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Hybrid documents co-citation analysis: making sense of the interaction between science and technology in technology diffusion

Author

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  • Ji-ping Gao

    (Institute of Science Studies and S&T Management and WISE Lab, Dalian University of Technology)

  • Kun Ding

    (Institute of Science Studies and S&T Management and WISE Lab, Dalian University of Technology)

  • Li Teng

    (Institute of Science Studies and S&T Management and WISE Lab, Dalian University of Technology)

  • Jie Pang

    (Institute of Science Studies and S&T Management and WISE Lab, Dalian University of Technology)

Abstract

The paper presents a methodology called hybrid documents co-citation analysis, for studying the interaction between science and technology in technology diffusion. Our approach rests mostly on patent citation, cluster analysis and network analysis. More specifically, with the patents citing Smalley RE in Derwent innovations index as the data sets, the paper implemented hybrid documents co-citation network through two procedures. Then spectrum cluster algorithm was used to reveal the knowledge structure in technology diffusion. After that, with the concordance between network properties and technology diffusion mechanisms, three indicators containing degree, betweenness and citation half-life, were calculated to discuss the basic documents in the pivotal position during the technology diffusion. At last, the paper summarized the hybrid documents co-citation analysis in practise, thus concluded that science and technology undertook different functions and acted dominatingly in the different period of technology diffusion, though they were co-activity all the time.

Suggested Citation

  • Ji-ping Gao & Kun Ding & Li Teng & Jie Pang, 2012. "Hybrid documents co-citation analysis: making sense of the interaction between science and technology in technology diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 459-471, November.
    • Handle: RePEc:spr:scient:v:93:y:2012:i:2:d:10.1007_s11192-012-0691-z
    DOI: 10.1007/s11192-012-0691-z
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    2. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Liu, Ziqiang & Yuan, Guoting, 2020. "Topic-linked innovation paths in science and technology," Journal of Informetrics, Elsevier, vol. 14(2).
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    5. 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.
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    7. Roh, Taeyeoun & Yoon, Byungun, 2023. "Discovering technology and science innovation opportunity based on sentence generation algorithm," Journal of Informetrics, Elsevier, vol. 17(2).
    8. Wang, Jean J. & Ye, Fred Y., 2021. "Probing into the interactions between papers and patents of new CRISPR/CAS9 technology: A citation comparison," Journal of Informetrics, Elsevier, vol. 15(4).
    9. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
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