This research aims to examine the correlation between the adoption of specific artificial intelligence (AI) tools, including diagnostic algorithms, predictive analytics and robotic process automation, and employee performance in Iran’s health-care institutions, specifically analyzing the mediating effects of perceived ease of use, innovative work behavior and skills enhancement. This study offers empirical insights from Iran, a context characterized by developmental challenges, thereby enhancing the understanding of how these AI tools influence employee performance across diverse health-care environments. By integrating the Technology Acceptance Model (TAM), Resource-Based View (RBV) and Sociotechnical Systems Theory, this study proposes a novel conceptual framework tailored to resource-constrained settings, offering unique insights into AI’s application in developing countries.
A quantitative study approach was used to investigate the correlations between AI adoption, perceived ease of use, innovative behavior, skills enhancement and employee performance. A structured questionnaire was used to gather data from a sample of 240 health-care professionals employed in health-care institutions across Iran. SmartPLS-structural equation modeling (SEM) was used to analyze the data and evaluate the proposed mediation model.
The findings demonstrate a significant positive correlation between the adoption of AI and employee performance. In addition, perceived ease of use, innovative work behavior and skills enhancement were identified as mediators in the relationship between AI and employee performance. AI demonstrated a positive correlation with perceived ease of use, which subsequently showed a positive association with employee performance. AI has positively influenced innovative work behavior and skills enhancement, which in turn has positively affected employee performance.
This research enriches the current body of knowledge by synthesizing the TAM, socio-technical systems theory and RBV theory to investigate the mediating influences of perceived ease of use, innovative work behavior and skills enhancement within the framework of AI adoption in health care. This study offers empirical insights from Iran, a context characterized by development challenges, thereby enhancing the comprehension of AI’s influence on employee performance across varied health-care environments. The findings of this study underscore the essential role of user perceptions and the cultivation of skills in the effective deployment of AI technologies.
