Journals / Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi / 2026 / Cilt: 16 Sayı: 31

ALGORITHMIC MANAGEMENT IN THE CONTEXT OF STRATEGIC MANAGEMENT AND ORGANIZATIONAL BEHAVIOR: A BIBLIOMETRIC ANALYSIS AND THEMATIC EVOLUTION

STRATEJİK YÖNETİM VE ÖRGÜTSEL DAVRANIŞ BAĞLAMINDA ALGORİTMİK YÖNETİM: BİBLİYOMETRİK BİR ANALİZ VE TEMATİK EVRİM

Elif Sis Atabay(TRABZON ÜNİVERSİTESİ)0000-0003-3862-6060
Pages
150–183
Publication Date
2026-06-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that the literature initially focused on the gig economy and digital control mechanisms, and over time shifted toward ethics, employee experience, and strategic decision-making processes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to reveal the intellectual structure and thematic evolution of the algorithmic management literature by analyzing it through a bibliometric method within the context of strategic management and organizational behavior.
📊
Key Findings & Data:The study examines 402 articles indexed in the Scopus database and published between 1983 and 2026.
💡
Conclusion & Impact:The findings indicate that the literature initially focused on the gig economy and digital control mechanisms, and over time shifted toward ethics, employee experience, and strategic decision-making processes.
🔬Design:Araştırma Makalesi
📍Scope:2026 Verileri
⚙️Variables:Algoritmik yönetim · Örgütsel Davranış · Tematik Evrim

Abstract

The aim of this study is to reveal the intellectual structure and thematic evolution of the algorithmic management literature by analyzing it through a bibliometric method within the context of strategic management and organizational behavior. The study examines 402 articles indexed in the Scopus database and published between 1983 and 2026. Using performance analysis and scientific mapping techniques, co-citation analysis, keyword co-occurrence analysis, and thematic evolution analysis were conducted. The findings indicate that the literature initially focused on the gig economy and digital control mechanisms, and over time shifted toward ethics, employee experience, and strategic decision-making processes. It was determined that algorithmic management has not yet achieved a comprehensive theoretical framework within the strategic management literature. By revealing the trajectory of the field’s conceptual development, the study provides a roadmap for future research.

Özet

Bu çalışma, algoritmik yönetim literatürünün stratejik yönetim ve örgütsel davranış bağlamındaki entelektüel yapısını ve tematik evrimini bibliyometrik analiz yöntemiyle incelemeyi amaçlamaktadır. Bu kapsamda, Scopus veri tabanında 1983–2026 yılları arasında yayımlanan 402 bilimsel yayın; performans analizi ve bilimsel haritalama teknikleri (ortak atıf, anahtar kelime eşleşme ve tematik evrim analizleri) aracılığıyla analiz edilmiştir. Bulgular, literatürün özellikle 2020 sonrasında ivme kazandığını ve 2025 yılında yayın üretiminin en yüksek seviyesine ulaştığını göstermektedir. Analiz sonuçları, alanın başlangıçta gig ekonomisi ve dijital kontrol gibi teknik odaklı konulardan, zamanla etik, çalışan deneyimi, anlamlandırma süreçleri ve stratejik karar verme gibi yönetsel temalara evrildiğine işaret etmektedir. Elde edilen veriler, algoritmik yönetimin salt bir dijitalleşme tartışmasından ziyade, stratejik ve örgütsel boyutları bütünleştiren disiplinlerarası bir araştırma alanına dönüştüğünü yansıtmaktadır. Çalışma, literatürün gelişim dinamiklerini sistematik biçimde ortaya koyarak gelecekteki araştırmalar için kavramsal ve metodolojik bir yol haritası sunmaktadır.

🔖

Cite This Article

Atabay, E. S. (2026). ALGORITHMIC MANAGEMENT IN THE CONTEXT OF STRATEGIC MANAGEMENT AND ORGANIZATIONAL BEHAVIOR: A BIBLIOMETRIC ANALYSIS AND THEMATIC EVOLUTION. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, Cilt: 16 Sayı: 31, 150–183. https://ajindex.com/articles/1116990

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (49)

  1. Agarwal, R., & Dhar, V. (2014). Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443–448. https://doi.org/10.1287/isre.2014.0546
  2. Ajunwa, I., & Greene, D. (2019). Platforms at work: Automated hiring platforms and other new intermediaries in the organization of work. In S. P. Vallas & A. Kovalainen (Eds.), Work and labor in the digital age (Research in the Sociology of Work, Vol. 33, pp. 61–91). Bingley, UK: Emerald Publishing Limited. https://doi.org/10.1108/S0277-283320190000033005
  3. Aria, M., & Cuccurullo, C. (2017). Bibliometric: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  4. Aşkun, V., & Çizel, R. (2019). Kompleks problem çözme üzerine R programı ile bir bibliyometrik analiz. Mediterranean Journal of Humanities, 9(1), 37–47.
  5. Atabay, E., Çizel, B., & Ajanoviç, E. (2019). Akıllı şehir araştırmalarının R programı ile bibliometrik analizi. 20. Ulusal Turizm Kongresi, Eskişehir, Türkiye.
  6. Aytekin, S. S. (2022). Yönetim kurullarını vekâlet teorisi perspektifiyle ele alan araştırmaların bibliyometrik analizi. The Journal of International Scientific Researches, 7(3), 317–335.
  7. Baiocco, S., Fernández-Macías, E., Rani, U., & Pesole, A. (2022). The algorithmic management of work and its implications in different contexts (No. 2022/02). JRC Working Papers Series on Labour, Education and Technology.
  8. Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104, 671–732.
  9. Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022). Algorithmic management: Bright and dark sides, practical implications, and research opportunities. Business & Information Systems Engineering, 64(6), 825–839. https://doi.org/10.1007/s12599-022-00764-w
  10. Block, J. H., & Fisch, C. (2020). Eight tips and questions for your bibliographic study in business and management research. Management Review Quarterly, 70(3), 307–312. https://doi.org/10.1007/s11301-020-00188-4
  11. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
  12. Broadus, R. N. (1987). Toward a definition of “bibliometrics.” Scientometrics, 12(5–6), 373–379. https://doi.org/10.1007/BF02016680
  13. Crane, D. (1972). Invisible colleges: Diffusion of knowledge in scientific communities. Chicago, IL: University of Chicago Press. http://refhub.elsevier.com/S1751-1577(17)30050-0/sbref0065
  14. Çavdar, E. (2021). Yeşil lojistik: WoS verilerine dayalı bibliyometrik bir analiz (2000–2021). Econder International Academic Journal, 5(2), 359–374.
  15. Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98.
  16. Edelman, B., Luca, M., & Svirsky, D. (2017). Racial discrimination in the sharing economy: Evidence from a field experiment. American Economic Journal: Applied Economics, 9(2), 1–22.
  17. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. New York, NY: St. Martin’s Press.
  18. Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005
  19. Fayyad, U. M., Grinstein, G. G., & Wierse, A. (Eds.). (2002). Information visualization in data mining and knowledge discovery. San Francisco, CA: Morgan Kaufmann.
  20. 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), 247–260. https://doi.org/10.1037/cap0000324
  21. Gerber, C., & Krzywdzinski, M. (2019). Brave new digital work? New forms of performance control in crowdwork. In S. P. Vallas & A. Kovalainen (Eds.), Work and labor in the digital age (pp. 121–143). Bingley, UK: Emerald Publishing. https://doi.org/10.1108/S0277-283320190000033008
  22. Heinrich, K., Vu, M. A., & Vysochyna, A. (2022). Algorithms as a manager: A critical literature review of algorithm management. In ICIS 2022 Proceedings. https://aisel.aisnet.org/icis2022/is_futureofwork/is_futureofwork/9
  23. Jarrahi, M. H., & Sutherland, W. (2019, March). Algorithmic management and algorithmic competencies: Understanding and appropriating algorithms in gig work. In International Conference on Information (pp. 578–589). Cham, Switzerland: Springer International Publishing.
  24. Jarrahi, M. H., Möhlmann, M., & Lee, M. K. (2023, April 5). Algorithmic management: The role of AI in managing workforces. MIT Sloan Management Review. Erişim adresi: https://sloanreview.mit.edu/article/algorithmic-management-the-role-of-ai-in-managing-workforces/
  25. Jiang, M. (2025, May). The influence of algorithmic control on work autonomy in gig economy. In Proceedings of the 2025 2nd International Conference on Digital Society and Artificial Intelligence (pp. 923–930). https://doi.org/10.1145/3748825.3748967
  26. Kaplan, T., & Sis Atabay, E. (2024). A bibliometric analysis of the relationship between dynamic capabilities and digital transformation. Global Journal of Business, Economics, and Management: Current Issues, 14(2), 143–157. https://doi.org/10.18844/gjbem.v14i2.9402
  27. Karoğlu, E. (2026). The Academic Discourse on Deunionization: A Bibliometric Review of Research Trends and Patterns. Uluslararası Ekonomi ve Yenilik Dergisi, 12(1), 55-78. https://doi.org/10.20979/ueyd.1791360
  28. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
  29. Korogodsky, A. (2025). Recursive impacts of algorithmic management on trust and employee productivity in professional work settings. In Proceedings of the 58th Hawaii International Conference on System Sciences (pp. 5417–5426). https://hdl.handle.net/10125/109499
  30. Koştı, N., & Kayadibi, S. (2025). A bibliometric analysis of artificial intelligence and machine learning applications for human resource management. Future Business Journal. https://doi.org/10.1186/s43093-025-00602-x
  31. Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), 2053951718756684. https://doi.org/10.1177/2053951718756684
  32. Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015, April). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1603–1612).
  33. Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w
  34. Levy, K., Chasalow, K. E., & Riley, S. (2021). Algorithms and decision-making in the public sector. Annual Review of Law and Social Science, 17(1), 309–334. https://doi.org/10.1146/annurev-lawsocsci-041221-023808
  35. 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), 1999–2022. https://doi.org/10.25300/misq/2021/15333
  36. Mufti, H., Fauzi, D. U., & Putra, T. R. (2026). Algorithmic management in the future of work: A systematic literature review in the context of gig workers. Bisman (Bisnis dan Manajemen): The Journal of Business and Management, 9(1), 36–53. https://doi.org/10.36815/bisman.v9i1.4589
  37. Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’. The Journal of Strategic Information Systems, 24(1), 3–14. https://doi.org/10.1016/j.jsis.2015.02.001
  38. Ocasio, W. (1997). Towards an attention‐based view of the firm. Strategic Management Journal, 18(S1), 187–206.
  39. Orsini, J. F. (1986). Artificial intelligence: A way through the strategic planning crisis? Long Range Planning, 19(4), 71–77. https://doi.org/10.1016/0024-6301(86)90273-6
  40. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,. & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71
  41. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Cambridge, MA: Harvard University Press.
  42. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2019.0144
  43. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784.
  44. Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
  45. Stark, D., & Vanden Broeck, P. (2024). Principles of algorithmic management. Organization Theory, 5(2), https://doi.org/10.1177/26317877241257213
  46. 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
  47. Yanık, C., & Engin, M. (2025). A bibliometric analysis of sociological studies on the social impacts of algorithmic management. Manisa Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 23(3), 15–36.
  48. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods, 18(3), 429-472.
  49. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,. & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71