Scenario-based technology roadmaps (TRM) have been used to analyse future uncertainties in the technology planning phase. However, there is a limitation in the existing related studies that the internal and external factors of the increasing uncertainty of the future are not considered. In response, this study proposes a framework that analyses the textual big data to understand the relationship between factors that may affect future uncertainty and build a TRM through scenarios based on these relationships. To do this, Fuzzy Cognitive Map technique, which analyses causal relationships between various factors in fuzzy graph form, is incorporated into TRM. To verify the effectiveness of the proposed framework, an illustrative case study was conducted using real data for an unmanned aerial vehicle. This study is expected to help integrate and utilise big data effectively in the technology planning stage.

This study proposes a framework for selecting and validating data sources for public-based technology foresight. In other words, it finds out which of the many online communities have valuable data sources. Specifically, we evaluate the usefulness of text data from online communities for technology foresight in terms of expertise and diversity. To this end, not only is a bibliographic analysis using metadata conducted, but also, topic modeling techniques for a semantic analysis of texts are utilized. As a case study, we selected 20 candidate communities where discussions and predictions related to technology are made and applied newly proposed metrics. As a contribution of this study, it is expected that it will provide a basis for public participation in technology foresight, not only leaving it to a few experts.