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Determinants of Cross-Regional R&D Collaboration Networks: An Application of Exponential Random Graph Models

In: The Geography of Networks and R&D Collaborations

Author

Listed:
  • Tom Broekel

    (Leibniz University of Hanover)

  • Matté Hartog

    (Utrecht University)

Abstract

This study investigates the usefulness of exponential random graph models (ERGM) to analyze the determinants of cross-regional R&D collaboration networks. Using spatial interaction models, most research on R&D collaboration between regions is constrained to focus on determinants at the node level (e.g. R&D activity of a region) and dyad level (e.g. geographical distance between regions). ERGMs represent a new set of network analysis techniques that has been developed in recent years in mathematical sociology. In contrast to spatial interaction models, ERGMs additionally allow considering determinants at the structural network level while still only requiring cross-sectional network data. The usefulness of ERGMs is illustrated by an empirical study on the structure of the cross-regional R&D collaboration network of the German chemical industry. The empirical results confirm the importance of determinants at all three levels. It is shown that in addition to determinants at the node and dyad level, the structural network level determinant “triadic closure” helps in explaining the structure of the network. That is, regions that are indirectly linked to each other are more likely to be directly linked as well.

Suggested Citation

  • Tom Broekel & Matté Hartog, 2013. "Determinants of Cross-Regional R&D Collaboration Networks: An Application of Exponential Random Graph Models," Advances in Spatial Science, in: Thomas Scherngell (ed.), The Geography of Networks and R&D Collaborations, edition 127, chapter 0, pages 49-70, Springer.
    • Handle: RePEc:spr:adspcp:978-3-319-02699-2_4
    DOI: 10.1007/978-3-319-02699-2_4
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    Citations

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    Cited by:

    1. Luigi Aldieri & Gennaro Guida & Maxim Kotsemir & Concetto Paolo Vinci, 2019. "An investigation of impact of research collaboration on academic performance in Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2003-2040, July.
    2. Peng, Tai-Quan, 2015. "Assortative mixing, preferential attachment, and triadic closure: A longitudinal study of tie-generative mechanisms in journal citation networks," Journal of Informetrics, Elsevier, vol. 9(2), pages 250-262.
    3. Brökel, Tom & Mewes, Lars, 2020. "Der Beitrag von Hochschulen zur Einbindung von Regionen in politisch induzierte Wissensnetzwerke," Forschungsberichte der ARL: Aufsätze, in: Postlep, Rolf-Dieter & Blume, Lorenz & Hülz, Martina (ed.), Hochschulen und ihr Beitrag für eine nachhaltige Regionalentwicklung, volume 11, pages 233-259, ARL – Akademie für Raumentwicklung in der Leibniz-Gemeinschaft.
    4. Zhang, Yanlu & Yang, Naiding, 2018. "Vulnerability analysis of interdependent R&D networks under risk cascading propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1056-1068.
    5. Abbasiharofteh, Milad & Broekel, Tom, 2021. "Still in the shadow of the wall? The case of the Berlin biotechnology cluster," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 53(1), pages 73-94.
    6. Iris Wanzenböck, 2016. "Measuring network proximity of regions in R&D networks," Innovation Studies Utrecht (ISU) working paper series 16-03, Utrecht University, Department of Innovation Studies, revised Nov 2016.
    7. Falk Strotebeck, 2014. "Running with the pack? The role of Universities of applied science in a German research network," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(2), pages 139-156, October.

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