Bengisu, Murat and Nekhili, Ramzi, 2006, Forecasting emerging technologies with the aid of science and technology databases, Technological Forecasting and Social Change, 73(7), 835-844.
Short term forecasting was applied to 20 emerging technologies under the "Machine and Materials" category based on the Vision 2023 foresight study previously conducted for Turkey. This scientometric approach uses the most suitable keywords linked to the technology in question and determines the number of publications and patents in those fields for a given year. Database analysis of publications and patents in the previous 11 years indicates that while the majority of the top 20 technologies identified by the experts are indeed emerging (i.e. the number of research and/or patenting in these technologies is increasing), some of them have not actually attracted too much interest in the science and technology (S&T) community. Forecasts based on S-curves indicate steady growth in some of the selected technologies. There is a high correlation between the number of scientific publications and patents in most of the technologies investigated. The method is proposed as a simple and efficient tool to link national foresight efforts to international S&T activities and to obtain quantitative information for prioritized technologies that could be used for technology management and decision making for research funding and technology investment.