IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v200y2024ics0040162523008284.html
   My bibliography  Save this article

Policy-induced cooperative knowledge network, university-industry collaboration and firm innovation: Evidence from the Greater Bay Area

Author

Listed:
  • Zhao, Yang
  • Yongquan, Yang
  • Jian, Ma
  • Lu, Angela
  • Xuanhua, Xu

Abstract

The major goal of this paper is to analyze the interactive impact of China's high-tech policies on university-industry (UI) collaboration and firm innovation by a network-based approach. Introducing high-tech policy to traditional innovative networks, we constructed a policy-induced UI collaboration network and applied social network analysis (SNA) method to empirically study the role of high-tech policies and the underlying mechanisms in firm innovation in the Greater Bay Area (GBA). This policy-induced innovative network consists of over 2000 innovative entities in GBA from 2012 to 2021, which reflects dynamic characteristics of innovative activities in this region. Based on this network, we find that high-tech policies play a positive and key role in regional innovation and we also clarify some potential mechanisms and routes in the effect of policies on UI collaboration and firm innovation. This paper advances our understanding of innovative networks and policy-induced regional innovation, which has implications for innovation policymaking in world-class innovative centers.

Suggested Citation

  • Zhao, Yang & Yongquan, Yang & Jian, Ma & Lu, Angela & Xuanhua, Xu, 2024. "Policy-induced cooperative knowledge network, university-industry collaboration and firm innovation: Evidence from the Greater Bay Area," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008284
    DOI: 10.1016/j.techfore.2023.123143
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523008284
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.123143?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    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:eee:tefoso:v:200:y:2024:i:c:s0040162523008284. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.