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e-Government in Europe. A Machine Learning Approach

Angelo Leogrande, Nicola Magaletti, Gabriele Cosoli and Alessandro Massaro

MPRA Paper from University Library of Munich, Germany

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. (search for similar items in EconPapers)
JEL-codes: O30 O31 O32 O33 O34 (search for similar items in EconPapers)
Date: 2022-03-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cse, nep-ict, nep-ino and nep-sbm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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