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Cited by 328

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

Unrelated knowledge combinations: the unexplored potential for regional industrial path development

Open access
Grillitsch, Markus, Asheim, Bjørn, Trippl, Michaela·2018·Cambridge Journal of Regions, Economy and Society

Abstract

The article engages in a critical discussion of the related variety/regional branching argument and foregrounds a more differentiated perspective on regional industrial path development. It contributes by (i) sharpening the definition of key concepts, namely specialisation and diversity, related and unrelated variety; (ii) discussing their relevance in local and non-local spaces; (iii) scrutinizing related variety as the source for regional branching; and (iv) developing a conceptual framework capturing the opportunity space for regional structural change that unveils the relevance of path upgrading, path importation, path branching, path diversification and new path creation as different forms of new path development.

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Registry

Crossref · 10.1093/cjres/rsy012

Cites

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Metadata

Authors
Grillitsch, M., Asheim, B., & Trippl, M.
Venue
Cambridge Journal of Regions, Economy and Society
Published
2018-05-14
Volume
11
Issue
2
Pages
257-274
ISSN
  • 1752-1378 (ISSN-L)
  • 1752-1386
OpenAlex
W2734535081
Cited by
328
References
89
FWCI
124.53
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Unrelated knowledge combinations: the unexplored potential for regional industrial path development. https://app.cochat.ai/papers/10.1093/cjres/rsy012
The paper
Grillitsch, M., Asheim, B., & Trippl, M. (2018). Unrelated knowledge combinations: the unexplored potential for regional industrial path development. Cambridge Journal of Regions, Economy and Society. https://doi.org/10.1093/cjres/rsy012

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.