The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Burnout and job performance: The moderating role of selection, optimization, and compensation strategies.
Open accessAbstract
The present study aims to explain why research thus far has found only low to moderate associations between burnout and performance. We argue that employees use adaptive strategies that help them to maintain their performance (i.e., task performance, adaptivity to change) at acceptable levels despite experiencing burnout (i.e., exhaustion, disengagement). We focus on the strategies included in the selective optimization with compensation model. Using a sample of 294 employees and their supervisors, we found that compensation is the most successful strategy in buffering the negative associations of disengagement with supervisor-rated task performance and both disengagement and exhaustion with supervisor-rated adaptivity to change. In contrast, selection exacerbates the negative relationship of exhaustion with supervisor-rated adaptivity to change. In total, 42% of the hypothesized interactions proved to be significant. Our study uncovers successful and unsuccessful strategies that people use to deal with their burnout symptoms in order to achieve satisfactory job performance.
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Cites
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Cited by
Job demands–resources theory: Taking stock and looking forward.
Bakker, A. B., & Demerouti, E. (2017) · Journal of Occupational Health Psychology · doi:10.1037/ocp0000056 · Cited by 6,979
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Self-citation
Metadata
- Authors
- Demerouti, E., Bakker, A. B., & Leiter, M.
- Venue
- Journal of Occupational Health Psychology
- Published
- 2014-01
- Volume
- 19
- Issue
- 1
- Pages
- 96-107
- ISSN
- 1076-8998 (ISSN-L)
- 1939-1307
- OpenAlex
- W2104773487
- Cited by
- 295
- References
- 63
- FWCI
- 33.86
- Type
- journal article
- Language
- en
- Open access
- green
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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.
