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Pasteur’s quadrant in AI: do patent-cited papers have higher scientific impact?

Author

Listed:
  • Xingyu Gao

    (University of Science and Technology of China)

  • Qiang Wu

    (University of Science and Technology of China)

  • Yuanyuan Liu

    (University of Science and Technology of China)

  • Ruilu Yang

    (University of Science and Technology of China)

Abstract

In scientific research, basic research that is both curiosity-driven and use-inspired is known as Pasteur’s Quadrant. The research on the impact and attention of Pasteur’s Quadrant is an essential research topic in academia. In view of the many milestone breakthroughs that Artificial Intelligence (AI) has brought to humanity, this paper delves into Pasteur’s Quadrant in AI through the citation of papers by patents. We empirically analyse the scientific impact of 3322 patent-cited papers and 6587 non-patent-cited papers published from 1999 to 2013, where scientific impact is measured by scientific citations and usage counts. Our main results show that patent-cited papers have a stronger scientific impact than non-patent-cited papers, and this impact is further enhanced in conference publications than in journal publications. Further, the relationship between the multidimensional characteristics of patent citations and scientific impact is investigated in terms of patent-cited papers. We find an inverted U-shaped relationship between the intensity of a paper’s patent citations and its scientific citations, as well as between the breadth of a paper’s patent citations and its scientific citations. In addition, the patent citation lag of a paper negatively relates to its scientific impact.

Suggested Citation

  • Xingyu Gao & Qiang Wu & Yuanyuan Liu & Ruilu Yang, 2024. "Pasteur’s quadrant in AI: do patent-cited papers have higher scientific impact?," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(2), pages 909-932, February.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:2:d:10.1007_s11192-023-04925-w
    DOI: 10.1007/s11192-023-04925-w
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