Predicting Tech Employee Job Satisfaction Using Machine Learning Techniques Sumali J. Conlon1 Lakisha L. Simmons2 Feng Liu3
Keywords:Predictive Analysis, Machine Learning, High-Tech Industry Employees, Employee Job Satisfaction
High-tech industry employees are among the most talented groups of people in the workforce, and are therefore difficult to recruit and retain. We analyze employee reviews submitted by employees from five technology companies. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the data science life cycle process, we use machine learning techniques to analyze employees’ reviews. Our goal is to predict an overall measure of whether employees are satisfied or not, using other information from the reviews, such as employer attitudes towards upper management. We also use predictive analysis to determine which features are more helpful in determining an employee’s overall job satisfaction. Finally, we analyze which prediction algorithm provides the most accurate predictions. We find the percentage of true positives we correctly identify in the holdout sample is 97.4%, while the percentage of true negatives correctly identified is 72.5%.
2017 Retention Report: Trends, Reasons & Recommendations: http://info.workinstitute.com/retentionreport2017 (accessed on January 1, 2021).
Abraham, R. (1999). The Impact of Emotional Dissonance on Organizational Commitment and Intention to Turnover. Journal of Psychology, Vol. 133, No. 4, 441–455.
Alao, D. & Adeyemo, A. (2013). Analyzing employee attrition using decision tree algorithms. Comput. Information System, Development Informatics and Allied Research Journal. Vol. 4, No. 1, 17–28.
Alduayj, S. S. & Rajpoot K. (2018). Predicting Employee Attrition using Machine Learning. International Conference on Innovations in Information Technology (IIT), 93-98.
Azari B. (2011). Job Satisfaction: A Literature Review. Management Research and Practice, Vol. 3, No. 4, 77-86. http://mrp.ase.ro/no34/f7.pdf.
Bendemra, H. (2019). Building an Employee Churn Model in Python to Develop a Strategic Retention Plan. https://towardsdatascience.com/building-an-employee-churn-model-in-python-to-develop-a-strategic-retention-plan-57d5bd882c2d (accessed on January 1, 2021).
Bolden-Barrett, V. (2017). Turnover costs employers $15,000 per worker. https://www.hrdive.com/news/study-turnover-costs-employers-15000-per-worker/449142/ (accessed on January 1, 2021).
Burkov, A. (2019). The Hundred-Page Machine Learning Book, ISBN-13: 978-1999579500.
Chen, Ling-Hsiu (2008). “Job satisfaction among information system (IS) personnel,” Computers in Human Behavior, Volume 24, Issue 1, 2008, Pages 105-118, ISSN 0747-5632, https://doi.org/10.1016/j.chb.2007.01.012.
Conlon, S. J. (2021). Why Do Data Scientists Want to Change Jobs: Using Machine Learning Techniques to Analyze Employees’ Intentions in Switching Jobs. INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY, 16, 59–71. https://doi.org/10.24297/ijmit.v16i.9058.
Cotton, J. L.& Tuttle J. M. (1986). Employee turnover: A meta-analysis and review with implications for research. Academy of management Review, Vol. 11, No. 1, 55-70. https://doi.org/10.2307/258331.
Fallucchi, F., Coladangelo, M., Giuliano, R., & William De Luca, E. (2020). “Predicting Employee Attrition Using Machine Learning Techniques.” Computers 9(4), 86. https://doi.org/10.3390/computers9040086.
Galup S, Klein G, Jiang J. (2008). The impacts of job characteristics on IS employee satisfaction: A comparison between permanent and temporary employees. Journal of Computer Information Systems, Vol. 48 No. 4, 58–68.
Golestani, A., Masli, M., Shami, N. S., Jones, J., Menon A., & Mondal J. (2018). Real-Time Prediction of Employee Engagement Using Social Media and Text Mining. 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, 2018, 1383-1387.
Harden, G., Boakye, K. G., & Ryan, S. (2018). Turnover intention of technology professionals: A social exchange theory perspective. Journal of Computer Information Systems, 58(4), 291–300. https://doi.org/10.1080/08874417.2016.1236356.
Hinkin, T. R. & Tracey, J. B. (2000). The cost of turnover: Putting a price on the learning curve. Cornell Hotel and Restaurant Administration Quarterly, Vol. 41, 14-21. https://hdl.handle.net/1813/71741.
Hinkin, T. R. & Tracey, J. B. (2006). Development and use of a web-based tool to measure the costs of employee turnover: Preliminary findings. Ithaca, NY: Cornell University School of Hotel Administration Center for Hospitality Research.
Holtom, B., Mitchell, T., Lee, T., & Eberly, M. (2008). Turnover and retention research: A glance at the past, a closer review of the present, and a venture into the future. The Academy of Management Annals, Vol. 2, No. 1, 231-274. https://doi.org/10.1080/19416520802211552.
Hulin, C. L., & Judge, T. A. (2003). Job attitudes. In W. C. Borman, D. R. Ligen, & R. J. Klimoski (Eds.), Handbook of psychology: Industrial and organizational psychology, Hoboken, NJ: Wiley. 255-276.
IBM HR Analytics Employee Attrition & Performance: IBM HR Analytics Employee Attrition & Performance | Kaggle (accessed on January 1, 2021).
IBM Knowledge Center https://www.ibm.com/support/knowledgecenter/SS3RA7_sub/modeler_crispdm_ddita/clementine/crisp_help/crisp_overview.html (accessed on January 1, 2021).
Jain D., Makkar S., Jindal L., Gupta M. (2021) Uncovering Employee Job Satisfaction Using Machine Learning: A Case Study of Om Logistics Ltd. In: Gupta D., Khanna A., Bhattacharyya S., Hassanien A., Anand S., Jaiswal A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, Vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_33
Larose, D. & Larose, C. (2014). Discovering knowledge in data: An introduction to data mining. John Wiley and Sons, Inc.
Lee, T., Mitchell, T., Sablynski, C., Burton, J., & Holtom B. (2004). The effect of job embeddedness on organizational citizenship, job performance, volitional absences and voluntary turnover. Academy of Management Journal, Vol. 47, No. 5, 711-22. https://doi.org/10.2307/20159613.
Locke, E.A. (1976). The nature and causes of job satisfaction. In M.D. Dunnette (Ed.), Handbook of industrial and organizational psychology, Chicago: Rand McNally. Vol. 1, 1297-1343.
Mishra, P.K. (2013). Job Satisfaction. Journal of Humanities and Social Science, Vol. 14, No. 5 (Sep. - Oct. 2013), 45-54.
Mobley, W. H. (1982). Employee turnover: Causes, consequences, and control, Reading, MA: Addison Wesley.
Moorman, R.H. (1993). The influence of cognitive and affective based job satisfaction measures on the relationship between satisfaction and organizational citizenship behavior. Vol. 46, No. 6, 759–776. https://doi.org/10.1177/001872679304600604.
Morrell, K., Loan-Clarke, J., & Wilkinson, A. (2001). Unweaving leaving: The use of models in the management of employee turnover. International Journal of Management Reviews, Vol. 3, 219-44. https://doi.org/10.1111/1468-2370.00065.
Morris, M. & V. Venkatesh. “Job Characteristics and Job Satisfaction: Understanding the Role of Enterprise Resource.” MIS Q. 34 (2010): 143-161. https://doi.org/10.2307/20721418
Mossholder, K., Sutton, R. P., & Henagan, S. C. (2005). A relational perspective on turnover: Examining structural, attitudinal, and behavioral predictors. Academy of Management Journal, Vol. 48, No. 4, 607- 18. https://doi.org/10.2307/20159682.
Mowday, R. T., Porter, L. W., & Steers R. M. (1982). Employee-organization linkages: The psychology of commitment, absenteeism, and turnover, New York: Academic Press.
Nagadevara, V. (2008). Early Prediction of Employee Attrition in Software Companies-Application of Data Mining Techniques. Research & Practice in Human Resource Management. 16, 2020–2032. https://doi.org/10.1109/IADCC.2018.8692137.
Punnoose, R. & Ajit, P. (2016). Prediction of employee turnover in organizations using machine learning algorithms. International Journal of Advanced Research in Artificial Intelligence, Vol. 5, Issue 9. 22–26. https://doi.org/10.14569/IJARAI.2016.050904.
Ray, A. N. & Sanyal, J. (2019). Machine Learning Based Attrition Prediction. 2019 Global Conference for Advancement in Technology (GCAT), BANGALURU, India, 1-4.
Saari, Lise. M. & Judge, T. (2004) Employee Attitudes and Job Satisfaction. Human Resource Management, Winter 2004, Vol. 43, No. 4, 395–407. https://doi.org/10.1002/hrm.20032.
Sacco, J. M. & Schmitt, N. (2005). A dynamic multilevel model of demographic diversity and misfit effects. Journal of Applied Psychology, Vol. 90, No. 2, 203-231. https://doi.org/10.1037/0021-9010.90.2.203.
Singh, J.K. & Jain, M. (2013). A Study of Employees’ Job Satisfaction and Its Impact of their performance. Journal of Indian Research Vol. 1, No. 4, 105-111.
Spector, P.E. (1997). Job satisfaction: Application, assessment, causes and consequences. Thousand Oaks, CA: SAGE.
Sumner, M. & Niederman, F. (2004). The impact of gender differences on job satisfaction, job turnover, and career experiences of information systems professionals. Journal of Computer Information Systems, 44: 29–39. https://doi.org/10.1145/512360.512395.
Thompson, E.R. & Phua F.T.T. (2012). A Brief Index of Affective Job Satisfaction. Group & Organization Management, Vol. 37, No. 3, 275–307. https://doi.org/10.1177/1059601111434201’
Tracey, J. B., & Hinkin, T. R. (2010). Contextual factors and cost profiles associated with employee turnover. In C. Enz (Ed.), The Cornell School of Hotel Administration handbook of applied hospitality strategy, Los Angeles, CA: SAGE. 736-753.
Vidal, M.E.S., Valle, R.S., & Aragón, B.M.I. (2007). Antecedents of repatriates’ job satisfaction and its influence on turnover intentions: Evidence from Spanish repatriated managers. Journal of Business Research, Vol. 60, 1272-1281. https://doi.org/10.1016/j.jbusres.2007.05.004.
How to Cite
Copyright (c) 2021 Sumali Conlon, Lakisha L. Simmons, Feng Liu
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Journal of Advances in Linguistics are licensed under a Creative Commons Attribution 4.0 International License.