TY - GEN
T1 - Understanding user preferences for gaining trust, when utilising conversational agents for mental health data disclosures
AU - Taylor, Debbie
AU - Buckley, Oliver
AU - Aung, Min Hane
PY - 2023/7/9
Y1 - 2023/7/9
N2 - Encouraging humans to disclose personal information is a complex process that is built upon trust, and this is especially true when related to sensitive topics such as mental health. Currently, this data is collected through trained professionals but COVID-19 has seen an increasing demand for support. This paper looks at maximising trust in mental health conversational agents. The study collected data from 177 participants, using survey questionnaires, to examine what human-like features help cultivate and encourage trust. Analysis suggests respondents prefer something that reflects themselves. For example, 78% stated a conversational agent should display a static avatar they can shape to their own preferences. Other factors found to have an impact were friendly greetings (preferred by 76%) and patience (99%). This initial study establishes that humans believe mental health conversational agents can, and should, exhibit a range of human-like features. Some preferences are largely universal across all demographics, whereas others are more specific. This study then delivers a framework of desirable attributes, traits and characteristics, which will be used to test if these features are more successful at establishing trust than standard online forms.
AB - Encouraging humans to disclose personal information is a complex process that is built upon trust, and this is especially true when related to sensitive topics such as mental health. Currently, this data is collected through trained professionals but COVID-19 has seen an increasing demand for support. This paper looks at maximising trust in mental health conversational agents. The study collected data from 177 participants, using survey questionnaires, to examine what human-like features help cultivate and encourage trust. Analysis suggests respondents prefer something that reflects themselves. For example, 78% stated a conversational agent should display a static avatar they can shape to their own preferences. Other factors found to have an impact were friendly greetings (preferred by 76%) and patience (99%). This initial study establishes that humans believe mental health conversational agents can, and should, exhibit a range of human-like features. Some preferences are largely universal across all demographics, whereas others are more specific. This study then delivers a framework of desirable attributes, traits and characteristics, which will be used to test if these features are more successful at establishing trust than standard online forms.
KW - Conversational agents
KW - Personal disclosure
KW - Trust
UR - http://www.scopus.com/inward/record.url?scp=85169037628&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-35992-7_24
DO - 10.1007/978-3-031-35992-7_24
M3 - Conference contribution
SN - 978-3-031-35991-0
VL - 1833
T3 - Communications in Computer and Information Science
SP - 167
EP - 174
BT - International Conference on Human-Computer Interaction
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Ntoa, Stavroula
A2 - Salvendy, Gavriel
PB - Springer
ER -
