Innovation Trends and Knowledge Dynamics

Innovation theory suggests that a variety of actors work together in different thematic and spatial domains in the generation and design of economic and social innovations. Against this background, the Business Unit Innovation Trends and Knowledge Dynamics seeks to document and analyse these actors' activities as well as their systemic cooperation at different levels. In addition, it works towards establishing the socio-economic contribution of science, research and technological development (impact analysis).

The Business Unit focuses on comparative analyses of science and innovation systems in Germany, Europe and Asia (especially China) to continuously appraise Germany's and Europe's global positioning. This includes traditional competition and cooperation analyses as well as the documentation and evaluation of emerging dynamics in the areas of socially inclusive, sustainable and global challenge oriented innovation.

In order to describe and analyse trends and dynamics in diverse innovation systems, a variety of factors must be taken into account. While some of them call for qualitative analysis, the Business Unit Innovation Trends and Knowledge Dynamics focuses primarily on those that yield themselves to quantitative approaches.

Accordingly, a central focus of the Business Unit's work lies on the identification and further development of indicators that provide meaningful reflections of innovation processes and outcomes in specific national economies or industries. In addition, we derive empirical and conceptual insights from existing and newly created indicator systems. The range of our offer includes the evaluation of established data sources such as publication, patent, foreign trade and company statistics, as well as the integration of secondary statistics for the purpose of bespoke composite indicators for specific purposes. The Business Unit derives unique analytical opportunities from establishing interfaces between existing databases as well as from linking them with complementary thematic data sources. In addition, we develop new measurement methods based on unstructured data (big data, text mining and web scraping) based on well-founded conceptual frameworks.