Job Satisfaction and the ‘Great Resignation’: An Exploratory Machine Learning Analysis

Mehmet Güney Celbis, Pui Hang Wong*, K. Kourtit, Peter Nijkamp

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Labor market dynamics is shaped by various social, psychological and economic drivers. Studies have suggested that job quit and labor market turnover are associated with job satisfaction. This study examines the determinants of job satisfaction using a large survey dataset, namely the LISS Work and Schooling module on an extensive sample of persons from the Netherlands. To handle these big data, machine learning models based on binary recursive partitioning algorithms are employed. Particularly, sequential and randomized tree-based techniques are used for prediction and clustering purposes. In order to interpret the results, the study calculates the sizes and directions of the effects of model features using computations based on the concept of Shapley value in cooperative game theory. The findings suggest that satisfaction with the social atmosphere among colleagues, wage satisfaction, and feeling of being appreciated are major determinants of job satisfaction.
Original languageEnglish
Pages (from-to)1097-1118
Number of pages22
JournalSocial Indicators Research
Volume170
Issue number3
DOIs
Publication statusPublished - Dec 2023

Keywords

  • job satisfaction
  • work conditions
  • job attitudes
  • satisfaction with coworker
  • pay satisfaction

Fingerprint

Dive into the research topics of 'Job Satisfaction and the ‘Great Resignation’: An Exploratory Machine Learning Analysis'. Together they form a unique fingerprint.

Cite this