Assessing Adults’ Privacy Risk, Attitude, and Behavioral Intention Toward Mobile Health Communities in Mianyang, China
Abstract
This study investigates the behavioral intention of adults in Mianyang, China, to use mobile health communities (MHCs). Drawing upon the Trust-Risk Framework, the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Social Cognitive Theory, a structural equation model was developed to examine how performance expectancy, trust, self-efficacy, privacy risk, social influence, and attitude influence user intention. A total of 450 valid questionnaires were collected from adult users in Mianyang, China, all of whom had prior experience using MHCs. Participants were recruited through a structured online survey. CFA was employed to verify construct validity, and SEM was used to test the model fit and hypothesized relationships. The findings reveal that attitude is the strongest predictor of behavioral intention, followed by trust and self-efficacy. Trust reduces perceived privacy risk and strengthens user attitude, forming a layered cognitive-affective pathway. Social influence shows a moderate yet significant effect, while performance expectancy exhibits limited impact—indicating that users prioritize emotional security and trust over functional efficiency in digital health settings. This research contributes to digital health adoption theory by contextualizing trust and risk in a high-sensitivity environment. It offers practical guidance for platform design, digital literacy training, and policy support aimed at improving user acceptance and sustained engagement.
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