Studying the European Union Summary Innovation Index (SII) and Its Affecting Factors

Georgios Pozios (), Melpomeni Masoura () and Sonia Malefaki ()
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Georgios Pozios: Hellenic Open University, School of Science and Technology
Melpomeni Masoura: School of Engineering, University of Patras, Department of Mechanical Engineering and Aeronautics
Sonia Malefaki: School of Engineering, University of Patras, Department of Mechanical Engineering and Aeronautics

Chapter Chapter 6 in Quantitative Methods and Data Analysis in Applied Demography - Volume 1, 2025, pp 63-76 from Springer

Abstract: Abstract The European Innovation Scoreboard (EIS) is a tool developed at the initiative of the European Commission to provide a comparative assessment of the innovation performance of European Union Member States on an annual basis through the Summary Innovation Index (SII). The assessment is based on a wide range of indicators covering four key innovation activities. The first major activity examines the main components of innovation related to the external environment of an organization, the second activity captures public and private investments made in Research and Innovation, the third activity outlines different types of innovations and intellectual property rights, and the fourth activity captures the impact of innovation on social, economic, and environmental level. In the current work, the level as well as the evolution of innovation in the European Union (EU) are assessed by studying the four main activities of the SII for the years 2014–2021. Additionally, our aim is to group EU countries according to their performance in the four main activities of the SII by applying the K-means clustering method. EU member states are classified in two groups with respect to their performance in the four categories presenting one group with High and one group with Low performance respectively, since the optimal number of clusters is two. The evolution of the performance of each member country and the possible transitions from one group to another during the years 2014–2021 is also another point of interest. The grouping of EU member states into the two clusters showed that socio-economic factors may affect the overall SII. Linear Mixed Effects Models confirm the effect of Gross Domestic Product per capita, the average number of weekly working hours, the renewable energy consumption, the public expenditure on education and the Digital Economy and Society Index on the SII, but this is not the case for unemployment, CO2 emissions and conventional energy consumption.

Keywords: Innovation; Summary Innovation Index (SII); Clustering; K-means; Linear Mixed Effect Models (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-82275-9_6

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