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© European Communities, 2011 Background & Context Effective management of technological innovation requires a strategic perspective based upon sound empirical intelligence. Achieving that blend is especially challenging in the case of Newly Emerging Science & Technologies (NESTs). We conduct an academic study to fill this gap by synthesizing an approach to 1) inform an innovation system model (TDS), 2) by drawing upon R&D data mining, 3) for NESTs. Research question: how can we better exploit R&D information resources concerning NESTS to identify innovation opportunities? FTA Process (Methodology) Contact Ying Guo Beijing Institute of Technology, Beijing, China Georgia Institute of Technology, Atlanta, Georgia, US Tel INTERNATIONAL CONFERENCE Empirically informing a technology delivery system model for an emerging technology: Illustrated for dye-sensitized solar cells Affiliation (Name Onl) Top Countries % since 2006 Research emphases Chinese Academy of Science China  Japan  52% of 88 TiO 2  films  CONVERSION  Swiss Federal Institute of Technology Switzerland  UK  Italy  24% of 66 TiO 2  CONVERSION  films  Ecole Polytechnique Federale de Lausanne Switzerland  UK  Netherlands  USA  54% of 54 TiO 2  films  CONVERSION  EFFICIENT  Univ London Imperial Coll Sci Technol & Med UK  Spain  Switzerland  31% of 48 TiO 2  films  recombine  National Institute of Advanced Industrial Science and Technology Japan 28% of 47 TiO 2  films  electron injection  Helping to answer the research question is the aim of our 10-step analytical process by combining quantitative (empirical) and qualitative analyses. Composing an innovation system model is a vital precondition for formulating effective policy and development strategy. We strive to offer the technology policy-maker or manager a systems model that incorporates empirical intelligence on key players, leverage points, and requirements. We exemplify our approach for a particular NEST case: dye-sensitized solar cells (DSSC). Output & Impacts C: Profile R&D DSSC publication & patent trends Top 10 DSSC Research Organizations [based on the Science Citation Index] E: Cross-charting Cross-chart: from nano material to application for DSSCs G. Internal actor analyses Relations among 3 interesting DSSC players and others I. Compose the TDS TDS for DSSCs Conclusion & policy implication 1. A correct NEST understanding moves the whole process in the right direction and ensures useful results. Accordingly, expert engagement is vitalthis occurred via personal contact and in workshops. 2. Ten months were spent in this case; Step B occupied most of this time. 3. The Cross-charting method helps to relate R&D advances to functional gains that they can provide and, thereafter, to potential applications. 4. For the future, we will continue to explore how cross-charting can apply at various granularities. We are also pursuing ways to apply Tech Mining methods, such as specialized thesauri and identification of triples (subject-verb-object), to facilitate composing cross-chart linkages. 5. When applying the 10-step process to other NESTs, the process can be adapted in various aspects. Technology Delivery System (TDS) is a useful framework to consider how enhanced technological capabilities can translate into a successful application. Ten-step process to compose a TDS for an NEST
Text Mining of Information Resources to Inform Forecasting of Innovation Pathways Lu Huang, 1 Ying Guo, 1 TingTing Ma, 1,2 Alan L. Porter 2,3 1 School.
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