Predicting public sector employee burnout using big data analysis

Bahar Razi zadeh; Abbas Zamani

doi:10.63053/ijmea.15

Authors

Bahar Razi zadeh PhD student, Department of Management, Islamic Azad University, Najaf Abad branch, Isfahan, Iran
Abbas Zamani Assistant Professor, Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran

DOI:

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

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.

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