İNSAN KAYNAKLARI YÖNETİMİNDE YAPAY ZEKA: BİBLİYOMETRİK BİR ANALİZ

Yıl 2022, Cilt: 7 Sayı: 2, 490 - 514, 26.12.2022

Nermin Kişi

https://doi.org/10.54452/jrb.1113164

Cited By: 1

Öz

Bu çalışmanın amacı, 1998-2021 yılları arasında Web of Science (WoS) veri tabanında indekslenen İnsan Kaynakları Yönetimi’nde (İKY) yapay zeka konusundaki bilimsel araştırmaları bibliyometrik analiz yöntemiyle incelemektir. Performans analizi ve entelektüel yapı analizi uygulanan çalışmada konuyla ilgili önde gelen ülkeler, yazarlar, dergiler ve yayınlar belirlenmekte, araştırma eğilimleri ortaya çıkarılmakta ve geleceğe yönelik beklentiler sunulmaktadır. Araştırmanın temel bulguları İKY’de yapay zeka konusuyla ilgili bilimsel üretimin son yıllarda arttığını, Çin ve ABD’nin en üretken ülkeler olduğunu, makalelerin çoğunlukla Elsevier ve Emerald yayınevlerine ait dergilerde yayınlandığını göstermektedir. Makalelerde en çok ortak atıf yapılan dergi Expert Systems with Applications ve en çok ortak atıf yapılan yazar elektronik İKY konusunda yaptığı çalışmalarla tanınan Stefan Strohmeier’dir. Ayrıca, yapay zeka teknolojisinin personel seçimi, işe alma, performans analizi ve çalışan devrinin tahmini gibi çeşitli işlevlerde kullanıldığı saptanmıştır. Araştırma alanının geliştirilmesi için yapay zekanın kariyer yönetimi, ödül yönetimi, ücret yönetimi ve Yeşil İKY üzerindeki etkileri potansiyel araştırma konuları olarak önerilebilir.

Anahtar Kelimeler

İnsan kaynakları yönetimi, yapay zeka, bibliyometrik analiz

ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT: A BIBLIOMETRIC ANALYSIS

Öz

The aim of this study is to examine scientific research with the bibliometric analysis method on artificial intelligence in Human Resources Management (HRM) indexed in the Web of Science (WoS) database between 1998 and 2021. Leading countries, authors, journals and publications related to the topic are determined, research trends are revealed and future prospects are presented in the study where performance analysis and intellectual structure analysis are applied. The main findings of the research show that scientific production on artificial intelligence in HRM has increased in recent years, China and USA are the most productive countries, articles are mostly published in journals belonging to Elsevier and Emerald publishing houses. The most commonly cited journal in articles is Expert Systems with Applications, and the most commonly cited author is Stefan Strohmeier, who is known for his studies on electronic HRM. In addition, it has been determined that artificial intelligence technology is used in various functions, such as personnel selection, recruitment, performance analysis and employee turnover forecasting. The effects of artificial intelligence on career management, reward management, compensation management and Green HRM can be suggested as potential research topics for the development of the research field.

Anahtar Kelimeler

Human resource management, artificial intelligence, bibliometric analysis

Kaynakça

• Abubakar, A. M., Namin, B. H., Harazneh, I., Arasli, H., & Tunc, T. (2017). Does gender moderates the relationship between favoritism/nepotism, supervisor incivility, cynicism and workplace withdrawal: A neural network and SEM approach. Tourism Management Perspectives, 23, 129-139.
• Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In D. Card & O. Ashenfelter (Eds.), Handbook of Labor Economics, 4, (pp. 1043-1171). USA & Netherlands: Elsevier.
• Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244.
• 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.
• Carneiro, D., Pimenta, A., Neves, J., & Novais, P. (2017). A multi-modal architecture for non-intrusive analysis of performance in the workplace. Neurocomputing, 231, 41-46.
• Choi, Y. & Choi, J. W. (2021). The prediction of workplace turnover using machine learning technique. International Journal of Business Analytics, 8(4), 1-10.
• 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.
• Dabirian, A., Kietzmann, J., & Diba, H. (2017). A great place to work!? Understanding crowdsourced employer branding. Business Horizons, 60(2), 197-205.
• De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for big data professions: A systematic classification of job roles and required skill sets. Information Processing & Management, 54(5), 807-817.
• Dickson, D. R. & Nusair, K. (2010). An HR perspective: The global hunt for talent in the digital age. Worldwide Hospitality and Tourism Themes, 2(1), 86-93.
• Eubanks, B. (2022). Artificial intelligence for HR: Use AI to support and develop a successful workforce. (2nd Ed.). London, N.Y: Kogan Page.
• European Commission, (2020). European skills agenda for sustainable competitiveness, social fairness and resilience. Erişim Adresi: https://ec.europa.eu/migrant-integration/sites/default/files/2020-07/SkillsAgenda.pdf
• Fareri, S., Fantoni, G., Chiarello, F., Coli, E., & Binda, A. (2020). Estimating Industry 4.0 impact on job profiles and skills using text mining. Computers in Industry, 118, 103222, 1-19.
• 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.
• Garg, V., Srivastav, S., & Gupta, A. (2018). Application of artificial intelligence for sustaining green human resource management. 2018 International Conference on Automation and Computational Engineering (ICACE), (pp. 113-116). Piscataway, NJ: IEEE.
• Garg, S., Sinha, S., Kar, A.K., & Mani, M. (2021). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, doi:10.1108/IJPPM-08-2020-0427.
• Gu, Z., Meng, F., & Farrukh, M. (2021). Mapping the research on knowledge transfer: A scientometrics approach. IEEE Access, 9, 34647-34659.
• Guest, D. E. (1997). Human resource management and performance: A review and research agenda. The International Journal of Human Resource Management, 8(3), 263-276.
• Haenlein, M. & Kaplan, A. A (2019). Brief history of artificial ıntelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th Ed.). Upper Saddle River, NJ, United States: Prentice Hall.
• Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106-121.
• 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.
• Herrera-Franco, G., Montalvan-Burbano, N., Carrion-Mero, P., Jaya-Montalvo, M., & Gurumendi-Noriega, M. (2021). Worldwide research on geoparks through bibliometric analysis. Sustainability, 13, 1175, 1-32.
• Hooper, R. S., Galvin, T. P., Kilmer, R. A., & Liebowitz, J. (1998). Use of an expert system in a personnel selection process. Expert Systems with Applications, 14(4), 425-432.
• Huang, M-J., Tsou, Y.-L., & Lee, S-C. (2006). Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 19(6), 396-403.
• Ivanov, S., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27/28, 1501-1517.
• Kaushal, N., Kaurav, R. P. S., Sivathanu, B., & Kaushik, N. (2021). Artificial intelligence and HRM: Identifying future research agenda using systematic literature review and bibliometric analysis. Management Review Quarterly, doi:10.1007/s11301-021-00249-2
• Laumer, S. & Morana, S. (2022). HR natural language processing - Conceptual overview and state of the art on conversational agents in human resources management. In S. Strohmeier (Ed.), Handbook of research on artificial intelligence in human resource management (pp. 226-242). UK & USA: Edward Elgar Publishing.
• Majumder, S. & Mondal, A. (2021). Are chatbots really useful for human resource management?. International Journal of Speech Technology, 24, 969-977.
• Ogbeibu, S., Chiappetta Jabbour, C. J., Burgess, J., Gaskin, J., & Renwick, D .W. S. (2022). Green talent management and turnover intention: The roles of leader STARA competence and digital task interdependence. Journal of Intellectual Capital, 23(1), 27-55.
• Pendharkar, P. C. & Rodger, J. A. (2003). Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption. Decision Support Systems, 36(1), 117-136.
• Shahhosseini, V. & Sebt, M. H. (2011). Competency-based selection and assignment of human resources to construction projects. Scientia Iranica, 18(2), 163-180.
• Singer, G. & Cohen, I. (2020). An objective-based entropy approach for ınterpretable decision tree models in support of human resource management: The case of absenteeism at work. Entropy, 22(8), 821, 1-14.
• Stavrou, E. T., Charalambous, C., & Spiliotis, S. (2007). Human resource management and performance: A neural network analysis. European Journal of Operational Research, 181(1), 453-467.
• Strohmeier, S. (2007). Research in e-HRM: Review and implications. Human Resource Management Review, 17(1), 19-37.
• Strohmeier, S. (2009). Concepts of e-HRM consequences: A categorisation, review and suggestion. The International Journal of Human Resource Management, 20(3), 528-543.
• Strohmeier, S. & Piazza, F. (2013). Domain driven data mining in human resource management: A review of current research. Expert Systems with Applications, 40(7), 2410-2420.
• 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.
• 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. The International Journal of Human Resource Management, doi: 10.1080/09585192.2020.1871398
• World Economic Forum (2021). Human-centred artificial intelligence for human resources: A toolkit for human resources professionals. Erişim adresi: https://www3.weforum.org/docs/WEF_Human_Centred_Artificial_Intelligence_for_Human_Resources_2021.pdf
• Wright, P. M. & Snell, S. A. (1991). Toward an integrative view of strategic human resource management. Human Resource Management Review, 1(3), 203-225.
• Yuegang, S. & Ruibing, W. (2021). Analysing human-computer interaction behaviour in human resource management system based on artificial intelligence technology. Knowledge Management Research & Practice, doi: 10.1080/14778238.2021.1955630