Understanding User Preferences in Developing a Mental Healthcare AI Chatbot: A Conjoint Analysis Approach

Abstract

The global population is experiencing a significant rise in cases of depressive disorders, which have been exacerbated by the COVID-19 pandemic. However, having limited resources and fear of social stigma have discouraged individuals from seeking professional psychological counseling or visiting hospitals. In response to this issue, psychiatrists have attempted the use of chatbots as a therapeutic aid. Therefore, in this study, users’ choice data about the mental healthcare AI chatbot are collected through conjoint analysis, and the collected data is analyzed using the mixed logit method to derive users’ preferences for the mental healthcare AI chatbot. Findings highlight a consistent preference among users for certain factors in both psychological counseling chatbots and traditional psychological counseling. At first, the findings indicate that users place the highest priority on pricing and the ability to connect with a professional counselor. Furthermore, users prefer chatbots that have a more human-like appearance and characteristics. By incorporating these preferences, chatbot developers can create a more user-centric mental healthcare AI chatbot.

Keywords:

• Mental healthcare
• AI chatbot
• conjoint analysis
• user-centric

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mirae Kim

Mirae Kim is a graduate student in the Department of Artificial Intelligence Convergence at Sungkyunkwan University. Her research interest includes Artificial Intelligence, specifically in Natural Language Processing using machine learning and data analysis.

Jaedong Oh

Jaedong Oh is a graduate student in the Department of Artificial Intelligence Convergence at Sungkyunkwan University. His research interests include data analysis, recommender systems and natural language processing techniques using machine learning and deep learning.

Doha Kim

Doha Kim is currently pursuing a PhD in the Department of Human-Artificial Intelligence Interaction at Sungkyunkwan University. Much of the research involves Human–Computer Interaction, designing and evaluating user experience from Human-centered AI approaches and multiple interdisciplinary social and behavioral sciences perspectives.

Jungwoo Shin

Jungwoo Shin received a PhD from Seoul National University’s Technology Management, Economics, and Policy Program. He is an associate professor at the Department of Industrial and Management Systems Engineering and Big Data Analytics at Kyung Hee University, Korea. His research interests include demand forecasting, consumer behavior, and R&D management.

Daeho Lee

Daeho Lee received BS in Electrical Engineering and PhD in Economics at Seoul National University. He is currently an associate professor in the Department of Interaction Science at Sungkyunkwan University. His research interests include user experience and behavior for new ICT products & services, including artificial intelligence.