YAPAY ZEKÂ DESTEKLİ KARAR ALMA SİSTEMLERİNİN ORTA KADEME YÖNETİCİLERİN OTONOMİSİ ÜZERİNDEKİ ETKİSİ: KARAR YAPISININ ARACI VE LİDERLİĞİN DÜZENLEYİCİ ROLÜ

Abstract

Bu çalışmanın amacı, yapay zekâ destekli karar alma sistemleri (YZ-DKS) kullanımına ilişkin algıların orta kademe yöneticilerin iş otonomisi üzerindeki etkisini araştırmak, bu ilişkinin karar yapısındaki değişim aracılığıyla nasıl gerçekleştiğini ortaya koymak ve dönüşümcü liderliğin alt boyutlarının bu ilişkideki düzenleyici rolünü incelemektir. Araştırma Türkiye’de kamu kurumlarında görev yapan orta kademe yöneticiler üzerinde yürütülmüş ve çalışma, YZ-DKS ile yönetsel özerklik arasındaki ilişkiyi aracılık ve moderasyon etkilerini birlikte ele alan çok katmanlı bir modelle incelemesi ve dönüşümcü liderliğin dört alt boyutunu analiz etmesi bakımından literatüre özgün katkı sunması beklenmektedir. Nicel araştırma kapsamında kartopu örnekleme yöntemiyle 390 kamu yöneticisinden elde edilen veriler doğrulayıcı faktör analizi, yapısal eşitlik modellemesi ve Hayes PROCESS analizleri kullanılarak analiz edilmiştir. Bulgular, YZ-DKS kullanım algılarının iş otonomisini negatif yönde etkilediğini ve bu etkinin büyük ölçüde karar yapısındaki değişim aracılığıyla gerçekleştiğini göstermektedir. Ayrıca dönüşümcü liderliğin özellikle entelektüel teşvik ve ilham veren motivasyon boyutlarının bu olumsuz etkiyi zayıflattığı belirlenmiştir. Çalışma, yapay zekâ tabanlı sistemlerin yönetsel özerklik üzerinde sınırlayıcı etkiler yaratabileceğini, ancak uygun liderlik yaklaşımlarının bu etkileri dengeleyebileceğini ortaya koymaktadır. Bu bağlamda insan odaklı teknoloji tasarımı ve dönüşümcü liderlik uygulamalarının güçlendirilmesi önerilmektedir.

Keywords

• Yapay Zeka
• Karar Destek Sistemleri
• Otonomi
• Orta Kademe Yönetim
• Dönüşümcü Liderlik

Ethical Statement

KARAR SAYISI TOPLANTI TARİHİ 2025.11.373 26.09.2025 "Yapılan görüşmeler sonucunda, aşağıdaki tabloda isimleri belirtilen araştırmacılar tarafından hazırlanan “Yapay Zeka Destekli Karar Alma Sistemlerinin Orta Kademe Yöneticilerin Otonomisi Üzerindeki Etkisi: Karar Yapısının Aracı ve Liderliğin Düzenleyici Rolü” isimli araştırma projesi dosyası ve ilgili belgeler araştırmanın gerekçe, amaç, yaklaşım ve yöntemleri dikkate alınarak incelenmiş olup, projenin gerçekleştirilmesinde etik sakınca bulunmadığına kurulumuz üyeleri tarafından oy birliği ile karar verilmiştir."

THE IMPACT OF ARTIFICIAL INTELLIGENCE-SUPPORTED DECISION-MAKING SYSTEMS ON THE AUTONOMY OF MIDDLE MANAGERS: THE MEDIATING ROLE OF DECISION-MAKING STRUCTURES AND THE REGULATORY ROLE OF LEADERSHIP

Abstract

The aim of this study is to examine the impact of perceptions regarding the use of artificial intelligence supported decision-making systems (AI-DMS) on the job autonomy of middle managers, to reveal how this relationship is mediated by changes in the decision-making structure, and to investigate the moderating role of the sub-dimensions of transformational leadership in this relationship. The research was conducted among middle managers working in public institutions in Türkiye. The study is expected to make an original contribution to the literature by examining the relationship between AI-DMS and managerial autonomy using a multi-level model that considers both mediating and moderating effects, and by analysing the four sub-dimensions of transformational leadership. Within the scope of this quantitative research, data collected from 390 public sector managers using snowball sampling was analysed using confirmatory factor analysis, structural equation modelling and Hayes PROCESS analyses. The findings indicate that perceptions of AI-DMS usage negatively affect job autonomy, and that this effect is largely mediated by changes in the decision-making structure. Furthermore, it was determined that transformational leadership, particularly its dimensions of intellectual stimulation and inspirational motivation, mitigates this negative effect. The study demonstrates that AI-based systems can create restrictive effects on managerial autonomy, but that appropriate leadership approaches can balance these effects. In this context, it is recommended that human-centred technology design and transformative leadership practices be strengthened.

Keywords

• Artificial Intelligence
• Decision Support Systems
• Autonomy
• Middle Management
• Transformational Leadership

Ethical Statement

DECISION NUMBER MEETING DATE 2025.11.373 26.09.2025 ‘As a result of the discussions, the research project file entitled ’The Impact of Artificial Intelligence-Supported Decision-Making Systems on the Autonomy of Middle Managers : The Mediating Role of Decision Structure and the Regulatory Role of Leadership" prepared by the researchers whose names are listed in the table below, along with the relevant documents, has been reviewed taking into account the rationale, purpose, approach, and methods of the research, and our board members have unanimously decided that there are no ethical concerns regarding the implementation of the project."

References

1. Avolio, B. J. and Bass, B. M. (2004). Multifactor Leadership Questionnaire (MLQ): Manual and sampler set (3rd ed.). Mind Garden.
2. Baird, A. and Maruping, L. M. (2021). The next generation of research on is use: A theoretical framework of delegation to and from agentic is artifacts. Management Information Systems Quarterly, 45(1), 315-341. https://doi.org/10.25300/MISQ/2021/15882
3. Bakker, A. B. and Demerouti, E. (2007). The job demands–resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115
4. Bass, B. M. (1985). Leadership: Good, better, best. Organizational Dynamics, 13(3), 26-40. https://doi.org/10.1016/0090-2616(85)90028-2
5. Bass, B. M. and Avolio, B. J. (1994). Improving organizational effectiveness through transformational leadership. Sage Publications.
6. Bass, B. M. and Avolio, B. J. (2011). Multifactor leadership questionnaire [Dataset]. https://doi.org/10.1037/t03624-000
7. Bass, B. M. and Riggio, R. E. (2006). Transformational leadership (2nd ed.). Psychology Press.
8. Berente, N., Gu, B., Recker, J. and Santhanam, R. (2021). Managing artificial intelligence. Management Information Systems Quarterly, 45(3), 1433-1450. https://doi.org/10.25300/MISQ/2021/16274

9. Bolden, R. and O’Regan, N. (2016). Digital disruption and the future of leadership: an interview with rick haythornthwaite, chairman of centrica and mastercard. Journal of Management Inquiry, 25(4), 438-446. https://doi.org/10.1177/1056492616638173
10. Breaugh, J. A. (1985). The measurement of work autonomy. Human Relations, 38(6), 551–570. https://doi.org/10.1177/001872678503800604
11. Byrne, B. M. (2013). Structural equation modeling with mplus: basic concepts, applications, and programming. Routledge.
12. Carton, F. (2008). The ımpact on decision making of centralisation in a multinational manufacturing company: The materials purchasing function. In Encyclopedia of Decision Making and Decision Support Technologies (pp. 443-454). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-59904-843-7.ch051
13. Chtioui, T. (2009). Understanding the impact of ERP standardization on business process performance. International Journal of Business, 14(2), 151-162
14. Davenport, T. H. and Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
15. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
16. Deci, E. L. and Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
17. Demerouti, E., Bakker, A. B., Nachreiner, F. and Schaufeli, W. B. (2001). The job demands–resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499
18. Demirel, E. ve Yaralı, C. (2023). İmalat işletmelerinin dijitalleşme süreçleri üzerine nitel bir çalışma. Yönetim ve Ekonomi Dergisi, 30, 21-41. https://doi.org/10.18657/yonveek.1379397
19. Erkutlu, H., Erdemir Ergün, E., Köseoğlu, İ. ve Vurgun, T. (2023). Yapay zekâ ve örgütsel davranış. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 13(3), 1403–1417. https://doi.org/10.30783/nevsosbilen.1246678
20. Fornell, C. and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
21. Glikson, E. and Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
22. Gök, B. (2021). Yönetim ve liderlik becerileri. Ed. D. Hıdıroğlu. Yalın yönetim, yalın liderlik ve yalın girişimcilik, (1. Baskı, ss. 67-86) içinde. Nobel Yayınevi.
23. Hackman, J. R. and Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16(2), 250–279. https://doi.org/10.1016/0030-5073(76)90016-7
24. Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2019). Multivariate data analysis. (8th Ed.) Cengage.
25. Hall, R. (2002). Enterprise resource planning systems and organizational change: Transforming work organization? Strategic Change, 11(5), 263–270. https://doi.org/10.1002/jsc.602
26. Hayes, A. F. (2017). Introduction to mediation, moderation and conditional process analysis: a regression-based approarch (Second Edition). The Guiford Press.
27. Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication monographs, 85(1), 4-40.
28. Hu, L. T. and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
29. Huy, Q. N. (2001). In praise of middle managers. Harvard Business Review, 79(8), 72-79, 160.
30. Ignatiadis, I. and Nandhakumar, J. (2007). The impact of enterprise systems on organizational routines. Information Systems Journal, 17(3), 287–310. https://doi.org/10.1111/j.1365-2575.2007.00234.x
31. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
32. Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E. and Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2), 1-14. https://doi.org/10.1177/20539517211020332
33. Jolly, A. A., Dunlop, P. D., Parker, S. K. and Kanse, L. (2026). It’s not my responsibility: working with autonomy-restricting algorithms facilitates unethical behavior and displacement of responsibility: AA Jolly et al. Journal of Business Ethics, 203(2), 405-424. https://doi.org/10.1007/s10551-025-06034-5
34. Karasek, R. A. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly, 24(2), 285-308. https://doi.org/10.2307/2392498
35. Kellogg, K. C., Valentine, M. A. and 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
36. Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford Publications.
37. Kocaman, O. (2024). Yapay zekâ uygulamalarının kamu yönetiminde karar almaya etkisi. Yasama Dergisi, 49, 153–192.
38. Kutner, M. H., Nachtsheim, C. J., Neter, J. and Li, W. (2005). Applied linear statistical models. McGraw, Irwin.
39. Lorenz, L., Meijer, A. and Schuppan, T. (2021). The algocracy as a new ideal type for government organizations: Predictive policing in Berlin as an empirical case. Information Polity, 26(1), 71–86. https://doi.org/10.3233/IP-200279
40. Madapusi, A. and Miles, G. (2011). Routines in enterprise application systems. Management Research Review, 34(10), 1106–1125. https://doi.org/10.1108/01409171111096487
41. McAfee, A. and Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.
42. Murphy, G. B., Trailer, J. W. and Hill, R. C. (1996). Measuring performance in entrepreneurship research. Journal of Business Research, 36(1), 15-23.
43. Murphy, G., Chang, A. and Unsworth, K. L. (2012). Differential effects of ERP systems on user outcomes: A longitudinal investigation. New Technology, Work and Employment, 27(2), 147–162. https://doi.org/10.1111/j.1468-005X.2012.00282.x
44. Noponen, N., Feshchenko, P., Auvinen, T., Luoma-aho, V. and Abrahamsson, P. (2023). Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management. Management Review Quarterly, 73(4), 1695–1744. https://doi.org/10.1007/s11301-023-00345-5
45. Nunnally, J. and Bernstein, I. (1994). Psychometric Theory 3rd edition. MacGraw-Hill.
46. Nural, D. (2024). Akıllı turizm. Y. Yağmur (Ed.) Dijitalleşmenin turizme yansımaları (1. Baskı, s. 30-56) içinde. Nobel Yayıncılık.
47. O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6
48. Orçanlı, K. ve Oruç, Ş. (2025). Detection and analysis of out-of-control observations in statistical process control using explainable artificial intelligence models. Acta Infologica, 9(2), 719-744. https://doi.org/10.26650/acin.1830356
49. Pääkkönen, J. (2021). Bureaucratic routines and error management in algorithmic systems. Hallinnon Tutkimus, 39(4), 1–15. https://doi.org/10.37450/ht.107880
50. Parent-Rocheleau, X. and Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), https://doi.org/10.1016/j.hrmr.2021.100838
51. Pierce, J. L. and Delbecq, A. L. (1977). Organization structure, individual attitudes and innovation. Academy of Management Review, 2(1), 27–37. https://doi.org/10.5465/amr.1977.4409154
52. Preacher, K. J. and Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
53. Raisch, S. and 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.2018.0072
54. Ryan, R. M. and Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
55. Seethamraju, Ravi, "ERP Systems and Decision Support – An Exploratory Study" (2007). ICDSS 2007 Proceedings. 3. http://aisel.aisnet.org/icdss2007/3
56. Seethamraju, R. (2014). Enterprise systems and demand chain management: a cross-sectional field study. Information Technology and Management, 15(3), 151-161.
57. Seethamraju, Ravi and Seethamraju, Jaya, "ERP Systems and Managerial Decision Making - A Model for Analysis" (2008). ICEB 2008 Proceedings (Hawaii, Waikoloa, Big Island). 8. https://aisel.aisnet.org/iceb2008/8
58. Sevinç, H. G. (2025). Yönetimin değişen pradigması: Yapay zekâ liderliği. Liberal Düşünce Dergisi, (118), 89-116. Shrestha, Y. R., Ben-Menahem, S. M. and 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
59. Tarafdar, M., Beath, C. M. and Ross, J. W. (2019). Using AI to enhance business operations. MIT Sloan Management Review, 60(4), 37–44.
60. van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D. and Petersen, J. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/1094670516679272
61. Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003
62. Wright, S. A. and Schultz, A. E. (2018). The rising tide of artificial intelligence and business automation: Developing an ethical framework. Business Horizons, Ethics, Culture, and Pedagogical Practices in The Global Context, 61(6), 823-832. https://doi.org/10.1016/j.bushor.2018.07.001
63. Yaralı, M. C. (2025). Rekabet üstünlüğünü yeniden tanımlamada dijital olgunluğunun ve bilgi yönetimi yöneliminin etkisi. Özgür Yayınları. https://doi.org/10.58830/ozgur.pub935
64. Zhang, M. M., Cooke, F. L., Ahlstrom, D. and McNeil, N. (2025). The rise of algorithmic management and implications for work and organisations. New Technology, Work and Employment, 40(3), 659-671.