Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

E-Government in Europe. A Machine Learning Approach

Version 1 : Received: 25 February 2022 / Approved: 28 February 2022 / Online: 28 February 2022 (12:10:28 CET)

How to cite: Leogrande, A.; Magaletti, N.; Cosoli, G.; Massaro, A. E-Government in Europe. A Machine Learning Approach. Preprints 2022, 2022020359 (doi: 10.20944/preprints202202.0359.v1). Leogrande, A.; Magaletti, N.; Cosoli, G.; Massaro, A. E-Government in Europe. A Machine Learning Approach. Preprints 2022, 2022020359 (doi: 10.20944/preprints202202.0359.v1).

Abstract

The following article analyzes the determinants of e-government in 28 European countries between 2016 and 2021. The DESI-Digital Economy and Society Index database was used. The econometric analysis involved the use of the Panel Data with Fixed Effects and Panel Data with Variable Effects methods. The results show that the value of “e-Government” is negatively associated with “Fast BB (NGA) coverage”, “Female ICT specialists”, “e-Invoices”, “Big data” and positively associated with “Open Data”, “e-Government Users”, “ICT for environmental sustainability”, “Artificial intelligence”, “Cloud”, “SMEs with at least a basic level of digital intensity”, “ICT Specialists”, “At least 1 Gbps take-up”, “At least 100 Mbps fixed BB take-up”, “Fixed Very High Capacity Network (VHCN) coverage”. A cluster analysis was carried out below using the unsupervised k-Means algorithm optimized with the Silhouette coefficient with the identification of 4 clusters. Finally, a comparison was made between eight different machine learning algorithms using "augmented data". The most efficient algorithm in predicting the value of e-government both in the historical series and with augmented data is the ANN-Artificial Neural Network.

Keywords

Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation

Subject

SOCIAL SCIENCES, Economics

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