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Why do employees worry about their jobs? A meta-analytic review of predictors of job insecurity.
Abstract
We used psychological contract theory as a framework to meta-analytically review subjective and objective predictors of employees' perceived job insecurity. Seventy-six samples from 68 studies were included in our review. Results revealed that lower levels of job insecurity are associated with having an internal locus of control, lower amounts of role ambiguity and role conflict, greater amounts of organizational communication, less organizational change, younger employees, and white-collar and permanent work. Moderator analyses further revealed that relations between job insecurity and age, gender, education, and formal contracts are moderated by unemployment rates, countries of origin, and type of job insecurity measure. We discuss theoretical and practical implications for psychological contract theory and occupational health, and offer directions for future research.
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Miraglia, M., & Johns, G. (2016) · Journal of Occupational Health Psychology · doi:10.1037/ocp0000015 · Cited by 494
Cognitive and Affective Job Insecurity: A Meta-Analysis and a Primary Study
Jiang, L., & Lavaysse, L. M. (2018) · Journal of Management · doi:10.1177/0149206318773853 · Cited by 422
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Metadata
- Authors
- Keim, A. C., Landis, R. S., Pierce, C. A., & Earnest, D. R.
- Venue
- Journal of Occupational Health Psychology
- Published
- 2014-07
- Volume
- 19
- Issue
- 3
- Pages
- 269-290
- ISSN
- 1076-8998 (ISSN-L)
- 1939-1307
- OpenAlex
- W1982837967
- Cited by
- 324
- References
- 95
- FWCI
- 28.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- Keim, A. C., Landis, R. S., Pierce, C. A., & Earnest, D. R. (2014). Why do employees worry about their jobs? A meta-analytic review of predictors of job insecurity. Journal of Occupational Health Psychology. https://doi.org/10.1037/a0036743
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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.
