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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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    5. 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.
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    7. 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.
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    11. 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.
    12. 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.
    13. Kenneth Zahringer & Christos Kolympiris & Nicholas Kalaitzandonakes, 2017. "Academic knowledge quality differentials and the quality of firm innovation," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(5), pages 821-844.
    14. 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.
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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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