Studies on the identification of science and technology trends in different areas are conducted by many organizations world-wide (international organisations, national research centers, universities, business companies and consultancies). Usually in all these projects the identification of technology trends is mostly based on expert methods (Delphi, expert panels, interviews and others). At the same time quantitative methods (bibliometrics, patent analysis, keywords search, etc.) that could be effectively used as an evidence-based tool to support expert opinion or to give them additional information are rarely integrated into the methodology.
The main goal of this research project is to elaborate an integrated methodology for the identification of global technology trends which may have the greatest impact on the economy and society in the long term. The proposed approach includes the use of different quantitative and qualitative methods and supporting IT-instruments for the clustering and processing of data. This can lead to the analysis of all relevant information sources: bibliometric databases (ISI Web of Knowledge, Scopus), patent databases (USPTO, EPO, JPO, WIPO), media resources (Factiva), Foresight databases (EFMN, EFP), conferences’ materials, CORDIS Europe database, web-resources, databases of dissertations (ProQuest) and presentations (SlideShare.com).
The methodology consists of the following five stages: 1) formation of keywords list; 2) data scanning (from different information sources); 3) сlustering of data; 4) formation of the trends list; 5) creation of a trends database (a description of trends using 18 characteristics, like technology applications, life cycle stage, disruptive potential, drivers, risks and the others).
In 2011, the methodology was tested for the field of semantic technologies (ICT). Five global trends were identified and described. Further development of the proposed methodology and its application for all Russian priority S&T fields is planned for 2012-2013.
The construction of such a large information database of the most important and prospective S&T areas creates a substantial empirical basis for much deeper and more systematic understanding of S&T development. The results obtained are suitable to be used by public authorities (development of long-term forecasts and identification of S&T priorities), business organisations (development of promising innovative strategies) and academic community (to revise and complement the methodologies of S&T Foresight).This work was carried out in collaboration with Ozcan Saritas (The University of Manchester, http://www.manchester.ac.uk/) and Mark Boden (The Institute for Prospective Technological Studies - Joint Research Centre of the European Commission http://ipts.jrc.ec.europa.eu/).