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Local emergence and global diffusion of research technologies: An exploration of patterns of network formation

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  • Loet Leydesdorff
  • Ismael Rafols

Abstract

Grasping the fruits of “emerging technologies” is an objective of many government priority programs in a knowledge‐based and globalizing economy. We use the publication records (in the Science Citation Index) of two emerging technologies to study the mechanisms of diffusion in the case of two innovation trajectories: small interference RNA (siRNA) and nanocrystalline solar cells (NCSC). Methods for analyzing and visualizing geographical and cognitive diffusion are specified as indicators of different dynamics. Geographical diffusion is illustrated with overlays to Google Maps; cognitive diffusion is mapped using an overlay to a map based on the ISI subject categories. The evolving geographical networks show both preferential attachment and small‐world characteristics. The strength of preferential attachment decreases over time while the network evolves into an oligopolistic control structure with small‐world characteristics. The transition from disciplinary‐oriented (“Mode 1”) to transfer‐oriented (“Mode 2”) research is suggested as the crucial difference in explaining the different rates of diffusion between siRNA and NCSC.

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  • Loet Leydesdorff & Ismael Rafols, 2011. "Local emergence and global diffusion of research technologies: An exploration of patterns of network formation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(5), pages 846-860, May.
    • Handle: RePEc:bla:jamist:v:62:y:2011:i:5:p:846-860
    DOI: 10.1002/asi.21509
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    Cited by:

    1. Alfonso Ávila-Robinson & Shintaro Sengoku, 2017. "Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1691-1720, September.
    2. Stefan Hennemann, 2012. "Evaluating the performance of geographical locations within scientific networks using an aggregation—randomization—re-sampling approach (ARR)," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2393-2404, December.
    3. Rousseau, Ronald & Liu, Yuxian & Ye, Fred Y., 2012. "A preliminary investigation on diffusion through a layered system," Journal of Informetrics, Elsevier, vol. 6(2), pages 177-191.
    4. Petersen, Alexander M. & Rotolo, Daniele & Leydesdorff, Loet, 2016. "A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of Medical Subject Headings," Research Policy, Elsevier, vol. 45(3), pages 666-681.
    5. Yi Zhang & Mengjia Wu & Guangquan Zhang & Jie Lu, 2023. "Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(7), pages 775-790, July.
    6. Leydesdorff, Loet & Rafols, Ismael, 2012. "Interactive overlays: A new method for generating global journal maps from Web-of-Science data," Journal of Informetrics, Elsevier, vol. 6(2), pages 318-332.
    7. J. J. Winnink & Robert J. W. Tijssen, 2015. "Early stage identification of breakthroughs at the interface of science and technology: lessons drawn from a landmark publication," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 113-134, January.
    8. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    9. Rafols, Ismael & Leydesdorff, Loet & O’Hare, Alice & Nightingale, Paul & Stirling, Andy, 2012. "How journal rankings can suppress interdisciplinary research: A comparison between Innovation Studies and Business & Management," Research Policy, Elsevier, vol. 41(7), pages 1262-1282.
    10. Yibo Lyu & Quanshan Liu & Binyuan He & Jingfei Nie, 2017. "Structural embeddedness and innovation diffusion: the moderating role of industrial technology grouping," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 889-916, May.
    11. Per Ahlgren & Peter Pagin & Olle Persson & Maria Svedberg, 2015. "Bibliometric analysis of two subdomains in philosophy: free will and sorites," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 47-73, April.
    12. Wang, Xuefeng & Huang, Meng & Wang, Hongyuan & Lei, Ming & Zhu, Donghua & Ren, Jie & Jabeen, Munazza, 2014. "International Collaboration Activity Index: Case study of dye-sensitized solar cells," Journal of Informetrics, Elsevier, vol. 8(4), pages 854-862.
    13. Igors Skute & Kasia Zalewska-Kurek & Isabella Hatak & Petra Weerd-Nederhof, 2019. "Mapping the field: a bibliometric analysis of the literature on university–industry collaborations," The Journal of Technology Transfer, Springer, vol. 44(3), pages 916-947, June.
    14. Liming Liang & Lixin Chen & Yishan Wu & Junpeng Yuan, 2012. "The role of Chinese universities in enterprise–university research collaboration," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(1), pages 253-269, January.
    15. Jos J. Winnink & Robert J. W. Tijssen & Anthony F. J. van Raan, 2016. "Theory‐changing breakthroughs in science: The impact of research teamwork on scientific discoveries," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1210-1223, May.
    16. Yi Bu & Mengyang Li & Weiye Gu & Win‐bin Huang, 2021. "Topic diversity: A discipline scheme‐free diversity measurement for journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 523-539, May.
    17. Daniele Rotolo & Ismael Rafols & Michael Hopkins & Loet Leydesdorff, 2014. "Scientometric Mapping as a Strategic Intelligence Tool for the Governance of Emerging Technologies," SPRU Working Paper Series 2014-10, SPRU - Science Policy Research Unit, University of Sussex Business School.

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