The Impact of Algorithmic Monitoring Systems on Employee Productivity and Psychological Stress

Abstract

This article examines the impact of algorithmic monitoring on employee productivity and work-related stress in contemporary organizations. Algorithmic monitoring refers to the real-time supervision of work processes through data-driven systems that assess employee performance based on a range of predefined metrics.
A synthesis of the existing research indicates that algorithmic monitoring enhances employee productivity in the short term by strengthening discipline and performance control. However, in the long term, it is frequently associated with increased psychological stress, reduced employee autonomy, and a higher risk of burnout.
The article concludes that the effectiveness of algorithmic monitoring depends on the design and transparency of monitoring systems, as well as the quality of organizational culture. These findings underscore the need to develop hybrid, human-centered monitoring models that balance technological efficiency with employee well-being.