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Cited by 891

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Employees and High‐Performance Work Systems: Testing inside the Black Box

Ramsay, Harvie, Scholarios, Dora, Harley, Bill·2000·British Journal of Industrial Relations

Abstract

Most work on high‐performance work systems has examined only the direct relationship between a set of management practices and performance outcomes. This presumes that any connection operates through the incentive and motivational effects captured as ‘high‐commitment’ or ‘high‐involvement’ employee outcomes. No attempt has been made to examine the alternative, Labour Process conceptualization, which expects performance gains from new management practices to arise instead from work intensification, offloading of taskcontrols, and increased job strain. Using data from WERS98, we tested models based on high‐performance work systems and labour process approaches. Both were found wanting, and we consider the possible implications of these failures.

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Crossref · 10.1111/1467-8543.00178

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Metadata

Authors
Ramsay, H., Scholarios, D., & Harley, B.
Venue
British Journal of Industrial Relations
Publisher
Wiley
Published
2000-12
Volume
38
Issue
4
Pages
501-531
ISSN
  • 0007-1080 (ISSN-L)
  • 1467-8543
OpenAlex
W2155563387
Cited by
891
References
2
FWCI
25.86
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Employees and High‐Performance Work Systems: Testing inside the Black Box. https://app.cochat.ai/papers/10.1111/1467-8543.00178
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Ramsay, H., Scholarios, D., & Harley, B. (2000). Employees and High‐Performance Work Systems: Testing inside the Black Box. British Journal of Industrial Relations. https://doi.org/10.1111/1467-8543.00178

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.