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

Artificial intelligence (AI)-based chatbots have emerged as potential tools to assist individuals in reducing anxiety and supporting well-being.

This study aimed to identify the factors that impact individuals' intention to engage and their engagement behavior with AI-based well-being chatbots by using a novel research model to enhance service levels, thereby improving user experience and mental health intervention effectiveness.

We conducted a web-based questionnaire survey of adult users of well-being chatbots in China via social media. Our survey collected demographic data, as well as a range of measures to assess relevant theoretical factors. Finally, 256 valid responses were obtained. The newly applied model was validated through the partial least squares structural equation modeling approach.

The model explained 62.8% (R²) of the variance in intention to engage and 74% (R²) of the variance in engagement behavior. Affect (β=.201; P=.002), social factors (β=.184; P=.007), and compatibility (β=.149; P=.03) were statistically significant for the intention to engage. Habit (β=.154; P=.01), trust (β=.253; P<.001), and intention to engage (β=.464; P<.001) were statistically significant for engagement behavior.

The new extended model provides a theoretical basis for studying users' AI-based chatbot engagement behavior. This study highlights practical points for developers of AI-based well-being chatbots. It also highlights the importance of AI-based well-being chatbots to create an emotional connection with the users.