The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On the Interface Between Operations and Human Resources Management
Abstract
Operations management (OM) and human resources management (HRM) historically have been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications, and staffing. Human responses to OM systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields.
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HUSELID, M. A. (1995) · Academy of Management Journal · doi:10.2307/256741 · Cited by 9,342
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Metadata
- Authors
- Boudreau, J., Hopp, W., McClain, J. O., & Thomas, L. J.
- Venue
- Manufacturing & Service Operations Management
- Published
- 2003-07
- Volume
- 5
- Issue
- 3
- Pages
- 179-202
- ISSN
- 1523-4614 (ISSN-L)
- 1526-5498
- OpenAlex
- W2004201361
- Cited by
- 451
- References
- 1
- FWCI
- 39.79
- Type
- journal article
- Language
- en
- Open access
- closed
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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.