TY - GEN
T1 - Understanding the Intention to Use Mental Health Chatbots Among LGBTQIA+ Individuals
T2 - 6th International Workshop on Chatbot Research and Design, CONVERSATIONS 2022
AU - Henkel, Tanja
AU - Linn, Annemiek J.
AU - van der Goot, Margot J.
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - LGBTQIA+ community
KW - Mental health chatbots
KW - Technology acceptance
KW - UTAUT
UR - http://www.scopus.com/inward/record.url?scp=85148696569&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-25581-6_6
DO - https://doi.org/10.1007/978-3-031-25581-6_6
M3 - Conference contribution
SN - 9783031255809
VL - 13815 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 100
BT - Chatbot Research and Design - 6th International Workshop, CONVERSATIONS 2022, Revised Selected Papers
A2 - Følstad, Asbjørn
A2 - Araujo, Theo
A2 - Papadopoulos, Symeon
A2 - Law, Effie L.-C.
A2 - Luger, Ewa
A2 - Goodwin, Morten
A2 - Brandtzaeg, Petter Bae
A2 - Følstad, A.
A2 - Araujo, T.
A2 - Papadopoulos, S.
A2 - Law, E.L.-C.
A2 - Luger, E.
A2 - Goodwin, M.
A2 - Brandtzaeg, P.B.
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
Y2 - 22 November 2022 through 23 November 2022
ER -