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A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology

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  • Hu, Xiaojun
  • Rousseau, Ronald

Abstract

In this contribution, we develop a new approach to explore the process of knowledge transition from discovery-oriented science to technological fields, via applications-oriented research, including a mediator set. This trajectory is referred to as the D-A-T trajectory. It is shown how it can be constructed and measures are proposed to characterize the relational strength among different environments (discovery oriented research, applications-oriented research and patents) and the speed of evolution. Our approach is illustrated by a case study of three fundamental restriction enzymes articles. Among other results we found that 387 patents cited 124 of the 988 articles (a share of 12.55%) in the mediator set. Defining the non-patent references (NPR) transition rate as the number of citing patents divided by the number of articles in the mediator set yields a value 0.392. Our results suggest that the D-A-T path acts as a backbone and reveals important “invisible contributions” of an original scientific work during its evolution from discovery oriented research to outside academia. Our contribution provides a useful tool for bridging the existing gap in detecting the transition of knowledge between science and technology.

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  • Hu, Xiaojun & Rousseau, Ronald, 2018. "A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology," Journal of Informetrics, Elsevier, vol. 12(3), pages 842-857.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:3:p:842-857
    DOI: 10.1016/j.joi.2018.07.004
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    Cited by:

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    2. Matteo Lascialfari & Marie-Benoît Magrini & Guillaume Cabanac, 2022. "Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6165-6189, November.
    3. Xiaoli Wang & Yun Liu & Lingdi Chen & Yifan Zhang, 2022. "Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network," SAGE Open, , vol. 12(4), pages 21582440221, December.
    4. Xian Li & Dangzhi Zhao & Xiaojun Hu, 2020. "Gatekeepers in knowledge transfer between science and technology: an exploratory study in the area of gene editing," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1261-1277, August.
    5. Yasuhiro Yamashita, 2020. "An attempt to identify technologically relevant papers based on their references," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1783-1800, November.
    6. Xiaojun Hu & Ronald Rousseau & Sandra Rousseau, 2019. "Does Environmental Economics lead to patentable research?," Papers 1905.02875, arXiv.org.
    7. Ba, Zhichao & Liang, Zhentao, 2021. "A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling," Journal of Informetrics, Elsevier, vol. 15(3).

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