AI Insight
This structured review of 41 studies examines how algorithmic management systems in digital workplaces affect worker autonomy and performance. The analysis reveals a paradox: while algorithmic tools improve coordination and scalability, they unevenly redistribute worker discretion across different facets of autonomy, with performance outcomes depending on which autonomy dimensions are preserved or constrained. The review finds that sustainable performance is most likely when workers retain meaningful control over work methods and decisions, when monitoring serves developmental rather than punitive purposes, and when algorithmic systems incorporate transparency, explanation, and human review mechanisms.
Why it matters
As organizations increasingly adopt AI-driven management systems for task allocation, monitoring, and evaluation, this research provides evidence-based guidance for designing these systems to support rather than undermine worker performance. The findings suggest that effective algorithmic management requires preserving specific forms of worker autonomy and building in accountability mechanisms, offering practical design principles for platform companies, remote work systems, and automated workplaces.
Understand the Science
Digital workplaces increasingly delegate managerial functions such as task allocation, scheduling, monitoring, evaluation, feedback, and sanctioning to software systems. These systems can improve coordination speed, scalability, and consistency, but they also redistribute worker discretion in uneven ways. This article examines that tension as an algorithmic management autonomy paradox. Rather than asking whether algorithmic management is simply empowering or controlling, the review asks which facet of autonomy is affected, through which psychological mechanism, and with what consequences for different types of performance. A structured integrative review was conducted across Scopus, Web of Science, PsycINFO, ABI/INFORM, and the ACM Digital Library, supported by backward and forward citation tracing. The core search covered 2015 to January 2026 and used search blocks related to algorithmic management, digital monitoring, platform work, autonomy, fairness, stress, empowerment, and performance. The screening process identified 358 records, retained 276 after de-duplication, assessed 94 full texts, and produced a final corpus of 41 sources. The synthesis used a thematic coding matrix to classify algorithmic practices, autonomy facets, psychological mediators, outcome categories, moderators, evidence type, and level of analysis. The review makes a deliberately bounded contribution. It does not claim to propose a wholly new theory of algorithmic management; instead, it develops a middle-range framework that connects five groups of algorithmic practices with three autonomy facets, key psychological mechanisms, and differentiated performance outcomes. The synthesis suggests that algorithmic management is most likely to support sustainable performance when workers retain meaningful method and decision-making autonomy, when monitoring is developmental rather than punitive, and when transparency is actionable through explanation, contestability, and human review. Six propositions and a practical governance agenda are offered for future research and organizational design.