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The Rise of Algorithmic Management and Implications for Work and Organisations

Citation. Zhang, M. M., Cooke, F. L., Ahlstrom, D., & McNeil, N. (2025). The rise of algorithmic management and implications for work and organisations. New Technology, Work and Employment, 40(3), 659–671. https://doi.org/10.1111/ntwe.12343

Summary. This open-access review article synthesizes research on algorithmic management (AM), understood as machine-learning-enabled automation of managerial functions, and organizes the literature into five theoretical perspectives. It argues that AM can improve coordination and efficiency while reshaping control, accountability, worker autonomy, and employment relations, making design and governance consequential for worker outcomes.

Key claims

  • The review distinguishes five perspectives on AM: surveillance and control system, neutral tool, agentic boss, socio-technical process, and contradictory unity.
  • AM can centralize managerial authority through continuous data collection, task allocation, performance evaluation, and automated decision-making, potentially limiting worker participation and contestation.
  • AM’s effects are not presented as deterministic: the review identifies recurring tensions between control and autonomy, transparency and opacity, efficiency and exploitation, empowerment and alienation, and fairness and discrimination.
  • A socio-technical account treats AM outcomes as contingent on institutional context and organizational design rather than on algorithmic capability alone.
  • The authors call for responsible AM research and practice centered on accountability, transparency, worker participation, fairness, human oversight, and worker well-being.

Evidence & limitations

  • This is a narrative review and special-issue introduction that synthesizes existing theory and studies; it does not supply a new causal test of AM effects or a systematic-review protocol.
  • The five-perspective framework is useful for organizing competing explanations, but its propositions require empirical testing across conventional workplaces as well as platform work.
  • Published open access in New Technology, Work and Employment; public DOI and Crossref metadata establish canonical provenance for the full-text Markdown artifact.

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