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

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Listed:
  • Zhang, Yi
  • Zhang, Guangquan
  • Chen, Hongshu
  • Porter, Alan L.
  • Zhu, Donghua
  • Lu, Jie

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.

Suggested Citation

  • Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    • Handle: RePEc:eee:tefoso:v:105:y:2016:i:c:p:179-191
    DOI: 10.1016/j.techfore.2016.01.015
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    References listed on IDEAS

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    1. Robinson, Douglas K.R. & Huang, Lu & Guo, Ying & Porter, Alan L., 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Technological Forecasting and Social Change, Elsevier, vol. 80(2), pages 267-285.
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