COMPARATIVE STUDY OF AI-BASED EMPLOYEE MONITORING SYSTEMS VS SELF-REPORTED PRODUCTIVITY ASSESSMENT ON EMPLOYEE WELLBEING AND JOB SATISFACTION

Abstract

The introduction of artificial intelligence (AI) systems into the workplace has changed how productivity of employees is evaluated. Monitoring employee productivity with AI systems that involve algorithmic surveillance, predictive performance models and real-time behaviour monitoring systems is increasingly becoming common more than the use of self-reported productivity assessment. Such systems are advantageous in terms of efficiency, objectivity and scalability but are harmful to the wellbeing, satisfaction, autonomy and psychological safety of workers. This paper presents the critical comparative analysis of AI-based systems and self-reported productivity measures in the light of the Job Demands-Resources (JD-R) model. The article is quantitative and statistical tests the impacts of such systems on employee wellbeing and job satisfaction in various organisational settings via SPSS. The results prove the hypothesis that surveillance by AI improves the perceived job demand, technostress and surveillance anxiety, which initiates poor employee wellbeing and job satisfaction. On the other hand, the methods of self-reported productivity promote the sense of autonomy, trust and psychological empowerment which positively affect the wellbeing of employees. This paper is a contribution to the literature because it synthesises the socio-technical systems theory, algorithmic management views, and wellbeing models at the workplace and provides empirical recommendations that organisations can adopt in optimizing the performance of technology and people-centered work design.