The Prediction of Employee Turnover and its Personal and Organizational Determinants Using Machine Learning Methods

Khandan, Abbas; Mohammadi, Soheila

doi:10.22059/jte.2025.380061.1008925

The Prediction of Employee Turnover and its Personal and Organizational Determinants Using Machine Learning Methods

Document Type : Research Paper

Authors

Faculty of Economics, University of Kharazmi, Tehran, Iran.

10.22059/jte.2025.380061.1008925

Abstract

This study analyzing the chain stores’ personnel data intends to predict the employees’ turnover rate and examine its personal and organizational (occupational) determining factors. A dataset of 17542 records including information of personnel's activity status (active or quit) and 12 personal and occupational characteristics in the period of November 2018 to 2014 are used in machine learning algorithms of decision tree and support vector machine (SVM) to predict the workforce turnover rate across the country in a pilot chain store. The results show that job characteristics have a greater effect on employees’ turnover. Specifically, among different characteristics, four factors of personnel transfers, their organizational position (line or staff), years of service and working hours have the most importance and influence on job turnover. Among the individual characteristics, it was also observed that job quit is more prevalent among young people with less than 30 years old. Based on these characteristics, support vector machine (SVM) model with 91% accuracy and F1-score above 90% and decision tree algorithm with 83% accuracy and F1-score have shown a good performance in classification and prediction of employees’ turnover. Based on personal and job characteristics and using data mining and machine learning methods, organizations can set policies to preserve human resources, which will reduce costs and also maintain their competitive advantages necessary for the progress and development of the company.

Keywords

Main Subjects

References

امیری، قاسم، و محمودزاده، سیدمجتبی. (1394). بررسی عوامل مؤثر بر کاهش ترک خدمت کارکنان در سازمان‌های دولتی ایران (مطالعه موردی: ستاد وزارت راه و شهرسازی). مدیریت فرهنگ سازمانی، 13(2)، 559-579.

پیرایش، رضا، محمدی، مهدی خان، و بادفر، عمران. (1399). عوامل مؤثر بر قصد ترک خدمت کارکنان و تأثیر آن بر کارایی کارکنان شرکت زرین روی زنجان. مطالعات کاربردی در علوم مدیریت و توسعه، 5(1)، 7-19.

چشم براه، الهام. (1397). بررسی عوامل مؤثر بر ترک خدمت در بین کارکنان (پایان‌نامه کارشناسی ارشد). دانشگاه آزاد اسلامی واحد بندرعباس، بندرعباس.

دباشی، فرزانه، نوری، ابوالقاسم، عریضی، حمیدرضا، و دیباجی، سیدمیثم. (1395). پیش‌بینی تمایل به ترک شغل کارکنان بر اساس عوامل فردی، شغلی و سازمانی. دانش و پژوهش در روان‌شناسی کاربردی، 17(2)، 45-54.

دیهیم پور، مهدی، و دولتی، حسن. (1396). تأثیر عوامل ایجادکننده ترومای سازمانی برمیزان ترک خدمت کارکنان نظامی. پژوهش‌های مدیریت منابع انسانی، 9(4)، 81-106.

زبانی شادباد، محمدعلی، حسنی، محمد، قاسم‌زاده علیشاهی، ابوالفضل، و قاسم‌زاده، ابوالفضل. (1396). رابطه درگیری شغلی و تناسب شغلی با اخلاق حرفه‌ای و تمایل به ترک خدمت. اخلاق در علوم و فناوری، 12(2)، 77-84.

طالقانی، غلامرضا، عبدالملکی، جمال، و غفاری، علی. (1397). بررسی عوامل فردی مؤثر بر قصد ترک شغل کارکنان اداره کل آموزش و پرورش استان کردستان. مدیریت دولتی، 8(1)، 219-232.

عابدین، بهاره، اکبری، شهناز، و عباس‌نژاد، طیبه. (1399). طراحی مدل مفهومی ترک خدمت افراد مستعد از سازمان‌های ایران: پژوهشی اکتشافی در صنعت ICT. مدیریت منابع انسانی پایدار، 2(2)، 49-64.

عارفی، صدیقه، سرخوش، سمانه، و رئوفی، سمیرا. (1398). تبیین عوامل مؤثر بر ترک شغل از دیدگاه پرستاران: یک مطالعه کیفی. پژوهش‌های سلامت محور، 5(1)، 13-28.

علیرحیمی، محمدمهدی، امیرخانی، امیرحسین، و رسولی، رضا. (1396). طراحی مدل کاهش ترک خدمت سازمانی (مورد مطالعه: شرکت پایانه‌های نفتی ایران). مدیریت منابع انسانی در صنعت نفت، 9(34)، 53-82.

هاشم زهی، ریحانه، نجف‌بیگی، رضا، و ذبیحی، محمدرضا. (1400). طراحی مدل ترک خدمت کارکنان دانشی در شرکت پخش فراورده‌های نفتی خراسان رضوی. مدیریت منابع انسانی در صنعت نفت، 12(47)، 22-39.

Abedin, B., Akbari Emami, S., & Abbasnejad, T. (2020). Designing a Conceptual Model of Talent Turnover among Iranian Organizations: An Exploratory Research in ICT industry. Journal of Sustainable Human Resource Management, 2(2), 49-64. [In Persian]

Al Akasheh, M., Faisal Malik, E., Hujran, O., & Zaki, N. (2024). A Decade of Research on Machine Learning Techniques for Predicting Employee Turnover: A Systematic Literature Review. Expert Systems with Applications, 238, Retrieved from https://doi.org/10.1016/j.eswa.2023.121794

Alirahimi M. M., Amirkhani A., & Rasouli R. (2018). Designing a Model for Turnover Reduction in Iranian Oil Terminals Company (IOTC). Strategic Studies in the Oil and Energy Industry, 9(34), 53-82. [In Persian]

Alsagheer, R. H., Alharan, A. F., & Al-Haboobi, A. S. (2017). Popular Decision Tree Algorithms of Data Mining Techniques: A Review. International Journal of Computer Science and Mobile Computing, 6(6), 133-142.

Amiri, G. and Mahmoudzadeh, S. M. (2015). The Examination Factors Affecting Reduce of Employees Turnover in the Iranian Public Organizations. Case Study: Ministry of Road and Urban Development (Center Staff). Organizational Culture Management, 13(2), 559-579. [In Persian]

Arefi S., Sarkhosh S., & Raoofi S. (2019). Explanation of the Effective Factors of Job Leave from Nurses' Point of View: A Qualitative Study. Health Based Research, 5(1), 13-28. [In Persian]

Arokiasamy, A. R. A. (2013). A Qualitative Study on Causes and Effects of Employee Turnover in the Private Sector in Malaysia. Middle-East Journal of Scientific Research, 16(11), 532-1541.

Bassok, D., Markowitz, A. J., Bellows, L., & Sadowski, K. (2021). New Evidence on Teacher Turnover in Early Childhood. Educational Evaluation and Policy Analysis, 43(1), 172-180.

Brownlee, J. (2020) Data Preparation for Machine Learning-Data Cleaning, Feature Selection and Data Transformation in Python: Machine Learning Mastery. Retrived from https://www.machinelearningmastery.com/data-preparation-for-machine-learning/

Chauhan, V. K., Dahiya, K., & Sharma, A. (2019). Problem Formulations and Solvers in Linear SVM: A Review. Artificial Intelligence Review, 52(2), 803-855.

Cheshmbarah, E. (2018). Study of Factors Affecting Employee Turnover (Master's Thesis). Islamic Azad University, Bandar Abbas. [In Persian]

Chowdhury, S., Joel-Edgar, S., Dey, P. K., Bhattacharya, S., & Kharlamov, A. (2022). Embedding Transparency in Artificial Intelligence Machine Learning Models: Managerial Implications on Predicting and Explaining Employee Turnover. The International Journal of Human Resource Management, 34(14), 2732–2764.

Dabbashi, F., Nouri, A., Oreyzi, H., & Dibaji, S. M. (2015). Predicting Employees' Turnover Intention by Individual, Occupational and Organizational Factors. Knowledge & Research in Applied Psychology, 17(2), 45-54. [In Persian]

Dulaty, H., & deyhimpuor, M. (2018). An Assessment of Factors Affecting Organizational Trauma on Leaving Service by Military Personnel. Journal of Research in Human Resources Management, 9(4), 81-106. [In Persian]

Dunegan, K. (1993). Framing, Cognitive Modes, and Image Theory: Toward an Understanding of a Glass Half Full, Journal of Applied Psychology, 78(3), 491-503.

Elaine, M. (1997). Job Tenure Shift for Men and Women. HR Magazine, 42(50), 1-20.

Hashemzehi R., Najafbeygi R., & Zabihi M. (2021). Designing the Model of Knowledge Workers Turnover in Khorasan Razavi Oil Products Distribution Company. Strategic Studies in the Oil and Energy Industry, 12(47), 22-39. [In Persian]

Healy, M. C., Lehman, M., & McDaniel, M. A. (1995). Age and Voluntary Turnover: A Quantitative Review. Personnel Psychology, 48(2), 335–345.

Marsh, R. M., & Mannari, H. (1977). Organizational Commitment and Turnover: A Prediction Study. Administrative Science Quarterly, 22(1), 57–75.

McDermid, F., Mannix, J., & Peters, K. (2020). Factors Contributing to High Turnover Rates of Emergency Nurses: A Review of the Literature. Australian Critical Care, 33(4), 390-396.

Park, J., Feng, Y. & Jeong, SP. Developing an Advanced Prediction Model For New Employee Turnover Intention Utilizing Machine Learning Techniques. Scientific Reports, 14, Retrieved from https://doi.org/10.1038/s41598-023-50593-4

Phillips, D. R., & Roper, K. O. (2009). A Framework for Talent Management in Real Estate. Journal of Corporate Real Estate, 11(1), 7-16.

Pirayesh, R., Mohammadi, M., & Badfar, I. (2019). Factors Affecting Employees' Intention to Leave and Its Impact on Employee Performance in Zarrin Roy Zanjan Company. Applied Studies in Management and Development Sciences, 5(1), 7-19. [In Persian]

Rokach, L., & Maimon, O. Z. (2014). Data Mining With Decision Trees: Theory and Applications. Singapore: World Scientific Publishing Co.

Summers, T. P., & Hendrix, W. H. (1991). Modelling the Role of Pay Equity Perceptions: A Field Study. Journal of Occupational Psychology, 64, 145-157

Taleghani, G., Abdolmaleki, J. and Ghafari, A. (2016). The Investigation of the Individual Factors on Turnover Intention of Employees in Education Administration of Kurdistan Province. Journal of Public Administration, 8(1), 219-232. [In Persian]

Waldman, J. D., Kelly, F., Aurora, S., & Smith, H. (2004). The Shocking Cost of Turnover in Health Care, Health Care Management Review, 29(1), 2-7.

Yun, M. R., & Yu, B. (2021). Strategies for Reducing Hospital Nurse Turnover in South Korea: Nurses' Perceptions and Suggestions. Journal of Nursing Management, 29(5), 1256-1262.

Zabani Shadabad, M. A., Hassani, M., & Ghasemzadeh, A. (2017). The Relationship between Job Engagement & Job Propriety with Professional Ethics & Intent to Leave. Ethics in Science and Technology, 12 (2). 77-84. [In Persian]

Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2018). Employee Turnover Prediction with Machine Learning: A Reliable Approach. In Proceedings of SAI Intelligent Systems Conference. Cham: Springer.