The Mechanism Through Which AI Algorithmic Recommendations Influence Users’ Behavioral Intentions: A Comparative Analysis of Consumption and Public Health Service Contexts
DOI:
https://doi.org/10.53469/ijomsr.2026.09(05).01Keywords:
AI algorithmic recommendation, User behavioral intention, User trust, Perceived usefulness, Public health services, PLS-SEMAbstract
With the widespread application of AI algorithmic recommendations on digital platforms, their influence on users’ behavioral decision-making has attracted increasing scholarly attention. Based on the stimulus–organism–response framework, the technology acceptance model, and trust theory, this study constructs a research model to examine how AI algorithmic recommendations affect users’ behavioral intentions, and further conducts a comparative analysis between consumption and public health service contexts. Questionnaire data were collected from 312 valid respondents, and partial least squares structural equation modeling and multi-group analysis were employed for empirical testing. The results show that recommendation accuracy and recommendation transparency significantly enhance user trust, while recommendation reliability significantly strengthens perceived usefulness. Both user trust and perceived usefulness have significant positive effects on behavioral intention and play mediating roles between algorithmic recommendation characteristics and behavioral intention. Further analysis reveals that the consumption context relies more heavily on personalized recommendations and interest matching, whereas the public health service context depends more strongly on the professionalism and reliability of recommendation systems as well as trust mechanisms. This study enriches research on AI algorithmic recommendations from a context-comparative perspective and provides practical implications for optimizing recommendation mechanisms on digital platforms and promoting intelligent governance in public health services.
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