Integrating metaverse technologies in medical education: examining acceptance factors among current and future healthcare providers

This study investigates behavioral intention to use healthcare metaverse platforms among medical students and physicians in Turkey, where such technologies are in early stages of adoption. A multi-theoretical research model was developed by integrating constructs from the Innovation Diffusion Theory, Embodied Social Presence Theory, Interaction Equivalency Theorem and Technology Acceptance Model. Data from 718 participants were analyzed using partial least squares structural equation modeling. Results show that satisfaction, perceived usefulness, perceived ease of use, learner interactions, and technology readiness significantly enhance adoption, while technology anxiety and complexity have negative effects. Learner learner and learner teacher interactions strongly predict satisfaction, which subsequently increases behavioral intention. Perceived ease of use fully mediates the relationship between technology anxiety and perceived usefulness. However, technology anxiety does not significantly moderate the effects of perceived usefulness or ease of use on behavioral intention. The model explains 71.8% of the variance in behavioral intention, indicating strong explanatory power. The findings offer practical implications for educators, curriculum designers, and developers aiming to integrate metaverse platforms into healthcare training in digitally transitioning educational systems.