Fang, ChenChenFangGuo, FuFuGuoBelpaeme, TonyTonyBelpaeme2026-07-232026-07-232025979-8-3315-8772-71944-9445https://imec-publications.be/handle/20.500.12860/59947Mental health conversational agents (CAs) are gaining increasing attention as accessible tools for social communication, emotional support, and stress relief. These agents introduce new forms of human-AI interaction, yet the factors influencing user trust remain underexplored. Prior research suggests that conversation type and presumed message source may shape users’ experience, but their effects on users’ intentional stance and trust in CAs are not well understood. To address this gap, we first conducted a pre-study to develop a questionnaire for measuring users’ intentional stance towards mental health CAs. We then carried out a 2 × 2 mixed-design experiment to examine how conversation type and presumed message source influence intentional stance and trust, and whether intentional stance mediates the relationship between conversation type and trust. Results show that conversation type significantly influences user trust, mediated by intentional stance, while presumed message source had no significant effect. These findings advance our understanding of how users form trust in mental health CAs and offer implications for designing more engaging and trustworthy conversational systems in mental health contexts.engProceedings paper10.1109/ro-man63969.2025.11217721WOS:001672967200089ROBOT ACTIONS