A Study on the Determinants of Burnout Among Firefighters Using Decision Tree Analysis

This study empirically analyzed the determinants of job burnout among firefighters using decision tree analysis. Furthermore, to consider the various detailed factors of job burnout, the study examined not only overall job burnout but also its subdivisions, including reduced sense of accomplishment, emotional exhaustion, and loss of self-identity. The decision tree analysis revealed that organizational system factors and organizational fairness factors were the characteristics of the group with the highest level of job burnout. Among organizational system factors, groups with a strong perception of task avoidance showed higher levels of reduced sense of accomplishment, emotional exhaustion, and overall job burnout. Additionally, groups experiencing conflicting instructions from higher authorities or supervisors exhibited higher levels of emotional exhaustion and overall job burnout. Regarding fairness factors, groups that perceived their compensation as inadequate relative to their duties and performance demonstrated higher levels of loss of self-identity and overall job burnout. Based on these findings, the policy implications of this study are as follows: First, there is a need to enhance programs aimed at alleviating job stress for firefighters. Second, an appropriate compensation system should be established based on job performance.

Authors

Publication Details

Journal
National Association of Korean Local Government Studies
Published
2024-08-31
DOI
https://doi.org/10.38134/klgr.2024.26.2.063
Primary Topic
Educational Systems and Policies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Study on the Determinants of Burnout Among Firefighters Using Decision Tree Analysis

Seong-Min Park, JungWook Seo, Donguk Kim
National Association of Korean Local Government Studies
Educational Systems and Policies
article

A Study on the Determinants of Burnout Among Firefighters Using Decision Tree Analysis

Seong-Min Park, JungWook Seo, Donguk Kim
article en

Abstract

This study empirically analyzed the determinants of job burnout among firefighters using decision tree analysis. Furthermore, to consider the various detailed factors of job burnout, the study examined not only overall job burnout but also its subdivisions, including reduced sense of accomplishment, emotional exhaustion, and loss of self-identity. The decision tree analysis revealed that organizational system factors and organizational fairness factors were the characteristics of the group with the highest level of job burnout. Among organizational system factors, groups with a strong perception of task avoidance showed higher levels of reduced sense of accomplishment, emotional exhaustion, and overall job burnout. Additionally, groups experiencing conflicting instructions from higher authorities or supervisors exhibited higher levels of emotional exhaustion and overall job burnout. Regarding fairness factors, groups that perceived their compensation as inadequate relative to their duties and performance demonstrated higher levels of loss of self-identity and overall job burnout. Based on these findings, the policy implications of this study are as follows: First, there is a need to enhance programs aimed at alleviating job stress for firefighters. Second, an appropriate compensation system should be established based on job performance.

National Association of Korean Local Government StudiesVol. 26(2)
Peace, Justice and strong institutions
Openalex Percentile: Top 74%
Educational Systems and Policies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.