ArticleScientific reports2026
Understanding continuance intention toward AI-driven healthcare services: the moderating role of e-Health Literacy.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
AI-driven healthcare services are increasingly used to support health consultation, symptom triage, appointment navigation, and routine health management. However, sustaining users' post-adoption engagement remains challenging, particularly because users differ in their ability to access, evaluate, and apply online health information. This study examines the determinants of continuance intention toward AI-driven healthcare services and investigates whether e-Health Literacy moderates selected relationships in the post-adoption process. Drawing on the Unified Theory of Acceptance and Use of Technology, the Information Adoption Model, and Privacy Calculus Theory, this study developed an integrated model of continuance intention. A cross-sectional survey was conducted among 580 users of AI-driven healthcare services in Shanghai, China. Data were analyzed using Partial Least Squares Structural Equation Modeling. Source trustworthiness, information quality, and effort expectancy were positively associated with performance expectancy. Performance expectancy, effort expectancy, and perceived interactivity were positively associated with continuance intention, whereas perceived privacy risk was negatively associated with continuance intention. e-Health Literacy positively moderated the relationship between information quality and performance expectancy and negatively moderated the relationship between effort expectancy and continuance intention. However, the moderation effects were small in magnitude and should be interpreted cautiously. This study extends post-adoption research on AI-driven healthcare services by showing how users' e-Health Literacy may shape the relative importance of information quality and ease of use. The findings suggest that inclusive AI healthcare design should combine trustworthy, high-quality information with interfaces that reduce use barriers for users with lower e-Health Literacy.
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