Analyzing Regional Disparities in E-Commerce Adoption Among Italian SMEs: Integrating Machine Learning Clustering and Predictive Models with Econometric Analysis

The article explores the diffusion of online sales tools among Italian enterprises with at least ten employees, considering regional inequalities through methods that help address economic policy. The study gives an overall assessment of the adoption of e-commerce among Italian SMEs, using multiple methods that help to identify regional disparities and provide insight for policymakers. The data were obtained from the ISTAT-BES database. Analysis was applied using the k-Means machine learning algorithm by comparing the Silhouette coefficient vs. the Elbow method. The elbow method reveals greater expository capacity, and the optimal number of clusters equals 3. The econometric analysis used the following methods: Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Least Squares-WLS, and Dynamic Panels at 1 Stage. The results show that cultural and creative employment and regular internet users are positively associated with SMEs active in e-commerce while negatively associated with the family's availability of at least one computer and internet connection. Finally, the article compares different machine learning algorithms to predict the future value of SMEs active in e-commerce. The results are discussed critically.