A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study

Area of Science:

• Human-Computer Interaction
• Mental Health Technology
• Artificial Intelligence Applications

Background:

• Artificial intelligence (AI)-based chatbots are emerging as tools for anxiety reduction and well-being support.
• Understanding user engagement is crucial for maximizing the effectiveness of these digital mental health interventions.

Purpose of the Study:

• To identify factors influencing user intention and engagement behavior with AI-based well-being chatbots.
• To enhance service levels, user experience, and mental health intervention effectiveness through a novel research model.

Main Methods:

• A web-based questionnaire survey was conducted among adult well-being chatbot users in China.
• Data from 256 valid responses were analyzed using partial least squares structural equation modeling.

Main Results:

• The model explained 62.8% of the variance in intention to engage and 74% in engagement behavior.
• Affect, social factors, and compatibility significantly predicted intention to engage.
• Habit, trust, and intention to engage significantly predicted engagement behavior.

Conclusions:

• A new extended model provides a theoretical framework for AI chatbot engagement.
• Findings offer practical insights for AI well-being chatbot developers.
• Emphasizes the importance of emotional connection in AI-driven mental health support.