Journal
SCIENTOMETRICS
Volume 99, Issue 1, Pages 55-75Publisher
SPRINGER
DOI: 10.1007/s11192-013-1090-9
Keywords
Triple Helix model; Semantic TRIZ; Technology roadmapping; DSSCs; Text mining; Emerging technology
Funding
- US National Science Foundation [1064146]
- Direct For Social, Behav & Economic Scie
- SBE Off Of Multidisciplinary Activities [1064146] Funding Source: National Science Foundation
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In recent years, the Triple Helix model has identified feasible approaches to measuring relations among universities, industries, and governments. Results have been extended to different databases, regions, and perspectives. This paper explores how bibliometrics and text mining can inform Triple Helix analyses. It engages Competitive Technical Intelligence concepts and methods for studies of Newly Emerging Science & Technology (NEST) in support of technology management and policy. A semantic TRIZ approach is used to assess NEST innovation patterns by associating topics (using noun phrases to address subjects and objects) and actions (via verbs). We then classify these innovation patterns by the dominant categories of origination: Academy, Industry, or Government. We then use TRIZ tags and benchmarks to locate NEST progress using Technology Roadmapping. Triple Helix inferences can then be related to the visualized patterns. We demonstrate these analyses via a case study for dye-sensitized solar cells.
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