B.E. (Computer Science). M.E. (Computer Science). Faculty of Information Sciences and Engineering. University of Canberra ACT 2601 Canberra. PhD Thesis.
Open Source Software Adoption in the Australian Public Sector
By
Kavitha Gurusamy B.E. (Computer Science) M.E. (Computer Science)
Faculty of Information Sciences and Engineering University of Canberra ACT 2601 Canberra
PhD Thesis
A Thesis submitted in fulfilment of the requirements of the Degree of Doctor of Philosophy (Information Sciences and Engineering)
(February 21, 2011)
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Abstract Despite a considerable body of literature on Open Source Software (OSS) adoption, there is little research into adoption or rejection of OSS by public sector organisations, and into their practical experiences of using OSS. This study explored various factors that may enable or inhibit OSS adoption by Australian Public Sector (APS) organisations from the perspectives of those involved in software procurement. This research used two major technology adoption theories to study OSS adoption within APS organisations: Diffusion of Innovation (DOI) theory and the Technology Acceptance Model (TAM). This research incorporated a survey of those involved in software procurement to identify enablers/inhibitors of OSS adoption within APS organisations. A case study was also conducted by interviewing those people to provide further theoretical insights on practical experiences in using OSS. The findings were analysed through the lenses of technology adoption theories and OSS adoption literature. The findings of this study showed that OSS provides economic advantages and is a flexible alternative to proprietary software. Success of OSS adoption in Australian Public Sector organisations was contingent upon critical factors such as software quality and features that better meet organisational business needs, maintainability and availability of support, economic value, and the attitude of staff towards OSS. On the other hand there were issues in adopting OSS applications. For example, perceived lack of availability of support and training to sustain long-term usage, economical disadvantages associated with OSS applications such as higher support, maintenance and training costs, lack of product quality, inability to meet business needs, and legal issues with licensing and intellectual property were organisational concerns about OSS adoption. This study contributes to the existing body of knowledge on the adoption of OSS, and the technology adoption theories in the context of OSS by identifying various factors that enable or inhibit OSS adoption within APS organisations. This research identified that innovation attributes are applicable to OSS technology adoption including relative advantage, compatibility, and complexity. Further, organisational attributes formalization and organisational size, and environmental attributes communication channels and adopter characteristics were also found to have an impact on OSS adoption. The other factors, trialability and organisational slack (in terms of human and financial resources), were not found to have an impact on OSS adoption. The findings of this research provided valuable insight into the OSS adoption process for OSS industry, OSS iii
community, and public sector policy makers. The findings of this research will: assist the OSS community to produce better OSS applications that meet organisational business needs; identify where OSS vendors should focus to offer better support and services to organisations using OSS; and provide guidance for public sector policy makers in the development of specific strategies to support OSS.
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Table of Contents 1
2
Introduction to the thesis ................................................................................................................. 1 1.1
Background............................................................................................................................ 2
1.2
Purpose of the research and research question ...................................................................... 2
1.3
Rationale for the study........................................................................................................... 4
1.4
Significance of the study ....................................................................................................... 6
1.5
Overview of the thesis ........................................................................................................... 7
Literature review ............................................................................................................................. 9 2.1
Introduction ........................................................................................................................... 9
2.2
Open Source Software ........................................................................................................... 9
2.2.1
Overview of Open Source Software................................................................................ 10
2.2.2
Open Source movement within public sector organisations around the world ............... 14
2.2.3
Open Source movement within Australia ....................................................................... 15
2.2.4
Research based on Open Source Software ...................................................................... 18
2.2.5
Conflicting findings in the OSS literature ....................................................................... 20
2.3 2.3.1
Innovation Theory ........................................................................................................... 21
2.3.2
Technology Acceptance Model ....................................................................................... 22
2.3.3
Research based on Innovation Theory ............................................................................ 23
2.3.4
Research based on TAM ................................................................................................. 28
2.3.5
Innovation adoption in an organisation ........................................................................... 31
2.4 3
Technology adoption theories ............................................................................................. 21
Summary of the chapter ....................................................................................................... 34
Research design ............................................................................................................................ 37 3.1
Introduction ......................................................................................................................... 37
3.2
Research development model .............................................................................................. 38
3.3
Research methodology ........................................................................................................ 41
3.3.1
Strength of multi-method approach and triangulation .................................................... 41
3.3.2
Strengths of the case study .............................................................................................. 42
3.3.3
Strengths of the survey .................................................................................................... 43
3.3.4
Conclusion....................................................................................................................... 45
3.4
Ethical considerations .......................................................................................................... 45
3.5
Survey .................................................................................................................................. 45
3.5.1
Sampling ......................................................................................................................... 45
3.5.2
Survey instrument development and pre-testing ............................................................. 47
3.5.3
Administration of the survey instrument ......................................................................... 56 vii
3.5.4 3.6
Case study............................................................................................................................ 59
3.6.1
Unit of analysis ............................................................................................................... 59
3.6.2
Qualitative data collection technique .............................................................................. 60
3.6.3
Data analysis strategy ...................................................................................................... 61
3.6.4
Validity and reliability .................................................................................................... 62
3.6.5
Construction of interview instrument .............................................................................. 63
3.6.6
Selection of interview participants .................................................................................. 64
3.6.7
Administration of the interviews ..................................................................................... 64
3.7 4
Data analysis strategy ...................................................................................................... 57
Summary of the chapter ....................................................................................................... 65
Survey results and findings ........................................................................................................... 67 4.1
Introduction ......................................................................................................................... 67
4.2
Survey description ............................................................................................................... 67
4.2.1
Organisational profile...................................................................................................... 67
4.2.2
Size of the organisations ................................................................................................. 68
4.2.3
Description about the participants’ role .......................................................................... 69
4.2.4
OSS usage within an organisation ................................................................................... 70
4.2.5
Data imputation for missing values ................................................................................. 70
4.3
Factor analysis results.......................................................................................................... 71
4.3.1
Factors enabling OSS adoption ....................................................................................... 75
4.3.2
Factors inhibiting OSS adoption ..................................................................................... 80
4.3.3
Organisational experience with OSS............................................................................... 84
4.3.4
Perceptions about the benefits of OSS ............................................................................ 86
4.3.5
Communication channels used to access OSS information ............................................ 88
4.3.6
Discussion of factor analysis results ............................................................................... 90
4.4
Survey findings .................................................................................................................... 93
4.4.1
Factors enabling OSS adoption ....................................................................................... 94
4.4.2
Factors inhibiting OSS adoption ..................................................................................... 95
4.4.3
Organisational experience with OSS............................................................................... 96
4.4.4
Perceptions about the benefits of OSS ............................................................................ 97
4.4.5
Communication channels ................................................................................................ 97
4.4.6
Impact of ICT policy on OSS adoption ........................................................................... 98
4.4.7
Impact of OSS guidelines on OSS adoption ................................................................... 98
4.4.8
Impact of organisational satisfaction with OSS products ............................................... 98
4.4.9
Impact of organisational satisfaction with resources ...................................................... 99
4.4.10
Impact of OSS trials on adoption .............................................................................. 100 viii
4.4.11 4.5 5
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Summary of the chapter ..................................................................................................... 106
Case study findings ..................................................................................................................... 107 5.1
Introduction ....................................................................................................................... 107
5.2
Interview description ......................................................................................................... 107
5.3
OSS usage within an organisation ..................................................................................... 108
5.4
Enablers of OSS adoption.................................................................................................. 110
5.5
Inhibitors of OSS adoption ................................................................................................ 123
5.5.1
Deployment problems ................................................................................................... 123
5.5.2
Practical difficulties in adopting OSS ........................................................................... 127
5.5.3
Migration issues ............................................................................................................ 135
5.5.4
Review of inhibitors of OSS adoption .......................................................................... 136
5.6
Organisational experience with OSS source code ............................................................. 137
5.7
Perceptions about benefits of OSS .................................................................................... 139
5.8
Communication channels .................................................................................................. 140
5.9
Impact of ICT policy on OSS adoption ............................................................................. 140
5.10
Impact of OSS guidelines on OSS adoption ...................................................................... 140
5.11
Impact of organisational satisfaction with OSS products .................................................. 142
5.12
Impact of organisational satisfaction with resources ......................................................... 143
5.13
Impact of OSS trials on adoption ...................................................................................... 144
5.14
Impact of IT budget on OSS adoption ............................................................................... 145
5.15
Impact of training requirement on OSS adoption .............................................................. 146
5.16
Impact of vendor-lock-in on OSS adoption ....................................................................... 147
5.17
Review of case study findings ........................................................................................... 148
5.18
Summary of the chapter ..................................................................................................... 155
Conclusions ................................................................................................................................. 157 6.1
Introduction ....................................................................................................................... 157
6.2
Summary of research conclusions ..................................................................................... 157
6.2.1
Enablers of OSS adoption ............................................................................................. 161
6.2.2
Inhibitors of OSS adoption............................................................................................ 162
6.2.3
Factors that do not affect OSS adoption........................................................................ 163
6.3 7
Discussion of survey findings ................................................................................... 102
Summary of the chapter ..................................................................................................... 164
Discussions ................................................................................................................................. 165 7.1
Introduction ....................................................................................................................... 165
7.2
Enablers of OSS adoption.................................................................................................. 165
7.3
Inhibitors of OSS adoption ................................................................................................ 171 ix
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7.4
Organisational experience with OSS source code ............................................................. 176
7.5
Perceptions about the benefits of OSS .............................................................................. 177
7.6
Communication channels .................................................................................................. 178
7.7
Impact of ICT policy on OSS adoption ............................................................................. 178
7.8
Impact of OSS guidelines on OSS adoption ...................................................................... 179
7.9
Impact of organisational satisfaction with OSS products .................................................. 179
7.10
Impact of organisational satisfaction with resources ......................................................... 180
7.11
Impact of OSS trials on adoption ...................................................................................... 180
7.12
Impact of IT budget on OSS adoption ............................................................................... 181
7.13
Impact of training requirements on OSS adoption ............................................................ 182
7.14
Impact of vendor-lock-in on OSS adoption ....................................................................... 182
7.15
Summary of the chapter ..................................................................................................... 183
Research contributions and implications .................................................................................... 187 8.1
Introduction ....................................................................................................................... 187
8.2
Summary of the research ................................................................................................... 187
8.3
Practical implications ........................................................................................................ 190
8.4
Contribution to theory ....................................................................................................... 192
8.4.1
Contribution to Innovation Theory ............................................................................... 192
8.4.2
Contribution to TAM .................................................................................................... 195
8.5
Limitations of this research ............................................................................................... 195
8.6
Future research .................................................................................................................. 196
8.7
Conclusions ....................................................................................................................... 197
Bibliography ....................................................................................................................................... 199 Appendices.......................................................................................................................................... 209 Appendix A-1 Technology adoption factors identified from the literature with references ........... 209 Appendix B ..................................................................................................................................... 213 Appendix B-1
Survey questionnaire ....................................................................................... 213
Appendix B-2
Ethics approval letter ....................................................................................... 221
Appendix B-3
Invitation e-mail to the survey participants ..................................................... 222
Appendix B-4
Reminder email to the survey participants ...................................................... 223
Appendix B-5
Interview instrument ....................................................................................... 224
Appendix B-6
Invitation email to the interview participants .................................................. 225
Appendix B-7
Participant Information form - Interview ........................................................ 226
Appendix B-8
Informed Consent form - Interview ................................................................ 228
Appendix C – Survey results .......................................................................................................... 229 Appendix C-1
Factor analysis results ..................................................................................... 229 x
Appendix C-2
Factors enabling OSS adoption ....................................................................... 232
Appendix C-3
Factors inhibiting OSS adoption ..................................................................... 234
Appendix C-4
Organisational experience with OSS............................................................... 236
Appendix C-5
Communication channels ................................................................................ 236
Appendix C-6
Organisational satisfaction with OSS products and resources ........................ 236
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List of Tables Table 2-1: Innovation characteristics that affect innovation adoption and implementation ................. 24 Table 2-2: Research based on Innovation Theory................................................................................. 26 Table 2-3: Research based on TAM ..................................................................................................... 29 Table 2-4: Summary of items identified from literature review ........................................................... 34 Table 3-1: Comparison of relative strengths of case study and survey methods .................................. 44 Table 3-2: Development of questionnaire constructs based on items from the literature review ......... 49 Table 4-1: Factor analysis of organisational perception of enablers of OSS adoption ......................... 79 Table 4-2: Factor analysis of organisational perception of inhibitors of OSS adoption ....................... 83 Table 4-3: Factor analysis of organisational experiences with OSS ..................................................... 86 Table 4-4: Factor analysis of organisational perceptions about the benefits of OSS ............................ 88 Table 4-5: Factor analysis of communication channels used to access OSS information .................... 90 Table 4-6: Mapping of the factors against theoretical attributes ........................................................... 92 Table 4-7: Summary of survey findings against theoretical attributes................................................ 103 Table 4-8: Survey findings: moderators versus theoretical attributes ................................................. 104 Table 5-1: Organisational profile – case study ................................................................................... 108 Table 5-2: List of OSS applications used by APS organisations ........................................................ 109 Table 5-3: Summary of case study findings against theoretical attributes .......................................... 150 Table 6-1: Summary of the research findings against theoretical attributes ....................................... 159 Table A-1.1: Summary of technology adoption factors identified from the literature with references ............................................................................................................................................................ 209 Table C-1.1: Communalities of organisational perception of items enabling OSS adoption ............. 229 Table C-1.2: Communalities of organisational perception of items inhibiting OSS adoption............ 230 Table C-1.3: Communalities of organisational experiences with OSS adoption ................................ 230 Table C-1.4: Communalities of perceptions about the benefits of OSS ............................................. 231 Table C-1.5: Communalities of communication channels used to access OSS information .............. 231 Table C-2.1: Independent samples t-test for OSS use with enablers .................................................. 232 Table C-2.2: One-way ANOVAs between organisational size and enablers ...................................... 232 Table C-2.3: One-way ANOVAs between organisational perception of increasing OSS use in future and enablers ........................................................................................................................................ 233 Table C-2.4: Independent samples t-test for organisational plan to use OSS with enablers ............... 233 Table C-3.1: One-way ANOVAs between organisational size and inhibitors .................................... 234 Table C-3.2: One-way ANOVAs between organisational perception of increasing OSS use in future and inhibitors ...................................................................................................................................... 234 Table C-3.3: Independent samples t-test for organisational plan to use OSS with inhibitors............. 235 Table C-3.4: Independent samples t-test for OSS use with inhibitors ................................................ 235 Table C-4.1: Independent samples t-test for organisational plan to use OSS with their experiences . 236 Table C-4.2: One-way ANOVAs between organisational perception of increasing OSS use in future and experiences on OSS...................................................................................................................... 236 Table C-5.1: Independent samples t-test for OSS use with communication channels ........................ 236 Table C-6.1: One-way ANOVAs between organisational size and satisfaction ................................. 236
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List of figures Figure 2-1: Australian Open Source industry ....................................................................................... 17 Figure 3-1: Research model .................................................................................................................. 39 Figure 3-2: Research Process ................................................................................................................ 40 Figure 4-1: Survey responses of organisation type versus frequency ................................................... 68 Figure 4-2: Number of people employed in the participated organisations .......................................... 69 Figure 4-3: Participants role in their organisation................................................................................. 69 Figure 4-4: OSS usage within organisation .......................................................................................... 70 Figure 4-5: Organisational perception of items enabling OSS adoption .............................................. 76 Figure 4-6: Scree plot of factors enabling OSS adoption ..................................................................... 77 Figure 4-7: Organisational perception of items inhibiting OSS adoption. ............................................ 81 Figure 4-8: Scree plot of factors inhibiting OSS adoption .................................................................... 82 Figure 4-9: Organisational experiences with OSS ................................................................................ 84 Figure 4-10: Scree plot of organisational experience with OSS ........................................................... 85 Figure 4-11: Organisational perceptions about the benefits of OSS ..................................................... 87 Figure 4-12: Scree plot of organisational perceptions about the benefits of OSS ................................ 87 Figure 4-13: Communication channels used to access OSS information ............................................. 89 Figure 4-14: Scree plot of communication channels used to access OSS information ......................... 89 Figure 4-15: OSS adoption after test................................................................................................... 101 Figure 4-16: Testing software products before deployment ............................................................... 101 Figure 5-1: Enablers of OSS adoption ................................................................................................ 122 Figure 5-2: Issues in OSS deployment ................................................................................................ 126 Figure 5-3: Deployment problems versus organisational plans to use OSS ....................................... 127 Figure 5-4: Practical difficulties in adopting OSS .............................................................................. 133 Figure 5-5: Practical difficulties versus organisations plans about OSS ............................................ 135 Figure 5-6: Perception about national economic development ........................................................... 139 Figure 5-7: Perception about satisfaction versus plans to use OSS .................................................... 143 Figure 7-1: Research model with findings .......................................................................................... 184
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Acknowledgements I am grateful to many people who assisted me throughout the process of this PhD journey. First, I would like to thank Professor John Campbell, my supervisor and chair of the supervisory panel, for accepting me as a doctoral candidate. I am honored to have an opportunity to work with him and thankful for his continued guidance, support, encouragement, and especially allowing me to see him at any time without prior appointment throughout the years. I would also like to express my sincere gratitude to my supervisory panels Associate Professor Craig McDonald and Assistant Professor Wanli Ma for their invaluable guidance and feedback. I thank them for the insights they provided to me on my thesis, as well as their encouragement to help me in every aspect of my doctoral thesis. I am thankful to Professor Dharmendra Sharma, Dean of Faculty of Information Sciences and Engineering, for his encouragement and support during the course of my study. Special thanks to Dr. Linda Li, Academic Skills Program, for giving me valuable suggestions in writing the thesis. My special thanks to Dr. David Pederson, statistical consultant and Assistant Professor Alice Richardson for their valuable advice in helping me analysing and reporting the survey data. Many thanks to research colleagues and staff in Faculty of Information Sciences and Engineering for sharing their research experience during my candidature and Pia Waugh, formerly OSS research coordinator at, Macquarie University, for advice on OSS. I am grateful to Jennifer Bradley, for taking on the hard task of proof reading this thesis. I also wish to thank the ACIS conference committee for helping shape this thesis through the reviewers‟ comments. I sincerely acknowledge the financial support offered to me through an Australian Postgraduate Research Award. Also I acknowledge the University of Canberra for providing me financial support as well as an opportunity to attend various seminars and workshops throughout my candidature. I am also thankful to participants in this study. Without their support, this project would not have been possible. Finally, I would like to thank my daughter Priyanka Ramkumar and my son Varun Ramkumar, and all of my family members and friends for their love, patience, enthusiasm and encouragement during this long research journey.
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Acronyms AGIMO
Australian Government Information Management Office
ANOVA
Analysis of Variance
APS
Australian Public Sector
ASK-OSS
Australian Service for Knowledge of Open Source Software
ATO
Australian Taxation Office
AUUG
Australian Unix and Open Systems User Group
CEHR
Committee for Ethics in Human Research
COTS
Commercial Off The Shelf
DOI
Diffusion of Innovation
EDT
Expectancy Disconfirmation Theory
EFA
Exploratory Factor Analysis
EM
Expectation Maximization
EPL
Evaluated Product List
FLOSS
Free / Libre / Open Source Software
FOSS
Free / Open Source Software
FOSSACT Free Open Source Software Australian Capital Territory ICT
Information and Communication Technology
IP
Intellectual Property
IS
Information Systems
KMO
Kaiser-Meyer-Olkin
MERRI
Managed Environment for Research Repository Infrastructure
MIS
Management Information Systems
ML
Maximum Likelihood
MLE
Maximum Likelihood Estimation
NASA
National Aeronautics and Space Administration
OSI
Open Source Initiative
OSIA
Open Source Industry Australia
OSS
Open Source Software xix
PAF
Principal Axis Factoring
PCA
Principal Components Analysis
PLS
Partial Least Squares
SPSS
Statistical Package for the Social Sciences
SEM
Structural Equation Modelling
TAM
Technology Acceptance Model
TCO
Total Cost of Ownership
TRA
Theory of Reasoned Action
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Introduction to the thesis
Open Source Software (OSS)1 is software that comes with source code and its licence allows users to modify and redistribute the modified work under the same license agreement. OSS is often available free or at low cost and allows organisations to customise the software to better meet their organisational business needs and to integrate it with existing technical environments. In contrast, proprietary software is closed in nature, available at a cost and the copyright is owned by the developer organisation(s). This means that end users generally do not have access to the source code, cannot make changes or customise the software and cannot redistribute it. OSS is often seen as a cost effective alternative to proprietary software. This attribute has attracted public sector organisations worldwide to invest in OSS research activities to sustain and improve its utilization. Research activities were focused on various aspects including advantages associated with OSS, how to derive benefits from OSS, OSS licences, OSS development processes and community coordination within OSS projects and analysing the success and failure of OSS projects. Though there was some research that investigated factors involved in the OSS adoption process, there has been little focus on organisational concerns in adopting OSS applications. So it was important to reassess the factors influencing OSS adoption in order to enhance OSS utilization. Consequently, this study investigated the enablers and inhibitors of Open Source Software adoption within Australian Public Sector (APS) organisations. The study findings contribute to the existing body of knowledge on the adoption of Open Source Software by providing insights into the factors that enable or inhibit OSS adoption within APS organisations. Further, the findings were used to test the veracity of the technology adoption theories in the domain of OSS adoption. In this chapter, a brief introduction about the theme concepts is provided. Then, the purpose of this study is discussed and the underlying research questions are introduced. Subsequently, the rationale and significance of the research are described. Finally an outline of the thesis is provided.
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Detailed description about OSS is available at http://www.opensource.org/docs/definition.php
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1.1 Background OSS is increasingly gaining public sector attention around the world because of its equivalent or superior functionality to proprietary software such as reliability, open standards, flexibility, compatibility, performance and quality in certain circumstances (Lorraine & Patrick, 2007; Ven & Verelst, 2006). As a result many public sector organisations are investing in OSS research such as assessing availability and potential of OSS based solutions in public sector organisations (GITOC, 2003; Schmitz, 2001). Internationally, many government organisations either use or plan to use OSS (Comino & Manenti, 2005; Evans & Reddy, 2003; Hwang, 2005; OGC, 2004). Governments have shown their interest by creating policies to promote OSS adoption within their country, mandating open standards and interfaces, increasing knowledge and understanding of OSS through publications and presentations, and initiating pilot projects and research programs (Comino et al., 2006; Comino & Manenti, 2005; GITOC, 2003; Hillenius, 2009; IDABC, 2008a, 2008b; OGC, 2004). In Australia the Commonwealth Government is taking significant steps in OSS adoption within APS organisations by publishing OSS guidelines and requesting government organisations to consider OSS applications. This research aims to help government policy makers and the OSS industry improve OSS usage by investigating APS organisations‟ experience with OSS usage and the practical difficulties experienced by APS organisations.
1.2 Purpose of the research and research question The Technology Acceptance Model (TAM) (Davis, 1989) and Innovation Theory (Rogers, 1995) identify a list of factors that drive technology usage. TAM is used to study individual‟s intention to adopt a technology. Innovation theory deals with technological as well as organisational characteristics that affect innovation adoption. Individuals‟ roles in an organisation is important as an organisation is a stable system of individuals who work together to achieve their goals (Lapointe & Rivard, 2005, 2007; Rogers, 2003). Further, these theories have gained substantial attention from researchers, who used them to test and identify the factors influencing new technology adoption (sections 2.3.3 and 2.3.4). This research uses both theories to assess organisational adoption of OSS technology. This research aims to build on earlier work by investigating factors involved in OSS adoption. The study will examine the specific factors at work and compare them with those reported in the theory and literature. An account will be given of why expected factors are not operational or what new factors are operational in OSS adoption. The outcomes of this research will contribute to: the existing body of knowledge on the adoption of OSS by identifying 2
organisational benefits and concerns in the adoption of OSS; and technology adoption theories by identifying the applicability of the attributes of technology adoption theories to OSS adoption. In order to achieve the research objective the following question is investigated: Do technology adoption theories account for Open Source Software adoption in Australian Public Sector organisations? The study will further explore the following two subsidiary questions to the main research question of the thesis: 1. What are the enablers of OSS adoption by Australian Public Sector (APS) organisations? 2. What are the inhibitors of OSS adoption by Australian Public Sector (APS) organisations? The first subsidiary research question explores the factors that influence an organisation to adopt OSS. These factors can vary from organisation to organisation depending upon organisational business need. Attention will be given to the following organisational issues: software requirements and how OSS fits these needs; the selection of OSS applications; and the practical experiences with OSS adoption. In this research the identified concepts are grouped based on similarities revealed in the study. Then, these factors are used to examine the specific factors at work and compare them with the factors identified from Innovation Theory and the technology adoption literature. Also an account is given of why expected factors are not operational or what new factors have emerged with OSS adoption within APS organisations. The second subsidiary research question investigates practical difficulties in using OSS. The inhibitors were explored in the following ways: (1) Practical difficulties experienced by adopters; (2) Issues associated with migrating applications from and to proprietary products; and (3) Perceived problems that organisations would expect in using OSS. Some of these perceived problems might have a negative impact on the willingness to adopt OSS. Therefore it is important to consider perceived problems along with real problems that inhibit OSS usage within APS organisations. The findings will be compared with the factors identified as inhibitors from technology adoption theories and literature. The factors identified as enablers and/or inhibitors from this study will be used to contribute to theory by reflecting on or 3
expanding the current state of technology adoption theories in the domain of OSS. The findings from this research will be used to examine the relationship between theory and practice. The factors from theory that are applicable for OSS adoption will be identified.
1.3 Rationale for the study Open Source Software is increasingly acknowledged as a viable alternative to proprietary products, with significant software reliability and value for money benefits for businesses of all kinds (Haider & Koronios, 2009). Public sector organisations around the world are acknowledging the potential of OSS and are spending large amounts of money to assess the sustainability of Open Source Software (Ouédraogo, 2005). The European Union has been a frontrunner in this process by actively pursuing investment in OSS research activities. For example, the European Union‟s Free / Libre / Open Source Software (FLOSS) 2 world project which is focused on Free and OSS to strengthen Europe's leadership in research into FLOSS and open standards. The European Commission‟s QualiPSo3 aims to reduce uncertainty about using OSS within public sector organisations (IDABC, 2008a). The public sectors‟ rationale for using OSS include superior features available in OSS, cost efficiency and reducing their dependency on foreign suppliers (Comino et al., 2006). Most of the research carried out around the world has looked at the following: advantages associated with OSS (Ghosh & Glott, 2005; Ouédraogo, 2005); how to realise benefits from OSS (Laplante et al., 2007); OSS licences; OSS development processes and community coordination within OSS projects (Bonaccorsi & Rossi, 2003; Crowston et al., 2007; DinhTrong & Bieman, 2005; Krogh et al., 2003; Mockus et al., 2005); and analysing the success and failure of OSS projects (Crowston et al., 2006; Feitelson et al., 2006; Israeli & Feitelson, 2007; Katsamakas & Georgantzas, 2007; Lerner & Tirole, 2005; Senyard & Michlmayr, 2004). However, there has been little research on what factors inhibit OSS usage (Goode, 2005; Holck et al., 2005; Larsen et al., 2004); the practical problems being faced by the organisations when adopting OSS (Haider & Koronios, 2008); and the factors influencing OSS adoption within public sector organisations (Hwang, 2005; Haider & Koronios, 2009). Although there has been some research on the success and failure of OSS projects, additional research was needed to provide better insights into the OSS adoption process, which help to narrow the gap between organisational requirements and the ability of OSS applications and 2
For details see the link http://www.flossworld.org/index.php This project is funded by the European Commission under its sixth framework program (FP6), as part of the Information Society Technologies (IST) initiative. QualiPSo was launched in synergy with Europe‟s technology initiatives such as NESSI and Artemis. http://www.qualipso.org/node/7 3
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services to meet those requirements. The Australian government promotes OSS adoption. As part of the Australian Government‟s interest in OSS adoption, the Australian Government Information Management Office (AGIMO) released a guide providing information to public sector organisations on the benefits of using OSS (AGIMO, 2005). AGIMO has conducted seminars with OSS service providers and vendors within Australia to promote greater awareness of OSS in public sector organisations. However, previous research conducted in Australia at state government and commercial organisations have shown OSS adoption rate in Australia is lagging behind compared with the rest of the world (Haider, 2008; Higgins et al., 2005). Yet, there is very little in-depth research on inhibitors to OSS adoption. Goode‟s (2005) research focused on barriers to OSS adoption within Australian organisations. However it was not an in-depth study and did not focus on Australian Public Sector organisations. Haider and Koronios‟s (2009) study focused on the benefits and risks of using OSS within three Australian state government organisations. It would be useful to reassess the reasons why public sector organisations choose OSS and problems experienced in using OSS, as it would be beneficial for the public sector organisations to know the factors that influence OSS adoption. In order to achieve the Government‟s goals on OSS and to maximize the benefits of using OSS, government needs to know the public sector organisations‟ experiences with OSS. Lack of in-depth research on APS organisations‟ experience with OSS and their perceived problems in using OSS can create an impediment for government to improve OSS usage within APS organisations even though government policy supports OSS. This research helps government to formulate effective policy on OSS by providing information on organisational expectations/difficulties of using OSS and how to take effective steps to minimize potential problems. Further, it provides information to the OSS community and service providers to assist in offering better products and services in the future. The consequences of the above will help to create a new way to leverage OSS usage within public sector organisations.
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1.4 Significance of the study This study is one of the first to explore factors behind OSS adoption by Australian Public Sector organisations and is significant as it has filled a gap by providing in-depth empirical research on factors involved in OSS adoption within APS organisations. This study is different from previous studies in the following ways: samples were collected from Commonwealth organisations (federal government), state and territory government organisations, local government organisations and government enterprises; the research framework was developed based on technology adoption theory attributes; used a multimethod approach that provided an opportunity to triangulate the study results. This study contributes to existing technology adoption theories by adding knowledge to Innovation Theory attributes with respect to OSS; and contributes to practice by identifying factors involved in OSS adoption within APS organisations. Consequently this research contributes valuable knowledge to research and practice. The academic contribution of the research findings reflects on and modifies the current state of technology adoption theories (Innovation theory and TAM) as applied to the OSS domain. This research identifies innovation characteristics that are applicable to OSS adoption from Innovation Theory. Further, this research has the potential to identify new adoption factors that influence OSS adoption. In so doing, it has the potential to contribute to technology adoption theories. The findings that support Innovation Theory attributes are used to add value to the existing technology adoption theories by research based on new innovations. New factors specific to OSS adoption will enhance an understanding of the attributes of innovation that influence technology adoption. The practical contributions of the research are multifold. Recommendations can also be suggested to the policy makers by providing knowledge on factors that influence OSS usage and problems faced when using OSS within APS organisations. This research identifies the tensions between the organisational business needs/expectations and what the OSS industry/community is providing. A better understanding of this tension helps to narrow the gap between OSS service providers and users. This helps the OSS industry/community to produce better products and subsequently expand OSS adoption within organisations. This research finding will help policy makers and OSS industry to make effective decisions on OSS adoption.
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1.5 Overview of the thesis This thesis is organized into eight chapters. Chapter one provides an introduction, aims and expected contributions from this research. This section also gives brief information about the organisation of the rest of the thesis. Chapter two reviews in detail literature on OSS, then discusses technology adoption theories such as Innovation Theory and the Technology Acceptance Model along with previous research based on those theories. Finally, a summary of those factors that impact on technology adoption (including OSS), as identified from theory and literature, is reported. Chapter three describes the research design used to investigate the factors enabling/inhibiting OSS adoption. Further, this chapter describes and justifies the research methodology adopted to address the research question. This research has used both quantitative (survey) and qualitative (case study) approaches. As part of the quantitative study, the questionnaire development process, and details of participants selection for the study are discussed. Afterward, the data collection process and the data analysis strategy are explained. For the qualitative study, data collection techniques, unit of analysis and data analysis strategy are discussed. Then, construction of the interview instrument and specific steps used in data collection process are outlined. Summary of technology adoption factors reported in Chapter two is used for the survey questionnaire and interview instrument development process. Chapter four reports the survey results and findings that include demographic details of the survey, handling of missing values from the survey responses followed by the statistical results obtained from the survey and findings based on significant statistical results. The data analysis strategy explained in Chapter three is used in Chapter four to analyse the survey responses. Then, this research employs statistical tests such as factor analysis, Chi-square, one way ANOVA and independent samples t-test. Detailed factor analysis results are reported. Then, the detailed statistical analysis test results for the factors that show significant impact on OSS adoption are reported with significant values. However, the details of all statistical test results, including both significant and non-significant results, are reported in Appendix C. The summary of findings reports their relationship to the research questions. Finally, the implications for technology adoption theory attributes are presented based on the identified enablers and inhibitors of OSS adoption. Chapter five reports the findings from the case study. Case study findings enrich the survey findings, and provide further theoretical insights into the OSS adoption process. 7
Data
analysis strategy explained in chapter three is used to derive findings from the case study. In the case study analysis, interconnected concepts are grouped as factors and used to draw conclusions from the case study. At the end of this chapter, a summary of findings from the case study is reported that addresses research questions. Finally, factors that enable and inhibit OSS adoption process are identified, and the implications for technology adoption theory attributes in an OSS context are discussed. Chapter six reports the findings from the study, which incorporated survey and case study approaches. Summary of enablers and inhibitors to OSS adoption and its relationship to theoretical attributes were also reported. Chapter seven discusses the findings from the survey and the case study. Then, findings are compared with the factors identified from technology adoption theories and existing literature. Explanations are given where inconsistent results are obtained between the studies (case study and survey) and theories. Then, factors that affect OSS technology adoption are identified. Conclusions are drawn, based on the research findings, about the role of technology adoption theories in OSS adoption. Consequently, the contributions to the existing body of knowledge on the adoption of OSS and technology adoption theories are described. Chapter eight discusses research contributions to theory and practice, and makes recommendations to government policy makers and OSS industry. Finally, limitations of this research and suggestions for future research directions are discussed. Thus, this research achieves its aim and potential contributions are made to the theory and practice.
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2
Literature review
2.1 Introduction This chapter introduces the global OSS phenomenon and technology adoption theories used in this study. The OSS literature is examined and conflicting research findings discussed. The technology adoption theories and their usage in innovation adoption are then presented. The research findings based on technology adoption theories in different technology domains are discussed. Finally, a summary of the factors influencing technology adoption, identified from the theory and literature, are reported.
2.2 Open Source Software The term OSS was first coined in February 1998 (Fitzgerald, 2005), although the idea has a longer history. Open Source Software (OSS) licensing agreements allow users to use, modify and redistribute software free of cost. Like many other terms, the exact meaning of “Open Source Software” is arguable (Holck et al., 2005; Larsen et al., 2004). The definition offered by the Open Source Initiative (OSI) focuses on the software licence. The most important term in this context is “copyleft”, a term introduced by Richard Stallman, which means that the copyright is used to ensure free software and free derivative works based on the software (Berg, 2007). In contrast to proprietary software licences, which mostly deal with restricting users‟ rights and limiting vendors‟ liabilities, OSS licences, according to OSI, must provide users with a number of rights, including The software source code is freely available. Anyone is free to distribute and use the software. However, the licence is not the only characteristic of “Open Source Software” as most people understand it. Another important characteristic is: Software developed and maintained through the “Open Source model”, in which many developers contribute code to a common repository. This research focuses on OSS adoption within APS organisations excluding freeware. Freeware is usually defined as proprietary software given away without cost, and does not provide the basic OSS rights to examine, modify, and redistribute the program‟s source code (Wheeler, 2005). Some prominent examples of OSS are the Linux operating system, Apache 9
web server, Open Office for word processing and Mozilla web browser. 2.2.1 Overview of Open Source Software The term OSS has been widely used in recent years. OSS developments involve enthusiastic volunteers around the world (Dinh-Trong & Bieman, 2005). Most developers have spent their own time in developing OSS projects. Some of them are paid for their contribution to OSS projects by their employers (Lakhani & Wolf, 2005). The OSS development community has very different motivations for contributing code to OSS projects, which can be either intrinsic or extrinsic. The developers‟ intrinsic motivations are altruism, creative pleasure, prestige and fighting against proprietary software; their extrinsic motivations are learning and developing new skills, future career opportunities and monetary rewards (Ghosh, 2005e; Lakhani & Wolf, 2005; Rossi & Bonaccorsi, 2005). OSS projects are usually initiated by a single person or a small number of groups (Dinh-Trong & Bieman, 2005; Lerner & Tirole, 2005). Even though OSS developers are scattered around the world, coordination among the developers is controlled by the strong centralization of authoritative leadership (Dinh-Trong & Bieman, 2005; Lerner & Tirole, 2005). Leading organisations around the world are demonstrating their interest in OSS. For example, large organisations such as Hewlett-Packard, IBM, and Sun have all launched projects to develop and use OSS (Lerner & Tirole, 2005). Some of these organisations are market leaders and have made significant investment in Open Source (Ebert, 2009). Many organisations have supported OSS development by investing money and allowing their employees to participate in OSS development and R&D (Dinh-Trong & Bieman, 2005). Most of these organisations however, seek to obtain revenue from OSS by supplying softwarerelated services, selling related products and selling packaged OSS possibly bundled with proprietary software (Ebert, 2009; Long, 2004). 2.2.1.1 The motivation of organisations in adopting OSS The reasons for organisations to adopt or migrate to OSS vary. There has been substantial research focus on factors that enable OSS adoption within organisations (Chau & Tam, 1997; Dedrick & West, 2003, 2004; Lorraine & Patrick, 2007; Ven & Verelst, 2006). While other research identified inhibitors to OSS adoption within organisations (Chau & Tam, 1997; Ghosh, 2005a; Goode, 2005; Holck et al., 2005). The OSS research literature has identified factors such as licence fee, budgetary reasons, cost savings, security and national values as the major enablers (Comino et al., 2006; Ebert, 2009; Moyle, 2004). Interestingly some of 10
these factors in other research are shown as barriers to adopt OSS (Ghosh, 2005a; Voth & Stone, 2003). The literature regarding the factors involved in OSS adoption is discussed below. Cost Concerns Licence fee: Many organisations have started considering OSS because of the costs associated with using proprietary industry products. The price and licence fee of proprietary industry products are constantly increasing
(Hwang, 2005). In some cases proprietary
licences are charged based on a sliding scale requiring additional licence fees for using the same software on additional machines (Evans & Reddy, 2003; Wheeler, 2005; Wong, 2004). High upgrade costs charged by the proprietary software vendors is also cited as one of the reasons for public sector organisations to migrate to OSS (Ghosh et al., 2002). Budgetary reasons: Increasing costs are particularly important when budgets are limited. In public sector organisations, budgetary constraints are one of the main reasons for moving towards OSS. For example, a study conducted by Australian educational departments shows that a lower Total Cost of Ownership (TCO) and governments‟ limited budgets have led to increased OSS usage within the educational sector (Moyle, 2004). Public sector organisations are interested in reducing their IT budget spent on software licences (Ghosh & Glott, 2005). By adopting OSS, organisations spend less on software and can spend more on new projects and development (Holck et al., 2005). But not all research agrees with this as Ghosh (2005a) for instance, argues that the budget cycle in public sector organisations is a barrier to using OSS because migration to new software requires high initial cost, while cost savings might only be made in following years. Cost saving: In general, cost is one of the major drivers for OSS adoption in organisations (Dedrick & West, 2004). European public sector reports show that considerable cost savings were made after adopting OSS in the workplace (Comino et al., 2006; DBOT, 2002; Ghosh, 2005c; Ghosh & Schmidt, 2006). Reducing software cost is one of the reasons for OSS adoption within South African government departments (Mtsweni & Biermann, 2008a). A study conducted on OSS usage in the Australian education sector shows that schools have saved money and time by adopting OSS (ASK-OSS, 2009). On the one hand, cost is one of the reasons for OSS adoption, but on the other hand, training costs have also led public sector organisations to reject OSS (Ghosh, 2005a), and migration cost is one of the challenges in OSS implementation (Mtsweni & Biermann, 2008a). In contrast, a case study conducted by 11
Larsen et al. (2004), shows that cost does not play a significant role in adoption. Despite these different research findings, cost is one of the important factors to investigate when looking into OSS adoption. Code transparency and Security concerns Code transparency (availability of source code or openness) is the major characteristic associated with OSS, in addition to its freedom to modify and redistribute the source code without any cost. Research studies have found code transparency plays a significant role in OSS adoption (Ebert, 2009; Hwang, 2005; Schmitz, 2001; Ven & Verelst, 2006), yet there have been different findings regarding the role of code transparency (Ghosh & Glott, 2005). Security of OSS products is often cited with OSS‟s open nature of the source code. As far as data security is concerned, Open Source Software is believed to be less vulnerable than proprietary software due to the availability of the source code (Ebert, 2009; Schmitz, 2001). Public sector organisations are concerned about the closed nature of the proprietary products and this concern has driven the adoption of OSS in many cases (Ghosh, 2005c; Hwang, 2005; Schmitz, 2001). Only one study has shown source code was not a reason for adoption (Ghosh & Glott, 2005). Due to national security issues, some countries have forced Microsoft to open its code to users and, in certain security related applications, banned proprietary software. For example, the European Union Commission forced Microsoft to disclose the Microsoft Windows XP source code in order to better understand developing products that enhance interoperability with Windows products (Ghosh et al., 2002; PressPass, 2006). As a result Microsoft agreed to disclose its Windows source code to a selective panel but not to the public. This discloser allows software developers to view the source code in order to better understand how to develop products that interoperate with windows, but not to copy the Microsoft‟s source code (Comino et al., 2006; PressPass, 2006). Code transparency is therefore perceived as an important factor in security issues. In defence and military organisations, security is a major concern. A survey conducted at organisations involved with the US Department of Defense shows that OSS was used where information security is important (MITRE, 2003). If there are vulnerabilities in proprietary software, being unable to inspect source code can cause serious problems (Ghosh et al., 2002; Hwang, 2005; MITRE, 2003). Microsoft, however, claims that proprietary software is more secure than OSS because OSS allows hackers to attack other users‟ systems (Voth & Stone, 2003).
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National values Public sector organisations are said to be interested in adopting OSS because the adoption of OSS creates economic value by developing local Information and Communication Technology capabilities (Ghosh et al., 2002; Ghosh & Schmidt, 2006; Haider & Koronios, 2009; Wong, 2004), provides employment to local people (Comino et al., 2006; Ghosh, 2005d), improves economic growth (Ghosh, 2006; Ghosh & Schmidt, 2006), improves skills (Ghosh, 2005d, 2006), keeps tax payers‟ money within a country, provides for local industry development through OSS support services (Hwang, 2005; Haider & Koronios, 2009), and reduces dependency from foreign suppliers (vendor independence) (Comino et al., 2006; Ghosh, 2005c; Ven & Verelst, 2006). Other reasons Public sector organisations are also considering OSS because of return on investment, openness and scalability (Voth & Stone, 2003). The US census bureau has chosen OSS because of its portability in a mixed environment and low maintenance cost (Management, 2003). The limitations to interoperability set by proprietary software vendors can motivate public sector organisations to consider OSS (Ghosh, 2005b; Schmitz, 2001). Studies have found reliability plays a significant role in OSS adoption (Hwang, 2005; Comino et al., 2006). Proprietary software groups‟ anti-pirating operations have prompted many organisations to gradually adopt OSS (Robert & Schütz, 2001). Mtsweni and Biermann (2008a) identified that government strategy, freedom of use, better performance, and customisability are the major reasons for implementing OSS in South African government departments while incompatibility of OSS with current proprietary solutions, lack of support, migration costs, lack of approved standards and user resistance were challenges in OSS implementation. Availability of support is identified as an important factor in OSS adoption but with conflicting results. For example, some studies (Dedrick & West, 2003; Haider & Koronios, 2009; Ven & Verelst, 2006) identified availability of external support as one of the reasons for organisational adoption of OSS applications, while studies (Lorraine & Patrick, 2007; Mtsweni & Biermann (2008a) found lack of support is one of the barriers or challenges to OSS adoption. Other studies4 have shown a number of factors that are also considered in OSS adoption, including customisation, subjective attitudes toward OSS, user appreciation, training, fear, functionality, enterprise architecture, existing ICT infrastructure, contractual 4
For detailed list of factors and its references cited in the literature refer Appendix A-1
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and legal engagement, external pressure, relevance to their job, flexibility and digital data durability. From the above discussion, a number of factors involved in OSS adoption have been identified. Due to the contradictory results, there is no clear evidence about the factors that enable or inhibit OSS adoption. Consequently, this research aims to explore various factors involved in OSS adoption in the context of APS organisations. 2.2.2 Open Source movement within public sector organisations around the world 2.2.2.1 Policies on OSS Governments‟ interest all over the world in OSS is steadily increasing. Research on OSS policies and standards demonstrates this, even though OSS is relatively new (Comino et al., 2006; Comino & Manenti, 2005; Ouédraogo, 2005). South African government‟s policy mandates OSS usage within ministries (Mtsweni & Biermann, 2008a). Singapore offers tax breaks to companies that use the Open Source Linux operating system instead of Microsoft Windows (Comino & Manenti, 2005; Evans & Reddy, 2003; Wong, 2004). Germany has reached an agreement with IBM aimed at offering discounts on IBM machines with preinstalled Linux (Comino & Manenti, 2005). A practical demonstration of an open standards policy is the decision by the Commonwealth of Massachusetts, USA to mandate open formats for office documents from 2007 (Ghosh, 2005b; Ghosh & Schmidt, 2006). The Government of Vietnam is encouraging OSS usage through the Open Source Software Master Plan (20042008) (Wong, 2004). The UK Government‟s policy considered OSS solutions along with proprietary software because of their value for money, interoperability, vendor independence, security and return on investment (OGC, 2004; Tannenbaum, 2003). Brazil is one of the leading users of OSS. Brazilian government initiated a program in 2003 called governmentled FOSS (Free and Open Source Software), to encourage federal and state governments to switch to OSS. In 2005, the president of Brazil Luis Inacio Lula da Silva announced a policy to mandate all Brazilian federal departments to switch to OSS. The rationale behind the Brazilian government‟s Open Source policy is based on three very closely related factors: economics, development and ideology (Richter et al., 2009). In Australia, the federal government‟s ICT procurement policy recommended organisations to consider OSS during procurement to achieve value for money and for meeting their business needs (AGIMO, 2005).
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2.2.2.2 OSS adoption around the world The National Aeronautics and Space Administration (NASA) is using OSS because of its compatibility with NASA‟s mission and enhanced collaboration with government laboratories, universities and industries (Moran, 2003). China, New Zealand, and Chicago banking sectors are using OSS (Linux) to reduce cost and time (Wheeler, 2005). Other countries are encouraging OSS usage, including France, Germany, Denmark, Finland, Italy, Norway, Spain, Sweden, Netherlands, UK, Brazil, Japan, Korea, South Africa, Malysia and Thailand (Hwang, 2005; Wong, 2004). Because of the benefits associated with OSS (discussed in section 2.2.1.1) governments around the world are spending money on OSS research and to raise awareness of OSS and to promote OSS usage in public sector organisations (discussed in section 2.2.4.2). Nevertheless, OSS usage in public sector organisations is still limited (Ghosh, 2005a). 2.2.3 Open Source movement within Australia 2.2.3.1 Open Source Software in the APS The Australian Government perceived a growing market for companies that implement and support Open Source solutions in business and government. Consequently, in Australia both federal and state governments are promoting the use of OSS. The Australian Government Information Management Office, (AGIMO, 2005) has published “A Guide to Open Source Software for Australian Government Agencies” to promote OSS usage within Australian Public Sector organisations. The guide recommends the usage of OSS along with proprietary software because of its potential to lead to significant savings in government expenditure on ICT. This guide is not intended to either advocate or reject OSS products. However, for agencies considering OSS solutions on merit, it provides advice on issues agencies should consider when evaluating the business case for OSS solutions. Where necessary, it highlights the differences between Open Source and proprietary software, to assist agencies to quickly understand the value of both types of software. Further, the guide assists agencies by providing practical information and approaches for agencies to consider when assessing Open Source solutions. The guide addresses risk management procedures and the different contractual considerations applying to Open Source Software issues, as well as cost of ownership issues, which are important because, under an Open Source model, costs are incurred at different phases of the implementation and operation of an information technology system. 15
Australian Service for Knowledge of Open Source Software (ASK-OSS) is one of the leading research groups funded under Managed Environment for Research Repository Infrastructure (MERRI) projects by the Australian government (ASK-OSS, 2009). According to ASK-OSS the Australian government has a number of reasons for its interest in promoting OSS in public sector organisations: OSS improves ICT skills within government, interoperability between government agencies, education and training opportunities and benefits ICT spending. In addition to the federal government‟s support, there is support from various state governments5. For example, the Victorian government is focusing on motivating educational institutes to use OSS because of its potential benefits; the Tasmanian government is working with ICT industries to research risks and benefits associated with OSS and to provide information on social and business benefits to be derived from using OSS. Other Australian state governments, such as Western Australia and New South Wales, are working with OSS industries to encourage OSS within the states. Figure 2.1 depicts various OSS organisations in Australia. 2.2.3.2 Open Source organisations in Australia In addition to the Australian government‟s interest in OSS, some private organisations are also supporting OSS by demonstrating the advantages of using OSS through case studies, publishing reports about OSS, conducting conferences for OSS and providing guidance on the use of OSS. For example, the Australian Unix and Open Systems User Group (AUUG 6), Linux Australia7, Open Source Industry Australia (OSIA8) and Free Open Source Software Australian Capital Territory (FOSSACT9) are some of the industry bodies working closely promoting OSS adoption in Australia. Some of the roles of these organisations related to OSS are explained below. AUUG and Linux Australia are organisations focusing on the Australian OSS community. Their main role is building a community by connecting, supporting and promoting people with an interest in interoperable computing. They regularly organize conferences to provide opportunities for participants to meet technical decision makers from industry, government and education sectors.
5
http://www.ask-oss.mq.edu.au/index.php?option=com_content&task=view&id=44&Itemid=62 A snapshot of OSS in Australian Government 6 http://www.auug.org.au/ the Organisation for Unix, Linux and Open Source professionals 7 http://linux.org.au/ Linux Australia 8 http://www.osia.net.au/about Open Source Industry Australia 9 http://fossact.org.au/about FOSSACT Promoting the use of free and open source software in ACT businesses and Government
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Open Source Industry Australia (OSIA) focuses on helping corporations, government and the education sector improve their business success in a growing ICT market. OSIA also publishes reports, guidelines and policies dealing with how to evaluate or migrate to Free/Open Source Software (FOSS). Some of their activities include presenting the business case for FOSS to Corporations, Government and Education, to coordinate events and activities relating to the business advocacy of FOSS, and to join with other FOSS organisations in the broader community to increase the uptake of Open Source in Australia. FOSSACT is a user group focusing on promotion and support of the use of free and Open Source Software in Business and Government in the Australian Capital Territory and surrounding regions. FOSSACT meets every month and aims to demonstrate OSS applications and provide information about OSS service providers. Further, it provides an opportunity to facilitate FOSS services providers and users getting to know each other so that collaborative relationships may form and for giving advice to the organisations that want to use Free or OSS applications. OSS Australia
Community
Linux Australia AUUG
Public Sector
Federal Govt
Industry
State Govt
AGIMO
ASK-OSS
Open Source WA Open Source Victoria Open Source Tasmania Open Source NSW
Figure 2-1: Australian Open Source industry
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OSIA
FOSSACT
2.2.3.3 Open Source Software adoption in Australian organisations Goode‟s (2005) survey on OSS usage in Australia‟s top firms has shown that only 26% of organisations surveyed have adopted OSS and that less than 50% of the respondents have considered using OSS in their organisation. Higgins et al.‟s (2005) survey shows that current Linux adoption in Australia and New Zealand is three times lower than in North America. Seventy seven percent of respondents (in Australia and New Zealand) have not used OSS and have no plans to adopt OSS over the next 12 months. Open Source adoption for desktop computing in Asia Pacific is almost three times that of Australia and New Zealand. Further, adoption of OSS for e-government in Australia was lagging the rest of the world (Haider, 2008). Despite these figures, Australia‟s interest in OSS adoption is increasing. This has been seen from the Australian government‟s involvement in encouraging organisations to make use of OSS by publishing the guideline “A guide to Open Source Software for Australian Government Agencies”.
In Western Australia, the Chair of the e-Government Sub
Committee Strategic Management council, M C Wauchope, asked all CEOs and Director Generals to consider OSS alternatives while procuring any software to gain value for money (Wauchope, 2005). Moyle (2004) reports Telstra has switched to Open Source Software by using Linux on the company‟s web servers and applications servers and is migrating to Linux on desktops. The same report also says the Australian Taxation Office (ATO) is investigating options for migration to Open Source Software. ATO has released revised policy10 on FOSS in 2009. This policy requires that the organisation give fair and reasonable consideration to FOSS-based ICT solutions that best support the organisation‟s business and integrate best into its technical environment. 2.2.4 Research based on Open Source Software Since the mid-1990s, there has been a surge of interest in OSS research among academics and practitioners. The following sub-sections discuss OSS research focus around the world and public sectors‟ interest with OSS research activities. 2.2.4.1 Research on OSS The prominence of OSS has generated much research interest. Most of the existing research has focused on the incentives of OSS developers (Bonaccorsi & Rossi, 2003; Ghosh, 2005e; Lakhani & Wolf, 2005; Subramanyam & Xia, 2008; Wu et al., 2007; Xu et al., 2009), 10
http://www.ato.gov.au/corporate/content.asp?doc=/content/48886.htm IT Policy 11- Free and Open Source Software
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motivations of firms‟ involvement with OSS (Rossi & Bonaccorsi, 2005), Open Source Software development processes (Bonaccorsi & Rossi, 2003; Crowston et al., 2007; DinhTrong & Bieman, 2005; Krogh et al., 2003; Mockus et al., 2005), economics in OSS (Garzarelli et al., 2008), benefits of using OSS (Laplante et al., 2007), determinants of OSS success such as a model to measure the success of OSS projects (Crowston et al., 2006), determinants for success (Feitelson et al., 2006; Israeli & Feitelson, 2007; Lerner & Tirole, 2005; Senyard & Michlmayr, 2004), current level of OSS usage (Mtsweni & Biermann, 2008a), and reasons for failure of OSS projects (Katsamakas & Georgantzas, 2007). Finally, some researchers have used OSS phenomena to explore the factors involved in OSS adoption (Dedrick & West, 2004; Glance et al., 2004; Hwang, 2005) and evaluation criteria for OSS adoption (Ven et al., 2008; Wang & Wang, 2001). As yet, very little research has focused on barriers to OSS adoption (Goode, 2005; Holck et al., 2005; Larsen et al., 2004) and challenges in implementing OSS (Mtsweni & Biermann, 2008a). 2.2.4.2 Research on OSS within public sector organisations Public sector organisations around the world are spending large amounts of money on OSS research to assess the potential of OSS to achieve their goals of OSS (Ouédraogo, 2005). The European Union‟s FLOSS World (Free / Libre / Open Source Software) research project is spending €660,000 on OSS research. Their research was focused on the use of OSS in firms and the public sector, implications of OSS, advantages of using OSS by improving local skills and economic growth, and employment opportunities (Ghosh & Glott, 2005; Ouédraogo, 2005). South African government‟s interest in OSS has led to the creation of OSS policy that mandates OSS usage within ministries. In order to improve OSS usage within South African government departments, a research was conducted to assess the current level of OSS usage on the server side as required by the OSS policy (Mtsweni & Biermann, 2008a, 2008b). A study conducted in French government organisations investigated how the use of OSS contributes to the process of improving government performance (Deller & Guilloux, 2008). In Australia, in early 2007 the Department of Finance and Administration conducted an OSS usage survey focused on the impact of adoption of OSS, to identify benefits and risks, advantages and disadvantages, future challenges and opportunities in Australian government agencies (DFA, 2006). The survey identified the APS organisations challenges such as perceived vendor support, perceived inability for trialability and lack of availability of
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information about OSS. Further, the survey identified that OSS evaluation is based on fit for purpose and value for money, not cost savings (Government, 2007). A study conducted by Haider and Koronios (2008) addressed the issues of OSS uptake in Australian and New Zealand government agencies from the perspective of representatives from AGIMO, various state and territory government and the University of South Australia. Based on concerns raised, they set the agenda for research into OSS within government organisations. Following that Haider and Koronios (2009) conducted a study in three Australian state government departments and found that success of OSS in government agencies depended on technical and economic value, maintainability, and availability of support to sustain the utilization of OSS, while some of the risks and challenges were shortage of skills to administer OSS, frequent releases, constantly changing interface designs, lack of support and incompatibility with existing infrastructure. 2.2.5 Conflicting findings in the OSS literature There are conflicting research findings regarding the factors that drive OSS adoption. For example, there is no clear result for Total Cost of Ownership (TCO) in using OSS. The results of TCO comparison studies conducted by OSS groups (Cybersource, 2004; Kenwood, 2001; Management, 2003; Wong, 2004) and proprietary software companies (Corporation, 2001; IDCWhitePaper, 2002) present contradicting results. A recent study on TCO comparison between OSS and proprietary software shows there is no difference in terms of cost (Riehle, 2007). Ven and Verelst (2006) identified that organisations are not sure whether the resulting TCO would be positive. Some of the other conflicting results were in regard to the availability of support (section 2.2.1.1), security and source code. It is argued that OSS is more secure because OSS allows more people to inspect and review the code than proprietary software (Ebert, 2009; Hoepman & Jacobs, 2007) which supports the Linus‟s law “Given enough eyeballs, every bug is shallow” (Fitzgerald, 2005, p-95; Raymond 1998). Others claim Open Source is less secure because it allows hackers to attack the code (Messmer, 2008). Ven et al. (2008) provides an excellent summary of the claims and counterclaims for OSS adoption.
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2.3 Technology adoption theories Open Source Software adoption in organisations is a form of technology adoption. It is important and would be useful to study, apply and enhance the theoretical underpinnings of technology adoption in organisations to OSS adoption. The two main technology adoption theories used to test the adoption of new technologies are Innovation Theory (Rogers, 1995) and Technology Acceptance Model (TAM) (Davis, 1989). This section of the thesis introduces those theories and describes how they have been applied, developed and tested. These theories are used in studies in various contexts such as exploring factors involved in new technology adoption, studying individual and organisational adoption of a technology, predicting the factors of technology adoption and developing models by adding new factors to the existing theories. The following section discusses studies of technology adoption and research findings based on these theories. 2.3.1 Innovation Theory Innovation Theory, also known as diffusion theory or Diffusion of Innovation Theory, was developed by Rogers in the 1960s (Rogers, 1995). In this thesis this theory is referred to as Innovation Theory. Innovation Theory attempts to explain the main elements in the diffusion of innovations. The main elements are characteristics of an innovation, communication channels, time and social system and the role of these elements in the innovation adoption rate. Moreover, this theory prescribes innovation and organisational characteristics that influence the technology adoption in an organisation. Innovation Theory identifies a series of factors that are associated with an innovation and its role in diffusion rates. Innovation characteristics such as relative advantage, compatibility, complexity, trialability and observability determine an innovation‟s rate of adoption (Rogers, 1995). Rogers defined those innovation characteristics in the following ways: (1) Relative advantage: the degree to which an innovation is perceived as being better than the idea it supersedes, (2) Compatibility: the degree to which an innovation is perceived as consistent with the existing values, past experiences and needs of potential adopters, (3) Complexity: the degree to which an innovation is perceived as relatively difficult to understand and use, (4) Trialability: the degree to which an innovation may be experimented with on a limited basis, and (5) Observability: the degree to which the results of an innovation are visible to others. Innovation Theory also covers various organisational characteristics that may impact on the adoption rate of a new technology. They range from an individual‟s attitude toward change to 21
organisational characteristics such as formalization (the degree to which an organisation requires its members to follow rules and procedures), centralization (the degree to which power and control in a system are concentrated in the hands of relatively few individuals), system openness (the patterns of information exchange a firm establishes with its environments, including customers as well as the technical experts outside the firm), complexity (the degree to which an organisation‟s members possess a relatively high level of knowledge and expertise, usually measured by the members‟ range of occupational specialties and their degree of professionalism), interconnectedness (the degree to which the units in a social system are linked by interpersonal networks), organisational slack (the degree to which uncommitted resources are available to an organisation) and organisational size (Depietro et al., 1990; Narayanan, 2001; Rogers, 2003). Rogers‟ studies show that organisational innovativeness has a low correlation with each of the organisational characteristics, though he suggested a list of organisational characteristics to study organisational innovativeness. 2.3.2 Technology Acceptance Model The Technology Acceptance Model (TAM) was created by Davis (1989) based on the Theory of Reasoned Action (TRA) to explain individuals‟ intentions to accept and use computer technology in the domain of Information Systems (Davis et al., 1989). TAM explains human computer-usage behaviour using TRA as the theoretical basis. The objective of TAM is to provide an explanation of the determinants of computer acceptance that is capable of explaining the behaviour of users across a broad range of end-user computing and user populations while simultaneously being parsimonious and theoretically justified. TAM proposes two specific beliefs/constructs: (1) perceived usefulness - is defined as “The degree to which a person believes that using a particular system would enhance his or her job performance”, and (2) perceived ease of use - as “The degree to which a person believes that using a particular system would be free of effort” for technology adoption (Davis, 1989, p320). Some researchers suggested that TAM needs to be given additional variables to provide a stronger model (Legris et al., 2003; Wu & Wang, 2005). Subsequently, TAM has been extended to include additional key adoption determinants: social influence processes (subjective norm, voluntariness, and image) and cognitive instrumental processes (job relevance, output quality, and result demonstrability) for technology adoption (Venkatesh & Davis, 2000). The results of TAM2 show that social influence processes, such as subjective norm, have a significant direct effect on usage intentions (Venkatesh & Davis, 2000). Legris 22
et al., (2003) reviewed 22 articles11 focused on integrating TAM‟s constructs and confirmed that TAM is a useful model for studying innovation adoption when integrated with variables related to both human and social change processes. Legris et al. (2003) cited the list of empirical research based on TAM‟s theoretical model that they reported were used to understand and explain adoption behaviour in IS innovations. They concluded that TAM‟s constructs influence technology adoption. Further they suggested that it may be difficult to increase the predictive capacity of TAM if it is not integrated into a broader model that includes organisational and social factors. 2.3.3 Research based on Innovation Theory Rogers‟ (1995) Innovation Theory has been used since the 1960s to study different types of innovations, ranging from agricultural tools to organisational innovations (Venkatesh et al., 2003). Especially, Innovation Theory has attracted a high level attention from the technology adoption researchers. For example, Innovation Theory in IS research has been used in different ways including the examination of factors influencing technology adoption (Tung & Rieck, 2005); the study of factors involved in post adoption behavior (continuance and discontinuance) (Parthasarathy & Bhattacherjee, 1998); predicting technology adoption (Jurison, 2000); developing a new model based on Innovation Theory (Moore & Benbasat, 1991); understanding the technology diffusion process and perceived attributes, and adopter versus non-adopter characteristics (Lu et al., 2009). Tornatzky and Klein (1982) reviewed 75 examples of innovation literature examining the relationships between the attributes of an innovation and its adoption. They identified the following ten attributes that have occupied the attention of the researchers: (1) Compatibility, (2) Relative advantage, (3) Complexity, (4) Cost, (5) Communicability, (6) Divisibility, (7) Profitability, (8) Social approval, (9) Trialability and (10) Observability. Innovation attributes have been modified by Tornatzky and Klein (1982) and are re-defined in table 2.1. Even though the above attributes had been cited in studies, only three innovation characteristics (compatibility, relative advantage and complexity) had the most consistent significant relationships with innovation adoption.
11
Articles were from the journals MIS quarterly, Decision Sciences, Information and Management, Management science, Journal of Management Information Systems, and Information systems research.
23
Table 2-1: Innovation implementation
characteristics
that
affect
innovation
adoption
and
1. Compatibility
May refer to compatibility with the values or norms of the potential adopters or may represent congruence with the existing practices of the adopters.
2. Relative advantage
The degree to which an innovation is perceived as being better than the idea it supersedes
3. Complexity
The degree to which an innovation is perceived as relatively difficult to understand and use
4. Cost
The cost of an innovation is assumed to be negatively related to the adoption and implementation of the innovation; the less expensive the innovation, the more likely it will be quickly adopted and implemented.
5. Communicability
The degree to which aspects of an innovation may be conveyed to others.
6. Divisibility
Extent to which an innovation can be tried on a small scale prior to adoption.
7. Profitability
The level of profit to be gained from adoption of the innovation
8. Social approval
Status gained in one‟s reference group, “a non financial aspect of reward”.
9. Trialability
The degree to which an innovation may be experimented with on a limited basis.
10. Observability
The degree to which the results of an innovation are visible to others.
Moreover, Innovation Theory has been extended to support research in an Information Systems context. For example, Moore and Benbasat (1991) developed an instrument intended to be a tool to measure the perceptions of adopting an IT innovation based on Rogers‟ innovation characteristics in the Information Systems context including initial adoption and eventual diffusion of IT innovations within organisations. They identified two further constructs beyond Rogers‟ classification, which were thought important in the decision to adopt an innovation: image and voluntariness of use. Further, they divided the factor 24
observability into two factors, i.e: visibility and result demonstrability. Finally, they elaborated a set of constructs consisting of eight factors, i.e. voluntariness, relative advantage, compatibility, image, ease of use, result demonstrability, visibility and trialability, to study individual technology acceptance rates. The extension was based on the following concepts: Rogers‟ construct observability is quite complex as it comprises two meanings within a single construct i.e. visible and communicable, consequently requiring two different labels such as visibility and result demonstrability (for communicability) in order to improve validity and internal cohesiveness; the attribute complexity is renamed ease of use as it gives simple meaning to the construct; the construct voluntariness was introduced because of the perception on voluntariness that could contribute to trialability; the construct image is defined as the degree to which the innovation enhances one‟s image or status within the organisation and is perceived as a characteristic of an innovation. The factors influencing adoption vary from study to study. For example, Jurison (2000) found that innovations with high relative advantage and compatibility are easily adopted. Tung and Reick (2005) found that, in addition to perceived benefits, external pressure and social influence have a significant impact on adoption decision. Parthasarathy and Bhattacherjee‟s (1998) study found that in addition to usefulness and compatibility, network externalities, external (mass media, advertisement) and interpersonal (friends, relatives) influential sources have a significant impact on adoption decisions. Lu et al. (2009) found that compatibility of the technology influences technology diffusion as the technology must operate with the existing technological environment. In general, the innovations that have greater relative advantages, compatibility, trialability, observability and less complexity are adopted more rapidly than other innovations (Narayanan, 2001; Rogers, 1995; Tung & Rieck, 2005) The table 2.2 summarizes the details of the research based on Innovation Theory. Two concepts emerged from that research. (1) Innovation Theory has often been used to investigate the factors influencing technology adoption as well as factors affecting the technology adoption. For example, Tung and Rieck (2005), Al-Gahtani (2003) examined factors influencing technology adoption; Lu et al. (2009) discover why the technology adoption was not successful consequently identified the concerns of non-adopters in using Wi-Fi technology intending to improve its usage in the future. (2) Innovation Theory has been used to study technology adoption at various levels from individuals to groups, and from organisations to geographical regions. For examples refer to the following table. 25
Table 2-2: Research based on Innovation Theory Study
Purpose of the research
Theories used
Unit of analysis
Innovation tested / used
Variables / concepts tested
Findings
Parthasarathy & Bhattacherjee, 1998
Extends DOI
Innovation Diffusion theory
Individual
Online services
Innovation attributes relative advantage, compatibility, complexity and network externalities (complementary products)
Perceived usefulness and compatibility are significant predictors of subsequent discontinuance behavior.
Lu et al., 2009
Investigates perceived attributes (difference between early adopters and non-adopters, concerns of nonadopters)
Diffusion theory
Individuals in an organisation
Wi-Fi technology
Tests innovation attributes relative advantage, compatibility, complexity, trialability, observability and diffusion gap between early and late adopters
Relative advantage, compatibility, complexity, and trialability influence technology adoption.
Jurison, 2000
Explores and predict technology adoption
Innovation Diffusion theory
Groups of end user in an organisation
Integrated office automation system
Perceived value
Relative advantage has positive impact in adoption.
(examines post adoption behavior discontinuation)
Consistent with Rogers‟ generalisation “innovations with a high rate of adoption have a low rate of discontinuance”.
Tung & Rieck, 2005
Examines factors influencing adoption
Diffusion of Innovation theory, literature
Organisation
e-government services
Innovation attributes, organisational and interorganisational constructs
Perceived benefits (Rogers), external pressure and social influence technology adoption.
Moore & Benbasat, 1991
Instrument development
Diffusion of Innovation theory, TAM‟s concept
Organisation
Personal Work Stations (PWS)
Voluntariness, image, result demonstrability, visibility, relative advantage, compatibility, ease of use, and trialability
Developed an instrument to study an innovation adoption in an organisation.
26
Lorraine & Patrick, 2007
Investigates the impact of perceived benefits and drawbacks in adoption
Innovation Theory
Organisation
Open Source Software
Tests innovation attributes relative advantage, compatibility, complexity, and trialability in terms of organisational, environmental, technological, and individual factors
Relative advantage, compatibility, complexity, and trialability influence technology adoption.
Al-Gahtani, 2003
Investigates perceived attributes influencing adoption
Diffusion of Innovation theory
Region
Computer technology
Relative advantage, compatibility, complexity, observability and trialability.
Relative advantage, compatibility, complexity, observability and trialability influence technology adoption.
27
2.3.4 Research based on TAM Research contributions to TAM have extended and enhanced the predictive power of the model. Many researchers have adopted this model to assess or predict the users‟ acceptance of various technologies. Some studies have elaborated the constructs‟ perceived usefulness and perceived ease of use in TAM to better understand the motivations behind the technology adoption. For example, TAM has been tested and elaborated in the following research: business-to-consumer e-commerce adoption (Lim, 2001), internet usage (Seyal & Rahman, 2003), Executive Information Systems (Ikart, 2005), on-line learning system (Saade & Bahli, 2005), Inter-organisational electronic medical record systems (Handy et al., 2001), broker workstation (Lucas & Spitler, 1999). The influence of TAM‟s constructs‟ perceived usefulness and perceived ease of use varies between studies. While some studies have found that TAM plays an important role in technology adoption (Seyal & Rahman, 2003; Handy et al., 2001; Roca et al., 2006), other studies have not found an impact between perceived ease of use and technology adoption (Saade & Bahli, 2005; Seyal & Rahman, 2003; Wu & Wang, 2005). However, a study by Lucas and Spitler (1999) shows that both TAM‟s constructs do not have any impact on technology adoption. However, they identified perceived ease of use as a significant predictor of perceived usefulness. Some studies have adapted TAM and other theories to investigate technology adoption by individuals. For example, TAM and Innovation Theory have been integrated and used to investigate individual‟s intentions in using Mobile commerce technology (Lopez-Nicolasa et al., 2008; Wu & Wang, 2005).
These studies found that TAM‟s perceived usefulness
construct plays an important role in adoption. TAM and Expectancy Disconfirmation Theory (EDT) were used by Roca et al. (2006) in E-learning adoption and their findings support the impacts of both TAM‟s constructs in e-learning adoption. The table 2.3 summarizes the details of the research based on TAM. From that research, the researcher identified the following concepts: (1) TAM constructs are widely used to test individuals‟ intentions and their impact in adoption of a technology; and (2) TAM has been used to study organisational adoption because individuals are the elements of an organisation.
28
Table 2-3: Research based on TAM Study
Purpose of the research
Theories used
Unit of analysis
Innovation tested / used
Variables / concepts tested
Findings
Lopez-Nicolasa et al., 2008
Integrating TAM and DOI
TAM, DOI
Individual
3G related technologies
Ease of use, perceived usefulness in terms of social influence and perceived benefits and mass media
Ease of use, perceived usefulness, social influence, perceived benefits and mass media have impact in adoption
Wu & Wang, 2005
Investigate and extends TAM by integrating TAM2 and DOI
TAM2, Innovation Diffusion theory
Individual
Mobilecommerce
Perceived usefulness, perceived ease of use, compatibility, cost, perceived risk
Perceived usefulness, compatibility, cost, perceived risk are the predictors of technology adoption.
Roca et al., 2006
Extends and investigates TAM using EDT as background
TAM, Expectancy Disconfirma tion Theory (EDT)
Individual
E-learning service
Satisfaction, perceived usability (cognitive absorption, perceived usefulness, perceived ease of use), perceived control, perceived quality, subjective norm
Perceived usefulness, satisfaction, perceived ease of use, cognitive absorption, and perceived quality are the determinants in continuing the technology usage.
Seyal & Rahman, 2003
Validating and extending TAM, identifying key factors in internet usage
TAM
Individual
Internet usage
Perceived usefulness, perceived ease of use, PC experience, task characteristics, task variety, institutional support
Perceived usefulness and external variables (PC experience, task characteristics, task variety, and institutional support) influences internet usage.
Saade & Bahli, 2005
Examines and extends TAM (TAM2)
TAM
Individual
On-line learning system
Perceived usefulness, perceived ease of use, behavioral intension, Cognitive absorption
Perceived usefulness and cognitive absorption have impact in technology usage.
Teo et al., 2008
Extends and Exams TAM
TAM
Group
Teachers‟ attitude towards computer
Perceived usefulness, perceived ease of use, subjective norms, facilitating conditions
Perceived usefulness, perceived ease of use, subjective norms were significant determinants.
Ikart, 2005
Extension of TAM by identifying factors that determine
TAM
Individuals in an organisation
Executive Information
Perceived usefulness, perceived ease of use, Habits, Facilitating
Perceived usefulness, perceived ease of use, cultural, social, individual and organisational variables are important
29
technology adoption
Systems (EIS)
conditions, Social factors
in adoption.
Lim, 2001
Examines and reconstructing the boundary of TAM
TAM
Individuals in an organisation
Business-toconsumer electronic commerce
Experience, self-efficacy, perceived risk, social influence
The paper does not contain results as it was an ongoing research.
Handy et al., 2001
Extends and investigates TAM
TAM
Individuals, groups in an organisation
Interorganisational electronic medical record systems
Perceived usefulness, perceived ease of use, perceived system acceptability, organisational characteristics, individual characteristics, system characteristics,
Perceived usefulness, perceived ease of use, Perceived system acceptability are important in system acceptance.
Lucas & Spitler, 1999
Extends and tests TAM
TAM
Individuals, groups in an organisation
Broker workstation
Social norms, user performance, perception of system quality.
Social norms, nature of the job, perception of system quality are important in predicting technology usage. No significant results for TAM‟s constructs.
(Predicting system usage)
30
2.3.5 Innovation adoption in an organisation Organisational adoption of technology is more complex than individual adoption for several reasons such as the higher number of stages involved in adoption process, organisation‟s authority structure and existing rules and regulations (Mierzjewska & Hollifield, 2006; Ram & Jung, 1991). Thus a more robust framework is needed to study organisational adoption. An influential framework for understanding MIS adoption in an organisational context has been developed by Depietro et al. (1990). Their model defines a “context for change” consisting of three elements: (1) Technology, (2) Organisation and (3) Environment (Dedrick & West, 2004). This model is subsequently adopted in many studies exploring factors involved in technology adoption. For example, Chau and Tam (1997) used these constructs to identify factors affecting adoption of OSS systems; Dedrick and West (2003, 2004) to explore the factors involved in Open Source platform adoption; Lorraine and Patrick (2007) adapted it to identify the influence of perceptions of Open Source Software in its adoption. Technological context is related to the technologies available to an organisation and focuses on how technology attributes influence the adoption process (Lorraine & Patrick, 2007). Chau and Tam (1997) adapted Depietro et al.‟s (1990) model and named technological context constructs as “characteristics of open systems technology innovation” to investigate factors affecting open systems adoption. Rogers (2003) identified five attributes associated with a technology that influence innovation adoption. They are: relative advantage, compatibility, complexity, trialability and observability. These factors are often identified as influencing technology adoption (section 2.3.3 Table 2.2). There has been criticism about Rogers‟ theory when applied to organisational adoption (Chau & Tam, 1997) as those attributes are focused on individuals‟ adoption. Despite the criticism, Depietro et al. (1990) identified “technology” as one of the main elements in organisational adoption. Moreover, based on the evaluation of Information Systems innovation research, Swanson (1994) suggested that Innovation Theory has been found useful for studying Information Systems in a broader organisational context. Innovation Theory especially offers a promising route for developing our understanding of the relationships of IS to the larger business. Following Swanson‟s concept, several studies have examined technology adoption within an organisation in light of Innovation Theory (Lorraine & Patrick, 2007; Moore & Benbasat, 1991; Tung & Rieck, 2005). The organisational context looks at the structure and processes of an organisation that constrain or facilitate the adoption and implementation of innovations (Tornatzky and 31
Fleischer, 1990). Rogers (2003) identified organisational characteristics that are related to innovation
adoption. They are
organisational
size,
centralization, formalization, organisational
interconnectedness,
complexity,
system
openness
and
slack, leader
characteristics. Some of these factors align with factors proposed by Narayanan (2001) and Depietro et al., (1990) in organisational adoption of innovation. Though most of the technology adoption research investigates innovation attributes in the technology adoption process, some studies attempted to investigate organisational constructs such as organisational size, organisational slack and interconnectedness in the OSS technology adoption process. Major findings include: organisational slack has an impact on OSS platform adoption (Dedrick & West, 2004); size of the organisation has an impact on OSS adoption within Australian firms (Goode, 2004); interconnectedness (named as boundary spanners) as having an impact on OSS adoption in Belgium organisations (Ven & Verelst, 2006). Environmental contexts such as the industry, competitors, availability of support structure, and relationship with governments can influence the degree to which an organisation brings in new technology. The environment presents both constraints and opportunities for technological innovations (Depietro et al., 1990). Research conducted by Chau and Tam (1997) and Dedrick and West (2003) found that environmental factors such as market conditions and available skills and services influence OSS adoption. Other factors such as training requirements (Ghosh, 2005a), enterprise architecture (Holck et al., 2005; Larsen et al., 2004), existing ICT infrastructure (Schmitz, 2001), contractual and legal engagement (Schmitz, 2001) and budgets (Ghosh, 2005a; Ghosh & Glott, 2005; Moyle, 2004) also appear to affect OSS adoption. An organisation is a stable system of individuals who work together to achieve common goals (Rogers, 2003). Individuals‟ attitudes are also important in organisational adoption as identified by Lapointe and Rivard (2005, 2007). Their study used individuals in an organisation as one of the embedded units in studying organisational adoption, and identified that individual resistance within an organisation has an impact on IT adoption. The study conducted by Lapointe and Rivard (2005, 2007) discussed resistance behaviours in IT implementations. Resistance is a critical obstacle preventing organisations from reaping the potential benefits of an IT implementation. Resistance is a means by which users communicate their discomfort with a system that might be flawed. Group resistance behaviors emerge from individual behaviors.
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Both technology adoption theories (Innovation Theory and TAM) have been used to study organisational innovation. While there has been a large number of studies that have used TAM to research the attitudes of individuals towards technology adoption, some researchers have attempted to apply TAM‟s constructs in organisational studies. For example, Handy et al., (2001) integrated TAM‟s constructs with organisational characteristics to study technology adoption by different groups of people within an organisation; Lucas and Spitler (1999) extended TAM‟s constructs with organisational variables such as social norms and nature of job, and identified its importance in predicting technology usage; further TAM has been used to study organisational adoption by studying individuals‟ perceptions on a technology (Ikart, 2005; Lim, 2001). Rogers‟ innovation attributes were used to study technology adoption within an organisation (Lorraine & Patrick, 2007; Moore & Benbasat, 1991; Tung & Rieck, 2005), groups of people within an organisation (Jurison, 2000), and individuals within an organisation (Lu et al., 2009). Based on discussions from previous sections (sections 2.3.3 and 2.3.5) it is evident that Innovation Theory is a valuable framework for understanding organisational adoption of new technologies. As discussed earlier in this section individuals are also important in organisational adoption of a technology. TAM is a useful theory to study individual‟s intention to adopt new technology. Further, the research based on TAM and Innovation Theory shows that some of their constructs are extremely similar and supplement one another. Some researchers indicate that the constructs employed in TAM were fundamentally a subset of the perceived innovation characteristics and, if integrated, could provide an even stronger model than if each theory was used on its own (Wu & Wang, 2005). Their suggestions align with Moore and Benbasat‟s (1991) contention that the TAM constructs are similar to Rogers‟ perceived relative advantage and perceived complexity. This suggests that both theories could be applicable to study innovation adoption in an organisation.
33
2.4 Summary of the chapter This chapter discussed literature on OSS and factors involved in technology adoption processes in light of technology adoption theories. Even though there has been significant research focused on OSS technology adoption, there is limited research in organisational adoption of OSS. Yet there is no clear evidence on what factors make organisations decide to use OSS and there is very limited research on public sector organisations‟ experiences in using OSS. Further, past research gave very little attention to barriers in using OSS within public sector organisations. This research therefore, has tried to explore the factors involved in OSS adoption within APS organisations. Ultimately this research provides a significant contribution to the technology adoption theories in the series of attributes of innovation affecting technology usage. Table 2.4 shows the summary of factors that influence general technology adoption as well as OSS adoption as identified from the literature. For a detailed list of factors and references please refer to Appendix A-1, Table A-1.1.
Table 2-4: Summary of items identified from literature review Technology (innovation) characteristics (attributes)
OSS features Security
Relative advantage
Interoperability
Compatibility
Reliability
Complexity
Portability
Trialability
Customisation
Observability
Code transparency
Perceived usefulness
Maintenance
Perceived ease of use
Software piracy
Output quality
Functionality
Result demonstrability
Flexibility
Visibility
Vendor independence
Subjective norm
Continuity of data format
Image
Scalability
Re-invention
License
Divisibility
34
Organisational characteristics
Elements in adoption process
Formalization
Communication channels
Centralization
Time
Interconnectedness
Communication networks
Organisational slack
Change agent
Size
Communicability
System openness
Voluntariness
Perceived benefits
Environmental characteristics
Cost
Availability of service and support
National IT independence
Budget
Economic development
Training requirement
Developing local industry
Enterprise architecture
Reduction of imports / conservation of foreign exchange
Existing ICT infrastructure Contractual & legal engagements
Employment opportunity Value for money
External pressure (government, industry, competitive)
Job relevance (requirement)
Social influence
Perceived quality
Social approval
Profitability
Adopter characteristics Subjective attitude User appreciation Fear Opinion leaders characteristics Perceived risk Satisfaction
35
36
3
Research design
3.1 Introduction This research is designed to address the question: Do technology adoption theories account for Open Source Software adoption in Australian Public Sector organisations? The study will further explore the following two subsidiary questions to the main research question of the thesis: 1. What are the enablers of OSS adoption by Australian Public Sector (APS) organisations? 2. What are the inhibitors of OSS adoption by Australian Public Sector (APS) organisations? This study used the framework shown in figure 3.1 to identify various factors involved in OSS technology adoption in APS organisations. The overall research process is depicted in figure 3.2. The factors identified from this research will be compared with the factors presented in the technology adoption theories and literature. The comparison will be used to reveal the extent factors identified from technology adoption theories account for OSS adoption in APS organisations. Also, an account will be given of why expected factors are not operational or what new factors are significant in the OSS adoption process. Unforeseen factors that influence OSS adoption may emerge from this research. The results will be used to reflect on, improve and modify the current state of technology adoption theories. The research methodologies include an exploratory case study and a survey, as multiple sources of evidence will strengthen the findings and provide greater understanding of the phenomena and the research problem.
37
3.2 Research development model From earlier research on technology adoption theories (section 2.3.3 and 2.3.4) and innovation adoption in organisations (section 2.3.5), it is evident that both technology adoption theories and the context of change have played important roles in explaining innovation adoption within organisations. Innovation Theory incorporates elements of context of change such as technological context in terms of characteristics of innovation, and organisational context in terms of organisational characteristics. There is, however, no direct link between environmental context and Innovation Theory attributes, but, Innovation Theory has identified that elements such as communication channels, communication networks and adopter characteristics are some of the variables involved in rate of adoption. Prior OSS adoption research has used market characteristics (Chau & Tam, 1997), availability of external support and services (Lorraine & Patrick, 2007) as the environmental context. This research has not explicitly represented the context of change elements in the research model as they are incorporated in Innovation Theory. Prior research integrated Innovation Theory and context of change elements in studying organisational adoption. For example, Lorraine and Patrick (2007) used them to study organisational adoption of OSS applications; Tung and Reick (2005) used them to study organisational adoption of e-government applications. Individuals in an organisation play important roles in organisational adoption of an innovation because an organisation is a system of individuals who work together to achieve common goals. So, individuals within an organisation are commonly used to study innovation adoption. See for example (Lapointe & Rivard, 2005, 2007). Davis‟ (1989) TAM is designed to study individuals‟ intentions in technology adoption. TAM has been used to study organisational adoption of different technologies (section 2.3.4). This research is specifically designed to study OSS adoption within APS organisations. Campbell et al. (2009) discussed systematic contextual differences between private and public sector organisations. While both sectors face similar managerial-level IT issues and challenges, the public sector has different layers of authority, which may lead to a lower implementation rate as decisions take longer to be finalized and resourced. Further, they argue that public policy limitations and legacy processes can make investments and decision-making difficult for the CIO and governance committees. Consequently, this research has included ICT policy as one of the elements to be studied in this study.
38
Following the above discussion, this research developed a model depicted in Figure 3.1 by integrating Innovation Theory and TAM‟s constructs along with factors identified from OSS literature to explore various factors involved in OSS adoption within APS organisations. Innovation Theory Innovation attributes Relative advantage Compatibility Complexity Trialability Observability Organisational attributes Formalization Slack Size Environmental attributes Communication channels Adopter characteristics OSS adoption TAM Perceived usefulness Perceived ease of use Moderators (from literature) ICT policy Economy External support Personal attitude Software characteristics Satisfaction Source code Migration issues Figure 3-1: Research model The figure 3.2 illustrates the overall research development process, designed in three phases, to address the research question. The first phase consists of technology adoption theories and OSS literature. This phase is used to develop the research model shown in figure 3.1 and used to construct the survey instrument (section 3.5.2) and the case study instrument (section 3.6.5). The second phase of the research explains the role of the survey. This part explains the development of the survey instrument followed by the survey administration and analysis. The survey instrument was developed from factors identified by the technology adoption
39
theories, and constructs that emerged from the OSS literature. The third phase of the research elaborates the case study approach. The findings from survey and literature are used in the third phase to collect detailed in-depth information on OSS adoption.
Figure 3-2: Research Process Finally, the findings from the survey and case study will be compared with the literature and conclusions drawn. The findings from the study will be used to test whether factors identified from technology adoption theories account for OSS adoption in APS organisations. The possible outcomes from this study will be either literal replication or theoretical replication. These terms are defined as follows: (1) literal replication is where similar results happen between cases and for predictable reasons, the evidence produced is seen to involve the same phenomena described in the theory; (2) theoretical replication is when the case study predicts contrasting results, but also for predictable reasons (Santos et al., 2001; Yin, 2003). If the factors identified from this research do not work as predicted by the technology adoption theories, modifications will be made to the theories by adding new factors to the technology adoption theories as suggested by Santos et al. (2001). If the findings align with factors identified from technology adoption theories, then a contribution will be made to technology adoption theories with respect to OSS adoption. Contributions will be made to practice by 40
providing information regarding factors that affect OSS adoption within APS organisations, to public sector policy makers and OSS industry.
3.3 Research methodology This research employs both qualitative and quantitative techniques to investigate the factors that influence OSS adoption in APS organisations. A case study is used to explore various factors involved in OSS adoption, while the survey is used to enhance the exploratory power of the case study approach as well as to increase reliability of findings obtained from the case study. The following sections describe the importance of a multi-method approach for this research and the benefits of using both case study and survey in this multi-method approach. 3.3.1
Strength of multi-method approach and triangulation
A mixed-method research design is a procedure for collecting, analysing, and “mixing” both quantitative and qualitative data in a single study to understand a research problem. It is a “legitimate inquiry approach” (Creswell, 2005). Qualitative research may be accurate in confirming the truth and potentially generalisable, but often over complex. Large-sample quantitative studies often use proxies to measure aspects of phenomena of interest and might be categorized as being simple and generalisable, but lacking in accuracy (Shah & Corley, 2006). Researchers have defined the power of a multi-method approach in many ways. For example, the value of combining qualitative and quantitative methods has proved especially valuable in Information Systems research (Kaplan & Duchon, 1988), and more specifically, produces reliable results (Gable, 1994); allows researchers to feel more confident of their results (Jick, 1983); provides an opportunity to view problems in different perspectives. This diversity of method implies rich opportunities for cross-validating research procedures, findings and theories (Brewer & Hunter, 2006). The process of compiling research materials using multi-methodologies is useful whether there is convergence or not. Where there is convergence, confidence in the results grows considerably. However, where divergent results emerge, alternative, and likely more complex, explanations are generated. In seeking explanations for divergent results, the researcher may uncover unexpected results or unseen contextual factors (Jick, 1983). Multi-method research results will be richer and more reliable than the results from single method studies (Mingers, 2001). Increased use of multiple methods is necessary to build accurate, generalisable, and useful theory (Shah & Corley, 2006).
41
Gable (1994) suggested that integration of case study and survey methods within a single research design is effective and also improves internal validity and interpretation of findings through triangulation. Integrating the main strength of case study (discoverability, complexity) with the main strength of the survey (generalisability/external validity) in a single research design can yield a superior piece of research (Gable, 1994). Triangulated measurement tries to pinpoint the values of a phenomenon more accurately by focusing on different methodological viewpoints (Brewer & Hunter, 2006). Triangulation obtained through a multi-method approach may be used not only to examine the same phenomenon from multiple perspectives, but also to enrich our understanding by allowing new or deeper dimensions to emerge (Jick, 1983). The value of triangulation rests on the premise that the weaknesses in each single method will be compensated by the counterbalancing strengths of the other methods (Jick, 1983), and that one source of data collection supplies strengths to offset weaknesses of the other data sources (Creswell, 2005). Triangulation can alert researchers to potential analytical errors and omissions. Mixing methods can also lead to new insights and modes of analysis that are unlikely to occur if only one method is used. Therefore, it is important to consider a variety of approaches to the study of Information Systems (Kaplan & Duchon, 1988). In addition to the above, there are many contexts where qualitative and quantitative methods can be used in conjunction to build and refine theory (Shah & Corley, 2006). This research used a multi-method approach by integrating case study and survey methods within the research design. The strengths of using both case study and survey for this research are explained in the following two sections. 3.3.2 Strengths of the case study Remenyi et al. (1998) defines case study as a detailed investigation of the context and processes that affect a phenomenon within an organisation. Major criticisms about case study are: that sample size is too small, biased samples and interpretations (Siggelkow, 2007). But Remenyi et al. (1998) argued that a well designed case study is a powerful and appropriate method to validate an already established theory. This is achieved by establishing a thorough theoretical framework, and having a research question that relies on theoretical propositions and reviews of literature to test or contribute to theory (Yin, 2003). Further, case study is the appropriate method to investigate the situations in which the intervention being evaluated has no clear set of outcomes (Tellis, 1997a), and limited knowledge exists concerning a particular
42
phenomenon (Benbasat et al., 1987; Siggelkow, 2007). Yin (2003) also suggested that case study is appropriate for exploratory investigations where research questions mainly focus on “what” questions such as What are the enablers of OSS adoption by Australian Public Sector (APS) organisations?. Case study can be used to establish valid and reliable evidence in order to expand and generalise theories (Remenyi et al., 1998; Yin, 2003). The depth of enquiry possible through the case study method is significantly greater than other research methods (Remenyi et al., 1998). A single case design may be sufficient to confirm, challenge, test, or extend a well formulated theory, if the theory specifies a clear set of propositions, as well as circumstances in which it is believed that these propositions will be true (Remenyi et al., 1998; Tellis, 1997a, 1997b; Yin, 2003). However, multiple cases within a study strengthen the results even further by replicating the pattern-matching, thus increasing confidence in the robustness of the theory (Tellis, 1997b; Yin, 2003). A case study approach helps to achieve the aims of this research because a well established research model is used to validate already established technology adoption theories and ultimately contribute to theory as recommended by Ramenyi et al. (1998) and Yin (2003) respectively. Prior research on OSS has yielded inconsistent findings regarding the reasons for using OSS (described in section 2.2.5). Tellis (1997a), Benbasat et al. (1987) and Siggelkow (2007) suggested that case study was the appropriate method to investigate situations where inconsistent outcomes occur, and limited knowledge exists about a particular phenomenon as is the current situation in OSS. Because case study methodology can be used to test or contribute to theory (Bryman, 1989; Darke & Shanks, 2002), information technology innovations have been investigated using this approach (Niederman & Davis, 2006; Tellis, 1997a) to identify factors influencing information technology adoption, for example, e-commerce adoption (Cenfetelli, 2004; Pease & Rowe, 2005), and Open Source platform adoption (Dedrick & West, 2004). Because of exploratory nature of this research and appropriateness of using case study in exploratory research this research applies exploratory case study approach. 3.3.3 Strengths of the survey Pinsonneault and Kraemer (1993) defined survey research as a quantitative method, requiring standardized information from and/or about the subject being studied. The subjects studied might be individuals, groups, organisations, or communities; they also might be projects, applications, or systems.
43
An assessment of survey research methodology in MIS research based on 141 articles published in MIS journals between 1980 and 1990 reported that survey research is most appropriate for two settings (Pinsonneault & Kraemer, 1993). These are: 1. Survey research is especially well suited for answering questions about what, how much, how many, and to a greater extent than is commonly understood, questions about how and why. 2. The phenomena of interest occur in current time or the recent past. Survey research can be used for exploration, description, or explanation purposes (Pinsonneault & Kraemer, 1993). Surveys have been very widely used in Information Systems research (Tanner, 2002). Newsted et al. (1998) reports that surveys are among the more popular methods used by the IS research community. Their argument includes (1) surveys provide responses that can be generalised to other members of the population studied and often to other similar populations and (2) surveys help confirm and quantify the findings of qualitative research. Comparison of relative strengths of case study and survey methods Both case study and survey methods have their own strengths and weaknesses as listed in table 3.1 (adapted from (Gable, 1994)). The case study provides an opportunity to capture rich information, seeks to understand the problem being investigated clearly, but may not be generalisable as its focus is on a particular organisation. In contrast, survey research contributes to greater confidence in the generalisability of the results, but it is less useful in developing a deeper understanding of a problem. Table 3-1: Comparison of relative strengths of case study and survey methods Case study
Survey
Controllability
Low
Medium
Deductibility
Low
Medium
Repeatability
Low
Medium
Generalisability
Low
High
Discoverability (explorability)
High
Medium
Representability (potential model complexity)
High
Medium
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3.3.4 Conclusion It is evident that there is no clear information about drivers and concerns of using OSS (section 2.2.5). Although a great deal of research focuses on OSS usage, very little attention has been given to practical problems in using OSS. Creswell (2005) suggested that qualitative research is appropriate where little is known about the research problem, or detailed understanding of a central phenomenon. However, the major criticisms of case study research are: small sample size, biased samples and interpretations (Siggelkow, 2007), and low generalisability (Gable, 1994). In contrast, survey research is more generalisable. Based on the benefits of multi-method approach discussed early in this section, this research employs a multi-method approach by combining and integrating the strengths of survey and case study approaches to address the research questions.
3.4 Ethical considerations It is important to protect the privacy and confidentiality of individuals who participate in the study and their organisation‟s. The researcher has a responsibility to keep personal and organisational details anonymous and not use these details for any other purpose other than the research. This project was approved by the University of Canberra Committee for Ethics in Human Research (CEHR). Participants were sent an invitation by e-mail, an ethics approval letter along with a Participant information form and Informed Consent form. Copies of the above documents are attached in Appendix B.
3.5 Survey The survey is used to collect quantitative data (described in section 3.3) as part of the multimethod approach. The following sections discuss the components of a survey design such as sample selection, survey instrument development, pre-testing followed by the survey administration. 3.5.1
Sampling
Sampling is concerned with drawing individuals or entities from a population in such a way as to permit generalisation about the phenomena of interest from the sample to the population. The most critical element of the sampling procedure is the choice of the sample frame that constitutes a representative subset of the population from which the sample is drawn. The sample frame must adequately represent the unit of analysis (Pinsonneault & Kraemer, 1993).
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Unit of analysis “Unit of analysis” refers to the unit (e.g., individual, family, organisation) the researcher uses to gather data (Creswell, 2005). This study aims to study OSS adoption at an organisational level. So, the unit of analysis for this study is the organisation. Therefore, in this study data collection and statistical analysis are conducted at the organisational level. Selection of organisations The sampling frame for this study consisted of the Australian Public Sector organisations, including Commonwealth organisations (federal government), state and territory government organisations, local government organisations and government enterprises. Organisation lists were collected from the Government online directory12. For each organisation, key personal involved in Information and Communication Technology (ICT) were identified and contact information, such as e-mail addresses and telephone numbers, were collected. Selection of survey participants Regardless of the unit of analysis, the unit for data collection in survey research is usually the individual (Pinsonneault & Kraemer, 1993). A sample unit is the smallest entity that provides one response. Ordinarily, survey sample units consist of individual people (Alreck & Settle, 1995). Participants for this survey are people involved in ICT processes and software procurement and include CIOs, CEOs, CTOs, Policy Officers, ICT Managers, IT Support Staff and System Analysts. The focus of this research is to explore factors that influence OSS adoption as well as the inhibitors of OSS adoption in APS organisations. Consequently, this research collected information from the people listed above as they are involved in stages of the OSS selection process. Participants‟ e-mail addresses were collected from government websites. Those people whose e-mail address was not published in the website were contacted by phone and asked to participate in the survey. Their e-mail addresses were collected by phone so that an invitation e-mail could be sent at a later stage. Some of the email addresses were collected by personal contact made at various OSS seminars held at Canberra.
12
http://www.gold.gov.au/ Government online directory.
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3.5.2 Survey instrument development and pre-testing Construction of survey questionnaire Designing a good survey instrument is a challenging and complex process (Creswell, 2005). The questionnaire for this survey was developed based on the factors identified from the technology adoption theories such as Innovation Theory, Technology Acceptance Model (TAM) and OSS literature, are listed in table 2.4. Each question focused on a single specific topic or issue (Alreck & Settle, 1995), to collect information that answers the research questions. Attention is given to make the items as brief and simple as possible. Questions are expressed with appropriate technical words in a way that suits the respondents (Alreck & Settle, 1995). Questions are grouped by topics as well as by scales that ensure respondents need less time and effort to answer the questions (Alreck & Settle, 1995). Pre-testing and validation processes are reported later in this section. Question types used in the questionnaire The copy of the survey questionnaire is included in Appendix B-1. The questionnaire includes both closed-ended questions as well as some open-ended questions. Closed-ended questions are effectively designed with comprehensive answer choices. This type of questioning is very useful in collecting information to support theories and concepts in the literature (Creswell, 2005). The inclusion of open-ended questions provides opportunities for the participants to raise their own concerns and experiences of using OSS and any comments participants might have beyond the responses to the closed-ended questions that the researcher has in his/her mind (Creswell, 2005). Closed-ended questions are constructed using multiple choices intend to obtain either a single or multiple responses for each question. The choice criterion is clearly defined to cover the maximum possible answers for the questions in the questionnaire. An „other‟ category is included in some of the questions, which provides respondents an opportunity to include any answers other than the listed (given/defined) categories. Some survey experts have recommended that „No option‟ routinely be offered. People who select „No option‟ have characteristics suggesting that they are least likely to have formed real opinions. Krosnick (1999) suggests that „No option‟ should increase the quality of data obtained by a questionnaire. By offering a „No option‟, respondents would be discouraged from offering meaningless opinions. In this research „No option‟ is adapted as „Uncertain‟. Some of the multiple-choice questions included an option „Uncertain‟ for those respondents who are
47
uncertain about the answer for the particular question. This „Uncertain‟ option ensures the respondents are not forced to select any one of the answers given in the response items. This ensures the minimum level of meaningless data collected to the survey. Item scales used in the questionnaire The questionnaire is labeled with item words rather than numbers because reliability and validity can be significantly improved as they clarify the meaning of the items (Krosnick, 1999). The Likert scale (strongly disagree, disagree, neutral, agree, strongly agree) are used in the questionnaire section Opinions about the Benefits of OSS to collect rate of agreement or disagreement with the statements given in the questionnaire. Nominal scale answers are used in the Demographics section. Various ordinal scale items used in this questionnaire include very low concern, low concern, neutral, high concern, very high concern; extremely unimportant, unimportant, neutral, important, extremely important; very unsatisfied, unsatisfied, neutral, satisfied, very satisfied; no impact, low impact, neutral, high impact, very high impact; little extent, some extent, great extent; not useful, useful, extremely useful. In a few places a verbal frequency scale never, rarely, sometimes, often, always is used in the questionnaire. Construction of questionnaire The questionnaire is developed based on the factors reviewed from the literature. The concepts discussed in the literature are grouped into the following 11 categories to develop a survey questionnaire: (1) Policy and guidelines: covers the items representing organisations‟ ICT policy addresses OSS and impact of availability of guidelines within the organisation in OSS adoption; (2) Diffusion of innovation: includes the items representing role of communication channels and trialability of OSS products; (3) National economic development: includes items focused on benefits to the nation by adopting the OSS; (4) Organisational constructs: includes the items representing organisational rules, existing contracts and their impact in OSS adoption; (5) Cost constructs: items cover various costs related to OSS applications and their role in adoption; (6) Personal attitude: items cover persons‟ attitude towards OSS; (7) Availability of support: enclose the items correspond to support and service available to the OSS products and its impact in OSS adoption; (8) Software characteristics: items that cover features of software including OSS; (9) Perceived benefits: items take account of perception of benefits of using OSS; (10) Level of satisfaction: items related to satisfaction about OSS products and resources available at the
48
organisation; and (11) Experiences with OSS adoption: items include migration experiences and source code modification. The detailed list of items used for the survey questionnaire and its references, grouped into 11 categories, are shown in the table 3.2. Table 3-2: Development of questionnaire constructs based on items from the literature review 1. Policy and guidelines Constructs
Questionnaire reference
Literature source
Questions 10 & 11
Comino et al., 2006; Comino & Manenti, 2005; Ghosh, 2005b; Ghosh & Schmidt, 2006; Holck et al., 2005; Mtsweni & Biermann, 2008a; OGC, 2004; Ouédraogo, 2005; Tannenbaum, 2003
Questions 12 to 15
Wheeler, 2005
ICT policy
OSS Guidelines
2. Diffusion Of Innovation Constructs
Questionnaire reference
Literature source
Sources for OSS information
Question16
Parthasarathy & Bhattacherjee, 1998; Rogers, 1995
Testing of OSS products
Question 17 to 19
Moore & Benbasat, 1991; Narayanan, 2001; Rogers, 1995; Tornatzky & Klein, 1982; Tung & Rieck, 2005
3. National economic development Constructs
Questionnaire reference
Literature source
Question 9 – item 1
Ghosh, 2006; Ghosh et al., 2002; Ghosh & Glott, 2005; Ghosh & Schmidt, 2006; Hwang, 2005; Wheeler, 2005
OSS usage reduces the use Question 9 – Item 2 of pirated software
Hwang, 2005; Management, 2003; Robert & Schütz, 2001; Wong, 2004
OSS usage creates a market Question 9 – Item 3 for local OSS support and
Ghosh et al., 2002; Ghosh & Glott, 2005; Ghosh & Schmidt, 2006; Hwang,
OSS usage helps to increase Australia‟s economic development/growth
49
services
2005; Wong, 2004
OSS usage develops local industry by creating demand for OSS related products
Question 9 – Item 4
Ghosh et al., 2002; Ghosh & Glott, 2005; Ghosh & Schmidt, 2006; Hwang, 2005; Wong, 2004
OSS usage will reduce reliance on proprietary software
Question 9 – Item 5
Hwang, 2005; Wong, 2004
OSS usage provides local employment opportunities
Question 9 – Item 6
Comino et al., 2006; Ghosh, 2005d; Ghosh & Glott, 2005
Question 9 – Item 7
Comino et al., 2006; Ghosh, 2005c; Ghosh et al., 2002; Haider & Koronios, 2009; Hwang, 2005; Lorraine & Patrick, 2007; Management, 2003; Schmitz, 2001; Wong, 2004
OSS usage removes the need for organisations to lock in with particular vendor
4. Organisational constructs Constructs
Questionnaire reference
Literature source
Question 3
Holck et al., 2005; Ikart, 2005; Narayanan, 2001; Rogers, 1995
Organisational rules
Question 5 item 14, & question 7 item 16
Holck et al., 2005; Larsen et al., 2004
Existing contracts
Question 5 item 15, & question 7 item 17
Schmitz, 2001
Legal engagements
Question 5 item 16, & question 7 18
Schmitz, 2001
External pressure
Question 5 item 17
Size of the organisation
Tung & Rieck, 2005
50
5. Cost constructs Constructs
Questionnaire reference
Limited budget
Question 5 item 10 Ghosh, 2005a, 2005c; Ghosh & Glott, 2005; & question 7 item 13 Moyle, 2004; Schmitz, 2001
Cost savings
Question 5 item 11
Value for money / Return on investment
Question 5 item 18 & question 7 item 19 Ghosh, 2005c; Ghosh & Glott, 2005; Management, 2003; Voth & Stone, 2003
Initial implementation cost
Question 7 item 11
Service cost
Question 7 item 12
Literature source
ASK-OSS, 2009; Comino et al., 2006; DBOT, 2002; Dedrick & West, 2004; Ghosh, 2005c; Ghosh & Glott, 2005; Ghosh et al., 2002; Ghosh & Schmidt, 2006; Hwang, 2005; Kenwood, 2001; Larsen et al., 2004; Madanmohan & De', 2004; Management, 2003; Martyris, 2003; Mtsweni & Biermann, 2008a; Moyle, 2004; Schmitz, 2001; Tung & Rieck, 2005; Wheeler, 2005; Wong, 2004; Wu & Wang, 2005
Ghosh, 2005a Management, 2003
6. Personal attitude Constructs
Questionnaire reference
Willingness to adopt OSS
Question 5 item 8
User appreciation
Question 5 item 9 & Larsen et al., 2004 question 7 item 8
Literature source
Larsen et al., 2004
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7. Availability of external support Constructs
Questionnaire reference
Literature source
Question 5 item 7 & question 7 item 7
Dedrick & West, 2004; Goode, 2005; Haider & Koronios, 2009; Kenwood, 2001; Management, 2003; Schmitz, 2001; West & Dedrick, 2008
Maintenance
Question 5 item 12 & question 7 item 14
Comino et al., 2006; Haider & Koronios, 2009; Management, 2003; Robert & Schütz, 2001
Training requirement
Question 7 item 9
Ghosh, 2005a; Ghosh & Glott, 2005; Goode, 2005; Martyris, 2003; Schmitz, 2001
Availability of technical support
Continuity support
of Question 7 item 10
Robert & Schütz, 2001; Wheeler, 2005
8. Software characteristics Constructs
Questionnaire reference
Security
Comino et al., 2006; Ghosh, 2005c; Ghosh et al., 2002; Hwang, 2005; Kenwood, 2001; Lorraine & Patrick, 2007; Management, 2003; Question 5 item 1 & Messmer, 2008; MITRE, 2003; PressPass, question 7 item 1 2006; Robert & Schütz, 2001; Schmitz, 2001; Voth & Stone, 2003; Wheeler, 2005; Wong, 2004
Interoperability
Question 5 item 2 & Ghosh, 2005b; Hwang, 2005; Kenwood, 2001; question 7 item 2 Management, 2003; Schmitz, 2001
Portability
Question 5 item 3 & Bonaccorsi & Rossi, 2003; Kenwood, 2001; question 7 item 3 Management, 2003
Reliability
Bonaccorsi & Rossi, 2003; Comino et al., Question 5 item 4 & 2006; Hwang, 2005; Kenwood, 2001; Wheeler, question 7 item 4 2005
Customisation
Question 5 item 5 & Hwang, 2005; Mtsweni & Biermann, 2008a question 7 item 7
Availability of source code
Question 5 item 6 & Ghosh, 2005c; Ghosh & Glott, 2005; Ghosh et al., 2002; Hwang, 2005; Management, 2003; question 7 item 6 MITRE, 2003; Schmitz, 2001; Voth & Stone,
Literature source
52
2003 Functionality
Question 5 item 13 & Ghosh & Glott, 2005; Schmitz, 2001 question 7 item 15
Flexibility
Question 5 item 20 & Comino et al., 2006; Kenwood, 2001; Wheeler, question 7 item 21 2005
Continuity of data Question 5 item 21 & Hwang, 2005 format question 7 item 22 Scalability
Question 5 item 22 & Kenwood, 2001; question 7 item 23 Wheeler, 2005
Voth
&
Stone,
2003;
9. Perceived benefits Constructs
Questionnaire reference
Perceived usefulness Question 27
Job relevance
Literature source Davis, 1989; Handy et al., 2001; Jurison, 2000; Moore & Benbasat, 1991; Narayanan, 2001; Parthasarathy & Bhattacherjee, 1998; Roca et al., 2006; Rogers, 1995; Saade & Bahli, 2005; Seyal & Rahman, 2003; Tornatzky & Klein, 1982; Tung & Rieck, 2005; Venkatesh & Davis, 2000; Wu & Wang, 2005
Question 5 item 19 & Goode, 2005 question 7 item 20
10. Level of satisfaction Constructs
Questionnaire reference
Literature source
Availability of human resources at your organisation that support OSS adoption
Question 21 item 2
Dedrick & West, 2004; Ikart, 2005; Narayanan, 2001; Rogers, 1995; Schmitz, 2001
Availability of financial resources at your
Question 21 item 3
Dedrick & West, 2004; Ikart, 2005; Narayanan, 2001; Rogers, 1995; Schmitz, 2001
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organisation that support OSS adoption Availability of OSS products that meet your organisational needs
Question 21 item 1
Larsen et al., 2004
Do OSS products generally satisfy organisational needs
Question 20
Larsen et al., 2004
11. Experiences with OSS adoption Constructs
Questionnaire reference
Modifying the source code available from OSS to satisfy our needs
Question 22 – item 1 Ghosh, 2005c; Ghosh & Glott, 2005; Ghosh et al., 2002; Hwang, 2005; Management, Question 5 – item 6 2003; MITRE, 2003; Schmitz, 2001; Voth & Question 7 – item 6 Stone, 2003
Experiencing difficulties in migrating from existing system (proprietary) to OSS products
Question 22 - item 2
Experiencing difficulties in migrating from OSS products to proprietary software
Question 22 – item 3 Ghosh, 2005a
Using OSS reduce our organisation's IT spending on software procurement
Question 22 - item 4
Literature source
54
Ghosh, 2005a
Ghosh & Glott, 2005
12. Others Constructs
Questionnaire reference
Literature source
OSS usage
Question 23 to 26
Wheeler, 2005
Questions 1,2 and 4
Constructs were developed participant‟s details.
Questions 28 to 30
Constructs were developed aimed to collect participant‟s interest in the follow up interviews and publications with this research in the future.
Demographics Opt-in
to
collect
Pre-testing the questionnaire The questionnaire was pretested to identify the questions that respondents had difficulty understanding or interpret differently from the researcher‟s intentions. Pre-testing improves validity and clarity. Pre-testing helps determine whether individuals in the sample are capable of completing the survey and that they can understand the questions. Pre-testing is a procedure in which a researcher makes changes to an instrument based on feedback from a small number of individuals who complete and evaluate the instrument (Creswell, 2005). Pre-testing the questionnaire is done in three different phases. First, each question‟s way of expression, clarity, focus, brevity, simplicity, ambiguity of wording, vocabulary and grammar is examined by academic professionals. Second, technical terms used in the questionnaire are reviewed by different kinds of technical people working in Information and Communication Technology (ICT) which included an OSS research coordinator, System Analyst and System Architect. Scales used for questions and groupings of questions were assessed by a statistical consultant which ensured the data captured would be effective and valid. The above three steps were repeated until the questionnaire was of a suitable standard. The questionnaire was refined based on suggestions given by the professionals wherever applicable in order to improve its validity.
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Pre-testing the online survey This research used LimeSurvey13 software to conduct an online survey. Testing of LimeSurvey software was done in three steps: software installation, pre-testing the survey within University of Canberra (UC), and pre-testing the survey within APS organisations. First „LimeSurvey‟ software was installed on a computer provided in the UC research center. A database was created on the university student web server to store the data received from the survey responses. A webpage was created to upload the online survey. The pretested questionnaire was uploaded in „LimeSurvey‟. Second, the online survey was pretested within UC by sending invitation mails, receiving responses, sending reminders, and storing the responses (backups) of 15 volunteers. This survey allowed participants to save their partial responses for completion and submission at a later time; allowed the researcher (surveyor) to check the number of complete responses and the number of partially saved responses. From the participants‟ side the following three steps were checked: saving the partial responses of the survey, retrieving the partially completed survey, and submitting the completed survey. Third, the online survey was tested at different APS organisations. The steps checked in the second phase were retested again here to ensure the survey was ready and stable enough to send to all APS organisations selected for this research. Public registration14 to participate in this survey was tested by using the steps provided in the second and third steps. 3.5.3 Administration of the survey instrument An online survey of APS organisations was conducted between November 2007 and January 2008. Participants were sent an invitation e-mail that contained the brief information about the research as well as the URL link to participate in the survey. We received 35 valid responses representing a response rate of 29%. Participants were informed that their participation was totally voluntary, and that they might withdraw at any stage and avoid answering questions they did not wish to answer. The researcher also assured participants that the information they provide would be kept anonymous. The participants were given two weeks to complete the survey. Once the participant submitted the completed survey a confirmation e-mail was sent back to the participants. Reminders were sent to those participants who had not submitted the survey within the specified time period of two weeks. A copy of reminder e-mail information is provided in 13
http://www.limesurvey.org/ A leading open source tool for conducting online surveys. Interested key personals involved in APS software procurement can register through this phase and can participate in the survey. Public registration was initiated through the participant‟s id collected through the website. Their id‟s are used to request them to participate in the survey as well as nominate other people for the survey through the public registration. 14
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Appendix B-4. Participants were encouraged to complete a paper based survey if they were not able to access the online survey. Three participants completed the paper based survey as their organisation did not support online surveys at that time. The three paper based survey responses were entered into the online survey database manually. This survey received one response from the public registration. 3.5.4 Data analysis strategy When exploration or description is the aim of the survey research, analysis frequently involves no more than developing marginal and cross-tabulations for the variables and using simple descriptive statistics such as means and medians. Thus, there are no design issues regarding data analysis (Pinsonneault & Kraemer, 1993). Descriptive statistics is a method for presenting quantitative descriptions in a manageable form. Inferential statistics, on the other hand, assists in drawing conclusions from the observations; typically, inferential statistics involves drawing conclusions about a population from the study of a sample drawn from that population (Babbie, 1990). In this study, descriptive statistics is used to analyse the survey response rate, organisational profile, and participants‟ profile as suggested by Babbie (1990). SPSS and excel software are used to analyse the survey data. In addition to descriptive statistics, the following tests are employed throughout the survey analysis process: chi-square test, t-test and one-way Analysis of Variance (ANOVA). In all tests, statistical significance is assessed based on p values less than 0.05 as suggested by Acton et al. (2009). Chi-square, also sometimes referred to as cross-tabulation, is by far the most common measure of association between survey variables (nominal or ordinal) that have a relatively small number of categories interpreted (Acton et al., 2009; Alreck & Settle, 1995). The chisquare test allows determination of whether or not there is a statistically significant association between two variables (Acton et al., 2009). Chi-square does not require one variable to be identified as dependent and the other independent, although that is often the case. Chi-square tests are common and popular because the method is effective and can easily be understood and interpreted (Alreck & Settle, 1995). Chi-square tests are used in this research to test the independence or relatedness of two categorical variables as suggested by Coakes and Steed (2007). In prior Information Systems research, chi-square tests were used to identify the relationships between variables. For example, Gonzalez et al. (2005) used chi-
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square tests to identify the relationship between outsourcing success factors and organisational characteristics. In this research, t-tests are used to assess the statistical significance of the difference between two independent sample means for a single dependent variable as suggested by Hair et al. (2006). T-tests are most commonly used to examine whether the means of two groups of data are significantly different from one another. The necessary conditions for t-test are that the independent variable is nominal or categorical and the dependent variable is measured at interval or ratio scale of measurement. The populations from which the two groups are drawn can be independent (or unrelated) or matched (related) (Acton et al., 2009). The t-test is employed in this research because it works with small group sizes and is quite easy to apply and interpret. It does face a couple of limitations: (1) it only accommodates two groups; and (2) it can only asses one independent variable at a time. To remove either or both of these restrictions, the researcher can utilize analysis of variance, which can test independent variables with more than two groups as well as simultaneously assessing two or more independent variables (Hair et al., 2006). The conditions for t-test are met with this research design and applied in the survey analysis (refer section 4.4). One-way analysis of variance (ANOVA) is used to measure differences between means for two or more distributions (Alreck & Settle, 1995). Analysis of Variance (ANOVA), or F test, is an extension of the independent groups t-test. ANOVA is a more general statistical procedure than the group t-test. The t-test is used when there are two levels of independent variables (for example OSS users and non-users) and to see how the groups differ on an interval/ratio variable. However, often there are categorical variables which have more than two levels. Analysis of variance is similar to the independent groups t-test but is employed when there are more than two levels of an independent variable. What an ANOVA does is to compare the variance between groups (or categories) with the variance within groups (or categories). If there is more difference between groups than there is between individuals within the groups, and the result is likely to be statistically significant (Acton et al., 2009). ANOVA has the ability to test the differences between more than two groups as well as test more than one independent variable (Hair et al., 2006). However, in this research ANOVA is used to test the differences between more than two groups (or more than two levels of each independent variable). With the increased flexibility, ANOVA can address additional issues, but ANOVA does not require the groups to be of the same size. The only restriction on group size is that the ratio of the smallest to the largest group should not be extremely small. The
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results tend to be unreliable if the smallest group is less than about 4 or 5 percent of the largest. If some groups are very small, with only a few respondents in them, the independent variables should be recoded in a way to make the difference smaller. The other requirements for ANOVA are the dependent variable must be from interval or ratio scales and each case must be independent of the other (or from a different person) (Alreck & Settle, 1995). These conditions are ensured while conducting ANOVA tests which are reported in section 4.4. Factor analysis is defined as a statistical variable reduction procedure, which extracts a small number of latent variables or “constructs” from among a larger set of observed variables (Santos & Clegg, 1999). Consequently, factor analysis is employed in this research (section 4.3) to reduce a large number of variables to a smaller set of underlying factors that summarise the essential information contained in the variables. Then the factor analysis results are used to conduct the tests described above such as t-test and ANOVA.
3.6 Case study A case study approach can be used as an exploratory method to investigate factors involved in OSS adoption as discussed in section 3.3.2. Yin (2003) suggested four important components that need to be considered before conducting case study research. They are Study‟s research question Its propositions, if any Its unit of analysis Data analysis strategy. The following sections describe the unit of analysis, data collection technique, data analysis strategy, selection of the participants, and administration of the interview process. The study‟s research question is introduced in section 1.2 (revisited in 3.1). Unit of analysis, data collection technique, data analysis strategy, validity and reliability checks, construction of interview instrument, selection of interview participants, and administration of the interview processes are discussed in the following sections. 3.6.1 Unit of analysis In case study research, the unit of analysis may be an individual, a group, an organisation, or it may be an event or some other phenomenon. It is related to the way the major research question is initially defined and is likely to be at the level being addressed by the question (Darke et al., 1998). Brewer and Hunter (2006) define units of analysis as those entities about
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which we collect data and about which we want to generalise or make inferences. Observational units may be defined as those units from which data are collected. Unit of analysis determines the limits of the data collection and analysis (Yin, 2003). As this research aims to explore factors employed in OSS adoption within APS organisations, the main unit of analysis for this research is APS organisation and the embedded unit of analysis are OSS usage within APS organisations, OSS policy, and experiences of using OSS (Yin, 2003). Unit of analysis can be different from data collection source (Yin, 2003). This research used individuals involved in the OSS selection process as the data collection source while APS organisations were the unit of analysis used in the research. 3.6.2 Qualitative data collection technique The proposed data collection technique for the case study is through semi-structured interviews, because this research needs in-depth qualitative data in order to answer the research questions. Interview is the best technique to collect in-depth and rich qualitative data (Pease & Rowe, 2005; Williamson, 2002), especially in a case study setting (Tellis, 1997a). Interviews provide valuable and in-depth information regarding the reasons for using or not using OSS from those involved in ICT decision making. One prior study used open-ended interviews to link empirical evidence and theory (Santos et al., 2001). Bryman (1989) suggested that semi-structured interviews were valuable in organisational case studies, particularly to collect the rich data that this research needs. Previous research on identifying factors that influence technology adoption has employed interviews as the data collection technique. For example, semi-structured interviews were used to explore factors influencing Open Source platform adoption (Dedrick & West, 2004); structured interviews were used to explore the factors that facilitate e-commerce adoption (Pease & Rowe, 2005). Semistructured interview techniques provide opportunities to clarify both the questions that will be asked and the answers that will be given. Furthermore, additional details can be extracted through follow-up questions. Case study is among the hardest types of research to undertake because of the absence of routine formulas. However there is a list of commonly required skills for case study investigations such as: asking good questions, being a good listener, being adaptive and flexible, having a firm understanding of the issues being studied, not having preconceived notions Yin (2003). The researcher developed these skills by conducting pilot interviews with academic as well as industry people.
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3.6.3 Data analysis strategy Data analysis consists of examining, categorizing, tabulating, testing, or otherwise recombining both the quantitative and qualitative evidence to address the research questions. Analysing the data is one of the most difficult parts of the case study as strategies and techniques are not well defined (Yin, 2003). However, Miles and Huberman‟s (1994) book is among one of the more useful sources to guide researchers in the qualitative data analysis process. They defined data analysis as having three concurrent flows of activity: data reduction, data display, and conclusion drawing/verification. Data reduction refers to the process of selecting, focusing, simplifying, abstracting, and transforming the data that appear in field notes or transcriptions. Data display is an organized, compressed assembly of information that permits conclusion drawing. Conclusion drawing is the process of drawing meaning from the evidence by noting regularities, patterns, explanations, possible configurations, causal flows, and propositions. The researcher incorporated these flows while analysing the interview data. Further, Yin (2003) suggested that every investigation should have a general analytic strategy, so as to guide the decision regarding what will be analysed and for what reason. These strategies will help reduce bias and produce compelling analytic conclusions. The theoretical proposition strategy is useful where the original objectives and design of the case study were based on theories, reviews of the literature and new hypothesis or propositions (Yin, 2003). For case study analysis, one of the most desirable techniques is using a patternmatching logic (Yin, 2003). Consequently this research uses the theoretical proposition strategy and pattern matching technique to analyse the case study evidence along with Miles and Huberman‟s (1994) three concurrent activities. Pattern matching is a technique which links two patterns when one is a theoretical pattern and the other is an observed or operational pattern (Trochim, 2006). Pattern matching is applicable to survey as well as qualitative research. In survey research it is accomplished by a test of significance such as t-test or ANOVA, and forms the basis of generalisations across different concepts or population subgroups. In qualitative research pattern matching lies at the heart of any attempt to conduct thematic analyses (Trochim, 2006). Pattern matching is a useful technique for linking data to the proposition (Tellis, 1997b). In particular, pattern matching is the most desirable strategy possible to use for exploratory research (Tellis, 1997a), to analyse and compare the results within cases as well as with existing theories (Santos et al., 2001). For example, Santos et al. (2001) used pattern matching to link empirical evidence with existing theory.
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When using theoretical propositions, the possible outcomes are (1) theoretical replication (predicts contrasting results but for predictable reasons) and (2) literal replication (predicts similar results within cases). The analysed evidence emerging through pattern matching can be used as evidence to support or contradict an already established theory (Remenyi et al., 1998). If some of the empirical cases do not work as predicted, modification must be made to the theory (Santos et al., 2001). Using the pattern matching technique, the observed results from this exploratory study are compared with the technology adoption theories (Santos et al., 2001). Further, the results from different organisations in this study are also compared as stated by Trochim (2006), Tellis (1997b) and Santos et al. (2001). Coding Each recorded interview was transcribed and stored in a MS Word document. The interview data was then coded using qualitative analysis tool NVivo 8. The researcher used NVivo 8 software to manipulate the interview data and to manage themes emerging from the analysis process. Prior to coding, transcribed data was verified by the participants. The following methods were used to code the interview transcripts: Coded into the category that was pre-determined based on the factors in the research framework Coded into a new category that was not in the research framework but had emerged through the interviews. Coded into multiple categories wherever appropriate Not coded if the information was found not to be related to the research question. 3.6.4 Validity and reliability Yin (2003) recommended four important tests to judge the quality of case study research. They are: (1) Construct validity: establishing correct operational measures for the concepts being studied; (2) Internal validity: (for explanatory or causal studies only, and not for descriptive or exploratory studies) establishing a causal relationship, whereby certain conditions are shown to lead to other conditions, as distinguished from spurious relationships; (3) External validity: establishing the domain to which a study‟s findings can be generalised; and (4) Reliability: demonstrating that the operations of a study such as the data collection
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procedures can be repeated, with the same results. This research is exploratory, therefore internal validity is not addressed for further discussion. The other three validity measures are explained below. Construct validity is ensured by reconciling multiple sources of evidence (triangulation) such as findings from the survey, case study, OSS literature and government reports related to OSS. Further construct validity is ensured by selecting the concepts to be studied for this research from the literature and survey results. The items that are used to measure the concepts are identified and used in interview questions. Further exploration is possible because a semi-structured interview was selected as a data collection strategy. External validity is achieved by using multiple cases within the study to ensure the generalisability of the case study findings. Yin (2003) said that the external validity problem has been a major weakness in doing case studies. Critics typically state that single cases offer a poor basis for generalising. However, in this study multiple cases were selected in order to achieve generalisability of the findings. Further, as per Yin‟s suggestion, external validity is ensured in the research design by incorporating survey and case study design and by using multiple cases. Generalisations could be achieved from the findings of survey and case study, and their ability to replicate the findings from each approach. Reliability is ensured by creating a case study database for the study as suggested by Yin (2003). The goal of reliability is to minimize the errors and biases in a study. Creating a case study database requires organizing and documenting the data collected for the case study. Data from every interview was transcribed, coded and stored in a manner to ensure easy retrieval of data as suggested by Yin (2003). Further reliability is achieved by using the same set of interview questions for all participants. As is the nature of semi-structured interviews, some questions differed slightly between cases. However, questions were asked in a way that covered the main research constructs. 3.6.5 Construction of interview instrument Interview questions were developed based on the factors identified from the OSS literature and technology adoption theories as described in chapter 2. Survey findings were considered while developing the interview instrument because it provided an opportunity to unveil problems in using OSS. Questions were grouped into relevant categories to help interviewees answer questions with minimal confusion. The interview instrument was pre-tested by both academic and industry professionals. A pilot interview was conducted to test the length of the
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interview and clarity of the questions used in the instrument. Based on the pilot interview, small changes were made to make the instrument more effective and understandable. A copy of the interview instrument is presented in Appendix B-5. 3.6.6 Selection of interview participants Interview subjects were identified from self-nominating survey participants. At the time of the survey, participants were asked about their interest to take part in further research as an interview participant. Fourteen of the thirty-five survey respondents declared an interest in participating in an interview. Five potential participants were not able to be contacted at the time of interview. Other interview participants were invited, but only one additional subject was recruited. 3.6.7 Administration of the interviews Participants‟ contact information collected at the time of survey was used to contact them during the interview process. Initially the participants were reminded about this research as well as their interest in participating in the interview through a telephone call. Then participants were sent an invitation e-mail along with an ethics approval letter from the University of Canberra and participant information form (see Appendix B-6 and B-2). Participants were requested to nominate a convenient time and place to conduct the interview. In order to maximize the responses, the researcher employed the following methods. Collecting new contact details from their colleagues for the people who had moved to other departments. Collected some new participant details from the agreed interview participants. Interview process Interviews were conducted in the period November 2008 to January 2009 and the duration of each interview varied between 30 and 60 minutes. Informed consent forms signed by the participants were collected at the time of interview. A total of ten people were interviewed from Commonwealth and state government organisations. Nine interviews were conducted face-to-face and one was a telephone interview. From ten participants, nine participated in the survey. At the beginning of each interview, the interviewee was asked for his/her permission to tape-record questions and answers. All interviewees agreed to this request. To reduce the possibility of misinterpretation, the interviewer briefly explained the interview
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topics and the intention of the research at the beginning of the interview. The researcher adopted a neutral role during the interview process to minimize the possibility of bias in the answers. The researcher, to the best of her knowledge, never expressed her own personal opinion. At the end of each interview, participants were asked to verify the interview transcript to reduce any misunderstanding between interviewer and the interviewee. Nine of the ten participants reviewed their transcripts and made minor changes related to technical terms.
3.7 Summary of the chapter This chapter discussed the overall research method, including the justification of the research methodology as well as data collection and data analysis techniques for both the case study and survey. The research methodology section discussed the significance of using a multimethod approach in this research. Detailed processes for the survey questionnaire and interview instrument development were reported. Data analysis strategy for both survey and case study discussions were made, and reliability and validity measures were introduced. Also the administration of survey and interview processes was discussed. Chapter 4 discusses results and findings from the survey and chapter 5 discusses findings from the case study.
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Survey results and findings
4.1 Introduction In this section, the survey responses are analysed using descriptive statistics, factor analysis, t-test, ANOVA and chi-square tests available from SPSS software. Detailed descriptions about the survey participants and organisations are reported. Different data imputation methods for missing values are discussed. For factor analysis, different factor extraction and rotation methods are also discussed. Tests to ensure practical significance of factor analysis and results are reported. Factors extracted from factor analysis are mapped with the theoretical model. This mapping is used to assess the relationship between survey findings and theoretical attributes in an OSS context. Factor analysis results are used to derive findings from the survey. Then findings based on the values of t-test, chi-square and ANOVA are reported. Finally, a summary of findings is reported which discusses the findings in relation to the research questions and the implications for technology adoption theory attributes.
4.2 Survey description A survey was conducted between November 2007 and January 2008. An e-mail invitation was sent to one hundred and sixteen (116) participants in various APS organisations. In order to maximize the survey response rate, a paper based questionnaire was also sent to three potential participants who could not access the online version. Overall one hundred and nineteen (119) invitations were sent for this survey. Thirty five valid responses including one public registered response and three paper based responses were received representing a response rate of twenty nine percent (29%). This survey was undertaken to collect information from individuals involved in software procurement from various APS organisations. The response rate was good considering that there was not a large population of senior personnel to survey. Respondent roles included CIO, CEO, CTO, policy officer and ICT manager. 4.2.1 Organisational profile Figure 4.1 provides information about the type of APS organisations which participated in the survey. Twenty-five responses were received from Commonwealth organisations, followed by three responses from State and Territory government organisations, and one response each from Local government organisations and Government enterprises. Five respondents reported 67
their organisation type as „other‟. Four of these respondents were from educational institutions and the fifth worked as a consultant employed by an APS organisation.
Figure 4-1: Survey responses of organisation type versus frequency 4.2.2 Size of the organisations Figure 4.2 shows the number of employees reported for each participant‟s organisation. Twenty three organisations employed 1000 or more employees; six organisations employed 500 to 999 employees; five organisations employed 100 to 499 employees; one organisation employed under 20 employees. The research model identified organisation size as a potential moderating variable. The distribution of responses poses a problem for any subsequent testing and could render the results unreliable (refer section 3.5.4). Alreck and Settle (1995) recommended recoding and reducing the number of categories for such variables. Consequently, the responses were recoded into three categories: “Under 500 employees”, “500-999 employees” and “1000 or more employees”. Any subsequent testing involving organisational size is based on the recoded variable.
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Figure 4-2: Number of people employed in the participated organisations 4.2.3 Description about the participants’ role Figure 4.3 reports the organisational roles held by participants. The highest responses were from ICT Managers (40%) followed by Systems Analysts (14.3%), CTO (5.7%), Policy Officers (5.7%), IT Support Staff (5.7%), CIO (2.9%) and CEO (2.9%). Eight participants (22.9%) reported their role as „other‟ which they described as Deputy CIO, Project Manager, Program Account Manager, Information and Records Manager, Group Executive, and Enterprise Architect (3 responses).
Figure 4-3: Participants role in their organisation
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4.2.4 OSS usage within an organisation As shown in the figure 4.4, OSS applications have been used at different levels within APS organisations. Server side applications were dominant followed by web applications and desktop applications. Only two organisations reported that they were using OSS applications in application areas other than the types specified in the survey. These were application layer components and various places.
Figure 4-4: OSS usage within organisation 4.2.5 Data imputation for missing values The treatment of missing values is important in all analyses. Missing data is defined as information not available for a subject (or case) about whom other information is available. Missing data often occurs when a respondent fails to answer one or more questions in a survey (Hair et al., 2006). Appropriately handled imputations for missing data can make a positive contribution to data analysis (Fowler, 2009). Mean substitution is one of the most widely used methods where missing values for a variable are replaced with the mean value of that variable calculated from all valid responses (Hair et al., 2006). In the absence of all other information, the mean for all cases provides a reasonable estimate about the value of a missing item response (Tabachnick & Fidell, 2007). Expectation Maximization (EM) imputation is an alternative data imputation method. EM imputation is based on Maximum Likelihood Estimation (MLE) rather than regression estimates15 of missing values. This is a standard method for dealing with missing data and is sometimes preferred to the regression approach because it handles nonlinearities and relies on fewer data assumptions. EM is especially appropriate for techniques such as exploratory 15
Regression estimates is a more complex method for estimating missing values. Cases with complete data are used to generate the regression equation. The equation is then used to predict missing values for incomplete cases.
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factor analysis (Tabachnick & Fidell, 2007). Consequently, this research employed both Expectation Maximization and series mean data imputation (mean substitution) methods for the correcting for missing values where each method was most appropriate.
4.3 Factor analysis results Exploratory factor analysis was employed with the following concepts: enablers of OSS adoption (items in survey question 5); inhibitors of OSS adoption (items in survey question 7); organisational experiences with OSS adoption (items in survey question 22); perceptions about the benefits of OSS (items in survey question 9); and communication channels used to collect information about OSS (items in survey question 16). A full copy of the questionnaire is provided in the Appendix B-1. This section provides detailed factor analysis results for the above concepts. These results are later used to derive findings from the survey using the data analysis techniques discussed in section 3.5.4. The factor analysis results are reported in subsections 4.3.1 to 4.3.5. Factor analysis was used to reduce a large number of variables to a smaller set of underlying factors that summarise the essential information contained in the original items. The technique is used as an exploratory technique when the researcher wishes to summarise the structure of a set of variables (Coakes & Steed, 2007). This procedure essentially removes metric redundancies from a survey and extracts the common thread that binds a set of observed variables together. This research is exploratory in nature. Gerber and Finn (2005) report that in exploratory factor analysis, there is no a priori assumption as to how the variables will combine to make factors. Consequently it was appropriate to use exploratory factor analysis on the 35 survey responses obtained in this survey. The tests employed to ensure the reliability of the factor analysis results are discussed below. Factorability of the correlation matrix Bartlett‟s test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy are both tests that determine data factorability. When Bartlett‟s test of sphericity is large and significant and the Kaiser-Meyer-Olkin measure is greater than 0.6, then factorability is assumed (Coakes & Steed, 2007).
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Choosing the Number of Factors to Retain Selecting the number of factors to retain is more critical than the selection of extraction and rotational techniques or communality values (Tabachnick & Fidell, 2007). Both overextraction and under-extraction of factors for rotation can have deleterious effects on results (Costello & Osborne, 2005). There are different approaches to selecting the number of factors to retain. They are latent root criterion (eigenvalues), scree test criterion, a priori criterion, and percentage of variance criterion. The rationale for using the latent root criterion is that any individual factor should account for the variance of at least a single variable if it is to be retained for interpretation. A priori criterion is a simple yet reasonable criterion when testing a theory or hypothesis about the number of factors to be extracted. The percentage of variance criterion is an approach based on achieving a specified cumulative percentage of total variance extracted by successive factors. The goal is to ensure practical significance for the derived factors by ensuring that they explain at least a specified amount of variance. The scree test is derived by plotting the latent roots against the number of factors in their order of extraction. The shape of the resulting curve is then used to evaluate the cut-off point. As a general rule, the scree test results in more factors being considered for inclusion than when using the latent root criterion (Hair et al., 2006). The latent root criterion is one of the most commonly used techniques. In latent root criterion, the factors having eigenvalues greater than one are considered significant (Hair et al., 2006). However, Costello and Osborne (2005) suggested that in exploratory factor analysis the scree test is the best choice for researchers in selecting the number of factors to retain. Consequently, this research used the scree test to select the number of factors to retain for further analysis. The reliability or internal consistency of each extracted factor was assessed by computing Cronbach’s alpha, it ranges in value from zero to one. The generally agreed upon lower limit for Cronbach’s alpha is 0.70 (Nunnally, 1967), although this may be relaxed to 0.6 in exploratory research (Hair et al., 2006). Chau and Tam (1997) also reported that a reliability of at least 0.6 suffices for early stages of basic research. Factor extraction method Even though there are many studies on the appropriate selection of factor extraction methods, some disagreement still exists. Based on a review of studies that used exploratory factor analysis, Costello and Osborne (2005) found that Principal Components Analysis (PCA) was the commonly preferred extraction method. However, PCA does not discriminate between
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shared and unique variance (Costello & Osborne, 2005). In contrast, principal factors method (in SPSS this procedure is called Principal Axis Factoring PAF) has the advantage of requiring no distributional assumptions. Principal factors are less likely than Maximum Likelihood (ML) to produce inappropriate solutions (Fabrigar et al., 1999). In general, ML or PAF will give the best results, depending on whether the data are generally normallydistributed or significantly non-normal (Costello & Osborne, 2005). Rotation method The most important tool for interpreting factors is factor rotation. The simplest case of rotation is orthogonal factor rotation, in which the axes are maintained at 90 degrees. When not constrained to being orthogonal, the rotational procedure is called an oblique factor rotation. Hair et al., (2006) claims that, in most cases, rotation will improve the interpretation by reducing some of the ambiguities that often accompany preliminary analysis. The ultimate goal of any rotation is to obtain some theoretically meaningful factors and, if possible, the simplest factor structure (Hair et al., 2006). Several different approaches are available for performing either orthogonal or oblique rotations. Varimax, quartimax, and equamax are commonly available orthogonal methods of rotation; direct oblimin, quartimin, and promax are oblique methods (Costello & Osborne, 2005). Orthogonal rotations produce factors that are uncorrelated; oblique methods allow the factors to correlate (Costello & Osborne, 2005). Using orthogonal rotation results in a loss of valuable information if the factors are correlated, and oblique rotation should theoretically render a more accurate, and perhaps more reproducible, solution. If the factors are truly uncorrelated, orthogonal and oblique rotation should produce almost identical results (Costello & Osborne, 2005). However, the oblique rotational method is more flexible as the factor axes need not be orthogonal (Hair et al., 2006). Oblique rotations are best suited to the goal of obtaining several theoretically meaningful factors or constructs because, realistically, few constructs in the real world are uncorrelated (Hair et al., 2006). Ensuring practical significance of factor analysis The practical significance of factor analysis can be strengthened by making a preliminary examination of the factor matrix in terms of factor loading. Using practical significance as the criteria, we can assess the loadings as follows: (a) factor loadings in the range of ± .30 to ± .40 are considered to meet the minimal level for interpretation of structure, (b) loadings ± .50 or greater are considered practically significant, (b) loadings exceeding + .70 are considered
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indicative of well-defined structure and are the goal of any factor analysis (Hair et al., 2006). If the variables used to interpret a factor do not have a multiple correlation with the factor of at least 0.50, the estimates of factor scores will be highly inexact (Nunnally, 1967). However, factor loadings are not constrained to a range of +1.00 and -1.00. In some rare cases in which the factors are strongly correlated, some loadings may be as much as 10 or even larger, which may make the interpretation of the pattern matrix difficult (Hatcher, 1994). A considerable part of the literature on sample size in factor analysis has been reviewed by (MacCallum et al., 1999) where the following recommendations were made. 1. When communalities are consistently high (all greater than 0.6), then that aspect of sampling that has a detrimental effect on model fit and precision of parameter estimates receives a low weight, thus greatly reducing the impact of sample size and other aspects of design. 2. It is desirable for the mean level of communality to be at least 0.7, preferably higher, and for communalities not to vary over a wide range. 3. If results show a relatively small number of factors and have moderate to high communalities, then the investigator can be confident that obtained factors represent a close match to population factors, even with moderate to small sample sizes. However, if results show a large number of factors and low communalities of variables, then the investigator can have little confidence that the resulting factors correspond closely to population factors unless sample size is extremely large. This research incorporated a number of factor analyses along with the concepts discussed above. However, depending upon the nature of items grouped in the questionnaire, two different approaches were employed: Principal Axis Factoring (PAF) with promax rotation where the items were expected to be correlated; and Principal Component analysis with varimax rotation where the items were expected to be uncorrelated. In order to ensure the reliability of factor analysis, different factor analysis methods were trialed for each solution which included changing the imputation, extraction and rotation methods. For each factor analysis there were at least two similar results obtained for different method combinations. This indicated that the factor analysis results were robust. PAF with promax rotation and Expectation Maximization imputation were used as these methods are well suited to exploratory factor analysis. However, either PC varimax method or series mean imputation method was chosen when the results for PAF promax or EM method failed to satisfy the 74
factor analysis criteria at different stages of analysis. For each factor analysis the following conditions were assessed: Bartlett‟s test of sphericity was used to test the factorability of the items; scree test was used to select the number of factors; Cronbach‟s alpha value greater than 0.6 was used to check reliability or internal consistency of the items grouped in each factor; and factor loading and communality values were used to ensure the practical significance of the factor analysis. 4.3.1 Factors enabling OSS adoption Figure 4.4 shows the responses for the 22 survey items relating to the enablers of OSS adoption for the 35 respondents. User responses to each item are separated into six categories: extremely unimportant, unimportant, neutral, important, extremely important, and uncertain. Based on this data, it appears that respondents perceived the following items as significant enablers of OSS adoption: (22) Functionality, (19) Interoperability, (1) Reliability, (4) Availability of technical support, (2) Security, (20) Scalability, (16) Value for money/return on investment, (12) Portability and (5) Maintenance. The responses to the remaining items were less clear cut. However, the percentage of neutral and uncertain responses was low for all items. The data was further analysed using factor analysis to reduce these items to a smaller set of underlying factors.
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Figure 4-5: Organisational perception of items enabling OSS adoption
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In the factor analysis “Uncertain” values were replaced by the series mean. The value of Bartlett's Test of Sphericity is significant (p = 0.001), and the mean level of communality (0.8) were well above the desirable level of 0.7 except for two items: “customisation” and “continuity of data format” which had communalities of 0.619 and 0.691. This shows that the data is factorable. Factors were extracted using Principal Axis Factoring and promax rotation method. Factor analysis reduced the 22 items into eight meaningful constructs based on the results of the scree plot shown in Figure Scree Plot 4.6. 6
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22
Factor Number
Figure 4-6: Scree plot of factors enabling OSS adoption The results of factor analysis and Cronbach‟s alpha for each factor extracted is provided in Table 4.1. Six out of eight factors extracted from the factor analysis have Cronbach‟s alpha values greater than 0.6 which satisfies the reliability test. Cronbach‟s alpha is not applicable for Factor 8 as it has a single item. While factor 6 has a Cronbach‟s alpha of 0.412 which is substantially lower than the acceptable level of 0.616. The total amount of variance explained by these eight factors is more than 80 percent. Loading scores for most of the items in each factor is greater than 0.5 except for four items (among them three items have loading scores higher than 0.4 and one item higher than 0.3). The loading values for two items in factor two is higher than one. As discussed earlier, pattern loadings are not always constrained to a range between +1.00 and -1.00 where the factors are strongly correlated (Hatcher, 1994). Factors 6 and 7 have two items each. Based on the underlying theme of the factor items, the 16
Subsequent survey analysis based on the factor Customisation produced one significant result with the variable Organisational Size. Particular attention is given to customisation in the case study.
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constructs identified from factor analysis are labelled as (1) Supportability, (2) Financial Constraints, (3) Contractual Obligations, (4) Reusability, (5) Organisational Fit, (6) Customisation for Integration, (7) Scalability, and (8) Functionality. Further analysis was undertaken using Principal Component analysis with varimax rotation employing series mean imputation method. This analysis also produced 8 factors consistent with PAF which showed that the items are meaningfully grouped.
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Table 4-1: Factor analysis of organisational perception of enablers of OSS adoption
Reliability Security Legal engagements Availability of technical support Maintenance Organisation rules Limited budget Cost savings Existing contracts External pressure Availability of source code Portability User appreciation Willingness to adopt OSS Job relevance Value for money / Return on investment Flexibility Customisation Interoperability Scalability Continuity of data format Functionality Eigenvalue Percent of total variance explained by the rotated component matrix
Factor 1: Supportability
Factor 2: Financial constraints
Factor 3: Contractual obligations
Factor 4: Reusability
Factor 5: Organisational fit
Factor 6: Customisation for integration
Factor 7: Scalability
Factor 8: Functionality
.823 .762 .612
.132 -.189 .177
-.103 .027 .116
-.304 .236 .112
-.045 .052 .023
.490 -.033 -.224
.011 .145 .187
-.106 -.287 .001
.609
-.242
-.053
-.017
.121
-.157
.078
.222
.550 .456 .062 -.141 .128 -.034 -.027 -.038 .274 -.054 .244
.319 -.066 1.080 1.056 .147 .020 -.003 -.008 .260 .225 .118
.183 .418 .058 .098 .894 .795 -.176 .118 -.057 .290 -.078
-.174 -.006 .026 .048 -.059 .064 .822 .809 .521 .343 .089
-.064 .083 .032 .048 .011 .013 -.062 .013 .238 -.336 .874
.253 -.106 .119 .209 -.043 -.136 .630 .087 -.133 -.085 -.206
-.022 -.117 .034 .066 -.182 -.061 -.071 -.064 -.008 .264 -.085
.343 .142 -.315 -.055 .024 .130 .296 -.271 .045 .028 .035
.018
.045
.063
-.080
.634
.106
.079
.353
-.305 -.075 .369 .217 .239 .012 5.807
-.003 .249 -.472 .155 -.371 -.354 3.773
.180 -.123 .148 -.216 .074 .156 2.067
-.031 .213 .306 -.057 -.150 -.031 1.662
.503 -.021 -.054 .069 -.167 .211 1.468
.326 .827 .491 -.016 -.119 .209 1.172
.349 -.020 -.027 .909 .412 .031 .959
.001 .181 -.050 .003 .209 .778 .903
26.398
17.149
9.395
7.555
6.674
5.324
4.361
4.106
0.850 0.925 0.830 0.713 0.733 0.412 0.622 Cronbach’s alpha Participants were asked to indicate the level of importance that enable organisation to adopt or to have intention to adopt OSS over proprietary software on each item using a six-point scale indicates “1 - extremely unimportant, 2 - unimportant, 3 - neutral, 4 - important, 5 - extremely important, 6 - uncertain”. Factors were extracted using Principal Axis Factoring Analysis and Promax rotation with convergence in 9 iterations.
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4.3.2 Factors inhibiting OSS adoption Figure 4.7 shows the responses from the 35 respondents for the 23 survey items relating to organisational concern about OSS adoption. User responses to each item are separated into six categories: very low concern, low concern, neutral, high concern, very high concern and uncertain. There were more neutral responses for the items (7, 8, 9, 10, 12, 15, 16, 17, 18, 19, 21, 22 and 23) and it appears that respondents perceived there were few significant concerns with these items. Based on the data, organisational concerns were expressed for items (13) Continuity of support, (11) Ongoing maintenance, (2) Security, (3) Reliability, (20) Training requirement, (5) Scalability, (4) Functionality and (14) Availability of service. The responses to the remaining items were less pronounced. The data was further analysed using factor analysis to reduce the large number of items to a smaller set of underlying factors. The “Uncertain” response values were replaced by the Expectation Maximization (EM) imputation available from SPSS. The significant value of Bartlett's Test of Sphericity (p
