Information Technology Diffusion with Influentials, Imitators, and Opponents: Model and Preliminary Evidence

• Main Authors: Cavusoglu, Hasan, HU, Nan, LI, Yingjiu, MA, Dan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: diffusion of innovation; information technology diffusion; technology opposition; Computer Sciences; Management Information Systems; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/sis_research/1324; https://ink.library.smu.edu.sg/context/sis_research/article/2323/viewcontent/Information_Technology_Diffusion_with_Influentials_Imitators_and_Opponents_av.pdf
• Institution: Singapore Management University

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.