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

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.

Automated summary

Prior studies on trust in artificial intelligence (AI) influencers and followers have been limited. This research used complexity theory to explore the causal recipes that can lead to high trust in AI influencers. The findings revealed that no single factor is sufficient for building trust, but a combination of source attractiveness, source credibility, and congruences acts as a driver of trust in an AI influencer. These results are valuable for practitioners as they offer new methods to enhance trust in AI influencers.