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Network structural properties for cluster long run dynamics. Evidence from collaborative R&D networks in the European mobile phone industry

Author

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  • Joan Crespo

    (LEREPS, University of Toulouse)

  • Raphaël Suire

    (CREM UMR CNRS 6211, University of Rennes 1, France)

  • Jérôme Vicente

    (PACTE-CNRS, Sciences-Po Grenoble)

Abstract

In a recent literature, the structural properties of knowledge networks have been pointed out as a critical factor for cluster structural changes and long run dynamics. Mixing evolutionary economic geography and network-based approach of clusters, this contribution aims at capturing and discussing the particular influence of hierarchy (degree distribution) and assortativity (degree correlation) in the innovative capabilities of clusters along the industry life cycle. We test our propositions in the field of the mobile phone industry in Europe from 1988 to 2008. We use EPO PATSTAT and OECD REGPAT to capture cluster trends, and R&D relations from European Framework Programs to capture knowledge networks and their evolving structural properties. Our findings provide new insights to understand the organization of clusters over time in order to perform along the industry life cycle

Suggested Citation

  • Joan Crespo & Raphaël Suire & Jérôme Vicente, 2013. "Network structural properties for cluster long run dynamics. Evidence from collaborative R&D networks in the European mobile phone industry," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 201336, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
  • Handle: RePEc:tut:cremwp:201336
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    3. Baek, Hyun & Lee, Heesang, 2021. "Framework of Socio-Technology Analysis and Prescriptions for a sustainable society: Focusing on the mobile technology case," Technology in Society, Elsevier, vol. 65(C).
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    6. Bastien Bernela & Marie Ferru & Marc-Hubert Depret, 2017. "When genesis shapes cluster life cycle? Applying mixed method on a French cluster case study," Working Papers halshs-01616929, HAL.
    7. Abbasiharofteh, Milad, 2020. "Endogenous effects and cluster transition: a conceptual framework for cluster policy," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 2508-2531.
    8. Pablo Galaso & Jaromír Kovářík, 2021. "Collaboration networks, geography and innovation: Local and national embeddedness," Papers in Regional Science, Wiley Blackwell, vol. 100(2), pages 349-377, April.
    9. Joan Crespo & Frédéric Amblard & Jérôme Vicente, 2015. "Simulating micro behaviours and structural properties of knowledge networks: toward a “one size fits one” cluster policy," Papers in Evolutionary Economic Geography (PEEG) 1503, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Feb 2015.
    10. Galaso, Pablo & Kovářík, Jaromír, 2018. "Collaboration Networks and Innovation: How to Define Network Boundaries," MPRA Paper 85108, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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