How do perceived AI algorithmic recommendation characteristics influence health information adoption? A PLS-SEM and fsQCA analysis.

OBJECTIVE: This study examines how users' perceptions of AI algorithmic recommendation characteristics are associated with health information adoption intention on online healthcare platforms. It further investigates the mediating roles of trust and perceived usefulness in this process. METHODS: A cross-sectional online survey was conducted among users of Haodf.com, a major online healthcare platform in China. After a pilot test, 310 valid responses were collected. Partial least squares structural equation modeling (PLS-SEM) was used to examine the hypothesized relationships, while fuzzy-set qualitative comparative analysis (fsQCA) was employed to identify multiple configurations associated with high health information adoption intention. RESULTS: Perceived AI algorithmic recommendation characteristics were positively associated with both trust and perceived usefulness. Trust was positively associated with perceived usefulness and health information adoption intention, while perceived usefulness showed a stronger direct association with adoption intention (β = 0.52, p < 0.001). Mediation analysis indicated that these recommendation characteristics were indirectly associated with adoption intention through trust, perceived usefulness, and the theoretically specified pathway linking trust and perceived usefulness. fsQCA further identified multiple configurations associated with high health information adoption intention. CONCLUSION: Users' adoption intention toward AI-recommended health information is jointly associated with perceived recommendation characteristics and cognitive evaluations. Trust represents a credibility-based evaluation, whereas perceived usefulness appears to be more proximally related to adoption intention. These findings provide insights for optimizing algorithmic recommendation strategies in digital health platforms.