AN INTEGRATED FRAMEWORK FOR AI ADOPTION AND RESISTANCE IN HEALTHCARE

This study identifies a critical gap in Artificial Intelligence (AI) adoption research. Existing research often examines adoption and resistance in isolation, overlooking their dynamic interplay, where factors can serve as enablers and barriers. These studies also typically rely on a single theoretical lens, limiting their ability to reflect the complexity of healthcare systems. To address this, we propose a comprehensive framework developed through a systematic literature review and theory-driven synthesis integrating Innovation Resistance Theory (IRT), Digital Agency (DA), and the Push-Pull-Mooring (PPM) Model. The framework captures functional, psychological, digital readiness, and contextual factors, highlighting how they can simultaneously motivate or hinder adoption. This pre-implementation approach offers healthcare leaders and policymakers a structured tool to understand healthcare providers' readiness for AI and proactively reduce resistance. By advancing theoretical understanding and providing practical insights, the framework supports smoother AI integration in healthcare and provides guidance applicable to other regulated sectors.