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Please note: the information in the course manual is binding.
The objective of this course is that students acquire knowledge and skills concerning
- the modelling and measurement of innovation, in particular the translation of innovation theories into indicators and models
- the data sources and data acquisition necessary to use these models and indicators
- the analysis of empirical data by using the software R
- the interpretation and reporting of empirical results and the formulation of recommendations in light of existing theories.
The course aids student in choosing, using and reporting on an appropriate research design and methods, with a specific emphasis on empirical (quantitative) data and on innovation measurements. Students acquire skills in using R to prepare and manipulate large-scale data sets, perform calculations and visualize them. It furthermore helps students to acquire appropriate skills for preparing a Master's Thesis which includes original or secondary data, and is also essential for those that have the ambition to get further involved in research in this area.
After completion of the course, the student is able to:
- describe and discuss different models of innovation;
- translate these theoretical models into suitable indicators and measures of innovation;
- know about the most important sources of data (such as the OECD, Eurostat, Scopus and patent data sources), their strengths and their limitations;
- use indicators and measurements to analyze the innovative performance of nations, sectors, industries, universities, researchers and firms.
- independently choose a problem, conduct relevant data analysis as well as derive and report practical implications of the empirical research
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This course approaches "Measuring and Modeling Innovation" through different perspectives. The science-technology-innovation system is one that is continuously and rapidly evolving. The dramatic growth over the last 20 years in the use of science, technology and innovation (STI) models and indicators is the result of a combination of the ease of computerized access to an increasing number of measures of STI and, on the other hand, the interest in a growing number of public policy and private business circles in such models and measurements.
As such the students obtain insight in the complex interactions between science, innovative technology and society and are able to reflect critically upon roles of science and technology in organizations and society.
The course Innometrics teaches students how to translate theories into models and use these models to analyze innovation at different levels of aggregation (such as firms, regions or nations). Particular relevance will be on the societal relevance of knowledge generation and innovation. As such, the students have to conduct empirical research of the dynamics and challenges of Innovation in a creative and independent way.
Several influential models of science and innovation are introduced (Functions of Innovation, Triple Helix, Network and Evolutionary models, etc). Furthermore, different approaches of quantitatively measuring the development of science and innovation are introduced using different sources of information such as bibliometric or patent databases.
The course is organized around the following broad themes:
- Science and technology (S&T) indicators
- Scientometrics
- Patentdata analysis
- (Social) Network Analysis
- Evolutionary Models of Science and Innovation
- The Geography of Science and Innovation
Individual assignments and group projects will contribute to the students’ ability to independently conduct empirical research and to communicate conclusions in form of short reports to an audience of specialists and non-specialists.
This course is the entry requirement for:
- Master’s Thesis IS (GEO4-2239X)
- Consultancy Project IS (GEO4-2252)
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