Search Results (7,887)

Despite growing attention to sustainability in the global tourism industry, empirical evidence explaining how internal organizational capabilities translate into superior environmental performance remains scarce—particularly in emerging markets. This study investigates the performance effects of green dynamic capabilities (GDC) in driving environmental performance in the hotel industry, with a particular focus on the mediating effect of eco-innovation (ECI) and the moderating effect of environmental management accounting (EMA). Although environmental sustainability in tourism has become a global imperative, limited empirical evidence exists on how internal capabilities and accounting practices jointly enhance hotels’ green performance—particularly within emerging economies such as Türkiye. Drawing on dynamic capabilities theory and resource orchestration perspectives, this study addresses this research gap by analyzing survey data collected from 108 managers of Green Key-certified hotels in Türkiye. The developed research framework was tested through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4. The results reveal that GDCs positively influence environmental performance, and this relationship is significantly mediated by ECI. Moreover, EMA strengthens the positive effect of GDCs on ECI, highlighting its role as an enabling internal infrastructure. These findings suggest that the realization of sustainability outcomes depends not only on the presence of capabilities but also on how these are embedded within innovation processes and internal organizing systems. The study contributes to sustainability and management literature by offering a context-specific understanding of the capability–infrastructure–performance nexus and providing actionable insights for hotel managers in emerging tourism markets. Full article

In response to the persistent failures of traditional election polling, this study introduces the Decentralized Prediction Market Voter Framework (DPMVF), a novel tool to empirically test and quantify the predictive capabilities of Decentralized Prediction Markets (DPMs). We apply the DPMVF to Polymarket, analysing over 11 million on-chain transactions from 1 September to 5 November 2024 against aggregated polling in the 2024 U.S. Presidential Election across seven key swing states. By employing Cross-Correlation Function (CCF) for linear analysis and Dynamic Time Warping (DTW) for non-linear pattern similarity, the framework provides a robust, multi-faceted measure of the lead-lag relationship between market sentiment and public opinion. Results reveal a striking divergence in predictive clarity across different electoral contexts. In highly contested states like Arizona, Nevada, and Pennsylvania, the DPMVF identified statistically significant early signals. Using a non-parametric Permutation Test to validate the observed alignments, we found that Polymarket’s price trends preceded polling shifts by up to 14 days, a finding confirmed as non-spurious with a high confidence (p < 0.01) and with an exceptionally high correlation (up to 0.988) and shape similarity. At the same time, in states with low polling volatility like North Carolina, the framework correctly diagnosed a weak signal, identifying a “low-signal environment” where the market had no significant polling trend to predict. This study’s primary contribution is a validated, descriptive tool for contextualizing DPM signals. The DPMVF moves beyond a simple “pass/fail” verdict on prediction markets, offering a systematic approach to differentiate between genuine early signals and market noise. It provides a foundational tool for researchers, journalists, and campaigns to understand not only if DPMs are predictive but when and why, thereby offering a more nuanced and reliable path forward in the future of election analysis. Full article

This paper represents initial research with the aim to establishes a baseline for subsequent research into AI-based construction monitoring, building upon the NORMENG project in Croatia, which previously integrated photogrammetry, laser scanning, and BIM-based methods. The study tests general purpose AI’s ability to detect materials and estimate quantities, aiming to assess whether a broad, context-aware AI system can match the precision of specialized, domain-specific tools or even human work needed for productivity estimations. While the AI demonstrated potential for basic entity detection and preliminary quantity estimations, it showed significant limitations in delivering fine-grained, temporally accurate breakdowns without targeted adaptation. The findings underscore the need for domain-specific fine-tuning and human-in-the-loop validation to transform AI into a reliable tool for construction management. This initial contribution provides empirical insights and actionable recommendations for advancing automated progress monitoring in the construction sector. Full article

Patent foramen ovale (PFO) is implicated in cryptogenic stroke and other clinical syndromes, with transcatheter closure demonstrating superiority over medical therapy in selected patients. Most closure devices are composed of nitinol, a nickel–titanium alloy, raising concerns in individuals with nickel hypersensitivity, one of the most prevalent contact allergies worldwide. Although typically manifesting as localized dermatitis, nickel allergy has been associated with systemic reactions after device implantation, including chest pain, palpitations, migraines, dyspnea, and cutaneous eruptions. Recent evidence indicates that nickel-sensitive patients experience a significantly higher incidence of post-procedural device-related symptoms. Nevertheless, severe reactions remain rare, and the benefits of PFO closure generally outweigh the risks. The predictive value of pre-implantation patch testing remains uncertain, and the lack of nickel-free alternatives constrains device selection. Management strategies are empirical, relying on symptomatic treatment with corticosteroids, antihistamines, or device explantation in refractory cases. Future research should focus on elucidating the pathophysiology of nickel-induced hypersensitivity in cardiovascular implants, improving diagnostic algorithms, and developing biocompatible, nickel-free devices. A multidisciplinary approach involving cardiologists, dermatologists, and allergists is essential to optimize outcomes in this complex subset of patients. Full article

The emergence of generative artificial intelligence (GAI) has significantly transformed higher education. As a linguistic assistant, GAI can promote equity and reduce barriers in academic writing. However, its widespread availability also raises ethical dilemmas about integrity, fairness, and skill development. Despite the growing debate, empirical evidence on how students’ ethical evaluations influence their predicted use of GAI in academic tasks remains scarce. This study analyzes the ethical determinants of students’ determination to use GAI as a linguistic assistant in essay writing. Based on the Multidimensional Ethics Scale (MES), the model incorporates four ethical criteria: moral equity, moral relativism, consequentialism, and deontology. Data were collected from a sample of 151 university students. For the analysis, we used a mix of partial least squares structural equation modeling (PLS-SEM), aimed at testing sufficiency relationships, and necessary condition analysis (NCA), to identify minimum acceptance thresholds or necessary conditions. The PLS-SEM results show that only consequentialism is statistically relevant in explaining the predicted use. Moreover, the NCA reveals that reaching a minimum degree in the evaluations of all ethical constructs is necessary for use to occur. While the necessary condition effect size of moral equity and consequentialism is high, that of relativism and deontology is moderate. Thus, although acceptance of GAI use in the analyzed context increases only when its consequences are perceived as more favorable, for such use to occur it must be considered acceptable, which requires surpassing certain thresholds in all the ethical factors proposed as explanatory. Full article

Introduction: Mpox emerged as a multi-country outbreak in 2022 and disproportionately affected gay, bisexual, and other men who have sex with men (GBMSM). Stigma is known to exacerbate health crises by discouraging testing, treatment, and vaccination. This review aimed to explore stigma associated with Mpox among GBMSM from July 2022, when mpox was declared a public health emergency of international concern. Methods: The PICO framework guided this narrative review. A search was conducted across the following databases from inception to June 2025: PubMed/MEDLINE, Embase, CINAHL, and Web of Science. The literature had to be empirical, peer-reviewed research that focused on mpox-related stigma in GBMSM. Results: Forty-seven studies were included in this review. The following themes were derived: (1) healthcare experiences, (2) media influence, (3) internalised and anticipated stigma, (4) public health messaging, (5) community responses, and (6) psychosocial impact. Healthcare experiences were marked by anticipated discrimination; many GBMSM delayed testing or vaccination for fear of being disclosed or labelled promiscuous. This was especially apparent in contexts where same-sex relationships are criminalised, leading some men to self-medicate or seek clandestine services. Media analyses revealed that social and traditional platforms often amplified blame and homophobia, though community-led counter-messaging helped shift narratives. Internalised and anticipated stigma resulted in shame, concealment of symptoms, avoidance of care, and heightened anxiety. Public health messaging that framed mpox as a behaviour-linked rather than identity-linked risk was more acceptable, and flexible vaccination strategies (e.g., offering less conspicuous injection sites) increased uptake. Stigma contributed to psychosocial distress and may have impeded outbreak control. Conclusions: Mpox-related stigma among GBMSM operates at individual, community, and structural levels, echoing patterns from the HIV era. Effective mitigation requires rights-based, destigmatising communication, culturally competent care, and collaboration. Addressing stigma is vital to controlling future outbreaks and ensuring equitable healthcare access. Full article

This study investigates the dynamic relationship between key macroeconomic indicators, specifically inflation (CPI), the Federal Funds Rate, GDP growth, unemployment, and money supply, and U.S. stock market returns, represented by the S&P 500 index, over the period January 2015 to June 2025. The objective is to understand how inflation and monetary policy affect market performance in both the short and long run. Using an Autoregressive Distributed Lag (ARDL) modeling framework and Error Correction Model (ECM), the study examines monthly S&P 500 returns alongside macroeconomic variables, accounting for lagged effects and potential cointegration. The model captures both immediate and delayed impacts, employing the Bounds Testing approach to confirm long-run equilibrium relationships. Results show significant mean-reversion in stock returns, a delayed negative impact of inflation and interest rate increases, and a positive contemporaneous response to GDP growth. Unemployment exhibits a counterintuitive positive effect on returns, suggesting forward-looking investor expectations. The money supply also positively influences equity prices, supporting liquidity-based asset pricing theories. This paper provides updated empirical evidence on macro-finance linkages and highlights the complex interplay of monetary policy, inflation, and market expectations in shaping U.S. equity returns. Full article

This paper investigates how digital finance affects corporate ESG performance through the following mediation paths. Based on agency theory and a resource-based view, we hypothesize that digital finance benefits ESG performance not only directly but also indirectly through enhancing TFP and analyst coverage. We test our hypotheses using 22,576 firm-year observations of Chinese listed firms from 2011 to 2023 by employing a fixed-effects mediation model. The empirical results support our hypotheses. Digital finance improves ESG performance directly, and part of its effect goes through higher TFP and better analyst monitoring. The results show that digital finance plays dual roles in improving efficiency and market monitoring, which is beneficial to corporate sustainability. By identifying the above two mediation paths, this paper enriches the theoretical understanding of the relationship between financial digitalization and sustainability and provides practical implications for policymakers and managers to improve ESG performance. Full article

High-quality corporate innovation serves as a critical driver for achieving corporate sustainable development. This study bridges the gap between macroeconomic fiscal sustainability and microeconomic innovation quality. Specifically, this paper investigates the influence of local fiscal sustainability on high-quality corporate innovation, examining the underlying mechanisms and heterogeneous effects. Methodologically, data were collected using Python-based retrieval and web-scraping techniques. A multi-dimensional index of local fiscal sustainability was constructed, comprising five key dimensions to quantitatively map provincial fiscal sustainability across China. Corporate innovation quality was measured using patent citation metrics. Employing panel data from A-share listed companies over the 2015–2023 period, we implemented a two-way fixed-effects model for rigorous empirical econometric analysis. The findings indicate a significant positive relationship between local fiscal sustainability and high-quality corporate innovation. This result remains robust after a battery of robustness tests, including the use of instrumental variable (IV) methods. Mechanism analysis reveals that the resource compensation effect is the primary channel. Furthermore, our analysis identifies heterogeneity across varying innovation environments, economic regions, and industry characteristics. The positive influence is particularly pronounced in provinces with stronger intellectual property protection, firms located in the eastern regions, and High-Tech Enterprises. Collectively, the conclusions drawn from this research offer valuable policy implications for strengthening local fiscal sustainability and enhancing high-quality corporate innovation. Full article

Addressing climate change, pursuing green development, and achieving high-quality development are rapidly coalescing into a global strategic consensus. Against this backdrop, this paper empirically examines the impact of green investor entry on the high-quality development of manufacturing enterprises. Using a sample of A-share listed manufacturing companies from 2015 to 2023, it employs fixed-effects and mediation models. The findings reveal: (1) Green investor entry significantly promotes high-quality development in manufacturing enterprises, a conclusion that holds after endogeneity and robustness tests. (2) Mechanism effects indicate that green investors empower manufacturing enterprises to achieve high-quality development through the integration of digital and physical technologies. (3) Heterogeneity tests indicate that in eastern regions and non-heavily polluting industries, the entry of green investors exerts a more pronounced promotional effect on the high-quality development of manufacturing enterprises. (4) Green investor entry significantly promotes high-quality development of manufacturing enterprises under the negative moderation of financing constraints. These findings confirm the catalytic role of green investor entry in advancing high-quality development within manufacturing enterprises, clarify the mechanism of digital–physical integration linking the two, and provide empirical evidence and policy insights to support strategic decisions promoting high-quality development through green investor entry in China’s manufacturing sector. Full article

This study investigates the relationship between economic growth and environmental performance in selected Middle East and North Africa (MENA) countries through the lens of the Environmental Kuznets Curve (EKC) hypothesis. Due to data availability constraints, our sample includes Algeria, Egypt, Lebanon, Mauritius, Morocco, and Oman, covering the period 1990–2022. Using annual panel data, we apply panel cointegration techniques alongside Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators, complemented by Granger causality tests, to examine the interaction among GDP per capita, renewable energy consumption, and transport service exports in determining CO₂ emissions per unit of GDP. The empirical findings provide only partial support for the EKC: while the DOLS results confirm an inverted U-shaped income–emissions relationship, the FMOLS estimations contradict it, suggesting a more complex and nonlinear pattern. Beyond testing the EKC, this study contributes two novel dimensions to the literature. First, it shows that renewable energy exerts a statistically significant negative effect on carbon intensity in the long run, despite weak short-run causality, highlighting the delayed but durable environmental benefits of clean energy adoption. Second, it introduces transport service exports as a proxy for structural economic transformation, capturing the role of trade-driven diversification in reducing emissions. By embedding renewable energy deployment and service-based trade dynamics into the EKC framework, the study advances a more policy-relevant and region-specific understanding of the growth–environment nexus in the selected MENA economies. The results underscore the importance of scaling renewable energy, promoting low-carbon service sectors, and aligning trade and environmental policies to ensure that economic growth supports long-term climate objectives. Full article

This paper proposes the lower-bounded Fréchet–log-logistic distribution (LFLD), a probability model designed for robust flood frequency analysis (FFA). The LFLD addresses key limitations of traditional distributions (e.g., generalized extreme value (GEV) and log-Pearson Type III (LP3)) by combining bounded support ($\alpha <x<\infty$) to reflect physical flood thresholds, flexible tail behavior via Fréchet–log-logistic fusion for extreme-value accuracy, and maximum entropy characterization, ensuring optimal parameter estimation. Thus, we obtain the LFLD’s main statistical properties (PDF, CDF, and hazard rate), prove its asymptotic convergence to Fréchet distributions, and validate its superiority through simulation studies showing MLE consistency (bias < 0.02 and mean squared error < 0.0004 for $\alpha$) and empirical flood data tests (52- and 98-year AMS series), where the LFLD outperforms 10 competitors (AIC reductions of 15–40%; Vuong test p < 0.01). The LFLD’s closed-form quantile function enables efficient return period estimation, critical for infrastructure planning. Results demonstrate its applicability to heavy-tailed, bounded hydrological data, offering a 20–30% improvement in flood magnitude prediction over LP3/GEV models. Full article

As has repeatedly been pointed out in recent years, the categories ESL/Outer Circle and EFL/Expanding Circle should not be considered as clear-cut as traditionally assumed. Consequently, recent research has made first attempts for an integrative approach to Englishes traditionally ascribed to one of these categories. The paper at hand introduces the Extra- and Intra-territorial Forces Model (EIF Model) as a successful attempt to bridge the traditional gap between the two categories and shows how the model works in practice by implementation to the cases of Greece and Cyprus. These two countries are particularly interesting for the application of this framework since their linguistic ecologies, with Greek and English in contact, are essentially similar. From a historical perspective, however, they are fundamentally different; Cyprus is a former colony of the British Empire, whereas Greece has never experienced British colonization. Therefore, the two countries offer the perfect basis for putting the traditional categories of EFL and ESL to the test and for illustrating how more recent models of World Englishes, such as the EIF Model, might offer more flexible theoretical alternatives to earlier, often more rigid theoretical approaches. Full article

Pedestrian crashes remain a leading cause of road traffic fatalities in developing countries (DCs); yet reliable crash data are scarce, constraining the ability to model pedestrian safety risks and evaluate countermeasure effectiveness. This study developed a methodological process for estimating the influence of contextual factors on pedestrian crashes using artificial data. The process integrated literature-derived trend analysis, artificial data generation, external face validity checks, correlation analysis, stepwise negative binomial regression, sensitivity testing, and mapping of results against the International Road Assessment Programme (iRAP) framework. Of the 26 contextual factors considered, 20 were retained in the negative binomial (NB) models, while six were excluded due to weak or inconsistent trend data. Results showed that behavioural and institutional factors, including ad hoc countermeasure implementation, gender composition of pedestrian flows, and vehicle age or technology, exerted stronger influence on crash outcomes than several geometric variables typically emphasised in global models. External validity testing confirmed broad alignment of the artificial dataset with published values, while sensitivity analysis demonstrated the robustness of factor influence values (Fi) across bootstrap resampling and scenario perturbations. The Fi values derived are illustrative rather than decision-ready, reflecting the artificial-data basis of this study. Nonetheless, the findings highlight methodological proof of concept that artificial-data modelling can provide credible and context-sensitive insights in data-scarce environments. Mapping results to the iRAP framework revealed complementarity, with opportunities to extend global models by incorporating behavioural and institutional variables more systematically. The approach provides a replicable pathway for improving pedestrian safety assessment in DCs and informs the development of an enhanced iRAP effectiveness model in subsequent research. Future applications should prioritise empirical calibration with real-world crash datasets and support policymakers in integrating behavioural and institutional factors into countermeasure prioritisation and safety planning. Full article

Industrial machine tools are both performance assets and environmental hotspots over their long service lives. Maintenance is traditionally optimized to safeguard availability, quality and cost. However, maintenance choices also determine the energy consumption, footprints, component duration and end-of-life pathways. In this study, we present a decision framework to compare performance-only maintenance (POM) with sustainability-aware maintenance (SAM) for machine tools. The framework integrates degradation and Remaining Useful Life (RUL) estimation, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Outcomes are summarized with a Sustainable Maintenance Balance (SMB) index. We test the proposed approach on a horizontal machining center for aluminum, validated by running a Monte Carlo simulation over a 1000 h functional unit. Across empirical data and simulation, SAM—compared to POM—demonstrated an ability to improve availability, reduces downtime and scrap, and lower total LCC while cutting carbon emissions. The proposed method is proposed as readily deployable in real plants, supporting robust sustainable-production decisions. Full article