Abstract
Introduction
Mental health challenges among university students have become increasingly prevalent, while existing digital interventions often struggle to sustain user engagement and provide meaningful emotional support. Although previous studies have proposed various explanations for the limited acceptance of AI-based mental health chatbots, the role of affective experience remains insufficiently understood. This study proposes an affect-centered framework to examine how different virtual agent persona styles (Listener, Guide, and Motivator) influence university students’ psychological acceptance of mental health chatbots.
Methods
Drawing on the Behavior Change Wheel (BCW) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study developed a dual-pathway model to investigate the mediating role of affective engagement in AI-based mental health chatbot acceptance. Data were collected from 266 university students who were randomly assigned to scenario-based chatbot stimuli representing three virtual agent persona styles. Partial least squares structural equation modeling (PLS-SEM), artificial neural network (ANN) analysis, and partial least squares multi-group analysis (PLS-MGA) were conducted to examine the proposed relationships and persona-related differences.
Results
The results indicate that most proposed hypotheses were supported. Affective engagement emerged as a central mechanism linking persona-related characteristics and psychological acceptance. Multi-group analysis further revealed that although the overall acceptance mechanism remained largely consistent across the three persona conditions, significant differences existed in the strength of key relationships. Specifically, the effect of affective engagement on behavioral intention was stronger in the Guide and Motivator conditions than in the Listener condition.
Discussion
These findings suggest that virtual agent persona styles do not fundamentally alter the psychological acceptance mechanism but can influence the strength of critical affective pathways. The study highlights the importance of affective engagement in AI-supported mental health interactions and provides practical implications for designing more adaptive, emotionally responsive, and user-centered mental health chatbots.