From Real to Artificial: How Profile Image Realism Shapes Warmth, Competence, and Trade Outcomes in Swapping Platforms

ABSTRACT This research investigates how value transparency and profile image realism jointly shape consumers' perceptions and trade behavior in Consumer‐to‐Consumer (C2C) swapping platforms. Across three studies, we examine whether realistic profile images enhance perceived warmth and competence, thereby fostering trade intentions under different levels of transparency. Study 1 analyzes behavioral data from an active swapping platform and finds that image realism positively predicts trade activity, particularly when value transparency is low. Study 2 uses an experiment to uncover underlying mechanisms; the results show that the effect of value transparency on perceived warmth and competence depends on image realism, which in turn influences trade intent. Study 3 contrasts realistic and AI‐generated images to test the boundaries of artificial realism and reveals that AI imagery evokes weaker persuasion in high‐transparency conditions. Together, these findings demonstrate that consumers rely on social cues from visual realism when informational cues are limited. This research extends models of authenticity signaling and the stereotype content framework to the context of C2C exchange, highlighting how visual and informational cues interact to influence fairness, trust, and persuasion in digital marketplaces that are increasingly shaped by AI‐generated content.