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Cited by 305

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

STRUCTURAL EMBEDDEDNESS AND SUPPLIER MANAGEMENT: A NETWORK PERSPECTIVE*

CHOI, THOMAS Y., KIM, YUSOON·2008·Journal of Supply Chain Management

Abstract

The concept of structural embeddedness refers to the importance of framing suppliers as being embedded in larger supply networks rather than in isolation. Such framing helps buying companies create more realistic policies and strategies when managing their suppliers. Simply put, the performance of a supplier is dependent on its own supply networks. By adopting the concept of structural embeddedness, we learn that a buying company needs to look at a supplier's extended supply network to arrive at a more complete evaluation of that supplier's performance. By doing so, a buying company may do a better job of selecting suppliers for long‐term relationships and may also find value in maintaining relationships with poorly performing suppliers who may potentially act as a conduit to other companies with technological and innovative resources.

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Crossref · 10.1111/j.1745-493x.2008.00069.x

Cites

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Metadata

Authors
CHOI, T. Y., & KIM, Y.
Venue
Journal of Supply Chain Management
Publisher
Wiley
Published
2008-09
Volume
44
Issue
4
Pages
5-13
ISSN
  • 1055-6001 (ISSN-L)
  • 1523-2409
  • 1745-493X
OpenAlex
W2156556105
Cited by
305
References
78
FWCI
16.10
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: STRUCTURAL EMBEDDEDNESS AND SUPPLIER MANAGEMENT: A NETWORK PERSPECTIVE*. https://app.cochat.ai/papers/10.1111/j.1745-493x.2008.00069.x
The paper
CHOI, T. Y., & KIM, Y. (2008). STRUCTURAL EMBEDDEDNESS AND SUPPLIER MANAGEMENT: A NETWORK PERSPECTIVE*. Journal of Supply Chain Management. https://doi.org/10.1111/j.1745-493x.2008.00069.x

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.