The growing digitalisation of public administration and the increasing number of countries affected by armed conflicts raise important questions about the role of e-government in sustaining government revenue mobilisation under institutional stress. This study examines how the development of digital government influences different sources of public revenue and whether armed conflict moderates this relationship across countries. The analysis uses cross-country panel data for 2003–2024, combining indicators of e-government development from the UN EGDI database, fiscal indicators from the World Bank Open Data, and conflict information from the UCDP/PRIO Armed Conflict Dataset, estimated using two-way fixed effects models with robust clustered standard errors. The results show that economic development is the strongest determinant of revenue mobilisation, with GDP per capita coefficients ranging from 0.95 to 1.22 across models. The overall EGDI does not demonstrate a statistically significant direct effect on tax revenue or revenue excluding grants, although weak interaction effects suggest that armed conflict may reduce the fiscal effectiveness of digital governance (−0.238; p ≈ 0.10). A significant negative relationship is observed for the telecommunications infrastructure component with tax revenue (−0.362; p = 0.005) and revenue excluding grants (−0.349; p = 0.011). For customs duties and social contributions, the results indicate no systematic relationship between e-government development and revenue performance, highlighting the dominant role of macroeconomic conditions in shaping fiscal capacity.

Digital transformation of the public sector is increasingly relevant because it can reshape the costs firms face in finance, logistics, and cross-border service provision. This study investigates how e-government development and its human-capital, online-service, and telecommunication components influence interest rate spread distortions, logistics performance, and services trade restrictiveness across countries, and whether these effects are linear or non-linear. The analysis uses three unbalanced country panels covering 1,299 observations for 130 countries, 906 observations for 163 countries, and 306 observations for 51 countries, estimated with two-way fixed-effects models and Driscoll–Kraay standard errors, with robustness checks and quadratic specifications implemented in R. The results show that a 0.1-point increase in EGDI is associated with an approximately 0.45-point reduction in the absolute interest rate spread, while the robustness coefficient remains negative at-0.677. Human capital and telecommunication infrastructure are especially important in this dimension, with baseline coefficients of-3.921 and-1.910, respectively. In the logistics specification, aggregate EGDI is insignificant, but HCI is positive and significant (β = 0.372), and a 0.1-point increase in HCI raises the Logistics Performance Index by about 0.037 points. In the services-trade specification, EGDI reduces STRI by-0.095. At the same time, HCI shows a U-shaped effect with a turning point at 0.975, indicating that digital human capital lowers restrictiveness up to very high levels before the relationship turns upward.

The rapid diffusion of generative artificial intelligence (GenAI) is reshaping higher education by challenging traditional roles of teaching, trust, and academic integrity. This study aims to explore how university staff cognitively and ethically evaluate GenAI by analysing perceptions of its pedagogical replacement potential, practical feasibility, academic integrity risks, and perceived reliability across national contexts. The analysis is based on an anonymous cross-sectional survey of 637 respondents conducted between May and September 2025, using descriptive statistics, correlations, regression models, and exploratory factor analysis. The findings show that perceived replacement potential is low (M = 2.47), with over 51% of respondents rejecting the idea of AI replacing teachers. Academic integrity concerns are the strongest dimension (M = 3.51), while trust in AI accuracy remains low (M = 1.99), indicating widespread scepticism. Perceived cost and complexity do not significantly influence beliefs about replacement (R² = 0.007; p = 0.117), suggesting a weak relationship between feasibility and perceived impact. Finally, moderate positive correlation (ρ = 0.34) and low reliability (α = 0.50; α = 0.45) provide evidence that perceptions of GenAI are fragmented and multidimensional rather than internally consistent.