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

Lems, Jens ; Klemmensen, Robert LU and Pihl-Thingvad, Signe (2025) In Public Management Review
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.
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
Public Management Review
publisher
Taylor & Francis
external identifiers
  • scopus:105018841582
ISSN
1471-9037
DOI
10.1080/14719037.2025.2565793
language
English
LU publication?
yes
id
3568c59d-8691-4d2d-bad2-4bcc3ace525d
date added to LUP
2025-10-11 12:48:33
date last changed
2026-01-22 14:05:33
@article{3568c59d-8691-4d2d-bad2-4bcc3ace525d,
  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.}},
  author       = {{Lems, Jens and Klemmensen, Robert and Pihl-Thingvad, Signe}},
  issn         = {{1471-9037}},
  language     = {{eng}},
  month        = {{10}},
  publisher    = {{Taylor & Francis}},
  series       = {{Public Management Review}},
  title        = {{Pruning the forest of turnover research: identifying important antecedents using predictive modelling}},
  url          = {{http://dx.doi.org/10.1080/14719037.2025.2565793}},
  doi          = {{10.1080/14719037.2025.2565793}},
  year         = {{2025}},
}