Search Results (1,156)

This study revisits the tourism–economic growth nexus in the EU-27 over 2014–2024 by combining an integrative conceptual synthesis of transmission mechanisms with a multi-method empirical strategy. We move beyond a single fixed-effects regression by integrating: (i) a dynamic panel System Generalized Method of Moments (System GMM) estimator that addresses endogeneity and persistence in growth, (ii) interaction terms between tourism intensity and moderating factors (institutional quality, human capital, trade openness), (iii) sub-sample analyses across mature versus emerging tourism destinations and Mediterranean versus Continental economies, and (iv) a Hansen-type threshold model that identifies non-linearities in the tourism–growth relationship. The baseline fixed-effects results confirm a positive and statistically significant tourism elasticity, while the dynamic and threshold specifications reveal that this effect is highly heterogeneous: it is amplified in economies with stronger institutions and higher human capital, but becomes statistically indistinguishable from zero in destinations with very high tourism intensity, consistent with congestion and overtourism mechanisms. Evidence on heterogeneous post-pandemic resilience is treated as exploratory rather than as a confirmed empirical contribution, since the present specification relies on a COVID-19 dummy and sub-sample patterns rather than a dedicated COVID-19 interaction model. The findings reposition tourism not as a uniformly positive growth engine, but as a moderated and bounded driver whose contribution depends on absorptive capacity and management quality. Full article

E-commerce has reshaped short-term financial management by altering transaction speed, payment structures, and supply chain coordination. This study examines how large publicly listed e-commerce firms, viewed as complex digital business systems, adjusted their working capital policies during and after the COVID-19 shock. The sample is based on the 100 largest e-commerce companies worldwide by market capitalization, as reported by CompaniesMarketCap (February 2026), and is reduced to 76 firms from 23 countries due to data availability, yielding 802 firm-year observations. Firm-level data are obtained from LSEG Datastream, while macroeconomic variables are sourced from the World Bank. The analysis distinguishes between two dimensions of working capital: flow-based operational adjustment, measured by the cash conversion cycle (CCC), and stock-based balance-sheet adjustment, captured by net working capital relative to total assets (WC/TA). Fixed-effects models with firm-clustered standard errors are employed. The results indicate a substantial contraction of the CCC during the pandemic, followed by partial persistence of that contraction rather than a return to pre-pandemic norms. In contrast, WC/TA remains broadly stable during the crisis but declines in the post-pandemic period, suggesting a delayed balance-sheet adjustment. Business-model heterogeneity is not statistically significant, which may reflect a common system-level response across e-commerce firm types. Leverage and supply-chain pressures are associated with working capital intensity (WC/TA), while inflation shapes operate cycle duration (CCC). The findings are consistent with a two-stage adaptive response to systemic disruption. Full article

Background/Objectives: While the descriptive trajectory of COVID-19 is well-documented, there is a methodological gap in quantifying the precise magnitude of risk reduction across multi-year pandemic phases in Brazilian subnational units. This study aimed to fill this gap by applying Generalized Linear Models (GLMs) to quantify the temporal transition of epidemiological risks (Incidence, Mortality, and Case Fatality) in Ceará (2020–2023), using the first year of the pandemic as a statistical baseline. Methods: Ecological time-series study was conducted using official surveillance data. We employed GLMs with Poisson distribution to calculate Rate Ratios (RRs) and 95% Confidence Intervals, allowing for a robust comparative risk modeling between 2020 (reference) and subsequent years (2021–2023). Results: Modeling revealed a significant epidemiological dissociation between transmission and severity. While the risk of incidence remained high through 2022 (RR = 1.42), the mortality risk showed an earlier and more drastic decline, with a 68% reduction as early as 2022 (RR = 0.32) and 99% in 2023 (RR = 0.01). The Case Fatality Rate (CFR) risk decreased consistently from 2021 onwards, reaching its lowest point in 2023 (RR = 0.09; 91% reduction). Conclusions: Between 2020 and 2023, Ceará transitioned to reduced COVID-19 severity. Despite ecological design and data limitations, these findings underscore the importance of resilient health systems and equitable immunization. Full article

Research on post-COVID tourism behaviour has expanded rapidly, yet inland UNESCO World Heritage cities remain underexamined, particularly in Mediterranean contexts. This study examines whether the pandemic produced durable changes in tourist behaviour and in willingness to pay for sustainable services in Cáceres, Spain. A structured face-to-face survey was administered to 421 visitors in March 2023, after public-health restrictions had been lifted. The analysis covered self-reported behavioural change, perceived impacts on different destination types, perceived effects on local sustainability objectives and changes in willingness to pay (WTP) for sustainable services. Descriptive statistics were complemented by an exploratory binary logistic regression predicting increased WTP. Because the model includes only sociodemographic predictors and shows modest fit, it is used to describe associations rather than to predict. Reported behavioural change was limited: mean scores for crowd avoidance, health–safety preferences, shorter stays and substitution towards rural and nature tourism ranged from 1.73 to 1.91 on a five-point scale. Respondents nevertheless perceived substantial spatial effects of the pandemic, particularly on natural parks (92.6%) and rural destinations (84.1%). Most believed that the pandemic had accelerated sustainability efforts mainly through greater institutional and business awareness (54.9%). WTP proved relatively stable, with 62.7% reporting no change and 26.1% an increase. Women and respondents with university education showed higher odds of reporting increased WTP. Because constructs such as institutional trust and pro-environmental values were not measured directly, these attitudes are interpreted—rather than demonstrated—as reflecting governance-related confidence and value orientations more than lingering health concerns. This governance-and-values reading is the study’s main interpretive contribution and requires confirmation with direct measures of the underlying constructs. Full article

This study investigates the key determinants of bank stability and profitability in commercial and Islamic banks listed on the Amman Stock Exchange (ASE) in Jordan, with a focus on credit risk and capital adequacy during the period 2018–2024. Using panel data from 15 banks, the study applies fixed effects regression models with clustered standard errors. Liquidity is proxied by the loan-to-deposit ratio (LDR), credit risk by the loans loss provisions-to-total loans ratio, and capital strength by the equity-to-assets ratio, alongside a COVID-19 dummy and an interaction term between liquidity and credit risk. Financial performance and stability are measured using return on assets (ROA), return on equity (ROE), and the logarithmic Z-score. The findings indicate that credit risk has a significant negative effect on both bank performance and financial stability, whereas capital adequacy exerts a positive and significant effect. The COVID-19 pandemic negatively affected financial performance and stability, while liquidity (LDR) shows no significant direct effect. The interaction between liquidity and credit risk was statistically insignificant across all estimated models, suggesting that credit risk remains the dominant determinant regardless of liquidity conditions. The study highlights the importance of effective credit risk management and strong capital buffers in enhancing bank resilience. It contributes to the literature by providing recent evidence from the Jordanian banking sector and by incorporating multiple performance measures, a pandemic shock variable, and risk interaction effects to better understand bank stability within a unified empirical framework for an emerging banking market. Full article

The present paper considers an empirical likelihood approach for testing equality of autocorrelation coefficients in two independent stationary time series. In the time domain, a two-sample blockwise empirical likelihood method is constructed for weakly dependent data. In the frequency domain, a two-sample frequency-domain empirical likelihood test is introduced using spectral moment restrictions for autocorrelation. Under suitable regularity conditions, the corresponding profiled empirical likelihood statistics converge to chi-square limits under the null hypothesis. To improve small-sample performance, a bootstrap Bartlett-type calibration is proposed for the profiled two-sample frequency-domain statistic. The finite-sample behavior of the proposed procedures is examined in a Monte Carlo study covering AR, ARMA, and ARFIMA models with both Gaussian and asymmetric heavy-tailed innovations. The results show that the frequency-domain empirical likelihood procedure provides reliable size control in the short-memory models considered and remains competitive in mild long-memory settings, while the benchmark procedures are more sensitive to parametric misspecification or block-length choice. The simulation study shows that the bootstrap Bartlett-type calibration improves performance in smaller samples. An empirical application to squared Nikkei 225 returns provides evidence of higher short-run volatility persistence during the COVID-19 regime than in the pre-pandemic period. Full article

The China Containerized Freight Index exhibits multi-scale periodicity and nonlinear responses to uncertainty, which challenge traditional forecasting methods. This study proposes a dynamic multi-stage deep learning framework with COVID-19 as an interval node to construct event windows. Breakpoint detection identifies shipping-related events. A three-stage procedure, including Maximal Information Coefficient, Boruta, and Granger causality, selects uncertainty risk indicators as core features, while K-shape clustering groups the exogenous variables. The proposed hybrid model integrates a Temporal Convolution Kolmogorov–Arnold Network with a Warped Fourier and Shock Kernel. Prophet decomposition supplies baseline and residual terms. Temporal Convolution Kolmogorov–Arnold Network unifies local temporal feature extraction and universal nonlinear approximation under sparse samples. The Warped Fourier component adapts to drifting and superimposed seasonality, and the Shock Kernel quantifies uncertainty shock intensity and decay. A gating fusion mechanism suppresses noise and enhances information efficiency. Comparative experiments demonstrate competitive accuracy and robustness, with statistically significant gains in several benchmark comparisons; ablation studies confirm incremental contributions of each component. Empirical analysis shows that under event-driven uncertainty, demand-side policy variables show stronger predictive relevance to China Containerized Freight Index fluctuations, while simultaneously transmitting effects to the carbon market and accelerating the green energy cost transition. These findings provide insights for freight rate forecasting and shipping market risk management. Full article

Background and Objectives: Healthcare workers (HCWs) involved in the COVID-19 response are at increased risk of mental health disturbances, including sleep disorders. This study aimed to assess the prevalence and predictors of insomnia among HCWs in Almaty, Kazakhstan. Materials and Methods: A cross-sectional study was conducted between 11 and 26 September 2021, including 553 HCWs. Insomnia symptoms were assessed using the Insomnia Severity Index (ISI). The primary binary outcome was defined as an ISI score ≥ 10, while ISI ≥ 15 was used descriptively to indicate moderate-to-severe insomnia symptoms. Associations were evaluated using chi-square tests and multivariable logistic regression models. Statistical significance was set at p < 0.05. Results: Overall, 38.10% of HCWs with complete ISI data had insomnia symptoms based on the predefined ISI ≥ 10 threshold. In multivariable analysis, Kazakh nationality (AOR = 2.11, 95% CI: 1.05–4.23), advanced education (AOR = 2.03, 95% CI: 1.13–3.65), physician role (AOR = 4.92, 95% CI: 1.25–19.30), and working with COVID-19 patients for >1 year (AOR = 2.28, 95% CI: 1.33–3.89) were significantly associated with increased odds of insomnia. Conclusions: Insomnia symptoms were common among surveyed HCWs during the COVID-19 pandemic and were associated with selected demographic and occupational characteristics, including professional role, education level, and duration of work with COVID-19 patients. These findings highlight the need for targeted mental health interventions and structural support systems for HCWs in Kazakhstan. Full article

While higher education institutions strive for academic excellence, they also bear the responsibility of caring for and ensuring the sustainable well-being of their students. After the COVID-19 pandemic, these institutions have transitioned to hybrid and digital education models and have begun to experience the opportunities and threats of digital learning ecosystems. With the introduction of AI technology, this transformation has taken on a new dimension: while students benefit from the flexibility, instant feedback, and personalized learning offered by AI tools, they have also begun to experience new challenges, including cognitive overload, digital fatigue, and social isolation. In this context, the aim of this research is to assess students’ overall psychological well-being and to provide a support system that promotes sustainable well-being by anticipating potential psychological strain and recommending necessary precautions. Accordingly, the purpose of this study, drawing on Self-Determination Theory and Conservation of Resources Theory, is to examine the direct effects of an AI-enabled student support system on sustainable well-being and academic engagement, as well as its indirect effects through self-efficacy and academic resilience. Data will be collected from undergraduate students from a public university in Istanbul. Data will be analyzed in the R statistical environment. We expect that academic resilience, and self-efficacy will mediate the relationship between an AI-enabled student support system and sustainable well-being. At the end of the study, we propose a conceptual model that can be tested empirically by further research. Managerial and further research directions, as well as limitations, are also discussed. Full article

This paper examines how value-added tax (VAT) reforms affected recorded point-of-sale (POS) spending in Saudi Arabia’s restaurant, café, and food service sector during a period of rapid payment digitalization. Two policy shocks are analyzed: the introduction of a 5% VAT in January 2018 and the increase to 15% in July 2020. Using monthly official POS data from January 2016 to January 2024, the study applies an interrupted time-series framework. Baseline estimates are obtained using Generalized Least Squares (GLS) with AR (1) correction. In contrast, seasonal SARIMAX and Error Correction Model (ECM) specifications are used as robustness checks and to distinguish short-run from long-run dynamics. Controls include food and beverage price indices, headline inflation, and COVID-19 disruptions. Results show statistically significant positive level shifts in recorded POS sales after both VAT reforms, with larger measured effects after the 2020 increase. However, the evidence suggests that these changes primarily reflect formalization of transactions, migration toward electronic payments, improved reporting compliance, and intertemporal expenditure timing rather than persistent growth in real demand. Post-reform trend coefficients indicate gradual normalization in subsequent months. ECM estimates suggest that approximately 56% of short-run disequilibrium is corrected within one month. Findings are robust across alternative specifications. The paper contributes new evidence from the Gulf region by showing that retail transaction indicators may overstate real consumption responses when tax reforms coincide with rapid financial digitalization. From a sustainability perspective, the findings highlight the role of digital financial systems and modern tax administration in improving economic transparency, strengthening fiscal sustainability, enhancing formal-sector integration, and supporting the institutional transformation objectives of Saudi Vision 2030. The results imply that fiscal-policy evaluations should jointly account for tax administration reforms and changes in payment technology. Full article

This study aimed to investigate the factors that predict the acceptance of distance learning among undergraduate nursing students during the COVID-19 pandemic and to examine Please check if the Citation part is missing the implications of these findings for nursing education in the post-pandemic era. A cross-sectional study was conducted in Greece with a convenience sample of undergraduate nursing students from the Hellenic Mediterranean University and the University of Patras. Data were collected between December 2020 and January 2021 using an online questionnaire. Statistical analysis was performed using IBM SPSS version 21.0. A total of 378 undergraduate nursing students (mean age: 22 years) participated in this study. The regression model predicting students’ attitudes toward distance learning (ATel) was statistically significant and explained 18.3% of the variance in ATel scores (R² = 0.183, adjusted R² = 0.158). Among the psychological and experiential factors, future career concerns (β = 0.237, p < 0.001), emotional distress related to social isolation (β = 0.186, p = 0.001), and perceived difficulties in group work (β = 0.140, p = 0.013) were revealed as significant predictors of the students’ attitudes toward distance learning. In contrast, digital readiness, flexibility, and perceived effectiveness of distance learning were not revealed as statistically significant predictors in the multivariate model but were positively associated with students’ attitudes. Demographic characteristics were not identified as statistically significant predictors of ATel scores. Psychosocial factors were significantly associated with nursing undergraduate students’ attitudes toward distance learning, underscoring the importance of incorporating blended learning in higher education in the post-pandemic era to secure group interaction, effective collaboration, and students’ well-being. Full article

This study investigates the emergence of psychosocial risks during the COVID-19 pandemic in the architecture, engineering, and construction (AEC) industry. It aims to enhance mental health outcomes for project professionals by identifying pandemic-related stressors, evaluating the role of organisational interventions, and developing a practical framework for psychosocial risk management. Guided by Job Demands–Resources (JDR) theory, the research involved a literature review, expert consultations, and a structured survey targeting AEC project managers. The findings reveal that COVID-19-related psychosocial risks such as work overload, isolation, job insecurity, and blurred work–life boundaries were negatively associated with mental health. Organisational interventions were positively associated with improved mental health. However, the moderating effect of organisational intervention on the relationship between psychosocial risks and mental health was not statistically significant. This study proposes a framework to guide AEC organisations in integrating proactive mental health strategies into everyday project practices. While the data are sector-specific and collected during a crisis period, the implications extend to broader project-based settings. This research offers practical insights for AEC firms, policymakers, and industry stakeholders on supporting workforce well-being through targeted interventions. It also contributes conceptually by linking pandemic-induced stressors to established theoretical models of occupational stress, highlighting the need for sector-specific strategies in promoting psychological safety in high-demand work environments. Full article

Background: Celiac disease (CeD) requires lifelong adherence to a strict gluten-free (GF) diet. During the COVID-19 pandemic, the prevailing clinical assumption was that food supply disruptions and dietary management difficulties would be the primary sources of patient distress. This exploratory cross-sectional study directly tested this assumption in an Italian CeD cohort. Methods: COVIMPACT is an exploratory observational, web-based study conducted in Italy (data collected: July–September 2024; participants retrospectively reported their experiences during the COVID-19 pandemic period 2020–2022). Participants with a confirmed CeD diagnosis were recruited through patient associations and online networks. A structured 26-item questionnaire addressed socio-demographic, nutritional, psychological, and healthcare-access domains. Descriptive statistics, chi-square bivariate analyses (Cramér’s V as effect size), and binary logistic regression were performed using R (v4.1) and Python. Results: Among 118 participants (78% female; median age 36 years; IQR 12–42), 27% reported self-perceived psychological distress. Against expectation, difficulties in accessing GF products and changes in gluten consumption showed no clear associations with distress. Instead, social exclusion showed the strongest association (Firth OR = 5.55, 95% CI: 1.80–17.09, p = 0.003), while reduced physical activity (Firth OR = 5.28, 95% CI: 1.86–14.99, p = 0.002, full model; Firth OR = 5.54, p = 0.001, reduced model) and negative economic impact (Firth OR = 3.77, 95% CI: 0.89–15.97, p = 0.071, trend) were additional associated factors. Female sex showed a non-significant trend (Firth OR = 4.21, p = 0.082). All estimates carry wide confidence intervals (EPV = 4.1) and should be treated as hypothesis-generating. Conclusions: These preliminary findings suggest that social exclusion and physical inactivity may be more strongly associated with self-perceived distress than dietary challenges in contexts where GF food access is structurally protected. Results are exploratory, hypothesis-generating, and should not be generalised beyond this selected Italian cohort. Full article

The COVID-19 pandemic caused an unprecedented disruption to global tourism, exposing the structural vulnerability of tourism-dependent economies and leading to a sharp decline in international mobility. This study examines post-pandemic tourism recovery in Kazakhstan, focusing on the association between travel expenditure and tourism demand, and on regional disparities in recovery patterns. The empirical strategy combines time-series econometric modelling, infrastructure index construction, and regional cluster analysis. Using annual data for 2000–2023, a parsimonious autoregressive distributed lag (ARDL) model is applied to estimate both short-run dynamics and long-run relationships. The results show a positive and statistically significant association between tourism demand and travel expenditure, supporting the interpretation of expenditure-related recovery dynamics. They also indicate persistence in demand and a significant negative association with the coronavirus disease 2019 (COVID-19) shock. The error correction mechanism indicates rapid adjustment toward long-run equilibrium. Infrastructure and regional analyses highlight substantial post-pandemic capacity expansion alongside pronounced spatial disparities, with activity concentrated in major urban centres. The findings suggest that recovery is closely associated with demand-side dynamics and accompanied by persistent structural and regional constraints, highlighting the need for coordinated policies that combine demand stimulation, infrastructure development, and balanced regional growth. Full article

Background/Objectives: Health system socio-economic inequities in dental care are a long-standing problem in Europe. The issue gained increased relevance during the recent pandemic due to service disruption and socio-economic inequities that become even more pronounced under such circumstances. However, while preventive dental programs are considered key elements of public health, little is known about their role in addressing equity in accessing dental care among different countries and over time between them. This research aims at investigating the relationship between preventive dental policy, socio-economic factors, and the inability to get appropriate dental care within EU member states. Methods: A longitudinal panel dataset at the country level, consisting of data collected during 2020 through 2024, was assembled using open sources of statistics from Europe and other international statistical databases. The dependent variable used in the study was the percentage of the population that had unmet dental care need because of cost. Independent variables were the presence or absence of preventive policies related to dentistry, educational attainment, gross domestic product per capita, unemployment rate, number of dentists, and out-of-pocket expenses. Balanced panel datasets and regressions with robust standard errors in random-effects models were estimated. Interaction terms were created to test the moderating effect of education level on the relationship between policies and access to care. Results: Cross-country variations in terms of the prevention policy environment, socio-economic status, and unmet dental care need were found from descriptive analysis. The higher level of out-of-pocket payment was always related to the higher unmet dental care need, while the lower GDP countries displayed poorer access. Using the balanced panel random-effects model, preventive dental policies and the interaction between preventive policies and educational level were insignificant factors predicting the unmet dental care need. On the other hand, higher out-of-pocket payments, education, and dentists per million population had nearly significant positive relationships. In the sensitivity analysis, GDP per capita showed a negative association, whereas dentists per million population remained positively associated with unmet dental care need. Conclusions: The findings suggest that inequalities in access to dental care during and after the COVID-19 period were shaped primarily by financial and structural determinants rather than by the presence of preventive policies alone. While preventive programs remain an important component of long-term oral health strategies, reducing direct household payment burden and strengthening health system capacity may represent more immediate mechanisms for maintaining equitable access to dental services during periods of system disruption. Full article