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

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

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.

Suggested Citation

  • Liu, Nicole Tsz Yeung & Kirshner, Samuel N. & Lim, Eric T.K., 2023. "Is algorithm aversion WEIRD? A cross-country comparison of individual-differences and algorithm aversion," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:joreco:v:72:y:2023:i:c:s0969698923000061
    DOI: 10.1016/j.jretconser.2023.103259
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