Entrepreneurship is important for the European Union’s competitiveness and green transition, yet financial market uncertainty may discourage business formation. This study examines the relationship between stock price volatility (SPV) and new business density (NBD) across 21 EU member states and investigates whether environmental tax revenue (ENVT) moderates this relationship. The analysis employs the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) estimator, with Fully Modified Ordinary Least Squares (FMOLS), Canonical Cointegrating Regression (CCR), and the Dumitrescu–Hurlin panel causality test for robustness and causality analysis. Results reveal a significant negative long-run association between SPV and NBD. In the interaction model, SPV has a negative and significant effect, whereas the SPV–ENVT interaction is positive and significant. Marginal effects indicate that the negative association weakens from −0.561 when ENVT is 0.86% of GDP to −0.289 when it is 4.84% of GDP. The direct effect of ENVT and the short-run interaction are statistically insignificant. These findings suggest that environmental tax revenue moderates the long-run relationship between financial market uncertainty and entrepreneurship, highlighting the relevance of environmental fiscal conditions for policies supporting business formation.
Platform-based accommodation has become an established component of tourism markets, yet consumer acceptance increasingly appears to rest on factors other than price. This study examines how digital trust, reputation, digital transparency, economic advantage, and perceived regulation relate to the acceptance of platform-based accommodation in the Slovak Republic. Empirical analysis is based on an online questionnaire survey of 455 respondents, combining Spearman’s rank correlations with multiple linear regression using heteroskedasticity-consistent standard errors. Reliability and construct validity were assessed using internal consistency, convergent validity, and discriminant validity measures. Digital trust, reputation mechanisms, digital transparency, and economic advantage were positively associated with acceptance, while perceived smart regulation showed a negative association. Wald tests indicated that digital trust and reputation mechanisms were significantly more strongly associated with acceptance than economic advantage, whereas the difference between digital transparency and economic advantage was not statistically significant. Higher age was associated with lower acceptance. The findings therefore highlight the importance of non-price digital factors, particularly trust and reputation, while also demonstrating that economic considerations remain relevant. Because the analysis is based on cross-sectional consumer responses, the findings represent associations rather than causal effects, and implications for small and medium-sized providers are derived rather than directly tested. The study contributes to debates on smart tourism and platform governance by providing a multidimensional perspective on consumer acceptance in mature platform markets.
Long-term environmental degradation may reshape regional labor markets through outward mobility without a corresponding rise in recorded unemployment. This study examines labor-market adjustment in environmentally exposed regions of the Aral Sea basin. We use administrative labor-register data for Uzbekistan’s 14 regions during 2007–2025, excluding 2017, yielding 252 region-year observations. The analysis combines fixed-effects models, event studies, a basin-wide desiccation index, wild-cluster bootstrap inference, and regional-pair randomization tests. Kazakhstan provides a descriptive comparison. High-exposure regions have an estimated external-migration share 3.35 percentage points above low-exposure regions, with limited bootstrap support (p = 0.076). The estimated post-2017 increase in this differential is 3.34 percentage points (p = 0.101). Differences in unemployment, informal employment, and inactivity are not robust under bootstrap inference. Only two regions have high exposure, and pretrend tests reject parallel trends, limiting causal interpretation. The findings are consistent with migration as a potential adaptation margin but do not establish that migration prevents unemployment or that environmental degradation causes the observed differences. Regional adaptation policy should monitor labor mobility alongside unemployment and strengthen local employment alternatives and migrant protection.
As AI applications diversify, evidence remains limited on how individuals differ in their patterns of actual use across application types. This study identifies AI-use profiles among respondents in Kazakhstan and examines their sociodemographic and macroregional correlates, purposes, experienced problems, perceived risks, and expectations of government support. Data were collected through an online survey of 4,602 respondents across Kazakhstan and analyzed in IBM SPSS Statistics 25. K-means clustering was applied to six AI-use frequency indicators among 3,971 complete cases; associations with Intensive-profile membership were estimated using binary logistic regression, and profile differences were tested using χ² tests with Holm correction. Three profiles were identified: Low-intensity, Moderate multi-application, and Intensive multi-application AI-use profiles. The profiles formed a predominantly level-based gradient in the intensity and breadth of use across application categories. Age, education, gender, and macroregion were associated with Intensive-profile membership, but the model had low explanatory power and poor discrimination. The strongest profile differences concerned purposes of use: broader and more intensive engagement was associated with a wider functional repertoire, whereas differences in experienced problems, perceived risks, and government-support expectations were more selective. These findings provide an empirical basis for more differentiated monitoring and policy responses to AI use in Kazakhstan.
This study develops a smart decision-support system for tourism management under post-pandemic and changing economic conditions. Using the Visegrad Group countries (the Czech Republic, Hungary, Poland, and Slovakia) as the empirical setting, the framework combines expert evaluation, knowledge-based analysis, fuzzy-set theory, fuzzy logic, and multidimensional membership functions. The system comprises an information model that evaluates destination accessibility and value for money through expected and actual positive and negative experiences, and a hybrid model that combines this assessment with regional attractiveness. It produces an aggregated tourism-recovery score and a linguistic assessment of tourism development. The approach was verified using data from 2,343 respondents and illustrated for seven Visegrad regions. The findings demonstrate that the model can transform uncertain and heterogeneous visitor evaluations into structured regional knowledge, distinguish differences in recovery potential, and support decisions by tourists, tourism businesses, destination managers, and public authorities. By connecting knowledge management with fuzzy decision support, the study offers a replicable tool for strengthening destination competitiveness, resilience, and adaptive regional tourism policy under volatile economic conditions.
