IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i7p1283-d1178708.html
   My bibliography  Save this article

Innovation Networks of Science and Technology Firms: Evidence from China

Author

Listed:
  • Chenxi Liu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
    Digital City Research Center, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Zhenghong Peng

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
    Digital City Research Center, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Lingbo Liu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
    Digital City Research Center, School of Urban Design, Wuhan University, Wuhan 430072, China
    Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA)

  • Shixuan Li

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
    Digital City Research Center, School of Urban Design, Wuhan University, Wuhan 430072, China)

Abstract

Examining and assessing the characteristics of innovation networks among science and technology firms at the city level is essential for comprehending the innovation patterns of cities and improving their competitiveness. Nevertheless, the majority of studies in this field solely rely on patent and paper data, neglecting the analysis of networks across diverse scales and dimensions. Websites offer a novel platform for companies to exhibit their products and services, and the utilization of hyperlink data better captures the dynamics of innovative cooperation. Thus, to attain a more realistic and precise comprehension of China’s technology enterprise cooperation networks, enhance the understanding of intra-city and cross-border cooperation within innovation networks, and offer more scientific guidance to cities in enhancing their innovation capabilities by investigating the factors influencing innovation scenarios and the mechanisms of their interactions, this study constructs an innovation network based on the hyperlink data extracted from Chinese science and technology enterprises’ websites in 2022. It explores the network’s inherent characteristics and spatial patterns across multiple dimensions and scales. Additionally, it employs GeoDetector to analyze the driving factors behind the heterogeneity of city quadrants across each dimension. The findings suggest the following: (1) Evident polarization of innovation capability exists, with a more pronounced differentiation of cities between high capability zones. (2) Contrary to the conventional notion of geographical proximity, cross-region website cooperation prevails, with cross-provincial cooperation being more prevalent than intra-provincial cross-city cooperation. (3) Enterprise cooperation tends to align with partners of similar scale, and small and medium-sized enterprises primarily engage in internal cooperation, primarily concentrated in second and third-tier cities. (4) Cities with high degree centrality and structure holes are primarily located in the construction areas of Chinese urban agglomerations, while those with low degree centrality and structure holes are situated near double-high cities. (5) The spatial heterogeneity of innovation networks across the four dimensions is primarily influenced by STI, while cooperation intensity and innovation capacity dimensions are strongly influenced by traffic capacity. The intra- and inter-city cooperation intensity dimensions are significantly impacted by administrative grade, and the enterprise scale and network location dimensions are most affected by the level of digital infrastructure.

Suggested Citation

  • Chenxi Liu & Zhenghong Peng & Lingbo Liu & Shixuan Li, 2023. "Innovation Networks of Science and Technology Firms: Evidence from China," Land, MDPI, vol. 12(7), pages 1-21, June.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1283-:d:1178708
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/7/1283/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/7/1283/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Rammer & Jan Kinne & Knut Blind, 2020. "Knowledge proximity and firm innovation: A microgeographic analysis for Berlin," Urban Studies, Urban Studies Journal Limited, vol. 57(5), pages 996-1014, April.
    2. Christian Zeller, 2010. "The Pharma-biotech Complex and Interconnected Regional Innovation Arenas," Urban Studies, Urban Studies Journal Limited, vol. 47(13), pages 2867-2894, November.
    3. Freeman, C., 1991. "Networks of innovators: A synthesis of research issues," Research Policy, Elsevier, vol. 20(5), pages 499-514, October.
    4. Sheng-qiang Jiang & An-na Shi & Zhi-hang Peng & Xin Li, 2017. "Major factors affecting cross-city R&D collaborations in China: evidence from cross-sectional co-patent data between 224 cities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1251-1266, June.
    5. Mingming Guan & Siyu Wu & Chengliang Liu, 2022. "Comparing China’s urban aviation and innovation networks," Growth and Change, Wiley Blackwell, vol. 53(1), pages 470-486, March.
    6. Jan Kinne & Janna Axenbeck, 2020. "Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2011-2041, December.
    7. Jiajia Hao & Chunling Li & Runsen Yuan & Masood Ahmed & Muhammad Asif Khan & Judit Oláh, 2020. "The Influence of the Knowledge-Based Network Structure Hole on Enterprise Innovation Performance: The Threshold Effect of R&D Investment Intensity," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
    8. De Noni, Ivan & Orsi, Luigi & Belussi, Fiorenza, 2018. "The role of collaborative networks in supporting the innovation performances of lagging-behind European regions," Research Policy, Elsevier, vol. 47(1), pages 1-13.
    9. Abdullah Gök & Alec Waterworth & Philip Shapira, 2015. "Use of web mining in studying innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 653-671, January.
    10. Xia Gao & Jiancheng Guan & Ronald Rousseau, 2011. "Mapping collaborative knowledge production in China using patent co-inventorships," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 343-362, August.
    11. Mariagrazia Squicciarini & Hélène Dernis & Chiara Criscuolo, 2013. "Measuring Patent Quality: Indicators of Technological and Economic Value," OECD Science, Technology and Industry Working Papers 2013/3, OECD Publishing.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xie, Qijun & Su, Jun, 2021. "The spatial-temporal complexity and dynamics of research collaboration: Evidence from 297 cities in China (1985–2016)," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Wei Fang & Lulu Tang & Pengxiao Cheng & Naveed Ahmad, 2018. "Evolution Decision, Drivers and Green Innovation Performance for Collaborative Innovation Center of Ecological Building Materials and Environmental Protection Equipment in Jiangsu Province of China," IJERPH, MDPI, vol. 15(11), pages 1-19, October.
    3. Menger Tu & Sandy Dall'erba & Mingque Ye, 2022. "Spatial and Temporal Evolution of the Chinese Artificial Intelligence Innovation Network," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    4. Yindan Ye & Kevin De Moortel & Thomas Crispeels, 2020. "Network dynamics of Chinese university knowledge transfer," The Journal of Technology Transfer, Springer, vol. 45(4), pages 1228-1254, August.
    5. Kolja Hesse & Dirk Fornahl, 2020. "Essential ingredients for radical innovations? The role of (un‐)related variety and external linkages in Germany," Papers in Regional Science, Wiley Blackwell, vol. 99(5), pages 1165-1183, October.
    6. Dörr, Julian Oliver & Kinne, Jan & Lenz, David & Licht, Georg & Winker, Peter, 2021. "An integrated data framework for policy guidance in times of dynamic economic shocks," ZEW Discussion Papers 21-062, ZEW - Leibniz Centre for European Economic Research.
    7. Motohashi, Kazuyuki & Zhu, Chen, 2023. "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    8. Christoph Stich & Emmanouil Tranos & Max Nathan, 2023. "Modeling clusters from the ground up: A web data approach," Environment and Planning B, , vol. 50(1), pages 244-267, January.
    9. Rammer, Christian & Es-Sadki, Nordine, 2023. "Using big data for generating firm-level innovation indicators - a literature review," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    10. Chengliang Liu & Caicheng Niu & Ji Han, 2019. "Spatial Dynamics of Intercity Technology Transfer Networks in China’s Three Urban Agglomerations: A Patent Transaction Perspective," Sustainability, MDPI, vol. 11(6), pages 1-24, March.
    11. 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).
    12. Pinto, Pablo E. & Vallone, Andres & Honores, Guillermo, 2019. "The structure of collaboration networks: Findings from three decades of co-invention patents in Chile," Journal of Informetrics, Elsevier, vol. 13(4).
    13. Gupeng Zhang & Jiancheng Guan & Xielin Liu, 2014. "The impact of small world on patent productivity in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 945-960, February.
    14. De Noni, Ivan & Ganzaroli, Andrea & Pilotti, Luciano, 2021. "Spawning exaptive opportunities in European regions: The missing link in the smart specialization framework," Research Policy, Elsevier, vol. 50(6).
    15. Zuo-jun Dong & Lan Xu & Jia-hui Cheng & Guo-jun Sun, 2021. "Major factors affecting biomedical cross-city R&D collaborations based on cooperative patents in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1923-1943, March.
    16. Jan Kinne & Janna Axenbeck, 2020. "Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2011-2041, December.
    17. Axenbeck, Janna & Breithaupt, Patrick, 2022. "Measuring the digitalisation of firms: A novel text mining approach," ZEW Discussion Papers 22-065, ZEW - Leibniz Centre for European Economic Research.
    18. Ebersberger, Bernd & Feit, Margarita & Mengis, Helen, 2023. "International knowledge interactions and catch-up. Evidence from European patent data for Chinese latecomer firms," International Business Review, Elsevier, vol. 32(2).
    19. MOTOHASHI Kazuyuki, 2023. "Identifying Technology Opportunity Using a Dual-attention Model and a Technology-market Concordance Matrix," Discussion papers 23024, Research Institute of Economy, Trade and Industry (RIETI).
    20. Schmidt, Sebastian & Kinne, Jan & Lautenbach, Sven & Blaschke, Thomas & Lenz, David & Resch, Bernd, 2022. "Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining," ZEW Discussion Papers 22-006, ZEW - Leibniz Centre for European Economic Research.

    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:gam:jlands:v:12:y:2023:i:7:p:1283-:d:1178708. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.