Paper Type

ERF

Abstract

Consumers today have a plethora of technological options from which to choose. Inundated by options, individuals now reject more technologies than ever. While IS researchers know that individuals feel overwhelmed by so many choices and sometimes regret their adoption decisions, most IS research focuses on the technologies consumers adopt, not the ones they reject. This paper aims to understand the regret that arises from individuals' counterfactual thinking about rejected consumer technologies. Drawing on the functional theory of counterfactual thinking, we propose that satisfaction with a chosen technology, expectations about a rejected technology, IT identity, social influence, network externalities, and environmental cues give rise to counterfactual thinking and post-rejection regret. The proposed research model will be tested using a longitudinal survey research design with two waves of data collection and covariance-based structural equation modeling techniques.

Paper Number

1655

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1655

Comments

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Aug 16th, 12:00 AM

Understanding Post-Rejection Regret: The Unexplored Path of Technology Rejection

Consumers today have a plethora of technological options from which to choose. Inundated by options, individuals now reject more technologies than ever. While IS researchers know that individuals feel overwhelmed by so many choices and sometimes regret their adoption decisions, most IS research focuses on the technologies consumers adopt, not the ones they reject. This paper aims to understand the regret that arises from individuals' counterfactual thinking about rejected consumer technologies. Drawing on the functional theory of counterfactual thinking, we propose that satisfaction with a chosen technology, expectations about a rejected technology, IT identity, social influence, network externalities, and environmental cues give rise to counterfactual thinking and post-rejection regret. The proposed research model will be tested using a longitudinal survey research design with two waves of data collection and covariance-based structural equation modeling techniques.

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