Prediction of Employees’ Burnout Level Using a Decision Tree Model

This paper provides a study that aims to predict levels of burnout by applying a decision tree model. In order to accomplish this, we employed the Maslach Burnout Inventory (MBI) questionnaire, which evaluates three main aspects of burnout: emotional exhaustion, disengagement, and a sense of personal accomplishments. The level of burnout was categorised into three levels, namely low, medium, and high, based on the MBI scores of the respondents. Additionally, both the Working Conditions and Control Questionnaire (WOCCQ) and the Positive and Negative Occupational States (PNOSI) questionnaire were used taking into account not only the emotional exhaustion of the employees but also the impact of working conditions, job strain and work engagement. The results indicate that the decision tree model successfully identifies the various levels of burnout with an accuracy rate of 82%.

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Published
2024-11-03
DOI
https://doi.org/10.1109/afros62115.2024.11037209
Primary Topic
AI and HR Technologies
Type
article
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article

Prediction of Employees’ Burnout Level Using a Decision Tree Model

Samia Chettouh, Saadia Saadi, Amira Zadem, Hamida Debz
AI and HR Technologies
article

Prediction of Employees’ Burnout Level Using a Decision Tree Model

Samia Chettouh, Saadia Saadi, Amira Zadem, Hamida Debz
article en

Abstract

This paper provides a study that aims to predict levels of burnout by applying a decision tree model. In order to accomplish this, we employed the Maslach Burnout Inventory (MBI) questionnaire, which evaluates three main aspects of burnout: emotional exhaustion, disengagement, and a sense of personal accomplishments. The level of burnout was categorised into three levels, namely low, medium, and high, based on the MBI scores of the respondents. Additionally, both the Working Conditions and Control Questionnaire (WOCCQ) and the Positive and Negative Occupational States (PNOSI) questionnaire were used taking into account not only the emotional exhaustion of the employees but also the impact of working conditions, job strain and work engagement. The results indicate that the decision tree model successfully identifies the various levels of burnout with an accuracy rate of 82%.

University of Batna 1 (DZ)
Openalex Percentile: Top 60%
AI and HR Technologies
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