Information Technology Diffusion with Influentials, Imitators, and Opponents

Information technology (IT) innovations follow a diverse set of diffusion patterns. Early diffusion models explaining technology diffusion patterns assumed that there is a single homogeneous segment of potential adopters. It was later shown that a two-segment model considering two groups of adopters explains variations in diffusion patterns better than the existing one-segment models. While the two-segment model considers a group of adopters promoting adoption by exerting a positive influence on prospective adopters, it does not consider the members of society who aim to inhibit the adoption process by exerting a negative influence on prospective adopters. In fact, most IT innovations face opposition. Yet it is not clear how opposition affects the diffusion process. In this paper, we model the diffusion of an IT innovation through its target population with three types of actors: influentials, who are autonomous in adopting new technology and promote its adoption; opponents, who are opposed to the technology and inhibit its adoption; and imitators, who are information seekers, thus affected by both influentials and opponents. We show that opponents play a crucial role in determining the diffusion path of an innovation. The empirical tests using real as well as simulated data sets demonstrate the ability of our model to fit the data better and to identify the segments of adopters correctly.

[1]  K. Isii,et al.  On a stochastic model concerning the pattern of communication , 1959 .

[2]  E. Mansfield TECHNICAL CHANGE AND THE RATE OF IMITATION , 1961 .

[3]  D. Shanks Solved and Unsolved Problems in Number Theory , 1964 .

[4]  J. Coleman Introduction to Mathematical Sociology , 1965 .

[5]  Gareth Horsnell,et al.  Stochastic Models of Buying Behavior , 1971 .

[6]  Ross A. Williams Growth in Ownership of Consumer Durables in the United Kingdom , 1972 .

[7]  F. Bass A new product growth model for consumer durables , 1976 .

[8]  D. Winterfeldt,et al.  Beyond acceptable risk: On the social acceptability of technologies , 1982 .

[9]  T. H. Kwon,et al.  Unifying the fragmented models of information systems implementation , 1987 .

[10]  R. Zmud,et al.  Information technology implementation research: a technological diffusion approach , 1990 .

[11]  Vijay Mahajan,et al.  New Product Diffusion Models in Marketing: A Review and Directions for Research: , 1990 .

[12]  Paul Ati ' Ewell TECHNOLOGY DIFFUSION AND ORGANIZATIONAL LEARNING: THE CASE OF BUSINESS COMPUTING* , 1992 .

[13]  Robert G. Fichman,et al.  International Conference on Information Systems ( ICIS ) 1992 INFORMATION TECHNOLOGY DIFFUSION : A REVIEW OF EMPIRICAL RESEARCH , 2017 .

[14]  Varun Grover,et al.  The Initiation, Adoption, and Implementation of Telecommunications Technologies in U.S. Organizations , 1993, J. Manag. Inf. Syst..

[15]  Sree Nilakanta,et al.  Implementation of Electronic Data Interchange: An Innovation Diffusion Perspective , 1994, J. Manag. Inf. Syst..

[16]  D. Nelkin Resistance to new technology: Forms of intrusion: comparing resistance to information technology and biotechnology in the USA , 1995 .

[17]  Arun Rai,et al.  A Structural Model for CASE Adoption Behavior , 1996, J. Manag. Inf. Syst..

[18]  Kar Yan Tam,et al.  Dynamic Price Elasticity and the Diffusion of Mainframe Computing , 1996, J. Manag. Inf. Syst..

[19]  Jay F. Nunamaker,et al.  Affective Reward and the Adoption of Group Support Systems: Productivity Is Not Always Enough , 1995, J. Manag. Inf. Syst..

[20]  G. Lilien,et al.  Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models , 1997 .

[21]  Neil C. Ramiller,et al.  The Organizing Vision in Information Systems Innovation , 1997 .

[22]  Varun Grover,et al.  The influence of information technology diffusion and business process change on perceived productivity: The IS executive's perspective , 1998, Inf. Manag..

[23]  T. Chow What is a Closed-Form Number? , 1998, math/9805045.

[24]  Rick Gibson Software process improvement: innovation and diffusion , 1998 .

[25]  G. Lilien,et al.  Erratum to Bias and Sysematic Change in the Parameter Estimates of Macro-Level Diffusion Models , 1998 .

[26]  K. Lewin,et al.  Group decision and social change. , 1999 .

[27]  P. Geroski Models of technology diffusion , 2000 .

[28]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[29]  Fred D. Davis,et al.  Investigating Determinants of Software Developers' Intentions to Follow Methodologies , 2003, J. Manag. Inf. Syst..

[30]  Cenk Kocas,et al.  Evolution of Prices in Electronic Markets Under Diffusion of Price-Comparison Shopping , 2002, J. Manag. Inf. Syst..

[31]  Erja Mustonen-Ollila,et al.  Why organizations adopt information system process innovations: a longitudinal study using Diffusion of Innovation theory , 2003, Inf. Syst. J..

[32]  Paul Beynon-Davies,et al.  The diffusion of information systems development methods , 2003, J. Strateg. Inf. Syst..

[33]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[34]  E. Burton Swanson,et al.  Innovating Mindfully with Information Technology , 2004, MIS Q..

[35]  Yogesh V. Joshi,et al.  New Product Diffusion with Influentials and Imitators , 2007 .

[36]  Toni M. Somers,et al.  The Impact of ERP Implementation on Business Process Outcomes: A Factor-Based Study , 2007, J. Manag. Inf. Syst..

[37]  Steve Sawyer Wired for Innovation: How Information Technology is Reshaping the Economy , 2010, J. Assoc. Inf. Sci. Technol..

[38]  Mihaela Ulieru,et al.  WIRED for Innovation: How Information Technology is Reshaping the Economy , 2011, Comput. J..

[39]  J. V. Rauff,et al.  Introduction to Mathematical Sociology , 2012 .