Driving and Inhibiting Factors in the
Adoption of Open Source Software
in Organisations
Neil Greenley; Jyoti Choudrie
University of Hertfordshire
Hertfordshire Business School
Hatfield
Hertfordshire. AL10 9EU.
UK
Contact Author E-mail: j.choudrie@herts.ac.uk
Overview of the Presentation
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Background
Aim of the research
Literature review
Research Approach
Findings-Pilot
Findings-Main study
Conclusions
Contributions
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Background to Open Source Software
(OSS)
• OSS has played a key role in the IT industry
• OSS originated in organizations in its current
form in 1996 when the Open Source Institute
(OSI) was formed.
• In recent years, OSS has emerged in the form
of: Online Social Networks such as, Facebook,
or online free encyclopaedias or online access
platforms such as, Wikis.
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Definition of OSS and PS
• OSS has been broadly defined by IS as:
“Software where the license model grants individuals, groups, and
organisations extensive rights to use, modify, and redistribute the
binary and source-code of the original and modified/derived works,
without requiring license royalty fees” (Fitzgerald, 2004 cited in
Macredie and Mijinyawa, 2011, p237)
• When considering OSS, Proprietary Software has to be mentioned,
which is defined as:
“Software that is available only in its binary form (i.e., not in a form
that can be easily modified), that generally requires the payment of
license fees by enterprises/users, and that legally restricts user rights
and vendor liabilities” (Macredie and Mijinyawa, 2011, p238).
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Examples of PS
• Commercial off the shelf (CoTS) packages that
many individuals use in their working lives
• Web browsers such as:
• MS Internet Explorer;
• Office automation suite such as:
• Microsoft Office;
• Email servers such as:
• Microsoft Exchange or
• A database system such as
• Oracle or IBM DB/2 (Sen, 2007, p234).
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OSS Growth (or not)
• IS research has shown that global OSS related
revenues are expected to be in the region of
USD8.1Bn in 2013 and grow at an annual rate
22.4% (Gwebu and Wang, 2011).
• Despite the free of charge aspect, OSS
revenues are forecast to be less than 3% of
global software industry annual revenue for
the same period.
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Aim of this research
Aim of this research
To identify and establish the extent to which
organisational adoption and usage of OSS can
be shown to be a function of the driving and
inhibiting salient beliefs of the managers
involved for a specific sample.
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Research Approach
• Philosophy: Largely Positivist
• Sampling: Self-selection approach similar to other studies in IS adoption
research was used (Alshare et al., 2009, Hilton et al., 2006).
• Small population size: 32 for the pilot phase and 45 for the main study.
• Developed a survey instrument
• Contents of survey instrument: (a) a Likert-type scale indicating strength
and direction of perception (i.e. driver or inhibitor for OSS adoption) and
(b) open ended questions designed to collect additional qualitative data
(i.e. in relation to OSS adoption) as in other IS research (Jinwei et al., 2006)
• Questions: Drawn from previous IS research in the adoption and usage
field; organisational profile (Barbosa and Musetti, 2010, Ngai et al., 2008)
and individual profile (Zhou et al., 2011, Karahanna et al., 1999).
• Questionnaire posting: Bristol On-line Survey (BOS) a web-based system
designed for researchers who wish to collect data from respondents via
the internet.
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Dominant Way of Considering
Adoption & Use
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Terms of Adoption, use and Diffusion
• Adoption has been defined as, “Whether a person or an organisation is an
adopter or a non-adopter of an innovation. This is usually measured as a
binary variable based on self-assessment” (Jeyaraj et al., 2006, p5, Table
4).
• Usage has been differentiated from adoption as post-adoption
“subsequent continued use” (Karahanna et al., 1999, p184).
• Diffusion has been defined as, “The extent to which a person or an
organization exploits an innovation. This is usually measured as a
percentage of available features used, possible sites adopted, or possible
applications” (Jeyaraj et al., 2006, p5, Table 4).
• When considering adoption, acceptance is another term which has
emerged. Acceptance is specifically associated with end-user acceptance,
which previous IS research has argued is important especially in
organisational settings, as logically end-users must accept innovation
before organisations can claim that a deployment has been successful
(Gwebu and Wang, 2011, p221).
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OSS, adoption and use
• Recently, IS research argued that there is a
paucity of OSS research in the field of
adoption and usage.
• Results: 88/ 1,355 scholarly articles (i.e. 7%)
were published in connection with OSS
diffusion.
• Of the 88, only 44 (i.e. 4%) of the scholarly
articles related to OSS adoption (Aksulu and
Wade, 2010, p583).
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OSS Research Published according to
Tiered journals
Publication
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Total
% of OSS
Total
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5 "Tier One"
Journals
0
1
0
0
0
0
0
4
2
5
5
15
7
7
9
0
55
14 "Tier Two"
Journals
2
0
4
7
1
2
5
0
6
4
14
8
7
13
14
1
88
1,185 "Tier Three"
Journals
7
32
28
62
102
136
185
273
342
417
475
434
443
518
436
50
3940
1.3%
2.2%
96.5%
J. Choudrie & N. Greenley
OSS
Total
9
33
32
69
103
138
190
277
350
426
494
457
457
538
459
51
4083
12
Tier One journals
• (1) Management Information Systems Quarterly
(MISQ),
• (2) Information Systems Research (ISR),
• (3) Journal of Management Information Systems
(JMIS),
• (4) Journal of the Association of Information
Systems (JAIS) and
• (5) European Journal of Information Systems
(EJIS) (Lyytinen et al., 2007, p318).
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Tier Two journals
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Information & Management,
Communications of the Association of Computer Machinery (ACM),
Journal of Computer Information Systems,
International Journal of Information Management,
Journal of Information Technology,
Industrial Management & Data Systems,
Decision Support Systems,
Journal of Strategic Information Systems,
Journal of Organizational Computing and Electronic Commerce,
Information Society,
Information Systems Journal,
Information Systems Management,
Database for Advances in Information Systems and
Journal of Global Information Management
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Theories of Adoption, Use and Diffusion used
more often in IS research
Theory of
Theory of
Diffusion of
Research
Technology
Reasoned Action
Planned Behaviour
(TRA)
(TPB)
Innovations (DoI)
Self-efficacy (SE)
Acceptance Model (TAM)
All
research
2,732
799
3,020
2,108
25,008
188
68
120
649
462
6.9
8.5
4.0
30.8
1.8
articles
IS
Research
articles
IS
Research
Contribution
(%)
Table 2.2: Comparison of and the Volume of Contribution of IS Research by Theory (Web-of-Knowledge, 2014)
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Examining the Organizational, Adoption,
Use, Diffusion Literature in the context of
OSS
Conceptual Area (Within OSS Research)
Number of Articles
Percentage Contribution (%)
Adoption , Usage, Diffusion and Acceptance
420
10.3
Organisation/Organization, Enterprise and Firm
653
16.0
Top Adoption and Usage Theories
19
0.50
Others
3,153
77.2
Total OSS Research
4,083
100.0
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Conceptual Framework of this
research
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Hypothesis 1
• IS adoption and usage research suggest demographic
variables such as, age, gender and length of service are
suitable individual profile data to collect.
• Useful for establishing whether demographic factors are
statistically significant independent variables in relation to
adoption behaviour (Adams et al., 1992, Venkatesh et al.,
2003).
• IS researchers-demographic data focused on education levels
(ranging from secondary school through to doctoral studies)
are also important attributes to test for statistical significance
in relation to adoption (Karahanna et al., 1999).
H1: Individual profile factors will be of statistical significance in
OSS adoption outcomes.
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Hypothesis 2
• Proposed that the motivation for OSS adoption
can be linked to whether or not organisations
actually employ software developers (i.e. have inhouse skills to adapt code) (Morad et al., 2005).
• Organisational profile has been further defined
by the North American Industry Classification
System (NAICS) a hierarchical categorisation
devised by the US Census Bureau (USCB, 2003).
H2: Organisational profile factors will be of
statistical significance in organisational OSS
adoption.
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Hypothesis 3
• Our review found that the extant research will
often investigate driving factors but not always
inhibiting factors (Goode, 2005).
• Therefore, this research will investigate both
driving and inhibiting factors.
• These factors, in conjunction with Theory of
Planned Behaviour (TPB), have been used to
deductively reason the creation of the hypothesis
in the context of OSS adoption as below:
H3: Attitudinal factors will be of statistical
significance in OSS adoption outcomes.
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Hypothesis 4
• Social influence such as, subjective norm can be
considered, “(1) informational influence, which occurs
when individuals accept information as evidence of reality,
and (2) normative influence, which occurs when individuals
conform to the expectations of others” (Karahanna et al.,
1999, p189).
• Therefore, this research has sought to establish whether
potential subjective norm factors associated with
organisational OSS adoption and; (a) the behaviour of
others (b) the influence of others and (c) the influence of
others expectations.
H4: Subjective norm factors will be of statistical significance
in organisational OSS adoption behaviour.
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Hypothesis 5
• Perceived behavioural control can be considered, (1)
facilitating conditions, described as, “the availability
of resources needed to engage in a behaviour, such
as time, money or other specialised resources”, and
(2) self-efficacy, described as, “an individual's selfconfidence in his/her ability to perform a behaviour”
(Taylor and Todd, 1995, p150)
H5: Perceived Behavioural Control factors will be of
statistical significance in organisational OSS adoption
behaviour.
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Research Approach
• Deductive approach in terms of establishing a
set of hypotheses against which the
quantitative data collected has been tested.
• Used an online survey website-Bristol Online
survey
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Research Approach pursued in this
study
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Demographics section
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Likert scale type questions
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Quantitative Data Analysis Process
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Results of Cronbach's Alpha Coefficient
Analysis for Pilot Study
Construct
Attitude (A)
Behavioural Beliefs - Driving Adoption (BB-DA)
Behavioural Beliefs - Inhibiting Adoption (BB-IA)
Subjective Norm (SN)
Behaviour of Others (SN-BO)
Influence of Others (SN-IO)
Influence of Others's Expectations (SN-IOE)
Perceived Behavioural Control (PBC)
Organisational (PBC-O)
Open Source Software (PBC-OSS)
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Questions
Number
of Items
Cronbach's
Alpha
22(a) to 22(q)
23(a) to 23(h)
17
8
0.97
0.90
25(a) to 25(c)
26(a) to 26(h)
27(a) to 27(l)
3
8
12
0.94
0.90
0.89
31(a) to 31(m)
32(a) to 32(f)
Total
13
6
67
0.92
0.88
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List of Attempts to Obtain Completed
Surveys and Completion Rates
Publicised via
Direct Email invitation to 378 local
government IT Managers obtained
from the yougov.org website
http://www.openforumeurope.org/
http://www.linuxquestions.org/
http://www.oss-survey.org/
http://forums.mysql.com/
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Start Date
End Date
Completed Incomplete Completion
Surveys
Surveys
Rate
28th Feb 2012
30th Mar 2012
21
38
36%
12th Feb 2012
12th Feb 2012
6th Feb 2012
14th Feb 2012
29th Feb 2012
29th Feb 2012
29th Feb 2012
29th Feb 2012
Total
6
6
1
0
34
16
12
2
0
68
27%
33%
33%
n/a
33%
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Statistically Significant Driving/Inhibiting
Factors and OSS Adoption for Pilot Study
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Radar Graph showing statistically
Significant Driving/Inhibiting Factors and
OSS Adoption for Pilot Study
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: Statistically Significant
Driving/Inhibiting Factors and Intention
to Adopt OSS for Pilot Study
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Summary of Conceptual Framework
resulting from the Pilot Study
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Results of Cronbach's Alpha Coefficient
Analysis for Main Study
Construct
Attitude (A)
Behavioural Beliefs - Driving Adoption (BB-DA)
Behavioural Beliefs - Inhibiting Adoption (BB-IA)
Subjective Norm (SN)
Behaviour of Others (SN-BO)
Influence of Others (SN-IO)
Influence of Others' Expectations (SN-IOE)
Perceived Behavioural Control (PBC)
Organisational (PBC-O)
Open Source Software (PBC-OSS)
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Questions
Number
of Items
Cronbach's
Alpha
20(a) to 20(p)
21(a) to 21(g)
16
7
0.940
0.792
23(a) to 23(c)
24(a) to 24(h)
25(a) to 25(l)
3
8
12
0.896
0.762
0.747
29(a) to 29(j)
30(a) to 32(i)
Total
10
9
65
0.873
0.834
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Main Study Compared to Pilot Study
Samples
Publicised or Invitations via
Start Date
Completed
Surveys
Incomplete Completion
Surveys
Rate
21
38
36%
29th Feb 2012
29th Feb 2012
29th Feb 2012
29th Feb 2012
Total
6
6
1
0
34
16
12
2
0
68
27%
33%
33%
n/a
33%
31st Dec 2012
45
42
51%
End Date
The Pilot Study
Direct Email invitation to 378 local
government IT Managers obtained from 28th Feb 2012
the yougov.org website
http://www.openforumeurope.org/
12th Feb 2012
http://www.linuxquestions.org/
12th Feb 2012
http://www.oss-survey.org/
6th Feb 2012
http://forums.mysql.com/
14th Feb 2012
The Main Study
Direct email invitation to 3,547 public
25th Oct 2012
sector IT managers
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30th
2012
Mar
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Summary of Hypotheses by OSS
Adoption (by year) and Confidence
Level Observed
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Summary of Conceptual Model
Successfully Tested during the Main
Study
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Conclusion
• Driving and inhibiting factors (or salient beliefs)
associated with organisational OSS adoption can,
and have, been identified for a specific sample of
managers in an organisational context.
• The model was shown to be the most accurate
for the selected sample for organisational OSS
adoption in 2012 yielding a 97.10% overall
predictive capability, which represented a 37.54%
improvement on “block zero” or straight forward
probability calculation (i.e. without the use of the
conceptual model).
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Example of Driving and Inhibiting
Factors for OSS Adoption
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Academic Contributions
• From the comprehensive literature review there is a
paucity of empirical IS research in OSS adoption in
organisations.
• Of the existing research it has been argued that many of
these theories perhaps do not lend themselves to the
complexities of the organisational context.
• Specifically, having utilised TPB (Ajzen, 1991) constructs
which are crucial to organisational scenarios, such as PBC,
can be taken into careful consideration. Furthermore, this
research has modestly advanced theory by incorporating
theoretical constructs from organisational diagnostics (i.e.
Force Field Analysis - FFA) and IS research (i.e. ITG multistage models).
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Industry Contributions
• From an industry perspective, and drawing on
‘design science’ principles (Hevner, 2004), this
research has designed a methodology and artefact
which can be easily reproduced in industry (i.e. the
survey instrument, statistical/content analysis and
graphical reporting) to best enable managers to
pragmatically and heuristically develop intervention
programmes to aid the adoption of OSS.
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Questions?
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