigated 49

3.3.6 Methodological Analysis 49

3.3.6.1 Research Methods 50

3.3.6.2 Types of Users 51

3.3.6.3 Sample Size 52

3.3.7 Theoretical Analysis 52

3.3.7.1 External Variables Analysis 53

3.3.7.2 External Theories Analysis 54

xvii Contents

3.3.7.3 Relationships of External Variables

with UTAUT Constructs 54

3.4 Discussion 55

3.5 Conclusion 57

References 58

4 Why Do People Reject Technologies: A Review of User Resistance Theories 63

Sven Laumer and Andreas Eckhardt 4.1 Introduction 64

4.2 Resistance, Rejection, and Non-Adoption 65

4.3 User Resistance Theories 67

4.3.1 Multilevel Model of Resistance to Information Technology Implementation 69

4.3.2 Power, Politics, and MIS Implementation 71

4.3.3 A Model of Users’ Perspective on Change 72

4.3.4 Passive Resistance Misuse 73

4.3.5 An Attributional Explanation of Individual Resistance 75

4.3.6 Inhibitors and Enablers as Dual Factor Concepts in Technology Usage 77

4.3.7 Physicians’ Resistance Toward Health-Care Information Technology 79

4.3.8 Analyzing Workplace Referents’ Social Influence on IT Non-adoption 80

4.3.9 Investigating User Resistance to Information Systems Implementation: A Status Quo Bias Perspective 80

4.4 Outlook 82

References 84

5 Task-Technology Fit Theory: A Survey and Synopsis of the Literature 87

Brent Furneaux 5.1 Introduction 88

5.2 The Theory 88

5.3 Literature Survey and Synopsis 90

5.3.1 Definition of Task-Technology Fit 91

5.3.2 Operationalization of Task-Technology Fit 93

5.3.3 Research Contexts Employed by TTF Research 95

5.3.4 Key Outcomes of Interest to TTF Researchers 97

5.3.5 Summary Framework 98

5.4 Discussion 101

5.5 Conclusion 102

References 102

xviii Contents

6 Migrating Processes from Physical to Virtual Environments:

Process Virtualization Theory 107

Eric Overby 6.1 Introduction 108

6.2 Definitions 108

6.3 Process Virtualization Theory: Constructs and Relationships 111

6.3.1 Dependent Variable 111

6.3.2 Independent Variables 111

6.3.2.1 Characteristics of the Process 112

6.3.2.2 Characteristics of the Virtualization Mechanism 113

6.3.3 Clarifications and Adjustments to Process Virtualization Theory 115

6.3.4 Comments on Empirical Testing 116

6.3.5 Illustration 116

6.4 Relationship of Process Virtualization Theory to IS Research 117

6.4.1 The Process Virtualization Theme Within IS 117

6.4.1.1 IS Research on Distributed Decision Support Systems and Virtual Teams 118

6.4.1.2 IS Research on Electronic Commerce 118

6.4.1.3 IS Research on Distance Learning 119

6.4.1.4 IS Research on Business Process Reengineering and Disaggregation 119

6.4.2 Process Virtualization Theory and Other IS Theories 119

6.4.3 A “Native” Information Systems Theory 120

6.5 Conclusion 122

References 122

7 The Theory of Deferred Action: Purposive Design as Deferred Systems for Emergent Organisations 125

Nandish V Patel 7.1 Introduction 125

7.2 The Adaptive IS Problem 126

7.3 A Theory of IS 128

7.4 Theorisation 129

7.5 Deferred Action as Controlled Emergence of Organisation and Systems 130

7.6 Implementing Deferred Action 136

7.7 Data, Information and Knowledge 137

7.8 Formal Models 138

7.8.1 Real Systems 139

7.8.2 Deferred Systems 140

7.8.3 Specified Systems 141

7.8.4 Autonomous Systems 141

xix Contents

7.9 Design Principles for the Practice Framework 142

7.9.1 Under-Specification 142

7.9.2 Functional Deferment Points 143

7.9.3 Self-Organising 143

7.9.4 Adaptation 143

7.9.5 Ethics 143

7.9.6 Deferred Design Decisions 144

7.10 Instantiations of Deferred Systems 144

7.10.1 Legal Arbitration IS 145

7.10.2 E-Learning 145

7.10.3 Deferred Information Technology 146

7.11 Discussion 146

7.12 Limitations and Further Theory Development Work 147

7.13 Conclusion 148

References 148

8 Resource-Based View Theory 151

Mahdieh Taher 8.1 Introduction 152

8.2 Literature Review 154

8.2.1 Competitive Advantage 154

8.2.2 Resources 155

8.2.2.1 Resource Characteristics 155

8.2.3 Capabilities 157

8.3 Application of RBV in IS Research 159

8.3.1 Information System Resources and Capabilities 159

8.4 Resource Orchestration 160

8.5 Conclusions and Future Research 160

References 161

9 On the Business Value of Information Technology: A Theory of Slack Resources 165

Yasser Rahrovani and Alain Pinsonneault 9.1 Introduction 166

9.2 Theoretical Background 167

9.2.1 Organizational Slack 167

9.2.1.1 Organizational Slack and Effectiveness 168

9.2.1.2 Organizational Slack and Efficiency 169

9.2.1.3 Organizational Slack and Redeployability 169

9.3 IT Slack Conceptualization 170

9.3.1 IT Slack and Redeployability 171

9.3.2 The Value of IT Slack 171

9.4 A Typology of IT Slack 172

9.4.1 Type 1 – IT Infrastructure-Artifact Slack 175

9.4.2 Type 2 – IT Infrastructure-Human Resource Slack 176

xx Contents

9.4.3 Type 3 – IT Infrastructure-Time Slack 176

9.4.4 Type 4 – IT Application-Artifact Slack 177

9.4.5 Type 5 – IT Application-Human Resource Slack 177

9.4.6 Type 6 – IT Application-Time Slack 178

9.5 A Slack View Toward the Value of IT 178

9.5.1 IT Slack and Organizational Efficiency 179

9.5.1.1 Type of IT Slack and Organizational Efficiency 180

9.5.2 IT Slack and Organizational Effectiveness 186

9.6 Implications and Contributions 188

9.7 Conclusion 191

Appendix A 192

References 195

10 Portfolio Theory: The Contribution of Markowitz’s Theory to Information System Area 199

Pietro Cunha Dolci and Antônio Carlos Gastaud Maçada 10.1 Introduction 200

10.2 Literature Review 201

10.2.1 Description of Portfolio Theory 201

10.2.2 Markowitz’s Theory and Information System Area 202

10.2.3 Information Technology Portfolio Management (ITPM) 204

10.2.3.1 Dimensions of ITPM 205

10.2.3.2 IT Projects Portfolio 206

10.3 Links from This Theory to Other Theories 208

10.4 Concluding Comments 208

References 209

11 The Theory of the Lemon Markets in IS Research 213

Jan Devos, Hendrik Van Landeghem, and Dirk Deschoolmeester 11.1 Introduction 214

11.2 Dissection of the Theory: Its Nomological Network and Constructs 215

11.3 Link with Other Theories 218

11.4 Literature Overview of IS Articles Using LMT 220

11.5 Bibliographical Analysis of the Original Akerlof Article 222

11.6 Conclusion 227

References 227

12 The Technology–Organization–Environment Framework 231

Jeff Baker 12.1 Introduction 232

12.1.1 The Technological Context 232

12.1.2 The Organizational Context 233

12.1.3 The Environmental Context 235

xxi Contents

12.2 The Technology–Organization–Environment Framework

in Research 23512.3 The Technology–Organization–Environment Framework

in Future Research 23712.3.1 Reasons for Lack of Development 23712.3.2 Future Directions for TOE Research 24112.4 Conclusions 243References 243

13 Contingency Theory in Information Systems Research 247

Jeff Reinking

13.1 Introduction 24813.2 Literature Review 24913.2.1 Seminal Literature 249

13.2.1.1 Environment 25013.2.1.2 Technology 25013.2.1.3 Leadership Traits 25113.2.2 Contingency Research in IS 251

13.2.2.1 Systems Design 25313.2.2.2 Implementation 25413.2.2.3 Performance 25513.2.2.4 User Involvement 25613.2.2.5 Internet 25713.2.2.6 Additional Constructs 25813.3 Research Methods 25813.4 Contingency Theory Limitations 26013.4.1 Performance 26013.4.2 Contingency Variables 26013.4.3 Culture 26113.5 Conclusion 261References 262

Sanjay Mohapatra

14.1 Introduction 26614.2 Understanding Porter’s Model 26614.2.1 Supplier’s Bargaining Power 26714.2.2 Bargaining Power of Buyers 26714.2.3 Threats of New Entrant 26714.2.4 Threat of Substitutes 26814.2.5 Threats of Rivalry Among Existing Players

in Present Market 26814.3 Strategic Significance of Information Technology 26914.4 Technology-Enabled Strategy 27014.5 How Five Forces Help Formulate Strategy 27114.6 IT Research and Porter’s Five Forces 272

xxii Contents

14.7 IT and Porter’s Five Forces 27414.7.1 IT and Buying Power 27514.7.2 IT and Entry Barrier 27514.7.3 IT and Threat of Substitutes 27514.7.4 IT and Industry Rivalry 27614.7.5 IT and Selling Power 27614.8 Changing Times with IT 27714.9 Role of Managers in IT-Enabled Strategy 27814.10 Conclusion 279References 280

15 Information Technology and Organisational Performance:

Reviewing the Business Value of IT Literature 283

Boumediene Ramdani

15.1 Introduction 28415.1.1 IT Assets 28415.1.2 IT Business Value 28515.1.3 IT Business Value Dimensions 28615.2 Early Research on IT Business Value 28615.3 Current Theoretical Paradigms 28815.3.1 Economics-Based IT Business Value Research 28815.3.2 Management-Based IT Business Value Research 291

15.3.2.1 Value Creation Models 29215.3.2.2 Performance Measurement Models 29315.3.2.3 IT Investment Models 29415.3.2.4 IT Governance Models 29515.3.3 Sociology-Based IT Business Value Research 29615.4 Conclusion and Future Research 296References 297

16 Applying “Business Case” Construct Using the “Diffusion

of Innovations” Theory Framework: Empirical Case Study

in the Higher Education 303

Francisco Chia Cua

16.1 Introduction 30416.1.1 Critical Reflective Lenses 30616.1.2 Outline 30716.2 The “Diffusion of Innovations” (DoI) Theory 30716.2.1 Perceived Attributes of the Innovation 30916.3 Methodology 31016.3.1 Research Questions 31116.3.2 The Literature Review 31116.3.3 Units of Analysis and Limitations 31116.3.4 Replication and Challenges in Data Gathering,

Analysis, and Narration, Threats to the Single-Case Study, and Control Self-assessment 313

xxiii Contents

16.4 The Empirical Evidence 31716.4.1 The “Business Case” Document 318

16.4.1.1 Section 1: The Evaluation Process 31816.4.1.2 Section 2: Why is a New Finance

System Needed? 31916.4.1.3 Section 3: Benefits of a New

Financial System 32016.4.1.4 Section 4: What is the Recommended

Solution? 32016.4.1.5 Section 5: What Will Happen if a New

Financial System Is Not Implemented? 32016.4.1.6 Section 6: Proposed Time Frame 32216.4.1.7 Section 7: What Resources

will be Required? 32216.4.1.8 Attachment A: Project Definition

(2 Pages) 32216.4.1.9 Attachment B: Project Strategic Evaluation

(1 Page) 32316.4.1.10 Attachment C: Project Risk Assessments

of the Four Options (4 Pages) 32316.4.1.11 Attachment D: Cost Summary (1 Page) 32316.4.1.12 Attachment E: Cost-Benefit Analysis 32316.5 Discussions 32416.6 Conclusion and Directions for Future Research 326References 327

Christopher T Street and James S Denford

17.1 Introduction 33617.2 Theory Description 33617.2.1 Theory Origins 33617.2.2 Application to Management 33717.2.3 Decomposing Punctuated Equilibrium 33817.2.4 Discussion of Theory 34017.3 Levels of Analysis, Alternative Theories, and Applications 34217.3.1 Persistent Gradualism 34317.3.2 Tectonic Shift 34317.3.3 Turbulent Adaptation 34417.4 Four Applications of Punctuated Equilibrium in IS Research 34417.4.1 Virtual Teams 34617.4.2 IS Implementation 34617.4.3 Organizational Change 34717.4.4 Strategic Alignment 34717.5 Operationalization of Punctuated Equilibrium 34817.5.1 Triggering Event: Was the Change

Event-Driven? 348

xxiv Contents

17.5.2 Pervasive Change: Was There a Transformation? 34917.5.3 Entire Organization: Was There an Entity-Wide

Systemic Change? 34917.5.4 Short Period of Time: Was the Occurrence Rapid? 34917.6 Conclusion 350References 351

18 Discrepancy Theory Models of Satisfaction in IS Research 355

James J Jiang, Gary Klein, and Carol Saunders

18.1 Introduction 35618.2 Origins of Discrepancy-Based Satisfaction 35718.2.1 Discrepancy Theory Overview 35818.2.2 Management Studies of Job Satisfaction 35918.2.3 Marketing Studies of Consumer Satisfaction 36018.3 Satisfaction in IS Research 36218.3.1 User Satisfaction with Information Systems 36318.3.2 Job Satisfaction in the Information Systems

Literature 36718.3.3 Discrepancy Theory Formation of Satisfaction 36718.4 Methodological Issues in Applying Discrepancy Theories 37118.4.1 Choosing the Components 37118.4.2 Measuring Discrepancy 37218.4.3 Choosing the Shape 37418.4.4 Analyzing the Relationship 37418.5 Conclusions 375References 376

19 Institutional Change and Green IS: Towards Problem-Driven,

Mechanism-Based Explanations 383

Tom Butler

19.1 Introduction 38419.1.1 Green IT and Green IS Defined 38519.2 Institutional Theory 38619.2.1 Mechanisms-Based Explanations from Institutional

and Social Movement Theory 38719.2.2 Institutional and Social Movement Theory

in IS Research 38819.2.3 Evidence of Institutional and Social Mechanisms

in IS Research 38919.3 Towards a Problem-Driven Explanatory Theory of Green IS 39019.3.1 Social Mechanisms Operating from the

Regulative Pillar 39119.3.2 The Role of Social Mechanisms in Shaping

Influences from the Normative Pillar 39319.3.3 Social Mechanisms and the

Cultural-Cognitive Pillar 397

xxv Contents

19.4 Conclusions 40019.4.1 Theoretical Development and Implications 401References 403

20 A Multilevel Social Network Perspective on IT Adoption 409

Heidi Tscherning

20.1 Introduction 41020.2 Multilevel Research on IT Adoption 41220.2.1 Levels of Analysis:

Society – Industries – Organizations 41420.2.2 Levels of Analysis: Industries – Organizations 41420.2.3 Levels of Analysis: Organizations – Groups/Teams 41520.2.4 Levels of Analysis: Groups – Individuals 41520.2.5 Levels of Analysis: Organizations – Individuals 41520.3 Multilevel Framework for Technology Adoption 41620.3.1 Individual Level 418

20.3.1.1 Attributes and Beliefs 41920.3.1.2 Intentions 41920.3.1.3 Adoption Behavior 42020.3.2 Network Level 420

20.3.2.1 Discourse 42020.3.2.2 Diffusion 42120.3.3 Individual Level and Network Level Interaction 42220.4 Social Network Theories 42320.4.1 Social Network Analysis 42320.4.2 Homophily 42520.4.3 Self-Interest and Collective Action 42620.4.4 Contagion 42820.5 Discussion 42920.5.1 Homophily 43020.5.2 Self-Interest and Collective Action 43120.5.3 Contagion 43220.5.4 Social Network Analysis 43220.6 Limitations and Future Research 43220.7 Conclusion 433References 434

21 Expectation–Confirmation Theory in Information

System Research: A Review and Analysis 441

Mohammad Alamgir Hossain and Mohammed Quaddus

21.1 Introduction 44221.2 A Review of ECT and ECM 44321.2.1 The Expectation–Confirmation Theory (ECT) 44321.2.2 The Evolution of Expectation–Confirmation

Model (ECM) 445

xxvi Contents

21.2.3 The Anomalies of ECT and ECM 446

21.2.3.1 Definition Anomaly 44721.2.3.2 Relationship Anomaly 44921.2.3.3 Measurement Anomaly 44921.2.3.4 Additional Variables 45021.2.3.5 Other Limitations 45121.3 Literature Analyses 45121.3.1 Research Methodology 45121.3.2 Results and Findings 452

21.3.2.1 Research Type Used 45221.3.2.2 Research Concentration 45221.3.2.3 Relevant Theories Used 45221.3.2.4 Dependent Variables 46221.3.2.5 Independent Variables 46221.3.2.6 Other Findings 46221.4 Promising Inquiry for the Future 46321.5 Conclusions 464References 465

22 Stakeholder Theory and Applications in Information Systems 471

Alok Mishra and Yogesh K Dwivedi

22.1 Introduction 47222.2 Stakeholder Theories of Management 47322.2.1 Origin of Stakeholder Theory 47322.2.2 Descriptive, Instrumental and Normative Views

of Stakeholder Theory 47422.3 Stakeholder Theories in Information Systems 47522.4 Applications of Stakeholder Theory in Information Systems 47822.5 Discussion 47822.6 Conclusions 485References 485

About the Author 489

Index 499

Y.K Dwivedi et al (eds.), Information Systems Theory: Explaining and Predicting

Our Digital Society, Vol 1, Integrated Series in Information Systems 28,

DOI 10.1007/978-1-4419-6108-2_1, © Springer Science+Business Media, LLC 2012

Abstract In order to provide a general and comprehensive definition of information

systems (IS) success that covers different evaluation perspectives, DeLone and McLean reviewed the existing definitions of IS success and their corresponding measures, and classified them into six major categories Thus, they created a multidi-mensional measuring model with interdependencies between the different success categories (DeLone and McLean 1992) Motivated by DeLone and McLean’s call for further development and validation of their model, many researchers have attempted

to extend or respecify the original model Ten years after the publication of their first model and based on the evaluation of the many contributions to it, DeLone and McLean proposed an updated IS Success Model (DeLone and McLean 2003) This chapter gives an overview of the current state of research on the IS Success Model Thereby, it offers a concise entry point to the theory’s background and its application, which might be specifically beneficial for novice readers

Keywords DeLone & McLean Model ‡ Information Systems Success ‡ IS Success

Model

Abbreviations

D&M DeLone & McLean

ICIS International Conference on Information Systems

Ind Individual

IS Information systems

N Urbach ( * )

Institute of Research on Information Systems (IRIS), EBS Business School,

EBS Universität für Wirtschaft und Recht, Wiesbaden 65201, Germany

e-mail: nils.urbach@ebs.edu`

Chapter 1

The Updated DeLone and McLean Model

of Information Systems Success

Nils Urbach and Benjamin Müller

2 N Urbach and B Müller

Org Organizational

ROI Return on investment

TAM Technology acceptance model

During the first International Conference on Information Systems (ICIS), Keen (1980) introduced his perspective on the key challenges of the information systems (IS) dis-cipline While today, 3 decades later, these questions remain core issues for many IS scholars, the last years have brought about tremendous progress in methodologies and theories Especially with respect to Keen’s second question, the search for the depen-dent variable in IS research, a lot of progress has been made Since Keen’s paper, work

on technology acceptance (e.g Davis 1989; Davis et al 1989), IS benefit frameworks (e.g Kohli and Grover 2008; Müller et al 2010; Peppard et al 2007; Shang and Seddon 2002), and the business value of IT (e.g Sambamurthy and Zmud 1994; Soh and Markus 1995) has been published One of the most prominent streams of research

on the dependent variable of IS research, however, is work connected to the DeLone and McLean IS Success Model (D&M IS Success Model) (1992, 2003)

Since its introduction in 1992, the D&M IS Success Model has created a broad response in the literature In fact, the 1992 article of DeLone and McLean (1992) was found to be the single-most heavily cited article in the IS literature (Lowry et al

2007) Through all this work, the model’s principal constituents and their relations have been investigated in a broad spectrum of settings (Petter et al 2008; Urbach

et al 2009b) While the original version of the model, presented in an earlier chapter

in this book, was a logical aggregation of research published on IS success, the model has been updated by its original authors to reflect and integrate some of the empirical work investigating the model’s propositions as well as to consider the measurement challenges of the growing e-commerce world (DeLone and McLean 2003) A recent meta-study has shown that this updated version of the model has not only received great appreciation in the IS community, too, but that most of its propositions explain-ing the success of an IS are actually supported (Petter et al 2008)

Through its popularity, DeLone and McLean’s work also managed to address another of Keen’s key challenges to the IS discipline: the lack of a cumulative tradi-tion in IS research Given its high citation counts and the intense investigation of the model’s propositions in a broad spectrum of contexts, we believe that the D&M IS Success Model should be part of a comprehensive compendium of IS theories

To present the updated D&M IS Success Model, we structure this chapter as follows The next section briefly introduces the updated model (DeLone and McLean

2003), especially highlighting its development after its first introduction (DeLone and McLean 1992) We then present the model’s different constructs in more detail and provide an exemplary selection of validated measures that can be reused in future applications Afterward, we present an analysis of the construct interrelations Furthermore, we give an overview on existing research that uses the D&M IS Success Model as theoretical basis and/or adapts the model to a specific domain To conclude,

1 The Updated DeLone and McLean Model of Information Systems Success

we discuss the significance of the D&M IS Success Model for the IS discipline and link the model to related theories Finally, we discuss future research opportunities in the field of IS success

1.2 Development of the D&M IS Success Model

In 1980, Peter Keen referred to the lack of a scientific basis in IS research and raised the question of what the dependent variable in IS research should be Keen argued that surrogate variables like user satisfaction or hours of usage would continue to mislead researchers and evade the information theory issue (Keen 1980) Motivated

by his request for clarification of the dependent variable, many researchers have tried to identify the factors contributing to IS success Largely, however, different researchers addressed different aspects of IS success, making comparisons difficult

In order to organize the large body of existing literature as well as to integrate the different concepts and findings and to present a comprehensive taxonomy, DeLone and McLean (1992) introduced their (first) IS Success Model.1

Building on the three levels of information by Shannon and Weaver (1949), together with Mason’s expansion of the effectiveness or influence level (Mason

1978), DeLone and McLean defined six distinct dimensions of IS success: system

quality, information quality, use, user satisfaction, individual impact, and tional impact Based on this framework, they classified the empirical studies pub-

organiza-lished in seven highly ranked IS journals between January 1981 and January 1988 Their examination supports the presumption that the many success measures fall into the six major interrelated and interdependent categories they present These authors’ IS Success Model was their attempt to integrate these dimensions into a comprehensive framework Judged by its frequent citations in articles published in leading journals, the D&M IS Success Model has, despite some revealed weak-nesses (Hu 2003), quickly become one of the dominant evaluation frameworks in IS research, in part due to its understandability and simplicity (Urbach et al 2009b).Motivated by DeLone and McLean’s call for further development and valida-tion of their model, many researchers have attempted to extend or re-specify the original model A number of researchers claim that the D&M IS Success Model

is incomplete; they suggest that more dimensions should be included in the model,

or present alternative success models (e.g Ballantine et al 1996; Seddon 1997; Seddon and Kiew 1994) Other researchers focus on the application and validation

of the model (e.g Rai et al 2002)

Ten years after the publication of their first model, and based on the evaluation of the many contributions to it, DeLone and McLean (2002, 2003) proposed an updated

IS success model.2

The primary differences between the original and the updated model are: (1) the

addition of service quality to reflect the importance of service and support in successful

1 A graphical representation of this model can be found in DeLone and McLean (1992, p 87).

2 A graphical representation of this model can be found in DeLone and McLean (2003, p 24).

4 N Urbach and B Müller

e-commerce systems; (2) the addition of intention to use to measure user attitude as

an alternative measure of use; and (3) the collapsing of individual impact and

orga-nizational impact into a more parsimonious net benefits construct The updated

model consists of six interrelated dimensions of IS success: information, system, and

service quality; (intention to) use; user satisfaction; and net benefits The arrows

demonstrate proposed associations between the success dimensions

Looking at its constructs and their interrelations, the model can be interpreted as follows: a system can be evaluated in terms of information, system, and service quality; these characteristics affect subsequent use or intention to use and user satis-faction Certain benefits will be achieved by using the system The net benefits will (positively or negatively) influence user satisfaction and the further use of the IS.Although DeLone and McLean have refined their first model and presented an updated version, they encourage other researchers to develop the model further and help

to continue its evolution In order to provide a basis for IS scholars to answer this call for future research, the following sections of this chapter will briefly introduce the con-structs, measures, and propositions used in research on the IS success model so far

1.3 Constructs and Measures

Although the D&M IS Success Model is a result of the attempt to provide an grated view on IS success that enables comparisons between different studies, the operationalization of the model’s different success dimensions varies greatly between the several studies which have been published in the past Especially, the diversity of different types of information systems the model has been adapted to leads to several construct operationalizations However, with a large amount of pub-lications using the D&M IS Success Model as theoretical basis (Lowry et al 2007; Urbach et al 2009b), typical item sets for each of the constructs have emerged which have often been used in several IS success studies

inte-In the following paragraphs we present the different success dimensions of the D&M IS Success Model in more detail and provide an exemplary selection of validated measures that can be reused for future application of the model While such a list can certainly not be a comprehensive account of measures, the studies cited should provide a first overview and a good starting point for a more (context-)specific search of the literature

1.3.1 System Quality

The success dimension system quality constitutes the desirable characteristics of an

IS and, thus, subsumes measures of the IS itself These measures typically focus on usability aspects and performance characteristics of the system under examination

A very common measure is perceived ease of use caused by the large amount of

research related to the Technology Acceptance Model (TAM) (Davis 1989)

1 The Updated DeLone and McLean Model of Information Systems Success

However, many additional measures have been proposed and used to capture the system quality construct as a whole Table 1.1 shows a sample of typical items for measuring system quality

1.3.2 Information Quality

The success dimension information quality constitutes the desirable characteristics of

an IS’s output An example would be the information an employee can generate using

a company’s IS, such as up-to-date sales statistics or current prices for quotes Thus,

it subsumes measures focusing on the quality of the information that the system

pro-duces and its usefulness for the user Information quality is often seen as a key cedent of user satisfaction Typical measurement items are presented in Table 1.2

ante-1.3.3 Service Quality

The success dimension service quality represents the quality of the support that the

users receive from the IS department and IT support personnel, such as, for example, training, hotline, or helpdesk This construct is an enhancement of the updated

Table 1.1 Exemplary measures of system quality

Items References

Access Gable et al (2008), McKinney et al (2002)

Convenience Bailey and Pearson (1983), Iivari (2005)

Customization Gable et al (2008), Sedera and Gable (2004b)

Data accuracy Gable et al (2008)

Data currency Hamilton and Chervany (1981), Gable et al (2008)

Ease of learning Gable et al (2008), Sedera and Gable (2004b)

Ease of use Doll and Torkzadeh (1988), Gable et al (2008), Hamilton and Chervany

(1981), McKinney et al (2002), Sedera and Gable (2004b) Efficiency Gable et al (2008)

Flexibility Bailey and Pearson (1983), Gable et al (2008), Hamilton and Chervany

(1981), Iivari (2005), Sedera and Gable (2004b) Integration Bailey and Pearson (1983), Gable et al (2008), Iivari (2005), Sedera and

Gable (2004b) Interactivity McKinney et al (2002)

Navigation McKinney et al (2002)

Reliability Gable et al (2008), Hamilton and Chervany (1981)

Response time Hamilton and Chervany (1981), Iivari (2005)

Sophistication Gable et al (2008), Sedera and Gable (2004b)

System accuracy Doll and Torkzadeh (1988), Hamilton and Chervany (1981), Gable et al

(2008), Sedera and Gable (2004b) System features Gable et al (2008), Sedera and Gable (2004b)

Turnaround time Hamilton and Chervany (1981)

6 N Urbach and B Müller

D&M IS Success Model that was not part of the original model The inclusion of

this success dimension is not indisputable, since system quality is not seen as an

important quality measure of a single system by some authors (e.g Seddon 1997)

A very popular measure for service quality in IS is SERVQUAL (Pitt et al 1995) However, several other measurement items have been proposed Table 1.3 presents

a sample of those

1.3.4 Intention to Use/Use

The success dimension (intention to) use represents the degree and manner in which

an IS is utilized by its users Measuring the usage of an IS is a broad concept that can

Table 1.2 Exemplary measures of information quality

Items References

Accuracy Bailey and Pearson (1983), Gable et al (2008), Iivari (2005), Rainer and

Watson (1995) Adequacy McKinney et al (2002)

Availability Gable et al (2008), Sedera and Gable (2004b)

Completeness Bailey and Pearson (1983), Iivari (2005)

Conciseness Gable et al (2008), Rainer and Watson (1995), Sedera and Gable (2004b) Consistency Iivari (2005)

Format Gable et al (2008), Iivari (2005), Sedera and Gable (2004b)

Precision Bailey and Pearson (1983), Iivari (2005)

Relevance Gable et al (2008), McKinney et al (2002), Rainer and Watson (1995),

Sedera and Gable (2004b) Reliability Bailey and Pearson (1983), McKinney et al (2002)

Scope McKinney et al (2002)

Timeliness Bailey and Pearson (1983), Gable et al (2008), Iivari (2005),

Doll and Torkzadeh (1988), McKinney et al (2002), Rainer and Watson (1995)

Understandability Gable et al (2008), McKinney et al (2002), Sedera and Gable (2004b) Uniqueness Gable et al (2008)

Usability Gable et al (2008), Sedera and Gable (2004b)

Usefulness McKinney et al (2002)

Table 1.3 Exemplary measures of service quality

Assurance Pitt et al (1995)

Flexibility Chang and King (2005)

Interpersonal quality Chang and King (2005)

Intrinsic quality Chang and King (2005)

IS training Chang and King (2005)

Reliability Pitt et al (1995)

Responsiveness Chang and King (2005), Pitt et al (1995) Tangibles Pitt et al (1995)

1 The Updated DeLone and McLean Model of Information Systems Success

be considered from several perspectives In case of voluntary use, the actual use of an

IS may be an appropriate success measure Previous studies measured use objectively

by capturing the connect time, the functions utilized, or the frequency of use As the amount of time a system is used is apparently not a sufficient success measure, other studies applied subjective measures by questioning users about their perceived use of

a system (e.g DeLone 1988) A more comprehensive approach for explaining the usage of an IS is TAM (Davis 1989) TAM uses the independent variables perceived

ease of use and perceived usefulness contributing to attitude toward use, intention to use, and actual use Due to difficulties in interpreting the dimension use, DeLone and

McLean suggest intention to use as an alternative measure to use for some contexts

Table 1.4 presents some typical measurement items for this success dimension

1.3.5 User Satisfaction

The success dimension user satisfaction constitutes the user’s level of satisfaction

when utilizing an IS It is considered as one of the most important measures of IS success Measuring user satisfaction becomes especially useful, when the use of an

IS is mandatory and the amount of use is not an appropriate indicator of systems cess Widely used user satisfaction instruments are the ones by Ives et al (1983) and Doll et al (2004) However, these instruments also contain items of system, informa-tion, and service quality, rather than only measuring user satisfaction Accordingly, other items have been developed to exclusively measure user satisfaction with an IS Table 1.5 presents some examples

suc-1.3.6 Net Benefits

The success dimension net benefits, constitutes the extent to which IS are contributing

to the success of the different stakeholders The construct subsumes the former

sep-arate dimensions individual impact and organizational impact of the original D&M

IS Success Model as well as additional IS impact measures from other researchers like work group impacts and societal impacts into one single success dimension The choice of what impact should be measured depends on the system being

Table 1.4 Exemplary measures of (intention to) use

Actual use Davis (1989)

Daily use Almutairi and Subramanian (2005), Iivari (2005) Frequency of use Almutairi and Subramanian (2005), Iivari (2005) Intention to (re)use Davis (1989), Wang (2008)

Nature of use DeLone and McLean (2003)

Navigation patterns DeLone and McLean (2003)

Number of site visits DeLone and McLean (2003)

Number of transactions DeLone and McLean (2003)

8 N Urbach and B Müller

evaluated, the purpose of the study, and the level of analysis Although use and user satisfaction are correlated with net benefits, there is still the necessity to measure net benefits directly Some studies look at the value of technology investments through quantifiable financial measures such as return on investment (ROI), market share, cost, productivity analysis, and profitability Some researchers argue that benefits in terms of numeric costs are not possible because of intangible system impacts and intervening environmental variables (McGill et al 2003) Most of the studies applying the D&M IS Success Model measure the benefits of utilizing an IS on the individual and organizational levels Accordingly, we present exemplary measurement items

of individual impact in Table 1.6 and organizational impact in Table 1.7

After the introduction of the original D&M IS Success Model (DeLone and McLean

1992), many authors have investigated the model both empirically and theoretically Beyond the constructs discussed above, also the construct interrelations received

Table 1.5 Exemplary measures of user satisfaction

Items References

Adequacy Almutairi and Subramanian (2005), Seddon and Yip (1992),

Seddon and Kiew (1994) Effectiveness Almutairi and Subramanian (2005), Seddon and Yip (1992),

Seddon and Kiew (1994) Efficiency Almutairi and Subramanian (2005), Seddon and Yip (1992),

Seddon and Kiew (1994) Enjoyment Gable et al (2008)

Information satisfaction Gable et al (2008)

Overall satisfaction Almutairi and Subramanian (2005), Gable et al (2008), Rai et al

(2002), Seddon and Yip (1992), Seddon and Kiew (1994) System satisfaction Gable et al (2008)

Table 1.6 Exemplary measures of individual impact

Awareness/Recall Gable et al (2008), Sedera and Gable (2004b)

Decision effectiveness Gable et al (2008), Sedera and Gable (2004b)

Individual productivity Gable et al (2008), Sedera and Gable (2004b)

Job effectiveness Davis (1989), Iivari (2005)

Job performance Davis (1989), Iivari (2005)

Job simplification Davis (1989), Iivari (2005)

Learning Sedera and Gable (2004b), Gable et al (2008)

Productivity Davis (1989), Iivari (2005), Torkzadeh and Doll (1999) Task performance Davis (1989)

Usefulness Davis (1989), Iivari (2005)

Task innovation Torkzadeh and Doll (1999)

1 The Updated DeLone and McLean Model of Information Systems Success

manifold attention In their revised model, DeLone and McLean (2003) have already accounted for and integrated some of these findings Similarly, Petter et al (2008) look at the literature on IS success published between 1992 and 2007 and aggregate their findings into an overall assessment of the theoretical and empirical support of the current model Drawing on their work, we would like to highlight the most impor-tant findings for the 15 pair-wise construct interrelations by looking at the dependent variables respectively Table 1.8 summarizes these relationships at the individual (Ind.) and organizational (Org.) levels Please note that Table 1.8 does not show the strength or direction of the relations, but highlights how strongly any relation is sup-ported by current studies For a detailed review of the directions (i.e., positive or negative relations), please see Tables 3 and 4 in Petter et al (2008)

1.4.1 System Use

At the individual level, the meta-analysis by Petter et al (2008) shows mixed to moderate support for the explanation of system use Of the three quality indicators, system quality has received the broadest attention in the literature However, only mixed support can be found to support the hypothesis that system use can be explained by system quality overall While a total of nine studies reported a positive association with system use, seven studies reported nonsignificant results for this model path The same is true for information quality, especially as only a total of six studies reviewed by Petter et al (2008) did look at this relation to start with Even fewer data is available for the investigation of service quality, which is why no

Table 1.7 Exemplary measures of organizational impact

Business process change Gable et al (2008), Sedera and Gable (2004b)

Competitive advantage Almutairi and Subramanian (2005), Sabherwal (1999) Cost reduction Almutairi and Subramanian (2005), Gable et al

(2008), Sedera and Gable (2004b) Enhancement of communication and

collaboration

Almutairi and Subramanian (2005), Sabherwal (1999)

Enhancement of coordination Almutairi and Subramanian (2005)

Enhancement of internal operations Almutairi and Subramanian (2005), Sabherwal (1999) Enhancement of reputation Almutairi and Subramanian (2005)

Improved outcomes/outputs Gable et al (2008), Sedera and Gable (2004b)

Improved decision making Almutairi and Subramanian (2005)

Increased capacity Gable et al (2008), Sedera and Gable (2004b)

Overall productivity Gable et al (2008), Sedera and Gable (2004b)

Overall success Almutairi and Subramanian (2005), Sabherwal (1999) Quality improvement Sabherwal (1999)

Customer satisfaction Torkzadeh and Doll (1999)

Management control Torkzadeh and Doll (1999)

10 N Urbach and B Müller

conclusive argument can be drawn for this relation to date User satisfaction, on the other hand, has been investigated by a high number of studies and was found to be positively linked in most of them The same is true for the feedback link from net benefits to system use The literature has shown that both links receive moderate support overall

The effects on system use at the organizational level are, as of yet, largely uninvestigated The impact of user satisfaction on system use in an organizational context, for example, has not been covered by a single study Only the impact of system quality has been covered in a sufficiently high number of studies The results, however, are somewhat inconclusive as positive, negative, mixed, and nonsignificant relations were found Especially at the organizational level, a lot of work remains to

be done to investigate the IS success model’s propositions

1.4.2 User Satisfaction

In comparison to actual system use, propositions related to user satisfaction received broad and often strong support for positive associations in the literature on the indi-vidual level of the D&M IS Success Model Both system and information quality were found to have strong positive relations with user satisfaction in most studies

Table 1.8 Construct interrelations (as discussed by Petter et al (2008))

Antecedents m Explained constructs Ind Org System use

Information quality m System use ~ o

Service quality m System use o o

User satisfaction m System use + o

User satisfaction

System quality m User satisfaction ++ o

Information quality m User satisfaction ++ o

Service quality m User satisfaction + o

System use m User satisfaction + o

Net benefits m User satisfaction + o

Net benefits

Information quality m Net benefits + o

Service quality m Net benefits + o

User satisfaction m Net benefits ++ o

++, strong support

+, moderate support

~, mixed support

o, insufficient data

1 The Updated DeLone and McLean Model of Information Systems Success

conducted to date The results on service quality, on the other hand, only provide mixed support for its ability to explain user satisfaction While investigated less often, the interrelation between use and user satisfaction shows only moderate support in the literature However, studies available to date mainly show positive associations (e.g., Chiu et al 2007; Halawi et al 2007) Additionally, the feedback effect from net benefits to user satisfaction has shown to be very strong (e.g., Hsieh and Wang 2007; Kulkarni et al 2007; Rai et al 2002)

At the organizational level, Petter et al (2008) highlight the lack of conclusive data

on the antecedents of user satisfaction None of the five constructs interrelations ing to user satisfaction were investigated more than four times Looking at the quality constructs, the studies conducted so far do, however, indicate a positive relationship The effects of system use and net benefits, on the other hand, show mixed results Similarly to the research on system use, the investigation of user satisfaction in an organizational context remains an interesting area for future research into IS success

lead-1.4.3 Net Benefits

As the D&M IS Success Model’s overall dependent variable, net benefits play a significant role in IS success research Looking at the individual level, current stud-ies have found at least moderate support for all interrelations System quality has mostly been found to have a positive association with net benefits, even though most

of the effect is moderated through system use and user satisfaction While gated less often, the same is also true for information and service quality System use, in turn, also has a moderate positive association with net benefits, even though six studies reviewed by Petter et al (2008) reported nonsignificant findings The construct covering user satisfaction was unanimously reported to be positively asso-ciated with a system’s net benefits by all the studies reviewed Accordingly, this interrelation was found to be supported strongly by current studies

investi-On the organizational level, insufficient overall data is a major hurdle for the assessment of the D&M IS Success Model Three of the five possible antecedents are not covered sufficiently to determine their associations with net benefits in a reliable way Only the constructs system quality and system use are covered in a sufficient manner to determine a moderate support for their positive association with net benefits Despite the lack of widespread investigation of net benefits at an organizational level, most of the studies conducted on the other constructs so far do indicate a positive association with net benefits

1.5 Existing Research on IS Success

During the last years, the D&M IS Success Model in its original and updated sion has become a widely used evaluation framework in IS research Several articles have been published that use the model as the theoretical basis In a recent literature

ver-12 N Urbach and B Müller

review, Urbach et al (2009b) explore the current state of IS success research by analyzing and classifying recent empirical articles with regard to their theoretical foundation, research approach, and research design The results show that the domi-nant research analyzes the impact that a specific type of IS has by means of users’ evaluations obtained from surveys and structural equation modeling The D&M IS Success Model is the main theoretical basis of the reviewed studies Several success models for evaluating specific types of IS – like knowledge management systems (Kulkarni et al 2007) or enterprise systems (Gable et al 2003) – have been developed from this theory

In order to give an overview on existing literature on IS success, we present an exemplary collection of research articles in Table 1.9 These are classified in terms

of the type of IS being evaluated and should provide a point of departure for context-specific research in these or additional areas

Taking a closer look at these publications, we see a broad variety of IS types that have been analyzed using the D&M IS Success Model Thereby, the D&M IS Success Model is used in different ways

Several authors use the model in its predefined form as a theoretical basis In these publications, only the operationalizations of the model’s success dimensions were adapted to the specific research context Iivari (2005), for example, evaluates

Table 1.9 Exemplary collection of IS success studies

Type of information system Publications

Data warehouse Nelson et al (2005), Shin (2003), Wixom and Watson

(2001), Wixom and Todd (2005) Decision support system Bharati and Chaudhury (2004)

e-Commerce system DeLone and McLean (2004), Molla and Licker (2001),

Wang (2008) e-Mail system Mao and Ambroso (2004)

Enterprise system Gable et al (2003), Lin et al (2006), Qian and Bock

(2005), Sedera (2006), Sedera and Gable (2004b), Sedera and Gable (2004a), Sedera et al (2004a, b) Finance and accounting system Iivari (2005)

Health information system Yusof et al (2006)

Intranet Hussein et al (2008), Masrek et al (2007), Trkman

and Trkman (2009) Knowledge management system Clay et al (2005), Halawi et al (2007), Jennex and

Olfman (2003), Kulkarni et al (2007), Velasquez

et al (2009), Wu and Wang (2006) Learning system Lin (2007)

Online communities Lin and Lee (2006)

Web-based system Garrity et al (2005)

Web sites Schaupp et al (2006)

1 The Updated DeLone and McLean Model of Information Systems Success

the finance and accounting system of a municipal organization The empirical data collected is used to validate the D&M IS Success Model in its original form However, the success dimensions are operationalized with regard to the specific research problem

Other authors use the model in its predefined form for constructing their research model, but add additional success dimensions that are necessary to fully capture the specifics of the type of IS under investigation As an example, Urbach et al (2010) use the D&M IS Success Model as the theoretical basis for investigating the success

of employee portals However, in contrast to other types of IS, employee portals are not only utilized to exchange information, but also to electronically support work processes as well as collaboration between users Accordingly, the two additional success dimensions, process quality and collaboration quality, were added to the research model

Finally, in some of the presented publications, the D&M IS Success Model is fully adapted to a specific research problem using newly developed constructs that are similar to those of the original model Wixom and Watson (2001), for example, develop and validate a model for empirically investigating data warehousing suc-cess on the basis of the D&M IS Success Model Instead of referring to the proposed success dimensions, however, context-specific constructs such as organizational, project, and technical implementation success are utilized

An additional observation is that many of the published studies only partially analyze the D&M IS Success Model (e.g., Garrity et al 2005; Kulkarni et al 2007; Velasquez et al 2009) Only few studies validate the model in its complete form (e.g., Iivari 2005; Urbach et al 2010; Wang 2008)

Despite the high number of studies already conducted in the context of the D&M IS Success Model, there are quite a few further research opportunities For example, DeLone and McLean (2003) themselves make recommendations for future research They highlight that the model, especially the interdependent relationships between its constructs, should be continuously tested and challenged In order to provide a basis for the much needed cumulative tradition of IS research, the authors urge future users of their model to consider using proven measures where possible Only

a significant reduction in the number of measures used can make results comparable beyond the various contexts of IS success studies Moreover, they emphasize that more field-study research is needed in order to investigate and incorporate net ben-efit measures into the model

As especially the summary of the meta-review of Petter et al (2008) has shown, additional research covering the IS success model from an organizational perspective

is required to be able to determine the degree of associations between the constructs.Looking at current work on the D&M IS Success Model, many studies con-ducted to date have focused on the measurement and assessment of selected parts of