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Measuring the elements of knowledge regimes and their links to technological catch-up

A synthesis based on the East Asian experience

Bibliographic Data

ID6149683
AuthorsKeun Lee (0000-0002-0403-6348, Seoul National University, corresponding author)
Year2013
Volume3
Issue1
Pages37-53
Publication date2013-02-25
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInnovation and Development (JOURNAL)
Journal identifiersISSN: 2157-9318 • E-ISSN: 2157-930X
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/2157930x.2012.713198
OpenAlexW2005812819
LanguageEN
Citations received3
References cited32

This paper provides a review of the roles of knowledge access and learning as determinants of technological catch-up and focuses on the relationship between the quantifiable measures of elements of the knowledge regimes of sectors and the possibility of catch-up. Catching-up implications of the various elements of knowledge regimes are discussed, after grouping them into two categories: those related to accessibility to the foreign knowledge base (degree of embodied technology transfer and modularity) and those related to the learning possibilities (tacitness of knowledge and cycle time of technologies). We find that catch-up possibilities are positively linked to the accessibility to foreign knowledge base, the degree of embodied technology transfer, and modularity and that a higher degree of knowledge tacitness tends to interfere with learning possibility while the impact of cycle time of technologies would be dubious depending upon the absorption capacity of the latecomers. It is also pointed out that the impact of the knowledge regimes would be different on different types of firms. Policy implications and remaining issues are discussed

Absorption capacity · Business · Embodied cognition · Industrial organization · Knowledge base · Knowledge management · Knowledge transfer · Modularity (biology · Computer Science · Economic Growth and Productivity · Engineering · Firm Innovation and Growth · Innovation Policy and RD · Artificial Intelligence

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Unique citing works3
Citations per year0,3
Citation span2016 - 2021 (6)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 3

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