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

Author

Listed:
  • Mehmet Güney Celbiş

    (University of Lyon
    United Nations University)

  • Pui-Hang Wong

    (United Nations University
    Maastricht University)

  • Karima Kourtit

    (Open University of the Netherlands
    Alexandru Ioan Cuza University)

  • Peter Nijkamp

    (Open University of the Netherlands
    Alexandru Ioan Cuza University)

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.

Suggested Citation

  • Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Job Satisfaction and the ‘Great Resignation’: An Exploratory Machine Learning Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 170(3), pages 1097-1118, December.
    • Handle: RePEc:spr:soinre:v:170:y:2023:i:3:d:10.1007_s11205-023-03233-3
    DOI: 10.1007/s11205-023-03233-3
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