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

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.

The search for training and skill improvement of seasonal workers in tourism

“…In absence of labour hoarding practices (Mason and Bishop, 2015) a deep and long-lasting recession could jeopardize workplace training in the entire tourism industry (Majumdar, 2007). Moreover, at the time of the recovery workers will enter the sector again, and the industry will need appropriate skills to support the rebound (Thomas and Long, 2001; Seyitoğlu and Costa, 2022) and to tackle the rise of the “Great Resignation” phenomenon, which identifies an unparalleled phenomenon of mass voluntary quitting that reached its peak at the end of the COVID-19 lockdown in 2021 (Fuller and Kerr, 2022; Celbis et al ., 2023). This is particularly true for seasonal labour markets since voluntary separations have been particularly widespread among people employed in temporary contract with unpredictable work schedules (Ravenelle and Kowalski, 2023), and unsurprisingly in the tourism industry, which has experienced one of its most severe labour shortages during this period (Formica and Sfodera, 2022).…”