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Evaluating technological emergence using text analytics: two case technologies and three approaches

Author

Listed:
  • Samira Ranaei

    (Lappeenranta University of Technology)

  • Arho Suominen

    (VTT Technical Research Centre of Finland)

  • Alan Porter

    (Search Technology, Inc.
    School of Public Policy, Georgia Tech)

  • Stephen Carley

    (Search Technology, Inc.)

Abstract

Scientometric methods have long been used to identify technological trajectories, but we have seldom seen reproducible methods that allow for the identification of a technological emergence in a set of documents. This study evaluates the use of three different reproducible approaches for identifying the emergence of technological novelties in scientific publications. The selected approaches are term counting technique, the emergence score (EScore) and Latent Dirichlet Allocation (LDA). We found that the methods provide somewhat distinct perspectives on technological. The term count based method identifies detailed emergence patterns. EScore is a complex bibliometric indicator that provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community. LDA traces emergence at the thematic level and provides insights on the linkages between emerging research topics. The results suggest that term counting produces results practical for operational purposes, while LDA offers insight at a strategic level.

Suggested Citation

  • Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    • Handle: RePEc:spr:scient:v:122:y:2020:i:1:d:10.1007_s11192-019-03275-w
    DOI: 10.1007/s11192-019-03275-w
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    Cited by:

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    3. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
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    6. 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).
    7. Manuel A. Vázquez & Jorge Pereira-Delgado & Jesús Cid-Sueiro & Jerónimo Arenas-García, 2022. "Validation of scientific topic models using graph analysis and corpus metadata," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5441-5458, September.
    8. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    9. Li, Munan & Porter, Alan L. & Suominen, Arho & Burmaoglu, Serhat & Carley, Stephen, 2021. "An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence," Technological Forecasting and Social Change, Elsevier, vol. 166(C).

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