Paper Type
Short
Paper Number
PACIS2025-1160
Description
With the growing global demand of mental healthcare professionals, voice AI assistants for mental healthcare are emerging as a viable solution. Given the critical role of patient trust in therapeutic relationships, this study leverages the theory of emotional contagion to explore how pitch variation in voice AI mental health assistants influences trust perception. We suggest that high emotion arousal, driven by greater pitch variation, enhances both affect-based and cognition-based trust. Moreover, a matched direction of pitch variation and level (both high) amplifies these trust perceptions most effectively. Additionally, we propose perceived anthropomorphism in voice AI assistants is primarily shaped by pitch variation. This research provides a nuanced understanding of how AI speech characteristics impact patient trust, enabling practitioners to strategically implement voice AI assistants for improved therapeutic relationships and enhanced treatment experiences.
Recommended Citation
Xian, Chunyi and Xu, David (Jingjun), "How Can I ‘Speak’ to Earn Your Trust: Impact of Voice on Trust in Mental Healthcare" (2025). PACIS 2025 Proceedings. 7.
https://aisel.aisnet.org/pacis2025/general_topic/general_topic/7
How Can I ‘Speak’ to Earn Your Trust: Impact of Voice on Trust in Mental Healthcare
With the growing global demand of mental healthcare professionals, voice AI assistants for mental healthcare are emerging as a viable solution. Given the critical role of patient trust in therapeutic relationships, this study leverages the theory of emotional contagion to explore how pitch variation in voice AI mental health assistants influences trust perception. We suggest that high emotion arousal, driven by greater pitch variation, enhances both affect-based and cognition-based trust. Moreover, a matched direction of pitch variation and level (both high) amplifies these trust perceptions most effectively. Additionally, we propose perceived anthropomorphism in voice AI assistants is primarily shaped by pitch variation. This research provides a nuanced understanding of how AI speech characteristics impact patient trust, enabling practitioners to strategically implement voice AI assistants for improved therapeutic relationships and enhanced treatment experiences.
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