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Cited by 285

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

Firms’ knowledge search and local knowledge externalities in innovation performance

Open access
Roper, Stephen, Love, James H., Bonner, Karen·2017·Research Policy

Abstract

We use an augmented version of the UK Innovation Surveys 4–7 to explore firm-level and local area openness externalities on firms’ innovation performance. We find strong evidence of the value of external knowledge acquisition both through interactive collaboration and non-interactive contacts such as demonstration effects, copying or reverse engineering. Levels of knowledge search activity remain well below the private optimum, however, due perhaps to informational market failures. We also find strong positive externalities of openness resulting from the intensity of local interactive knowledge search—a knowledge diffusion effect. However, there are strong negative externalities resulting from the intensity of local non-interactive knowledge search—a competition effect. Our results provide support for local initiatives to support innovation partnering and counter illegal copying or counterfeiting. We find no significant relationship between either local labour quality or employment composition and innovative outputs.

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Metadata

Authors
Roper, S., Love, J. H., & Bonner, K.
Venue
Research Policy
Publisher
Elsevier BV
Published
2017-02
Volume
46
Issue
1
Pages
43-56
ISSN
0048-7333 (ISSN-L)
OpenAlex
W2540426773
Cited by
285
References
64
FWCI
42.43
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Firms’ knowledge search and local knowledge externalities in innovation performance. https://app.cochat.ai/papers/10.1016/j.respol.2016.10.004
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Roper, S., Love, J. H., & Bonner, K. (2017). Firms’ knowledge search and local knowledge externalities in innovation performance. Research Policy. https://doi.org/10.1016/j.respol.2016.10.004

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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.