Pruning the forest of turnover research: identifying important antecedents using predictive modelling

Summary, in English

While extant research has identified numerous antecedents of turnover, our understanding of their relative influence on turnover behaviour remains limited. This article evaluates the predictive power of established turnover antecedents and determines which are most important for predicting turnover. Drawing on administrative and survey data from public employees in a large Danish municipality, we use predictive modelling to demonstrate how demographic characteristics are the strongest predictors. In contrast, antecedents related to the work environment, job characteristics, and work attitudes do not significantly enhance predictive accuracy. We discuss the implications of these findings for both theory and practice.

• Författare: Jens Lems; Robert Klemmensen; Signe Pihl-Thingvad
• Avdelning/ar: Statsvetenskapliga institutionen; LU profilområde: Naturlig och artificiell kognition
• Publiceringsår: 2025-10-11
• Språk: Engelska
• Publikation/Tidskrift/Serie: Public Management Review
• Länkar: http://dx.doi.org/10.1080/14719037.2025.2565793
• Dokumenttyp: Artikel i tidskrift
• Förlag: Taylor & Francis
• Ämne: Political Science (excluding Peace and Conflict Studies)
• Aktiv: Epub
• ISBN/ISSN/Övrigt: ISSN: 1471-9037