Information Systems Research: Foundations, Design and Theory 3031254694, 9783031254697

Citation preview

INFORMATION SYSTEMS RESEARCH FOUNDATIONS, DESIGN AND THEORY

MOHAMMED ALI

Information Systems Research

Mohammed Ali

Information Systems Research Foundations, Design and Theory

Mohammed Ali Salford Business School University of Salford Manchester, UK

ISBN 978-3-031-25469-7 ISBN 978-3-031-25470-3 (eBook) https://doi.org/10.1007/978-3-031-25470-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: Digital_Art This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my colleague Professor Trevor Wood-Harper, and my beloved mother, father, wife, and children without whom this book would have not been completed, and I dearly thank them all from the bottom of my heart.

Foreword

Information systems (IS) have emerged as the cornerstone of global industry development by leveraging modern technologies that have improved business processes, such as creating efficient supply chains, empowering people to use technology to support their roles, and integrating a modern infrastructure that sets them apart from other traditional organisations. This book defines contemporary IS as a collection of elements—people, processes, and technologies—that work in concert to gather, retrieve, process, store, and share information to assist contemporary organisations in making decisions, monitoring their day-to-day activities, and ultimately keeping up with current business trends. Despite the abundance of literature and theory on IS in business settings, there is little discourse about how IS can be used in other fields, like research development, especially among modern IS researchers. This demonstrates a clear need for knowledge in the real-world applications of current IS research initiatives. For the aforementioned reasons, this book examines the useful uses of research design in contemporary IS research. It concentrates on IS research concepts, analysis processes, and modern methods and tools for analysing IS research. In addition to discussing the current social paradigm shift, global changes, and the need for new methodological tools, all of which have revolutionised the use of contemporary IS, the book also focuses on methodological elements such as the approach, research strategy, and data collection and analysis methods required to effectively conduct research on contemporary IS issues. Dr. Mohammed Ali, a committed and knowledgeable author in the IS field, introduces a series of chapters dedicated to research design in contemporary IS research. Although the book may present theory and information that are widely available in other texts, what sets it apart from other texts is the way in which Dr. Mohammed Ali has applied novel ideas on the applications of contemporary IS in research designs through case studies and examples of practical applications of contemporary IS issues, including the use of innovations powered by cloud computing, the internet of things (IoT), artificial intelligence, and big data, among others, to provide in-depth analysis of contemporary IS issues. In light of this, “Information Systems Research Foundations, Design, and Theory” offers a distinctive viewpoint on methodology and useful considerations in research projects. With the passion of an artist, Dr. Mohammed Ali weaves the phenomena in this book into a stunning and intricate work of art that can be appreciated by students, vii

viii

Foreword

research practitioners, and IS researchers and experts. The editor’s dedication to and goal of knowledge sharing in their field, as well as their foresight into potential problems, can be represented by this, much like a knowledgeable researcher who needs to more effectively allocate knowledge as a public good. The book combines a practical viewpoint on how to link this to research design with the conceptual depth of a novel method for identifying knowledge gaps that currently exist in the contemporary IS domain. In addition to establishing a clear connection between IS researchers’ research and current IS interventions, the book identifies contemporary IS trends that can help IS researchers innovate, revolutionise, and modernise their research projects in ways that set them apart from the rest. The book also lays the groundwork for future contemporary IS research designs, which is something I think will be increasingly important in the coming years, and thus Dr. Mohammed’s book illustrates the route to future contemporary IS research designs and theory. As the technological landscape continues to evolve at a breakneck pace, IS researchers will need to keep up with contemporary technological trends to ensure their research remains current and viable in the future. It is an honour and a source of great humility for me to have been chosen to write the foreword to this ground-breaking book. Emeritus Chair of Information Systems (IS) and Systemic Change Trevor Wood-Harper University of Manchester Manchester, UK

Preface

I am very pleased to present the very first edition of “Information Systems Research: Foundations, Design, and Theory.” The aim of the book is to explore the phenomenon of research design in contemporary IS research by concentrating on the methodological elements such as the approach, research strategy, and data collection and analysis methods required to effectively conduct research on contemporary IS issues, supported by practical case studies and real-world scenarios. The primary market for this book is a combination of academics, students, and practitioners who are interested in, are studying, or are experts in the multidisciplinary areas of contemporary IS, such as information management, digital business, ICT, and information science. Additionally, the book may be of interest to a variety of professions involved in IS. The book appeals to both researchers and practitioners and will represent a truly multidisciplinary approach to the field of contemporary IS and their respected research methodologies. The book may also be of some interest to students of other subjects to support their research methods courses. Since this book caters to students, it can be used as a core or supplementary text in various courses and modules related to contemporary IS, such as cloud computing, big data, artificial intelligence, the internet of things, management information technology and systems, business IS, information security, and sociotechnical systems, among others. The chapters covered in this book are distributed across five parts and include: . . . . . . . . .

Part 1: Introduction to Information Systems Research Concepts Chapter 1: Historical Background of IS Research Chapter 2: Research Philosophies in Social Science Research Chapter 3: Applications of Research Designs in IS Research Chapter 4: Applying Contemporary IS Theory to Study Social Paradigm Shifts and Global Changes Part 2: Practical Applications of Information Systems Research Chapter 5: Methodological Approaches to Studying the Multidisciplinary Areas of IS Chapter 6: Application of Research Methodologies in Contemporary IS Research Chapter 7: Data Collection Procedures for Contemporary IS Research ix

x

Preface

. Chapter 8: Piloting and Feasibility Studies in IS Research . Part 3: Analysis Procedures and Tools for Analysing Contemporary Information Systems Research . Chapter 9: Planning for the Analysis Phase: A Framework of Data Analysis Procedures . Chapter 10: Analytical Methods Used in Contemporary IS Research . Chapter 11: Analytical Tools Used in Information Management, Digital Business, ICT, and Information Science . Part 4: Ethics and Ethical Procedures in Information Systems Research . Chapter 12: Data Protection, Confidentiality and Anonymity . Chapter 13: Ethical Procedures and Processes in IS Research . Part 5: Development of Findings and Concluding the Information Systems Research Project . Chapter 14: Writing and Transferring the Findings in IS Research Projects . Chapter 15: Concluding the IS Research Project and VIVA I would like to thank various reviewers who provided input to the development of this book. Finally, thanks to Alec Selwyn and his colleagues at Palgrave Macmillan for their help and support in the development of this book.

Manchester, UK November 2022

Yours, Dr. Mohammed Ali

Acknowledgements

The author would like to acknowledge the help of many people who gave their input during the development of this book. The author would like to thank all the contributors who provided advice and assistance, more notably Professor Trevor Wood-Harper, a specialist in the field of IS. The author is also appreciative of the editorial team at Palgrave Macmillan, in particular Alec Selwyn, who provided his unlimited support and assistance throughout the development of the book. The author also appreciates the unending and inspiring support of his peers, friends, and family. All the aforementioned people owe the author a debt of gratitude, and without your help, this book would not have been possible. The author is appreciative that you have given him the chance to expand his research portfolio and share his insights, ideas, and knowledge with the IS research community.

xi

Introduction

Even though many texts discuss Information Systems (IS), few, if any, specifically address the research design in IS. Even though IS has been around for several decades, few texts acknowledge the subtle role of contemporary IS in research design. This book attempts to strike a more equitable balance by focusing on not only IS research designs of the past, but also contemporary IS research designs. As experts in the field will attest, that research designs of an IS nature are fraught with issues and difficulties. In other fields, a solid understanding of research designs and the ability to apply it to their chosen design to this field can be a challenging endeavour and IS is no exception. However, researchers and practitioners in the field of IS require a much broader set of skills. Researchers must have not only a high level of technical competence, but also competence in the social and organisational process side aspect of IS as many researchers can fall into the trap of merely focusing on the technological aspect. The book’s overarching theme is the question of what makes a “good” contemporary IS researcher. The book examines this issue from a variety of perspectives. Early chapters, for example, emphasise important IS research concepts in IS research. Later chapters cover the practical applications of IS research, as well as analysis procedures and tools for analysing contemporary IS research and ethical considerations in IS research. The text’s emphasis on numerous practical and contemporary IS issues is a critical component. In addition to the theoretical knowledge that students and practitioners require, the text provides a wealth of useful advice and direction. Facts, definitions, and exercises are included in this advice and can be used to track learning progress. The authors’ and other practitioners’ experiences provide additional guidance and advice. Much of the research in contemporary IS research design has been scarce with very few textbooks covering the issue. Whilst the text acknowledges the contribution of academics and practitioners in this field, the content of the book is presented in a form suited to international researchers and not just limited to a single country audience. This means that traditional approaches and methods are presented in parallel with those techniques used in contemporary IS research. As an example, the text includes case studies and scenarios of contemporary IS issues, in addition to considering contemporary technologies used in IS research projects such as cloud computing, big data, artificial intelligence, and blockchain. Students and practitioners should find that the content of the text follows a natural progression xiii

xiv

Introduction

and has a logical structure. The following list provides an overview of the content of the book. . . . . . . . . . . . . . . .

Chapter 1: Historical Background of IS Research Chapter 2: Research Philosophies in Social Science Research Chapter 3: Applications of Research Designs in IS Research Chapter 4: Applying Contemporary IS Theory to Study Social Paradigm Shifts and Global Changes Chapter 5: Methodological Approaches to Studying the Multidisciplinary Areas of IS Chapter 6: Application of Research Methodologies in Contemporary IS Research Chapter 7: Data Collection Procedures for Contemporary IS Research Chapter 8: Piloting and Feasibility Studies in IS Research Chapter 9: Planning for the Analysis Phase: A Framework of Data Analysis Procedures Chapter 10: Analytical Methods Used in Contemporary IS Research Chapter 11: Analytical Tools Used in Information Management, Digital Business, ICT, and Information Science Chapter 12: Data Protection, Confidentiality and Anonymity Chapter 13: Ethical Procedures and Processes in IS Research Chapter 14: Writing and Transferring the Findings in IS Research Projects Chapter 15: Concluding the IS Research Project and VIVA

Several student features have been incorporated into the text to make the subject matter more approachable for readers. Each chapter begins with a brief introduction and a list of specific learning objectives. Similarly, each chapter concludes with a summary of the main points discussed. Each chapter includes a variety of self-evaluation exercises, such as activity questions and self-evaluation questions. There is also a thorough case studies featured throughout each chapter.

Contents

Part I Introduction to Information Systems Research Concepts 1

Historical Background of IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 What Is an Information System? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Historical and Developmental Perspective of IS . . . . . . . . . . . . . . . . 1.4 Types of Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Transaction Processing Systems . . . . . . . . . . . . . . . . . . . . . 1.4.2 Office Automation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Knowledge Work Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.4 Virtual Reality Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.5 Management Information Systems (MIS) . . . . . . . . . . . . 1.4.6 Decision Support Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.7 Executive Support Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 IS Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Overview of IS Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.1 Action Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.2 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.3 Ethnography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.4 Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.5 Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 3 3 6 10 10 12 12 13 13 14 14 14 15 16 16 17 18 18 18 18 19 20 20

2

Research Philosophies in Social Science and Information Systems Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Philosophical Underpinnings of Social Science . . . . . . . . . . . . . . . . 2.2.1 What Is a Research Philosophy? . . . . . . . . . . . . . . . . . . . . . 2.2.2 What Is a Research Paradigm? . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Social Science Research in the IS Domain . . . . . . . . . . .

23 23 24 24 25 26 xv

xvi

Contents

2.3

3

4

Key Research Paradigms and Philosophies . . . . . . . . . . . . . . . . . . . . . 2.3.1 Research Philosophies in IS Research . . . . . . . . . . . . . . . . 2.3.2 Research Paradigms in IS Research . . . . . . . . . . . . . . . . . . 2.3.3 Justifying Chosen Research Philosophy in IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

26 26 28

Applications of Research Designs in IS Research . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Introducing the Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 What Is a Research Design? . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Qualitative and Quantitative Research Designs . . . . . . . 3.2.3 Fixed vs. Flexible Research Design . . . . . . . . . . . . . . . . . . 3.3 Research Questions and Hypotheses Development . . . . . . . . . . . . . 3.3.1 What Are Research Questions and Hypotheses? . . . . . . 3.3.2 Differences Between Research Questions and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Research Design Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 What Is a Research Methodology? . . . . . . . . . . . . . . . . . . . 3.4.2 Types of Research Methodologies . . . . . . . . . . . . . . . . . . . 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35 35 36 36 36 37 40 40

Contemporary IS Theory and Methodological Applications . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Theoretical Foundations of IS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Overview of IS Theories and Frameworks in IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Contemporary IS Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Contemporary IS Approaches . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Methodological Streams of Research Reporting IS Theory . . . . . 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 53 54

31 33 34

42 43 43 43 50 51

54 57 72 81 82 83

Part II Practical Applications of Information Systems Research 5

Methodological Approaches to Studying the Multidisciplinary Areas of IS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Inductive and Deductive Approaches in Contemporary IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Overview of Methodological Approaches . . . . . . . . . . . . 5.2.2 Inductive and Deductive Reasoning . . . . . . . . . . . . . . . . . . 5.2.3 Quantitative and Qualitative Research Typologies . . . . 5.2.4 Comparison of Research Typologies . . . . . . . . . . . . . . . . .

89 89 90 90 90 93 94

Contents

xvii

5.2.5 General Research Typologies . . . . . . . . . . . . . . . . . . . . . . . . 94 5.2.6 Research Typologies Used in IS Research . . . . . . . . . . . . 97 5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6

7

8

Application of Research Methodologies in Contemporary Information Systems Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Typology of Research Methodologies in Contemporary IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Classifying Contemporary IS Approaches and Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Contemporary IS Research Methodologies . . . . . . . . . . . . . . . . . . . . . 6.4.1 Recap of Research Methodologies . . . . . . . . . . . . . . . . . . . 6.4.2 Contemporary IS Research Typology . . . . . . . . . . . . . . . . 6.5 Comparing Contemporary IS Research Methodologies . . . . . . . . . 6.5.1 Comparative Review of IS Research Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 104 106 108 108 108 115 115 117 118

Data Collection Procedures for Contemporary IS Research . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Starting the IS Fieldwork Journey: Ethical Approval Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Research Ethics Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Ethical Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 IS Data Collection Procedures, Methods and Tools . . . . . . . . . . . . 7.3.1 Quantitative Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Qualitative Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Novel IS Data Collection Methods . . . . . . . . . . . . . . . . . . . 7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

121 121

Piloting and Feasibility Studies in IS Research . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Pilot Study Framework for IS Research Projects: Determining Feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Research Ethics Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Definitions of Feasibility Study and Pilot . . . . . . . . . . . . 8.2.3 Testing the Research Problem in Action . . . . . . . . . . . . . 8.3 Methods and Research Frameworks for Conducting IS Pilot Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Development and Evaluation of Complex Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Data Collection Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 137

122 122 123 125 125 128 130 133 134

138 138 138 140 140 140 141 141

xviii

Contents

8.4

IS Pilot Study Processes and Procedures . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Designing the Pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 Planning and Preparation of Data Collection . . . . . . . . . 8.4.3 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.4 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.5 Data Analysis and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

142 143 143 144 144 145 148 148

Part III Analysis Procedures and Tools for Analysing Contemporary Information Systems Research 9

Planning for the Analysis Phase: A Framework of Data Analysis Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 What Is Data Analysis? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Methods & Research Frameworks for Conducting IS Pilot Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Qualitative Data Analysis Procedures . . . . . . . . . . . . . . . . 9.3.2 Quantitative Data Analysis Procedures . . . . . . . . . . . . . . . 9.4 Interpreting and Writing Up the Data . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Writing Up Qualitative Data . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.2 Writing Up Quantitative Data . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153 153 153 154 154 155 156 156 160 161 162

10 Analytical Methods Used in Contemporary IS Research . . . . . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 What is a Data Analysis Approach? . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Data Analysis Methods in IS Research . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Qualitative Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 Quantitative Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . 10.4 Comparing and Justifying Analytical Methods . . . . . . . . . . . . . . . . . 10.4.1 Qualitative versus Quantitative Procedures . . . . . . . . . . . 10.4.2 Analysis Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

163 163 164 164 164 167 168 168 168 171 172

11 Analytical Tools Used in Information Management, Digital Business, ICT and Information Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Software Packages and Tool for Analysing IS Data . . . . . . . . . . . . 11.2.1 Qualitative Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.2 Quantitative Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . .

173 173 174 174 180

Contents

xix

11.3 Linking Data Analysis Tools to IS Research Design . . . . . . . . . . . 184 11.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Part IV Ethics and Ethical Procedures in Information Systems Research 12 Data Protection, Confidentiality and Anonymity . . . . . . . . . . . . . . . . . . . 12.1 Ethics of Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Laws and Procedures Governing Data Protection in IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 Notions(s) of Research in the GDPR . . . . . . . . . . . . . . . . . 12.2.2 Lawful Basis for Scientific Research Purposes . . . . . . . 12.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

189 189

13 Ethical Procedures and Processes in IS Research . . . . . . . . . . . . . . . . . . . 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Ethical Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Ethical Process of Conducting IS Research . . . . . . . . . . . . . . . . . . . . 13.3.1 Overview of Ethics Process . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.2 Principles of IS Research Ethics . . . . . . . . . . . . . . . . . . . . . 13.4 Application and Justification of Ethics in IS Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.1 IS Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.2 IS Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.3 Data Analysis Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5 Tools of Obtaining Ethical Consent . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

199 199 199 201 201 201

192 193 193 197 197

205 206 207 212 213 215 215

Part V Development of Findings and Concluding the Information Systems Research Project 14 Writing and Transferring the Findings in IS Research Projects . . . . 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Framework of IS Data Write Up and Transference . . . . . . . . . . . . . 14.2.1 Typology of Gerneraisability and Transferability in IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 Comparing Generalisability and Transferability . . . . . . 14.3 Validating IS Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3.1 Methodological Lens of Validating IS Research Findings to Address the Research Questions . . . . . . . . .

219 219 220 221 225 226 227

xx

Contents

14.3.2

Reliability and Validity of the Research Contributions in IS Research: The Qualitative Versus Quantitative Debate . . . . . . . . . . . . . . . . . . . . . . . . . . 230 14.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 15 Concluding the IS Research Project and VIVA . . . . . . . . . . . . . . . . . . . . . 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Post-Research Process in IS Research . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.1 VIVA Arrangements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.2 VIVA Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.3 Venue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.4 Technical Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.5 Attendants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.6 Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.7 Examiner Deliberations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.8 Commencing the VIVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.9 Questioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.10 Reaching to a Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 VIVA Preparation and Post-Research Presentation in IS Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.1 VIVA Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.2 So, What Happens? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.3 Methods of Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.4 What Are Examiners Expecting? . . . . . . . . . . . . . . . . . . . . 15.3.5 Typical VIVA Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.6 Tough Mentality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.7 Post-VIVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.8 Food for Thought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

233 233 234 234 234 234 235 235 235 235 236 236 236 236 237 237 239 239 240 241 241 242 243 243

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255

Abbreviations

AI ANT AR CAD CC CIS CISRT DOI DPS EDI EDP ESP GT I5.0 ICT IoT IS IT KWS MIS OAS OO PS SSADM SSM STT TAM TPS UML VR

Artificial Intelligence Actor Network Theory Action Research Computer Aided Design Cloud Computing Computerised Information System Contemporary IS Research Typology Diffusion of Innovation Decision Support Systems Electronic Data Interchange Electronic Data Processing Executant Support Systems Grounded Theory Industry 5.0 Information Communication Technology Internet of Things Information Systems Information Technology Knowledge Work Systems Management Information Systems Office Automation System Object Oriented Pilot Study Structured Systems Analysis and Design Method Soft Systems Methodology Sociotechnical Theory Technology Acceptance Model Transaction Support Systems Unified Modelling Language Virtual Reality

xxi

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3

Fig. 1.4 Fig. 1.5 Fig. 2.1 Fig. 3.1 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. Fig. Fig. Fig. Fig.

4.5 4.6 4.7 4.8 4.9

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

4.10 4.11 4.12 5.1 5.2 6.1 6.2 6.3 6.4

IS research model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Origins of IS (Maddikunta et al., 2022, p. 2) . . . . . . . . . . . . . . . . Development of IS through the Industrial Lens (inspired by Maddikunta et al., 2022; Xu et al., 2021 and Zhong et al., 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PPT framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IS model research process (Inspired by Oates, 2006) . . . . . . . . . IS model research process (Inspired by Oates, 2006, 2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary research designs in IS research . . . . . . . . . . . . . . . . . . . . Categorised IS theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diffusion of Innovation Model (Adapted from Rogers, 1995) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology Acceptance Model (Adapted from Davis et al., 1989) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IS Success Model (Adapted from DeLone & McLean, 1992) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Activity Model (Adapted from Engeström, 2014) . . . . . . . . . . . . ANT Model (Adapted from Callon, 1996) . . . . . . . . . . . . . . . . . . . Sociotechnical Model (Adapted from Mumford, 2006) . . . . . . . Practice Model (Nash et al., 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . SSM Model (Adapted from Checkland, 1981, 1985, 2000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Categorised IS Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Waterfall Model (Sherman, 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . Spiral model (Conrad et al., 2012) . . . . . . . . . . . . . . . . . . . . . . . . . . Inductive reasoning (Mauldin, 2020) . . . . . . . . . . . . . . . . . . . . . . . . Deductive reasoning (Mauldin, 2020) . . . . . . . . . . . . . . . . . . . . . . . Contemporary IS research approaches . . . . . . . . . . . . . . . . . . . . . . . Contemporary IS research typology . . . . . . . . . . . . . . . . . . . . . . . . . Generic research model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Practical illustration of action research . . . . . . . . . . . . . . . . . . . . . .

4 7

9 15 17 27 38 57 59 60 61 63 64 65 66 69 72 73 74 91 92 105 107 109 112

xxiii

xxiv

Fig. Fig. Fig. Fig. Fig. Fig.

List of Figures

6.5 6.6 7.1 7.2 7.3 8.1

Fig. 8.2 Fig. Fig. Fig. Fig.

9.1 10.1 11.1 11.2

Fig. 11.3 Fig. 11.4 Fig. Fig. Fig. Fig. Fig.

11.5 11.6 11.7 11.8 11.9

Fig. 11.10 Fig. 11.11 Fig. 13.1

Contemporary IS SLR process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aligning contemporary IS research with a typology . . . . . . . . . . Ethical procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data collection methods (Lalehzari, 2021) . . . . . . . . . . . . . . . . . . . Triangulation of netnogaphy tools (Ashmore, 2021) . . . . . . . . . . Example analysis matrix for the pilot interview format and the interviewers’ management of the interviews (Malmqvist et al., 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example theoretical model for a pilot analysis of the interviews (Malmqvist et al., 2019) . . . . . . . . . . . . . . . . . . . Writing up data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Types of data analysis methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MAXQDA (https://www.maxqda.com/products) . . . . . . . . . . . . . NVivo (https://www.qsrinternational.com/nvivo-qualit ative-data-analysis-software/enabling-research/the-newnvivo-en/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ATLAS.ti (https://atlasti.com/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QDA Miner (https://provalisresearch.com/products/qualit ative-data-analysis-software/freeware/) . . . . . . . . . . . . . . . . . . . . . . Quirkos (https://www.quirkos.com/) . . . . . . . . . . . . . . . . . . . . . . . . . Dedoose (https://www.dedoose.com/) . . . . . . . . . . . . . . . . . . . . . . . Taguette (https://taguette.org/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MonkeyLearn (https://monkeylearn.com/) . . . . . . . . . . . . . . . . . . . SPSS (https://www.ibm.com/uk-en/products/spss-statis tics/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Power BI (https://powerbi.microsoft.com/en-us/desktop/) . . . . . Tableau (https://www.tableau.com/products/desktop/) . . . . . . . . . Principles of IS research ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115 116 123 125 132

145 147 160 165 174

175 176 177 177 178 179 179 180 182 183 202

List of Tables

Table Table Table Table Table Table Table Table Table Table Table

1.1 2.1 2.2 3.1 3.2 3.3 4.1 4.2 5.1 5.2 6.1

Table Table Table Table Table Table

8.1 9.1 10.1 10.2 10.3 11.1

Types of IS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of research paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of research philosophies & paradigms . . . . . . . . . . Quantitative vs. qualitative research design . . . . . . . . . . . . . . . . . . Research questions vs. hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of research methodologies . . . . . . . . . . . . . . . . . . . . . . . . IS theories & approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IS theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IS research approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IS research typologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Characteristics of Contemporary IS Research Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples of assessing feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . Data extract & Coding sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data analysis characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coding example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Converting codes into themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research design and analytical tools . . . . . . . . . . . . . . . . . . . . . . . .

11 29 30 37 40 44 55 81 91 95 117 142 155 169 169 170 184

xxv

Part I Introduction to Information Systems Research Concepts

1

Historical Background of IS Research

1.1

Introduction

Overview

– This chapter offers a history of IS research, which dates to the 1980s. It provides a historical background to the application of research designs in IS research by holistically exploring the origins of the contemporary applications of IS research that we have become accustomed to today. Learning outcomes include: . Understanding the various facets of IS history and contemporary future directions . Identifying types of IS and the multifaceted areas of IS.

> Definition Information Systems refer to a group of components, namely people, processes, and technologies that work together to collect, retrieve, process, store, and share information to help an organisation make decisions and keep track of its daily operations.

1.2

What Is an Information System?

Over the years, there have been many interpretations of information systems (IS) leaving many researchers confused. Researchers can easily fall into the trap of misleading IS because researchers often believe that IS and information technology (IT) are synonymous, and IT is arguably only one component of an IS. “An information system can be defined technically as a set of interrelated components that collect (or retrieve), process, store, and distribute information to support decision

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ali, Information Systems Research, https://doi.org/10.1007/978-3-031-25470-3_1

3

4

1

Historical Background of IS Research

Fig. 1.1 IS research model

making and control in an organisation” (Laudon et al., 2020, p. 16). Information systems can help managers and workers make decisions, collaborate, and monitor their daily activities, as well as analyse problems, visualise difficult topics, and develop new products. Information systems also keep track of key people, locations, and objects within an organisation or the global community. When we refer to information, we are referring to data that has been formatted in a way that people can comprehend and use. Data, on the other hand, is streams of unstructured facts about things that occur in organisations or the physical world before they are organised and put into a form that people can understand and use. Drawing inspiration from various scholars specialising in the IS domain (e.g., Avison and Wood-Harper, 1991; Baskerville and Wood-Harper, 1996; Bryant et al., 2013; Fitzgerald et al., 1985; Myers and Avison, 2002; Wood-Harper, 1985), we define an information system as a group of components, namely people, processes, and technologies that work together to collect, retrieve, process, store, and share information to help an organisation make decisions and keep track of its daily operations (see Fig. 1.1). Since this book places much emphasis on contemporary IS, this definition can be expanded to include contemporary organisations that use contemporary IS to keep up with modern industrial trends. Example

Did you know that Herman Hollerith’s census tabulator was the first large-scale mechanical information system. Hollerith’s machine, which he built in time for the 1890 U.S. census, was a big step toward automation and a source of ideas for computerised information systems.< Three activities within an information system enable organisations to obtain the information necessary for making decisions, managing operations, analysing problems, and developing new products. Input, processing, and output are referred to as input, processing, and output, respectively. Input obtains raw data from either within the organisation or externally. This information is transformed into a form that makes sense through processing. Output transmits the information that has been processed to the individuals or tasks that will utilise it. Information systems

1.2 What Is an Information System?

5

require feedback, which is output that is sent back to the appropriate individuals within the organisation to assist them in evaluating or modifying the input stage. A tall building is an effective way to demonstrate how an information system operates. Bricks, concrete, and glass are used to construct buildings, but they do not constitute the structure itself. The building’s architecture, design, setting, and landscaping, as well as all the decisions that went into their creation, all contribute to solving the problem of providing businesses with a place to work. The bricks, glass, and concrete that comprise computer-based information systems are computers and programmes. However, computers and programmes cannot provide an organisation with all the information it requires. To comprehend information systems, it is necessary to comprehend the problems they are intended to solve, how they are constructed and designed, and how organisational processes lead to solutions. Example

Did you know that information systems are different from information technology in that they include the technology, people, and processes that deal with information, while information technology is the design and use of information or data in an information system.
Definition IS Modelling referring to the PPT (people process and technology) framework for successful digital transformation in organisations.

1.5 IS Modelling

15

Fig. 1.4 PPT framework

People Process & Technology Framework (PPT): As previously stated, IS consists of people, processes, and technology (see Fig. 1.4). Together, these three components comprise the core of IS, and an IS cannot survive without all three. For instance, technology is the foundation of an IS because it consists of the tools necessary to support its functionality. A database cannot be searched for information if there are no search or query systems to locate a particular type of information. The people, process, and technology (PPT) framework has been utilised since the early 1960s. Most businesses utilised it to enhance the performance of their employees and equipment. In the late 1990s, Bruce Schneier made it well-known in the information security community. The framework for information technology management is currently utilised by most software companies. It is simple to comprehend why. It facilitates the creation of a map of the people, processes, and technologies that comprise the value streams. This aids in providing highperforming teams with complete control and visibility, allowing them to enhance operations and ship faster. This section will describe the PPT framework and how you can utilise it to benefit your organisation.

1.5.1

People

The term “people” refers to the workforce available to the business. People perform the tasks described in the process, sometimes with the assistance of technology. Recruiting the appropriate personnel is one of the most crucial tasks. Businesses must identify their key employees and ensure they possess the necessary skills, experience, and attitude for the position. But most of the time, key personnel are engaged in other activities. Therefore, the managers must wait until the appropriate personnel are available, hire new personnel for the position, transfer personnel from other projects, or delegate the tasks to a consultant or agency. These are crucial choices that must be made for the PPT framework impact to function. Additionally, people require distinct roles so that everyone is aware of their responsibilities. This will aid in making decisions, selecting technologies, implementing

16

1

Historical Background of IS Research

processes, and hiring personnel. So many businesses fall into the trap of focusing excessively on technology and processes while almost ignoring their employees. Consequently, it is essential to ensure that the team has the right members and that they can communicate effectively. Lastly, businesses require the support of individuals. They must understand what is expected of them, why, and how the changes will affect them. Without the support of the entire population, no new processes or technologies can be implemented. If not, businesses will experience sluggish adoption and subpar utilisation. People can be given the necessary authority if they receive the proper training.

1.5.2

Process

A process is a series of actions or steps that work together to achieve a particular objective. The process primarily illustrates “how” within the PPT framework. How will we achieve the desired outcome? How can we use people and technology to solve the business problem? Processes are a set of actions that can be repeated and should yield the same result regardless of who performs them. When creating and implementing processes, there are a few considerations to keep in mind. People must understand their role in a process. They must understand the procedure, their role, and their responsibilities. This entails providing the correct instructions to the appropriate individuals and instructing them on what to do. They should have a significant say in how the process is created and inspected. First, businesses should identify and prioritise the most crucial steps. Most likely, these essential steps will enhance the final product. Therefore, improving these steps will have the greatest impact on the efficiency of the process. Once these elements are in place, they can begin working on the specifics, special cases, and supporting processes. Therefore, each process must have a method for measuring its effectiveness. Organizations must consider which metrics to monitor and how to measure them. For the process to function optimally, it is essential to obtain feedback and continue adjusting. Numerous businesses pay consultants a great deal of money to come up with innovative methods of doing things. On the other hand, organisations are always changing. People change, so does technology, so does the market, and so on. Over time, this can cause the processes to lose some of their value. Therefore, businesses must constantly monitor and evaluate their processes to determine what works and what doesn’t. Then, they must use this feedback to modify the processes so that they continue to provide significant value to the business. Once people and processes are in place, organisations should consider how to support them technologically.

1.5.3

Technology

People can complete the process with the aid of the tools provided by technology. It also simplifies the process by automating certain steps. In a perfect world, the

1.6 Overview of IS Research Methods

17

most influential technology would be the newest and fastest. It is simple to become interested in new, flashy tools. However, organisations must ensure that the technology serves their needs. Too frequently, companies invest heavily in technology to gain a competitive advantage. People and processes are not prioritised. Then, they attempt to integrate the people and processes with the new technology. But this is not the best way to achieve your goal. Technology is meaningless if it is not utilised by the proper individuals in the proper manner. After the problem has been clearly defined, the right people have been trained and hired, and the process requirements have been established, technology should always be considered last. If the people do not know how to use the technology or if the process does not utilise it effectively, the technology will not provide the best return on investment. Therefore, technology alone will not be sufficient to solve problems. To maximise the benefits of technology, businesses must define their objectives and train their employees.

1.6

Overview of IS Research Methods

Example

Did you know that common research designs in IS research are positivist (quantitative) or interpretivist (qualitative).< This section provides a brief synopsis of IS research methods. Further details of these methods are provided in the later chapters. Typical IS research methods include actions research, case studies, ethnography, experiments, and surveys (see Fig. 1.5).

Fig. 1.5 IS model research process (Inspired by Oates, 2006)

18

1

1.6.1

Historical Background of IS Research

Action Research

Example

Did you know that survey methods are typically quantitative and case study is typically qualitative. Action research is typically qualitative, while experimentation is typically quantitative. Ethnography is typically qualitative.< Research conducted in practise. The researchers intend to do something in the real world, then reflect on what transpired or what they have learned. They then initiate the procedure once more.

1.6.2

Case Studies

These concentrate on a single instance of the “thing” being studied, such as an organisation, department, information system, discussion forum, systems developer, development project, decision, etc. The objective is to gather as much information as possible about the “life” of the case and all its intricate connections and processes.

1.6.3

Ethnography

Example

Did you know that survey methods are typically quantitative and case study is typically qualitative. Action research is typically qualitative, while experimentation is typically quantitative. Ethnography is typically qualitative.< The study of the culture and worldview of a group of people. Instead of simply observing from afar, the researcher spends time in the field and becomes acquainted with the locals. Similarly, ethnography based on the ethnographic model has been used in IS research to study social phenomenon within internet communities.

1.6.4

Experimentation

A method for investigating cause-and-effect relationships, testing hypotheses, and attempting to prove or disprove a link between a cause and an effect. There are “before” and “after” measurements, and all variables other than the one believed to be responsible for the “after” result are eliminated from the study.

1.6 Overview of IS Research Methods

1.6.5

19

Survey

A tool for collecting the same types of data from many people (or events) in a standardised and systematic manner. Then, you use statistics to search for patterns in the data to generalise to a larger group than the one you began with. Typical research studies often adopt one model. If a second method is needed, this is usually reflecting more than one research question. For example, mixed method studies may employ survey (quantitative) and case study (qualitative) with a research question dedicated to each method. Case Study 1.3

Rise of Enterprise Resource Planning (ERP) Systems J.I. Case, a manufacturer of tractors and construction equipment, collaborated with IBM in the 1960s to develop what is believed to be the first material requirements planning (MRP) system. Large manufacturers subsequently developed their own MRP systems. Even though they were difficult to produce, costly, and required a great deal of space, early MRP systems helped businesses keep track of inventory and production. This allowed manufacturers to better plan production runs by making it easier to purchase and transport raw materials to the factory. Even though MRP systems became more popular in the 1970s, only large companies with the resources to develop them in-house utilised them. Large software companies such as Oracle and JD Edwards worked to make this software accessible to more businesses. ERP’s history of manufacturing: The first manufacturing resource planning (MRP II) systems were introduced in the 1980s. This was a major milestone in the development of ERP systems. In addition to inventory management and the procurement of raw materials, these advanced solutions facilitated additional manufacturing processes. MRP II systems made it possible for the various departments of a manufacturing company to collaborate and made it simpler to schedule production. Other businesses quickly realised that manufacturing companies were on the right track. The future of enterprise resource planning (ERP) systems will be shaped by major technological trends such as artificial intelligence (AI) and the internet of things (IoT). Soon, ERP solutions will be able to eliminate manual tasks and predict future business trends using machine learning, a type of artificial intelligence in which a system learns to identify patterns in data and draw conclusions from them. Machine learning uses new data and feedback to become smarter and more effective over time. ERP systems have come a long way, and in the coming years, machine learning, the Internet of Things (IoT), and other innovations will render ERP obsolete. In fact, 65% of CIOs anticipate incorporating AI into their ERPs by 2022. Source https://www.g2.com/articles/history-of-erp/

20

1

1.7

Historical Background of IS Research

Conclusion

Key Takeaways

1. Information systems refer to a collection of people, processes, and technologies. 2. IS have been around since the late 18th century but gained prominence in the 1960s with the rise of computing technology and developing research in the field. 3. Five industrial revolutions and currently in industry 4.0 (digitalisation) and transiting to industry 5.0 (personalisation). 4. There are six types of IS: transaction processing systems (TPS), office automation systems (OAS), knowledge work systems (KWS), management information systems (MIS), decision support systems (DSS) and executive support systems. 5. Typical IS research methods include actions research, case studies, ethnography, experiments, and surveys.

Exercises

1. Around which period did information systems originate? 2. What are the key types of information systems and at what organisational levels do they serve? 3. Which industrial revolution saw the rise of electronics or electrical technology? 4. Which IS component would refer to the operations of supply chains and why? 5. If you were to study a particular IS of an organisation, which IS research method would you adopt? 6. Research several key definitions of IS from journals and books related to IS research, and then compare them to the definition provided in this chapter 7. What conclusions can you draw about the IS concept? 8. Industry 1.0-5.0 is an important IS concept, what is your understanding of the evolution of IS and how does it play a role in contemporary IS research?

References Avison, D. E., & Wood-Harper, A. (1991). Information systems development research: An exploration of ideas in practice. The Computer Journal, 34(2), 98–112. Baskerville, R. L., & Wood-Harper, A. T. (1996). A critical perspective on action research as a method for information systems research. In Enacting research methods in information systems: Volume 2 (pp. 169–190). Springer.

References

21

Bryant, A., Black, A., Land, F., & Porra, J. (2013). Information systems history: What is history? What is IS history? What IS history? … and why even bother with history? Journal of Information Technology, 28(1), 1–17. Duggal, A. S., Malik, P. K., Gehlot, A., Singh, R., Gaba, G. S., Masud, M., & Al-Amri, J. F. (2022). A sequential roadmap to Industry 6.0: Exploring future manufacturing trends. IET Communications, 16(5), 521–531. https://doi.org/10.1049/cmu2.12284 Fitzgerald, G., Hirschheim, R. A., Mumford, E., & Wood-Harper, A. T. (1985). Information systems research methodology: An introduction to the debate. Research Methods in Information Systems, 1–7. Laudon, K. C., & Laudon, J. P. (2018). Management information systems: Managing the digital firm. Pearson. Laudon, K. C., Staff, P.-H., & Laudon, J. P. (2020). Management information systems. Prentice Hall PTR. Maddikunta, P. K. R., Pham, Q.-V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T. R., Ruby, R., & Liyanage, M. (2022). Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration, 26, 100257. Myers, M. D., & Avison, D. (1997). Qualitative research in information systems. MIS Quarterly, 21(2), 241–259. Myers, M. D., & Avison, D. (2002). Qualitative research in information systems: A reader. Sage Publications. Oates, B. J. (2006). Researching information systems and computing. Sage Publications. Wood-Harper, T. (1985). Research methods in information systems: Using action research. Research Methods in Information Systems, 169, 191. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception, and perception. Journal of Manufacturing Systems, 61, 530–535. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5), 616–630.

2

Research Philosophies in Social Science and Information Systems Research

2.1

Introduction

Overview

– This chapter offers a philosophical background to the research paradigms and approaches associated with IS research. It covers contemporary IS through the lens of design science, as well as the ontological and epistemological perspectives of positivist, interpretivist and critical worldviews when studying the social paradigm shifts and global changes resulting from such systems. Learning outcomes include: . Understanding the epistemological, ontological, and axiological stances of social science research projects . Identify the main research paradigms that are significant for IS research, explaining the relevance to IS research of philosophical positions such as positivism, critical realism, interpretivism, postmodernism and pragmatism . Justifying the chosen paradigm and stance based on the nature of the IS research project.

> Definition A research philosophy is a mode of thought that leads to the discovery of new information about the object of study, and the development of research assumptions, knowledge, and nature.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ali, Information Systems Research, https://doi.org/10.1007/978-3-031-25470-3_2

23

24

2

Research Philosophies in Social Science and Information Systems Research

2.2

Philosophical Underpinnings of Social Science

2.2.1

What Is a Research Philosophy?

Assumptions about what constitutes “valid” research and which research methods are appropriate underlie all research, whether quantitative or qualitative (Myers & Avison, 1997, p. 5). Saunders et al. (2019) refers the term ‘research philosophy’ as “…a system of beliefs and assumptions about the development of knowledge” (p. 130). A scientific research philosophy can also be a mode of thought that leads to the discovery of new information about the object of study. According to these definitions, the foundation of a research project is the selection of a research strategy, formulation of an issue, data collection, and analysis. The paradigm for scientific research consists of ontology, epistemology, methodology, and methods. The chosen methodology should be based on the researcher’s philosophical stance and the social science phenomenon being investigated. Several philosophical approaches are possible in the field of research; however, extreme approaches can be limiting. Philosophy, methodology, and the research problem can only be reconciled using a third-party philosophical approach. However, the philosophies and methodologies of qualitative and quantitative research are vastly different, and it is common to combine the two. Therefore, it is essential to comprehend the advantages and disadvantages of various strategies. As a result, researchers can plan their work more effectively and gain a deeper understanding of the issue at hand. Figures depicting levels of organisational culture and their interactions, i.e., stages of corporate social responsibility, reflect the research philosophy and paradigm and guide the research in this monograph, which seeks to determine the level of management culture development required to implement corporate social responsibility. > Definition A research paradigm refers to a broad framework that encompasses perceptions, beliefs, and knowledge of various scientific investigation theories and practises. As a compass, each researcher’s approach to the research itself serves as their guide. Some claim that Mill (1906) was the first to challenge representatives of ancient science to a duel, promising that social science would suddenly mature if his advice was followed. Likewise, their education was influenced by philosophical and theological frameworks that similarly constrained them. This recommendation was accepted by the social sciences for a variety of other reasons. Research philosophy is the development of research assumptions, knowledge, and nature (Saunders et al., 2019, p. 130). This assumption may appear to be a preliminary statement of reasoning, but it is based on the philosopher’s acquired knowledge and insights. In research, assumptions also play a significant role. Regarding the nature of truth and knowledge, as well as their acquisition, various researchers may hold divergent viewpoints. In the context of research, scientific research

2.2 Philosophical Underpinnings of Social Science

25

philosophy is a method that permits scientists to convert ideas into knowledge. Multiple authors have identified and discussed four distinct philosophical trends in research: positivism, interpretivism and realism (Guba & Lincoln, 1994) (see Key Research Paradigms & Philosophies for more details).

2.2.2

What Is a Research Paradigm?

The scientific research paradigm defines the philosophy of scientific research. All research aims to produce valid knowledge. In other words, the source of this validity is a consensus on a set of values that has led to knowledge claims that have stood the test of time. This value system is referred to as a “research paradigm” (Kanellis & Papadopoulos, 2009, p. 2). It is possible for paradigms to change, evolve, and even be completely abandoned. An investigator’s philosophical, theoretical, instrumental, and methodological paradigms or worldviews must have crystal clear philosophical, theoretical, instrumental, and methodological foundations. These prerequisites are required for the study of paradigms. In view of Cohen et al. (2017), the paradigm of scientific research is a broad framework that encompasses perceptions, beliefs, and knowledge of various scientific investigation theories and practises (p. 8). Similarly, the scientific research paradigm is defined by a series of distinct steps. After completing the preceding steps, the researcher connects the research objectives and research questions. “Normal science” and paradigms share many similarities. The same rules and standards of scientific practise apply to all scientists working within the same paradigm. Example

Did you know that a research paradigm derives from the term paradigm shift in which the term was first used by Thomas Kuhn in his famous 1962 book: “The Structure of Scientific Revolutions.”< Research paradigm and philosophy are influenced by a variety of factors, including the individual’s mental model and worldview, as well as numerous beliefs and attitudes about reality. The beliefs and values of researchers are crucial in determining the validity of their arguments and terminology, according to this concept. In some instances, the researcher’s position can have a substantial effect on the research outcome. In some areas of natural science, free discussion permits the discovery of both significant and insignificant “discoveries” In the social sciences, it is extremely difficult to reach such an agreement. According to academic philosophers, the humanities and social sciences are characterised by “multi-paradigmatic” or the coexistence and competition of numerous theoretical paradigms (see Key Research Paradigms & Philosophies for more details).

26

2

2.2.3

Research Philosophies in Social Science and Information Systems Research

Social Science Research in the IS Domain

Information systems are increasingly seen as a social science in its narrower association with the natural sciences. There are still two camps regarding whether information systems are a discipline. Some argue that it is an interdisciplinary field of study and advocate incorporating concepts from more established social sciences to promote its development. Instead of accepting it as an adjunct or interdisciplinary, the opposing group believes it should be recognised as a distinct discipline in and of itself. This latter group is primarily responsible for the arguments supporting the development of information systems as a social science. Information systems, on the other hand, tends to adopt the fundamental characteristics of the so-called liberal social sciences. This largely “neutral” conception must give up some professional characteristics that some opposing camp members find desirable and deserving of retention. For instance, traditional liberal social sciences place a high premium on value neutrality as a librarian’s professional outlook. Due to the profession’s unwavering support, “Normative” and “prescriptive” approaches proposed by interested outside observers as a means of enhancing its social standing have been rejected. Hence information systems research is seen as a sub-domain of the social sciences.

2.3

Key Research Paradigms and Philosophies

Example

Did you know that interpretivist philosophies are often qualitative in nature where different social phenomena are interpreted through the personal experiences of subjects.
Definition A research design refers to a structural framework of various research methods and techniques that a researcher employs.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ali, Information Systems Research, https://doi.org/10.1007/978-3-031-25470-3_3

35

36

3

3.2

Applications of Research Designs in IS Research

Introducing the Research Design

Example

Did you know that qualitative designs are highly common in social sciences research, particularly in information systems studies.
Definition A hypothesis refers to statements that predict either positive or negative results.

42

3

Applications of Research Designs in IS Research

On the other hand, the purpose of proposing a research hypothesis is to predict either positive or negative results, and are frequently used in scientific, experimental, and quantitative research (Creswell and Creswell, 2018). A study’s hypothesis serves as a guide for its eventual outcome. Consider the influence of time and temperature on the transport of biological samples. This study employs in-depth statistical analysis and data-driven research to investigate the effects of different temperatures and times on the transportation of biological specimens. The findings of the study will demonstrate that biological samples can be transported safely at a specific temperature. The results are a temperature range within which people can travel comfortably, regardless of whether it is too hot or too cold. The interpretation of such results requires in-depth mathematical models, statistical analysis, scientific experiments, and other biological investigations.

3.3.2

Differences Between Research Questions and Hypotheses

The identified problem or gap is used to develop a research question, while existing knowledge is used to formulate the hypothesis. Multiple research questions are posed when conducting a study, but only one hypothesis is tested at the conclusion of the study. Research hypotheses necessitate in-depth subject knowledge and a large quantity of data or research studies, whereas research questions can be stated with a small amount of data or information. This demonstrates that the correlation between variables in a research question is uncertain, while the correlation between variables in a hypothesis is robust. A research question may be considered “short,” but it contains all necessary details and leaves room for interpretation, which typically results in a wide variety of intriguing findings. In contrast, the research hypothesis is a formal assertion that two or more variables selected for the study are related. Variables include, for instance, the number of participants, the size of the population, and the sampling technique used in the study. Although the hypothesis can be proposed in quantitative and experimental studies, qualitative studies are more likely to do so. Depending on the purpose of the study, the research questions can be classified as causal, descriptive, or comparative, and the hypotheses can be causal, null, directional, or non-directional. It is required to respond to a research question and to test a hypothesis. In contrast, the hypothesis is more scientific and prescriptive, while the research question is more elaborate. Therefore, research questions are so prevalent in the study of humanities disciplines such as language, literature, and art. And, as stated previously, the process is relatively straightforward. Consider how an information system impacts the productivity of an organisation. It is possible and likely that the results of the study will either confirm or refute the research hypothesis, necessitating the use of mathematical equations and statistical analysis. Hypotheses are indispensable to scientific, biological, and social research (that must be tested first).

3.4 Research Design Methodologies

43

Temperature and time duration, for instance, can have a significant impact on the transport and storage of samples.

3.4

Research Design Methodologies

> Definition A research methodology is the philosophical and theoretical foundations that shape how research is conducted and their influence on the selected method(s).

3.4.1

What Is a Research Methodology?

Firstly, as a quick note, this section will explain the research methodologies associated with each research design. Chapter 6 will provide greater details of these methodologies applied in contemporary IS research. The research methodology consists of the philosophical and theoretical foundations that shape how research is conducted and their influence on the selected method(s) (Myers & Avison, 2002). In other words, “…methodology refers to the theory of how research should be undertaken” (Saunders et al., 2019, p. 4). Research methodologies have been interpreted in numerous ways throughout the methodological literature. Due to the emphasis on ‘how’ research is conducted, we argue that the methodology is synonymous with the research strategy. Methodology is a way of doing something. Therefore, methodology and strategy constitute the plan for conducting research.

3.4.2

Types of Research Methodologies

Researchers can employ various methodologies to support their research designs. Because some methodologies can be employed in more than one approach, this chapter combines them for clarity (Table 3.3). Experimentation: There are typically two types of experimentation, laboratory, and field. In a controlled laboratory setting, quantitative analytical techniques are used. The goal of this method is to produce results that are generalizable to the real world. The experiment chosen reflects a specific area of interest and can be classified as simulation, small group, man-machine, or prototype. The experimental approach has the advantage of allowing detailed study of a small number of independent (cause) and dependent (effect) variables; the disadvantage is that it is extremely difficult to control all variables assumed to be constant and that laboratory experiments take place in an artificial environment (for example, students comparing prototyping and life-cycle methodologies of software development). Field experiments, on the other hand, are carried out in the real world to compensate for the laboratory’s artificial nature. There is a significant disadvantage in that it makes controlling or compensating for variables assumed to be constant even more difficult. Forecasting is also an experimentational positivist technique

Description

Experiments are used to disentangle cause and effect by manipulating the presumed causal, or independent, variable and observing its effect on the dependent variable(s) (Callegaro & Yang, 2018)

Surveys is a requirement gathering technique used to gather information from large groups of people by asking a series of structured questions (Callegaro & Yang, 2018)

Action research is an emergent and iterative process of inquiry that aims to address real organisational problems through a participatory and collaborative method that incorporates multiple forms of knowledge and has long-term implications for participants and the organisation (Myers & Avison, 2002)

Case study is a strategy for doing research which involves an empirical investigation of a particular contemporary phenomenon within its real life context using multiple sources of evidence (Robson, 2016; Yin, 2018)

Methodology

Experimentation

Surveys

Action research

Case study

Table 3.3 Summary of research methodologies IS example

(continued)

3

A single case study to examine how colleagues within an organisation were using an outdated FinTech system in their daily work and to see if senior managers, departmental managers, and front-line operatives used the FinTech system differently. The embedded cases were those of senior managers, department managers, and front-line operatives. Quantitative surveys could be used to measure the differences in uses of the FinTech system, but qualitative interviews could capture a more in-depth data about the lived experiences of the embedded cases’ use of the FinTech system

Five phases of action research (diagnosing, planning action, acting, evaluating action, and specifying learning phases) have been applied to determine the potential utility of Internet of Things or technology embedded objects (e.g., remote sensors) in enhancing patient care in hospitals. Each phase can assist Action Researchers in determining the feasibility of IoTs as a solution for enhancing patient care and promoting patient well-being

A survey study that investigates peoples’ reliance on big data. A survey could be used to measure people’ habits of using major big data storage facilities such as DropBox, MS OneDrive and GoogleDocs and how much data is produced from such tools

Experimentation and causal inference are two of Netflix’s Data Science and Engineering organisation’s primary focus areas. Results from experiments, such as A/B tests on existing members are used throughout Netflix to deliver more joy to current and future members of the video streaming service (Netflix, 2022)

44 Applications of Research Designs in IS Research

Supplementary documentation in contemporary IS studies range from system policy documents to background manuals and support guides of existing systems. Company background data may also be included. For example, studies exploring modern uses of Enterprise Resource Planning systems may request ERP documentation regarding the type of ERP and for what purpose it serves. Studies may reveal that the ERP may not align with the existing business process as the system may serve another purpose for a completely different process; documentation may help to reveal these details that cannot be obtained from primary research findings such as interviews

Documents refer to text, images, and audio files that can all be stored together in one document for long-term use

Ethnography is a method for gaining insight into a group’s culture or social world (Lichterman, 2015)

Grounded theory refers to the construction of theories through data collection (Hughes & Howcroft, 2000)

Ethnography

Grounded theory

Researchers may use grounded theory to develop a user-centric model to improve the acceptance of novel technologies among those who a resistant towards such technologies or suffer from technophobia. Research data collected from users suffering technophobia will inspire the development of the new user-centric model

An ethnographer may spend five months closely observing a group of students using a learning management system to comprehend and describe the group’s social customs. The ethnographer may explain to the group that they are curious about the benefits of LMSs and what it is like to be a member of this social group of online learners. They may take an active role in enhancing the research’s immersion. The ethnographer observes not only the students’ social dynamics and where they access the LMS when learning, but also participates in some of their common LMS activities

IS example

Description

Methodology

Documentation

Table 3.3 (continued)

3.4 Research Design Methodologies 45

46

3

Applications of Research Designs in IS Research

that employs techniques such as regression analysis and time series analysis. This method is dependent on the accuracy of historical data and the assumption of continuity. Futures research is interpretivist in nature, and it is especially useful for investigating the societal impacts of information technology. It can also be used to generate alternative future scenarios within organisations. The usefulness of futures research is highly dependent on the capability of those involved in scenario development. Simulation can also be used to simulate the behaviour of a real-world phenomenon, allowing future behaviour to be predicted. Human-computer interaction research in contemporary IS and computing has relied heavily on experiments. As an example, several studies were conducted to compare the speed at which people using a mouse and drop-down menus entered data with those who used keyboard control keys. Example

Did you know that surveys have contributed significantly to our understanding of the contemporary big data ecosystem because survey data can reveal objective trends, patterns and factual data used to support anything from organisational decision-making to developing contemporary technologies.< Surveys: Deductive research methods are frequently associated with the survey strategy. This strategy is frequently used in contemporary IS research to answer questions such as “what,” “who,” “where,” “how much,” “how many,” and “how many.” As a result, exploratory and descriptive researchers frequently employ this methodology. Surveys are typically conducted via mail, phone, or in-person (given current technology, they could also be carried out through email or the Internet). These techniques take a snapshot of a situation, which can then be quantitatively analysed. This technique’s strength is that it allows for the use of large sample sizes to establish a foundation for generalisation. Surveys are useful for describing phenomena, but they are less useful for gaining a more in-depth understanding of them. Bias in survey design and respondent selection is difficult to eliminate. The level of non-response has an impact on the validity of the results as well. Contemporary IS and computing research frequently employs surveys. For example, surveys are commonly used in user evaluations of systems, as well as to investigate the practises or views of IT or computing managers. Action Research: In 1946, Lewin coined the term “action research” for the first time. Several common and related themes have been identified in the subsequent literature by contemporary IS researchers, who have interpreted it in various ways. Action research is a very common approach in qualitative research. Although there are various interpretations of action research, Rapoport’s16 perspective is one of the most famous in the literature. Rapoport explains that action research aims to contribute both to the practical concerns of people in an immediate problematic situation and to the goals of social science by collaboration within a mutually acceptable ethical typology. The researcher plays an active role in assisting in the achievement of practical goals in an organisational context. This research’s breadth

3.4 Research Design Methodologies

47

of experience is a significant strength, while potential ethical issues and interpretation difficulties are weaknesses. To encourage organisational learning, Action Research entails identifying problems, formulating a plan of action, carrying it out, and assessing the results. Therefore, action research is a type of research that takes place in the context of action rather than in the context of research. This is because action research focuses on solving real-world problems and making a difference in the lives of people. Example

Did you know that case studies are used for both confirmatory (theory testing) and exploratory purposes (theory-building). For instance, a case study can be used to compare theories about the implementation of computing systems in an organisation and to assess each theory’s predictions. By conducting an inductive study on the user requirements for a new computing system, new theory could be generated from the theory testing case study.< Case Study: In contemporary IS, case study research is the most frequently used qualitative technique. Although there are numerous literatures on case study design, Robert Yin is one of the most citated scholars in this area of research design. However, there is a need to apply case studies more in contemporary IS research as Yin mostly comments about the case design in a more holistic sense. A person, a group, an organisation, an association, a joint venture, a change process (e.g., restructuring a corporation), an event (e.g., an annual general meeting), as well as many other sorts of case subjects, are all examples of ‘cases’ in the context of case study research. Setting the scope and limits of one’s study is an important part of the definition of a case study. When a topic is defined, case study research aims to understand the dynamics of the subject matter in its context or environment. It is important to understand the dynamics of a topic to understand how the subject interacts with its environment. Case studies entail writing a detailed description of a situation, which yields a much richer picture than the previous methodologies. Because the research boundaries are not clearly defined at the outset, experimental control of variables is inappropriate. It has been argued that case study results are difficult to generalise due to differing interpretations and a lack of control over variables. In addition, researchers will need to determine how their case study research will be organised. Based on two distinct dimensions, there are four case study methodologies to consider: single case versus multiple cases and holistic cases versus embedded case. As an example, a single case can be used to represent an important situation or a one-of-a-kind situation. However, a single case may be purposefully selected because it is typical or because it offers an opportunity to observe and analyse a phenomenon that few have previously considered. Defining a single case is an essential part of using a single case. Many part-time students are employed by this organisation. The most important thing here is to make sure that this method is appropriate for the research question and goals. Multiple cases, i.e., more than one case, can be included in a case study

48

3

Applications of Research Designs in IS Research

strategy. The reason for using multiple cases is to see if findings can be replicated. Cases will be selected with care based on the expectation that each will yield similar results. Documentation: Data digitization, online archives, and open data initiatives by governments and businesses have expanded researchers’ options for archival and documentary research. This means that these sources can now be accessed online from anywhere in the world. These could allow researchers to design a research project that takes advantage of the wide variety of secondary data sources. It is possible to conduct effective and efficient archival or documentary research based on the appropriateness of the research question and the availability of relevant documents. Access to certain documents or data may be restricted due to their sensitivity. The quality of the document’s researchers locate may also differ, particularly if they originate from various sources. Researchers may have difficulty comparing or analysing their findings if some data is missing or is not presented in a consistent manner. As a result, archival research strategies may require researchers to determine what documents are available and design their research to take advantage of those. This may necessitate a combination of this and another research strategy. For example, researchers could conduct documentary research alongside a Grounded Theory strategy based on qualitative interviews and use a similar analysis procedure for both sets of data. A case study strategy that incorporates documentary research could be another option. Example

Did you know that anthropologists frequently travel to different communities and live within them for an extended period, using ethnography to conduct research on their people and culture through sustained observation and participation.< Ethnography: People or ethnic groups can be documented in an ethnography, which simply refers to the writings of an ethnographer. Qualitative research has its roots in colonial anthropology, making it the oldest method. Anthropology developed in the late 1700s and early 1900s to study cultures in so-called “primitive” societies brought under the rule of a colonial power, to facilitate imperialist control and administration. When conducting an interpretive ethnography, the emphasis is more on the researcher’s personal impressions than on the study’s supposed objectivity. Rather than attempting to pin down a single, correct interpretation, the interpretive ethnographer considers the likelihood of multiple interpretations. The participants’ socially constructed interpretations will yield multiple meanings. Interpretive ethnography focuses on understanding meanings rather than subjects, and those being observed are treated as participants rather than subjects in this approach. When conducting ethnographic research, the researcher must constantly reflect on their findings to ensure their reliability/dependability and validity/credibility/transparency. Ethnography with a critical leaning seeks out and analyses the ways in which privilege, dominance, and authority affect the

3.4 Research Design Methodologies

49

lives of those on the periphery of these systems. However, does this have any relevance to contemporary IS research that relies on gaining access to an organisation? Criticised ethnographers often assume an advocacy role to bring about change in their work. This method may be useful for investigating the impact of a problematic issue within an organisation’s IT system and advocating for internal or external change using the constraints of critical ethnography. Whether it is a matter of strategy, decision-making procedures, regulation, governance, management, reward and promotion, communication, or involvement, such an issue could be relevant. Grounded Theory: Using the term “grounded theory” can refer to anything from a research process to a research methodology to an investigation method. Theory that is based on or developed inductively from a set of data is known as inductively grounded or inductively developed theory. In positivism, reality is viewed as a separate entity that exists outside of ourselves (to human cognition). Positivism is a good fit for scientific research, but they argued that social research should use a different philosophy. Using an interpretive approach in sociological research, reality is seen as being socially constructed through the meanings that social actors assign to their experiences or actions. To better understand how people interpret and explain their everyday experiences. Hence, Grounded Theory was devised to examine, interpret, and explain the meanings that social actors give to those experiences. Case Study 3.2

Application of mixed research designs in IS research Faisal aimed to investigate the use of cloud computing as an alternative to traditional storage of learning materials on VLEs in Universities. He planned to use two research strategies: a quantitative survey and a qualitative case study. Faisal discovered that each strategy would help him address a different research question he proposed in his study. The survey would help Faisal to address the question of whether there is a relationship between the adoption of cloud computing, efficiency, and productivity, namely speeding up processes and reducing cost. While the case study would help to unravel any potential issues that could impede the adoption of cloud to promote efficiency in University settings.

50

3

3.5

Applications of Research Designs in IS Research

Conclusion

Key Takeaways

1. Research designs are structural frameworks of various research methods and techniques that a researcher employs. 2. Research designs can be qualitative, quantitative or mixture of the two, and there are five types: correlational, descriptive, diagnostic, experimental and explanatory. 3. Research questions are based on the topic of the study that must be answered in the conclusion. 4. Hypotheses refer to statements that predict either positive or negative results. 5. A research methodology is the philosophical and theoretical foundations that shape how research is conducted and their influence on the selected method(s). 6. There are several types of methodologies such as experimentation, surveys, action research, case study, documentation, ethnography, and grounded theory.

Exercises

1. Why is it important to consider a research design in IS research? 2. Which research design can be applied to a survey methodology? Why? 3. Which research methodology involves examining a place or individual? Why? 4. What are the differences between design and methodology? 5. What is the difference between research question and hypotheses and when should they be appropriately used. 6. What are the differences Between Qualitative & Quantitative Designs? 7. What is your understanding of qualitative research designs based on the definitions you have read and researched? 8. What is your understanding of quantitative research designs based on the definitions you have read and researched? 9. Based on your definitions of the two terms, identify the differences between the two types of research designs. 10. In Faisal’s case, for what reasons do you think Faisal chose mixed methodologies. 11. Would a mono methodology work in Faisal’s case? Justify why 12. Are there alternative methodologies you suggest that are more appropriate for Faisal’s study? If not, why? 13. Think of an IS topic of your choice and determine an appropriate research methodology. Justify your choice.

References

51

14. Based on the chosen methodology, what research design is appropriate? Justify your choice.

References Callegaro, M., & Yang, Y. (2018). The role of surveys in the era of “big data.” In D. L. Vannette & J. A. Krosnick (Eds.), The Palgrave handbook of survey research (pp. 175–192). Springer International Publishing. https://doi.org/10.1007/978-3-319-54395-6_23 Cater-Steel, A. (2008). Information systems research methods, epistemology, and applications. Information Science Reference. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications. Eriksson, P., & Kovalainen, A. (2008). Qualitative methods in business research: A practical guide to social research. Sage. Eriksson, P., & Kovalainen, A. (2015). Qualitative methods in business research: A practical guide to social research. Sage. Hughes, J., & Howcroft, D. A. (2000). Grounded theory: Never knowingly understood. Information Systems Review, 4(1), 181–197. Lichterman, P. (2015). Interpretive reflexivity in ethnography. Ethnography. Myers, M. D., & Avison, D. (2002). Qualitative research in information systems: A reader. Sage Publications. Netflix. (2022). Experimentation is a major focus of Data Science across Netflix. Retrieved 2nd February from https://netflixtechblog.com/experimentation-is-a-major-focus-of-data-scienceacross-netflix-f67923f8e985 Oates, B. J. (2006). Researching information systems and computing. Sage Publications. Robson, C. (2016). Real world research: A resource for social scientists and practitionerresearchers. Wiley. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students. Pearson. Yin, R. K. (2018). Case study research and applications: Design and methods. Sage publications.

4

Contemporary IS Theory and Methodological Applications

4.1

Introduction

Overview

– This chapter aims to draw attention to contemporary theory that can be applied to IS research for studying Social Paradigm Shifts and Global Changes, including IS theories such as “Sociomateriality” (the relationship between material and human existence, or technology and social interactions). Similar and other relevant IS theories include actor-network theory (the study of the cultural traces that the material objects and human actors leave in the process of forming groups), structuration theory (explaining connections between situated interactions and social structures of meaning, norms, and power) or technology acceptance (models how users come to accept and use a technology). This provides researchers with a novel perspective on the methodological theory pertaining to contemporary IS research. Learning outcomes include: . Critically appraise the social paradigm shifts and global changes in IS research, recognising the different methodological worldviews and which of these worldviews would be best suited based on the nature of the IS research project . Understand the theoretical foundations of IS research methods.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Ali, Information Systems Research, https://doi.org/10.1007/978-3-031-25470-3_4

53

54

4

4.2

Contemporary IS Theory and Methodological Applications

Theoretical Foundations of IS

Example

Did you know that IS theory is concerned with information transformation and can be regarded as the theoretical foundation of information computer technology, and have been around for the past 70 years, with the earliest theories emerging in the 1940s.