The Research Field Innovation Dynamics & Modelling focuses on the quantitative analysis and modelling of innovation systems. It addresses key questions such as: How can innovation systems be empirically captured in their temporal, spatial and structural development? How do changing framework conditions and policy interventions affect organisations as well as the innovation system as a whole? And which data sources and indicators are suitable for measuring the performance of innovation systems and their individual institutions?
The research in this field is based on strong expertise in quantitative methods, particularly mathematical and statistical approaches, as well as comprehensive data infrastructures for the empirical analysis of different dimensions of innovation systems. By establishing and maintaining our own databases, we also contribute to pan-European research infrastructures (risis.eu). In addition to further developing our established databases, such as the AIT EUPRO database for mapping networks in the EU Framework Programs, we generate new data on emerging topics using approaches such as big data analytics and horizon scanning. In addition, we contribute to improving the openness, interoperability, and reusability of research data (FAIR Data).
Methodologically, our research places a strong emphasis on network analysis, in particular social network analysis. We also use advanced econometric approaches, especially from the fields of spatial econometrics and spatial interaction modelling, to identify determinants shaping the development and orientation of innovation systems. To identify innovation potential at an early stage, we develop novel indicators for measuring technological complexity. We also use our agent-based model RI:COMPASS, which enables micro-level analysis of innovation dynamics and supports ex-ante evaluation of policy interventions . Other methodological focal points include science mapping and technology mapping, for example through publication and patent analysis, and the growing use of social media data to assess emerging trends.
