Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Public Management Review

DOI: 10.1080/14719037.2025.2565793

Authors: Jens Lemb, Robert Klemmensen, Signe Pihl-Thingvad

Institutions: Lund University, University of Copenhagen

Language: English

Topic: Job Satisfaction and Organizational Behavior

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Funders: Australian Government, Syddansk Universitet, Division of Human Resource Development