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A 3-dimensional analysis for evaluating technology emergence indicators

Author

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  • Xiaoyu Liu

    (Beijing Institute of Technology)

  • Alan L. Porter

    (Georgia Institute of Technology
    Search Technology, Inc)

Abstract

Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study.

Suggested Citation

  • Xiaoyu Liu & Alan L. Porter, 2020. "A 3-dimensional analysis for evaluating technology emergence indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 27-55, July.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:1:d:10.1007_s11192-020-03432-6
    DOI: 10.1007/s11192-020-03432-6
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    1. Esmaelian, Majid & Tavana, Madjid & Di Caprio, Debora & Ansari, Reza, 2017. "A multiple correspondence analysis model for evaluating technology foresight methods," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 188-205.
    2. Foster, John & Metcalfe, J. Stan, 2012. "Economic emergence: An evolutionary economic perspective," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 420-432.
    3. Qi Wang, 2018. "A bibliometric model for identifying emerging research topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(2), pages 290-304, February.
    4. Martin, Ron & Sunley, Peter, 2012. "Forms of emergence and the evolution of economic landscapes," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 338-351.
    5. Harper, David A. & Endres, Anthony M., 2012. "The anatomy of emergence, with a focus upon capital formation," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 352-367.
    6. Sanjay K. Arora & Alan L. Porter & Jan Youtie & Philip Shapira, 2013. "Capturing new developments in an emerging technology: an updated search strategy for identifying nanotechnology research outputs," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 351-370, April.
    7. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2017. "A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2077-2087, June.
    8. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    9. Serhat Burmaoglu & Olivier Sartenaer & Alan Porter & Munan Li, 2019. "Analysing the theoretical roots of technology emergence: an evolutionary perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 97-118, April.
    10. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    11. Corrocher Nicoletta & Malerba Franco & Montobbio Fabio, 2003. "The emergence of new technologies in the ICT field: main actors, geographical distribution and knowledge sources," Economics and Quantitative Methods qf0317, Department of Economics, University of Insubria.
    12. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    13. 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.
    14. Kim, Dong-hyu & Lee, Heejin & Kwak, Jooyoung, 2017. "Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network," Research Policy, Elsevier, vol. 46(7), pages 1234-1254.
    15. Ozcan Saritas & Serhat Burmaoglu, 2015. "The evolution of the use of Foresight methods: a scientometric analysis of global FTA research output," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 497-508, October.
    16. Henry Small, 1999. "Visualizing science by citation mapping," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(9), pages 799-813.
    17. M. Rader & A. L. Porter, 2008. "Fitting Future-Oriented Technology Analysis Methods to Study Types," Springer Books, in: Cristiano Cagnin & Michael Keenan & Ron Johnston & Fabiana Scapolo & Rémi Barré (ed.), Future-Oriented Technology Analysis, chapter 3, pages 25-40, Springer.
    18. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.
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    Cited by:

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    2. 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.
    3. Woo, Seokkyun & Youtie, Jan & Ott, Ingrid & Scheu, Fenja, 2021. "Understanding the long-term emergence of autonomous vehicles technologies," Technological Forecasting and Social Change, Elsevier, vol. 170(C).

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    More about this item

    Keywords

    Technology emergence indicators; Technology forecasting; Emerging technologies; Technology emergence assessment; R&D emergence; Predictive indicators;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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