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A study of scientometric methods to identify emerging technologies via modeling of milestones

Author

Listed:
  • Robert K. Abercrombie

    (Computational Sciences and Engineering)

  • Akaninyene W. Udoeyop

    (Cisco Systems, Advanced Security Initiatives Group)

  • Bob G. Schlicher

    (Computational Sciences and Engineering)

Abstract

This work examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, patents, news archives, and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain we investigated.

Suggested Citation

  • Robert K. Abercrombie & Akaninyene W. Udoeyop & Bob G. Schlicher, 2012. "A study of scientometric methods to identify emerging technologies via modeling of milestones," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 327-342, May.
    • Handle: RePEc:spr:scient:v:91:y:2012:i:2:d:10.1007_s11192-011-0614-4
    DOI: 10.1007/s11192-011-0614-4
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    References listed on IDEAS

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    Cited by:

    1. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    2. Ansgar Moeller & Martin G. Moehrle, 2015. "Completing keyword patent search with semantic patent search: introducing a semiautomatic iterative method for patent near search based on semantic similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 77-96, January.
    3. Konstantin Fursov & Alina Kadyrova, 2017. "How the analysis of transitionary references in knowledge networks and their centrality characteristics helps in understanding the genesis of growing technology areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1947-1963, June.
    4. Chao Yang & Donghua Zhu & Xuefeng Wang & Yi Zhang & Guangquan Zhang & Jie Lu, 2017. "Requirement-oriented core technological components’ identification based on SAO analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1229-1248, September.
    5. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    6. Johannes Pol & Jean-Paul Rameshkoumar, 2018. "The co-evolution of knowledge and collaboration networks: the role of the technology life-cycle," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 307-323, January.
    7. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    8. Parraguez, Pedro & Škec, Stanko & e Carmo, Duarte Oliveira & Maier, Anja, 2020. "Quantifying technological change as a combinatorial process," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    9. Marisela Rodriguez-Salvador & Leonardo Azael Garcia-Garcia, 2018. "Additive Manufacturing in Healthcare," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 12(1), pages 47-55.
    10. Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    11. Rousseau, Ronald & Hu, Xiaojun, 2013. "Two time series, their meaning and some applications," Journal of Informetrics, Elsevier, vol. 7(3), pages 603-610.

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