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Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references

Author

Listed:
  • Julie Callaert

    (Faculty of Business and Economics, K.U. Leuven)

  • Joris Grouwels

    (Faculty of Business and Economics, K.U. Leuven)

  • Bart Looy

    (Faculty of Business and Economics, K.U. Leuven)

Abstract

Indicators based on non-patent references (NPRs) are increasingly being used for measuring and assessing science–technology interactions. But NPRs in patent documents contain noise, as not all of them can be considered ‘scientific’. In this article, we introduce the results of a machine-learning algorithm that allows identifying scientific references in an automated manner. Using the obtained results, we analyze indicators based on NPRs, with a focus on the difference between NPR- and scientific non-patent references-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological domain. These results signal the relevancy of delineating scientific references when using NPRs to assess the occurrence and impact of science–technology interactions.

Suggested Citation

  • Julie Callaert & Joris Grouwels & Bart Looy, 2012. "Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 383-398, May.
    • Handle: RePEc:spr:scient:v:91:y:2012:i:2:d:10.1007_s11192-011-0573-9
    DOI: 10.1007/s11192-011-0573-9
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    References listed on IDEAS

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    3. Matt Marx & Aaron Fuegi, 2020. "Reliance on science: Worldwide front‐page patent citations to scientific articles," Strategic Management Journal, Wiley Blackwell, vol. 41(9), pages 1572-1594, September.
    4. Mu-Hsuan Huang & Ssu-Han Chen & Chia-Ying Lin & Dar-Zen Chen, 2014. "Exploring temporal relationships between scientific and technical fronts: a case of biotechnology field," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1085-1100, February.
    5. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    6. Cassiman, Bruno & Veugelers, Reinhilde & Arts, Sam, 2018. "Mind the gap: Capturing value from basic research through combining mobile inventors and partnerships," Research Policy, Elsevier, vol. 47(9), pages 1811-1824.
    7. Masashi Shirabe, 2014. "Identifying SCI covered publications within non-patent references in U.S. utility patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 999-1014, November.
    8. Leten, Bart & Landoni, Paolo & Van Looy, Bart, 2014. "Science or graduates: How do firms benefit from the proximity of universities?," Research Policy, Elsevier, vol. 43(8), pages 1398-1412.
    9. Bart Leten & Rene Belderbos & Bart Van Looy, 2016. "Entry and Technological Performance in New Technology Domains: Technological Opportunities, Technology Competition and Technological Relatedness," Journal of Management Studies, Wiley Blackwell, vol. 53(8), pages 1257-1291, December.
    10. Leonardo Costa Ribeiro & Glenda Kruss & Gustavo Britto & Américo Tristão Bernardes & Eduardo Motta e Albuquerque, 2014. "A methodology for unveiling global innovation networks: patent citations as clues to cross border knowledge flows," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 61-83, October.
    11. Chen, Lixin, 2017. "Do patent citations indicate knowledge linkage? The evidence from text similarities between patents and their citations," Journal of Informetrics, Elsevier, vol. 11(1), pages 63-79.
    12. Yi-Ching Liaw & Te-Yi Chan & Chin-Yuan Fan & Cheng-Hsin Chiang, 2014. "Can the technological impact of academic journals be evaluated? The practice of non-patent reference (NPR) analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 17-37, October.
    13. Veugelers, Reinhilde & Wang, Jian, 2019. "Scientific novelty and technological impact," Research Policy, Elsevier, vol. 48(6), pages 1362-1372.
    14. Julie Callaert & Maikel Pellens & Bart Looy, 2014. "Sources of inspiration? Making sense of scientific references in patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1617-1629, March.
    15. Chul Lee & Gunno Park & Klaus Marhold & Jina Kang, 2017. "Top management team’s innovation-related characteristics and the firm’s explorative R&D: an analysis based on patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 639-663, May.
    16. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.
    17. Jaimin Goh & Jaehong Lee & Wonchang Hur & Yunchang Ju, 2019. "Do Analysts Fully Reflect Information in Patents about Future Earnings?," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    18. Appio, Francesco Paolo & Martini, Antonella & Fantoni, Gualtiero, 2017. "The light and shade of knowledge recombination: Insights from a general-purpose technology," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 154-165.

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    More about this item

    Keywords

    Science–technology interaction; Non-patent references; Indicators; Machine learning;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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