Visual Analyses and Reputation Signals: Role of Facial Characteristics on Sharing-Economy Platforms

Peer-to-peer (P2P) platforms such as Airbnb, where individual providers transact directly with consumers, rely not only on well-studied traditional signals, such as ratings, but also on less-examined visual cues to build trust and facilitate transactions jointly. However, how consumers process and integrate these intertwined multimodal signals remains insufficiently understood. This study, informed by signaling theory and the stereotype content model, uses a dataset from Airbnb to examine the impact of multimodal signals. First, our results provide further evidence that ratings positively influence occupancy rates. More importantly, we show that this effect is not uniform across hosts: warmth-related facial cues—such as smiling and perceived trustworthiness—significantly amplify the impact of ratings, whereas competence-related facial cues exhibit a much weaker moderating role. These interaction effects remain robust across propensity score matching (PSM) and alternative model specifications. Theoretically, this study advances the literature on multimodal signals and complementarity in peer-to-peer (P2P) platforms, particularly by juxtaposing and analyzing the interplay among multimodal signals. In practice, the findings help hosts and the platform understand the value of profile photos, whether amplifying existing advantages or compensating for temporary setbacks in ratings.