Fraunhofer Institute for Systems and Innovation Research ISI
Fraunhofer Institute for Systems and Innovation Research ISI

Innovative Regions

The Innovative Regions Unit supports decision-makers from businesses, politics and research in strategically accessing knowledge within innovation ecosystems. The experts in the research unit analyze emerging technologies or transforming industries and regions and apply this expertise to create an overview in an increasingly complex knowledge economy. The network- and knowledge-based understanding of innovation ecosystems embedded there, which is operationalized in the so-called AIR model (considering actors, interactions and framework conditions), serves to identify and analyse development paths, knowledge bases, diversification potentials and cooperations. In this way we support the transformation paths of regions, businesses and research institutions.

We explore what constitutes innovative regions and how our clients can shape them sustainably or benefit from them. Through ecosystem analyses, analyses of regional knowledge bases, market analyses and technology environment analyses, we provide a clear picture of the constitution of regional and sectoral innovation ecosystems and the basis for their evaluation and design. We support our clients' design and analysis processes by developing indicator systems, municipal and regional strategies as well as web-based analysis tools. We accompany and evaluate economic, regional and innovation policy promotion measures. Furthermore, we facilitate access to innovation ecosystems for companies and research institutions by identifying cooperation opportunities or cooperation partners, location analyses and location strategies.

With a wide variety of quantitative and qualitative methods, the researchers in the Innovative Regions Unit create structure in the mass of data. We specialize in the analysis of innovation indicators using bibliometric, technological, socio-economic and digital social data. We combine this with our expertise in regional development, innovation and economic policy and technology genesis to develop meaningful indicators for analyzing knowledge bases and ecosystems that are tailored to our clients' needs. Using methods of econometrics, statistics and network analysis, we visualize, evaluate and interpret complex relationships. We approach unstructured text data by means of semantic analysis (sentiment analysis, resonance indicators). Local knowledge from stakeholders is integrated into our analyses through interviews, workshops, systematic qualitative analyses (qualitative comparative analysis) or participatory methods (e.g., Bayesian networks).

In the Innovative Regions Unit, an interdisciplinary team of geographers, regional researchers and economists is conducting research into questions of applied regional development. This is guided by an evolutionary understanding of the economy, in which regional and sectoral innovation ecosystems play a key role in the emergence of innovations. We understand these innovation ecosystems as networks of technologies and actors. In recent years, technological, social and ecological change has increasingly led to regional and sectoral transformations. At the interface between business, science and society, we investigate technologies with an impact on people and the environment and how the associated transformations can be managed and made sustainable.

Our methods

Using a wide range of quantitative and qualitative methods, the researchers in the Innovative Regions Unit bring structure to the mass of data. We specialize in the analysis of innovation indicators that utilize bibliometric, technological, socioeconomic, and digital social data. We combine these with our expertise in regional development, innovation and economic policy, and technology genesis to develop meaningful indicators tailored to client needs for the analysis of knowledge bases and ecosystems. We use methods from econometrics, statistics, and network analysis to visualize, evaluate, and interpret complex relationships. We address unstructured text data through semantic analysis (sentiment analysis, resonance indicators). We incorporate local knowledge from stakeholders into our analyses through interviews, workshops, systematic qualitative analyses (Qualitative Comparative Analysis), or participatory methods (e.g., Bayesian networks).

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  • To analyze regions and industries undergoing transformation, it is essential to be able to map the available knowledge within organizations, regions, or industries. This is because the available knowledge—stored within individuals or organizations—is the most important foundation for the emergence of new activities, such as diversification into new fields of research, technologies, industries, products, or business areas. Particularly in processes of structural change, it is necessary to deepen existing knowledge bases, recombine them, translate them into new fields of application, or supplement them with new external impulses—for example, through the establishment of new research institutions.

    The analysis and evaluation of regional and organizational knowledge bases benefits from a network-based understanding of knowledge. The observed activities of a region, organization, or industry serve to define knowledge landscapes in which knowledge is broken down into the smallest building blocks (e.g., topics, see NLP). Using the “relatedness” method (Hidalgo et al. 2018), the similarity of these building blocks can be measured. It has been shown that relatedness plays a major role in processes of transformation, cooperation, and diversification. Based on existing knowledge, it is thus possible to determine which development paths can and should be pursued in the future.

    The research unit defines and visualizes knowledge landscapes for regions and industries to identify their development potentials and pathways. These can be explored interactively via online platforms. The relatedness between knowledge stocks is calculated to determine potential for diversifying the economic structure, assess the fit and impact of investments, generate ideas for new development paths, and match cooperation partners.