Please use this identifier to cite or link to this item: http://212.1.86.13:8080/xmlui/handle/123456789/4824
Title: Analysis of key university leadership factors based on their international rankings (QS World University Rankings and Times Higher Education)
Authors: Polyakov, Maxim
Bilozubenko, Vladimir
Korneyev, Maxim
Nebaba, Natalia
Keywords: globalization
universities
competition
international rankings
clustering
classification
leadership factors
Issue Date: 6-Dec-2022
Publisher: LLC “Consulting Publishing Company “Business Perspectives”
Citation: Maxim Polyakov, Vladimir Bilozubenko, Maxim Korneyev and Natalia Nebaba (2020). Analysis of key university leadership factors based on their international rankings (QS World University Rankings and Times Higher Education). Problems and Perspectives in Management, 18(4), 142-152.
Series/Report no.: Problems and Perspectives in Management;2020 №18(4)
Abstract: 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.
URI: http://biblio.umsf.dp.ua/jspui/handle/123456789/4824
ISSN: 1727-7051
1810-5467
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