NEST: a Model for Detecting Weak Signals of Emerging Trends Using Global Monitoring Expert Network

The importance of analyzing R&D environment changes and forecasting future technologies for supporting policy decision and efficient resource distribution has been increasingly recognized. Many futurists are forecasting future technology based on Delphi study, brainstorming, expert survey, trend analysis, data mining, etc. However, these processes still need to be formalized. In this paper, we introduce the NEST (New & Emerging Signals of Trends) model, which is a systematic collective intelligence model for collecting information from expert network worldwide and detecting weak signals of emerging technologies, developed by KISTI. The most outstanding feature of NEST model is that it is based on both quantitative and qualitative methods. In the stages of quantitative methods, NEST performs clustering, pattern recognition, scientometrics, and cross impact analysis. In the stages of qualitative methods, NEST conducts environmental scanning, brainstorming, and Delphi study. For illustration purpose, a result of experiment for detecting weak signals of emerging technologies is presented.