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Cited by 9,670

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness

Uzzi, Brian·1997·Administrative Science Quarterly

Abstract

This chapter aims to develop one of perhaps multiple specifications of embeddedness, a concept that has been used to refer broadly to the contingent nature of economic action with respect to cognition, social structure, institutions, and culture. Research on embeddedness is an exciting area in sociology and economics because it advances understanding of how social structure affects economic life. The chapter addresses propositions about the operation and outcomes of interfirm networks that are guided implicitly by ceteris paribus assumptions. While economies of time due to embeddedness have obvious benefits for the individual firm, they also have important implications for allocative efficiency and the determination of prices. Under the conditions, social processes that increase integration combine with resource dependency problems to increase the vulnerability of networked organizations. The level of investment in an economy promotes positive changes in productivity, standards of living, mobility, and wealth generation.

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Metadata

Authors
Uzzi, B.
Venue
Administrative Science Quarterly
Publisher
JSTOR
Published
1997-03
Volume
42
Issue
1
Pages
35
ISSN
0001-8392 (ISSN-L)
OpenAlex
W1999216122
Cited by
9,670
References
1
FWCI
201.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. https://app.cochat.ai/papers/10.2307/2393808
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Uzzi, B. (1997). Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Administrative Science Quarterly. https://doi.org/10.2307/2393808

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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.