The new power of proactive human resources management: HR analytics and artificial intelligence (AI)
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Copyright (c) 2021 Yasemin Bal- Mert Bal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
HR Analytics is among the topics that have become increasingly important in recent years. HR Analytics, which is used to analyze the institution's human resources data, identify the problems, and determine the strategy, provides a significant competitive advantage. HR analytics enables businesses to move away from a spreadsheet-based data repository, allowing keeping data in real-time and analyzing it together with the existing data flow of the organization. With artificial intelligence methods in this field, more and more businesses are investing in HR analytics, which provides increased efficiency and savings. With the HR analytics modules developed, businesses can manage their HR processes more effectively, make better recruitment decisions, anticipate employees who intend to leave, and make workforce optimization and planning for the future more effectively. Thus, thanks to HR analytics, it becomes easier to make data-based decisions and determine strategies. Within the scope of this study, the importance of HR analytics will be examined, and it will be emphasized in which HR functions HR analytics are used, which artificial intelligence methods are used in this field and how they benefit businesses. The importance of this study is that both HR analytics and artificial intelligence are gaining an increasing trend in business. Few theoretical studies in the literature deal with HR Analytics and Artificial Intelligence and examine them in terms of their value to businesses. The study guides academics and practitioners working in the relevant field.
- Agrawal, R. & Srikant, R. (1994). Fast Algorithms for Mining Association Rules. Proceedings of the 20th VLDB Conference, Santiago, Chile.
- Ahmed, O. (2018). Artificial intelligence in HR. International Journal of Research and Analytical Reviews, 5 (4), 971-978.
- Alao, D. & Adeyemo, A.B. (2013). Analyzing employee attrition using decision tree algorithms. Computing, Information Systems and Development Informatics, 4(1), 17-28.
- Alduayj., S. S., & Rajpoot K. (2018). Predicting employee attrition using machine learning. 2018 Proceedings of International Conference on Innovations in Information Technology (IIT). IEEE, 93 - 98.
- Alpaydın, E. (2011). Yapay Öğrenme. Boğaziçi Üniversitesi Yayınları, İstanbul.
- Breiman, L. (2001). Random forests. Machine Learning. 45, 5-32.
- Cheng, X. (2020). Obtain Employee Turnover Rate and Optimal Reduction Strategy Based on Neural Network and Reinforcement Learning. Publication eprint: arXiv:2012.00583.
- Efe, M. Önder & Kaynak, O. (2000). Yapay Sinir Ağları ve Uygulamaları. Boğaziçi Üniversitesi Yayınları, İstanbul.
- El-Rayes, N., Fang, M., Smith, M. & Taylor S.M. (2020). Predicting employee attrition using tree-based models. International Journal of Organizational Analysis. 28(6), 1273 – 1291.
- Faliagka, E., Illiadis,L., Karydis, I., Rigou, M., Sioutas, S., Tsakaladis, A. & Tzimas, G. (2014). On-line consistent ranking on e-recruitment: seeking the truth behind a well formed CV. Artificial Intelligence Review, 42 (3), 515-528.
- Fitz-enz, J. (2009). Predicting people: from metrics to analytics. Employment Relations Today, 36 (3), 1-11.
- Frye, A., Boomhower, C., Smith, M., Vitovsky, L. & Fabricant, S. (2018). Employee attrition: what makes an employee quit? SMU Data Science Review. 1(1), 1 – 28.
- Gartner Analytics Maturity Model. (2021). Erişim adresi: www.gartner.com.
- Girmanova, L. & Gasparova, Z. (2018). Analysis of data on staff turnover using association rules and predictive techniques. Quality Innovation Prosperity, 22 (2), 82-95.
- Gurusinghe, R.N., Arachchige, B.J.H. & Dayarathna, D. (2021). Predictive HR analytics and talent management: a conceptual framework. Journal of Management Analytics, 8 (2), 195-221.
- Hamilton, R.H. & Sodeman, W.A. (2020). The questions we ask: opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63 (1), 85-95.
- Heuvel, V.D.S. & Bondarouk, T. (2017). The rise and fall of HR analytics. Journal of Organizational Effectiveness, People and Performance, 4 (2), 127-148.
- HR and AI: Making Human Resources More With Algorithms. (2021). Erişim adresi: https://apro-software.com/hr-and-ai/
- Jia, Q., Guo, Y., Li, R., Li, Y. & Chen, Y. (2018). A conceptual artificial intelligence application framework in human resource management. ICEB Conference Proceedings 91.
- Jurafsky, D. & Martin, J.H. (2020). An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Pearson Education, USA.
- Karimi-Majd, A.- M., Mahootchi, M. & Zakery, A. (2017). A reinforcement learning methodology for a human resource planning problem considering knowledge-based promotion. Simulation Modelling Practice and Theory. (79), 87 - 99.
- Karmanska, A. (2020). The benefits of HR analytics. Research Papers of Wroclaw University of Economics and Business, 64 (8), 30-40.
- LeCun, Y, Bengio, Y & Hinton, G. (2015). Deep learning. Nature. 521, 434-444.
- Levenson, A. & Fink, A. (2017). Human capital analytics: too much data and analysis, not enough models and business insight. Journal of Organizational Effectiveness: People and Performance, 4 (2), 145-156.
- Liu, Y. (2020). Analysis of human resource management mode and its selection factors based on decision tree algorithm. International Conference on Advance in Ambient Computing and Intelligence Proceedings. IEEE.
- Maimon, O. & Rokach, L. (2010). The Data Mining and Knowledge Discovery Handbook. Springer Science.
- Margherita, A. (2021). Human resources analytics: a systematization of research topics and directions for future research. Human Resource Management Review,
- Marler, J. & Boudreau, J. (2017). An evidence based review of HR analytics. The International Journal of Human Resource Management, 28 (1), 3-26.
- Matyunina, J. (2020). How machine learning is changing HR industry? Erişim adresi: https://codetiburon.com/machine-learning-changing-hr-industry/
- McCarthy, J. (2004). What is Artificial Intelligence? Stanford University, Computer Science Department. (www-formal.stanford.edu/jmc(whatisai.pdf).
- Merlin, R. & Jayam, R. (2018). Artificial intelligence in human resource management. International Journal of Pure and Applied Mathematics, 119 (17), 1891-1895.
- Mitchell, M.T. (1997). Machine Learning. McGraw Hill.
- Mortensen, M. J., Doherty, N.F. & Robinson, S. (2015). Operational research from taylorism to terabytes: a research agenda for the analytics age. European Journal of Operational Research, 241 (3), 583-595.
- Murgai, A. (2018). Role of artificial intelligence in transforming human resource management. International Journal of Trend In Scientific Research and Development, 2(3), 877-881.
- Narula, S. (2015). HR analytics, its use, techniques and impact. International Journal of Research in Commerce & Management, 6 (8), 47-53.
- Pağda, Z. (2018). Yapay zekâ ve insansız insan kaynakları. Harvard Business Review, 103-107.
- Pwc. (2017). Artificial intelligence in HR: a no-brainer. Erişim adresi: https://www.pwc.nl/nl/assets/documents/artificial-intelligence-in-hr-a-no-brainer.pdf.
- Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
- Russell, S. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach, Third Edition. Prentice Hall, USA.
- Setiawan, I., Suprihanto, S., Nugraha, A.C. & Hutahaean, J. (2020). HR analytics: Employee attrition analysis using logistic regression. IOP Conference Series: Mater. Sci. and Engineering. 830(3), 1 – 7.
- Sharma, A. & Sharma, T. (2017). HR analytics and performance appraisal system: a conceptual framework for employee performance improvement. Management Research Review, 40 (6), 684-697.
- Sooraska, N. (2021). A survey of using computational intelligence and artificial intelligence in human resource analytics. 7th International Conference on Engineering, Applied Sciences and Technology. 1-3 Nisan 2021, Pattaya, Tayland.
- Srivastava, K. D. & Nair, P. (2017). Employee attrition analysis using predictive techniques. Proceedings of International Conference on Information and Communication Technology for Intelligent Systems. Springer, Cham. 293 – 300.
- Strohmeier, S., & Piazza, F. (2015). Artificial intelligence techniques in human resource Management- a conceptual exploration. In Intelligent Techniques in Engineering Management, 149-172, Springer, Cham.
- Sushman, B. (2021). The beginners guide to AI in HR. Erişim adresi: https://www.toolbox.com/hr/hr-innovation/articles/the-beginners-guide-to-ai-in-hr/
- Turing A. (1950). Computing machinery and intelligence. Mind, 49, 433 – 460.
- Van Vulpen, E. (2019). The Basic Principles of People Analytics. Academy to Innovative HR, AIHR. Erişim adresi: https://www. aihr.com/resources /The_Basic_principles_of _People _Analytics.pdf
- Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York, USA.
- Writer, S. (2018). 3 Ways machine learning can transform HR. Erişim adresi: https://www.hrtechnologist.com/articles/digital-transformation/futures-at-the-door-why-machine-learning-can-transform-hr/
- Yadav, S., Jain, A. & Singh, D. (2018). Early Prediction of Employee Attrition using Data Mining Techniques. Proceedings of IEEE 8th International Advance Computing Conference (IACC), 349 – 354.
- Yang, S. & Islam, Md.T. (2020). IBM Employee Attrition Analysis. Publication eprint: arXiv:2012.01286