Research orientation and agglomeration

Can every region become a Silicon Valley?

Ward Ooms*, Claudia Werker, Marjolein C. J. Caniels, Herman van den Bosch

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Not only success stories, such as Silicon Valley, but also non-success stories can inform regional innovation policy. In order to provide a benchmark for regional innovation systems we compare both success and non-success stories. Regional innovation systems differ in structural and functional requirements, because development processes are path dependent. We suggest that regions' development paths emerge from agglomeration patterns and research orientation. Accordingly, we have developed a typology of regions including (1) their agglomeration patterns (either MAR or Jacobs' type) and (2) the degree to which their research is predominantly oriented towards obtaining fundamental understanding or addressing considerations of use. We combine qualitative and quantitative data on thirty-six European regions to categorize them according to research orientation and agglomeration, thereby developing a typology. We use this typology and some basic quantitative economic data to see how success and non-success regions are distributed. Our results indicate that a better understanding of how to combine agglomeration patterns with research orientation can guide context-sensitive policy.
Original languageEnglish
Pages (from-to)78-92
Number of pages15
JournalTechnovation
Volume45-46
DOIs
Publication statusPublished - 2015

Keywords

  • Regional innovation systems
  • Research orientation
  • Pasteur's Quadrant
  • Agglomeration effects
  • Context-sensitive policy
  • TECHNOLOGICAL-INNOVATION SYSTEMS
  • OLD INDUSTRIAL-AREAS
  • POLICY
  • KNOWLEDGE
  • CITIES
  • SCIENCE
  • UNIVERSITIES
  • PERSPECTIVE
  • PERFORMANCE
  • ECONOMIES

Cite this

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title = "Research orientation and agglomeration: Can every region become a Silicon Valley?",
abstract = "Not only success stories, such as Silicon Valley, but also non-success stories can inform regional innovation policy. In order to provide a benchmark for regional innovation systems we compare both success and non-success stories. Regional innovation systems differ in structural and functional requirements, because development processes are path dependent. We suggest that regions' development paths emerge from agglomeration patterns and research orientation. Accordingly, we have developed a typology of regions including (1) their agglomeration patterns (either MAR or Jacobs' type) and (2) the degree to which their research is predominantly oriented towards obtaining fundamental understanding or addressing considerations of use. We combine qualitative and quantitative data on thirty-six European regions to categorize them according to research orientation and agglomeration, thereby developing a typology. We use this typology and some basic quantitative economic data to see how success and non-success regions are distributed. Our results indicate that a better understanding of how to combine agglomeration patterns with research orientation can guide context-sensitive policy.",
keywords = "Regional innovation systems, Research orientation, Pasteur's Quadrant, Agglomeration effects, Context-sensitive policy, TECHNOLOGICAL-INNOVATION SYSTEMS, OLD INDUSTRIAL-AREAS, POLICY, KNOWLEDGE, CITIES, SCIENCE, UNIVERSITIES, PERSPECTIVE, PERFORMANCE, ECONOMIES",
author = "Ward Ooms and Claudia Werker and Caniels, {Marjolein C. J.} and {van den Bosch}, Herman",
year = "2015",
doi = "10.1016/j.technovation.2015.08.001",
language = "English",
volume = "45-46",
pages = "78--92",
journal = "Technovation",
issn = "0166-4972",
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}

Research orientation and agglomeration : Can every region become a Silicon Valley? / Ooms, Ward; Werker, Claudia; Caniels, Marjolein C. J.; van den Bosch, Herman.

In: Technovation, Vol. 45-46, 2015, p. 78-92.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Research orientation and agglomeration

T2 - Can every region become a Silicon Valley?

AU - Ooms, Ward

AU - Werker, Claudia

AU - Caniels, Marjolein C. J.

AU - van den Bosch, Herman

PY - 2015

Y1 - 2015

N2 - Not only success stories, such as Silicon Valley, but also non-success stories can inform regional innovation policy. In order to provide a benchmark for regional innovation systems we compare both success and non-success stories. Regional innovation systems differ in structural and functional requirements, because development processes are path dependent. We suggest that regions' development paths emerge from agglomeration patterns and research orientation. Accordingly, we have developed a typology of regions including (1) their agglomeration patterns (either MAR or Jacobs' type) and (2) the degree to which their research is predominantly oriented towards obtaining fundamental understanding or addressing considerations of use. We combine qualitative and quantitative data on thirty-six European regions to categorize them according to research orientation and agglomeration, thereby developing a typology. We use this typology and some basic quantitative economic data to see how success and non-success regions are distributed. Our results indicate that a better understanding of how to combine agglomeration patterns with research orientation can guide context-sensitive policy.

AB - Not only success stories, such as Silicon Valley, but also non-success stories can inform regional innovation policy. In order to provide a benchmark for regional innovation systems we compare both success and non-success stories. Regional innovation systems differ in structural and functional requirements, because development processes are path dependent. We suggest that regions' development paths emerge from agglomeration patterns and research orientation. Accordingly, we have developed a typology of regions including (1) their agglomeration patterns (either MAR or Jacobs' type) and (2) the degree to which their research is predominantly oriented towards obtaining fundamental understanding or addressing considerations of use. We combine qualitative and quantitative data on thirty-six European regions to categorize them according to research orientation and agglomeration, thereby developing a typology. We use this typology and some basic quantitative economic data to see how success and non-success regions are distributed. Our results indicate that a better understanding of how to combine agglomeration patterns with research orientation can guide context-sensitive policy.

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KW - Pasteur's Quadrant

KW - Agglomeration effects

KW - Context-sensitive policy

KW - TECHNOLOGICAL-INNOVATION SYSTEMS

KW - OLD INDUSTRIAL-AREAS

KW - POLICY

KW - KNOWLEDGE

KW - CITIES

KW - SCIENCE

KW - UNIVERSITIES

KW - PERSPECTIVE

KW - PERFORMANCE

KW - ECONOMIES

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SN - 0166-4972

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