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Knowledge-transfer analysis based on co-citation clustering

Author

Listed:
  • Xuezhao Wang

    (National Science Library, Chinese Academy of Sciences)

  • Yajuan Zhao

    (National Science Library, Chinese Academy of Sciences)

  • Rui Liu

    (Institute of Physics, Chinese Academy of Sciences)

  • Jing Zhang

    (National Science Library, Chinese Academy of Sciences)

Abstract

Based on co-citation cluster analysis, we propose a knowledge-transfer analysis model for any technology field. In this model, patent data with backward citations to non-patent literature and forward citations by later patents would be analyzed. Co-citation clustering of the cited articles defines scientific knowledge sources, while that of the patents themselves defines technology fronts. According to the citation between the article and patent clusters, the landscape of knowledge-transfer including route and strength between scientific knowledge sources and technology fronts can be mapped out. The model has been applied to the field of transgenic rice. As a result of the analysis, ten scientific knowledge sources and eight technology fronts have emerged, and reasonable links between them have been established, which clearly show how knowledge has been transferred in this field.

Suggested Citation

  • Xuezhao Wang & Yajuan Zhao & Rui Liu & Jing Zhang, 2013. "Knowledge-transfer analysis based on co-citation clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 859-869, December.
    • Handle: RePEc:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-1077-6
    DOI: 10.1007/s11192-013-1077-6
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hanlin You & Mengjun Li & Jiang Jiang & Bingfeng Ge & Xueting Zhang, 2017. "Evolution monitoring for innovation sources using patent cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 693-715, May.
    2. Mehdi Rhaiem, 2017. "Measurement and determinants of academic research efficiency: a systematic review of the evidence," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 581-615, February.
    3. Amara, Nabil & Rhaiem, Mehdi & Halilem, Norrin, 2020. "Assessing the research efficiency of Canadian scholars in the management field: Evidence from the DEA and fsQCA," Journal of Business Research, Elsevier, vol. 115(C), pages 296-306.
    4. Hanlin You & Mengjun Li & Keith W. Hipel & Jiang Jiang & Bingfeng Ge & Hante Duan, 2017. "Development trend forecasting for coherent light generator technology based on patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 297-315, April.
    5. Guijie Zhang & Guang Yu & Yuqiang Feng & Luning Liu & Zhenhua Yang, 2017. "Improving the publication delay model to characterize the patent granting process," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 621-637, May.
    6. Juan Antonio Dip, 2021. "What does U-multirank tell us about knowledge transfer and research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3011-3039, April.
    7. Guijie Zhang & Yuqiang Feng & Guang Yu & Luning Liu & Yanqiqi Hao, 2017. "Analyzing the time delay between scientific research and technology patents based on the citation distribution model," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1287-1306, June.
    8. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    9. Mehdi Rhaiem & Nabil Amara, 2020. "Determinants of research efficiency in Canadian business schools: evidence from scholar-level data," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 53-99, October.

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