Type and Duration
FFF-Förderprojekt, July 2024 until March 2025Coordinator
Technology & InnovationMain Research
Growth and ComplexityDescription
The discipline of data-driven foresight (DDF) requires a constantly evolving selection of foresight data sources and methods for addressing the various tasks within a foresight process. Our research project taps and analyzes such new or alternative data sources as well as combines and couples them for the purpose of trend identification and validation. On the one hand, we test the use of online job postings data for the foresight of technology convergence and/or fusion in the AI technology field as an addition to patent data. On the other hand, we carry out trend analyses in different application contexts based on scientific publications as an established data source and test their coupling with various publications from corporate practice (job postings, annual reports, sustainability reports, etc.). Thus, the research project not only contributes new scientific trend studies, but also opens the possibility of comparing current R&D efforts by industrial companies with technology trends, validating their practical relevance and identifying technology leaders.Practical Application
The practical impact of the project lies on the one hand, in the tapping of new or alternative data sources for foresight practices of industrial companies and, on the other hand, in the publication of scientifically sound trend studies for technology-leading companies. In particular, the planned data mapping of NETCULATOR results with company publications for the purposes of technology foresight holds potential for future transfer projects on individual use cases with companies in the region and beyondReference to Liechtenstein
The topic of data-driven foresight in general and the tapping of alternative data sources in particular proves to be highly relevant for local companies and institutions in Liechtenstein, as demonstrated by the constant exchange with representatives from practice (see expert interviews in the predecessor project "lbs_23_01" or the exchange with "Digital Lichtenstein"). In addition, the project strengthens cooperation with Fraunhofer INT in Germany and promotes cross-border networking and the presence of the University of Liechtenstein, while also representing the country of Liechtenstein as an attractive research location (see partner workshop at the University of Liechtenstein campus).Project Manager
Project Collaborator
Publications
Scheuffele, M., & Brecht, L. (2024). Anticipating Technology Convergence with Online Job Postings Data. Paper presented at the ISPIM Innovation Symposium, Manchester, UK.
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Scheuffele, M. (2024, 20.03). Datengetriebene Früherkennung von Technologiekonvergenz und wie wir sie uns zunutze machen können. Institutsseminar des Fraunhofer INT.
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