Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

The Innovation Index in Europe

Version 1 : Received: 23 April 2022 / Approved: 25 April 2022 / Online: 25 April 2022 (04:40:06 CEST)

How to cite: Leogrande, A.; Laureti, L.; Costantiello, A. The Innovation Index in Europe. Preprints 2022, 2022040216 (doi: 10.20944/preprints202204.0216.v1). Leogrande, A.; Laureti, L.; Costantiello, A. The Innovation Index in Europe. Preprints 2022, 2022040216 (doi: 10.20944/preprints202204.0216.v1).

Abstract

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

Keywords

innovation and invention: processes and incentives; management of technological innovation and R&D; diffusion processes; open innovation

Subject

SOCIAL SCIENCES, Economics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.