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Globalization of collaborative creativity through cross-border patent activities

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  • Huang, Mu-Hsuan
  • Dong, Huei-Ru
  • Chen, Dar-Zen

Abstract

This study examines global collaborative creativity through patentometrics and social network analysis. Because patents are a direct output of innovative activities, cross-border patents are used to analyze the trend of global collaborative creativity. The results show linear growth of cross-border patents, while numerous inventors have grown exponentially for collaborative creativity. The number of inventors for global collaborative creativity trends have increased more rapidly than the number of patents. The network for global collaborative creativity is denser and shows a growing trend over a five-year interval. Both observed and cosine-normalized numbers of k-cores in global collaborative creativity show a growing trend, while the cosine-normalized k-cores increase slowly compared to the observed one. Similarly, the social network analysis confirms a growing network of global collaborative creativity, which is dense despite its small degree of growth. This study also found that high values of “betweenness” tend to spread from core countries to periphery countries. Collaborative creativity has globalized but remains concentrated in core countries such as the U.S., the UK, France, Germany, and Canada.

Suggested Citation

  • Huang, Mu-Hsuan & Dong, Huei-Ru & Chen, Dar-Zen, 2012. "Globalization of collaborative creativity through cross-border patent activities," Journal of Informetrics, Elsevier, vol. 6(2), pages 226-236.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:2:p:226-236
    DOI: 10.1016/j.joi.2011.10.003
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    Citations

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    Cited by:

    1. Liu, Ju & Chaminade, Cristina, 2014. "Exploring the interplay, differences, and commonalities between global production networks and global innovation networks of two multinational companies," Papers in Innovation Studies 2014/7, Lund University, CIRCLE - Centre for Innovation Research.
    2. Wei Yang & Xiang Yu & Ben Zhang & Ziyang Huang, 2021. "Mapping the landscape of international technology diffusion (1994–2017): network analysis of transnational patents," The Journal of Technology Transfer, Springer, vol. 46(1), pages 138-171, February.
    3. Sari Pekkala Kerr & William R. Kerr, 2018. "Global Collaborative Patents," Economic Journal, Royal Economic Society, vol. 128(612), pages 235-272, July.
    4. Hui-Yun Sung & Chun-Chieh Wang & Dar-Zen Chen & Mu-Hsuan Huang, 2014. "A comparative study of patent counts by the inventor country and the assignee country," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 577-593, August.
    5. Kilian Buehling & Matthias Geissler & Dorothea Strecker, 2022. "Free access to scientific literature and its influence on the publishing activity in developing countries: The effect of Sci‐Hub in the field of mathematics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1336-1355, September.
    6. Zhao Qu & Shanshan Zhang & Chunbo Zhang, 2017. "Patent research in the field of library and information science: Less useful or difficult to explore?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 205-217, April.
    7. Sari Pekkala Kerr & William R. Kerr, 2018. "Global Collaborative Patents," Economic Journal, Royal Economic Society, vol. 128(612), pages 235-272, July.
    8. repec:zbw:bofrdp:2017_003 is not listed on IDEAS
    9. Angelou, K. & Maragakis, M. & Kosmidis, K. & Argyrakis, P., 2021. "The evolution of triangular research and innovation collaborations in the European area," Journal of Informetrics, Elsevier, vol. 15(3).
    10. Weiwei Liu & Yuan Tao & Zhile Yang & Kexin Bi, 2019. "Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    11. Jiang, Hongxun & Fan, Shaokun & Zhang, Nan & Zhu, Bin, 2023. "Deep learning for predicting patent application outcome: The fusion of text and network embeddings," Journal of Informetrics, Elsevier, vol. 17(2).

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