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Cited by 276

The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.

Strategizing by the government: Can industrial policy create firm‐level competitive advantage?

Lazzarini, Sergio G.·2015·Strategic Management Journal

Abstract

Despite the prevalence of governmental action devised to foster firms and industries, the link between industrial policy ( IP ) and competitive advantage has received scant attention in strategic management. I propose a model where such a link is mediated by the accumulation and churning of local resources and capabilities. I also introduce the concept of support‐adjusted sustainable competitive advantage ( SASCA ), which occurs if a firm's observed performance is superior to the expected performance of competitors had they received the same array of policies. I argue that achieving SASCA through IP is a difficult endeavor and requires the interplay of three conditions: insertion in global production networks, geographical specificity, and governmental capability. Thus, the model expands the potential determinants of competitive advantage into the context of governmental intervention . Copyright © 2013 John Wiley & Sons, Ltd.

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Metadata

Authors
Lazzarini, S. G.
Venue
Strategic Management Journal
Publisher
Wiley
Published
2013-12-20
Volume
36
Issue
1
Pages
97-112
ISSN
  • 0143-2095 (ISSN-L)
  • 1097-0266
OpenAlex
W1570322089
Cited by
276
References
94
FWCI
25.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Strategizing by the government: Can industrial policy create firm‐level competitive advantage? https://app.cochat.ai/papers/10.1002/smj.2204
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Lazzarini, S. G. (2015). Strategizing by the government: Can industrial policy create firm‐level competitive advantage? Strategic Management Journal. https://doi.org/10.1002/smj.2204

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.