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Measuring the knowledge-based economy of China in terms of synergy among technological, organizational, and geographic attributes of firms

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  • Loet Leydesdorff

    (University of Amsterdam)

  • Ping Zhou

    (Zhejiang University)

Abstract

Using the possible synergy among geographic, size, and technological distributions of firms in the Orbis database, we find the greatest reduction of uncertainty at the level of the 31 provinces of China, and an additional 18.0 % at the national level. Some of the coastal provinces stand out as expected, but the metropolitan areas of Beijing and Shanghai are (with Tianjin and Chongqing) most pronounced at the next-lower administrative level of (339) prefectures, since these four “municipalities” are administratively defined at both levels. Focusing on high- and medium-tech manufacturing, a shift toward Beijing, Shanghai, and Tianjin (near Beijing) is indicated, but the synergy is on average not enhanced. High- and medium-tech manufacturing is less embedded in China than in Western Europe. Knowledge-intensive services “uncouple” the knowledge base from the regional economies mostly in Chongqing and Beijing. Unfortunately, the Orbis data is incomplete since it was collected for commercial and not for administrative or governmental purposes. However, we provide a methodology that can be used by others who may have access to higher-quality statistical data for the measurement.

Suggested Citation

  • Loet Leydesdorff & Ping Zhou, 2014. "Measuring the knowledge-based economy of China in terms of synergy among technological, organizational, and geographic attributes of firms," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1703-1719, March.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:3:d:10.1007_s11192-013-1179-1
    DOI: 10.1007/s11192-013-1179-1
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    References listed on IDEAS

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    1. Loet Leydesdorff, 2003. "The mutual information of university-industry-government relations: An indicator of the Triple Helix dynamics," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(2), pages 445-467, October.
    2. Park, Han Woo & Leydesdorff, Loet, 2010. "Longitudinal trends in networks of university-industry-government relations in South Korea: The role of programmatic incentives," Research Policy, Elsevier, vol. 39(5), pages 640-649, June.
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    4. Balazs Lengyel & Loet Leydesdorff, 2011. "Regional Innovation Systems in Hungary: The Failing Synergy at the National Level," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 677-693.
    5. Leydesdorff, Loet & Fritsch, Michael, 2006. "Measuring the knowledge base of regional innovation systems in Germany in terms of a Triple Helix dynamics," Research Policy, Elsevier, vol. 35(10), pages 1538-1553, December.
    6. Yuan Sun & Masamitsu Negishi, 2010. "Measuring the relationships among university, industry and other sectors in Japan’s national innovation system: a comparison of new approaches with mutual information indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 677-685, March.
    7. Loet Leydesdorff & Øivind Strand, 2013. "The Swedish system of innovation: Regional synergies in a knowledge-based economy," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(9), pages 1890-1902, September.
    8. Leydesdorff, Loet & Dolfsma, Wilfred & Van der Panne, Gerben, 2006. "Measuring the knowledge base of an economy in terms of triple-helix relations among 'technology, organization, and territory'," Research Policy, Elsevier, vol. 35(2), pages 181-199, March.
    9. W. Garner & William McGill, 1956. "The relation between information and variance analyses," Psychometrika, Springer;The Psychometric Society, vol. 21(3), pages 219-228, September.
    10. William McGill, 1954. "Multivariate information transmission," Psychometrika, Springer;The Psychometric Society, vol. 19(2), pages 97-116, June.
    11. Han Woo Park & Heung Deug Hong & Loet Leydesdorff, 2005. "A comparison of the knowledge-based innovation systems in the economies of South Korea and the Netherlands using Triple Helix indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 65(1), pages 3-27, October.
    12. Loet Leydesdorff & Yuan Sun, 2009. "National and international dimensions of the Triple Helix in Japan: University–industry–government versus international coauthorship relations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(4), pages 778-788, April.
    13. Strand, Øivind & Leydesdorff, Loet, 2013. "Where is synergy indicated in the Norwegian innovation system? Triple-Helix relations among technology, organization, and geography," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 471-484.
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    2. Tao Zhuang & Zhixia Zhou & Qingjun Li, 2021. "University‐industry‐government triple helix relationship and regional innovation efficiency in China," Growth and Change, Wiley Blackwell, vol. 52(1), pages 349-370, March.
    3. Inga Ivanova, 2022. "The relation between complexity and synergy in the case of China: different ways of predicting GDP growth in a complex and adaptive system," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 195-215, February.
    4. Inga Ivanova & Oivind Strand & Duncan Kushnir & Loet Leydesdorff, 2015. "The Efficiency of Triple-Helix Relations in Innovation Systems: Measuring the Connection between a Country’S Net Income and its Knowledge Base," HSE Working papers WP BRP 55/STI/2015, National Research University Higher School of Economics.
    5. Weimin Kang & Shuliang Zhao & Wei Song & Tao Zhuang, 2019. "Triple helix in the science and technology innovation centers of China from the perspective of mutual information: a comparative study between Beijing and Shanghai," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 921-940, March.
    6. Jiao, Hao & Zhou, Jianghua & Gao, Taishan & Liu, Xielin, 2016. "The more interactions the better? The moderating effect of the interaction between local producers and users of knowledge on the relationship between R&D investment and regional innovation systems," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 13-20.
    7. Shouji Fujimoto & Takayuki Mizuno & Atushi Ishikawa, 2022. "Interpolation of non-random missing values in financial statements’ big data using CatBoost," Journal of Computational Social Science, Springer, vol. 5(2), pages 1281-1301, November.
    8. Inga Ivanova & Oivind Strand & Loet Leydesdorff, 2019. "The Synergy and Cycle Values in Regional Innovation Systems: The Case of Norway," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 13(1), pages 48-61.
    9. Tao, Zhuang & Shuliang, Zhao, 2022. "Collaborative innovation relationship in Yangtze River Delta of China: Subjects collaboration and spatial correlation," Technology in Society, Elsevier, vol. 69(C).
    10. Zhang, Yi & Chen, Kaihua & Fu, Xiaolan, 2019. "Scientific effects of Triple Helix interactions among research institutes, industries and universities," Technovation, Elsevier, vol. 86, pages 33-47.
    11. Porto-Gomez, Igone & Zabala-Iturriagagoitia, Jon Mikel & Leydesdorff, Loet, 2019. "Innovation systems in México: A matter of missing synergies," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    12. Inga Ivanova & Oivind Strand & Loet Leydesdorff, 2014. "Synergy cycles in the Norwegian innovation system: The relation between synergy and cycle values," Papers 1409.2760, arXiv.org.

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