Comparison of the digital economy development parameters in the EU countries in the context of bridging the digital divide

  • Received March 15, 2020;
    Accepted May 27, 2020;
    Published June 4, 2020
  • Author(s)
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  • Article Info
    Volume 18 2020, Issue #2, pp. 206-218
  • Cited by
    14 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

The widespread use of information and communication technologies and subsequent transformations have led to the formation of a digital economy (DE). The European Union, as an international organization, has become the subject of building such an economy, striving to bring member countries closer in the field of digitalization.
The aim of this paper is to compare the DE development parameters of the EU countries based on cluster analysis and determine the most significant of them to solve the problems of bridging the digital divide between countries. For clustering, a feature DE vector of 20 indicators was created and the k-means algorithm and the Euclidean distance metric were used. For classification, the decision tree method was applied.
Three clusters of EU countries were identified by the level of DE development (leaders, followers and outsiders), which allowed assessing their positions relative to each other. Key parameters that determine countries’ positions in the general rating are identified. A parameter chart is generated to control the establishment of DE in the EU countries, which, in addition to key parameters, includes maximum, minimum and harmonic mean values of these parameters by cluster. This characterizes the landscape of DE development in the EU countries, assesses the digital divide and is the basis for decision-making in the area of bridging this divide.

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    • Table 1. EU DE feature description
    • Table 2. Parameter chart to manage DE development in EU countries
    • Table 3. Values of existing ratings, which reflect DE, for EU countries with the distribution by clusters received
    • Conceptualization
      Vladimir Bilozubenko, Maxim Korneyev
    • Investigation
      Vladimir Bilozubenko, Tetiana Serediuk, Maxim Korneyev
    • Supervision
      Vladimir Bilozubenko, Elżbieta Wolanin, Maxim Korneyev
    • Writing – review & editing
      Vladimir Bilozubenko, Maxim Korneyev
    • Data curation
      Olha Yatchuk, Elżbieta Wolanin, Tetiana Serediuk
    • Formal Analysis
      Olha Yatchuk
    • Software
      Olha Yatchuk
    • Visualization
      Olha Yatchuk, Elżbieta Wolanin
    • Writing – original draft
      Elżbieta Wolanin, Tetiana Serediuk
    • Methodology
      Tetiana Serediuk