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Managers' Attitudes on The Use of Artificial Intelligence Technology in Human Resources Management: A Qualitative Research in The Central District of Edirne Province

Yıl 2025, Cilt: 24 Sayı: 3, 1407 - 1438, 18.07.2025

Özgür Tezel , Agah Sinan Ünsar

https://doi.org/10.21547/jss.1574355

Öz

With the spread of Industry 4.0, many technologies that have emerged have been included in the business models and business processes of businesses. Companies in Edirne Central District, Turkey's gateway to Europe, have also integrated industry 4.0 technologies into their business. The research aims to reveal the attitudes of company managers operating in Edirne, Turkey's border province to Europe, regarding the use of artificial intelligence in HRM functions and to guide future research on this subject. It has been observed that the most used industry 4.0 technology in the Central District of Edirne Province is artificial intelligence. In this research, with a phenomenological pattern in which a qualitative approach was adopted, a semi-structured interview method was used to benefit more from the opinions of the participants. In the research, it was determined that company managers in the Central District of Edirne Province (despite some concerns) adopted a supportive attitude towards the use of artificial intelligence in HRM. Executives say that in the current situation, it is not possible to completely transfer HRM to AI; however, they stated that it could benefit from conceptual issues. Managers underlined the combination of AI and human factors for effective HRM. For this reason, in the conclusion of the research, a model in which artificial intelligence and human factors are considered together is proposed. The research offers unique value in terms of considering human resources management together with the artificial intelligence phenomenon.

Anahtar Kelimeler

Human Resources Management Artificial Intelligence Digital Transformation Edirne Turkey

Etik Beyan

Before the research, the ethics committee permission was received from Trakya University Social Sciences and Humanities Research Ethics Committee for interview questions and research, dated 24.01.2024, session number 2024/01 and decision number 2024.01.15. This report has been added to the additional materials under the name "Ethics Report".

Kaynakça

  • Abdul-Rahman, F. (2023). the effect of applying neural network information systems in achieving parallel processing of decisions and streamlining smart solutions for human resources: An applied study of a sample of educational leaders at al-mustansiriyah university. International Journal of Research in Social Sciences and Humanities, 13 (3), 340-350.
  • Alfawareh, H., Jusoh, S. (2019). Intelligent decision support system for CV evaluation based on natural language processing. International Journal of Advanced and Applied Sciences, 6 (4), 1-8.
  • Bukrek (2018). İnsan kaynakları departmanında yapay zekâ uygulama alanları. URL: https://www.bukrek.com/insan-kaynaklarinda-yapay-zeka. (Accessed: 01.03.2024).
  • Carneiro, D., Pimenta, A., Neves, J., Novais, P. (2017). A multi-modal architecture for non-ıntrusive analysis of performance in the workplace. Neurocomputing, 231, 41-46.
  • Choi, J.-G., Ko, I., Kim, J., Jeon, Y., Han, S. (2021). machine learning framework for multi-level classification of company revenue. IEEE Access, 9, 96739-96750.
  • Choi, Y., Choi, J. W. (2021). A study of job involvement prediction using machine learning technique. International Journal of Organizational Analysis, 29 (3), 788-800.
  • Coleman, J. P. (2021). AI and our understanding of intelligence. Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference, 1, 183- 190.
  • Cui, J., Gu, Y. (2023). Application of machine learning in digital human resource management. SHS Web of Conferences, 170, 01002. EDP Sciences.
  • Curran, R., Purcell, B. (2017). Techradar: Artificial intelligence technologies, Q1 2017. A Market Research Report. URL: https://www.forrester.com/report/TechRadar+Artificial+Intelligence+Technologies+Q1+2017/-/E-RES129161#, (Accessed: 23.01.2024).
  • De Canio, S. J. (2016). Robots and humans-complements or substitutes?. Journal of Macroeconomics, 49, 280-291. DOI: https://doi.org/10.1016/j.jmacro.2016.08.003.
  • Dorel, D., Bradic-Martinovic, A. (2011). The role of information systems in human resource management. URL: https://mpra.ub.uni-muenchen.de/35286/, (Accessed: 02.03.2024).
  • Eubanks, B. (2022). Artificial intelligence for HR: Use AI to support and develop a successful workforce, (2nd Edition). London, N.Y: Kogan Page.
  • Fanni, S. C., Febi, M., Aghakhanyan, G., Neri, E. (2023). Natural Language Processing, Klontzas, M. E., Fanni, S. C. & Neri E. (Eds.). Introduction to Artificial Intelligence (1st Edition, pp. 87-99). Cham: Springer International Publishing.
  • Ghazzawi K., Accoumeh, A. (2014). Critical success factors of the e-recruitment system. Journal of Human Resources Management and Labor Studies, 2 (2), 159-170.
  • Guo, F., Gallagher, C. M., Sun, T., Tavoosi, S., Min, H. (2021). Smarter people analytics with organizational text data: Demonstrations using classic and advanced NLP models. Human Resource Management Journal, 34 (1), 39-54. DOI: 10.1111/1748-8583.12426.
  • Haenlein, M., Kaplan, A. A. (2019). Brief history of artificial intelligence: on the past, present, and future of artificial intelligence. California Management Review, 61 (4), 5-14.
  • Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future? AI, 1, 143-155.
  • Huet, E. (2016). The humans hiding behind the chatbots. URL: https://www.bloomberg.com/news/articles/2016-04-18/the-humans-hiding-behind-the-chatbots, (Accessed: 01.03.2024).
  • Jain, P. K., Jain, M., Pamula, R. (2020). Explaining and predicting employees’ attrition: A machine learning approach. SN Applied Sciences, 2, 757. DOI: 10.1007/s42452.020.2519-4.
  • Jain, S. (2017). Is artificial intelligence-the next big thing in HR (human resources)?. International Conference On Innovative Research In Science Technology And Management Modi Institute Of Management And Technology, Dadabari, Kota, Rajasthan.
  • Kang, I. G. Kim, N., Loh, W. Y., Bichelmeyer, B. A. (2021). A machine-learning classification tree model of perceived organizational performance in U.S. Federal Government Health Agencies. Sustainability, 13, 10329, 1-13.
  • Kanojia, D., Joshi, A. (2023). Applications and Challenges af SA in Real-Life Scenarios. Computational Intelligence Applications for Text and Sentiment Data Analysis, 49-80.
  • Kişi, N. (2022). İnsan kaynakları yönetiminde yapay zekâ: Bibliyometrik bir analiz. Journal of Research in Business, 7 (2), 490-514.
  • Kumar, M. R., Sharma, A., Bhargavi, Y. K., Ramesh, G. (2022, August). Human resource management using machine learning-based solutions. 3rd International Conference on Electronics and Sustainable Communication Systems, 801-806.
  • Laumer, S., Morana, S. (2022). HR natural language processing-conceptual overview and state of the art on conversational agents ın human resources management, Strohmeier, S. (Ed.). Handbook of research on artificial intelligence in human resource management (pp. 226-242). UK & USA: Edward Elgar Publishing.
  • Leslie, D., Burr, C., Aitken, M., Cowls, J., Katell, M., Briggs, M. (2021). Artificial intelligence, human rights, democracy, and the rule of law: A primer. Arxiv Preprint Arxiv:2104.04147.
  • Li, J., Zhou, Z. (2022). Design of human resource management system based on deep learning. Computational Intelligence and Neuroscience, (1), 9122881, 1-9.
  • Lin, Y., Wang, X., Xu, R. (2020). Semi-supervised human resource scheduling based on deep presentation in the cloud. EURASIP Journal on Wireless Communications and Networking, 2020 (73), 1-9.
  • Liu, Q., Wan, H., Yu, H. (2023). The application of deep learning in human resource management: a new perspective on employee recruitment and performance evaluation. Academic Journal of Management and Social Sciences, 3 (1), 101-104.
  • Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., Forghani, R. (2020). Brief history of artificial. Machine Learning and Other Artificial Intelligence Applications, An Issue of Neuroimaging Clinics of North America, E-Book: Machine Learning and Other Artificial Intelligence Applications, An Issue of Neuroimaging Clinics of North America, E-Book, 30 (4).
  • Mukherji, S. K., Forghani, R. (Eds.). Machine learning and other artificial ıntelligence applications (pp. 393-399).
  • Niu, J., Tang, W., Xu, F., Zhou, X., Song, Y. (2016). Global research on artificial intelligence from 1990–2014: Spatially-explicit bibliometric analysis. ISPRS International Journal of GeoInformation, 5 (5), 66-92. DOI: https://doi.org/10.3390/ijgi5050066.
  • Ochmann, J., Laumer, S. (2020). AI recruitment: Explaining job seekers' acceptance of automation in human resource management. Proceeding of 15th International Conference on Wirtschaftsinformatik (Zentrale Tracks), Postdam, Germany, 1633-1648.
  • OECD (2024). AI principles. URL: https://www.oecd.org/en/topics/ai-principles.html, (Accessed: 02.04.2025).
  • Patalas‐Maliszewska, J., Halikowski, D., Damasevicius, R. (2021). An automated recognition of work activity in industrial manufacturing using convolutional neural networks. Electronics, 10 (23), 2946, 1-18.
  • Perez-Campdesuner, R., De Miguel-Guzan, M., Garcia-Vidal, G., Sanchez-Rodriguez, A., Martinez-Vivar, R. (2020). Incidences of variables in labor absenteeism: An analysis of neural networks. Management and Production Engineering Review, 11 (1), 3-12.
  • Pomperada, J. R. (2022). Human resource information system with machine learning integration. Qubahan Academic Journal, 2 (2), 5-8.
  • Russell, S. J., Norvig P. (2022). Artificial Intelligence: A Modern Approach, (4th Edition). Harlow: Pearson.
  • Samoili, S., Lopez Cobo, M., Gomez Gutierrez, E., De Prato, G., Martinez-Plumed, F., Delipetrev, B. (2020). AI watch. Defining Artificial Intelligence. Luxembourg: Publications Office of the European Union. DOI: https://doi.org/10.2760/382730.
  • Sarıalai, A. D. & Yanpar, T. (2024). Öğretmen adaylarının yapay zekâ kullanım durumlarının incelenmesi. Ramazanoğlu, M. (Ed.). Dijital çağda eğitim: Yapay zekâ, yaratıcılık ve liderlik (1st Edition, pp. 29-53). Ankara: BİDGE Yayınları.
  • Shilpa, V., Gopal, R. (2011). The implications of implementing electronic-human resource management (E-HRM) systems in, companies. Journal of Information Systems and Communication, 2 (1), 10-29.
  • Shneiderman, B. (2020). Design lessons from AI’s two grand goals: Human emulation and useful applications. IEEE Transactions on Technology and Society, 1 (2), 73–82. DOI: http://doi.org/10.1109/TTS.2020.2992669.
  • Song, Y., Wu, R. (2021). Analysing human-computer interaction behaviour in human resource management system based on artificial intelligence technology. Knowledge Management Research & Practice, 1-10. DOI: 10.1080/14778.238.2021.1955630.
  • Tambe, P., Cappelli, P., Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61 (4), 15-42. DOI: https://doi.org/10.1177/0008125619867910.
  • Tiftik, C. (2021). İnsan kaynakları yönetiminde yapay zekâ teknolojileri ve uygulamaları. IBAD Sosyal Bilimler Dergisi, 9, 374-390. DOI: https://doi.org/10.21733/ibad.833256.
  • Turing, A. M. (1950). I-Computing machinery and intelligence. Mind, LIX (236), 433-460. DOI: https://doi.org/10.1093/mind/LIX.236.433.
  • UNESCO (2021). Intergovernmental meeting of experts (Category II) related to a draft recommendation on the ethics of artificial intelligence. URL: https://unesdoc.unesco.org/ark:/48223/pf0000377898, (Accessed: 02.04.2025).
  • Van Esch, P., Black, S., Feroliec, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222.
  • Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., Trichina, E. (2021). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. Artificial intelligence and international HRM, 172-201.
  • Wang, X., Zhi, J. (2021). A machine learning-based analytical framework for employee turnover prediction. Journal of Management Analytics, 8 (3), 351-370.
  • Yan, X., Deng, X., Sun, S. (2020). Analysis and simulation of the early warning model for human resource management risk based on the BP neural network. Complexity, 1, 1-11. DOI: https://doi.org/10.1155/2020/8838468.
  • Yawalkar, V. Y. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research And Analytical Review (IJRAR), 6 (1), 20-24. E-ISSN 2348-1269.
  • Yeşilkaya, N. (2022). Yapay zekâya dair etik sorunlar. Şarkiyat İlmi Araştırmalar Dergisi, 14 (3), 949-963.
  • Yuan, S., Qi, Q., Dai, E., Liang, Y. (2022). Human resource planning and configuration based on machine learning. Computational Intelligence and Neuroscience, (1), 3605722, 1-6.
  • Zhou, D. (2022). Application of data mining technology in enterprise digital human resource management. Security and Communication Networks, (1), 7611623, 1-9.

Managers' Attitudes on The Use of Artificial Intelligence Technology in Human Resources Management: A Qualitative Research in The Central District of Edirne Province

Yıl 2025, Cilt: 24 Sayı: 3, 1407 - 1438, 18.07.2025

Özgür Tezel , Agah Sinan Ünsar

https://doi.org/10.21547/jss.1574355

Öz

Endüstri 4.0'ın dünya genelinde yaygınlaşmasıyla birlikte ortaya çıkan birçok teknoloji, işletmelerin iş modellerine ve iş süreçlerine dahil edilmiştir. Türkiye'nin Avrupa'ya açılan kapısı Edirne Merkez İlçe'deki firmalar da endüstri 4.0 teknolojilerini iş süreçlerine ve iş modellerine entegre etmişlerdir. Araştırma, Türkiye'nin Avrupa'ya açılan sınır ili Edirne'de faaliyet gösteren şirket yöneticilerinin yapay zekanın İKY fonksiyonlarında kullanımına ilişkin tutumlarını ortaya çıkarmayı ve bu konuda gelecekte yapılacak araştırmalara yol gösterici olmayı amaçlamaktadır. Edirne İli Merkez İlçe'de en çok kullanılan endüstri 4.0 teknolojisinin yapay zekâ olduğu gözlemlenmiştir. Nitel yaklaşımın benimsendiği fenomenolojik desenli bu araştırmada katılımcıların görüşlerinden daha fazla faydalanmak amacıyla yarı yapılandırılmış görüşme yöntemi kullanılmıştır. Araştırmada Edirne İli Merkez İlçesindeki şirket yöneticilerinin (bazı endişelere rağmen) yapay zekanın İKY'de kullanımına yönelik destekleyici bir tutum benimsedikleri tespit edilmiştir. Yöneticiler, mevcut durumda İKY’nin tamamen YZ’ye devredilmesinin mümkün olmadığını; ancak, kavramsal konularda fayda sağlayabileceğini aktarmışlardır. Yöneticiler, etkin bir İKY için YZ ve insan faktörü birlikte kullanılmasının altını çizmişlerdir. Bu sebeple araştırmanın sonuç kısmında yapay zekâ ve insan faktörünün birlikte ele alındığı bir model önerilmiştir. Araştırma, Türkiye’nin Avrupa’ya açılan şehri Edirne’nin Merkez İlçe’de yapılması ve insan kaynakları yönetimini yapay zekâ fenomeni ile birlikte ele alınması açısından benzersiz bir değer sunmaktadır.

Anahtar Kelimeler

İnsan Kaynakları Yönetimi Yapay Zeka Dijital Dönüşüm Edirne Türkiye

Kaynakça

  • Abdul-Rahman, F. (2023). the effect of applying neural network information systems in achieving parallel processing of decisions and streamlining smart solutions for human resources: An applied study of a sample of educational leaders at al-mustansiriyah university. International Journal of Research in Social Sciences and Humanities, 13 (3), 340-350.
  • Alfawareh, H., Jusoh, S. (2019). Intelligent decision support system for CV evaluation based on natural language processing. International Journal of Advanced and Applied Sciences, 6 (4), 1-8.
  • Bukrek (2018). İnsan kaynakları departmanında yapay zekâ uygulama alanları. URL: https://www.bukrek.com/insan-kaynaklarinda-yapay-zeka. (Accessed: 01.03.2024).
  • Carneiro, D., Pimenta, A., Neves, J., Novais, P. (2017). A multi-modal architecture for non-ıntrusive analysis of performance in the workplace. Neurocomputing, 231, 41-46.
  • Choi, J.-G., Ko, I., Kim, J., Jeon, Y., Han, S. (2021). machine learning framework for multi-level classification of company revenue. IEEE Access, 9, 96739-96750.
  • Choi, Y., Choi, J. W. (2021). A study of job involvement prediction using machine learning technique. International Journal of Organizational Analysis, 29 (3), 788-800.
  • Coleman, J. P. (2021). AI and our understanding of intelligence. Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference, 1, 183- 190.
  • Cui, J., Gu, Y. (2023). Application of machine learning in digital human resource management. SHS Web of Conferences, 170, 01002. EDP Sciences.
  • Curran, R., Purcell, B. (2017). Techradar: Artificial intelligence technologies, Q1 2017. A Market Research Report. URL: https://www.forrester.com/report/TechRadar+Artificial+Intelligence+Technologies+Q1+2017/-/E-RES129161#, (Accessed: 23.01.2024).
  • De Canio, S. J. (2016). Robots and humans-complements or substitutes?. Journal of Macroeconomics, 49, 280-291. DOI: https://doi.org/10.1016/j.jmacro.2016.08.003.
  • Dorel, D., Bradic-Martinovic, A. (2011). The role of information systems in human resource management. URL: https://mpra.ub.uni-muenchen.de/35286/, (Accessed: 02.03.2024).
  • Eubanks, B. (2022). Artificial intelligence for HR: Use AI to support and develop a successful workforce, (2nd Edition). London, N.Y: Kogan Page.
  • Fanni, S. C., Febi, M., Aghakhanyan, G., Neri, E. (2023). Natural Language Processing, Klontzas, M. E., Fanni, S. C. & Neri E. (Eds.). Introduction to Artificial Intelligence (1st Edition, pp. 87-99). Cham: Springer International Publishing.
  • Ghazzawi K., Accoumeh, A. (2014). Critical success factors of the e-recruitment system. Journal of Human Resources Management and Labor Studies, 2 (2), 159-170.
  • Guo, F., Gallagher, C. M., Sun, T., Tavoosi, S., Min, H. (2021). Smarter people analytics with organizational text data: Demonstrations using classic and advanced NLP models. Human Resource Management Journal, 34 (1), 39-54. DOI: 10.1111/1748-8583.12426.
  • Haenlein, M., Kaplan, A. A. (2019). Brief history of artificial intelligence: on the past, present, and future of artificial intelligence. California Management Review, 61 (4), 5-14.
  • Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future? AI, 1, 143-155.
  • Huet, E. (2016). The humans hiding behind the chatbots. URL: https://www.bloomberg.com/news/articles/2016-04-18/the-humans-hiding-behind-the-chatbots, (Accessed: 01.03.2024).
  • Jain, P. K., Jain, M., Pamula, R. (2020). Explaining and predicting employees’ attrition: A machine learning approach. SN Applied Sciences, 2, 757. DOI: 10.1007/s42452.020.2519-4.
  • Jain, S. (2017). Is artificial intelligence-the next big thing in HR (human resources)?. International Conference On Innovative Research In Science Technology And Management Modi Institute Of Management And Technology, Dadabari, Kota, Rajasthan.
  • Kang, I. G. Kim, N., Loh, W. Y., Bichelmeyer, B. A. (2021). A machine-learning classification tree model of perceived organizational performance in U.S. Federal Government Health Agencies. Sustainability, 13, 10329, 1-13.
  • Kanojia, D., Joshi, A. (2023). Applications and Challenges af SA in Real-Life Scenarios. Computational Intelligence Applications for Text and Sentiment Data Analysis, 49-80.
  • Kişi, N. (2022). İnsan kaynakları yönetiminde yapay zekâ: Bibliyometrik bir analiz. Journal of Research in Business, 7 (2), 490-514.
  • Kumar, M. R., Sharma, A., Bhargavi, Y. K., Ramesh, G. (2022, August). Human resource management using machine learning-based solutions. 3rd International Conference on Electronics and Sustainable Communication Systems, 801-806.
  • Laumer, S., Morana, S. (2022). HR natural language processing-conceptual overview and state of the art on conversational agents ın human resources management, Strohmeier, S. (Ed.). Handbook of research on artificial intelligence in human resource management (pp. 226-242). UK & USA: Edward Elgar Publishing.
  • Leslie, D., Burr, C., Aitken, M., Cowls, J., Katell, M., Briggs, M. (2021). Artificial intelligence, human rights, democracy, and the rule of law: A primer. Arxiv Preprint Arxiv:2104.04147.
  • Li, J., Zhou, Z. (2022). Design of human resource management system based on deep learning. Computational Intelligence and Neuroscience, (1), 9122881, 1-9.
  • Lin, Y., Wang, X., Xu, R. (2020). Semi-supervised human resource scheduling based on deep presentation in the cloud. EURASIP Journal on Wireless Communications and Networking, 2020 (73), 1-9.
  • Liu, Q., Wan, H., Yu, H. (2023). The application of deep learning in human resource management: a new perspective on employee recruitment and performance evaluation. Academic Journal of Management and Social Sciences, 3 (1), 101-104.
  • Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., Forghani, R. (2020). Brief history of artificial. Machine Learning and Other Artificial Intelligence Applications, An Issue of Neuroimaging Clinics of North America, E-Book: Machine Learning and Other Artificial Intelligence Applications, An Issue of Neuroimaging Clinics of North America, E-Book, 30 (4).
  • Mukherji, S. K., Forghani, R. (Eds.). Machine learning and other artificial ıntelligence applications (pp. 393-399).
  • Niu, J., Tang, W., Xu, F., Zhou, X., Song, Y. (2016). Global research on artificial intelligence from 1990–2014: Spatially-explicit bibliometric analysis. ISPRS International Journal of GeoInformation, 5 (5), 66-92. DOI: https://doi.org/10.3390/ijgi5050066.
  • Ochmann, J., Laumer, S. (2020). AI recruitment: Explaining job seekers' acceptance of automation in human resource management. Proceeding of 15th International Conference on Wirtschaftsinformatik (Zentrale Tracks), Postdam, Germany, 1633-1648.
  • OECD (2024). AI principles. URL: https://www.oecd.org/en/topics/ai-principles.html, (Accessed: 02.04.2025).
  • Patalas‐Maliszewska, J., Halikowski, D., Damasevicius, R. (2021). An automated recognition of work activity in industrial manufacturing using convolutional neural networks. Electronics, 10 (23), 2946, 1-18.
  • Perez-Campdesuner, R., De Miguel-Guzan, M., Garcia-Vidal, G., Sanchez-Rodriguez, A., Martinez-Vivar, R. (2020). Incidences of variables in labor absenteeism: An analysis of neural networks. Management and Production Engineering Review, 11 (1), 3-12.
  • Pomperada, J. R. (2022). Human resource information system with machine learning integration. Qubahan Academic Journal, 2 (2), 5-8.
  • Russell, S. J., Norvig P. (2022). Artificial Intelligence: A Modern Approach, (4th Edition). Harlow: Pearson.
  • Samoili, S., Lopez Cobo, M., Gomez Gutierrez, E., De Prato, G., Martinez-Plumed, F., Delipetrev, B. (2020). AI watch. Defining Artificial Intelligence. Luxembourg: Publications Office of the European Union. DOI: https://doi.org/10.2760/382730.
  • Sarıalai, A. D. & Yanpar, T. (2024). Öğretmen adaylarının yapay zekâ kullanım durumlarının incelenmesi. Ramazanoğlu, M. (Ed.). Dijital çağda eğitim: Yapay zekâ, yaratıcılık ve liderlik (1st Edition, pp. 29-53). Ankara: BİDGE Yayınları.
  • Shilpa, V., Gopal, R. (2011). The implications of implementing electronic-human resource management (E-HRM) systems in, companies. Journal of Information Systems and Communication, 2 (1), 10-29.
  • Shneiderman, B. (2020). Design lessons from AI’s two grand goals: Human emulation and useful applications. IEEE Transactions on Technology and Society, 1 (2), 73–82. DOI: http://doi.org/10.1109/TTS.2020.2992669.
  • Song, Y., Wu, R. (2021). Analysing human-computer interaction behaviour in human resource management system based on artificial intelligence technology. Knowledge Management Research & Practice, 1-10. DOI: 10.1080/14778.238.2021.1955630.
  • Tambe, P., Cappelli, P., Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61 (4), 15-42. DOI: https://doi.org/10.1177/0008125619867910.
  • Tiftik, C. (2021). İnsan kaynakları yönetiminde yapay zekâ teknolojileri ve uygulamaları. IBAD Sosyal Bilimler Dergisi, 9, 374-390. DOI: https://doi.org/10.21733/ibad.833256.
  • Turing, A. M. (1950). I-Computing machinery and intelligence. Mind, LIX (236), 433-460. DOI: https://doi.org/10.1093/mind/LIX.236.433.
  • UNESCO (2021). Intergovernmental meeting of experts (Category II) related to a draft recommendation on the ethics of artificial intelligence. URL: https://unesdoc.unesco.org/ark:/48223/pf0000377898, (Accessed: 02.04.2025).
  • Van Esch, P., Black, S., Feroliec, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222.
  • Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., Trichina, E. (2021). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. Artificial intelligence and international HRM, 172-201.
  • Wang, X., Zhi, J. (2021). A machine learning-based analytical framework for employee turnover prediction. Journal of Management Analytics, 8 (3), 351-370.
  • Yan, X., Deng, X., Sun, S. (2020). Analysis and simulation of the early warning model for human resource management risk based on the BP neural network. Complexity, 1, 1-11. DOI: https://doi.org/10.1155/2020/8838468.
  • Yawalkar, V. Y. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research And Analytical Review (IJRAR), 6 (1), 20-24. E-ISSN 2348-1269.
  • Yeşilkaya, N. (2022). Yapay zekâya dair etik sorunlar. Şarkiyat İlmi Araştırmalar Dergisi, 14 (3), 949-963.
  • Yuan, S., Qi, Q., Dai, E., Liang, Y. (2022). Human resource planning and configuration based on machine learning. Computational Intelligence and Neuroscience, (1), 3605722, 1-6.
  • Zhou, D. (2022). Application of data mining technology in enterprise digital human resource management. Security and Communication Networks, (1), 7611623, 1-9.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilim ve Teknoloji Sosyolojisi ve Sosyal Bilimler
Bölüm İşletme Finans
Yazarlar

Özgür Tezel 0000-0003-2572-4980

Agah Sinan Ünsar 0000-0003-2719-8689

Yayımlanma Tarihi 18 Temmuz 2025
Gönderilme Tarihi 27 Ekim 2024
Kabul Tarihi 8 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 24 Sayı: 3

Kaynak Göster

APA Tezel, Ö., & Ünsar, A. S. (2025). Managers’ Attitudes on The Use of Artificial Intelligence Technology in Human Resources Management: A Qualitative Research in The Central District of Edirne Province. Gaziantep Üniversitesi Sosyal Bilimler Dergisi, 24(3), 1407-1438. https://doi.org/10.21547/jss.1574355
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