Breaking new ground in data-driven foresight - An exploration of alter-native data sources for strategic and technology foresight
Project Description
In the data-driven foresight (DDF) discipline, researchers and practitioners make use of a variety of data sources to gain data-based insights into future-oriented research questions. The most established and most frequently used data sources for these purposes are scientific publications and patent data, with many examples of successfully conducted trend studies. While scientific publications and patents can provide valuable insights into emerging technologies and their early downstream maturing phases, the later development phases are not sufficiently covered by these data sources, as the focus is no longer primarily on technical but rather on strategic and market-related factors. Furthermore, these science- and technology-heavy data sources alone do not contain all the information needed to anticipate an innovation's market success, nor can they single-handedly cover all aspects of a multi-perspective foresight. For this reason, foresight research constantly strives to identify and utilize additional data sources and to examine them for early trend signals. As part of this research project, we also continue our ongoing application of online job postings as an innovative foresight data source for DDF and test other data sources to which we can apply our findings.
Relevance to Liechtenstein
For the Principality of Liechtenstein, the cooperation with Fraunhofer INT and our research topic represent opportunities to be internationally present in the scientific foresight discourse and to allow local companies to benefit from the exchange and transfer of knowledge in this regard. The "Data-Driven Foresight Lab", which is currently being set up as part of our cooperation with Fraunhofer INT and is intended to train companies in the use of data-driven foresight, also pursues the same goal.
Scientific, Economic and Societal Impact
The research project not only offers a strong application link to business practice due to its relevance in terms of content and its underlying innovative character, but also due to its diverse project participants. In terms of content, the topic of data-driven technology foresight in general and the identification of new data sources in particular is proving to be a constant topic of further training for local companies and institutions in the Alpine Rhine Valley and the Lake Constance region. This is demonstrated by the regular exchange with regional practitioners as part of our INNOPro Talk series at the University of Liechtenstein. At a personnel level, the practical relevance is particularly evident in the cooperation with the Fraunhofer Institute for Technological Trend Analysis INT, which, as part of the Fraunhofer-Gesellschaft, is strongly oriented towards application-oriented research.
