Innometrics

Course goals

1. the measurement of innovation, in particular the translation of innovation theories into indicators
2. the data sources and data acquisition necessary to use these indicators
3. the analysis of empirical data by using the software R
4. the interpretation and reporting of empirical results
5. the formulation of (policy) recommendations in light of existing theories.

The course aids students in choosing, using, and reporting on appropriate research designs and methods, with a specific emphasis on innovation measurements and quantitative data analysis. Students acquire basic skills in using R to prepare and manipulate large-scale data sets, perform calculations and visualize them. 

The course helps students to acquire appropriate skills for preparing a Master's Thesis which includes original or secondary innovation data. It is essential for those that have the ambition to get further involved in research in this area.
 
After completion of the course, students have the knowledge and skills to:

• translate theoretical innovation models into meaningful indicators and measures;
• assess the strength and limitations of important innovation data sources (e.g. OECD, Eurostat, Web of Science, patent data sources)
• conduct large-scale data analysis using the software R
• use indicators to analyze and compare the innovative performance of territories, sectors, industries, or organizations over time;
• independently choose a (societal/policy) problem and specify the problem statement for the research, conduct relevant data analysis as well as derive and report practical implications of the empirical research.

Content

The dramatic growth over the last 20 years in the use of science, technology and innovation (STI) measures and indicators is the result of a combination of easy access to an increasing number of data and STI measures, on the one hand, and the growing interest in public policy and private business circles in such measurements and their implications for future decision making, on the other hand.

This course approaches "Measuring Innovation" through different perspectives. The students obtain insight into the complex interactions between science, innovative technology and society and are able to reflect critically upon roles of knowledge creation and innovation in organizations and society. The course Innometrics teaches students how to translate innovation theories and frameworks (Innovation Systems, Network, Geographical or Evolutionary approaches) into meaningful indicators and metrics to analyze innovation at different levels of aggregation (such as firms, industries, regions or countries). Particular relevance will be on the societal relevance of knowledge creation and its implications for policy. As such, the students have to conduct empirical research of the dynamics, opportunities and challenges of innovation in a creative and independent way. To this end, different approaches to quantitatively measure the development of knowledge creation are introduced using different secondary data sources such as bibliometric, patent, innovation survey or research collaboration databases. 

The course is organized around the following broad themes:

• Science, technology & innovation (STI) indicators
• Scientometrics
• Patent data analysis 
• (Social) Network and Ecosystem Analysis
• Geographical and evolutionary Models of Science and Innovation

Individual assignments and group projects will contribute to the students’ ability to independently gather data and conduct empirical research using the software R, and to effectively communicate conclusions in form of a policy brief to an audience of specialists and non-specialists.