Оleksandr Mosin
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Indicators differentiating the sustainability profiles of leading universities in the QS Sustainability Rankings
Vladimir Bilozubenko
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Fedir Zhuravka
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Olha Hryhorash
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Yuri Tovt
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Andriy Zhydyk
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Оleksandr Mosin
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Maxim Korneyev
doi: http://dx.doi.org/10.21511/kpm.10(3).2026.03
Knowledge and Performance Management Volume 10, 2026 Issue #3 pp. 32-49
Views: 364 Downloads: 121 TO CITE АНОТАЦІЯType of the article: Research Article
Higher education plays an important role in advancing the sustainable development paradigm. The corresponding contribution of universities is measured, in particular, by the QS Sustainability Rankings, which summarizes nine comprehensive indicators for assessing various aspects of sustainability. The study aims to analyze the structure of the QS Sustainability Rankings for leading global universities and identify the indicators that most significantly distinguish the ranked profiles of these institutions. The study covered the top 30 universities in the QS World University Rankings: Sustainability 2026 (31 universities due to tied ranking scores). The Pearson coefficient showed a fairly strong pairwise relationship with the ranking score for indicators such as “Equality”, “Environmental Research”, “Impact of Education”, and “Employability & Opportunities”; however, the typology of university profiles is more complex. Therefore, based on the principal component analysis method, the indicators most strongly associated with the variation of universities in the Ranking were identified: “Health and Wellbeing”, “Knowledge Exchange”, and “Environmental Sustainability”. A cluster analysis showed that university profiles in the QS Sustainability Rankings vary across clusters: cluster I (17 institutions) is predominantly oriented toward strengthening social impact, cluster II (7 institutions) toward strengthening environmental impact, and cluster III (7 institutions) has a mixed orientation. Therefore, based on classification analysis, a combination of indicators (“Environmental Sustainability”, “Impact of Education”, “Health and Wellbeing”) was identified as most strongly distinguishing the obtained clusters, that is, differentiating the ranked profiles of leading universities. Other institutions can use this to substantiate the priorities of their sustainable transformation strategy.
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GovTech maturity and digital payment adoption in transition economies: Delayed associations and divergent deployment models
Liudmyla Zakharkina
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Svitlana Stender
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Olena Lahovska
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Оleksandr Mosin
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Yuliia Pereguda
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Perizat Buzaubayeva
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Aghavni G. Hakobyan
doi: http://dx.doi.org/10.21511/bbs.21(3).2026.16
Type of the article: Research Article
Abstract
Digital government platforms are expected to accelerate the shift to cashless payments, and banks stand between the two: they hold the accounts that digital credentials open and process the government-to-person and person-to-government flows that digital services generate. Cross-country evidence for transition economies remains scarce and largely contemporaneous. The study aims to determine whether digital government maturity is associated with the uptake of cashless payment instruments contemporaneously or with a delay, and whether the deployment model shapes that association beyond aggregate index scores. Wave panels combining the Global Findex database (2011–2024) with the UN E-Government Development Index for eleven transition economies were estimated using pooled, fixed-effects, between-country, lagged, and first-difference specifications, supplemented by an exploratory annual panel of ATM density and a structured comparison of three deployment models. A strong cross-country association between GovTech maturity and both digital payment adoption (0.551, p < 0.001) and account ownership (0.730, p < 0.001) did not survive within-country identification: fixed-effects coefficients turned negative and insignificant, so H1-H3 are not supported. With a four-to-five-year lag, the Online Service Index entered positively and significantly in the baseline specification (0.290, p < 0.05); as significance is not retained with controls, the evidence is consistent with a delayed association rather than establishing it. Adoption expanded under all three deployment models, from 47% to 85% in Kazakhstan, 48% to 83% in Ukraine, and 12% to 61% in Armenia; what distinguished the cases was the interface between the state and private bank ecosystems, which aggregate indices do not capture.Acknowledgment
Liudmyla Zakharkina’s contribution to this article was made within the framework of the research project “GovTech for Ukraine: A Digital, Secure, Transparent, and Equitable State in Times of War and Post-War Reconstruction” (registration number: 0126U000544), funded by the Ministry of Education and Science of Ukraine.
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