How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study

Yi Zhang (), Xiao Zhou (), Alan L. Porter () and Jose M. Vicente Gomila ()
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Yi Zhang: Beijing Institute of Technology
Xiao Zhou: Beijing Institute of Technology
Alan L. Porter: Georgia Institute of Technology
Jose M. Vicente Gomila: Universitat Politecnica de Valencia

Scientometrics, 2014, vol. 101, issue 2, No 28, 1375-1389

Abstract: Abstract Competitive technical intelligence addresses the landscape of both opportunities and competition for emerging technologies, as the boom of newly emerging science & technology (NEST)—characterized by a challenging combination of great uncertainty and great potential—has become a significant feature of the globalized world. We have been focusing on the construction of a “NEST Competitive Intelligence” methodology that blends bibliometric and text mining methods to explore key technological system components, current R&D emphases, and key players for a particular NEST. This paper emphasizes the semantic TRIZ approach as a useful tool to process “Term Clumping” results to retrieve “problem & solution (P&S)” patterns, and apply them to technology roadmapping. We attempt to extend our approach into NEST Competitive Intelligence studies by using both inductive and purposive bibliometric approaches. Finally, an empirical study for dye-sensitized solar cells is used to demonstrate these analyses.

Keywords: Semantic TRIZ; Text mining; Technology roadmapping; DSSCs (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (20)

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DOI: 10.1007/s11192-014-1262-2

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