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Paper-patent citation linkages as early signs for predicting delayed recognized knowledge: Macro and micro evidence

Author

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  • Du, Jian
  • Li, Peixin
  • Haunschild, Robin
  • Sun, Yinan
  • Tang, Xiaoli

Abstract

In this study, we investigate the extent to which patent citations to papers can serve as early signs for predicting delayed recognized knowledge in science using a comparative study with a control group, i.e., instant recognition papers. We identify the two opposite groups of papers by the Bcp measure, a parameter-free index for identifying papers which were recognized with delay. We provide a macro (Science/Nature papers dataset) and micro (a case chosen from the dataset) evidence on paper-patent citation linkages as early signs for predicting delayed recognized knowledge in science. It appears that papers with delayed recognition show a stronger and longer technical impact than instant recognition papers. We provide indication that in the more recent years papers with delayed recognition are awakened more often and earlier by a patent rather than by a scientific paper (also called “prince”). We also found that patent citations seem to play an important role to avoid instant recognition papers to level off or to become a so called “flash in the pan”, i.e., instant recognition. It also appears that the sleeping beauties may firstly encounter negative citations and then patent citations and finally get widely recognized. In contrast to the two focused fields (biology and chemistry) for instant recognition papers, delayed recognition papers are rather evenly distributed in biology, chemistry, psychology, geology, materials science, and physics. We discovered several pairs of “science sleeping”-“technology inducing”, such as “biology-biotechnology/pharmaceuticals”, “chemistry-chemical engineering”, as well as some trans-fields science-technology interactions, such as “psychology - computer technology/control technology/audio-visual technology”, “physics - computer technology”, and “mathematics-computer technology”. We propose in further research to discover the potential ahead of time and transformative research by using citation delay analysis, patent & NPL analysis, and citation context analysis.

Suggested Citation

  • Du, Jian & Li, Peixin & Haunschild, Robin & Sun, Yinan & Tang, Xiaoli, 2020. "Paper-patent citation linkages as early signs for predicting delayed recognized knowledge: Macro and micro evidence," Journal of Informetrics, Elsevier, vol. 14(2).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:2:s1751157719301555
    DOI: 10.1016/j.joi.2020.101017
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    References listed on IDEAS

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    1. Avery Sen, 2017. "Island + Bridge: how transformative innovation is organized in the federal government," Science and Public Policy, Oxford University Press, vol. 44(5), pages 707-721.
    2. Anthony F. J. Raan & Jos J. Winnink, 2018. "Do younger Sleeping Beauties prefer a technological prince?," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 701-717, February.
    3. Ke, Qing, 2018. "Comparing scientific and technological impact of biomedical research," Journal of Informetrics, Elsevier, vol. 12(3), pages 706-717.
    4. Ratnadeep Dey & Anurag Roy & Tanmoy Chakraborty & Saptarshi Ghosh, 2017. "Sleeping beauties in Computer Science: characterization and early identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1645-1663, December.
    5. Michael Roach & Wesley M. Cohen, 2013. "Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research," Management Science, INFORMS, vol. 59(2), pages 504-525, October.
    6. Anthony F. J. van Raan, 2004. "Sleeping Beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(3), pages 467-472, March.
    7. Jian Du & Yishan Wu, 2018. "A parameter-free index for identifying under-cited sleeping beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 959-971, August.
    8. Anthony F. J. Raan, 2017. "Sleeping beauties cited in patents: Is there also a dormitory of inventions?," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1123-1156, March.
    9. Small, Henry & Tseng, Hung & Patek, Mike, 2017. "Discovering discoveries: Identifying biomedical discoveries using citation contexts," Journal of Informetrics, Elsevier, vol. 11(1), pages 46-62.
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    Cited by:

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    5. Shi, Xuanyu & Du, Jian, 2022. "Distinguishing transformative from incremental clinical evidence: A classifier of clinical research using textual features from abstracts and citing sentences," Journal of Informetrics, Elsevier, vol. 16(2).

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