Akila, R. (2012). A study on employee retention among executives at BGR Energy Systems Ltd, Chennai.
International Journal of Marketing, Financial Services & Management Research, 1(9), 18–32.
Alao, D. A. B. A., & Adeyemo, A. B. (2013). Analyzing employee attrition using decision tree algorithms. Computing, Information Systems, Development Informatics and Allied Research Journal, 4(1), 17–28.
Alduayj, S. S., & Rajpoot, K. (2018). Predicting employee attrition using machine learning. In Proceedings of the 2018 13th International Conference on Innovations in Information Technology (IIT 2018) (pp. 93–98). IEEE. https://doi.org/10.1109/INNOVATIONS.2018.8605976
Alizadeh, M., Baoosh, M., & Rahimi, A. (2022). One hundred years of human resource management progress at three levels in the world, Iran and municipality of Tehran.
International Journal of Human Capital in Urban Management, 7(1), 125–142.
https://doi.org/10.22034/IJHCUM.2022.01.10
Alsheref, F. K., Fattoh, I. E., & Ead, W. M. (2022). Automated prediction of employee attrition using ensemble model based on machine learning algorithms.
Computational Intelligence and Neuroscience, 2022, Article 7728668.
https://doi.org/10.1155/2022/7728668
Amin, F. A. B. M., Mokhtar, N. M., Ibrahim, F. A. B., & Nordin, M. N. B. (2021). A review of the job satisfaction theory for special education perspective.
Turkish Journal of Computer and Mathematics Education, 12(11), 5224–5228.
https://doi.org/10.17762/turcomat.v12i11.6737
Anantharaja, A. (2009). Causes of attrition in BPO companies: Study of a mid-size organization in India.
The IUP Journal of Management Research, 8(11), 13–27.
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.
https://doi.org/10.1111/1748-8583.12090
Avrahami, D., Pessach, D., Singer, G., & Chalutz Ben-Gal, H. (2022). A human resources analytics and machine-learning examination of turnover: Implications for theory and practice.
International Journal of Manpower, 43(6), 1405–1424.
https://doi.org/10.1108/IJM-12-2020-0548
Banerjee, S., & Guha, S. (2010). Employee attrition in engineering firms: Case study of DCIPS Pvt. Ltd., India. In
Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management.
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization.
Journal of Machine Learning Research, 13(10), 281–305.
Boushey, H., & Glynn, S. J. (2012). There are significant business costs to replacing employees.
Center for American Progress,
16, 1-9.
https://www.americanprogress.org/article/there-are-significant-business-costs-to-replacing-employees/
Cai, X., Shang, J., Jin, Z., Liu, F., Qiang, B., Xie, W., & Zhao, L. (2020). DBGE: Employee turnover prediction based on dynamic bipartite graph embedding.
IEEE Access, 8, 10390–10402.
https://doi.org/10.1109/ACCESS.2020.2965544
Cascio, W. F., & McEvoy, G. (2006).
Managing human resources: Productivity, quality of work life, profits. McGraw-Hill/Irwin.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique.
Journal of Artificial Intelligence Research, 16, 321–357.
https://doi.org/10.1613/jair.953
Chowdhury, S., Joel-Edgar, S., Dey, P. K., Bhattacharya, S., & Kharlamov, A. (2023). 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.
https://doi.org/10.1080/09585192.2022.2066981
Chung, D., Yun, J., Lee, J., & Jeon, Y. (2023). Predictive model of employee attrition based on stacking ensemble learning.
Expert Systems with Applications, 215, Article 119364.
https://doi.org/10.1016/j.eswa.2022.119364
Colomo-Palacios, R., Casado-Lumbreras, C., Misra, S., & Soto-Acosta, P. (2014). Career abandonment intentions among software workers.
Human Factors and Ergonomics in Manufacturing & Service Industries, 24(6), 641–655.
https://doi.org/10.1002/hfm.20509
Conlon, S. J., Simmons, L. L., & Liu, F. (2021). Predicting tech employee job satisfaction using machine learning techniques.
International Journal of Management & Information Technology, 16, 72–88.
https://doi.org/10.24297/ijmit.v16i.9072
Das, B. L., & Baruah, M. (2013). Employee retention: A review of literature.
Journal of Business and Management, 14(2), 8–16.
DeNisi, A. S., & Griffin, R. W. (2005).
Human resource management. Dreamtech Press.
Denton, J. W. (2000). Using web-based projects in a systems design and development course.
Journal of Computer Information Systems, 40(3), 84–87.
https://doi.org/10.1080/08874417.2000.11647459
Dessler, G., Cole, N. D., & Chhinzer, N. (2015).
Management of human resources: The essentials (pp. 1-385). London: Pearson.
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.
https://doi.org/10.1108/IJOA-10-2019-1903
Fallucchi, F., Coladangelo, M., Giuliano, R., & William De Luca, E. (2020). Predicting employee attrition using machine learning techniques.
Computers, 9(4), Article 86.
https://doi.org/10.3390/computers9040086
French, W. L. (1978).
The personnel management process: Human resources administration and development (4th ed.). Houghton Mifflin.
Frye, A., Boomhower, C., Smith, M., Vitovsky, L., & Fabricant, S. (2018). Employee attrition: What makes an employee quit?
SMU Data Science Review, 1(1), Article 9.
https://scholar.smu.edu/datasciencereview/vol1/iss1/9
Gberevbie, D. E. (2008). Employee retention strategies and organizational performance. IFE Psychologia: An International Journal, 16(2), 148-173.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees.
Machine Learning, 63(1), 3–42.
https://doi.org/10.1007/s10994-006-6226-1
Han, J., Kamber, M., & Pei, J. (2012).
Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.
Hashemifard, S., & Okhravi, A. (2025). Investigating the effect of organizational commitment and organizational justice perception on knowledge sharing motivation.
System Engineering and Productivity, 5(1), 93–111.
https://doi.org/10.22034/sep.2025.2046866.1239
Hastie, T., Tibshirani, R., & Friedman, J. (2009).
The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
https://doi.org/10.1007/978-0-387-84858-7
Heneman, H. G., III, Schwab, D. P., Fossum, J. A., & Dyer, L. (1989).
Personnel/human resource management (4th ed.). Irwin.
Hom, P. W., & Griffeth, R. W. (1991). Structural equations modeling test of a turnover theory: Cross-sectional and longitudinal analyses.
Journal of Applied Psychology, 76(3), 350–366.
https://doi.org/10.1037/0021-9010.76.3.350
Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research.
Journal of Applied Psychology, 102(3), 530–545.
https://doi.org/10.1037/apl0000103
Hosseini, S. A., & Moslemi, F. (2023). Investigating the motivational prerequisites of employees' innovative behavior (Case study: Tehran Province Social Security Organization).
System Engineering and Productivity, 2(4), 23–44.
https://doi.org/10.22034/sep.2023.704328
Jain, N., Tomar, A., & Jana, P. K. (2021). A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning.
Journal of Intelligent Information Systems, 56(2), 279–302.
Kang, I. G., Croft, B., & Bichelmeyer, B. A. (2021). Predictors of turnover intention in U.S. federal government workforce: Machine learning evidence that perceived comprehensive HR practices predict turnover intention.
Public Personnel Management, 50(4), 538–558.
https://doi.org/10.1177/0091026020977562
Lazzari, M., Alvarez, J. M., & Ruggieri, S. (2022). Predicting and explaining employee turnover intention.
International Journal of Data Science and Analytics, 14, 279–292.
https://doi.org/10.1007/s41060-022-00329-w
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In
Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297). University of California Press.
Maertz, C. P., Jr., & Campion, M. A. (1998). 25 years of voluntary turnover research: A review and critique. In C. L. Cooper & I. T. Robertson (Eds.),
International review of industrial and organizational psychology (Vol. 13, pp. 49–81). Wiley.
Marvin, G., Jackson, M., & Alam, M. G. R. (2021). A machine learning approach for employee retention prediction. In
2021 IEEE Region 10 Symposium (TENSYMP) (pp. 1–8). IEEE.
https://doi.org/10.1109/TENSYMP52854.2021.9550921
Marvin, G., Jackson, M., & Alam, M. G. R. (2021). A machine learning approach for employee retention prediction. In
2021 IEEE Region 10 Symposium (TENSYMP) (pp. 1–8). IEEE.
https://doi.org/10.1109/TENSYMP52854.2021.9550921
Mathis, R. L., & Jackson, J. H. (2011).
Human resource management (13th ed.). South-Western Cengage Learning.
McCarthy, J. (2007).
What is artificial intelligence? Stanford University, Computer Science Department.
Mozaffari, F., Rahimi, M., Yazdani, H., & Sohrabi, B. (2023). Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data.
Benchmarking: An International Journal, 30(10), 4140–4173.
https://doi.org/10.1108/BIJ-11-2021-0664
Najafi-Zangeneh, S., Shams-Gharneh, N., Arjomandi-Nezhad, A., & Hashemkhani Zolfani, S. (2021). An improved machine learning-based employees attrition prediction framework with emphasis on feature selection.
Mathematics, 9(11), 1226.
https://doi.org/10.3390/math9111226
Naz, K., Siddiqui, I. F., Koo, J., Khan, M. A., & Qureshi, N. M. F. (2022). Predictive modeling of employee churn analysis for IoT-enabled software industry.
Applied Sciences, 12(20), Article 10495.
https://doi.org/10.3390/app122010495
Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2007). Fundamentals of human resource management (2nd ed.). McGraw-Hill/Irwin.
Opatha, H. H. D. N. P. (2021). A simplified study of definitions of human resource management.
Sri Lankan Journal of Human Resource Management, 11(1), 15–35.
https://doi.org/10.4038/sljhrm.v11i1.5672
Pandey, N., & Kaur, G. (2011). Factors influencing employee attrition in Indian ITeS call centres.
International Journal of Indian Culture and Business Management, 4(4), 419–435.
https://doi.org/10.1504/IJICBM.2011.040959
Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources.
European Journal of Operational Research, 252(2), 687–698.
https://doi.org/10.1016/j.ejor.2016.01.052
Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources.
European Journal of Operational Research, 252(2), 687–698.
https://doi.org/10.1016/j.ejor.2016.01.052
Powers, D. M. W. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation.
arXiv.
https://arxiv.org/abs/2010.16061
Punnoose, R., & Ajit, P. (2016). Prediction of employee turnover in organizations using machine learning algorithms.
International Journal of Advanced Research in Artificial Intelligence, 5(9), C5.
https://doi.org/10.14569/IJARAI.2016.050904
Radha, K., & Rohith, M. (2021). An experimental analysis of work-life balance among the employees using machine learning classifiers.
International Journal of Computer Trends and Technology, 69(4), 39–48.
https://doi.org/10.14445/22312803/IJCTT-V69I4P108
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.
Journal of Computational and Applied Mathematics, 20, 53–65.
https://doi.org/10.1016/0377-0427(87)90125-7
Schwind, H. F., Das, H., Wagar, T. H., & Fassina, N. (2013).
Canadian human resource management: A strategic approach (10th ed.). McGraw-Hill Ryerson.
Setiawan, I., Suprihanto, S., Nugraha, A. C., & Hutahaean, J. (2020). HR analytics: Employee attrition analysis using logistic regression.
IOP Conference Series: Materials Science and Engineering, 830(3), Article 032001.
https://doi.org/10.1088/1757-899X/830/3/032001
Singh, M., Varshney, K. R., Wang, J., Mojsilovic, A., Gill, A. R., Faur, P. I., & Ezry, R. (2012). An analytics approach for proactively combating voluntary attrition of employees. In
2012 IEEE 12th International Conference on Data Mining Workshops (pp. 317–323). IEEE.
Sisodia, D. S., Vishwakarma, S., & Pujahari, A. (2017). Evaluation of machine learning models for employee churn prediction. In
2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 1016–1020). IEEE.
https://doi.org/10.1109/ICICI.2017.8365293
Stone, T. H., & Meltz, N. M. (1983).
Personnel management in Canada. Holt, Rinehart and Winston of Canada.
Stovel, M., & Bontis, N. (2002). Voluntary turnover: Knowledge management—friend or foe? Journal of Intellectual Capital, 3(3), 303–322. https://doi.org/10.1108/14691930210435633
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. https://doi.org/10.1177/0008125619867910
Thompson, S. C., Holmgren, A. J., & Ford, E. W. (2022). Information system use antecedents of nursing employee turnover in a hospital setting. Health Care Management Review, 47(1), 78–85. https://doi.org/10.1097/HMR.0000000000000308
Vardarlier, P., & Zafer, C. (2019). Use of artificial intelligence as business strategy in recruitment process and social perspective. In U. Hacioglu (Ed.), Digital business strategies in blockchain ecosystems: Transformational design and future of global business (pp. 355–373). Springer. https://doi.org/10.1007/978-3-030-29739-8_17
Varian, H. R. (2019). Artificial intelligence, economics, and industrial organization. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 399–419). University of Chicago Press.
Wirth, R., & Hipp, J. (2000). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining (Vol. 1, pp. 29–39).
Zelenski, J. M., Murphy, S. A., & Jenkins, D. A. (2008). The happy-productive worker thesis revisited. Journal of Happiness Studies, 9(4), 521–537. https://doi.org/10.1007/s10902-008-9087-4
