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

Samira Ranaei, Arho Suominen (Corresponding Author), Alan Porter, Stephen Carley

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)215-247
Number of pages33
JournalScientometrics
Volume122
Issue number1
Early online date2 Nov 2019
DOIs
Publication statusPublished - 1 Jan 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Emergence score (EScore)
  • Technological emergence
  • Text analytics
  • Topic modeling

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