Uncovering patterns of digital transformation of European economies using self-organizing maps

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Type of the article: Research Article

Abstract
Digital technologies have become a key driver of economic growth, competitiveness, and social inclusion, while significant disparities in digital development persist across national economies. The aim of this study is to map and interpret the trajectories of digital transformation in 30 selected European countries (EU member states, associated economies, and Ukraine) during 2011–2022. The study employs the self-organizing map (SOM) with Ward hierarchical clustering to uncover latent structures of digital development, using a balanced panel of 20 indicators across three domains: ICT sector development, digital infrastructure, and digital technology adoption and skills. Cluster validity was assessed via the Elbow Method, Silhouette Coefficient, Calinski-Harabasz, and Davies-Bouldin indices. Results indicate that the two-cluster solution is statistically robust, while the three-cluster solution provides additional insight into transitional patterns of digital transformation. The two-cluster solution revealed a clear distinction between digital leaders and less advanced economies, with the greatest disparities observed in online banking (71% vs. 29%), online purchases (68% vs. 32%), and e-government use (68% vs. 34%). The three-cluster solution provided further nuance, showing that in 2011 most European economies were concentrated in the weakest cluster, while only Northern Europe achieved high levels of digitalization. By 2020, all European countries had reached at least the middle cluster, reflecting a shift from strong polarization toward a more balanced distribution of digital development. Despite progress, structural gaps remain, emphasizing the need for policies that advance digital skills, encourage inclusive adoption, and build trust in online services to sustain digital transformation.

Acknowledgment
The authors acknowledge with gratitude the financial support provided by the Ministry of Education and Science of Ukraine for the research project “Cybersecurity and digital transformations of the country’s wartime economy: the fight against cybercrime, corruption and the shadow sector”, state registration number 0124U000544).

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    • Figure 1. Cluster validation results (Elbow, Silhouette, Calinski-Harabasz, Davies-Bouldin)
    • Figure 2. Silhouette plot for a two-cluster solution
    • Figure 3. Silhouette plot for a three-cluster solution
    • Figure 4. Geographical distribution of European countries by clusters (Two-cluster solution, 2011–2022)
    • Figure 5. Geographical distribution of European countries by clusters (Three-cluster solution, 2011–2022)
    • Table 1. Indicators (SOM method)
    • Table 2. Data sources for the indicators
    • Table 3. Cluster characteristics: Two-cluster solution (SOM results)
    • Table 4. Cluster characteristics: Three-cluster solution (SOM results)
    • Conceptualization
      Olena Pakhnenko, Hanna Yarovenko, Andrii Semenog
    • Funding acquisition
      Olena Pakhnenko, Hanna Yarovenko, Oleksii Tarasenko
    • Investigation
      Olena Pakhnenko, Hanna Yarovenko, Andrii Semenog
    • Methodology
      Olena Pakhnenko, Hanna Yarovenko
    • Project administration
      Olena Pakhnenko, Hanna Yarovenko
    • Resources
      Olena Pakhnenko, Andrii Semenog, Oleksii Tarasenko
    • Writing – original draft
      Olena Pakhnenko, Andrii Semenog, Yevgeniya Mordan, Oleksii Tarasenko
    • Writing – review & editing
      Olena Pakhnenko, Hanna Yarovenko
    • Supervision
      Hanna Yarovenko
    • Visualization
      Hanna Yarovenko, Andrii Semenog
    • Data curation
      Andrii Semenog, Oleksii Tarasenko
    • Validation
      Andrii Semenog, Yevgeniya Mordan, Oleksii Tarasenko
    • Formal Analysis
      Yevgeniya Mordan
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Problems and Perspectives in Management
Problems and Perspectives in Management