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Cited by 197

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Employee well-being: A multilevel model linking work and nonwork domains

Ilies, Remus, Schwind, Kelly M., Heller, Daniel·2007·European Journal of Work and Organizational Psychology

Abstract

In this article, we review recent methodological developments that have enabled conceptual advances addressing intraindividual processes leading to psychological well-being. We contend that the introduction of dynamic assessment methodologies for sampling experiences, feelings, and behaviours on and off the job, together with the implementation of multilevel modelling strategies in organizational research on well-being, should lead to the development of richer models of employee well-being (compared to existing theoretical models). Accordingly, we develop a model of employee well-being that considers both personal and situational predictors, and both work and nonwork well-being indicators, as well as the real-time relationships between well-being antecedents and indicators across these two life spheres.

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Registry

Crossref · 10.1080/13594320701363712

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Metadata

Authors
Ilies, R., Schwind, K. M., & Heller, D.
Venue
European Journal of Work and Organizational Psychology
Published
2007-08-08
Volume
16
Issue
3
Pages
326-341
ISSN
  • 1359-432X (ISSN-L)
  • 1464-0643
OpenAlex
W2150122962
Cited by
197
References
7
FWCI
10.88
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Employee well-being: A multilevel model linking work and nonwork domains. https://app.cochat.ai/papers/10.1080/13594320701363712
The paper
Ilies, R., Schwind, K. M., & Heller, D. (2007). Employee well-being: A multilevel model linking work and nonwork domains. European Journal of Work and Organizational Psychology. https://doi.org/10.1080/13594320701363712

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.