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Understanding the Intention to Use Mental Health Chatbots Among LGBTQIA+ Individuals: Testing and Extending the UTAUT

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13815 LNCS, Page: 83-100
2023
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  • 29
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Conference Paper Description

This empirical study aims to test and extend the unified theory of acceptance and use of technology (UTAUT) in the context of mental health chatbot usage among LGBTQIA+ individuals. The proposed model uses UTAUT variables (performance expectancy, effort expectancy and social influence) as well as chatbot-related variables (willingness to self-disclose, perceived loss of privacy, and trust) to predict the intention to use a mental health chatbot. The online survey (N = 305) indicates that performance expectancy, social influence, and willingness to self-disclose positively predict chatbot usage intention, whereas effort expectancy negatively influences this intention. Moreover, previous experience with healthcare chatbots moderated the relationship between social influence and intention, age moderated the relationship between willingness to self-disclose and intention, and gender identity moderated the relationship between perceived loss of privacy and intention. Overall, the extended UTAUT proved to be useful in explaining technology acceptance of mental health chatbots among the LGBTQIA+ community.

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