Adult education demand and competitiveness patterns across European countries

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Type of the article: Research Article

Abstract
The study aims to empirically group European countries based on competitiveness determinants and adult education demand to form a generalized cluster representation of their socio-economic characteristics. The sample covers 36 European countries from 2015 to 2024. The information base was formed using a set of indicators derived from the Global Competitiveness Index (GCI), together with an indicator reflecting adult education demand. The methodology includes standardization of indicators, selection of relevant variables using principal component analysis, and cluster analysis. The first two principal components explain 76.3% of the total variance, allowing a substantial reduction in the dimensionality of the dataset while preserving most of the information contained in the initial indicators. Clustering was conducted using Ward’s hierarchical method and the k-means algorithm, with verification of differences between clusters by analysis of variance (p < 0.05). To examine structural changes over time, clustering was performed for three benchmark years: 2015, 2020, and 2024. The results reveal five clusters of countries differing in institutional development, innovation potential, business environment characteristics, and adult education participation. A relatively stable core of highly competitive economies was identified, including Austria, Belgium, Germany, France, Ireland, Luxembourg, Denmark, the Netherlands, Norway, Sweden, Finland, and Switzerland. Other clusters show greater variability in composition. Across the benchmark years selected within the 2015–2024 observation period, Ukraine remained within the cluster characterized by the lowest values of competitiveness determinants and adult education demand, reflecting persistent structural constraints in the development of human capital and lifelong learning systems.

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    • Figure 1. Scree plot of principal components
    • Figure 2. Average cluster values for the selected indicators in 2015
    • Figure 3. Average values of clusters in 2020
    • Figure 4. Average values of clusters obtained in 2024
    • Table 1. Principal component method for selecting the most relevant determinants of a country’s competitiveness
    • Table 2. Factor loadings of indicators in the first and second components
    • Table 3. Cluster composition of European countries according to competitiveness determinants and demand for adult education, 2015
    • Table 4. Qualitative assessment of K-means clustering in 2015
    • Table 5. Cluster composition of European countries according to competitiveness determinants and demand for adult education in 2020
    • Table 6. Qualitative assessment of clustering performed using the k-means method in 2020
    • Table 7. Cluster composition of European countries according to competitiveness determinants and demand for adult education in 2024
    • Table 8. Qualitative assessment of clustering performed using the k-means method in 2024
    • Table 9. Structural characteristics of clusters of European countries according to competitiveness determinants and demand for adult education in 2015–2024
    • Table A1. Array of input data on the study of system relationships in the chain “demand for adult education – competitiveness of the country” (extract)
    • Conceptualization
      Yuriy Petrushenko, Victor Chentsov, Mila Razinkova, Tetiana Yakovenko, Vladyslav Riabovolenko, Kateryna Onopriienko, Hanna Filatova
    • Formal Analysis
      Yuriy Petrushenko, Tetiana Yakovenko, Kateryna Onopriienko, Hanna Filatova
    • Methodology
      Yuriy Petrushenko, Kateryna Onopriienko, Hanna Filatova
    • Supervision
      Yuriy Petrushenko
    • Writing – original draft
      Yuriy Petrushenko, Kateryna Onopriienko, Hanna Filatova
    • Writing – review & editing
      Yuriy Petrushenko, Victor Chentsov, Mila Razinkova, Tetiana Yakovenko, Vladyslav Riabovolenko, Kateryna Onopriienko, Hanna Filatova
    • Investigation
      Victor Chentsov, Mila Razinkova, Vladyslav Riabovolenko, Kateryna Onopriienko
    • Resources
      Victor Chentsov
    • Validation
      Victor Chentsov
    • Data curation
      Mila Razinkova, Tetiana Yakovenko, Vladyslav Riabovolenko
    • Visualization
      Mila Razinkova, Vladyslav Riabovolenko
    • Software
      Tetiana Yakovenko
    • Project administration
      Hanna Filatova