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.
Author(s): Yi Zhang, Xiao Zhou, Alan L. Porter, Jose M. Vicente Gomila
Organization(s): Beijing Institute of Technology, Georgia Institute of Technology, Universitat Politecnica de Valencia
Source: Scientometrics http://link.springer.com/article/10.1007/s11192-014-1262-2