Exploring Digital Competitiveness through Bayesian Belief Networks

Abstract

This study assesses national digital competitiveness by analyzing interdependencies among key factors influencing overall performance. Unlike conventional ranking models that assume equal weighting of pillars, this study uses Bayesian belief network (BBN) models to capture complex, non-linear relationships, offering a more precise identification of critical determinants. The methodology involves constructing BBN models using data from the IMD Digital Competitiveness Ranking 2023 for 64 countries. Three states were assigned to variables—low, medium, and high performance—and the tree augmented naive Bayes (TAN) algorithm was applied to model interdependencies. Thefindings highlight future readiness and knowledge as the most influential pillars, with high-performing countries demonstrating strengths in these areas. Additionally, critical sub-pillars such as adaptive attitudes and regulatory frameworks play pivotal roles. Unlike traditional approaches, this study identifies ripple effects within sub-pillars, demonstrating how targeted improvements in key areas can amplify digital transformation. The results emphasize the importance of a holistic strategy that considers these interconnections rather than isolated improvements. By providing a data-driven prioritization of key factors, this study offers policymakers a novel framework for resource allocation and strategic interventions. It contributes to the literature by challenging traditional schemes, advocating for a more comprehensive understanding of digital competitiveness, and offering guidance for targeted interventions tailored to each country's unique context