Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research

Yi Zhang*, Guangquan Zhang, Hongshu Chen, Alan L. Porter, Donghua Zhu, Jie Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

159 Citations (Scopus)

Abstract

The number and extent of current Science, Technology & Innovation topics are changing all the time, and their induced accumulative innovation, or even disruptive revolution, will heavily influence the whole of society in the near future. By addressing and predicting these changes, this paper proposes an analytic method to (1) cluster associated terms and phrases to constitute meaningful technological topics and their interactions, and (2) identify changing topical emphases. Our results are carried forward to present mechanisms that forecast prospective developments using Technology Roadmapping, combining qualitative and quantitative methodologies. An empirical case study of Awards data from the United States National Science Foundation, Division of Computer and Communication Foundation, is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R&D management and science policy in practice.

Original languageEnglish
Pages (from-to)179-191
Number of pages13
JournalTechnological Forecasting and Social Change
Volume105
DOIs
Publication statusPublished - 1 Apr 2016

Keywords

  • Technical intelligence
  • Technological forecasting
  • Text clustering
  • Text mining
  • Topic analysis

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