From adoption to continued usage: unpacking configurations and motivational mechanisms in AI therapist utilization

Purpose Grounded in the decision factors classification framework, this paper examines how decision object factors, decision entity factors, and context factors combine as configurations that explain user adoption and continued usage of AI therapists. Furthermore, comparing the configurations for initial adoption and continued usage reveals three mechanisms—habit, belief updating, and belief revision—that facilitate the transition from adoption to continued use. Design/methodology/approach Employing a mixed-method approach, Study 1 conducts interviews to identify decision-related factors. Then, Study 2 employs fuzzy-set qualitative comparative analysis (fsQCA) to elucidate how combinations of these factors lead to outcomes. Findings The results reveal six fsQCA configurations that suggest a strong intention to adopt the AI therapist, and five that indicate a strong intention to continue using it. Furthermore, comparing motivations for initial adoption and continued usage reveals three distinct mechanisms that encourage adopters to become continued users. Research limitations/implications On a theoretical level, this study pioneers the exploration of the motivations underlying both the initial adoption and sustained usage of AI therapists from a configurational perspective. Practical implications On a practical level, these findings help managers develop tailored strategies that encourage both initial adoption and continued use. Originality/value Although AI-powered virtual therapists offer a promising solution to the growing demand for mental health services, many individuals remain hesitant to adopt them, and sustaining user engagement over time remains a challenge. This study identifies the key factors underlying adoption and continued-use intentions and reveals three mechanisms that explain the transition from initial adoption to sustained usage.