A mapping and summary of measurement methods for innovation systems
DOI:
https://doi.org/10.15170/MM.2020.54.KSZ.I.02Keywords:
innovation systems, methodology, composite indicators, knowledge production function, simulation modelsAbstract
THE AIMS OF THE PAPER
This study aims to summarize, what kind of methods can be used for measuring and modeling the performance of innovation systems. The presented methods can be classified into three groups according to their functions. The first group consists of methods that can measure and compare the performance of innovation systems of different nations or regions. The second group involves statistical models that explain the effect of different factors on innovative performance. The third group includes simulation methods, which can be used to model complex and dynamic relationships.
METHODOLOGY
This is a descriptive study, which presents and groups the methods for modeling and measuring innovation systems. After introducing the different tools, illustrative examples are given to show what kind of analyses can be conducted with each methodology.
MOST IMPORTANT RESULTS
The examined methods are different according to their functions, complexity and the quality and quantity of required data. Therefore, one can choose the appropriate method according to the aim of the analysis and available data. Composite indicators are good starting points since they are able to capture a wide range of indicators with one value. However, the measurement of innovation systems’ efficiency can be an important complement to them. The different statistical and econometrical methods are able to analyze the correlation between the inputs and outputs of innovation. The complexity of innovation systems can be best tackled by the simulation methods.
RECOMMENDATIONS
Each of the examined methods can be used for supporting policy-making. With the help of composite indicators, one can analyze its different dimensions and define the strengths and weaknesses of the system, where policy intervention is needed. Simulation models are suitable for ex-ante analysis of policy interventions by comparing different scenarios.
Acknowledgements: This publication/research has been supported by the European Union and Hungary and co-financed by the European Social Fund through the project EFOP-3.6.2-16-2017-00017, titled "Sustainable, intelligent and inclusive regional and city models".
