🤖 AI Summary
This study investigates users’ willingness to use AI chatbots and their self-disclosure of health information across physical and mental health topics varying in sensitivity. Employing a 2 (physical vs. psychological) × 2 (low vs. high sensitivity) mixed online experiment with a representative sample of 1,388 Dutch participants, the research systematically examines how topic sensitivity and individual characteristics jointly shape user decisions—an aspect previously unexplored. Findings reveal that perceived benefits positively predict both usage intention and information disclosure, whereas perceived risks exert a negative influence. Usage intention is significantly higher for low-sensitivity topics, and individual traits notably moderate these relationships. The study offers theoretical grounding and practical implications for the design of health-focused AI systems.
📝 Abstract
AI chatbots are increasingly used for answering health-related questions. This study examines the role of topic type discussed with an AI chatbot and individual characteristics on perceived benefits and risks, intention to use an AI chatbot, and willingness to self-disclose health information. We conducted an online experiment with a 2 (topic type: physical versus psychological, between-subjects) x 2 (topic sensitivity: low versus high, within-subjects) mixed design among a Dutch representative sample (N = 1,388). Results showed that perceived benefits were positively associated with intention and willingness to self-disclose, while perceived risks were negatively associated. Moreover, participants reported higher usage intentions for low-sensitive topics compared to high-sensitive topics. Furthermore, perceptions, intention, and willingness to self-disclose varied by individual characteristics. Overall, our findings suggest that intentions to use AI chatbots and self-disclosure of health-related information are primarily related to perceived benefits and risks and to personal characteristics rather than to topic type.