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Empirical research on supply chain management: A critical review and recommendations

Ho, Danny C. K., Au, K. F., Newton, Edward·2002·International Journal of Production Research

Abstract

Despite the growing interest in supply chain management (SCM), our understanding of the subject issue is still limited, largely due to inadequate attention being placed to theory building. To address this problem, this paper highlights some major weaknesses of the extant literature with respect to the conceptualization, operationalization, and modelling of SCM, identifies potential causes underlying these shortcomings, and gives some recommendations to improve theory development in this area. Specifically, it demonstrates that (1) the SCM construct has been perceived narrowly from the perspective of a particular traditional function of a firm; (2) the construct validity is threatened due to inadequate pre-operational explication of the content domain, among other operationalization problems; and (3) the dominant conceptual SCM models focus mainly on the practices-performance relationship, overlooking the context-practices relationship. A greater advance in theory development is possible if researchers adopt a process-based view of SCM, develop conceptual SCM models based on a context-practices-performance framework, and synthesize theories and research of SCM and those of related fields such as organization studies.

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Metadata

Authors
Ho, D. C. K., Au, K. F., & Newton, E.
Venue
International Journal of Production Research
Published
2002-01
Volume
40
Issue
17
Pages
4415-4430
ISSN
  • 0020-7543 (ISSN-L)
  • 1366-588X
OpenAlex
W2123050129
Cited by
268
References
0
FWCI
11.98
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Empirical research on supply chain management: A critical review and recommendations. https://app.cochat.ai/papers/10.1080/00207540210157204
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Ho, D. C. K., Au, K. F., & Newton, E. (2002). Empirical research on supply chain management: A critical review and recommendations. International Journal of Production Research. https://doi.org/10.1080/00207540210157204

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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.