PREDICTIVE ANALYTICS IN HUMAN RESOURCES USING MACHINE LEARNING AND DATA MINING

Öz

Human resource management information systems (HRIS) are rapidly evolving as a result of today's technologies and global technological developments. With the digitalization of businesses, it is widely used in predictive applications in human resources (HR) and HRIS. HR and HRIS, better managing human resources data and making more accurate and reliable decisions are of critical importance for businesses. In this field, data mining and machine learning approaches are used to reveal meaningful relationships and trends between data in management decisions through predictive analysis. Both approaches are very important in the field of HR and are very effective for businesses to transform data sets into useful information. It helps businesses understand trends that can lead to more accurate and reliable business decisions by using analytical capabilities. Within the scope of this study, research was conducted on the use of the HRIS system with white-collar employees of a company in the automotive sector in Bursa. The cost, time saving and strategic impact of the human resources information system on the company and information technology infrastructure, its differences and relationships according to the department worked, age, gender and education level were investigated through statistics and data mining. Knime and SPSS Statistics programs, which are machine learning tools, were used in the research. HRIS results were evaluated and suggestions were made for future planning.

Anahtar Kelimeler

• Human Resources Management
• Human Research Information System
• Data Mining
• Machine Learning
• Automotive
• Classification
• Knime
• IBM SPSS Statistics

Kaynakça

1. 1. Kavanagh, M. J., H. G. Gueutal, and S. Tannenbaum, “Human resource systems: Development and application information.” Boston, MA: PWS-Kent, 1990.
2. 2. Kovach, K.A., Cathcart, C.E., “Human Resource Information Systems (HRIS): Providing Business with Rapid Data Access, Information Exchange and Strategic Advantage”, Public Personnel Management, Vol. 28, Pages 275-282, 1999.
3. 3. Hendrickson, A.R., “Human resource information systems: backbone technology of contemporary human resources”, Journal of Labor Research, Vol. 24, Pages 382-394, 2003.
4. 4. Aycan, Z., “The Interplay Between Cultural and Institutional/Structural Contingencies in Human Resource Management Practices,” International Journal of Human Resource Management, Vol. 16, Issue 7, Pages 1083-1119, 2005.
5. 5. Tayfur, O., “Convergence or divergence? evaluation of human resource practices in Turkey”, Journal of Economics and Behavioral Studies, Vol. 5, Issue 9, Pages 625- 638, 2013.
6. 6. Yarkın, C., “BSH ev gereçleri A.Ş.’nin insan kaynakları uygulamalarının karşılaştırıması”, Trakya Üniversitesi Sosyal Bilimler Enstitüsü İktisat Anabilim Dalı Yüksek Lisans Tezi, 2011.
7. 7. Andersen A., “2001 ’e Doğru insan kaynakları araştırması”, İstanbul: Sabah Yayıncılık A.Ş, 2000.
8. 8. Dereli, T., “Teknolojik değişmeler-çalışma ilişkileri ve yeni istihdam türleri”, İş Güç Endüstri İlişkileri ve İnsan Kaynakları Dergisi, Sayı 3, Cilt 2, 2001.

9. 9. Sözer, S., “An evaluation of current human resource management practices in the Turkish private sector”, M.S. - Master of Science, Middle East Technical University, 2004.
10. 10. Kuzeyli, H. S., “Türkiye'de insan kaynakları yönetimi. Z. Ayçan (Der.), Türkiye'de yönetim, liderlik ve insan kaynakları uygulamaları, Ankara: Türk Psikologlar Derneği Yayınları, Sayfa 163-169 2000.
11. 11. Üsdiken, B., Wasti, A., “Türkiye’de Bir İnceleme Alanı Olarak Personel veya İnsan Kaynakları Yönetimi”, 9. Ulusal Yönetim ve Organizasyon Kongresi, İstanbul Üniversitesi İşletme Fakültesi, Pages 61-63, 2001.
12. 12. Emre, C., “Yönetim Bilimi. Cumhuriyet Döneminde Türkiye’de Bilim: Sosyal Bilimler II”, Ankara: TÜBA, Pages: 35-54,1998.
13. 13. Kleynhans, R., “Human resource management”, Pearson/Prentice Hall South Africa, 2006.
14. 14. Waddill, D., Marquardt, M., “The e-HR Advantage: The Complete Handbook for Technology-enabled Human Resources”, Boston, MA: Brealey, 2011.
15. 15. Kbmanage website, “Human resources information systems definition”, “https://www.kbmanage.com/concept/human-resources-information-systems-hris, 2006”, Access Date 10 September, 2023.
16. 16. Legendre, A.M., “Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot”, Paris, “Sur la Méthode des moindres quarrés” appears as an appendix, 1805.
17. 17. Angrist, J. D., Pischke, J. S., “Mostly Harmless Econometrics: An Empiricist's Companion”, Princeton University Press, 2008.
18. 18. “KDnuggets website”, “History of data mining”, “https://www.kdnuggets.com/2020/08/top-10-lists-data-science.html.”, Access Date 12 September, 2023.
19. 19. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., “Knowledge discovery and data mining: towards a unifying framework”, In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, Pages 82–88, 1996.
20. 20. Ersöz, F., “Veri madenciliği teknikleri ve uygulamaları”, Seçkin Yayıncılık, Ankara, 2019.
21. 21. “KDnuggets website”, “10 best data mining tools”, Access Date 04.09.2021.
22. 22. Fisher, S.L., Howell, A., “Beyond user acceptance: An examination of employee reactions to information technology systems”, Hum. Resour. Manage., Vol. 43, Pages 243-258
23. 23. Doherty, N.F., King, M., “From Technical Change to Socio-technical Change: Towards a Proactive Approach to the Treatment of Organizational Issues. In: Sociotechnical and Human Cognition Elements of Information Systems”, Clarke, S. (Ed.). Idea Group Inc., USA., Pages 22-40, 2002.
24. 24. Ziebell, R., Albors, J., Schoeneberg, K. P., Perello-Marin, M.R., “Adoption and Success of e-HRM in a Cloud Computing Environment: A Field Study”, International Journal of Cloud Applications and Computing. Vol. 9, Pages 1-27, 2019.
25. 25. Rogers, E.M., “Diffusion of innovations. (5th ed.)”, The Free Press, New York, 2003.
26. 26. Marler, J.H., Boudreau, J.W., “An evidence-based review of HR Analytics”, The International Journal of Human Resource Management. Vol. 28, Issue 1. Pages 3-26, 2017.
27. 27. DeSanctis, G., “Human Resource Information Systems: A Current Assessment. MIS Quarterly”, Vol. 10, Issue 1, Pages 15-27, 1986.
28. 28. Kavanagh, M. J., Gueutal, H. G., Tannenbaum, S. I., “Human resource information systems”, Boston: PWS-Kent, 1990.
29. 29. Wang, J., “Data Mining: Opportunities and Challenges”, IGI Global Publisher, Page 484, 2003. 30. Zeebaree, R.M.S., Shukur, H., Hussan, B.K., “Human resource management systems for enterprise organizations: A review”, Periodicals of Engineering and Natural Sciences”, Vol. 7, Issue 2, Pages 660-669, 2019.
30. 31. Masum, A.K.M., Abid, F.B., Yasir, A.B.M., Beh, L.S., “Factors Influencing Practice of Human Resource Information System in Organizations: A Hybrid Approach of AHP and Dematel”, International Journal of Advanced Computer Science and Applications, Vol. 11, Pages 700-706, 2020.
31. 32. Ghazi, A., Elsayed, S., Khedr, A., “A proposed model for predicting employee turnover of information technology specialists using data mining techniques”, International journal of electrical and computer engineering systems. Vol. 12, Pages 113-121, 2021.
32. 33. Ersöz, F., Ersöz, T., “İstatistik-I”, Pages 113,180. Seçkin Yayıncılık, Ankara, Türkiye, 2020.
33. 34. Ersöz, F., Ersöz, T., “İstatistik II”, Pages 119-190, Seçkin Yayıncılık, Ankara, Türkiye, 2020.
34. 35. Ersöz, F., Ersöz, T., “SPSS ile İstatistiksel Veri Analizi”, Pages 151-188, Seçkin Yayıncılık, Ankara, Türkiye, 2019.
35. 36. Analytics Vidhya, “Understanding Random Forest”, “https://www.analyticsvidhya.com/blog /2021/06/understanding-random-forest/ Access Date 10 September, 2023.
36. 37. “KDnuggets website”, (2020). https://www.kdnuggets.com/2021/01/machine-learning-algorithms-2021.html, Access Date 10 September, 2023.
37. 38. Chamchoun, Y. “Should Data Science Be Considered as Its Own Discipline?”, https://thedatascientist.com/data-science-considered-own-discipline/.Access Date 3 March, 2023