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Paper Number
1608
Abstract
Despite algorithms' ability to provide decision-makers with superior recommendations to their human counterparts, the reluctance to use algorithms – known as algorithm aversion – continues to emerge in research investigating algorithmic decision-making. This research in progress study investigates the role of construal levels and psychological distance in influencing the willingness to use algorithms in decision-making contexts. In examining algorithm aversion, we look to draw upon Construal Level Theory (CLT), a social psychology theory that describes how an individual’s psychological distance to a subject affects how they process information surrounding it. We will collect data through an online experiment that evaluates participant’s construal perceptions of ‘algorithmic versus human’ advisers in ‘visual versus verbal’ tasks before comparing their decisions to hire either a human or algorithmic agent. The study aims to expand upon the current understanding of IS research algorithms and clarify the causal factors that contribute to algorithm aversion.
Recommended Citation
Feng, Herman; Kirshner, Sam; and Cahalane, Michael, "Exploring Algorithm Aversion Through Construal Level Theory" (2021). ECIS 2021 Research-in-Progress Papers. 41.
https://aisel.aisnet.org/ecis2021_rip/41
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