The influence of recommendation algorithms on users' intention to adopt health information: does trust belief play a role?

Objectives: Recommendation systems have emerged as prevalent and effective tools. Investigating the impact of recommendation algorithms on users' health information adoption behavior can aid in optimizing health information services and advancing the construction and development of online health community platforms.

Materials and methods: This study designed scenario experiments for social- and profile-oriented recommendations and collected data accordingly. The Theory of Knowledge-Based Trust was applied to explain users' trust beliefs in algorithmic recommendations. Nonparametric tests, logistic regression, and bootstrapping were used to test the variables' main, mediating, and moderating effects.

Results: Social-oriented and profile-oriented recommendations were significantly correlated with users' intentions to adopt information. Competence belief (CB), benevolence belief (BB), and integrity belief (IB) mediated this relationship. Overall, the moderating effect of privacy concerns (PCs) is significant.

Discussion: Both social- and profile-oriented recommendations can enhance users' willingness to adopt health information by facilitating their knowledge-based trust, with integrity beliefs playing a more substantial mediating role. Privacy concerns negatively moderate the impact of profile-oriented recommendations on benevolence and competence beliefs on information adoption intention.

Conclusions: This study enriches the theoretical foundation of user health information adoption behavior in algorithmic recommendation contexts and provides new insights into the practice of health information on social media platforms.