User Satisfaction and Continuance Intention in Mental Health Counseling Using General-Purpose Conversational AI : The Effects of Individual Characteristics, Chatbot Characteristics, Interaction Quality, and the Mediating Role of Satisfaction

Key Points

• This research aims to analyze factors influencing user satisfaction and continuance intention in mental health counseling using general-purpose conversational AI.
• Analyzed data from 1,100 users of general-purpose AI counseling services.
• Examined individual characteristics, chatbot characteristics, and interaction quality as dimensions affecting satisfaction.
• Utilized hierarchical regression analysis to assess the relationships between these factors and the outcomes.
• Self-disclosure, self-efficacy in technology, social support seeking, chatbot anthropomorphism, and empathy recognition positively influence user satisfaction.
• Continuance intention was most strongly predicted by self-disclosure, while emotional sensitivity and social support seeking had no significant effect.
• Empathy recognition and chatbot anthropomorphism impacted continuance intention only through user satisfaction, indicating indirect and direct effects.