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

Zhang, Y; Zhang, G; Chen, H; Porter, AL; Zhu, D; Lu, J

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

Zhang, Y

Zhang, G

Chen, H

Porter, AL Zhu, D Lu, J

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Publication Type:: Journal Article
Citation:: Technological Forecasting and Social Change, 2016, 105 pp. 179 - 191
Issue Date:: 2016-04-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Chen, H https://orcid.org/0000-0002-0893-1817	en_US
dc.contributor.author	Porter, AL	en_US
dc.contributor.author	Zhu, D	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.available	2020-05-25T19:07:10Z
dc.date.issued	2016-04-01	en_US
dc.identifier.citation	Technological Forecasting and Social Change, 2016, 105 pp. 179 - 191	en_US
dc.identifier.issn	0040-1625	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41599
dc.description.abstract	© 2016 Elsevier Inc. 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.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	Technological Forecasting and Social Change	en_US
dc.relation.isbasedon	10.1016/j.techfore.2016.01.015	en_US
dc.subject.classification	Science Studies	en_US
dc.title	Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research	en_US
dc.type	Journal Article
utslib.citation.volume	105	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	10 Technology	en_US
utslib.for	14 Economics	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	105	en_US

Abstract:

© 2016 Elsevier Inc. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/41596