Even though Information technology (IT) assimilation and diffusion has been widely studied most of this type of research has been conducted from within a limited set of perspectives and from within a dominant paradigm. This research proposal is a response to calls to go beyond the dominant paradigm as well as a response to growing calls for more: use of pragmatism as a philosophical foundation for IS research; more use of mixed methods research grounded in a single appropriate philosophical paradigm; as well as calls for the employment of the methods of complexity science in IS research. Unified communications (UC) was chosen as an exemplar of a complex socio-technical innovation. It is proposed to use a combination of theoretical perspectives as lenses to understand the underlying causes enabling the adoption of UC in organisations in South Africa. It is expected that causes described in social contagion theory such as the institutional perspective, management fashion theory, efficient choice perspectives, as well as organisational innovativeness and possibly other specific South African pressures could influence organisational predisposition to adopt UC technology. A longitudinal study using a mixed methods approach will be undertaken from a pragmatist epistemological position. Pragmatism was chosen as a research paradigm because it supports the use of a mix of different research methods as well as modes of analysis and a continuous cycle of abductive reasoning while being guided primarily by the researcher’s desire to produce socially useful knowledge. The locus of adoption that will be studied will be organisational level adoption. Complexity science and agent-based modelling was chosen because real-world organisational adoption has been shown to be both highly complex and too slow to develop to be analysed using more traditional IS research methods. An agent-based model will be iteratively developed using aspects of complexity science as a guide to assist with explanation and prediction of organisational adoption intentions

1. 13 June 2013
Predicting and Explaining Organisational
Intention to Adopt Complex Socio-Technical
Innovations: A Complexity Science Approach
Brian Pinnock : PhD Research Proposal

2. 213 June 2013
AGENDA
Background
Literature
Questions
Methodology
Schedule
Contribution

3. 313 June 2013
Introduction and Background

4. 413 June 2013
My IS Journey (So Far)
Ontology: Realist
Epistemology: Agnostic
Post Positivist, Maybe Critical Realist, Pragmatist - Practioner
Prediction: Not complete determination but surely distinct outcomes are predictable?
Philosophy
Mixed methods: Both quant and qual have a role to play
Statistics: linearly additive causal models reached limit of what they can offer
Assumptions about distributions don’t match real world
Assumptions about linearity and order don’t match the real world
IS Approach
Real world is complex but theories aim at parsimony
Interaction terms a grudging recognition to the actual complexity of the world
How do we take complexity seriously and say how it might be investigated?
Real world
complexity

5. 513 June 2013
Why My Interest in Innovation Diffusion and
Assimilation?
Desirability
Feasibility
Viability

6. 613 June 2013
Summary
Diffusion of innovations
Organisational adoption
Adoption of complex ICT: Socio-technical systems: e.g. Unified Communications
Area of Interest
Post-positivist: Pragmatism
Mixed Methods
Longitudinal
Approach
Organisation-Technology-Environment (OTE)
Social Contagion: Institutional Theory + Fashion Theory + OI Theory
Complexity Science: CAS Theory
Theory
Simulation: Agent-based modeling
Qualitative: Interviews
Quantitative: Survey
Methodology
Frambach and Schillewaert (2002), Fichman (2004)

7. 713 June 2013
Literature Review

8. 813 June 2013
Terminology
Decision to adopt or process of adoption. Sometimes primary (formal organisational)
and secondary (individual) adoption are differentiated
Adoption
The way in which an innovation or a process spreads across a population. Sometimes
referred to as “learning” or “communication” or “contagion” effects.
Diffusion
Refers to a process within organizations starting from initial awareness of the
innovation, to potentially, formal adoption and subsequent user acceptance and
continued use
Assimilation
Generally used in the context of user adoption and continued use.Acceptance
Frambach and Schillewaert (2002)

9. 913 June 2013
Early Diffusion Studies: S-Curves
Diffusion of Iowa hybrid corn
Ryan and Gross
(1943)
Institutional diffusion
(postage stamps, school ethics
codes)
Pemberton (1936)
Anthropology:
Kroeber, Ratzel, Frobenius
(Intercultural diffusion) – Chinese
porcelain, alphabets
Early Studies
Anthropology
Late 1800s
Diffusion rate of antibiotic
prescriptions
Coleman, Katz, &
Menzel
(1958)

10. 1013 June 2013
Rates and patterns of adoption are affected by
characteristics of innovations and the adopters
An idea, practice, or object that is perceived as new by an individual or other unit of adoptionInnovation
The means by which messages get from one individual to anotherCommunication channels
The length of time required to pass through the innovation-decision processDecision Period
A set of interrelated units that are engaged in joint problem solving to accomplish a common goalSocial System
Rogers (1962)
Elements of the Innovation Process

11. 1113 June 2013
Wide Application across Management
Sciences (Marketing, IS etc.)
Market to one group at time
Moore (1991)
The
pragmatists

12. 1213 June 2013
Adoption and Diffusion Studies in
Organisations is Persisting in IS Research
Basole (2008), (Williams et al 2009)
Full text search of 390 articles in “highly ranked journals” focused solely on adoption of
ICT with firm/organisation as the unit of analysis. 66% in IS and Comp Sci Journals.
Basole (2008)
Search of 345 articles in 19 IS/IT Journals focused on adoption and diffusion of ICT in
general. 69% in IS and Comp Sci journals. 35% organisational level & 9% on SMME
Williams et al (2009)
0.0% 10.0% 20.0% 30.0% 40.0%
Survey
Frameworks &…
Mathematical Model
Case Study
Interview
Secondary Data
Qualitative Research
Field Study
Speculation/commentary
Library research
Content analysis
Field experiment
Laboratory experiment
0
10
20
30
40
50
60
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Basole (2008)
Williams et al (2009)

13. 1313 June 2013
Explaining & Predicting Adoption in
Organisations is Persisting in IS Practice
Visibility
Time
Less than 2 years
2 to 5 years
5 to 10 years
More than 10 years
Years to mainstream adoption:
Obsolete before plateau
Peak of Inflated
Expectations
Plateau of Productivity
Slope of
Enlightenment
Trough of
Disillusionment
Technology
Trigger
Fenn and Raskino (2008)

14. 1413 June 2013
One way to measure hype:
Google Trends
Google Trends (2013)
“Cloud Computing”
“Unified Communications”

15. 1513 June 2013
Technology Complexity
Socio-Technical System vs. Technology
Fichman (1992), Swanson (1994)
TYPE 2:
High knowledge
burden or high
user/technology inter-
dependencies
TYPE 1:
Low knowledge
burden or low
user/technology inter-
dependencies
Individual Organizational
Locus of adoption
Interdependence of
Technology-User
Socio-Technical
Artifacts
Socio-Technical
Artifacts
Technical
Artifact
Technical
Artifact
TAM++ IDT++
HighLow

16. 1613 June 2013
Other Classifications of Innovation
Complexity
Innovation
Types IS Process Business Process
Business
Product
Business
Integration
Admin Technology Admin Technology
Ia
Ib
II
*
IIIa
*
IIIb
IIIc
Primary FocusStrong Order Effects Weak Order Effects
Swanson (1994)

17. 1713 June 2013
3 Stages of Organizational Diffusion &
Adoption Theory in IS
Fichman (2004) – 7 promising opportunities (many from
outside of IS)
Social contagion (specificlly institutional theory), fashion
theory, mindfulness/context, extent and impact of
adoption
Emergent
Bandwagons and Context
Extended by Tornatzky and Fleischer (1990) TOE model
to include environmental drivers such as competition,
market uncertainty and regulation
Extension of IDT
Technology-organization-
environment
Rates and patterns of adoption are affected by
characteristics of both the innovation and the adopter.
Characterised by multistage adoption process, S shaped
curves. (Rogers, 1983). Narrow focus on technology and
organisational factors
Dominant Paradigm
Innovation Diffusion Theory
Pro-innovation
bias and
assumes
rational choice
Assumes
choice is
socially and
possibly
fashion driven
(Jeyaraj, Rottman, and Lacity (2006)

18. 1813 June 2013
The Dominant Paradigm
Size & Structure
Innovator
profile
Knowledge & Resources
Management Support
Compatibility
Competitive Environment
Quantity of
Adoption
Earliness
Frequency
Intent
Extent
Independent Variables Dependent Variables
Fichman (2004)

19. 1913 June 2013
(Some Key) Authors involved in publishing IS
adoption & diffusion research (1984-2006)
(Williams et al 2009)

20. 2013 June 2013
Some Other Theories of Adoption used in IS
Literature
• Theory of Reasoned Action (Fishbein & Ajzen, 1975)
• Innovation Diffusion Theory (E. M. Rogers, 1983)
• Innovation Diffusion Theory for organizations (E. Rogers, 1995)
• Social Cognitive Theory (Bandura, 1986)
• Technology Acceptance Model (TAM) (Davis, 1989)
• Theory of Planned Behavior (Ajzen, 1991)
• Perceived Characteristics of Innovating (Moore & Benbasat, 1991),
• TAM2 (Venkatesh & Davis, 2000)
• Unified Theory of Acceptance and Use of Technology
(Venkatesh, Morris, Davis, & Davis, 2003)
• Diffusion/Implementation Model (Kwon & Zmud, 1987)
• Tri-Core Model of IS Innovations (Swanson, 1994).

21. 2113 June 2013
Going Beyond the Dominant Paradigm
An innovation configuration is a specific combination of factors that are
collectively sufficient to produce a particular innovation-related outcome.
Innovation
Configurations
Exists when organizations feel social pressure to adopt an innovation that
increases in proportion to the extent of prior adoptions.
Social Contagion
Management fashion waves are relatively transitory collective
beliefs, disseminated by the discourse of management-knowledge
entrepreneurs
Management Fashion
An organization innovates mindfully to the extent that it attends to the
innovation with reasoning grounded in its own facts and specifics
Mindfulness
The quality of innovation is the extent to which an organization has adopted
the “right” innovation, at the “right” time and in the “right” way.
Quality of Innovation
Performance impacts capture the effect an innovation has on business
process measures, firm level measures, and market-based measures.
Performance Impacts
IntentExtent
Fichman (2004)

22. 2213 June 2013
Organisational Primary Adoption Theory
Frameworks
Frambach and
Schillewaert’s
(2002)
perspective
Tornatzky and
Fleischer’s
(1990)
perspective
Institutional
Perspective
(DiMaggio &
Powel, 1983)
Management
Fashion
Perspective
(Abrahamson,
1991, 1999)
Organisational
Innovativeness
Perspective (OI)
(Wolfe, 1994)
Efficient Choice
Perspective (EC)
(Tan & Fichman
2002)
External Factors
• Supplier
marketing
efforts
• Social Network
• Environmental
Influences
External
environmental
Context
Mimetic
pressures,
Normative
pressures,
Coercive
pressures
Fashion setters
Perceived
progressiveness
Adopter
Characteristics
Organisational
context
• Organisational
dispositional
innovativeness
• Leading edge
status
Economic benefits
and adoption by
end users
Perceived
Innovation
Characteristics
Technological
context
Perceived internal
benefits

23. 2313 June 2013
Innovating mindfully with information
technology
The role of institutional pressures and
organizational culture in the firm's
intention to adopt …..
Best Theories Are Hybrids!
(DiMaggio, 1995)
Predicting intention to adopt
interorganizational linkages: an
institutional perspective
Teo, Wei, Benbasat (2004)
Environmental and organizational
drivers influencing the adoption of
VoIP
Basaglia, Caporarello,
Magni, Pennarola (2009)
Institutional
Perspective
Fashion
Perspective
Organisation
Innovativeness
Efficient
Choice
Technological
Context
LocalContext
(Swanson and Ramiller,
2004)
Liu, Wei, Kwok, Chen (2010)
External Organizational Technological

24. 2413 June 2013
1. Adaptable Innovation2. Administrative Intensity 3. Age4. Anxiety 5. Attitudes6. Behavioral
Intention7. Business Computerization 8. Buying Center Participation 9. Career Ladder13.
Communication Amount 14. Communication18. Competitor Scanning 19. Complexity20.
Computer Avoidance 21. Computer Experience 22. Computer Self-Efficacy 23.
Consequences 24. Cost25. Culture26. Customer Interaction 27. Customer Power 28.
Customer Support29. Delegation Of IT Tasks 30. Developer Involvement 31. Ease Of Use
32. Education33. Elapsed Time34. End-User Characteristics 35. Environmental Complexity
36. Environmental Dynamism 37. Environmental Instability 38. Evolution Level Of IS 39.
Experience40. External Pressure 41. Extrinsic Motivation 42. Facilitating Conditions43.
Formalization of Systems Development 44. Gender45. Government46. Hierarchical Level
47. Image48. Impact On Jobs 49. Industry Type50. Influence (Coercive) 51. Influence
(Peer)52. Information Intensity53. Information Sources (External) 54. Information Sources
(Internal)10. Centralized Planning And Control 55. Information Sources 11. Championship
12. Communicability56. Infusion57. Internal Experimentation 58. Internal Pressure 59.
Intrinsic Motivation15. Communications Media Quality 60. IS Department Size 16.
Compatibility 17. Competition61. IS Maturity 62. IS Planning 63. IS Slack64. IS Structure65.
Job Task Difficulty 66. Job Task Variation 67. Job/Role Definition 68. Job/Role Rotation69.
Learning Responsibility70. Management Risk Perception 71. Managerial Training72. Middle
Management Support 73. Maturity74. Net Dependence75. Network Externality 76. Network
Size 77. Observability78. Opinion Leadership 79. Org Culture 80. Org Size81. Org Structure
(Centralization) 82. Org Structure (Formalization) 83. Org Structure (Integration) 84. Org
Structure (Routinization) 85. Org Structure (Specialization)90. Perceived Behavioral Control
91. Perceived Benefits 92. Perceived Usefulness 93. Performance Gap94. Personal
Innovativeness 95. Playfulness96. Problem Difficulty 97. Problem Importance 98. Process
Integration 99. Production Scale 100. Productivity Index 101. Professionalism 102.
Professionalism103. Quality Orientation 104. Quality Orientation 105. Relative Advantage
106. Resources107. Response To Risk108. Result Demonstrability 109. Risk (Operational)
110. Risk (Strategic) 111. Satisfaction 112. Scope 113. Sector114. Slack Resources115.
Strategic Role Of IS 116. Strategy117. Subjective Norms 118. System Quality 119.
Teamwork120. Technological Diversity 121. Technology Policy 122. Tenure123. Top
Management Characteristics 124. Top Management Support 125. Trialability 126. Trust127.
Uncertainty128. User Involvement 129. User Participation86. Outcome Expectations
(Performance) 130. User Satisfaction 87. Outcome Expectations (Personal) 88. Outsourcing
propensity131. User Support 132. User Training89. Perceived barriers 133. Vertical
Coordination134. Visibility 135. Voluntariness
A review of the predictors, linkages, and
biases in IT innovation adoption research
Jeyaraj, Rottman, Lacity (2006)
135
Independent
Variables

25. 2513 June 2013
Parsimony, Co-variance, Feedback Loops
and Interaction Effects
“… there is value in
sacrificing
parsimony to include
a richer set of
antecedents to
predict adoption”
Plouffe, Hulland &
Vandenbosch (2001)
Plouffe, Hulland & Vandenbosch (2001), Pinnock (2011)

26. 2613 June 2013
Science and Complexity (Weaver, 1948)
A few variables: e.g: Current, Resistance, Voltage, Population vs Time
19th Century Science
Problems of
Simplicity
Billions or Trillions of variables: e.g: Laws of temperature and pressure.
Science of averages.
Few or weak interactions among variables
Problems of
Disorganized
Complexity
Moderate number of variables:
Social and biological sciences
Strong non-linear interactions among variables
Problems of
Organized
Complexity
Encompasses more than one theoretical framework and is highly
interdisciplinary, seeking the answers to some fundamental questions about
living, adaptable, changeable systems.
Complexity Science

27. 2713 June 2013
Rich History of Complexity Science
Wikipedia (2013)

28. 2813 June 2013
Complexity Theory & CAS
Dissipative structures
chemistry-physics (Prigogine)
Complex Adaptive Systems
evolutionary biology (Kauffman)
Autopoiesis (self generation)
biology/cognition (Maturana)
Chaos Theory (Lorenz,
Feigenbaum)
Natural Sciences
Increasing Returns
Economics (Arthur)
Social Sciences
Generic
characteristics
of complex
adaptive
systems
Self-organisation
Emergence
Connectivity
Interdependence
Feedback
Far from equilibrium
Space of possibilities
Co-evolution
Path dependence
Creation of new order
Theories
Mitleton-kelly (2003), Merali & McKelvey (2006), Byrne (2001)
SystemsTheory

29. 2913 June 2013
Complex Adaptive Behaviour
Wikipedia (2013)

30. 3013 June 2013
Complexity Science and IS
Journal/Book Authors Theme
Communications of the
ACM
(Cline & Girou, 2000) Adaptable software frameworks
Communications of the
ACM
(Augustine, Payne, Sencindiver, & Woodcock, 2005; Coutaz,
Crowley, Dobson, & Garlan, 2005; Desai, 2005; Jones &
Deshmukh, 2005; Nerur, Mahapatra, & Mangalaraj, 2005;
Ramnath & Landsbergen, 2005; Tan, Wen, & Awad, 2005)
Special issue on complexity science
Information Technology
and People
(Benbya & McKelvey, 2006; Canessa & Riolo, 2006; Jacucci et
al., 2006; Merali & McKelvey, 2006; Merali, 2006)
Special issue on complexity science
European Journal of
Information Systems
(Lyytinen & Newman, 2008) Socio-technical change
IFIP International
Federation for
Information Processing
(Vuokko & Karsten, 2007) Complexity Theory and Research
Information Systems
Research
(Vidgen & Wang, 2009) Co-evolving systems, complex
adaptive systems and agile
development
21st Australasian
Conference on
Information Systems
(Knight & Halkett, 2010) Information Systems, systems
theory, complex systems
MIS Quarterly (Nan, 2011) Complex Adaptive Systems model
for capturing bottom-up IT use
33rd International
Conference on
Information Systems
(Kautz, 2012) ISD projects as complex adaptive
systems
Computational and
Mathematical
(Nan, Zmud, & Yetgin, 2013) Use of CAS for modelling diffusion

31. 3113 June 2013
Research Questions

32. 3213 June 2013
Pragmatism and Research Questions
“Key action questions could be related to: What action is
being performed? Who is actor? What are the results of
the actions? What is the time-context of the action? What
is the place-context of the actions? Who is the receiver of
the actions? What are the intended (and unintended)
effects or purposes arising from the actions?”
“Pragmatism doesn’t stop at these kinds of questions but
also requires that the fundamental “action” questions are
accompanied by questions specific to the research
context”
(Goldkuhl, 2004; Feilzer, 2009)

33. 3313 June 2013
Research Questions: Action Questions
• How can ABMs improve explanation using multiple perspectives?
• What is the most appropriate method of arriving at agent behavioral rules (actions-
actors-results-time-context-receiver) related to organizational adoption of
innovations in a CAS model?
• Can a relatively simple set of agent behavioral rules generate the observations in
diffusion of complex socio-technical innovations?
How can CAS
explain causal
mechanisms in
the chosen
framework?
• Will combining theoretical perspectives from social contagion theory using
complexity science approaches provide better predictive power of causal
connections to that already achieved using equilibrium-based aggregate
approaches?
• Can a relatively simple set of agent behavioral rules generate socially useful
predictions for diffusion of complex socio-technical innovations?
Will combining
perspectives +
CAS approach
improve
prediction?

34. 3413 June 2013
Research Questions: Context Questions
• Can diffusion/adoption systems be described as CAS for complex socio-technical
innovations (such as UC)?
• Is CAS-theory appropriate for going beyond the dominant paradigm for the study of
diffusion of complex socio-technical innovations?
• Does a CAS approach sufficiently address (i.e. address them to the point that they
become socially useful) the limitations of the dominant and emergent paradigms for
the study of diffusion of complex socio-technical innovations?
Is CAS
appropriate for
adoption studies
of UC?
• Will combining theoretical perspectives from institutional and social contagion
theory using complexity science approaches provide better or different explanatory
insights of causal connections to that already achieved using variance-based,
equilibrium-based aggregate approaches in prior studies?
• Who could use such insights?
Is combining
perspectives +
CAS appropriate
for adoption
studies of UC?

35. 3513 June 2013
Methodology

36. 3613 June 2013
Responses
Existing Data Set from 2011
Dependent VariablesIndependent Variables
Institutional
Theory
Fashion Theory
Efficient
Choice
Adoption
Intentions
Adoption
Characteristics
….
Response 1
Response 2
Response 3
Response 331
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
….
………....
Inductive Qualitative
Study
Deductive
Quantitative Study

37. 3713 June 2013
Existing Data Set from 2011
Decision
Makers
80%
Influencers
20%
Respondents (N=331)
20
29
6
67
20
4
38
30
19
14
8 10
Respondents: Decision Makers (N=265)
CEO
CIO
CFO
IT/IS Manager
Network Manager
Facilities/Infrastructure
Manager
Other Management Role
Business Owner
Managing Director
CTO
0
10
20
30
40
50
60
70
80
90
Industry Sector
Geographic Locations

38. 3813 June 2013
Research Strategy
Integrated
framework
Simulation
Quantitative Survey
& Analysis
Qualitative
Interviews &
Analysis
Agent Based Model
Analysis of
Literature & Prior
Research
Informs
Enables
Informs
Informs
Informs
Further validates and
possibly informs
Revised Integrated
frameworkis validated by
Netlogo3D
Smart-PLS –
segmentation & non-
linear effects
Smart-PLS –
segmentation & non-
linear effects
Emergent behaviour

39. 3913 June 2013
Agent Based Modeling
Epstein (2006)

40. 4013 June 2013
Agent Behavioral Rules
Frambach and Schillewaert (2002)
Primary
Adoption
Secondary
Adoption /
Acceptance
Organisational
Facilitators
Training
Social Persuasion
Organisational
Support
Personal
Characteristics
Demographics
Tenure
Product Experience
Personal Values
Social Usage
Network Externalities
Peer Usage
Attitude
Beliefs
Affects
Personal
Innovativeness
Beliefs
Individual
Acceptance

41. 4113 June 2013
Agent Based Modeling
Set up model parameter
values
Create agents
Set up social network ties
among agents
Allow agents to interact via
behavioral rules
Repeat n times
Conduct a
series of
experiments
Agent attributes
Innovation attributes
Social network attributes
Environment attributes

42. 4213 June 2013
Potential Contribution

43. 4313 June 2013
Contribution(s)
• A CAS-based multistage model of adoption/acceptance/diffusion for complex socio-
technical innovations
• Reveal how ABM approaches can improve both the explanatory (and possibly) the
predictive power of a theoretical framework in diffusion research
• A wider more substantive definition of type II technology than Fichman (1992) or
Swanson (1994)
• ABM offers a generative approach to explanation (Epstein 2006)
Theoretical
• Can show how other researchers can follow a similar MM + ABM process to study
other complex socio-technical innovation adoption
• Quantitative analysis of non-linear phenomena could inform agent behavioral rules
• Can (possibly use the approach) to study non-adoption decisions
Methodological

44. 4413 June 2013
Planned Research Schedule

45. 4513 June 2013
Schedule

46. 4613 June 2013
Conclusion

47. 13 June 2013
Questions