This study aims to systematically map the intersection between the literature on algorithmic management and organizational behavior. With the increasing prevalence of digitalization and data-driven decision-making processes, algorithms have assumed a central role in organizational control, performance monitoring, and decision-making mechanisms. This development has necessitated a comprehensive examination of their effects on variables such as employee autonomy, organizational trust, perceived justice, commitment, and psychological well-being. Accordingly, this study analyzes the intellectual structure, thematic concentrations, and temporal evolution of the algorithmic management literature through bibliometric methods. The dataset of the study consists of 1,256 publications retrieved from the Web of Science Core Collection database on February 3, 2026. The analyses were conducted using the VOSviewer software, including performance analysis, document-level citation analysis, co-authorship network analysis, keyword co-occurrence analysis, thematic clustering, and overlay (temporal) analysis. The findings indicate that the number of publications has increased significantly, particularly during the period between 2023 and 2025. The thematic clustering analysis identified seven main research clusters, with the strongest thematic focus structured around the concept of “algorithmic management.” The overlay analysis further reveals that themes such as job design and organizational justice were prominent in the early stages of the literature; platform-mediated work and algorithmic control gained prominence in later years; and more recently, discussions related to AI-based governance and ethics have come to the forefront. Overall, the findings demonstrate a growing body of research examining the effects of algorithmic management on employee attitudes and psychosocial outcomes.