Does digital banking adoption mediate the link between public-sector digital maturity and banking stability? Evidence from post-socialist transition economies, with a focus on Ukraine, Armenia, and Kazakhstan
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DOIhttp://dx.doi.org/10.21511/bbs.21(2).2026.13
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Article InfoVolume 21 2026, Issue #2, pp. 176–204
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Type of the article: Research Article
Abstract
Whether digital transformation in the public sector and in financial services jointly contributes to banking stability – or whether the two strands proceed along parallel trajectories – remains an open empirical question for post-socialist economies undergoing both reforms simultaneously. This study addresses the question in three components. First, a cross-country mediation analysis covers up to 130 economies over 2018–2024 (853 country-year observations), drawing on the World Bank GovTech Maturity Index, the IMF Financial Access Survey, and the IMF Financial Soundness Indicators, with panel OLS, country-clustered standard errors, and bootstrap mediation tests. Second, the results are decomposed via fixed-effect deviations for three post-Soviet economies from distinct EBRD regions: Ukraine, Armenia, and Kazakhstan. Pre-shock GovTech maturity is positively associated with digital banking adoption (β = +2.91, p = 0.017); sub-pillars differ in channel: core government systems for transaction intensity, public service delivery for account ownership. Bootstrap mediation tests do not support the indirect path through digital banking adoption (six specifications, lowest p = 0.132). GovTech maturity instead shows a substantial direct association with the non-performing-loan ratio – a 13-percentage-point reduction per unit increase in GTMI (p = 0.037) – plausibly operating through institutional infrastructure such as property registries, e-courts, and tax-credit information systems. The two strands are linked but not chained: GovTech is associated with digital banking adoption, yet the route to lower non-performing loans runs through institutional infrastructure. Country-level decomposition reveals heterogeneous GTMI trajectories and identifies reform priorities across public service delivery and core government systems.
Acknowledgment
This article was prepared based on the results of a study funded by the Ministry of Education and Science of Ukraine entitled “GovTech for Ukraine: A Digital, Secure, Transparent, and Equitable State in Times of War and Post-War Reconstruction” (registration number: 0126U000544).
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JEL Classification (Paper profile tab)G21, G28, O33, H11
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References76
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Tables14
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Figures2
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- Figure 1. Country trajectories: GovTech, digital banking adoption, and bank stability, 2018–2024
- Figure 2. Pairwise Pearson correlations among Stage 1 and Stage 2 variables, transition sub-sample (n = 28 economies, 2018–2024)
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- Table 1. Analytical sample composition
- Table 2. Descriptive statistics by sub-sample, 2018–2024
- Table 3. Stage 1 a-path estimates: log(M) on pre-shock GTMI
- Table 4. Sub-pillar decomposition: log(M) on GTMI sub-pillars
- Table 5. Cross-sectional Preacher-Hayes mediation: indirect effect of GTMI on bank stability through digital banking adoption
- Table 6. Panel two-way fixed-effects mediation: indirect effect of GTMI on the NPL ratio
- Table 7. Country-specific deviations from the panel two-way fixed-effects model
- Table A1. Full country list and sample membership
- Table B1. Pearson correlation matrix of principal regression variables (cross-country averages, 2018–2024)
- Table C1. Country fixed-effects estimates and leapfrog/lagging classification, Sample A (FA37N)
- Table D1. Country fixed-effects estimates and leapfrog/lagging classification, Sample B (FA30N)
- Table E1. Full-sample and outlier-excluded coefficient comparison
- Table E2. Sample sizes: full vs outlier-excluded
- Table F1. Definitions and data sources for all model variables
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