The Impact of Employees’ AI Acceptance on Productivity in the Construction of Smart Hospitals: The Mediating Role of Collaboration

Abstract

Against the backdrop of China’s smart-hospital initiative, this review examines how frontline employees’ AI acceptance translates into productivity. Synthesizing 31 empirical studies published between 2020 and 2024 across clinical and managerial settings, the evidence is synthesized and organized around four acceptance dimensions—awareness, understanding, trust, and skills—as framed by the Technology Acceptance Model (TAM), Protection Motivation Theory (PMT), and Person–Environment Fit (PE Fit) theory. Across studies, understanding and trust are most consistently linked with faster decisions and higher service quality; skills underpin effective workflow integration. Crucially, employee-AI–AI collaboration mediates these effects in most samples, converting favorable mind-sets into operational gains via shared diagnosis, decision support, and scheduling optimization. We discuss boundary conditions (leadership, culture, prior experience) and propose an integrated pathway from acceptance to collaboration to productivity. The review provides a conceptual baseline for an upcoming large-scale survey and offers actionable recommendations: prioritize trust-building, formalize collaboration protocols, and invest in continuous AI-skilling to fully realize AI's potential for enhancing productivity in smart hospitals.

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