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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

Listed:
  • Joan Crespo

    (URU - Urban and Regional Research Centre Utrecht - Universiteit Utrecht / Utrecht University [Utrecht])

  • Raphaël Suire

    (MARSOUIN - Môle Armoricain de Recherche sur la SOciété de l'information et des usages d'INternet - UR - Université de Rennes - UEB - Université européenne de Bretagne - European University of Brittany - UBS - Université de Bretagne Sud - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - UBO EPE - Université de Brest - Télécom Bretagne - IMT - Institut Mines-Télécom [Paris] - UR2 - Université de Rennes 2, CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

  • Jérôme Vicente

    (LEREPS - Laboratoire d'Etude et de Recherche sur l'Economie, les Politiques et les Systèmes Sociaux - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - UT2J - Université Toulouse - Jean Jaurès - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - Institut d'Études Politiques [IEP] - Toulouse - ENSFEA - École Nationale Supérieure de Formation de l'Enseignement Agricole de Toulouse-Auzeville, IEP Toulouse - Sciences Po Toulouse - Institut d'études politiques de Toulouse)

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 to perform along the industry life cycle.

Suggested Citation

  • Joan Crespo & Raphaël Suire & Jérôme Vicente, 2016. "Network structural properties for cluster long-run dynamics: evidence from collaborative R&D networks in the European mobile phone industry," Post-Print halshs-01238577, HAL.
    • Handle: RePEc:hal:journl:halshs-01238577
    DOI: 10.1093/icc/dtv032
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    Cited by:

    1. 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.
    2. Galaso, Pablo & Kovářík, Jaromír, 2018. "Collaboration Networks and Innovation: How to Define Network Boundaries," MPRA Paper 85108, University Library of Munich, Germany.
    3. 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.
    4. Anna Cabigiosu, 2018. "When do modular dominant designs emerge? A theoretical framework," Working Papers 05, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
    5. Qin, Xionghe & Wang, Xueli & Kwan, Mei-Po, 2023. "The contrasting effects of interregional networks and local agglomeration on R&D productivity in Chinese provinces: Insights from an empirical spatial Durbin model," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    6. Lucena-Piquero, D. & Vicente, Jérôme, 2019. "The visible hand of cluster policy makers: An analysis of Aerospace Valley (2006-2015) using a place-based network methodology," Research Policy, Elsevier, vol. 48(3), pages 830-842.
    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, vol. 28(12), pages 2508-2531.
    8. Andrea Ancona & Roy Cerqueti & Gianluca Vagnani, 2023. "A novel methodology to disambiguate organization names: an application to EU Framework Programmes data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4447-4474, August.
    9. Lorenzo Zirulia, 2023. "Path dependence in evolving R&D networks," Journal of Evolutionary Economics, Springer, vol. 33(1), pages 149-177, January.
    10. 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).
    11. Alejandro J. Gutiérrez Rodríguez & Nini Johanna Barón & José Manuel Guaita Martínez, 2020. "Validity of Dynamic Capabilities in the Operation Based on New Sustainability Narratives on Nature Tourism SMEs and Clusters," Sustainability, MDPI, vol. 12(3), pages 1-18, January.
    12. 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.
    13. Grashof, Nils, 2021. "Putting the watering can away –Towards a targeted (problem-oriented) cluster policy framework," Research Policy, Elsevier, vol. 50(9).

    More about this item

    Keywords

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    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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