Araştırma Makalesi
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İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz

Yıl 2025, Cilt: 13 Sayı: 1, 97 - 120, 27.03.2025
https://doi.org/10.22139/jobs.1594699

Öz

Bu çalışma, İnsan Kaynakları Yönetimi’nde yapay zekâ uygulamalarına yönelik bilimsel araştırmaların genel eğilimlerini ve entelektüel yapısını bibliyometrik analiz yöntemiyle ortaya koymayı amaçlamaktadır. Konunun akademik ve sektörel açıdan hızla önem kazanması, İKY süreçlerinde yapay zekânın etkilerini anlamaya yönelik sistematik bir değerlendirme yapılmasını gerekli kılmaktadır. Web of Science (WoS) veri tabanından elde edilen 236 araştırma verisi, R dilinde programlanmış “Bibliometrix” uygulaması kullanılarak analiz edilmiştir. Bu kapsamda, konuyla ilgili önde gelen yayınlar, yazarlar, dergiler ve ülkeler belirlenmekte, araştırma eğilimleri ortaya çıkarılmakta ve geleceğe yönelik beklentiler sunulmaktadır. Çalışmada elde edilen bulgular, konuya ilişkin araştırma ilgisinin ve bilimsel yayın sayısının son yıllarda arttığını, Çin ve ABD’nin en üretken ülkeler olduğunu göstermektedir. İşe alım süreçlerinde yapay zekâ uygulaması ve büyük veri analitiği, araştırmalarda sıklıkla kullanılan trend anahtar kavramlardır. Nesnelerin interneti (IoT) teması, konu ile ilgili en güncel araştırma eğilimini göstermektedir. Gelecek araştırmalar, yapay zekânın işe alım dışındaki diğer İKY fonksiyonları üzerindeki etkilerini gündemine almalıdır. İKY’nde yapay zekânın işe bağlılık, gig, ekonomik güvenlik, yasal görünüm ve sürdürülebilir gelişim üzerindeki etkileri gelecekteki araştırma gündemi için potansiyel çalışma konuları olarak belirlenmiştir. Çalışma, İKY’nde yapay zekâ uygulamalarına bilimsel ve sektörel açıdan ilgi duyan kişilere konunun araştırma kapsamı ve entelektüel yapısı hakkında genel bir bakış sunmaktadır.

Kaynakça

  • Abdeldayem, M. M., & Aldulaimi, S. H. (2020). Trends and opportunities of artificial ıntelligence in human resource management: Aspirations for public sector in Bahrain. International Journal of Scientific and Technology Research, 9(1), 3867-3871.
  • Acikgoz, Y., Davison, K. H., Compagnone, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28(4), 399-416.
  • Agarwal, A. (2022). AI adoption by human resource management: A study of its antecedents and impact on hr system effectiveness. Foresight, 25(1), 67-81.
  • Allal-Chérif, O., Aranega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822.
  • Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.
  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Arruda, P. L. D., Dutra, A., & Mussi, C. C. (2022). Organizational knowledge retention: International literature review. Perspectivas em Ciência da Informação, 27, 213-242.
  • Arslan, E. (2022). Sosyal bilim araştırmalarında VOSviewer ile bibliyometrik haritalama ve örnek bir uygulama. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(Özel Sayı 2), 33-56.
  • Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022). AI decision making with dignity? Cont rasting workers’ justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers, 24(3), 857-875.
  • Bhardwaj, G., Singh, S. V., & Kumar, V. (2020). An empirical study of artificial intelli gence and its impact on human resource functions. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (Pp. 47-51). IEEE.
  • Bhatt, P. (2023). AI adoption in the hiring process–important criteria and extent of AI adoption. Foresight, 25(1), 144-163.
  • Birkle, C., Pendlebury, D. A., Schnell, J., & Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363-376.
  • Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Data & Society Research Institute.
  • Bostrom, N. S. (2014). Paths, dangers, strategies. Oxford University Press.
  • Boyack, K. W., & Klavans, R. (2010). Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?. Journal of the American Society for information Science and Technology, 61(12), 2389-2404.
  • Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence challenges and opportunities for international HRM: A review and research agenda. The International Journal of Human Resource Management, 33(6), 1065-1097.
  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for information Science and Technology, 57(3), 359-377.
  • Chen, C., Ibekwe‐SanJuan, F., & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple‐perspective cocitation analysis. Journal of the American Society for information Science and Technology, 61(7), 1386-1409.
  • Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: Removing human prejudices in employment. Cognition, Technology & Work, 25(1), 135-149.
  • Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899.
  • Çeliker N., & Gürsoy, S. (2023). İşe alım sürecinde yapay zekayapay zekâ uygulamaları: Kavramsal bir inceleme. İçinde A. Kara & R. Bazancir (Eds.), Sosyal, insan ve idari bilimlerde öncü ve çağdaş çalışmalar (1. basım, ss. 785–805). Duvar Yayınları.
  • Dirik, D., Eryılmaz, İ., & Erhan, T. (2023). Post-truth kavramı üzerine yapılan çalışmaların Vosviewer ile bibliyometrik analizi. Sosyal Mucit Academic Review, 4(2), 164-188.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
  • Figueroa-Armijos, M., Clark, B. B., & Da Motta Veiga, S. P. (2023). Ethical perceptions of AI in hiring and organizational trust: The role of performance expectancy and social influence. Journal of Business Ethics, 1-19.
  • Fraij, J., & László, V. (2021). A literature review: Artificial intelligence impact on the recruitment Process. International Journal of Engineering and Management Sciences, 6(1), 108 119.
  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280.
  • Gläser, J., & Laudel, G. (2001). Integrating scientometric indicators into sociological studies: Methodical and methodological problems. Scientometrics, 52(3), 411-434.
  • Gonzalez, M. F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe, C. E., Justenhoven, R., & Martin, N. R. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior, 130, 107179.
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Artificial Intelligence in Human Resource Management: A Bibliometric Analysis on Trends, Prospects and Future Research Agenda

Yıl 2025, Cilt: 13 Sayı: 1, 97 - 120, 27.03.2025
https://doi.org/10.22139/jobs.1594699

Öz

This study aims to reveal the general trends and intellectual structure of scientific research on artificial intelligence applications in Human Resource Management through bibliometric analysis. The fact that the subject is rapidly gaining importance in academic and sectoral terms makes it necessary to make a systematic evaluation to understand the effects of artificial intelligence in HRM processes. The 236 research data obtained from the Web of Science (WoS) database were analyzed using the “Bibliometrix” application programmed in R language. In this context, leading publications, authors, journals and countries on the subject are identified, research trends are revealed and future expectations are presented. The findings of the study show that research interest and the number of scientific publications on the subject have increased in recent years, with China and the US being the most productive countries. The application of artificial intelligence in recruitment processes and big data analytics are key trending concepts frequently used in research. The Internet of Things (IoT) theme shows the most recent research trend on the topic. Future research should consider the impact of AI on other HRM functions other than recruitment. The impact of AI in HRM on work engagement, gig, economic security, legal outlook and sustainable development have been identified as potential study topics for the future research agenda. The study provides an overview of the research scope and intellectual structure of the topic for those with a scientific and industry interest in the application of AI in HRM.

Kaynakça

  • Abdeldayem, M. M., & Aldulaimi, S. H. (2020). Trends and opportunities of artificial ıntelligence in human resource management: Aspirations for public sector in Bahrain. International Journal of Scientific and Technology Research, 9(1), 3867-3871.
  • Acikgoz, Y., Davison, K. H., Compagnone, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28(4), 399-416.
  • Agarwal, A. (2022). AI adoption by human resource management: A study of its antecedents and impact on hr system effectiveness. Foresight, 25(1), 67-81.
  • Allal-Chérif, O., Aranega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822.
  • Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.
  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Arruda, P. L. D., Dutra, A., & Mussi, C. C. (2022). Organizational knowledge retention: International literature review. Perspectivas em Ciência da Informação, 27, 213-242.
  • Arslan, E. (2022). Sosyal bilim araştırmalarında VOSviewer ile bibliyometrik haritalama ve örnek bir uygulama. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(Özel Sayı 2), 33-56.
  • Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022). AI decision making with dignity? Cont rasting workers’ justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers, 24(3), 857-875.
  • Bhardwaj, G., Singh, S. V., & Kumar, V. (2020). An empirical study of artificial intelli gence and its impact on human resource functions. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (Pp. 47-51). IEEE.
  • Bhatt, P. (2023). AI adoption in the hiring process–important criteria and extent of AI adoption. Foresight, 25(1), 144-163.
  • Birkle, C., Pendlebury, D. A., Schnell, J., & Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363-376.
  • Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Data & Society Research Institute.
  • Bostrom, N. S. (2014). Paths, dangers, strategies. Oxford University Press.
  • Boyack, K. W., & Klavans, R. (2010). Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?. Journal of the American Society for information Science and Technology, 61(12), 2389-2404.
  • Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence challenges and opportunities for international HRM: A review and research agenda. The International Journal of Human Resource Management, 33(6), 1065-1097.
  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for information Science and Technology, 57(3), 359-377.
  • Chen, C., Ibekwe‐SanJuan, F., & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple‐perspective cocitation analysis. Journal of the American Society for information Science and Technology, 61(7), 1386-1409.
  • Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: Removing human prejudices in employment. Cognition, Technology & Work, 25(1), 135-149.
  • Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899.
  • Çeliker N., & Gürsoy, S. (2023). İşe alım sürecinde yapay zekayapay zekâ uygulamaları: Kavramsal bir inceleme. İçinde A. Kara & R. Bazancir (Eds.), Sosyal, insan ve idari bilimlerde öncü ve çağdaş çalışmalar (1. basım, ss. 785–805). Duvar Yayınları.
  • Dirik, D., Eryılmaz, İ., & Erhan, T. (2023). Post-truth kavramı üzerine yapılan çalışmaların Vosviewer ile bibliyometrik analizi. Sosyal Mucit Academic Review, 4(2), 164-188.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
  • Figueroa-Armijos, M., Clark, B. B., & Da Motta Veiga, S. P. (2023). Ethical perceptions of AI in hiring and organizational trust: The role of performance expectancy and social influence. Journal of Business Ethics, 1-19.
  • Fraij, J., & László, V. (2021). A literature review: Artificial intelligence impact on the recruitment Process. International Journal of Engineering and Management Sciences, 6(1), 108 119.
  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280.
  • Gläser, J., & Laudel, G. (2001). Integrating scientometric indicators into sociological studies: Methodical and methodological problems. Scientometrics, 52(3), 411-434.
  • Gonzalez, M. F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe, C. E., Justenhoven, R., & Martin, N. R. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior, 130, 107179.
  • Goodfellow, I. (2016). Deep learning. Massachusetts Institute of Technology.
  • Günbayi, I., & Sorm, S. (2018). Social paradigms in guiding social research design: The functional, interpretive, radical humanist and radical structural paradigms. Online Submission, 9(2), 57-76.
  • Gürsoy, S., & Çeliker, N. (2023). Endüstri 4.0 ve yapay zekânın insan kaynakları yönetim anlayışına etkisi. İçinde S. Kılıç (Ed.), Yapay zekâ & teori ve uygulamalar (1. basım, ss. 93–114). Nobel Bilimsel.
  • Hamouche, S., Rofa, N., & Parent-Lamarche, A. (2023). Systematic bibliometric review of artificial intelligence in human resource development: Insights for HRD researchers, practitioners and policymakers. European Journal of Training and Development.
  • Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014, January). Word cloud explorer: Text analytics based on word clouds. In 2014 47th Hawaii international conference on system sciences (pp. 1833-1842). IEEE.
  • Hemalatha, A., Kumari, P. B., Nawaz, N., & Gajenderan, V. (2021). Impact of Artificial Intelligence on Recruitment and Selection of Information Technology Companies. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (Pp. 60-66). IEEE.
  • Holloway, I., & Galvin, K. (2023). Qualitative research in nursing and healthcare. John Wiley & Sons.
  • Jatobá, M. N., Ferreira, J. J., Fernandes, P. O., & Teixeira, J. P. (2023). Intelligent human resources for the adoption of artificial intelligence: A systematic literature review. Journal of Organizational Change Management.
  • Kaur, M., Rekha, A. G., Resmi, A. G., & Gandolfi, F. (2023). Research on artificial intelligence in human resource management: Trends and prospects. Global Journal of Management and Business Research: An Administration and Management, 23(5), 31-46.
  • Kaushal, N., Kaurav, R. P. S., Sivathanu, B., & Kaushik, N. (2023). Artificial intelligence and HRM: Identifying future research Agenda using systematic literature review and bibliometric analysis. Management Review Quarterly, 73(2), 455-493.
  • Kelan, E. K. (2023). Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring. Human Resource Management Journal, 1- 14.
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  • Laviola, F., Cucari, N., & Novic, H. (2023). Human resource development and artificial ıntelligence in the view of personal development: A literature review and bibliometric analysis. Rediscovering local roots and interactions in management, (Electronic Conference Proceedings-Long Papers), 347-372.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
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  • Maity, S. (2019). Identifying opportunities for artificial intelligence in the evolution of training and development practices. Journal of Management Development, 38(8), 651-663.
  • Mathushan, P., Gamage, A. S., & Wachissara, V. (2023). Human resource management and artificial intelligence: A bibliometric exploration. Journal of Business Research and Insights (former Vidyodaya Journal of Management), 9(I).
  • McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI magazine, 27(4), 12-12.
  • Mitchell, T. M., & Mitchell, T. M. (1997). Machine learning (Vol. 1, No. 9). McGraw-hill.
  • Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la Información/Information Professional, 29(1).
  • Morshidi, A. B., Satar, N. S. M., Azizan, A. A. D. A., Idris, R. Z., Idris, R., Radzi, M. S. M., ... & Sarjono, F. (2024). A bibliometric analysis of artificial intelligence and human resource management studies. In Exploring the intersection of AI and human resources management (pp. 85–117). IGI Global.
  • Oswal, N., Khaleeli, M., & Alarmoti, A. (2020). Recruitment in the era of Industry 4.0: Use of artificial intelligence in recruitment and its impact. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(8), 39-47.
  • Palos-Sánchez, P. R., Baena-Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence, 36(1), 2145631.
  • Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125- 1147.
  • Paul, J., A. Merchant, Y. K. Dwivedi, & G. Rose. (2021). Writing an impactful review article: What do we know and what do we need to know? Journal of Business Research, 133, 337–40.
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  • Qamar, Y., R. K. Agrawal, T. A. Samad, & C. J. Chiappetta Jabbour. 2021. When technology meets people: The interplay of artificial intelligence and human resource management. Journal of Enterprise Information Management, 34(5):1339–70.
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  • Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. International Journal of Human Resource Management, 33(6), 1237–1266.
  • Web of Science Group Core Collection (2024), Clarivate analytics, https://clarivate.com/products/scientific-and-academic-research/research-discovery-and-workflow-solutions/webofscience-platform/web-of-science-core-collection/
  • Wijaya, E. F., & Qamari, I. N. (2024). Analysis of research on artificial intelligence in human resources management: A bibliometric analysis. International Research Journal of Multidisciplinary Scope, 5(2), 108-121.
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  • Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472.
Toplam 75 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme , İş Sistemleri (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Nuri Çeliker 0000-0002-3865-5489

Sergen Gürsoy 0000-0002-9032-2999

Erken Görünüm Tarihi 20 Mart 2025
Yayımlanma Tarihi 27 Mart 2025
Gönderilme Tarihi 2 Aralık 2024
Kabul Tarihi 10 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 1

Kaynak Göster

APA Çeliker, N., & Gürsoy, S. (2025). İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz. İşletme Bilimi Dergisi, 13(1), 97-120. https://doi.org/10.22139/jobs.1594699
AMA Çeliker N, Gürsoy S. İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz. About the Journal. Mart 2025;13(1):97-120. doi:10.22139/jobs.1594699
Chicago Çeliker, Nuri, ve Sergen Gürsoy. “İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler Ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz”. İşletme Bilimi Dergisi 13, sy. 1 (Mart 2025): 97-120. https://doi.org/10.22139/jobs.1594699.
EndNote Çeliker N, Gürsoy S (01 Mart 2025) İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz. İşletme Bilimi Dergisi 13 1 97–120.
IEEE N. Çeliker ve S. Gürsoy, “İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz”, About the Journal, c. 13, sy. 1, ss. 97–120, 2025, doi: 10.22139/jobs.1594699.
ISNAD Çeliker, Nuri - Gürsoy, Sergen. “İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler Ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz”. İşletme Bilimi Dergisi 13/1 (Mart 2025), 97-120. https://doi.org/10.22139/jobs.1594699.
JAMA Çeliker N, Gürsoy S. İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz. About the Journal. 2025;13:97–120.
MLA Çeliker, Nuri ve Sergen Gürsoy. “İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler Ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz”. İşletme Bilimi Dergisi, c. 13, sy. 1, 2025, ss. 97-120, doi:10.22139/jobs.1594699.
Vancouver Çeliker N, Gürsoy S. İnsan Kaynakları Yönetimi’nde Yapay Zekâ: Trendler, Beklentiler ve Gelecek Araştırma Gündemi Üzerine Bibliyometrik Bir Analiz. About the Journal. 2025;13(1):97-120.