Is algorithm aversion WEIRD? A cross-country comparison of individual-differences and algorithm aversion

Nicole Tsz Yeung Liu, Samuel N. Kirshner and Eric T.K. Lim

Journal of Retailing and Consumer Services, 2023, vol. 72, issue C

Abstract: Although algorithms offer superior performance over humans across many tasks, individuals often exhibit algorithm aversion, resisting algorithmic advice in favour of human recommendations. However, most algorithm aversion studies rely on American samples, potentially limiting the generalisability of the findings. Given the increasing adoption of algorithms globally, we explore if the impact of two crucial factors driving algorithm aversion, uniqueness neglect and familiarity, differ between culturally different countries. Drawing on the individualism-collectivism cultural dimension, we conducted two online studies comparing algorithm aversion between people in India and the United States in medical and financial services scenarios. While our results suggest that there is no difference in the degree of algorithm aversion between Indians and Americans at an aggregate level, we find important cross-cultural differences: Uniqueness neglect strengthens algorithm aversion for Americans more than Indians, while familiarity weakens algorithm aversion more for Indians than Americans. Thus, our results reveal generalisability issues within the algorithm aversion literature, as factors influencing algorithm aversion can be culturally dependent.

Keywords: Algorithm aversion; Uniqueness neglect; Familiarity; Individualism-collectivism (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:72:y:2023:i:c:s0969698923000061

DOI: 10.1016/j.jretconser.2023.103259

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