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.
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Acknowledgments
We acknowledge support from the US National Science Foundation (Award #1064146—“Revealing Innovation Pathways: Hybrid Science Maps for Technology Assessment and Foresight”). The findings and observations contained in this paper are those of the authors and did not necessarily reflect the views of the National Science Foundation.
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Technology and Innovation Including Patent Analysis (Topic 5) and Visualization and Science Mapping: Tools, Methods and Applications (Topic 8).
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Zhang, Y., Zhou, X., Porter, A.L. et al. 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. Scientometrics 101, 1375–1389 (2014). https://doi.org/10.1007/s11192-014-1262-2
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DOI: https://doi.org/10.1007/s11192-014-1262-2