Algorithmic Management and Its Impact on Employee Autonomy, Job Satisfaction, and Performance

The growing use of algorithm based systems to manage employees has transformed how work is organized and controlled in contemporary organizations. Commonly referred to as algorithmic management, this approach relies on automated decision making to allocate tasks, monitor performance, and evaluate employee outcomes. While such systems are often adopted to enhance efficiency, their implications for human work remain insufficiently understood. This study examined the relationship between algorithmic management and key employee outcomes, with a particular focus on employee autonomy, job satisfaction, and performance. Using a quantitative research design, data were collected through an online survey from employees working in algorithmically managed environments. Established measurement scales were employed to assess the study variables, and statistical analyses were conducted to test the proposed hypotheses. The findings indicate that algorithmic management is negatively associated with employee autonomy and job satisfaction, while demonstrating a significant relationship with employee performance. These results suggest that algorithmic management may improve performance related outcomes but can also constrain employees’ sense of control and satisfaction at work. The study contributes to the growing literature on digital and technology-mediated management by highlighting the dual effects of algorithmic management on human work. From a practical perspective, the findings underscore the importance of designing algorithmic systems that balance organizational efficiency with employee well-being. The study also offers directions for future research on the evolving role of algorithms in shaping the future of work.