Online shopping: How can algorithm performance expectancy enhance impulse buying?

Steffie Gallin and Audrey Portes

Journal of Retailing and Consumer Services, 2024, vol. 81, issue C

Abstract: Building on the literature on trust and Persuasion Knowledge Theory, this study examines the mediating role of trust in the relationship between algorithm performance expectancy and impulse buying in online retailing. Furthermore, it examines how impulse buying among online shoppers is influenced by the number of product recommendations displayed on a retailer's website (a small vs. large number of recommendations), along with the presence or absence of recommendation ratings. A survey-based study and an experiment indicated that consumer trust in algorithm-driven product recommendations correlates with heightened impulse buying of a recommended product, particularly when a large number of recommendations are presented. However, the presence (vs. absence) of recommendation ratings had no impact on impulse buying. This study extends literature on the impact of product recommendation design. This contributes to a deeper understanding of how product recommendation formats influence consumer behavior and offers insights for retailers regarding the strategic presentation of personalized product recommendations to enhance impulse buying.

Keywords: Personalized product recommendations; Trust; Algorithm performance expectancy; Impulse buying (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924002844

DOI: 10.1016/j.jretconser.2024.103988

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