Psychological Distance and Algorithm Aversion: Congruency and Advisor Confidence

Samuel N. Kirshner ()
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Samuel N. Kirshner: UNSW Business School, University of New South Wales, Sydney, New South Wales 2052, Australia

Service Science, 2025, vol. 17, issue 2-3, 74-91

Abstract: Employees and consumers have varying preferences between human and algorithmic advisors. Drawing on construal level theory, I hypothesize that individual differences in algorithm aversion can be explained by the perception that algorithms are psychologically farther away than human advisors. The first set of studies ( n = 266) shows that algorithms are viewed as abstract and distant compared with humans, even when their outputs are perceived at a low-level construal, challenging prior research. Leveraging construal congruency, the second set of studies ( n = 1,148) shows that farther within-task psychological distance generally increases preference for algorithmic advisors due to differences in advisor confidence. Specifically, I contribute to the literature by showing that a far psychological distance within a task reduces confidence in human advisors. In contrast, confidence in algorithms remains stable, increasing algorithm appreciation at farther within-task distances.

Keywords: algorithm aversion; psychological distance; construal level theory; confidence (search for similar items in EconPapers)
Date: 2025
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