ALGORITHMIC MANAGEMENT AND THE FUTURE OF WORK: A COMPREHENSIVE REVIEW OF EMPLOYEE AUTONOMY, TRUST, WELL-BEING, AND ORGANIZATIONAL CONTROL IN AI-DRIVEN WORKPLACES
Keywords:
Algorithmic management, Employee autonomy, Employee trust, Workplace well-being, Organizational control
Abstract
Algorithmic management has become a key part of AI-driven workplaces, changing task allocation, performance monitoring, decision-making, and control. This comprehensive review synthesizes current evidence on the implications of algorithmic management for employee autonomy, trust, well-being, and the future of work. A structured search of academic literature identified relevant studies, which was organized by thematic synthesis to explore common themes, conflicting results, theories and governance issues. The review reveals that algorithmic management can improve coordination, flexibility, and decision support, while at the same time limiting discretion by closely monitoring, standardizing the evaluation, and automating the control. Transparency, explainability, accountability, perceived fairness and contextual legitimacy are all important factors of employee trust. The wellbeing effects are also mixed; while increased efficiency may have positive effects, so can increased work intensification, psychological strain, surveillance and dehumanization. Important conditions that come to light are human-AI collaboration, participatory design, worker voice, and clear institutional protections to balance organizational efficiency with employee agency. The review suggests that the development of algorithmic management will not be solely about technologies but also about responsible governance, human oversight and work design to ensure autonomy, fairness, trust and well-being.
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References
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2. Balliester, T., & Elsheikhi, A. (2018). The future of work: a literature review. ILO research department working paper, 29, 1-54.
3. Burdziej, S., Haffer, R., Moszyńska, A., & Karwacki, A. (2024). Organizational justice and employee acceptance of management decisions: the mediating role of perceived organizational adaptation to hindering external conditions. Employee Relations: The International Journal, 46(5), 1045-1063.
4. Çeri, Ş. Ö., & Erhan, T. (2025). A Conceptual Study on the Effects of Artificial Intelligence in Managerial Decision-Making. European Journal of Digital Economy Research, 6(1), 5-21.
5. Cram, W. A., Wiener, M., Tarafdar, M., & Benlian, A. (2020). Algorithmic controls and their implications for gig worker well-being and behavior.
6. Darmawan, D. (2024). Algorithmic Management in Digital Platforms and Work Dehumanization in the Gig Economy. Studi Ilmu Sosial Indonesia, 4(2), 183-216.
7. de Fine Licht, K., & de Fine Licht, J. (2020). Artificial intelligence, transparency, and public decision-making: Why explanations are key when trying to produce perceived legitimacy. AI & society, 35(4), 917-926.
8. De Stefano, V. (2019). " Negotiating the algorithm": automation, artificial intelligence, and labor protection. Comp. Lab. L. & Pol'y J., 41, 15.
9. Elbasheer, M., Longo, F., Nicoletti, L., Padovano, A., Solina, V., & Vetrano, M. (2022). Applications of ML/AI for decision-intensive tasks in production planning and control. Procedia Computer Science, 200, 1903-1912.
10. Gagné, M., Parent-Rocheleau, X., Bujold, A., Gaudet, M. C., & Lirio, P. (2022). How algorithmic management influences worker motivation: A self-determination theory perspective. Canadian Psychology/Psychologie canadienne, 63(2), 247.
11. Galière, S. (2020). When food‐delivery platform workers consent to algorithmic management: a Foucauldian perspective. New technology, work and employment, 35(3), 357-370.
12. Ghelani, D. (2024). Optimizing resource allocation: Artificial intelligence techniques for dynamic task scheduling in cloud computing environments. International Journal of Advanced Engineering Technologies and Innovations, 3(1), 132-156.
13. Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021). Human-machine collaboration in managerial decision making. Computers in Human Behavior, 119, 106730.
14. Hosseini Tabaghdehi, S. A., & Ayaz, Ö. (2025). AI ethics in action: a circular model for transparency, accountability and inclusivity. Journal of Managerial Psychology.
15. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
16. Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big data & society, 8(2), 20539517211020332.
17. Jiang, J. (2023). Covert resistance against algorithmic control on online labor platforms–A systematic literature review.
18. Jo, A. A., & Raj, E. D. (2024, June). Ethical dimensions in data-driven business: Balancing fairness, accountability, transparency, and explainability. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
19. Kinowska, H., & Sienkiewicz, Ł. J. (2023). Influence of algorithmic management practices on workplace well-being–evidence from European organisations. Information Technology & People, 36(8), 21-42.
20. Krzywdzinski, M., Schneiß, D., & Sperling, A. (2025). Between control and participation: The politics of algorithmic management. New Technology, Work and Employment, 40(1), 60-80.
21. Kubicek, B., Paškvan, M., & Bunner, J. (2017). The bright and dark sides of job autonomy. In Job demands in a changing world of work: Impact on workers' health and performance and implications for research and practice (pp. 45-63). Cham: Springer International Publishing.
22. Kurian, N., Cherian, J. M., Sudharson, N. A., Varghese, K. G., & Wadhwa, S. (2023). AI is now everywhere: AI is now everywhere. British Dental Journal, 234(2), 72-72.
23. Lee, M. K., Nigam, I., Zhang, A., Afriyie, J., Qin, Z., & Gao, S. (2021, July). Participatory algorithmic management: Elicitation methods for worker well-being models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 715-726).
24. Montealegre-López, N. (2025). Exploring the role of trust in AI-driven decision-making: a systematic literature review. Management Review Quarterly, 1-51.
25. Noponen, N., Feshchenko, P., Auvinen, T., Luoma-Aho, V., & Abrahamsson, P. (2024). Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management. Management review quarterly, 74(3), 1695-1721.
26. Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied psychology, 71(4), 1171-1204.
27. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of management review, 46(1), 192-210.
28. Stark, D., & Vanden Broeck, P. (2024). Principles of algorithmic management. Organization Theory, 5(2), 26317877241257213.
29. Trunk, A., Birkel, H., & Hartmann, E. (2020). On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research, 13(3), 875-919.
30. Tschang, F. T., & Almirall, E. (2021). Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), 642-659.
31. Vignola, E. F., Baron, S., Abreu Plasencia, E., Hussein, M., & Cohen, N. (2023). Workers’ health under algorithmic management: Emerging findings and urgent research questions. International Journal of Environmental Research and Public Health, 20(2), 1239.
32. Wang, A., Kapoor, S., Barocas, S., & Narayanan, A. (2024). Against predictive optimization: On the legitimacy of decision-making algorithms that optimize predictive accuracy. ACM Journal on Responsible Computing, 1(1), 1-45.
33. Williams, M., & Rani, U. (2025). Resilience through resistance: the role of worker agency in navigating algorithmic control. Frontiers in artificial intelligence, 8, 1600044.
34. Wood, A. J. (2021). Algorithmic management consequences for work organisation and working conditions (No. 2021/07). JRC working papers series on labour, education and technology.
35. Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, employment and society, 33(1), 56-75.
36. Wood, S., & Ogbonnaya, C. (2018). High-involvement management, economic recession, well-being, and organizational performance. Journal of Management, 44(8), 3070-3095.
37. Xavier, D. F., & Korunka, C. (2025). Integrating Artificial Intelligence across cultural orientations: A longitudinal examination of creative self-efficacy and employee autonomy. Computers in Human Behavior Reports, 18, 100623.
38. Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & governance, 12(4), 505-523.
39. Zhang, L., & Li, H. (2025, October). Risk Prevention for the Intelligent Transformation of Discipline Inspection and Supervision from the Perspective of Algorithmic Governance. In Proceedings of the 2025 International Conference on Artificial Intelligence, Virtual Reality and Interaction Design (pp. 322-328).
40. 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.
41. Zheng, J., Zhang, J. Z., Kamal, M. M., Liang, X., & Alzeiby, E. A. (2025). Unpacking human-AI interaction: Exploring unintended consequences on employee Well-being in entrepreneurial firms through an in-depth analysis. Journal of Business Research, 196, 115406.
Published
2026-08-27
How to Cite
Dr. Muhammad Ahsan Khan. (2026). ALGORITHMIC MANAGEMENT AND THE FUTURE OF WORK: A COMPREHENSIVE REVIEW OF EMPLOYEE AUTONOMY, TRUST, WELL-BEING, AND ORGANIZATIONAL CONTROL IN AI-DRIVEN WORKPLACES. IJRDO - Journal of Business Management, 12(3), 30-41. https://doi.org/10.69980/bm.v12i3.6802
Section
Articles
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