ArticleDigital health
How do perceived AI algorithmic recommendation characteristics influence health information adoption? A PLS-SEM and fsQCA analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
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 ( 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.
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