Digitalization is the core component of future development in the 4.0 industrial era. It represents
a powerful mechanism for enhancing the sustainable competitiveness of economies worldwide.
Diverse triggering effects shape future digitalization trends. Thus, the main research goal in
this study is to use sustainable competitiveness pillars (such as social, economic, environmental
and energy) to evaluate international digitalization development. The proposed empirical model
generates comprehensive knowledge of the sustainable competitiveness-digitalization nexus. For
that purpose, a nonlinear regression has been applied on gathered annual data that consist of 33
European countries, ranging from 2010 to 2016. The dataset has been deployed using Bernoulli’s
binominal distribution to derive training and testing samples and the entire analysis has been
adjusted in that context. The empirical findings of artificial neural networks (ANN) suggest
strong effects of the economic and energy use indicators on the digitalization progress. Nonlinear
regression and ANN model summary report valuable results with a high degree of coefficient
of determination (R2>0.9 for all models). Research findings state that the digitalization process
is multidimensional and cannot be evaluated as an isolated phenomenon without incorporating
other relevant factors that emerge in the environment. Indicators report the consumption of
electrical energy in industry and households and GDP per capita to achieve the strongest effect.