Research proposal presentation ver 3.0

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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

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  • Philosophical Journey:Ontological: Realist (not naïve realist)Epistemological: Agnostic (Empirical is accessible but maybe not the actual or the real)Flirted with Critical Realism because causation (and hence prediction) is flawed (Hume etc.) Prediction: abandoning complete determination I can live with but the complete abandonment of any way of viewing the future (even in terms of distinct possibilities) seems too much.Some views I’ve collected along the way:The IS research agenda should be about systems of people and IT artifactsQuantitative analysis has a role in all Social Sciences including in an exploratory roleStatistics is a key part of quantitative analysis but linearly additive causal models are limited in what they can offerAssumptions about distributions don’t match real worldLinearity and order being forced on a world that isn’t really like thatNon linear statistics holds promise (MARS, SMART PLS etc.)Interaction (when the effects of multiple variables are not additive), covariance and feedback loops are a statistical headache but should be seen as exciting from a scientific perspectiveStatistical techniques use interaction as a grudging recognition to the actual complexity of the worldParsimony: (If the real world is complex why should our theories and methods not be complex too?)How do we take complexity seriously and say how it might be investigated?How do we handle inter disciplinarity?
  • Its what I am interested in on a daily basis ie. How can organisations innovate and then how can these innovations diffuse.
  • it was found that the rate of diffusion appeared to correspond with “the cumulative curve of a normal frequency distribution”(Pemberton, 1936).
  • Rogers is the most widely cited work on innovation diffusion
  • Several authors built on Rogers work including Moore in MarketingChasm is only applicable for discontinuous innovation. i.e. how do you convince the pragmatists (early majority) that they must change.
  • Adoption and diffusion is still being studied several decades later.
  • Diffusion is important to practitioners.Usedextensively by practitioners e.g. STREET processNon Newtonian time involved
  • Google is a quick way to evaluate hype (at least for the early part of the hype cycle)
  • to create an appreciation that the technology being studied is used in a social contextSocio-technical does not imply the same nuanced history and meaning as the Tavistock Institute of Human Relations (Mumford, 2006)“sociotechnical systems” is meant to include the things that information systems, work practices and organisations are made of and the study of these includes the exploration of humans and technology entanglement, co-constitution and co-performanceWhy not just use the word technology? That would imply type 1 type adoptionDifferent theories used in the literature to measure Individual and OrganisationalTAM++ = Technology Acceptance Model extensionsIDT = Innovation Diffusion Theory extensions
  • Type I innovation may focus upon the IS administrative task or an IS technical taskType 2 innovation applies IS products and services to the administrative core of the host organization busisness. Core business technology for the production of the organization's goods and services is not directly affected.Type 3 innovation integrates IS products and services with core business technology, and typically impacts upon general business administration as well. The whole business is potentially affected
  • According to a recent review of IT innovation adoptionstudies (Jeyaraj, Rottman, and Lacity, 2006), IDT is a dominant theory used to examine organizational adoption of IT over the prior two decades.Three stages of diffusion theory can be identified in the literature: dominant, technology-organization-environment and emergent. According to the dominant paradigm, the rate and pattern of the adoption and diffusion of ideas, practices or objects through populations of potential adopters is affected by the characteristics of both the innovation and the adopter (Rogers, 1983). The multistage adoption process is affected by the actions of ‘key adopters’, the resulting profile being characterized by an S-shaped curve. Information systems (IS) researchers have applied the dominant paradigm to examine a variety of IT-based innovations, including electronic scanners in supermarket chains (Zmud & Apple, 1992), spreadsheet software (Brancheau & Wetherbe, 1990), IT outsourcing (Loh & Venkatraman, 1992), and internet computing (Lyytinen & Rose, 2003). Examining this literature, Swanson (1994) concludes that firm-level effects are salient in explaining the adoption and diffusion of IS innovations. A drawback of the dominant paradigm, however, is its narrow focus on only two innovation drivers, namely, technology and organizational factors.The dominant paradigm was extended by Tornatzky & Fleischer (1990), who added the external environment as another factor driving adoption and diffusion of innovations. In the context of internet-based innovations, this extension suggests that organization-level factors, technology factors, as well as the firm's external environment all impact the diffusion process. Zhu et al. (2004) test this framework by analysing innovations associated with the electronic pre-processing, negotiation, performance and post processing of business transactions among firms via the internet. The authors find that internal factors – technology competence and firm scope and size – and external factors – consumer readiness and competitive pressure – are significant adoption drivers, providing support for the inclusion of external factors in studies of IT innovation diffusion. However, despite its expanded focus, application of the technology-organization-environment paradigm remains limited in the IS context as it largely ignores the primary function of IT, i.e. processing, storing and capturing information.Finally, in the emergent stage of diffusion theory, Fichman (2004) suggests seven ‘promising opportunities’ to extend the dominant paradigm: contagion effects, management fashion, innovation mindfulness, innovation configurations, technology destiny, quality of innovation, and the performance impacts of innovative IT. Several of these approaches offer the potential of extending and refining our understanding of IT innovation diffusion from an industry perspective. As an example, social contagion may be an important factor among firms within a single industry. Firms may be more likely to be affected by other firms in their industry who have already adopted an IT innovation.Although the dominant paradigm, technology-organization-environment extension and emergent stages of diffusion theory have improved understanding of the antecedents of IT adoption and diffusion, they do not explicitly address the role of information processing requirements. We thus extend our focus to move beyond diffusion theory, consistent with the suggestion of Cooper & Zmud (1992, p. 137) that studies will be improved if they ‘adequately account for the “fit” between the technology being examined and the work context within which the technology is being introduced.’ We adopt the information processing view of the firm to shed light on the connection between information processing requirements across industries and the adoption and diffusion of IT innovations.Information processing viewThe information processing view provides an alternative rationale as to why firms adopt IT innovations: to respond to information processing requirements. According to the information processing view, organizations can be viewed as information processing systems that develop information processing mechanisms to deal with environmental sources of uncertainty (March & Simon, 1958; Galbraith, 1977; Tushman & Nadler, 1978; Van de Ven & Ferry, 1980). Given imperfect knowledge and imprecise decision-making, firms develop patterns and routines of behaviour to complete individual organizational tasks. When changes in the environment occur, they give rise to uncertainty, as established routines can no longer be used. Firms face new information requirements as additional information must now be collected, processed and distributed in order for the firm to make decisions and complete its activities. To continue to operate efficiently, a firm must align its information processing capabilities with its new information requirements. The firm responds to complexity by either increasing its information processing capabilities or by reducing its information processing needs (Galbraith, 1977). System effectiveness is contingent upon the degree of fit between system and requirements, and organization scholars have found empirical associations between complexity and information processing modes (Tushman & Nadler, 1978; Van de Ven & Ferry, 1980).IS scholars have used the information processing view to explore the relationship between IS and information requirements. Anandarajan & Arinze (1998) examine the extent to which a client/server IS architecture matches contextual information needs. Using survey data from 89 client/server implementations, the authors find that efficiency is a function of the fit between task characteristics – task analysability and task interdependence – to the client/server architecture – client- vs. server-centric. Indeed, the overall performance of a firm is associated with how well a firm matches the information processing requirements it faces with the degree of alignment between the role of IS, centralization of IS and the formalization of IS (Wang, 2003). The information processing view has also informed analysis of environmental uncertainty and task characteristics in the context of user satisfaction with data (Karimiet al., 2004).Existing research shows that information processing capabilities adopted in response to information processing requirements can vary. An examination of the US and Japanese automobile industries uncovers five configurations of interorganizational relationships between buyers and suppliers (Bensaou & Venkatraman, 1995). In one configuration – electronic interdependence – the authors found that in the case of highly customized and complex products, firms tend to be significant users of electronic communications tools as well as traditional means as a response to substantial environmental uncertainty. The authors conclude that there may be several ways to design interorganizational relationships that are equally effective. Similarly, variation in response has been identified in an investigation of production maintenance operations. Specifically, maintenance operations respond to complexity by using various combinations of computerized maintenance management systems, preventive and predictive maintenance systems, coordination and workforce size (Swanson, 2003).The information processing view in the IS context is also supported by studies of organizational response to industry clock speed (Mendelson & Pillai, 1998; 1999). In a study of manufacturers of computers and related products, for example, Mendelson & Pillai (1998) adopt the view that information processing requirements underlie the adoption and diffusion of communication technologies. In highly dynamic business environments, firms must adopt new ways of processing increased volumes of information, with IT being a major enabler of such processing. Results support the information processing view of the firm in that higher clock speed environments are associated with greater use of IT by firms.In sum, the information processing view is useful in the context of IT-based innovation as it provides an alternative perspective of why firms adopt such innovations. Internal and environmental uncertainty, whether based in production methods, supply chains or the larger competitive landscape, gives rise to new information requirements – a key driver of firm adoption of information technologies. We now develop a conceptual framework of IT-based innovation based on the information requirements paradigm.
  • Most studies used aggregate models with linear characteristics
  • Most studies remain firmly ensconced within the positivist paradigm more generally; there are, of course, other notable innovation threads outside the positivist paradigm, including structuration (Orlikowski, 1992), adaptive structuration (DeSanctis and Poole, 1994), and socio-technical approaches (Bijker, 1995). These streams, and other non-positivist approaches that may emerge are certainly in keeping with the goal of breaking with the dominant innovation paradigm, but are beyond the scope of this proposal.
  • Just under 100 studies from 1992 to 2003Org adoption and individual adoption
  • Move from a parsimonius base model to a complicated model very quicklyThere are a number of benefits arising from an integrative model. First, it helps us understand how previous research fits within a broader model of small business growth. Thus, it provides an opportunity to gauge how much we really know about orgnisational diffusion when we simultaneously consider the constructs from the dominant perspectives. Second, we are able to investigate the relationship of constructs and small business growth, while control- ling for possible redundancies. This provides the opportunity of better assessing the contribution of each perspective to our understanding of organisation diffusion. Third, we not only investigate the relationships proposed within a perspective, but also relationships that only exist across perspectives, which further increases explanatory ability. That is, we examine the indirect effects that some constructs might have on organisational diffusion, which have not been adequately considered to date. Fourth, to some extent, we consider different levels of analysis. This creates some challenges, but opens up considerable opportunities for future research.
  • How can a complexity-based approach such as agent-based modeling be used to explain the causal mechanisms implied in the institutional perspective, management fashion perspective, efficient-choice perspective and organizational innovativeness 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?In a quest for elegance or parsimony, which of the perspectives can be excluded from the model without significantly reducing explanatory power and in what contexts? Phrased alternatively, can a relatively simple set of agent behavioral rules generate the observations in diffusion of complex socio-technical innovations?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?In a quest for elegance or parsimony, which of the perspectives can be excluded from the model without significantly impacting predictive power and in what contexts? Phrased alternatively, can a relatively simple set of agent behavioral rules generate socially useful predictions for diffusion of complex socio-technical innovations?
  • A simple linear aggregate model can predict a standing ovation but it can’t explain it. To do that you need an agent-based model of the audience.
  • Contribution: Theoretical – From Stahl 2004 - (van de Ven, 1989). Weick (1989 p. 517) suggests that a good theory should be "interesting rather than obvious, irrelevant or absurd, obvious in novel way:, a source of unexpected connections, high in narrative rationality, aesthetically pleasing, or correspondent with presumed realities". Maybe the ultimate quality criterion of the paper will be whether the author can "convince and cajole [his] colleagues about the directions we should now pursue" (Benbasat & Weber, 1996 p. 398).
  • Research proposal presentation ver 3.0

    1. 1. 13 June 2013Predicting and Explaining OrganisationalIntention to Adopt Complex Socio-TechnicalInnovations: A Complexity Science ApproachBrian Pinnock : PhD Research Proposal
    2. 2. 213 June 2013AGENDABackgroundLiteratureQuestionsMethodologyScheduleContribution
    3. 3. 313 June 2013Introduction and Background
    4. 4. 413 June 2013My IS Journey (So Far)Ontology: RealistEpistemology: AgnosticPost Positivist, Maybe Critical Realist, Pragmatist - PractionerPrediction: Not complete determination but surely distinct outcomes are predictable?PhilosophyMixed methods: Both quant and qual have a role to playStatistics: linearly additive causal models reached limit of what they can offerAssumptions about distributions don’t match real worldAssumptions about linearity and order don’t match the real worldIS ApproachReal world is complex but theories aim at parsimonyInteraction terms a grudging recognition to the actual complexity of the worldHow do we take complexity seriously and say how it might be investigated?Real worldcomplexity
    5. 5. 513 June 2013Why My Interest in Innovation Diffusion andAssimilation?DesirabilityFeasibilityViability
    6. 6. 613 June 2013SummaryDiffusion of innovationsOrganisational adoptionAdoption of complex ICT: Socio-technical systems: e.g. Unified CommunicationsArea of InterestPost-positivist: PragmatismMixed MethodsLongitudinalApproachOrganisation-Technology-Environment (OTE)Social Contagion: Institutional Theory + Fashion Theory + OI TheoryComplexity Science: CAS TheoryTheorySimulation: Agent-based modelingQualitative: InterviewsQuantitative: SurveyMethodologyFrambach and Schillewaert (2002), Fichman (2004)
    7. 7. 713 June 2013Literature Review
    8. 8. 813 June 2013TerminologyDecision to adopt or process of adoption. Sometimes primary (formal organisational)and secondary (individual) adoption are differentiatedAdoptionThe way in which an innovation or a process spreads across a population. Sometimesreferred to as “learning” or “communication” or “contagion” effects.DiffusionRefers to a process within organizations starting from initial awareness of theinnovation, to potentially, formal adoption and subsequent user acceptance andcontinued useAssimilationGenerally used in the context of user adoption and continued use.AcceptanceFrambach and Schillewaert (2002)
    9. 9. 913 June 2013Early Diffusion Studies: S-CurvesDiffusion of Iowa hybrid cornRyan and Gross(1943)Institutional diffusion(postage stamps, school ethicscodes)Pemberton (1936)Anthropology:Kroeber, Ratzel, Frobenius(Intercultural diffusion) – Chineseporcelain, alphabetsEarly StudiesAnthropologyLate 1800sDiffusion rate of antibioticprescriptionsColeman, Katz, &Menzel(1958)
    10. 10. 1013 June 2013Rates and patterns of adoption are affected bycharacteristics of innovations and the adoptersAn idea, practice, or object that is perceived as new by an individual or other unit of adoptionInnovationThe means by which messages get from one individual to anotherCommunication channelsThe length of time required to pass through the innovation-decision processDecision PeriodA set of interrelated units that are engaged in joint problem solving to accomplish a common goalSocial SystemRogers (1962)Elements of the Innovation Process
    11. 11. 1113 June 2013Wide Application across ManagementSciences (Marketing, IS etc.)Market to one group at timeMoore (1991)Thepragmatists
    12. 12. 1213 June 2013Adoption and Diffusion Studies inOrganisations is Persisting in IS ResearchBasole (2008), (Williams et al 2009)Full text search of 390 articles in “highly ranked journals” focused solely on adoption ofICT 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 ingeneral. 69% in IS and Comp Sci journals. 35% organisational level & 9% on SMMEWilliams et al (2009)0.0% 10.0% 20.0% 30.0% 40.0%SurveyFrameworks &…Mathematical ModelCase StudyInterviewSecondary DataQualitative ResearchField StudySpeculation/commentaryLibrary researchContent analysisField experimentLaboratory experiment010203040506019741976197819801982198419861988199019921994199619982000200220042006Basole (2008)Williams et al (2009)
    13. 13. 1313 June 2013Explaining & Predicting Adoption inOrganisations is Persisting in IS PracticeVisibilityTimeLess than 2 years2 to 5 years5 to 10 yearsMore than 10 yearsYears to mainstream adoption:Obsolete before plateauPeak of InflatedExpectationsPlateau of ProductivitySlope ofEnlightenmentTrough ofDisillusionmentTechnologyTriggerFenn and Raskino (2008)
    14. 14. 1413 June 2013One way to measure hype:Google TrendsGoogle Trends (2013)“Cloud Computing”“Unified Communications”
    15. 15. 1513 June 2013Technology ComplexitySocio-Technical System vs. TechnologyFichman (1992), Swanson (1994)TYPE 2:High knowledgeburden or highuser/technology inter-dependenciesTYPE 1:Low knowledgeburden or lowuser/technology inter-dependenciesIndividual OrganizationalLocus of adoptionInterdependence ofTechnology-UserSocio-TechnicalArtifactsSocio-TechnicalArtifactsTechnicalArtifactTechnicalArtifactTAM++ IDT++HighLow
    16. 16. 1613 June 2013Other Classifications of InnovationComplexityInnovationTypes IS Process Business ProcessBusinessProductBusinessIntegrationAdmin Technology Admin TechnologyIaIbII*IIIa*IIIbIIIcPrimary FocusStrong Order Effects Weak Order EffectsSwanson (1994)
    17. 17. 1713 June 20133 Stages of Organizational Diffusion &Adoption Theory in ISFichman (2004) – 7 promising opportunities (many fromoutside of IS)Social contagion (specificlly institutional theory), fashiontheory, mindfulness/context, extent and impact ofadoptionEmergentBandwagons and ContextExtended by Tornatzky and Fleischer (1990) TOE modelto include environmental drivers such as competition,market uncertainty and regulationExtension of IDTTechnology-organization-environmentRates and patterns of adoption are affected bycharacteristics of both the innovation and the adopter.Characterised by multistage adoption process, S shapedcurves. (Rogers, 1983). Narrow focus on technology andorganisational factorsDominant ParadigmInnovation Diffusion TheoryPro-innovationbias andassumesrational choiceAssumeschoice issocially andpossiblyfashion driven(Jeyaraj, Rottman, and Lacity (2006)
    18. 18. 1813 June 2013The Dominant ParadigmSize & StructureInnovatorprofileKnowledge & ResourcesManagement SupportCompatibilityCompetitive EnvironmentQuantity ofAdoptionEarlinessFrequencyIntentExtentIndependent Variables Dependent VariablesFichman (2004)
    19. 19. 1913 June 2013(Some Key) Authors involved in publishing ISadoption & diffusion research (1984-2006)(Williams et al 2009)
    20. 20. 2013 June 2013Some Other Theories of Adoption used in ISLiterature• 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. 21. 2113 June 2013Going Beyond the Dominant ParadigmAn innovation configuration is a specific combination of factors that arecollectively sufficient to produce a particular innovation-related outcome.InnovationConfigurationsExists when organizations feel social pressure to adopt an innovation thatincreases in proportion to the extent of prior adoptions.Social ContagionManagement fashion waves are relatively transitory collectivebeliefs, disseminated by the discourse of management-knowledgeentrepreneursManagement FashionAn organization innovates mindfully to the extent that it attends to theinnovation with reasoning grounded in its own facts and specificsMindfulnessThe quality of innovation is the extent to which an organization has adoptedthe “right” innovation, at the “right” time and in the “right” way.Quality of InnovationPerformance impacts capture the effect an innovation has on businessprocess measures, firm level measures, and market-based measures.Performance ImpactsIntentExtentFichman (2004)
    22. 22. 2213 June 2013Organisational Primary Adoption TheoryFrameworksFrambach andSchillewaert’s(2002)perspectiveTornatzky andFleischer’s(1990)perspectiveInstitutionalPerspective(DiMaggio &Powel, 1983)ManagementFashionPerspective(Abrahamson,1991, 1999)OrganisationalInnovativenessPerspective (OI)(Wolfe, 1994)Efficient ChoicePerspective (EC)(Tan & Fichman2002)External Factors• Suppliermarketingefforts• Social Network• EnvironmentalInfluencesExternalenvironmentalContextMimeticpressures,Normativepressures,CoercivepressuresFashion settersPerceivedprogressivenessAdopterCharacteristicsOrganisationalcontext• Organisationaldispositionalinnovativeness• Leading edgestatusEconomic benefitsand adoption byend usersPerceivedInnovationCharacteristicsTechnologicalcontextPerceived internalbenefits
    23. 23. 2313 June 2013Innovating mindfully with informationtechnologyThe role of institutional pressures andorganizational culture in the firmsintention to adopt …..Best Theories Are Hybrids!(DiMaggio, 1995)Predicting intention to adoptinterorganizational linkages: aninstitutional perspectiveTeo, Wei, Benbasat (2004)Environmental and organizationaldrivers influencing the adoption ofVoIPBasaglia, Caporarello,Magni, Pennarola (2009)InstitutionalPerspectiveFashionPerspectiveOrganisationInnovativenessEfficientChoiceTechnologicalContextLocalContext(Swanson and Ramiller,2004)Liu, Wei, Kwok, Chen (2010)External Organizational Technological
    24. 24. 2413 June 20131. Adaptable Innovation2. Administrative Intensity 3. Age4. Anxiety 5. Attitudes6. BehavioralIntention7. 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 Use32. Education33. Elapsed Time34. End-User Characteristics 35. Environmental Complexity36. 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 Level47. 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. Championship12. 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. MiddleManagement Support 73. Maturity74. Net Dependence75. Network Externality 76. NetworkSize 77. Observability78. Opinion Leadership 79. Org Culture 80. Org Size81. Org Structure(Centralization) 82. Org Structure (Formalization) 83. Org Structure (Integration) 84. OrgStructure (Routinization) 85. Org Structure (Specialization)90. Perceived Behavioral Control91. Perceived Benefits 92. Perceived Usefulness 93. Performance Gap94. PersonalInnovativeness 95. Playfulness96. Problem Difficulty 97. Problem Importance 98. ProcessIntegration 99. Production Scale 100. Productivity Index 101. Professionalism 102.Professionalism103. Quality Orientation 104. Quality Orientation 105. Relative Advantage106. 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. TopManagement 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. Outsourcingpropensity131. User Support 132. User Training89. Perceived barriers 133. VerticalCoordination134. Visibility 135. VoluntarinessA review of the predictors, linkages, andbiases in IT innovation adoption researchJeyaraj, Rottman, Lacity (2006)135IndependentVariables
    25. 25. 2513 June 2013Parsimony, Co-variance, Feedback Loopsand Interaction Effects“… there is value insacrificingparsimony to includea richer set ofantecedents topredict adoption”Plouffe, Hulland &Vandenbosch (2001)Plouffe, Hulland & Vandenbosch (2001), Pinnock (2011)
    26. 26. 2613 June 2013Science and Complexity (Weaver, 1948)A few variables: e.g: Current, Resistance, Voltage, Population vs Time19th Century ScienceProblems ofSimplicityBillions or Trillions of variables: e.g: Laws of temperature and pressure.Science of averages.Few or weak interactions among variablesProblems ofDisorganizedComplexityModerate number of variables:Social and biological sciencesStrong non-linear interactions among variablesProblems ofOrganizedComplexityEncompasses more than one theoretical framework and is highlyinterdisciplinary, seeking the answers to some fundamental questions aboutliving, adaptable, changeable systems.Complexity Science
    27. 27. 2713 June 2013Rich History of Complexity ScienceWikipedia (2013)
    28. 28. 2813 June 2013Complexity Theory & CASDissipative structureschemistry-physics (Prigogine)Complex Adaptive Systemsevolutionary biology (Kauffman)Autopoiesis (self generation)biology/cognition (Maturana)Chaos Theory (Lorenz,Feigenbaum)Natural SciencesIncreasing ReturnsEconomics (Arthur)Social SciencesGenericcharacteristicsof complexadaptivesystemsSelf-organisationEmergenceConnectivityInterdependenceFeedbackFar from equilibriumSpace of possibilitiesCo-evolutionPath dependenceCreation of new orderTheoriesMitleton-kelly (2003), Merali & McKelvey (2006), Byrne (2001)SystemsTheory
    29. 29. 2913 June 2013Complex Adaptive BehaviourWikipedia (2013)
    30. 30. 3013 June 2013Complexity Science and ISJournal/Book Authors ThemeCommunications of theACM(Cline & Girou, 2000) Adaptable software frameworksCommunications of theACM(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 scienceInformation Technologyand People(Benbya & McKelvey, 2006; Canessa & Riolo, 2006; Jacucci etal., 2006; Merali & McKelvey, 2006; Merali, 2006)Special issue on complexity scienceEuropean Journal ofInformation Systems(Lyytinen & Newman, 2008) Socio-technical changeIFIP InternationalFederation forInformation Processing(Vuokko & Karsten, 2007) Complexity Theory and ResearchInformation SystemsResearch(Vidgen & Wang, 2009) Co-evolving systems, complexadaptive systems and agiledevelopment21st AustralasianConference onInformation Systems(Knight & Halkett, 2010) Information Systems, systemstheory, complex systemsMIS Quarterly (Nan, 2011) Complex Adaptive Systems modelfor capturing bottom-up IT use33rd InternationalConference onInformation Systems(Kautz, 2012) ISD projects as complex adaptivesystemsComputational andMathematical(Nan, Zmud, & Yetgin, 2013) Use of CAS for modelling diffusion
    31. 31. 3113 June 2013Research Questions
    32. 32. 3213 June 2013Pragmatism and Research Questions“Key action questions could be related to: What action isbeing performed? Who is actor? What are the results ofthe actions? What is the time-context of the action? Whatis the place-context of the actions? Who is the receiver ofthe actions? What are the intended (and unintended)effects or purposes arising from the actions?”“Pragmatism doesn’t stop at these kinds of questions butalso requires that the fundamental “action” questions areaccompanied by questions specific to the researchcontext”(Goldkuhl, 2004; Feilzer, 2009)
    33. 33. 3313 June 2013Research 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 ofinnovations in a CAS model?• Can a relatively simple set of agent behavioral rules generate the observations indiffusion of complex socio-technical innovations?How can CASexplain causalmechanisms inthe chosenframework?• Will combining theoretical perspectives from social contagion theory usingcomplexity science approaches provide better predictive power of causalconnections to that already achieved using equilibrium-based aggregateapproaches?• Can a relatively simple set of agent behavioral rules generate socially usefulpredictions for diffusion of complex socio-technical innovations?Will combiningperspectives +CAS approachimproveprediction?
    34. 34. 3413 June 2013Research Questions: Context Questions• Can diffusion/adoption systems be described as CAS for complex socio-technicalinnovations (such as UC)?• Is CAS-theory appropriate for going beyond the dominant paradigm for the study ofdiffusion of complex socio-technical innovations?• Does a CAS approach sufficiently address (i.e. address them to the point that theybecome socially useful) the limitations of the dominant and emergent paradigms forthe study of diffusion of complex socio-technical innovations?Is CASappropriate foradoption studiesof UC?• Will combining theoretical perspectives from institutional and social contagiontheory using complexity science approaches provide better or different explanatoryinsights of causal connections to that already achieved using variance-based,equilibrium-based aggregate approaches in prior studies?• Who could use such insights?Is combiningperspectives +CAS appropriatefor adoptionstudies of UC?
    35. 35. 3513 June 2013Methodology
    36. 36. 3613 June 2013ResponsesExisting Data Set from 2011Dependent VariablesIndependent VariablesInstitutionalTheoryFashion TheoryEfficientChoiceAdoptionIntentionsAdoptionCharacteristics….Response 1Response 2Response 3Response 331….….….….….….….….….….….….….….….….….….….….….….….….………....Inductive QualitativeStudyDeductiveQuantitative Study
    37. 37. 3713 June 2013Existing Data Set from 2011DecisionMakers80%Influencers20%Respondents (N=331)2029667204383019148 10Respondents: Decision Makers (N=265)CEOCIOCFOIT/IS ManagerNetwork ManagerFacilities/InfrastructureManagerOther Management RoleBusiness OwnerManaging DirectorCTO0102030405060708090Industry SectorGeographic Locations
    38. 38. 3813 June 2013Research StrategyIntegratedframeworkSimulationQuantitative Survey& AnalysisQualitativeInterviews &AnalysisAgent Based ModelAnalysis ofLiterature & PriorResearchInformsEnablesInformsInformsInformsFurther validates andpossibly informsRevised Integratedframeworkis validated byNetlogo3DSmart-PLS –segmentation & non-linear effectsSmart-PLS –segmentation & non-linear effectsEmergent behaviour
    39. 39. 3913 June 2013Agent Based ModelingEpstein (2006)
    40. 40. 4013 June 2013Agent Behavioral RulesFrambach and Schillewaert (2002)PrimaryAdoptionSecondaryAdoption /AcceptanceOrganisationalFacilitatorsTrainingSocial PersuasionOrganisationalSupportPersonalCharacteristicsDemographicsTenureProduct ExperiencePersonal ValuesSocial UsageNetwork ExternalitiesPeer UsageAttitudeBeliefsAffectsPersonalInnovativenessBeliefsIndividualAcceptance
    41. 41. 4113 June 2013Agent Based ModelingSet up model parametervaluesCreate agentsSet up social network tiesamong agentsAllow agents to interact viabehavioral rulesRepeat n timesConduct aseries ofexperimentsAgent attributesInnovation attributesSocial network attributesEnvironment attributes
    42. 42. 4213 June 2013Potential Contribution
    43. 43. 4313 June 2013Contribution(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) thepredictive power of a theoretical framework in diffusion research• A wider more substantive definition of type II technology than Fichman (1992) orSwanson (1994)• ABM offers a generative approach to explanation (Epstein 2006)Theoretical• Can show how other researchers can follow a similar MM + ABM process to studyother 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 decisionsMethodological
    44. 44. 4413 June 2013Planned Research Schedule
    45. 45. 4513 June 2013Schedule
    46. 46. 4613 June 2013Conclusion
    47. 47. 13 June 2013Questions

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