The Role of Algorithmic Management in HR Practices and Ethical Challenges

https://doi.org/10.35912/simo.v6i3.4642
Published: Oct 29, 2025

Abstract:

Purpose: This study aims to systematically explore the development of algorithmic management in HR practices, focusing on emerging ethical challenges.

Methodology/approach: Using a Systematic Literature Review (SLR), this study analyzes findings from the past five years on the use of algorithms in managerial decision-making and their impact on workers' rights, justice, and welfare.

Results/findings: While algorithms bring efficiency, they present significant ethical, social, and legal challenges. Organizations must balance technological efficiency with principles of fairness, transparency, and privacy protection. A collaborative approach between humans and technology, coupled with strict regulation, is essential.

Conclusions: Algorithmic management in HR boosts efficiency but raises ethical concerns about fairness and transparency. Its success depends on creating accountable systems that balance technology with human values. Researchers advocate for human-technology collaboration, with algorithms as tools, not substitutes for human decision-making, and the integration of "responsible and explainable AI" to foster fairness and inclusivity.

Limitations: The study’s focus on references from developed countries limits its applicability to developing countries like Indonesia. Additionally, most of the literature is conceptual and lacks long-term data.

Contribution: The study suggests exploring contextual and participatory case studies across sectors and regions, along with both quantitative and qualitative research on algorithms’ impact on job satisfaction and employee rights. Further research on the role of national and international regulations is required.

Keywords:
1. Algorithmic Management
2. Ethics
3. Fairness
4. Human Resource Practices
5. Privacy
6. Transparency
Authors:
1 . Ryan Firdiansyah Suryawan
2 . Muhammad Yusuf
3 . Rousilita Suhendah
4 . Nandan Lima Krisna
5 . Karnawi Kamar
View PDF
How to Cite
Suryawan, R. F., Yusuf, M. ., Suhendah, R. ., Krisna, N. L. ., & Kamar, K. . (2025). The Role of Algorithmic Management in HR Practices and Ethical Challenges. Studi Ilmu Manajemen Dan Organisasi, 6(3), 925–934. https://doi.org/10.35912/simo.v6i3.4642
Download Citation
  1. Endnote/Zotero/Mendeley (RIS)
  2. BibTeX

Downloads

Download data is not yet available.
Issue & Section
Vol. 6 No. 3 (2025): Oktober
Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

References

    Adolphs, M., & als Anerkennungsgeschehen, D. M. G. (n.d.). Neuerwerbungen im Dezember 2023.

    Ajunwa, I. (2019). The paradox of automation as anti-bias intervention. Cardozo L. Rev., 41, 1671.

    Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media and Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645

    Ayu. (2024). The Ethical Perspective of Digital-Technology-Driven Economic Disruption Emeralda Ayu Kusuma 1* Wahyu Widhi Wicaksono 2* 2.

    Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health care. Science, 373(6552), 284–286.

    Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of Machine Learning Research, 81(2016), 149–159.

    Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). “It’s Reducing a Human Being to a Percentage” Perceptions of Justice in Algorithmic Decisions. Proceedings of the 2018 Chi Conference on Human Factors in Computing Systems, 1–14.

    BPS. (2022). Badan Pusat Statistik (BPS) 2022. Statistik Indonesia, 1101001.

    Chan, J., & Wang, J. (2018). Hiring preferences in online labor markets: Evidence of a female hiring bias. Management Science, 64(7), 2973–2994. https://doi.org/10.1287/mnsc.2017.2756

    Deobald, U. L., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The Challenges of Algorithm ? Based HR Decision ? Making for Personal Integrity.

    Draude, C., Klumbyte, G., Lücking, P., & Treusch, P. (2020). Situated algorithms: a sociotechnical systemic approach to bias. Online Information Review, 44(2), 325–342.

    Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258

    Fenech, R., Baguant, P., & Ivanov, D. (2019). The changing role of human resource management in an era of digital transformation. Journal of Management Information and Decision Sciences, 22(2), 176–180.

    Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

    Gal, U., Jensen, T. B., & Stein, M. K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301

    Hanseth, O., & Modol, J. R. (2021). The dynamics of architecture-governance configurations: An assemblage theory approach. Journal of the Association for Information Systems, 22(1), 130–155. https://doi.org/10.17705/1jais.00656

    Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data and Society, 8(2). https://doi.org/10.1177/20539517211020332

    Jooss, S., McDonnell, A., & Conroy, K. (2021). Flexible global working arrangements: An integrative review and future research agenda. Human Resource Management Review, 31(4), 100780. https://doi.org/10.1016/j.hrmr.2020.100780

    Kadolkar, I., Kepes, S., & Subramony, M. (2024). Algorithmic management in the gig economy: A systematic review and research integration. Journal of Organizational Behavior.

    Kellogg, K. C., Valentine, M., & Christin, A. (2020). ALGORITHMS AT WORK: THE NEW CONTESTED TERRAIN OF CONTROL Academy of Management Annals ALGORITHMS AT WORK: THE NEW CONTESTED TERRAIN OF CONTROL. Acad. Management Ann, 14(1), 366–410.

    Li, L., Lassiter, T., Oh, J., & Lee, M. K. (2021). Algorithmic Hiring in Practice: Recruiter and HR Professional’s Perspectives on AI Use in Hiring. In AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (Vol. 1, Issue 1). Association for Computing Machinery. https://doi.org/10.1145/3461702.3462531

    Möhlmann, M., & Zalmanson, L. (2018). Hands on the Wheel: Navigating Algorithmic Management and Uber Driversâ€TM Autonomy. ICIS 2017: Transforming Society with Digital Innovation, December.

    Mateescu, A., & Nguyen, A. (2019). Explainer: workplace monitoring and surveillance.

    Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021). Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM. International Journal of Human Resource Management, 32(12), 2545–2562. https://doi.org/10.1080/09585192.2021.1925326

    Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic management of work on online labor platforms: When matching meets control. MIS Quarterly, 45(4).

    Muhyiddin, M., Annazah, N. S., & Tobing, H. (2024). The Ambiguity of Employment Relationship in Indonesia ’ s Gig Economy?: A Study of Online Motorcycle Taxi Drivers The Ambiguity of Employment Relationship in Indonesia ’ s Gig Economy?: A Study of Online Motorcycle Taxi Drivers. January 2025. https://doi.org/10.47198/naker.v19i3.416

    Pea-Assounga, J. B. B., & Bindel Sibassaha, J. L. (2024). Impact of technological change, employee competency, and law compliance on digital human resource practices: Evidence from congo telecom. Sustainable Futures, 7(March), 100195. https://doi.org/10.1016/j.sftr.2024.100195

    Peccei, R., & Van De Voorde, K. (2019). Human resource management–well-being–performance research revisited: Past, present, and future. Human Resource Management Journal, 29(4), 539–563. https://doi.org/10.1111/1748-8583.12254

    Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 469–481.

    Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.

    Rani, U., & Furrer, M. (2021). Digital labour platforms and new forms of flexible work in developing countries: Algorithmic management of work and workers. Competition and Change, 25(2), 212–236. https://doi.org/10.1177/1024529420905187

    Ryan, M. J. (2020). Secret Algorithms, IP Rights, and the Public Interest. Nev. LJ, 21, 61.

    Sharifah, N., Wajdi, F., Susila, I., & Achmed, N. (2024). The Impact of Algorithm Management on Employee Job Satisfaction: Exploring the Mediating Role of Job Autonomy and the Moderating Effect of Employee Attitude: A Case Study on Two Premier Universitas Muhammadiyah (UMS and UMY). Journal of Business and Management Studies, 6(3), 233–251. https://doi.org/10.32996/jbms.2024.6.3.20

    Veen, A., Barratt, T., & Goods, C. (2020). Platform-Capital’s ‘App-etite’ for Control: A Labour Process Analysis of Food-Delivery Work in Australia. Work, Employment and Society, 34(3), 388–406. https://doi.org/10.1177/0950017019836911

    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). https://doi.org/10.3390/ijerph20021239

    Wang, R., Harper, F. M., & Zhu, H. (2020). Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences. Conference on Human Factors in Computing Systems - Proceedings, 1–14. https://doi.org/10.1145/3313831.3376813

    Widianto, S., Lestari, Y. D., Adna, B. E., Sukoco, B. M., & Nasih, M. (2021). Dynamic managerial capabilities, organisational capacity for change and organisational performance: the moderating effect of attitude towards change in a public service organisation. Journal of Organizational Effectiveness, 8(1), 149–172. https://doi.org/10.1108/JOEPP-02-2020-0028

    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. https://doi.org/10.1177/0950017018785616

    Yoon, D. J., Muir, C. P., Yoon, M. H., & Kim, E. (2022). Customer courtesy and service performance: The roles of self?efficacy and social context. Journal of Organizational Behavior, 43(6), 1015–1037.

    Zuboff, S. (2023). The age of surveillance capitalism. In Social theory re-wired (pp. 203–213). Routledge.

  1. Adolphs, M., & als Anerkennungsgeschehen, D. M. G. (n.d.). Neuerwerbungen im Dezember 2023.
  2. Ajunwa, I. (2019). The paradox of automation as anti-bias intervention. Cardozo L. Rev., 41, 1671.
  3. Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media and Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
  4. Ayu. (2024). The Ethical Perspective of Digital-Technology-Driven Economic Disruption Emeralda Ayu Kusuma 1* Wahyu Widhi Wicaksono 2* 2.
  5. Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health care. Science, 373(6552), 284–286.
  6. Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of Machine Learning Research, 81(2016), 149–159.
  7. Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). “It’s Reducing a Human Being to a Percentage” Perceptions of Justice in Algorithmic Decisions. Proceedings of the 2018 Chi Conference on Human Factors in Computing Systems, 1–14.
  8. BPS. (2022). Badan Pusat Statistik (BPS) 2022. Statistik Indonesia, 1101001.
  9. Chan, J., & Wang, J. (2018). Hiring preferences in online labor markets: Evidence of a female hiring bias. Management Science, 64(7), 2973–2994. https://doi.org/10.1287/mnsc.2017.2756
  10. Deobald, U. L., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The Challenges of Algorithm ? Based HR Decision ? Making for Personal Integrity.
  11. Draude, C., Klumbyte, G., Lücking, P., & Treusch, P. (2020). Situated algorithms: a sociotechnical systemic approach to bias. Online Information Review, 44(2), 325–342.
  12. Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258
  13. Fenech, R., Baguant, P., & Ivanov, D. (2019). The changing role of human resource management in an era of digital transformation. Journal of Management Information and Decision Sciences, 22(2), 176–180.
  14. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  15. Gal, U., Jensen, T. B., & Stein, M. K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301
  16. Hanseth, O., & Modol, J. R. (2021). The dynamics of architecture-governance configurations: An assemblage theory approach. Journal of the Association for Information Systems, 22(1), 130–155. https://doi.org/10.17705/1jais.00656
  17. Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data and Society, 8(2). https://doi.org/10.1177/20539517211020332
  18. Jooss, S., McDonnell, A., & Conroy, K. (2021). Flexible global working arrangements: An integrative review and future research agenda. Human Resource Management Review, 31(4), 100780. https://doi.org/10.1016/j.hrmr.2020.100780
  19. Kadolkar, I., Kepes, S., & Subramony, M. (2024). Algorithmic management in the gig economy: A systematic review and research integration. Journal of Organizational Behavior.
  20. Kellogg, K. C., Valentine, M., & Christin, A. (2020). ALGORITHMS AT WORK: THE NEW CONTESTED TERRAIN OF CONTROL Academy of Management Annals ALGORITHMS AT WORK: THE NEW CONTESTED TERRAIN OF CONTROL. Acad. Management Ann, 14(1), 366–410.
  21. Li, L., Lassiter, T., Oh, J., & Lee, M. K. (2021). Algorithmic Hiring in Practice: Recruiter and HR Professional’s Perspectives on AI Use in Hiring. In AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (Vol. 1, Issue 1). Association for Computing Machinery. https://doi.org/10.1145/3461702.3462531
  22. Möhlmann, M., & Zalmanson, L. (2018). Hands on the Wheel: Navigating Algorithmic Management and Uber Driversâ€TM Autonomy. ICIS 2017: Transforming Society with Digital Innovation, December.
  23. Mateescu, A., & Nguyen, A. (2019). Explainer: workplace monitoring and surveillance.
  24. Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021). Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM. International Journal of Human Resource Management, 32(12), 2545–2562. https://doi.org/10.1080/09585192.2021.1925326
  25. Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic management of work on online labor platforms: When matching meets control. MIS Quarterly, 45(4).
  26. Muhyiddin, M., Annazah, N. S., & Tobing, H. (2024). The Ambiguity of Employment Relationship in Indonesia ’ s Gig Economy?: A Study of Online Motorcycle Taxi Drivers The Ambiguity of Employment Relationship in Indonesia ’ s Gig Economy?: A Study of Online Motorcycle Taxi Drivers. January 2025. https://doi.org/10.47198/naker.v19i3.416
  27. Pea-Assounga, J. B. B., & Bindel Sibassaha, J. L. (2024). Impact of technological change, employee competency, and law compliance on digital human resource practices: Evidence from congo telecom. Sustainable Futures, 7(March), 100195. https://doi.org/10.1016/j.sftr.2024.100195
  28. Peccei, R., & Van De Voorde, K. (2019). Human resource management–well-being–performance research revisited: Past, present, and future. Human Resource Management Journal, 29(4), 539–563. https://doi.org/10.1111/1748-8583.12254
  29. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 469–481.
  30. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
  31. Rani, U., & Furrer, M. (2021). Digital labour platforms and new forms of flexible work in developing countries: Algorithmic management of work and workers. Competition and Change, 25(2), 212–236. https://doi.org/10.1177/1024529420905187
  32. Ryan, M. J. (2020). Secret Algorithms, IP Rights, and the Public Interest. Nev. LJ, 21, 61.
  33. Sharifah, N., Wajdi, F., Susila, I., & Achmed, N. (2024). The Impact of Algorithm Management on Employee Job Satisfaction: Exploring the Mediating Role of Job Autonomy and the Moderating Effect of Employee Attitude: A Case Study on Two Premier Universitas Muhammadiyah (UMS and UMY). Journal of Business and Management Studies, 6(3), 233–251. https://doi.org/10.32996/jbms.2024.6.3.20
  34. Veen, A., Barratt, T., & Goods, C. (2020). Platform-Capital’s ‘App-etite’ for Control: A Labour Process Analysis of Food-Delivery Work in Australia. Work, Employment and Society, 34(3), 388–406. https://doi.org/10.1177/0950017019836911
  35. 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). https://doi.org/10.3390/ijerph20021239
  36. Wang, R., Harper, F. M., & Zhu, H. (2020). Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences. Conference on Human Factors in Computing Systems - Proceedings, 1–14. https://doi.org/10.1145/3313831.3376813
  37. Widianto, S., Lestari, Y. D., Adna, B. E., Sukoco, B. M., & Nasih, M. (2021). Dynamic managerial capabilities, organisational capacity for change and organisational performance: the moderating effect of attitude towards change in a public service organisation. Journal of Organizational Effectiveness, 8(1), 149–172. https://doi.org/10.1108/JOEPP-02-2020-0028
  38. 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. https://doi.org/10.1177/0950017018785616
  39. Yoon, D. J., Muir, C. P., Yoon, M. H., & Kim, E. (2022). Customer courtesy and service performance: The roles of self?efficacy and social context. Journal of Organizational Behavior, 43(6), 1015–1037.
  40. Zuboff, S. (2023). The age of surveillance capitalism. In Social theory re-wired (pp. 203–213). Routledge.