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Significant difference of dependence upon scientific knowledge among different technologies

Author

Listed:
  • Schumpeter Tamada

    (Research Institute of Economy, Trade and Industry)

  • Yusuke Naito

    (Research Institute of Economy, Trade and Industry)

  • Fumio Kodama

    (Research Institute of Economy, Trade and Industry)

  • Kiminori Gemba

    (Research Institute of Economy, Trade and Industry)

  • Jun Suzuki

    (Research Institute of Economy, Trade and Industry)

Abstract

Summary The authors have constructed an original database of the full text of the Japanese Patent Gazette published since 1994. The database includes not only the front page but also the body text of more than 880,000 granted Japanese patents. By reading the full texts of all 1,500 patent samples, we found that some inventors cite many academic papers in addition to earlier patents in the body texts of their Japanese patents. Using manually extracted academic paper citations and patent citations as “right” answers, we fine-tuned a search algorithm that automatically retrieves cited scientific papers and patents from the entire texts of all the Japanese patents in the database. An academic paper citation in a patent text indicates that the inventor used scientific knowledge in the cited paper when he/she invented the idea codified in the citing patent. The degree of science linkage, as measured by the number of research papers cited in patent documents, is particularly strong in biotechnology. Among other types of technology, those related to photographic-sensitized material, cryptography, optical computing, and speech recognition also show strong science linkage. This suggests that the degree of dependence on scientific knowledge differs from technology to technology and therefore, different ways of university-industry collaboration are necessary for different technology fields.

Suggested Citation

  • Schumpeter Tamada & Yusuke Naito & Fumio Kodama & Kiminori Gemba & Jun Suzuki, 2006. "Significant difference of dependence upon scientific knowledge among different technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(2), pages 289-302, August.
    • Handle: RePEc:spr:scient:v:68:y:2006:i:2:d:10.1007_s11192-006-0112-2
    DOI: 10.1007/s11192-006-0112-2
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    Citations

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    Cited by:

    1. Bryan, Kevin A. & Ozcan, Yasin & Sampat, Bhaven, 2020. "In-text patent citations: A user's guide," Research Policy, Elsevier, vol. 49(4).
    2. Goto, Akira & Motohashi, Kazuyuki, 2007. "Construction of a Japanese Patent Database and a first look at Japanese patenting activities," Research Policy, Elsevier, vol. 36(9), pages 1431-1442, November.
    3. Alfonso Ávila-Robinson & Shintaro Sengoku, 2017. "Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1691-1720, September.
    4. Suma Athreye & Martha Prevezer, 2008. "R&D offshoring and the domestic science base in India and China," Working Papers 26, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.
    5. Byeongdeuk Jang & Jae-Yong Choung & Inje Kang, 2022. "Knowledge production patterns of China and the US: quantum technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5691-5719, October.
    6. onder Nomaler & Bart Verspagen, 2008. "Knowledge Flows, Patent Citations and the Impact of Science on Technology," Economic Systems Research, Taylor & Francis Journals, vol. 20(4), pages 339-366.
    7. Inoue, Hiroyasu & Souma, Wataru & Tamada, Schumpeter, 2010. "Analysis of cooperative research and development networks on Japanese patents," Journal of Informetrics, Elsevier, vol. 4(1), pages 89-96.
    8. MOTOHASHI Kazuyuki & TOMOZAWA Takanori, 2014. "Differences in Science Based Innovation by Technology Life Cycles: The case of solar cell technology," Discussion papers 14005, Research Institute of Economy, Trade and Industry (RIETI).
    9. Gurney, Thomas & Horlings, Edwin & van den Besselaar, Peter & Sumikura, Koichi & Schoen, Antoine & Laurens, Patricia & Pardo, Daniel, 2014. "Analysing knowledge capture mechanisms: Methods and a stylised bioventure case," Journal of Informetrics, Elsevier, vol. 8(1), pages 259-272.
    10. IKEUCHI Kenta & MOTOHASHI Kazuyuki & TAMURA Ryuichi & TSUKADA Naotoshi, 2017. "Measuring Science Intensity of Industry using Linked Dataset of Science, Technology and Industry," Discussion papers 17056, Research Institute of Economy, Trade and Industry (RIETI).
    11. Huang, Mu-Hsuan & Huang, Wei-Tzu & Chen, Dar-Zen, 2014. "Technological impact factor: An indicator to measure the impact of academic publications on practical innovation," Journal of Informetrics, Elsevier, vol. 8(1), pages 241-251.
    12. Ekaterina Albats & Irina Fiegenbaum & James A. Cunningham, 2018. "A micro level study of university industry collaborative lifecycle key performance indicators," The Journal of Technology Transfer, Springer, vol. 43(2), pages 389-431, April.
    13. Ryuichi Tamura, 2017. "The Effect of High-speed Railways on Knowledge Transfer: Evidence from Japanese Patent Citations," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 13(3), pages 325-342, November.
    14. Nagaoka, Sadao & Motohashi, Kazuyuki & Goto, Akira, 2010. "Patent Statistics as an Innovation Indicator," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 1083-1127, Elsevier.
    15. 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.
    16. Suzuki, Jun, 2011. "Structural modeling of the value of patent," Research Policy, Elsevier, vol. 40(7), pages 986-1000, September.

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