[1] J. Berengueres, G. Duran, and D. Castro, “Happiness, an inside job?: Turnover prediction using employee likeability, engagement and relative happiness,” in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. ACM, 2017, pp. 509–516.
[2] V. V. Saradhi and G. K. Palshikar, “Employee churn prediction,” Expert Systems with Applications, vol. 38, no. 3, pp. 1999–2006, 2011.
[3] J. L. Cotton and J. M. Tuttle, “Employee turnover: A meta-analysis and review with implications for research,” Academy of management Review, vol. 11, no. 1, pp. 55–70, 1986.
[4] D. G. Allen and R. W. Griffeth, “Test of a mediated performance–turnover relationship highlighting the moderating roles of visibility and reward contingency.” Journal of Applied Psychology, vol. 86, no. 5, p. 1014, 2001.
[5] D. Liu, T. R. Mitchell, T. W. Lee, B. C. Holtom, and T. R. Hinkin, “When employees are out of step with coworkers: How job satisfaction trajectory and dispersion influence individual-and unit-level voluntary turnover,” Academy of management journal, vol. 55, no. 6, pp. 1360–1380, 2012.
[6] B. W. Swider and R. D. Zimmerman, “Born to burnout: A meta-analytic path model of personality, job burnout, and work outcomes,” Journal of Vocational Behavior, vol. 76, no. 3, pp. 487–506, 2010.
[7] T. M. Heckert and A. M. Farabee, “Turnover intentions of the faculty at a teaching-focused university,” Psychological reports, vol. 99, no. 1, pp. 39–45, 2006.
[8] C. O. Trevor, “Interactions among actual ease-of-movement determinants and job satisfaction in the prediction of voluntary turnover,” Academy of management journal, vol. 44, no. 4, pp. 621–638, 2001.
[9] D. N. Dickter, M. Roznowski, and D. A. Harrison, “Temporal tempering: An event history analysis of the process of voluntary turnover,” Journal of Applied Psychology, vol. 81, no. 6, p. 705, 1996.
[10] R. W. Griffeth and P. W. Hom, Retaining valued employees. Sage Publications, 2001.
[11] S. Wang and X. Yi, “It’s happiness that counts: Full mediating effect of job satisfaction on the linkage from LMX to turnover intention in Chinese companies,” International Journal of Leadership Studies, vol. 6, no. 3, pp. 337–356, 2011.
[12] P. Ajit, “Prediction of employee turnover in organizations using machine learning algorithms,” Algorithms, vol. 4, no. 5, p. C5, 2016.
[13] “Happyforce.” [Online]. Available: https://myhappyforce.com/en/
[14] S. N. Mishra, D. R. Lama, and Y. Pal, “Human resource predictive analytics (hrpa) for hr management in organizations,” International Journal of Scientific & Technology Research, vol. 5, no. 5, pp. 33–35, 2016.
[15] I. Ajzen, “The theory of planned behavior,” Organizational behavior and human decision processes, vol. 50, no. 2, pp. 179–211, 1991.
[16] J. Berengueres, “Daily Happiness & Employee Turnover,” 2017. [Online]. Available: https://kaggle.com/harriken/employeeturnover
[17] J. Berengueres, “How many unlikes it takes to get fired?” 2017. [Online]. Available: https://www.kaggle.com/harriken/how-many-unlikes-it-takes-to-get-fired
[18] M. A. Hall, “Correlation-based feature subset selection for machine learning,” PhD Thesis, University of Waikato, 1998.
[19] L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks, 1984.
[20] L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.
[21] J. K. Martin and D. Hirschberg, “On the complexity of learning decision trees,” in International Symposium on Artificial Intelligence and Mathematics. Citeseer, 1996, pp. 112–115.
[22] Y. Freund and L. Mason, “The alternating decision tree learning algorithm,” in ICML, vol. 99, 1999, pp. 124–133.
[23] W.Iba and P.Langley, “Induction of one-level decision trees,” in Machine Learning Proceedings 1992. Elsevier, 1992, pp. 233–240.
[24] T. R. Patil, S. Sherekar et al., “Performance analysis of Naïve Bayes and J48 classification algorithm for data classification,” International Journal of Computer Science and Applications, vol. 6, no. 2, pp. 256–261, 2013.
[25] T. Mitchell, Machine learning, 2nd ed., McGraw Hill, USA, 1997.
[26] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10–18, 2009.
[2] V. V. Saradhi and G. K. Palshikar, “Employee churn prediction,” Expert Systems with Applications, vol. 38, no. 3, pp. 1999–2006, 2011.
[3] J. L. Cotton and J. M. Tuttle, “Employee turnover: A meta-analysis and review with implications for research,” Academy of management Review, vol. 11, no. 1, pp. 55–70, 1986.
[4] D. G. Allen and R. W. Griffeth, “Test of a mediated performance–turnover relationship highlighting the moderating roles of visibility and reward contingency.” Journal of Applied Psychology, vol. 86, no. 5, p. 1014, 2001.
[5] D. Liu, T. R. Mitchell, T. W. Lee, B. C. Holtom, and T. R. Hinkin, “When employees are out of step with coworkers: How job satisfaction trajectory and dispersion influence individual-and unit-level voluntary turnover,” Academy of management journal, vol. 55, no. 6, pp. 1360–1380, 2012.
[6] B. W. Swider and R. D. Zimmerman, “Born to burnout: A meta-analytic path model of personality, job burnout, and work outcomes,” Journal of Vocational Behavior, vol. 76, no. 3, pp. 487–506, 2010.
[7] T. M. Heckert and A. M. Farabee, “Turnover intentions of the faculty at a teaching-focused university,” Psychological reports, vol. 99, no. 1, pp. 39–45, 2006.
[8] C. O. Trevor, “Interactions among actual ease-of-movement determinants and job satisfaction in the prediction of voluntary turnover,” Academy of management journal, vol. 44, no. 4, pp. 621–638, 2001.
[9] D. N. Dickter, M. Roznowski, and D. A. Harrison, “Temporal tempering: An event history analysis of the process of voluntary turnover,” Journal of Applied Psychology, vol. 81, no. 6, p. 705, 1996.
[10] R. W. Griffeth and P. W. Hom, Retaining valued employees. Sage Publications, 2001.
[11] S. Wang and X. Yi, “It’s happiness that counts: Full mediating effect of job satisfaction on the linkage from LMX to turnover intention in Chinese companies,” International Journal of Leadership Studies, vol. 6, no. 3, pp. 337–356, 2011.
[12] P. Ajit, “Prediction of employee turnover in organizations using machine learning algorithms,” Algorithms, vol. 4, no. 5, p. C5, 2016.
[13] “Happyforce.” [Online]. Available: https://myhappyforce.com/en/
[14] S. N. Mishra, D. R. Lama, and Y. Pal, “Human resource predictive analytics (hrpa) for hr management in organizations,” International Journal of Scientific & Technology Research, vol. 5, no. 5, pp. 33–35, 2016.
[15] I. Ajzen, “The theory of planned behavior,” Organizational behavior and human decision processes, vol. 50, no. 2, pp. 179–211, 1991.
[16] J. Berengueres, “Daily Happiness & Employee Turnover,” 2017. [Online]. Available: https://kaggle.com/harriken/employeeturnover
[17] J. Berengueres, “How many unlikes it takes to get fired?” 2017. [Online]. Available: https://www.kaggle.com/harriken/how-many-unlikes-it-takes-to-get-fired
[18] M. A. Hall, “Correlation-based feature subset selection for machine learning,” PhD Thesis, University of Waikato, 1998.
[19] L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks, 1984.
[20] L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.
[21] J. K. Martin and D. Hirschberg, “On the complexity of learning decision trees,” in International Symposium on Artificial Intelligence and Mathematics. Citeseer, 1996, pp. 112–115.
[22] Y. Freund and L. Mason, “The alternating decision tree learning algorithm,” in ICML, vol. 99, 1999, pp. 124–133.
[23] W.Iba and P.Langley, “Induction of one-level decision trees,” in Machine Learning Proceedings 1992. Elsevier, 1992, pp. 233–240.
[24] T. R. Patil, S. Sherekar et al., “Performance analysis of Naïve Bayes and J48 classification algorithm for data classification,” International Journal of Computer Science and Applications, vol. 6, no. 2, pp. 256–261, 2013.
[25] T. Mitchell, Machine learning, 2nd ed., McGraw Hill, USA, 1997.
[26] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10–18, 2009.
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Affiliations
Xiaojun Ma
Carnegie Mellon University, Pennsylvania
Shengjun Zhai
The University of Chicago, Illinois
Yingxian Fu
emple University, Pennsylvania
Leonard Yoonjae Lee
Seoul International School, Seongnam, Korea
Jingxuan Shen
Dalian Royal School, Dalian, China
Predicting the Occurrence and Causes of Employee Turnover with Machine Learning
Abstract
This paper looks at the problem of employee turnover, which has considerable influence on organizational productivity and healthy working environments. Using a publicly available dataset, key factors capable of predicting employee churn are identified. Six machine learning algorithms including decision trees, random forests, naïve Bayes and multi-layer perceptron are used to predict employees who are prone to churn. A good level of predictive accuracy is observed, and a comparison is made with previous findings. It is found that while the simplest correlation and regression tree (CART) algorithm gives the best accuracy or F1-score, the alternating decision tree (ADT) gives the best area under the ROC curve. Rules extracted in the if-then form enable successful identification of the probable causes of churning.