Examining Preferences for Human vs. AI Sources in Online Health Information Seeking: Integrating the RISP and Source Credibility Perspectives.

Abstract

With the growing use of artificial intelligence (AI) chatbots and online medical consultation, individuals seeking health information increasingly face not only the question of whether to seek information, but also which source to consult. Integrating the risk information seeking and processing (RISP) framework with the concept of source credibility, this study examines how risk-related motivations and credibility-related evaluations shape preferences for AI chatbots versus online human doctors. In an online experimental survey (N = 248), participants were randomly assigned to a higher-sensitivity topic (sexually transmitted diseases) or a lower-sensitivity topic (seasonal allergies). Regression analyses revealed an overall preference for online human doctors. Informational subjective norms emerged as the strongest predictor of source choice, increasing the likelihood of preferring online human doctors, while positive channel beliefs in AI significantly predicted preference for AI agents across conditions. Importantly, higher perceived information-gathering capacity was associated with greater preference for AI in low sensitive context. Topic sensitivity influenced source preference indirectly through subjective norms, whereas affective responses and knowledge-related factors showed limited effects. These findings suggest that RISP-related motivations explain when health information seeking becomes salient, whereas source credibility considerations help explain how individuals translate those motivations into source preferences. The study contributes to research on digital health communication by clarifying how human and AI sources are evaluated in contemporary health information environments.