Keywords

Innovation diffusion, Innovation adoption, outgroup aversion, polarization

Abstract

Individuals' decisions to adopt an innovation can be influenced by the group identity of previous adopters or non-adopters in their social network. Previous research in innovation diffusion considered initial innovators and word-of-mouth imitator effects using analytical models. Simulations and agent-based models have been developed to address the heterogeneity of decision makers and the non-linearity of the process. A further refinement modeled adoption based on networks of social relationships between potential decision makers, analogous to the spread of disease on networks. In addition, adoption or non-adoption of some innovations has been characterized as a means of signaling identification with or aversion to a group. While identity signaling and outgroup aversion effects on adoption have been considered in a geo-spatial environment, this work extends these concepts to social network environments. The results show that adoption levels were significantly different in a network environment with outgroup effects present. Additionally, as outgroup and imitation factors increase, adoption levels decrease, and polarization increases in network environments. With group effects present, adoption was found to be higher when modularity and eigenvector centrality are high in a social network. Next, to test the model, Covid-19 vaccination adoption behavior was examined to find statistical evidence of the outgroup effect with groups defined by political affiliation. Finally, a model was developed to address gaps in the original model and evaluated with vaccine adoption data. In today's polarized social environment, understanding these effects is critical to the adoption of emerging innovations such as mitigating climate change, combating novel viruses, or decentralizing financial transactions. While innovators are often focused on solving technical challenges to advance adoption of an innovation, equal emphasis on understanding and solving social and potential outgroup effects will be needed to accurately project the rate of adoption and to achieve the desired outcome.

Completion Date

2023

Semester

Fall

Committee Chair

Garibay, Ivan

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Format

application/pdf

Identifier

DP0028068

URL

https://purls.library.ucf.edu/go/DP0028068

Language

English

Release Date

December 2023

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Campus Location

Orlando (Main) Campus

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