AI-Supported Healthcare Technology Resistance and Behavioral Intention: A Serial Mediation Empirical Study on the JD-R Model and Employee Engagement

This study combines innovation resistance theory, the stimulus–organism–response (SOR) framework, and the job demands–resources model to facilitate an in-depth exploration of the barriers faced by healthcare professionals and the psychological responses they exhibit when adopting AI-supported healthcare technologies. We conducted a questionnaire survey and obtained 296 valid responses from healthcare professionals to examine the relationship between resistance to AI-supported healthcare technologies and AI adoption behavioral intentions. Using the SOR framework as a basis, this study validated a serial mediation model with moderating effects, demonstrating that resistance to AI-supported healthcare technologies influenced AI adoption behavioral intentions through job resource, job demand, and levels of employee engagement. Further, this study sought to validate the relationship between age-moderated job resource and job demand in employees exhibiting resistance to AI-supported healthcare technologies and their associated AI adoption behavioral intentions. The results indicated that job resources, job demands, and employee engagement serially mediated the relationship between resistance to AI-supported healthcare technologies and AI adoption behavioral intentions. Additionally, age only exhibited significant moderating effects on the relationship between job demands in resistance to AI-supported healthcare technologies and AI adoption behavioral intentions. The findings of this study can aid in promoting the adoption of AI-supported healthcare technologies by healthcare professionals, generating new insights and broadening the scope and vision of existing literature.