The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
High-involvement work processes, work intensification and employee well-being
Abstract
Using a national population survey, this article examines how high-involvement work processes affect employee well-being. The analysis shows that greater experiences of autonomy and participation in decision-making have positive or neutral effects. Higher involvement is a key factor predicting higher job satisfaction and better work–life balance while it has no relationship to stress or fatigue. In contrast, higher levels of work intensity increase fatigue and stress and undermine work–life balance. If the quality of working life is a key objective in a reform based on greater employee involvement, close attention needs to be paid to the balance between processes that release human potential and those that increase the intensity of work.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
'Best practice' human resource management: perfect opportunity or dangerous illusion?
Marchington, M., & Grugulis, I. (2000) · The International Journal of Human Resource Management · doi:10.1080/09585190050177184 · Cited by 384
Veldhoven, M. V., Jonge, J. D., Broersen, S., Kompier, M., & Meijman, T. (2002) · Work & Stress · doi:10.1080/02678370210166399 · Cited by 246
Cited by
Human resource management and employee well-being: towards a new analytic framework
Guest, D. E. (2017) · Human Resource Management Journal · doi:10.1111/1748-8583.12139 · Cited by 1,362
Anwar, M. A., & Graham, M. (2021) · Competition & Change · doi:10.1177/1024529420914473 · Cited by 429
Kaufman, B. E. (2015) · Human Resource Management · doi:10.1002/hrm.21720 · Cited by 171
The (potential) demise of HRM?
Dundon, T., & Rafferty, A. (2018) · Human Resource Management Journal · doi:10.1111/1748-8583.12195 · Cited by 144
An orchestrated negotiated exchange: Trading home-based telework for intensified work.
Bathini, D. R., & Kandathil, G. M. (2019) · Journal of Business Ethics · doi:10.1007/s10551-017-3449-y · Cited by 106
Metadata
- Authors
- Boxall, P., & Macky, K.
- Venue
- Work, Employment and Society
- Publisher
- SAGE Publications
- Published
- 2014-06-12
- Volume
- 28
- Issue
- 6
- Pages
- 963-984
- ISSN
- 0950-0170 (ISSN-L)
- 1469-8722
- OpenAlex
- W2078842021
- Cited by
- 408
- References
- 2
- FWCI
- 45.26
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.