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Identifying cooperation for innovation: A comparison of data sources

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  • Fritsch, Michael
  • Piontek, Matthias
  • Titze, Mirko

Abstract

The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidised collaborative Research and Development projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers. The key role of universities that connect many regional actors is identified in all three types of data.

Suggested Citation

  • Fritsch, Michael & Piontek, Matthias & Titze, Mirko, 2019. "Identifying cooperation for innovation: A comparison of data sources," IWH Discussion Papers 1/2019, Halle Institute for Economic Research (IWH).
    • Handle: RePEc:zbw:iwhdps:12019
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    Cited by:

    1. Michael Fritsch & Muhamed Kudic, 2022. "Micro dynamics and macro stability in inventor networks," The Journal of Technology Transfer, Springer, vol. 47(2), pages 353-382, April.
    2. Aleksandra Zygmunt, 2020. "Do Human Resources and the Research System Affect Firms’ Innovation Activities? Results from Poland and the Czech Republic," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    3. Michael Fritsch & Moritz Zoellner, 2022. "Actor Fluidity and Knowledge Persistence in Regional Inventor Networks," Economies, MDPI, vol. 10(12), pages 1-20, November.
    4. Gergő Tóth & Sándor Juhász & Zoltán Elekes & Balázs Lengyel, 2021. "Repeated collaboration of inventors across European regions," European Planning Studies, Taylor & Francis Journals, vol. 29(12), pages 2252-2272, December.
    5. Zhu, Shanshan & Hagedoorn, John & Zhang, Shuhui & Liu, Fengchao, 2021. "Effects of technological distance on innovation performance under heterogeneous technological orientations," Technovation, Elsevier, vol. 106(C).
    6. Prokop, Viktor & Hajek, Petr & Stejskal, Jan, 2021. "Configuration Paths to Efficient National Innovation Ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    7. Brachert, Matthias & Brautzsch, Hans-Ulrich & Dettmann, Eva & Giebler, Alexander & Schneider, Lutz & Titze, Mirko, 2020. ""Evaluation der Gemeinschaftsaufgabe 'Verbesserung der regionalen Wirtschaftsstruktur' (GRW)" durch einzelbetriebliche Erfolgskontrolle: Endbericht," IWH Online 5/2020, Halle Institute for Economic Research (IWH).
    8. Abbasiharofteh, Milad & Kinne, Jan & Krüger, Miriam, 2021. "The strength of weak and strong ties in bridging geographic and cognitive distances," ZEW Discussion Papers 21-049, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    Keywords

    knowledge interactions; social network analysis; regional innovation systems; data sources;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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