The mediating role of transformational leadership in the effect of artificial intelligence anxiety and job performance: a field study

Abstract

The rapid integration of artificial intelligence (AI) technologies into healthcare systems has transformed clinical processes while simultaneously generating AI-related anxiety among healthcare professionals. Characterized by concerns about job security, professional competence, and ethical responsibilities, AI anxiety may negatively influence employees’ job performance. In this context, transformational leadership may play a critical role in mitigating the adverse effects of AI anxiety and sustaining employee performance during digital transformation processes. Therefore, this study aims to examine the effect of artificial intelligence (AI) anxiety on job performance and to investigate the mediating role of transformational leadership in this relationship. A quantitative research design and analyzed data collected from a sample of 418 healthcare professionals (including physicians, nurses, midwives, health technicians, and health officers) working in the province of Kayseri. Data were collected using the Artificial Intelligence Anxiety Scale, the Transformational Leadership Scale, and the Job Performance Scale. The data were analyzed using descriptive statistics, correlation, regression, and mediation analyses. The findings show that artificial intelligence anxiety negatively affects both job performance and transformational leadership. Furthermore, transformational leadership has a mediating role in this relationship. This study highlights the strategic role of transformational leadership in mitigating healthcare professionals’ anxiety and maintaining their job performance during the ongoing process of technological transformation. In this regard, national and institutional digital health policies may benefit from integrating structured transformational leadership training and capacity-building programs to facilitate adaptive change management and protect workforce performance during AI-driven transitions.

Ähnliche Arbeiten