İNSAN KAYNAKLARI YÖNETİMİNDE YAPAY ZEKÂ UYGULAMALARININ BENİMSENMESİNİN T.O.E. MODELİ İLE İNCELENMESİ

Öz

Bu çalışma, TOE modeli (Teknoloji-Organizasyon-Çevre) çerçevesini kullanarak, insan kaynakları yöneticilerinin İnsan Kaynakları Yönetiminde (İKY) Yapay Zeka (YZ) uygulamalarını benimseme eğilimlerini incelemektedir. Bu doğrultuda, araştırma verileri çevrimiçi anket yöntemiyle toplanmış ve analiz sürecinde Kısmi En Küçük Kareler Yapısal Eşitlik Modellemesi (PLS-SEM) kullanılmıştır. Araştırma bulguları, İKY'de YZ uygulamalarının teknolojik bağlamda sağladığı maliyet etkinliği ve göreceli avantajların YZ benimsemesini olumlu yönde etkilediğini ortaya koymuştur. Buna karşın, gizlilik ve güvenlik endişeleri ile teknolojik karmaşıklığın benimsemeyi olumsuz yönde etkilediği belirlenmiştir. Organizasyon bağlamında, üst yönetimin sağladığı desteğin ve insan kaynakları departmanının hazır olma durumunun, YZ'nin benimsenmesini pozitif yönde etkilediği tespit edilmiştir. Ancak çevresel bağlamda, rekabetçi baskı ve tedarikçi desteği değişkenlerinin YZ'nin benimsenmesi üzerinde istatistiksel olarak anlamlı bir etkisi olmadığı görülmüştür. Sonuç olarak, bu çalışma İKY’de YZ uygulamalarının örgütsel düzeyde benimsenmesini etkileyen faktörleri ortaya koyarak, insan kaynakları yöneticilerinin YZ adaptasyon süreçlerine ilişkin önemli bulgular sunmaktadır. Türkiye’de YZ uygulamalarının benimsenmesine yönelik faktörlerin belirlenmesi, literatürdeki önemli bir boşluğun doldurulmasına katkı sağlamaktadır.

Anahtar Kelimeler

• Yapay Zekâ
• İnsan Kaynakları Yönetimi
• Teknoloji-Organizasyon-Çevre Modeli (TOE)

EXAMINING THE ADAPTATION OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN HUMAN RESOURCES MANAGEMENT WITH THE T.O.E MODEL

Öz

This study investigates human resource managers’ tendencies to adopt artificial intelligence (AI) applications in human resource management (HRM) by employing the Technology-Organization-Environment (TOE) framework. Accordingly, the research data were collected through an online survey method and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).The findings reveal that, in the technological context, cost efficiency and relative advantage provided by AI applications in HRM positively influence AI adoption. In contrast, privacy and security concerns and technological complexity exert a negative impact. In the organizational context, top management support and the readiness of the HR department were identified as significant positive factors influencing AI adoption. However, in the environmental context, competitive pressure and supplier support did not affect AI adoption statistically significantly.In conclusion, this study contributes to understanding the factors influencing the adoption of AI applications in HRM at the organizational level, providing valuable insights into HR managers’ adaptation to AI in the Turkish context. The study addresses a significant gap in the literature by identifying the factors affecting AI adoption in HR departments in Turkey.

Anahtar Kelimeler

• Artificial Intelligence
• Human Resource Management
• Technology-Organization-Environment Modal

Kaynakça

1. Ahmed, I. (2020). Technology organization environment framework in cloud computing. Telkomnika, 18(2), 716–725.
2. Alam, M. G. R., Masum, A. K. M., Beh, L., & Hong, C. S. (2016). Critical factors influencing decision to adopt human resource information system (HRIS) in hospitals. PLOS ONE, 11, e0160366. https://doi.org/10.1371/journal.pone.0160366
3. Aligarh, F., Sutopo, B., & Widarjo, W. (2023). The antecedents of cloud computing adoption and its consequences for MSMEs’ performance: A model based on the Technology-Organization-Environment (TOE) framework. Cogent Business & Management, 10(2). https://doi.org/10.1080/23311975.2023.2220190
4. Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial intelligence adoption: AI-readiness at firm-level. In Pacific Asia Conference on Information Systems 2018 (p. 37). Association for Information Systems. https://aisel.aisnet.org/pacis2018/
5. Anawar, S., Othman, N. F., Selamat, S. R., Ayop, Z., Harum, N., & Abdul Rahim, F. (2022). Security and privacy challenges of big data adoption: A qualitative study in telecommunication ındustry. International Journal of Interactive Mobile Technologies (iJIM), 16(19), pp. 81–97. https://doi.org/10.3991/ijim.v16i19.32093
6. Andress, J. (2014). The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice / Edition 2, Syngress. ISBN: 9780128008126
7. Aşkun, V. (2024). ChatGPT gibi üretken yapay zekalar ile insan kaynakları yönetimi etkileşimi: Daha fazla çalışma için görüşler ve yollar. Turkish Studies - Economy, 19(2), 679-699. https://dx.doi.org/10.7827/TurkishStudies.75863
8. Baker, J. (2012) The Technology-Organization-Environment Framework. In: Dwivedi, Y.K., Scott, L.M., Schneberger, L. and Systems, I.S., Eds., Information Systems Theory: Explaining and Predicting Our Digital Society, University of Hamburg, Hamburg, 232-243. https://doi.org/10.1007/978-1-4419-6108-2_12

9. Bal, Y., & Bal, M. (2021). Proaktif insan kaynakları yönetiminin yeni gücü: İK analitiği ve yapay zekâ, BMİJ, 9 (3): 1198-1216, doi: https://doi.org/10.15295/bmij.v9i3.1863
10. Balcıoğlu, Y.S. (2023). “İnsan Kaynakları Yönetiminde Yapay Zeka”, Yönetim Biliminde Yapay Zekâ (içinde), Nobel Yayıncılık, s.199-218.
11. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108 (Original work published 1991)
12. Bhagyalakshmi, R., & Maria, E. F. (2021). Artificial intelligence and HRM: an empirical study on decision-making skills of HR through AI in HRM Practices. Annals of the Romanian Society for Cell Biology, 25(6), 11568-11578. https://doi.org/10.1145/1456659.1456684
13. Black, J. S., van Esch, P. (2021). AI-enabled recruiting in the war for talent. Business Horizons, 64(4), 513-524.
14. Boonsiritomachai, W., McGrath, G. M., & Burgess, S. (2016). Exploring business ıntelligence and ıts depth of maturity in thai smes. Cogent Business & Management, 3, Article ID: 1220663. https://doi.org/10.1080/23311975.2016.1220663
15. Bryan, J.D. and Zuva, T., 2021. A review on TAM and TOE framework progression and how these models integrate. Advances in Science, Technology and Engineering Systems Journal, 6(3), 137-145. https://doi.org/10.25046/aj060316.
16. Chin, W. W., Peterson, R. A. & Brown, P. S. (2008). Structural equation modelling in marketing: Some practical reminders. Journal of Marketing Theory and Practice, 16(4), 287–298. https://doi.org/10.2753/MTP1069-6679160402
17. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
18. Falk, R. F. & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.
19. Faustine, P., & Rachmawati, R. (2024). AI adoption determinants and its impacts on HRM effectiveness within MES in TANZANİA. Open Journal of Business and Management, 12, 2532-2552. https://doi.org/10.4236/ojbm.2024.124131
20. Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. https://doi.org/10.2307/3151312.
21. Frey, C. B. and Osborne, M. A. (2013). “The Future of Employment: How Susceptible Are Jobs to Computerization?”, Working Paper, Oxford: Oxford Martin. https://doi.org/10.1016/j.techfore.2016.08.019
22. Göçoğlu, V., & Kurt, İ. D. (2018). Kamu kurumlarında insan kaynakları yönetimi ve teknoloji: Gelecek odaklı bir değerlendirme. Uluslararası Yönetim Akademisi Dergisi, 1(3), 357-367. https://dergipark.org.tr/tr/download/article-file/626338
23. Granić, A. (2023). Technology Acceptance and Adoption in Education. In: Zawacki-Richter, O., Jung, I. (eds) Handbook of Open, Distance and Digital Education. Springer, Singapore. https://doi.org/10.1007/978-981-19-2080-6_11
24. Gür, Y. E., Ayden, C., & Yücel, A. (2019). Yapay zekâ alanındaki gelişmelerin insan kaynakları yönetimine etkisi. Fırat Üniversitesi Uluslararası İktisadi Ve İdari Bilimler Dergisi, 3(2), 137-158. https://dergipark.org.tr/en/download/article-file/911917
25. Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2016). Multivariate data analysis (Global Ed.). Pearson Education.
26. Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2022). A Primer on partial least squares structural equation modeling (PLS-SEM), (3rd ed.) Thousand Oaks, CA: Sage.
27. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
28. Hsu, P. F., Kraemer, K. L., & Dunkle, D. (2006). Determinants of e-business use in U.S. firms. International Journal of Electronic Commerce, 10(4), 9–45. https://doi.org/10.2753/JEC1086-4415100401
29. Hu, L. V & Bentler, P. M.(1998). Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychological Methods, 3(4): 424-453.
30. Ifinedo, P. (2011). An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada. International journal of information technology & decision making, 10(04), 731-766.
31. Jöhnk, J., Weißert, M. & Wyrtki, K. (2021). Ready or not, AI comes— an ınterview study of organizational AI readiness factors. Bus Inf Syst Eng 63, 5–20. https://doi.org/10.1007/s12599-020-00676-7
32. Kamble, S. S., Gunasekaran, A., & Arha, H. (2019). Understanding the blockchain technology adoption in supply chains–Indian context. International Journal of Production Research, 57(7), 2009–2033. https://doi.org/10.1080/00207543.2018.1518610
33. Kambur, E. (2022).Yapay zekâ çağında insan kaynakları yönetimi konusunda yazılmış Türkçe makaleler üzerine bir araştırma, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 48, 139-152. https://doi.org/10.30794/pausbed.872606
34. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly: Management Information Systems, 23(2), 183-213. https://doi.org/10.2307/249751
35. Kaur, M., Rekha, A. G. & Vikas, S. (2021). Adoption of artificial intelligence in human resource management: a conceptual model. Indian Journal of Industrial Relations, 57(2), 331–342. https://www.jstor.org/stable/27282554.
36. Kim, Y., & Crowston, K. (2011). Technology adoption and use: Theory review for studying scientists continued use of cyber-infrastructure. Proceedings of American Society for Information Science and Technology, 48(1), 1-10. doi:10.1002/meet.2011.14504801197
37. Kişi, N., & Özer, M. A. (2024). İnsan kaynakları yönetiminde yapay zekâ teknolojisinin benimsenmesi üzerine güç alanı analizi. Kocatepe İİBF Dergisi, 26(Özel Sayı), 35- 52. https://doi.org/10.33707/akuiibfd.1406096
38. Lafreniere, K.C., Hunter, M.G., & Deshpande, S. (2011). Comparing and prioritizing the factors affecting purchase decisions in ınnovation adoption in a post-secondary educational setting. https://doi.org/10.28945/1557. Lemos, S.I.C., Ferreira, F.A.F., Zopounidis, C. et al. Artificial intelligence and change management in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives. Ann Oper Res (2022). https://doi.org/10.1007/s10479-022-05159-4
39. Liang, J., Zhu, Y., Wu, J. et al. “When I Have the Advantage, I Prefer AI!” The Influence of an Applicant’s Relative Advantage on the Preference for Artificial Intelligence Decision-making. J Bus Psychol (2025). https://doi.org/10.1007/s10869-025-10012-z
40. Lippert, S. K., & Govindarajulu, C. (2006). Technological, organizational, and environmental antecedents to web services adoption. Communications of the IBIMA, 6(1), 14. DOI: https://doi.org/10.58729/1941-6687.1303
41. Lodha, S. R., Deshpande, P.A., Kakade, A., Kakade, S., Sundaram, S. K. & Deshmukh (2024). data privacy and security in HR systems, The Empirical Economics Letters 23(2):1-7, https://doi.org/10.5281/zenodo.12740943
42. Lutfi, A., Alsyouf, A., Almaiah, M. A., Alrawad, M., Abdo, A. A. K., Al-Khasawneh, A. L. et al. (2022). Factors ınfluencing the adoption of big data analytics in the digital transformation era: case study of Jordanian SMES. Sustainability, 14, Article 1802.
43. Majumder, S., Mondal, A. (2021). Are chatbots really useful for human resource management? International Journal of Speech Technology, 24(4), 969-977.
44. Malik, A., Budhwar, P., Patel, C., & Srikanth, N. R. (2020). May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE. The International Journal of Human Resource Management, 33(6), 1148–1178. https://doi.org/10.1080/09585192.2020.1859582
45. MeBeBot.( May 07, 2025). The business case for AI in HR: Streamlining processes and reducing costs. Erişim tarihi: 31.05.2025. https://www.mebebot.com/the-business-case-for-ai-in-hr-streamline-save-costs/
46. Melkamu, M. (2025). Artifıcial intelligence implementation challenges in industries: Developing countries prospective. Journal of Trends and Challenges in Artificial Intelligence, 2(1). https://doi.org/10.61552/jai.2025.01.004
47. Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., Forghani, R. (2020). Brief history of artificial ıntelligence. Neuroimaging Clinics, 30(4), 393-399.
48. Nadal, C., Gavin, D., & Sas, C. (2019). Technology acceptability, acceptance and adoption—Definitions and measurement. CHI Conference on Human Factors in Computing Systems, Lancaster University. https://eprints.lancs.ac.uk/id/eprint/131906/1/WISH_extended_abstract.pdf
49. Ndubisi, N.O., Gupta, O.K. & Massoud, S. (2003). Organizational learning and vendor support quality by the usage of application software packages: A study of Asian entrepreneurs. J. Syst. Sci. Syst. Eng. 12, 314–331. https://doi.org/10.1007/s11518-006-0138-2
50. Neumann, O., Guirguis, K., & Steiner, R. (2022). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114–141. https://doi.org/10.1080/14719037.2022.2048685
51. Oktay, Ş. & Azbay, Ş. (2021). Türkiye’nin ilk ulusal yapay zeka stratejisi açıklandı, Anadolu Ajansı, 24.08.2021, Erişim Tarihi: 04.10.2022, https://www.aa.com.tr/tr/bilim-teknoloji/turkiyenin-ilk-ulusal-yapay-zeka-stratejisi-aciklandi/2344314.
52. Oliveira, T., Martins, R., Sarker, S., Thomas, M., & Popovič, A. (2019). Understanding SaaS adoption: The moderating impact of the environment context. International Journal Of Information Management, 49 (December), 1-12. Doi: https://doi.org/10.1016/j.ijinfomgt.2019.02.009
53. Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic journal of information systems evaluation, 14(1), pp110-121. https://academic-publishing.org/index.php/ejise/article/view/389/352
54. Owusu, A. (2020). Determinants of cloud business intelligence adoption among Ghanaian SMEs. International Journal of Cloud Applications and Computing, 10(4), 48–69. https://doi.org/10.4018/IJCAC.2020100104
55. Paiva, J. (2024). Exploring the Drivers of AI Adoption: A meta-analysis of technological, organizational and environmental (TOE) Factors.https://doi.org/10.21203/rs.3.rs-5634577/v1
56. Park, Woosung. (2018). Artificial intelligence and human resource management: new perspectives and challenges. https://www.jil.go.jp/profile/documents/w.park.pdf
57. Pillai, R. & Sivathanu, B. (2020), Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations, Benchmarking: An International Journal, 27( 9), 2599-2629. https://doi.org/10.1108/BIJ-04-2020-0186
58. Rachim, M. N. I. A., & Aligarh, F. (2024). The role of the TOE-framework in enhancing the financial performance of gojek partner MSMES through the gobiz application. In Proceeding of the International Conference Economic Management Accounting (ICEMA) (Vol. 2, No. 1, pp. 894-914).
59. Renaud, K., & Van Biljon, J. (2008, October). Predicting technology acceptance and adoption by the elderly: a qualitative study. In Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology (pp. 210-219).
60. Revillod, G. (2024). What drives the diffusion of aı recruitment systems in swiss hrm? The ımportance of technological expertise, ınnovative climate, competitive pressure, employees’ expectations and contextual factors. International Journal of Engineering and Management Sciences, 9(4), 42-84. https://doi.org/10.21791/IJEMS.2024.029.
61. Roberts, C., Kundavaram, R. R., Onteddu, A. R., Kothapalli, S., Tuli, F. A., & Miah, M. S. (2020). Chatbots and virtual assistants in HRM: Exploring Their Role in Employee Engagement and Support. NEXG AI Review of America, 1(1), 16-31.
62. Rogers, E.M. (1995). Diffusion of Innovation. New York: Free Press.
63. Salwani, M. I., Marthandan, G., Norzaidi, M. D., & Chong, S. C. (2009). E-commerce usage and business performance in the malaysian tourism sector: empirical analysis. Information Management and Computer Security, 17, 166-185. https://doi.org/10.1108/09685220910964027
64. Selçuk, S. (2021). Technology acceptance model to evaluate factors affecting adoption of the industrial internet of things (IIOT) by the industrial professionals (Yayın No: 702989). [Doktora tezi, Orta Doğu Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi.
65. Shakeel, A., & Siddiqui, D.A. (2021). The effect of technological, organizational, environmental, and task technology fit on the adoption and usage of artificial intelligence (AI) for talent acquisition (TA): Evidence from the Pakistani banking sector. (October 15. http://dx.doi.org/10.2139/ssrn.3943071
66. Sharma, A., Tyagi, S., Kanthalia, S., Tyagi, & S., Shashikant (2025). Quantitative assessment on investigation on the impact of artificial intelligence on hr practices and organizational efficiency for industry 4.0. In: Singh, R., Gehlot, A. (eds) Business Data Analytics. ICBDA 2023. Communications in Computer and Information Science, 2358. Springer, Cham. https://doi.org/10.1007/978-3-031-80778-7_6
67. Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M. & Ringle, C. M. (2022). Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662-1677. https://doi.org/10.1108/EJM-08-2020-0636
68. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S. & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLS predict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
69. Siradhana, N. K., & Arora, R. G. (2024). Examining the ınfluence of artificial intelligence ımplementation in hrm practices using T-O-E model. Vision: The Journal of Business Perspective, 1- 15. https://doi.org/10.1177/09722629241231458
70. Swanson, E. B., & Ramiller, N. C. (1997). The organizing vision in information systems innovation. Organization Science, 8(5), 458–474. https://sci-hub.se/10.1287/orsc.8.5.458
71. Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia manufacturing, 22, 960-967.
72. Tapkan, P. Z. (2022). Yapay zekâ ve gençlerin dijital dönüşüm süreci, Journal of Communication, Sociology and History Studies, 2 (1). http://dx.doi.org/10.53723/cosohis.22
73. Taşlıyan, M., & Yılmaz, Ö.İ. (2022). Yapay zekâ ve işletmeler açısından sonuçları, International Academic Social Resources Journal, (eISSN: 2636-7637), 7 (36), 463-471. https://asrjournal.org/files/asrjournal/e461fe8a-094c-4a0f-a60c-497acf1aacff.pdf
74. Tenenhaus, M., Vinzi, V. E., Yves-Marie, C. & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
75. Thomas, D., & Yao, Y. (2023). Technology-Organization-Environment Meta- Review and Construct Analysis: Insights for Future Research, Proceedings of the 56th Hawaii International Conference on System Sciences, 5811-5821. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1637&context=hicss-56
76. Tiftik, C. (2021). İnsan kaynakları yönetiminde yapay zekâ uygulamaları. IBAD Sosyal Bilimler Dergisi, (9), 374-390.https://doi.org/10.21733/ibad.833256
77. Toprak, M., Özel, D., & Çalışkan, S. (2022). Yapay zeka kullanımı ve insan kaynakları yönetimi. Uluslararası Eşitlik Politikası Dergisi, 2(2), 76-103.https://dergipark.org.tr/tr/download/article-file/2851619
78. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington, MA: Lexington Books.
79. V. H. Jr Carr (1999). Technology adoption and diffusion, Technology Center for Interactive Technology.
80. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
81. Wang, Y.M., Wang, Y.S. and Yang, Y.F., 2010. Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77, pp.803-815.
82. Wong, J.W., & Yap, K.H.A. (2024). Factors influencing the adoption of artificial intelligence in accounting among micro, small medium enterprises (MSMEs). Quantum Journal of Social Sciences and Humanities, 5(1), 16-28. https://doi.org/10.55197/qjssh.v5i1.323
83. Wood, S. (1999). Human resource management and performance. International Journal of Management Reviews, 1(4), 367-413.
84. Yabanci, O. (2019). From human resource management to intelligent human resource management: a conceptual perspective. Hum.-Intell. Syst. Integr. 1, 101–109. https://doi.org/10.1007/s42454-020-00007-x
85. Zafar, H. (2013). Human resource information systems: Information security concerns for organizations. Hum. Resour. Manag. Rev. 23 (1), 105–113. https://doi.org/10.1016/j.hrmr.2012.06.010