AI-Driven Performance Monitoring and Employee Psychological Well-being: A Bibliometric and Science Mapping Analysis (2000–2026)

¹ Symbiosis Institute of Business Management, Nagpur, Symbiosis International (Deemed) University, Pune
² Symbiosis Centre of Management and Human Resource Development, Pune, Symbiosis International (Deemed) University, Pune

Abstract

The swiftness with which artificial intelligence (AI) has been integrated into the performance management in the workplace has significantly changed the way it is that organizations monitor and assess the effectiveness of the employees. Although the use of AI-enhanced performance monitoring systems can help in efficiency, objectivity, and decision making based on information, it presents such severe issues as the mental well-being of employees. Applying the bibliometric and science mapping methods based on the Human Capital Theory, Technostress Theory, and Conservation of Resources (COR) Theory, this paper found 350 documents on the intellectual structure of AI-based monitoring and its effects on employee well-being published between 2000 and 2026. After going through the PRISMA 2020 guidelines, 221 studies that met the requirements of the method were picked to be analyzed finally. The performance analysis and science mapping (co-authorship, keyword co-occurrence, and co-citation analysis) as bibliometric methods were performed with the help of VOSviewer and Biblioshiny to identify important tendencies, themes, and research clusters. It appeared that scholarly interest in AI-driven monitoring increased substantially after 2018. Nonetheless, the perceived transparency and organizational support are moderating factors that help to reduce these negative impacts, and this study has filled the knowledge gap by bringing together the bibliometric analysis with the systematic review model due to its extensive intellectual framework coverage in the field. It incorporates useful information that organizations can use to strike a balance between technological efficiency and human well-being by implementing AI ethically and in a supportive working environment. The paper also highlights the main research gaps and provides the guidelines to future empirical research.