AI-human interactions in healthcare: exploring users’ post-adoption behaviors of AI mental health chatbots

The rise in mental health challenges has spurred innovative technological solutions, including AI-based chatbots for mental care management. This study explores how individuals engage with these chatbots for health decision-making, examining relationships between utilitarian and hedonic values, satisfaction with well-being outcomes, IT identity, social norms, and post-adoption behaviours. Using IT identity theory and a quantitative approach, we surveyed 309 current users of AI-based chatbots in the US. Structural equation modeling (SEM) was employed to analyse the data. Results show that both utilitarian and hedonic values positively influence user satisfaction with well-being outcomes. Satisfaction, in turn, shapes IT identity, reflecting personalised engagement with technology. IT identity positively influences behavioural intentions, including exploring new features, word of mouth, and continued use of AI chatbots. The study reveals mixed moderating effects of social norms, with significant moderation for word of mouth but not for behavioural intentions. Mediation effects of satisfaction in the relationships between values and IT identity provide insights into how technology becomes integral to individual identities and well-being routines. This research contributes to both theoretical understanding and practical applications of AI chatbots in mental health and well-being. It offers a robust framework for future research and interventions in AI-enabled healthcare.