The research field Innovation Dynamics & Modelling shifts attention to the quantitative analysis and modelling of innovation systems. Analyzing such systems – as sets of actors interlinked via joint research and innovation activities – we address questions like: How can we empirically observe the (spatial, thematic and institutional) development of such systems? How do framework characteristics like policy interventions determine their evolution? What are the best indicators to measure the performance of innovating actors and the innovation system as a whole? Which data can we use to construct such indicators, and how can we collect them?
The backbone of our Research Field is comprised of methodological competencies in advanced quantitative – statistical and mathematical – methods on the one hand, and the development and maintenance of large-scale data infrastructures on the other, providing a solid basis for empirical insights into innovation dynamics. With our data infrastructures, we contribute to the largest pan-European research infrastructure on science, technology and innovation studies (risis.eu). Next to the advancement of our established datasets, most notably the EUPRO database on R&D networks within the EU Framework Programmes (FP), we develop new datasets for different questions using Big Data and Horizon Scanning methods. Moreover, we actively contribute to Open Science supporting FAIR data.
Methodologically, we focus on network analytic approaches, e.g. on Social Network Analysis. Further, we use advanced econometric techniques, in particular spatial econometrics and spatial interaction modelling, to get a better understanding on determinants and drivers of innovation dynamics. We develop new indicators of technological complexity for timely identification of innovation potentials. To characterize the dynamics of innovation systems a micro-level, we make use of simulation and Agent Based Modelling (ABM) techniques, e.g. for assessing ex-ante the influence of specific policy interventions into innovation systems. An additional methodological focus of our group lies in Science and Technology Mapping to identify emerging research fields and technologies as well as trends, using publication and patent data and increasingly also data from social media.