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 and Jie Lu

Technological Forecasting and Social Change, 2016, vol. 105, issue C, 179-191

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.

Keywords: Topic analysis; Technological forecasting; Text mining; Text clustering; Technical intelligence (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (45)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:105:y:2016:i:c:p:179-191

DOI: 10.1016/j.techfore.2016.01.015

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