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

Yanrong Yang¹, Jorge Tavares¹, Tiago Oliveira¹

¹NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Lisboa, Portugal.

JMIR Human Factors

|November 11, 2024

Summary

AI chatbots can help reduce anxiety. User engagement with well-being chatbots is influenced by affect, social factors, compatibility, habit, and trust, which are key for effective mental health support.

Keywords:

AI-based chatbot artificial intelligence–based chatbot engagement behavior intention to engage mental well-being mobile phone theoretical models

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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.