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Global networks of genetically modified crops technology: a patent citation network analysis

Author

Listed:
  • Jiaojiao Ji

    (University of Science and Technology of China (USTC)
    Nanjing University)

  • George A. Barnett

    (University of California)

  • Jianxun Chu

    (University of Science and Technology of China (USTC))

Abstract

This paper employs the patent data of four major genetically modified (GM) crops, soybeans, cotton, maize and rapeseed, to illustratee how the innovation of GM crop technology diffused and distributed globally over time. Data collected from the Derwent Innovation Index, were employed to construct country patent citation networks, from 1984 to 2015, and the results revealed that developed countries were early adopters, and the primary actors in the innovation of GM crop technology. Only seven developing countries appeared in the country citation network. Most developed countries were reluctant to apply GM crop technology for commercial cultivation. Private businesses stood out in the patent citation network. The early adoption and better performance of developed countries can be explained by the activities of large established private companies.

Suggested Citation

  • Jiaojiao Ji & George A. Barnett & Jianxun Chu, 2019. "Global networks of genetically modified crops technology: a patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 737-762, March.
    • Handle: RePEc:spr:scient:v:118:y:2019:i:3:d:10.1007_s11192-019-03006-1
    DOI: 10.1007/s11192-019-03006-1
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    Cited by:

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    3. Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    4. Li, Xiaotao & Yuan, Xiaodong, 2022. "Tracing the technology transfer of battery electric vehicles in China: A patent citation organization network analysis," Energy, Elsevier, vol. 239(PD).
    5. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    6. Yuan, Xiaodong & Li, Xiaotao, 2021. "The evolution of the industrial value chain in China's high-speed rail driven by innovation policies: A patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    7. Lidan Jiang & Jingyan Chen & Yuhan Bao & Fang Zou, 2022. "Exploring the patterns of international technology diffusion in AI from the perspective of patent citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5307-5323, September.
    8. Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
    9. Kuan, Chung-Huei & Lin, Jia-Tian & Chen, Dar-Zen, 2021. "Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory," Journal of Informetrics, Elsevier, vol. 15(4).

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    More about this item

    Keywords

    Genetically modified technology; Technology diffusion; Patent citation network; Globalization;
    All these keywords.

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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