This article analyzes the determinants of the "Broadband Price Index" in Europe. The data used refer to 28 European countries between 2016 and 2021. The database used is the Digital, Economy and Society Index-DESI of the European Commission. The data were analyzed using the following econometric techniques, namely Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS and Dynamic Panel. The value of the "Broadband Price Index" is positively associated with the DESI Index, and "Connectivity" while it is negatively associated with "Fixed Broadband Take Up", "Fixed Broadband Coverage", "Mobile Broadband", "e-Government", "Advanced Skills and Development", "Integration of Digital Technology", "At Least Basic Digital Skills ", "Above Basic Digital Skills "," At Least Basic Software Skills ". A cluster analysis was carried out below using the k-Means algorithm optimized with the Silhouette coefficient. The analysis revealed the existence of three clusters. Finally, an analysis of the machine learning algorithms was carried out to predict the future value of the "Broadband Price Index". The result shows that the most useful algorithm for prediction is the Artificial Neural Network-ANN with an estimated value equal to an amount of 9.21%.

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The role of socioeconomic determinants for superstore location is analyzed. Data are collected by Italian Minister of Economic Development and ISTAT. Results show a positive effect of superstore location in respect to GDP, Employment and population. Panel data analysis shows positive relations among superstore location and Instruction and Formation, Economic Wellness, Social Relationships.

In this article the value of “Fixed Broadband Take Up” in Europe is investigated. Data are collected from the DESI-Digital Economy and Society Index for 28 countries in the period 2016-2021. Data are analyzed with Panel Data with Fixed Effects and Random Effects. The Fixed Broadband Take-Up value is positively associated with the value of "Connectivity", "Human Capital", "Desi Index", "Fast BB NGA Coverage", "Fixed Very High-Capacity Network VHCN coverage". Fixed Broadband Take-Up value is negatively associated with "Digital Public Services for Businesses", "e-Government", "At least Basic Digital Skills", "At Least Basic Software Skills", "Above Basic Digital Skills", "Advanced Skills and Development", "Integration of Digital Technology", "Broadband Price Index", "Mobile Broadband", "Fixed Broadband Coverage". Subsequently the k-Means algorithm optimized by the Silhouette coefficient was used to identify the number of clusters. The analysis shows the presence of the two clusters. Eight different machine learning algorithms were then used to predict the future value of the "Fixed Broadband Take-Up in Europe". The analysis shows that the most efficient algorithm for the prediction is "ANN-Artificial Neural Network" with an estimated value of the prediction equal to 26.39%.

Il paper analizza il ruolo delle determinanti socio-economiche del franchising in Europa nel periodo 2007Europa nel periodo -2016. L Copyright © FrancoAngeli Opera pubblicata con Licenza Creative Commons Attribuzione -Non commerciale -Non opere derivate.Per i termini e le condizioni di utilizzo di questa opera consultare il sito: http://creativecommons.org/.