IDEAS home Printed from https://ideas.repec.org/a/ids/ijcome/v8y2018i3-4p325-344.html
   My bibliography  Save this article

Endogenous dynamics of innovation networks in the German automotive industry: analysing structural network evolution using a stochastic actor-oriented approach

Author

Listed:
  • Daniel Hain
  • Tobias Buchmann
  • Muhamed Kudic
  • Matthias Müller

Abstract

The generation of innovation is well known to be a social process depending on mutual interactions, aiming at accessing and exchanging knowledge in order to generate novel goods and services. Accordingly, interest in interfirm innovation networks has increased sharply over the last decade. Preceding research indicates that the structural dynamics of networks is driven both by endogenous and exogenous forces. In particular, we focus on the role of the endogenous determinants of the network evolution of interfirm networks - a category of often underestimated forces. We employ a longitudinal dataset that comprises German automotive firms' performance between 2002 and 2006 and apply a stochastic actor-oriented model (SAOM) designed to analyse both the endogenous and exogenous determinants of network change. Our results show that endogenous determinants - approximated by measures for local and global clustering - exhibit greater explanatory power than exogenous firm characteristics such as age, size, and R%D activity.

Suggested Citation

  • Daniel Hain & Tobias Buchmann & Muhamed Kudic & Matthias Müller, 2018. "Endogenous dynamics of innovation networks in the German automotive industry: analysing structural network evolution using a stochastic actor-oriented approach," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 8(3/4), pages 325-344.
    • Handle: RePEc:ids:ijcome:v:8:y:2018:i:3/4:p:325-344
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=96392
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Muhamed Kudic & Matthias Müller & Tobias Buchmann & Andreas Pyka & Jutta Günther, 2021. "Network dynamics, economic transition, and policy design—an introduction," Review of Evolutionary Political Economy, Springer, vol. 2(1), pages 1-8, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijcome:v:8:y:2018:i:3/4:p:325-344. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=311 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.