Which recommendation system do you trust the most? Exploring the impact of perceived anthropomorphism on recommendation system trust, choice confidence, and information disclosure

Recommendation systems (RSs) leverage data and algorithms to generate a set of suggestions to reduce consumers' efforts and assist their decisions. In this study, we examine how different framings of recommendations trigger people's anthropomorphic perceptions of RSs and therefore affect users' attitudes in an online experiment. Participants used and evaluated one of four versions of a web-based wine RS with different source framings (i.e. "recommendation by an algorithm," "recommendation by an AI assistant," "recommendation by knowledge generated from similar people," no description). Results showed that different source framings generated different levels of perceived anthropomorphism. Participants indicated greater trust in the recommendations and greater confidence in making choices based on the recommendations when they perceived an RS as highly anthropomorphic; however, higher perceived anthropomorphism of an RS led to a lower willingness to disclose personal information to the RS.