Analysis of key university leadership factors based on their international rankings (QS World University Rankings and Times Higher Education)

  • Received August 28, 2020;
    Accepted November 10, 2020;
    Published November 24, 2020
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.18(4).2020.13
  • Article Info
    Volume 18 2020, Issue #4, pp. 142-152
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This work is licensed under a Creative Commons Attribution 4.0 International License

In the context of globalization of the educational services market, competition between universities is becoming more intense. This manifests itself, among other things, in the struggle for positions in international university rankings. Given that universities are evaluated according to many criteria in such rankings, it becomes necessary to identify the most significant factors in determining their positions.
This study aims to identify the key factors determining the world’s leading universities’ leadership in international university rankings. The numerical values of the criteria for compiling the QS World University Rankings (QS) and Times Higher Education (THE) rankings were an empirical basis for the study. The analysis covered the Top 50 universities (according to the QS ranking) and was conducted based on reports for 2020 and 2021.
At first, clustering was carried out (method – k-means); the data set was the combination of numerical values of QS and THE criteria (six and five criteria, respectively). The universities were divided into three clusters in 2020 (23, 19, 8 universities) and 2021 (23, 17, 10 universities). This showed the universities’ leadership relative to each other for each year.
At the second stage, classification processing was performed (method – decision trees). As a result, criteria combinations that give an absolute separation of all clusters (2020 – five combinations; 2021 – eight combinations) were identified. The obtained combinations largely determine universities’ affiliation to clusters; their criteria are recognized as key factors of their leadership in the rankings. This study’s results can serve as guidelines for improving universities’ positions in the rankings.

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    • Table 1. List of criteria for calculating QS and THE, whose numerical values are combined in an OPT
    • Table 2. Clustering results of the Top 50 universities by numerical values of the QS and THE calculation criteria, 2020
    • Table 3. Clusterization results of the Top 50 universities by the numerical values of the criteria for calculating QS and THE, 2021
    • Table 4. Combinations of criteria with 100% discriminating ability, with each of them belonging to the rankings and an assessment of individual significance, 2020
    • Table 5. Combinations of criteria with 100% discriminating ability, with each of them belonging to the rankings and assessment of individual significance, 2021
    • Conceptualization
      Maxim Polyakov, Vladimir Bilozubenko
    • Formal Analysis
      Maxim Polyakov, Vladimir Bilozubenko, Maxim Korneyev, Natalia Nebaba
    • Investigation
      Maxim Polyakov, Vladimir Bilozubenko, Maxim Korneyev, Natalia Nebaba
    • Methodology
      Maxim Polyakov, Vladimir Bilozubenko
    • Project administration
      Maxim Polyakov
    • Software
      Maxim Polyakov, Vladimir Bilozubenko
    • Supervision
      Maxim Polyakov
    • Writing – original draft
      Maxim Polyakov, Vladimir Bilozubenko, Maxim Korneyev
    • Resources
      Vladimir Bilozubenko, Maxim Korneyev, Natalia Nebaba
    • Data curation
      Maxim Korneyev, Natalia Nebaba
    • Validation
      Maxim Korneyev, Natalia Nebaba
    • Writing – review & editing
      Maxim Korneyev, Natalia Nebaba