IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v102y2015i1d10.1007_s11192-014-1451-z.html
   My bibliography  Save this article

Early stage identification of breakthroughs at the interface of science and technology: lessons drawn from a landmark publication

Author

Listed:
  • J. J. Winnink

    (Leiden University
    Leiden University Dual PhD Centre The Hague
    Netherlands Patent Office)

  • Robert J. W. Tijssen

    (Leiden University
    Leiden University Dual PhD Centre The Hague
    Stellenbosch University)

Abstract

Certain scholarly publications or patent publications may signal breakthroughs in basic scientific research or radical new technological developments. Are there bibliographical indicators that enable an analysis of R&D dynamics to help identify these ‘local revolutions’ in science and technology? The focus of this paper is on early stage identification of potential breakthroughs in science that may evolve into new technology. We analyse bibliographic information for a typical example of such a breakthrough to pinpoint information that has the potential to be used as bibliographic indicator. The typical example used is the landmark research paper by Novoselov et al. (Science 306(5696): 666–669, 2004) concerning graphene. After an initial accumulation of theoretical knowledge about graphene over a period of 50 years this publication of the discovery of a method to produce graphene had an immediate and significant impact on the R&D community; it provides a link between theory, experimental verification, and new technological applications. The publication of this landmark discovery marks a sharp rise in the number of scholarly publications, and not much later an increase in the number of filings for related patent applications. Noticeable within 2 years after publication is an above average influx of researchers and of organisations. Changes in the structure of co-citation term maps point to renewed interest from theoretical physicists. The analysis uncovered criteria that can help in identifying at early stage potential breakthroughs that link science and technology.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1451-z
    DOI: 10.1007/s11192-014-1451-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1451-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-014-1451-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Luís M. A. Bettencourt & David I. Kaiser & Jasleen Kaur & Carlos Castillo-Chávez & David E. Wojick, 2008. "Population modeling of the emergence and development of scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 495-518, June.
    2. Hand, David J., 2009. "Mining the past to determine the future: Problems and possibilities," International Journal of Forecasting, Elsevier, vol. 25(3), pages 441-451, July.
    3. Anthony F. J. van Raan, 2004. "Sleeping Beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(3), pages 467-472, March.
    4. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    5. Ponomarev, Ilya V. & Williams, Duane E. & Hackett, Charles J. & Schnell, Joshua D. & Haak, Laurel L., 2014. "Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 49-55.
    6. Marc Julius & Charles E. Berkoff & Alvin E. Strack & Frank Krasovec & A. Douglas Bender, 1977. "A very early warning system for the rapid identification and transfer of new technology," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 28(3), pages 170-174, May.
    7. 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.
    8. Jonathan Adams, 2005. "Early citation counts correlate with accumulated impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(3), pages 567-581, June.
    9. Hand, David J., 2009. "Mining the past to determine the future: Rejoinder," International Journal of Forecasting, Elsevier, vol. 25(3), pages 461-462, July.
    10. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, George Yunxiong & Ascani, Andrea & Iammarino, Simona, 2024. "The material basis of modern technologies. A case study on rare metals," Research Policy, Elsevier, vol. 53(1).
    2. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    4. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    5. Byunghoon Kim & Gianluca Gazzola & Jaekyung Yang & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong & Young-Seon Jeong, 2017. "Two-phase edge outlier detection method for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 1-16, October.
    6. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    2. 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.
    3. Krzysztof Klincewicz, 2016. "The emergent dynamics of a technological research topic: the case of graphene," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 319-345, January.
    4. J. J. Winnink & Robert J. W. Tijssen, 2014. "R&D dynamics and scientific breakthroughs in HIV/AIDS drugs development: the case of Integrase Inhibitors," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 1-16, October.
    5. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    6. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    7. J Mingers, 2008. "Exploring the dynamics of journal citations: Modelling with s-curves," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1013-1025, August.
    8. Srayan Datta & Eytan Adar, 2018. "A generative model for scientific concept hierarchies," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.
    9. Jianhua Hou & Xiucai Yang, 2019. "Patent sleeping beauties: evolutionary trajectories and identification methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 187-215, July.
    10. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    11. Zeng, Michael A., 2018. "Foresight by online communities – The case of renewable energies," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 27-42.
    12. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    13. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    14. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    15. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    16. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.
    17. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    18. M. Laura Frigotto & Massimo Riccaboni, 2011. "A few special cases: scientific creativity and network dynamics in the field of rare diseases," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 397-420, October.
    19. Lachance, Christian & Larivière, Vincent, 2014. "On the citation lifecycle of papers with delayed recognition," Journal of Informetrics, Elsevier, vol. 8(4), pages 863-872.
    20. Nguyen, Ai Linh & Liu, Wenyuan & Khor, Khiam Aik & Nanetti, Andrea & Cheong, Siew Ann, 2020. "The golden eras of graphene science and technology: Bibliographic evidences from journal and patent publications," Journal of Informetrics, Elsevier, vol. 14(4).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1451-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.