The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Predicting safe employee behavior in the steel industry: Development and test of a sociotechnical model
Abstract
Industrial safety is an important issue for operations managers — it has implications for cost, delivery, quality, and social responsibility. Minor accidents can interfere with production in a variety of ways, and a serious accident can shut down an entire operation. In this context, questions about the causes of workplace accidents are highly relevant. There is a popular notion that employees' unsafe acts are the primary causes of workplace accidents, but a number of authors suggest a perspective that highlights influences from operating and social systems. The study described herein addresses this subject by assessing steelworkers' responses to a survey about social, technical, and personal factors related to safe work behaviors. Results provide evidence that a chain reaction of technical and social constructs operate through employees to influence safe behaviors. These results demonstrate that safety hazards, safety culture, and production pressures can influence safety efficacy and cavalier attitudes, on a path leading to safe or unsafe work behaviors. Based on these results, we conclude with prescriptions for operations managers and others who play roles in the causal sequence.
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Cited by
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Metadata
- Authors
- Brown, K. A., Willis, P. G., & Prussia, G. E.
- Venue
- Journal of Operations Management
- Publisher
- Wiley
- Published
- 2000-06
- Volume
- 18
- Issue
- 4
- Pages
- 445-465
- ISSN
- 0272-6963 (ISSN-L)
- 1873-1317
- OpenAlex
- W1971180591
- Cited by
- 364
- References
- 40
- FWCI
- 11.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Brown, K. A., Willis, P. G., & Prussia, G. E. (2000). Predicting safe employee behavior in the steel industry: Development and test of a sociotechnical model. Journal of Operations Management. https://doi.org/10.1016/s0272-6963(00)00033-4
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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.