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Trust me, I'm an influencer! - Causal recipes for customer trust in artificial intelligence influencers in the retail industry

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  • Alboqami, Hassan

Abstract

Prior examinations of relationship development and the leverage of trust among artificial intelligence (AI) influencers and followers have been few. This study employed complexity theory to understand the main causal recipes that can lead to high trust in AI influencers. Asymmetrical fuzzy set qualitative comparative analysis (fsQCA) was used to explore the recipes that can drive high customer trust. Data were collected from 683 consumers who are familiar with AI influencers in Saudi Arabia. Our findings indicated that no single factor is sufficient to drive trust in influencers, but five causal recipes were explored for their power to secure high levels of trust in AI influencers. The findings revealed that a configuration of source attractiveness (i.e., physical attractiveness, homophily), source credibility (i.e., authenticity, expertise) and congruences (i.e., influencer, product, consumer) act as driver of consumers' trust in an AI influencer. These results are useful for practitioners since they provide new methods for boosting trust in AI influencers.

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  • Alboqami, Hassan, 2023. "Trust me, I'm an influencer! - Causal recipes for customer trust in artificial intelligence influencers in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:joreco:v:72:y:2023:i:c:s0969698922003356
    DOI: 10.1016/j.jretconser.2022.103242
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