Capturing the Risk Signals for a Specific Emerging Technology: An Integrated Framework of Text Mining

The risk forecasting, analyses, and insights of a specific emerging technology (ET) are very important for relevant strategic management, anticipatory governance, and policymaking. However, the risk signals for a specific ET are usually weak and sparse and involve many different research areas, resulting in inefficient traditional queries; however, the techniques of text mining and decision methods are also seldom applied to this issue. To facilitate the understanding of the risk of ET (RoET) and improve the efficiency of capturing risk signals for a specific ET, an integrated text-mining framework is proposed in this article, based on multiattribute decision making and a nonlinear programming model, and a case study on three-dimensional printing/additive manufacturing is conducted. Compared with the previous studies, the proposed text-mining approach presents better performance and could further leverage the conceptualization of RoET.