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The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.

Job demands-resources theory in times of crises: New propositions

Open access
Demerouti, Evangelia, Bakker, Arnold B.·2023·Organizational Psychology Review

Abstract

This theoretical paper presents an extended Job Demands–Resources (JD–R) theory aimed at understanding how organizations and their employees can best deal with COVID-19 and other crises in the workplace. The crisis showed that job characteristics alone are insufficient to explain employee health and motivation, i.e., the two focal outcomes of the JD-R theory. Rather, demands and resources of the individual, the family, the job and the organization interact with each other to predict outcomes. Moreover, next to individual regulatory strategies also the regulatory strategies of the family, the leader and organization/team are suggested to modify the impact of demands and resources on outcomes. This was possible by integrating the crisis management literature in JD-R theory. Viewing the crisis from a job design perspective helped us to introduce several new and testable propositions that specify how employee well-being and functioning are impacted by crises and turbulent times.

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Metadata

Authors
Demerouti, E., & Bakker, A. B.
Venue
Organizational Psychology Review
Published
2022-11-07
Volume
13
Issue
3
Pages
209-236
ISSN
  • 2041-3866 (ISSN-L)
  • 2041-3874
OpenAlex
W4308386695
Cited by
631
References
125
FWCI
91.94
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Job demands-resources theory in times of crises: New propositions. https://app.cochat.ai/papers/10.1177/20413866221135022
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Demerouti, E., & Bakker, A. B. (2023). Job demands-resources theory in times of crises: New propositions. Organizational Psychology Review. https://doi.org/10.1177/20413866221135022

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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.