Predicting public sector employee burnout using big data analysis

Authors

  • Bahar Razi zadeh PhD student, Department of Management, Islamic Azad University, Najaf Abad branch, Isfahan, Iran.
  • Abbas Zamani Assistant Professor, Department of Management, Islamic Azad University, Najaf Abad Branch, Isfahan, Iran. (Responsible author)

DOI:

https://doi.org/10.63053/ijmea.15

Keywords:

burnout, big data, public sector employees, machine learning, prediction

Abstract

Job burnout is a common phenomenon among public sector employees that has negative effects on their performance and health. This study aims to predict job burnout using big data analysis. In this research, data related to 5000 employees from different government departments were collected and analyzed using machine learning techniques. The results showed that factors such as age, gender, work experience, level of job stress, and work-life balance are among the most important predictors of job burnout. Predictive models were able to detect job burnout with 85% accuracy. These results show the possibility of using big data analysis in identifying and managing job burnout.

References

• Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology, 52(1), 397-422. doi:10.1146/annurev.psych.52.1.397 (Sci-Hub).

• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.

• Wang, S., Liu, H., & Parker, S. K. (2015). The Role of Big Data in Human Resource Management: A Review and Future Research Agenda. Journal of Business Research, 69(4), 1536-1541.

• Schaufeli, W. B., & Enzmann, D. (1998). The burnout companion to study and practice: A critical analysis. CRC Press.

• Bakker, A. B., & Demerouti, E. (2007). The Job Demands-Resources model: state of the art. Journal of Managerial Psychology.

• George, G., Haas, M. R., & Pentland, A. (2016). Big data and management. Academy of Management Journal, 59(2), 575-623.

• McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.

• Mishra, P., Sharma, R., & Singh, U. (2016). Application of machine learning algorithms for better prediction of job burnout. Procedia Computer Science, 89, 556-561.

• Kumar, R., Arora, R., & Gupta, V. (2017). Analyzing the impact of machine learning algorithms in predicting employee burnout. IEEE International Conference on Computer and Applications.

• Avolio, B. J., Walumbwa, F. O., & Weber, T. J. (2004). Leadership: Current theories, research, and future directions. Annual Review of Psychology, 60(1), 421-449.

• Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches. Sage Publications.

• Judge, T. A., & Piccolo, R. F. (2004). Transformational and transactional leadership: A meta-analytic test of their relative validity. Journal of Applied Psychology, 89(5), 755.

• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.

• Quick, J. C., Quick, J. D., Nelson, D. L., & Hurrell Jr, J. J. (1997). Preventive stress management in organizations. American Psychological Association.

• Breiman, L. (1984). Classification and Regression Trees. Wadsworth International Group.

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

• Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall.

Published

2024-06-29

How to Cite

Razi zadeh, B., & Zamani, A. (2024). Predicting public sector employee burnout using big data analysis. International Journal of Applied Research in Management, Economics and Accounting, 1(2), 91–99. https://doi.org/10.63053/ijmea.15

Issue

Section

Articles