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Patterns of scientific and technological knowledge flows based on scientific papers and patents

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  • Hyun Woo Park

    (Korea Institute of Science and Technology Information)

  • Jay Kang

    (San Francisco State University)

Abstract

This paper examines how Korean technological development is linked with scientific activities and spreads to industrial fields through knowledge flows. It empirically assesses the linkages between scientific and technological knowledge flows and technological innovation by determining whether the quantity and quality of scientific papers cited by, and the knowledge being absorbed in, Korean patents filed in USPTO varied over time, and between technology fields. We conducted MANOVA and then canonical discriminate analysis. Our findings are: the patterns of both the absorption of scientific knowledge and the diffusion of technological knowledge differ by period and by field, and the speed of knowledge diffusion differs by technology field. This implies that the time required for Korean investment in basic and applied research to impact her industrial innovation differs by technology field.

Suggested Citation

  • Hyun Woo Park & Jay Kang, 2009. "Patterns of scientific and technological knowledge flows based on scientific papers and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 811-820, December.
    • Handle: RePEc:spr:scient:v:81:y:2009:i:3:d:10.1007_s11192-008-2224-3
    DOI: 10.1007/s11192-008-2224-3
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    References listed on IDEAS

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    Cited by:

    1. Shu-Hao Chang, 2018. "A pilot study on the connection between scientific fields and patent classification systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 951-970, March.
    2. Sung, Hui-Yun & Wang, Chun-Chieh & Huang, Mu-Hsuan & Chen, Dar-Zen, 2015. "Measuring science-based science linkage and non-science-based linkage of patents through non-patent references," Journal of Informetrics, Elsevier, vol. 9(3), pages 488-498.
    3. 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.
    4. 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.
    5. Ji-ping Gao & Kun Ding & Li Teng & Jie Pang, 2012. "Hybrid documents co-citation analysis: making sense of the interaction between science and technology in technology diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 459-471, November.
    6. Zhang, Chengzhi & Liu, Lifan & Wang, Yuzhuo, 2021. "Characterizing references from different disciplines: A perspective of citation content analysis," Journal of Informetrics, Elsevier, vol. 15(2).
    7. Chihmao Hsieh, 2011. "Explicitly searching for useful inventions: dynamic relatedness and the costs of connecting versus synthesizing," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 381-404, February.

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