Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1054
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dc.contributor.authorDutta, Amitava
dc.contributor.authorPuvvala, Abhinay
dc.contributor.authorRoy, Rahul Kumar
dc.contributor.authorSeetharaman, Priya
dc.date.accessioned2021-08-26T06:03:23Z-
dc.date.available2021-08-26T06:03:23Z-
dc.date.issued2017
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85012936229&doi=10.1016%2fj.techfore.2017.01.024&partnerID=40&md5=22c8276b26872ba3d99f23985d6ef103
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/1054-
dc.descriptionDutta, Amitava; George Mason University, 4400 University Drive, Fairfax, VA 22030, United States; Puvvala, Abhinay; MIS Group, Indian Institute of Management Calcutta, Kolkata, West Bengal 700104, India; Roy, Rahul Kumar, MIS Group, Indian Institute of Management Calcutta, Kolkata, West Bengal 700104, India; Seetharaman, Priya, MIS Group, Indian Institute of Management Calcutta, Kolkata, West Bengal 700104, India
dc.descriptionISSN/ISBN - 00401625
dc.descriptionpp.28-43
dc.descriptionDOI - 10.1016/j.techfore.2017.01.024
dc.description.abstractThe diffusion of technology artifacts is often marked by abrupt events and incremental evolutionary moves, resulting in shifts in diffusion parameters as well as the underlying mechanics. In this paper, we model the diffusion of Android and iOS based handsets, where new models and operating system versions are released periodically. We relax a common assumption in IT diffusion studies, of holding diffusion parameters constant, and find that there are clear breaks in their values at specific points in time. Using the system dynamics methodology, we then develop and calibrate a causal model of the underlying mechanics. Significant events during evolution of the two platforms are matched temporally with the observed breaks, and the changing mechanics of diffusion across the breakpoints are identified using this causal structure. We find that iOS and Android handset diffusion patterns, although superficially similar, were driven by different mechanics. Our study contributes to the IT diffusion literature by (i) establishing the need to test for, and model, shifts in diffusion parameters over the horizon of interest (ii) offering a method to identify changes in diffusion mechanisms accompanying these shifts and (iii) demonstrating that similar temporal diffusion patterns need not imply similar underlying mechanics. � 2017 Elsevier Inc.
dc.publisherSCOPUS
dc.publisherTechnological Forecasting and Social Change
dc.publisherElsevier Inc.
dc.relation.ispartofseries118
dc.subjectCausal models
dc.subjectMobile handsets
dc.subjectStructural breaks
dc.subjectSystem dynamics
dc.subjectTechnology diffusion
dc.titleTechnology diffusion: Shift happens — The case of iOS and Android handsets
dc.typeArticle
Appears in Collections:Management Information Systems

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