Search Results (1,770)

The research examines regional disparities in the diffusion of e-commerce among enterprises employing at least 10 people in Italy, using an integrated analytical framework that blends econometric modeling, machine learning, and network analysis. Instrumental Variable (IV) panel models overcome endogeneity arising from digital infrastructure, socioeconomic factors, and online business activity, with geographic slope as a suitable instrument for broadband penetration. Machine learning models—regularized regressions, random forests, and boosting—augment causal inference by registering nonlinear effects and sorting variable salience. The results, in all cases, emphasize internet use, household digital connectivity, and the prevalence of remote work as the most important predictors of the diffusion of e-commerce. Cluster analysis identifies regional digital profiles that distinguish northern-central regions from southern-insular regions, characterizing persistently distinct digital divides. The network analysis, in turn, identifies digital inclusion variables—such as internet penetration and ICT infrastructure—that occupy central positions within the entirety of the economic and technological interdependencies’ regime. Innovation and income levels, while practiced, hold peripheral positions, indicating that digital capacity, rather than economic affluence in the singular, drives online business participation. Italy’s case can particularly illustrate this beyond its national borders. Being a high-income economy with significant regional disparities, it reproduces challenges common elsewhere in the world, among which the cases of Spain, Germany, the USA, the Republic of Korea, and Japan come to mind, where regional disparities inhibit inclusive digital development. The Italian case presents, then, a transferable model for the diffusion of digital tools, the reduction in regional disparities, and the encouragement of economic integration. By synthesizing the causal, predictive, and systemic methodologies, the study offers a theoretical and practical response to digital transformation across diverse terrains. Full article

Background: High-grade serous ovarian carcinoma (HG-SOC) remains the most lethal gynecological malignancy, largely due to intrinsic or acquired resistance to platinum-based chemotherapy. Although large-scale sequencing studies have delineated the genomic landscape of HG-SOC, clinically actionable biomarkers predictive of platinum response and outcome are still lacking. This study aimed to identify genomic alterations associated with platinum sensitivity, resistance, or refractoriness, and to assess their prognostic relevance. Methods: Tumor DNA from 24 HG-SOC patients with optimal cytoreductive resection, classified as platinum-sensitive (n = 9), platinum-resistant (n = 8), or platinum-refractory (n = 7) underwent targeted next-generation sequencing of 409 cancer-associated genes. Somatic variants were filtered and classified for oncogenicity using established criteria incorporating predicted functional impact, REVEL scores, and population allele frequencies. Associations between mutational profiles, platinum response, and overall survival (OS) were evaluated using Kaplan–Meier and Cox regression analyses. Key findings were validated in the TCGA ovarian serous carcinoma (TCGA-OV) dataset using survival analyses. Results: A total of 1367 protein-altering somatic variants across 301 genes were identified. While TP53 mutations were ubiquitous, platinum-resistant and platinum-refractory tumors showed enrichment of pathogenic alterations affecting DNA repair, transcriptional regulation, epigenetic modification, and oncogenic signaling, including FANCA, ATF1, MAF, NCOA2, PIK3CA, and TET1. Mutations in these genes were associated with reduced overall survival in exploratory analyses (median 2.5–9 months vs. 27.5–45 months). Multivariate analysis identified FANCA and ATF1 as potential independent predictors in exploratory modeling. In the TCGA-OV cohort, patients harboring pathogenic variants in a multi-gene panel derived from this study (excluding BRCA1/2) exhibited significantly worse survival compared with both BRCA1/2-mutated cases and the overall cohort. Conclusions: This exploratory study identifies a set of genomic alterations converging on transcriptional and epigenetic regulation, DNA repair, and oncogenic signaling that are associated with platinum resistance and adverse prognosis in HG-SOC. Independent validation in TCGA supports the potential clinical relevance of this mutational signature. These findings warrant further validation in larger prospective cohorts and functional studies to clarify their role as biomarkers of aggressive disease and therapeutic vulnerability. Full article

Despite the growing importance of Environmental, Social, and Governance (ESG) performance in shaping brand perception and consumer trust, limited empirical evidence exists on how ESG indicators translate into measurable digital consumer engagement outcomes, particularly in ethically driven markets such as the vegan food sector. This study addresses this gap by examining how ESG performance translates into digitally observable consumer engagement and frames this relationship as a strategic performance measurement and decision-support problem. Building on the sector’s reliance on ethical positioning, trust, and online visibility, we integrate ESG indicators with digital marketing and web analytics metrics (e.g., traffic and engagement proxies) for a panel of five leading vegan food firms [Nestlé SA (Vevey, Switzerland), Kellanova (Chicago, IL, USA), Beyond Meat Inc. (El Segundo, CA, USA), Danone SA (Paris, France), and Conagra Brands Inc. (Chicago, IL, USA)], using data from the Semrush web analytics platform and the Eikon Refinitiv ESG database for the period January–December 2024. We employ a mixed-method design combining descriptive analytics with correlation analysis and simple linear regression to estimate the direction and strength of ESG–digital performance links, and we extend inference through Fuzzy Cognitive Mapping (FCM) using the MentalModeler platform to simulate “what-if” scenarios that support managerial foresight under digital uncertainty. Results indicate that stronger ESG profiles are associated with more favorable digital outcomes, with specific ESG mechanisms (e.g., human-capital and environmental initiatives) aligning with deeper engagement signals. The FCM scenarios further suggest that coordinated ESG improvements can amplify digital traction and reinforce sustainable brand growth. The proposed framework contributes to strategic management by operationalizing an ESG-enabled digital performance measurement system and a lightweight Decision Support System (DSS) that can guide resource allocation, KPI monitoring, and risk-aware positioning in sustainability-oriented markets. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

This study investigates the determinants of GDP per capita across 26 European Union member states over the period of 2006–2024, with a particular focus on entrepreneurship, institutional quality, and macroeconomic factors. Given the presence of long-run income differences across EU countries, the analysis explicitly accounts for structural heterogeneity in economic development and institutional capacity. To ensure robust estimation in the presence of cross-sectional dependence and slope heterogeneity, the study employs advanced panel econometric techniques, including tests for cross-sectional dependence, unit roots, and cointegration. Long-run relationships and short-run dynamics are estimated using the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model, complemented by robustness checks based on the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Method of Moments Quantile Regression (MMQR) is applied to capture heterogeneity across different points of the income distribution, thereby reflecting long-run income disparities among EU member states. The empirical results confirm the existence of a stable long-run equilibrium relationship among the variables. The baseline CS-ARDL estimates indicate that institutional quality, entrepreneurial activity, trade openness, and government expenditure exert positive and statistically significant effects on GDP per capita, while financial development exhibits a negative effect and foreign direct investment remains insignificant. In the short run, entrepreneurship and trade openness contribute positively to GDP per capita, whereas government expenditure and credit expansion generate contractionary effects. The robustness analysis using AMG and CCEMG estimators largely supports these findings, as the direction of the coefficients remains consistent across alternative specifications, although some variation in statistical significance is observed due to differences in the treatment of cross-sectional dependence and unobserved common factors. The MMQR results further reveal substantial heterogeneity across the income distribution, indicating that the effects of key determinants vary depending on countries’ long-run income levels. In particular, trade openness and institutional quality exert stronger positive effects in lower-income quantiles, while the adverse effects of excessive financial development are more pronounced in higher-income quantiles. Overall, the findings underscore the importance of promoting productive entrepreneurship, strengthening institutional frameworks, facilitating trade integration, and ensuring efficient financial intermediation to enhance GDP per capita within the European Union. The results also highlight the need for differentiated policy approaches that explicitly account for long-run income heterogeneity, structural differences, and varying institutional capacities across EU member states. Full article

This study examines whether the relationship between provincial fiscal revenue and expenditure measures and grain production in China is nonlinear. Using a balanced panel of 31 provinces from 2007 to 2021, we analyze major revenue-side and expenditure-side fiscal instruments, including the cultivated land occupation tax, value-added tax, agricultural insurance subsidies, agricultural loan interest subsidies, rural minimum living security subsidies, education expenditure, and transportation infrastructure expenditure. To identify regime-dependent changes in estimated associations, we employ panel kink and double-kink regression models with endogenously estimated kink points. The results suggest that the estimated relationships are intensity-dependent rather than constant. The cultivated land occupation tax exhibits a kinked relationship with grain production, with a more positive association beyond a certain level. Agricultural insurance subsidies display a double-kink pattern, with the strongest positive estimated association concentrated in an intermediate range of the subsidy measure. Rural minimum living security subsidies are positively associated with grain production at lower levels, but this association weakens and may become negative after the estimated kink point. Overall, the findings suggest that the relationship between fiscal variables and grain production depends not only on policy direction but also on the levels of the fiscal measures. Full article

In the context of the transition of the European energy sector and economy towards sustainable systems, this study aims to investigate the influence of digital enablers on affordable and clean energy in the European Union, using an econometric approach based on panel data regression. In accordance with the literature review and the main programmatic documents that mark the sustainable transition of the energy system, as well as the role of digitalization in this process, 4 research hypotheses and 16 sub-hypotheses were developed regarding the influence of digital enablers specific to the digitalization of the population and enterprises on clean and affordable energy. To confirm the hypotheses, a panel data regression was used for the period 2016–2024 in the European Union states. From a methodological perspective, the panel data regression was carried out using estimation of fixed effects and random effects models, Hausman tests for model selection, diagnostic testing, and correction of standard errors using Driscoll–Kraay estimators. The panel data regression analysis was carried out using R software, version 4.5.1. The results obtained showed that not all independent variables that express the digitalization of the population have the same influence on the share of renewable energy. The performed analysis shows the influence of the level of digitalization of enterprises on the share of renewable energy in the final energy consumption value, but also of the digitalization of the population on the price of energy, as a synthetic expression of affordable energy. Therefore, an essential contribution of the research is represented by highlighting the differentiated impact of digital enablers on clean and affordable energy, using a dual perspective of digitalization, at both the population and enterprise levels. Full article

In this article, we analyze the combined impact of sustainability activities and family governance on firm-level risk, measured by earning volatility, with particular attention to the timing of ESG involvement. Using panel regression models, we distinguish between short- and long-term ESG performance and between family ownership and family management. The empirical analysis reveals a negative correlation between long-term ESG performance and corporate risk, but short-term ESG impact is insignificant. Family ownership and having a family CEO both decrease firm risk; however, family ownership moderates the link between ESG risks and firm risk. Full article

The transition to a circular economy creates winners and losers, challenging the assumption that green growth is inherently inclusive. While environmental benefits are documented, the distributional consequences remain poorly understood. This study analyzes how socioeconomic and labor inequalities shape the effectiveness of circular economy policies. Using panel data from 90 countries (2019–2024) combined with global governance indicators, we employ fixed-effects models, instrumental variables, and configurational analysis (fsQCA) to identify causal mechanisms. The results reveal a critical institutional threshold: circular economy policies reduce inequality only in countries with high institutional quality (WGI > 1.39). In contexts with weak institutions or positive Skill Structure Balance (SSB), these policies are regressive. Social protection and digital financial inclusion moderate these effects, acting as buffers against distributional risks. These findings challenge the “automatic social benefits” narrative, suggesting that environmental ambition requires parallel investments in institutional capacity and human capital to achieve a just transition. Full article

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

Specialized, Refined, Unique and Novel (SRUN) listed firms are pivotal to the high-quality development of China’s real economy, and their growth quality underpins the security of industrial and supply chains. This study empirically examines the relationship between R&D investment persistence and growth quality of Chinese A-share SRUN listed firms from 2006 to 2024, with technology conversion efficiency as the mediating variable. R&D investment persistence is measured from the dual dimensions of investment intensity and stability, and firm growth quality is a comprehensive indicator constructed via principal component analysis (PCA) from revenue growth, profitability and risk resilience. Panel data regression models, combined with mechanism, endogeneity, robustness and heterogeneity tests, are adopted for empirical analysis. The results show a significantly positive correlation between R&D investment persistence and SRUN firms’ growth quality, with the regression coefficient of R&D investment persistence on growth quality reaching 0.189 (p < 0.01); both investment intensity and stability exert significant positive effects on all dimensions of growth quality, with their regression coefficients on growth quality being 0.156 and 0.132 (both p < 0.01) respectively. Technology conversion efficiency plays a partial mediating role in this relationship, with the mediating effect ratio of R&D investment persistence on growth quality through technology conversion efficiency at 34.2%, as R&D investment persistence indirectly improves growth quality by enhancing patent output and new product conversion efficiency. Heterogeneity analysis indicates that this positive correlation is more pronounced in high-tech industries, small and medium-sized enterprises (SMEs) and eastern China-based firms, driven by differences in industrial R&D dependence, resource endowments and financing frictions. Though endogeneity is mitigated by instrumental variables, propensity score matching (PSM) and difference-in-differences (DID), strict causal identification is constrained by data availability. This study enriches the theories of R&D investment and firm growth, and provides empirical insights for SRUN firms to optimize their R&D strategies and for the government to formulate targeted support policies, so as to promote the high-quality development of SRUN firms and the transformation of China’s manufacturing industry. Full article

Financial technologies (Fintech) have rapidly reshaped access to financial services, particularly in developing countries where traditional banking remains limited. This study investigates fintech’s role in advancing financial inclusion by analyzing panel data from 89 developing economies gathered from Global Findex reports (2011–2021), complemented by International Monetary Fund (IMF), UNU-WIDER, and PRIO datasets. We applied a random-effects regression model and GMM, incorporating fintech adoption alongside macroeconomic and institutional variables such as education, governance quality, and trade openness. Our results show that fintech is the most significant driver of financial inclusion, especially in expanding account ownership, with education and institutional quality further enhancing outcomes. Conversely, we show that population growth and income disparities constrain progress, while government expenditure and GDP growth display mixed effects. We also find that fintech reduces transaction costs and barriers, yet its impact depends on digital literacy, infrastructure, and governance. In conclusion, our findings highlight that fintech represents a transformative but unevenly utilized tool, capable of fostering broader economic participation and reducing inequality when paired with supportive policies and institutional frameworks. Full article

Non-muscle-invasive bladder cancer (NMIBC) is characterized by high recurrence rates and necessitates lifelong cystoscopic surveillance, underscoring the need for minimally invasive biomarkers to improve early detection and risk stratification. Therefore, this study aimed to investigate the role of trimethylamine-N-oxide (TMAO) and its precursors as diagnostic biomarkers for NMIBC. A total of 50 male patients with NMIBC (25 pTa and 25 pT1) were included in this study. Additionally, 52 age-matched healthy individuals were included as controls. Serum TMAO and its dietary precursors were quantified using liquid chromatography–tandem mass spectrometry. Group differences were analyzed using nonparametric tests, associations were assessed using Spearman’s correlation, and diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Multivariate logistic regression was performed to identify independent predictors, and a composite risk score was generated. Serum TMAO, carnitine, and choline levels were significantly higher in patients with NMIBC than in controls (p ≤ 0.0001), whereas betaine showed a nonsignificant trend toward higher levels (p ≥ 0.05). The pathological stage (pTa vs. pT1) showed the strongest correlation with TMAO levels. The ROC analysis revealed that TMAO had the highest individual diagnostic accuracy (area under the curve [AUC] = 0.875, 95% confidence interval [CI] 0.812–0.939), whereas carnitine and choline provided complementary diagnostic performance. In multivariate models, TMAO, carnitine, and choline remained independent predictors of NMIBC (p ≤ 0.0001). A composite risk score integrating all four metabolites demonstrated excellent discriminatory capacity (AUC = 0.958, 95% CI 0.926–0.991). The TMAO metabolic axis can be used as a minimally invasive biomarker panel for NMIBC. Further large, prospective, multicenter studies integrating metabolomic and microbiome profiling are needed to validate the findings. Full article

This study evaluates the performance of a semi-dynamic negative binomial model with cubic spline smoothing to characterize the spatiotemporal dynamics of COVID-19 mortality in Peru, a setting marked by significant data inconsistency and reporting delays. Using nationwide weekly mortality data, we compared a Poisson regression against a semi-dynamic NB model with a population offset and cubic splines (df = 6). The models were evaluated using Akaike Information Criterion and log-likelihood to handle overdispersion and temporal non-stationarity. The NB model demonstrated a superior fit, reducing the AIC from 136,596.4 to 75,668.25 and improving log-likelihood by over 30,000 points. Demographic analysis revealed an 81.6% higher risk of death in males (IRR = 1.816; 95% CI: 1.753–1.881) and an exponential gradient with age, peaking at an IRR of 4.717 (95% CI: 4.499–4.945) for individuals ≥80 years. Departmental fixed effects identified significant spatial heterogeneity, with higher diffusion in coastal regions. The semi-dynamic NB model with splines provides a robust, parsimonious, and scalable framework for epidemiological surveillance in resource-limited settings. By effectively correcting for overdispersion and stabilizing weekly reporting fluctuations, this approach offers a reliable tool for public health decision making in environments with fragmented data quality. Full article

Digital economy and agricultural technological innovation are key drivers of agricultural economic resilience and sustainable development. However, existing research has yet to clarify how they jointly affect agricultural economic resilience, particularly through potential nonlinear patterns and spatial spillover effects. Using panel data from 30 Chinese provinces, this study measures digital economy development and agricultural economic resilience via the entropy weight method. It systematically examines the direct impact, transmission mechanisms, threshold effects, and spatial spillover effects using two-way fixed effects, mediation, threshold regression, and spatial Durbin models. The findings are as follows. First, the digital economy significantly improves agricultural economic resilience, a result robust to various tests and endogeneity treatments. Second, agricultural technological innovation plays a partial mediating role, accounting for 19.37% of the total effect. Third, the resilience-enhancing effect of agricultural technological innovation exhibits a double-threshold pattern: its positive impact gradually strengthens as the digital economy develops to a higher level. Fourth, the digital economy generates a positive spatial spillover effect on agricultural economic resilience. Fifth, although the digital economy and agricultural technological innovation show synergistic development, their coupling coordination degree remains relatively low, indicating substantial untapped potential for synergy. From a sustainable development perspective, this study reveals the mechanisms through which the digital economy and agricultural technological innovation enhance agricultural economic resilience, providing empirical evidence and policy insights for strengthening agricultural risk resistance and achieving agricultural sustainability via digital transformation and technological progress. Full article