Search Results (583)

Digital transformation (DT) has emerged as a key strategic lever for enhancing firm resilience and competitiveness, yet its influence on non-productive investment behaviors, such as corporate financial investment, remains underexplored. Existing studies have largely focused on DT’s role in innovation and operational efficiency, leaving a significant gap in understanding how DT reshapes firms’ financial asset allocation. Drawing on a unique panel dataset of A-share main board-listed firms in China from 2011 to 2023, this study provides novel empirical evidence that DT significantly restrains financial investment, with pronounced heterogeneity across ownership types. More importantly, this paper uncovers a multi-layered mechanism: DT enhances the corporate information environment, which subsequently reduces financial investment. In addition, the analysis reveals a moderated mediation mechanism wherein economic uncertainty dampens the information-enhancing effect of DT. Unlike previous research that treats corporate risk-taking as a parallel mediator, this study identifies a sequential mediation pathway, where improved information environments suppress financial investment indirectly by influencing firms’ risk-taking behavior. These findings offer new theoretical insights into the financial implications of DT and contribute to the broader understanding of enterprise behavior in the context of digitalization and economic volatility. Full article

This study investigates the influence of corporate governance on reducing financial statement fraud (FSF) in Jordanian service and industrial companies listed on the Amman Stock Exchange from 2018 to 2022. To achieve this, the study employed the Beneish M-score model to assess the likelihood of FSF and logistic regression to examine the influence of corporate governance structure on fraud mitigation. The study identified 13 independent variables, including board size, board director’s independence, board director’s compensation, non-duality of CEO and chairman positions, board diversity, audit committee size, audit committee accounting background, number of annual audit committee meetings, external audit fees, board family business, the presence of women on the board of directors, firm size, and market listing on FSF. The study included 74 companies from both sectors—33 from the industrial sector and 41 from the service sector. Primary data was collected from financial statements and other information published in annual reports between 2018 and 2022. The results of the study revealed a total of 295 cases of fraud during the examined period. Out of the 59 companies analyzed, 21.4% demonstrated a low probability of fraud, while the remaining 78.6% (232 observations) showed a high probability of fraud. The results indicate that the following corporate governance factors significantly impact the mitigation of financial statement fraud (FSF): independent board directors, board diversity, audit committee accounting backgrounds, the number of audit committee meetings, family business involvement on the board, and firm characteristics. The study provides several recommendations, highlighting the importance for companies to diversify their boards of directors by incorporating different perspectives and experiences. Full article

Recent theoretical and empirical studies highlight that information asymmetry and owner–manager conflict of interest can distort corporate investment decisions. Building on this premise, we hypothesize that superior environmental, social, and governance (ESG) performance mitigates these frictions by (H1) alleviating financing constraints and (H2) intensifying external analyst scrutiny. To test these hypotheses, we examine all Shanghai and Shenzhen A-share non-financial firms from 2009 to 2023. Using panel fixed-effects and two-stage least squares with an industry–province–year instrument, we find that higher ESG performance significantly reduces investment inefficiency; the effect operates through both lower financing constraints and greater analyst coverage. Heterogeneity analyses reveal that the improvement is pronounced in small non-state-owned, non-high-carbon firms but absent in large state-owned high-carbon emitters. These findings enrich the literature on ESG and corporate performance and offer actionable insights for regulators and investors seeking high-quality development. Full article

This article describes the design, methods, and baseline characteristics of the social needs assessment (SNA) of participants enrolled in an ongoing randomized clinical trial implementing a comprehensive approach to improving diabetes self-management and providing an intensive Diabetes Self-Management Education and Support (iDSMES) Program at St. Vincent’s House Clinic, a primary care practice serving resource-challenged diverse populations in Galveston, Texas. Standardized SNA was conducted to collect information on financial needs, psychosocial well-being, and other chronic health conditions. Based on their identified needs, participants were referred to non-medical existing community resources. A series of in-depth interviews were conducted with a subset of participants. A team member independently categorized these SNA narratives and aggregated them into two overarching groups: medical and social needs. Fifty-nine participants (with a mean age of 53 years and equal representation of men and women) completed an SNA. Most (71%) did not have health insurance. Among 12 potential social needs surveyed, the most frequently requested resources were occupational therapy (78%), utility assistance (73%), and food pantry services (71%). SNA provided data with the potential to address barriers that may hinder participation, retention, and outcomes in diabetes self-management. SNA findings may serve as tertiary prevention to mitigate diabetes-related complications and disparities. Full article

This study investigates the impact of environmental, social, and governance (ESG) disclosure on value creation in a balanced panel of 100 non-financial Sharia-compliant firms listed on the Saudi Stock Exchange over the period 2014–2023. The analysis employs a combination of econometric techniques, including fixed effects models with Driscoll–Kraay standard errors, Pooled Ordinary Least Squares (POLS) with Driscoll–Kraay standard errors and industry and year dummies, and two-step system generalized method of moments (GMM) estimation to address potential endogeneity and omitted variable bias. Value creation is measured using Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The models also control for firm-specific variables such as firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization. The findings reveal that ESG disclosure has a positive and statistically significant effect on firm value across all three performance measures. Furthermore, firm size significantly moderates this relationship, with larger Sharia-compliant firms experiencing greater value gains from ESG practices. These results align with agency, stakeholder, and signaling theories, emphasizing the role of ESG in enhancing transparency, reducing information asymmetry, and strengthening stakeholder trust. The study provides empirical evidence relevant to policymakers, investors, and firms striving to achieve Saudi Arabia’s Vision 2030 sustainability goals. Full article

Recent years have seen a rise in combining deep learning and technical analysis for stock price prediction. However, technical indicators are often prioritized over technical charts due to quantification challenges. While some studies use closing price charts for predicting stock trends, they overlook charts from other indicators and their relationships, resulting in underutilized information for predicting stock. Therefore, we design a novel framework to address the underutilized information limitations within technical charts generated by different indicators. Specifically, different sequences of stock indicators are used to generate various technical charts, and an adaptive relationship graph learning layer is employed to learn the relationships among technical charts generated by different indicators. Finally, by applying a GNN model combined with the relationship graphs of diverse technical charts, temporal patterns of stock indicator sequences are captured, fully utilizing the information between various technical charts to achieve accurate stock price predictions. Additionally, we further tested our framework with real-world stock data, showing superior performance over advanced baselines in predicting stock prices, achieving the highest net value in trading simulations. Our research results not only complement the existing applications of non-singular technical charts in deep learning but also offer backing for investment applications in financial market decision-making. Full article

This study investigates farmers’ knowledge, perceptions, and adaptation strategies to climate change in Rwanda’s Eastern Province, integrating social and physical science approaches. Analyzing meteorological data (1981–2021) and surveys from 204 farmers across five districts, we assessed climate trends and adaptation behaviors using statistical methods (descriptive statistics, Chi-square, logistic regression, Regional Kendall test, dynamic linear state-space model). Results show that 85% of farmers acknowledge climate change, with 54% observing temperature increases and 37% noting rainfall declines. Climate data confirm significant rises in annual minimum (+0.76 °C/decade) and mean temperatures (+0.48 °C/decade), with the largest seasonal increase (+0.86 °C/decade) in June–August. Rainfall trends indicate a non-significant decrease in March–May and a slight increase in September–December. Farmers report crop failures, yield reductions, and food shortages as major climate impacts. Common adaptations include agroforestry, crop diversification, and fertilizer use, though financial limitations, information gaps, and input scarcity impede adoption. Despite limited formal education (53.9% primary, 22.3% no formal education), indigenous knowledge aids seasonal prediction. Farm location, group membership, and farming goal are key adaptation enablers. These findings emphasize the need for targeted policies and climate communication to enhance rural resilience by strengthening smallholder farmer support systems for effective climate adaptation. Full article

This study examines the influence of ethical leadership on corporate sustainability and financial performance, highlighting the moderating effect of firms’ commitment to the United Nations Sustainable Development Goals (SDGs). Utilizing panel data from 420 automotive companies spanning 2015 to 2024, the analysis applies the System Generalized Method of Moments (GMM) to control for endogeneity and unobserved heterogeneity. All data were gathered from the Refinitiv Eikon Platform (LSEG) and annual reports. Panel GMM regression is used to estimate the relationship to deal with the endogeneity problem. The results reveal that ethical leadership significantly improves corporate sustainability performance—measured by ESG scores from Refinitiv Eikon and Bloomberg—as well as financial indicators like Return on Assets (ROA) and Tobin’s Q. Additionally, firms that demonstrate breadth (the range of SDG-related themes addressed), concentration (the distribution of non-financial disclosures across SDGs), and depth (the overall volume of SDG-related information) in their SDG disclosures gain greater advantages from ethical leadership, resulting in enhanced ESG performance and higher market valuation. This study offers valuable insights for corporate leaders, policymakers, and investors on how integrating ethical leadership with SDG alignment can drive sustainable and financial growth. Full article

Objectives: Scarce data are available regarding preventive medicine in forcibly displaced populations especially regarding non-communicable diseases like neoplasia, while even more limited data are available on Ukrainian refugees in Romania. To address this research gap, the present analysis was performed to investigate Ukrainian refugee women’s beliefs, attitudes, and opinions towards the Romanian and Ukrainian healthcare system in a comparison model while focusing on the HPV immunization rates and factors influencing the uptake for themselves and their children. Methods: Participants were recruited using the snowball sampling method through their General Practitioner (GP) and a health mediator. Results: In total, 105 women completed the online or physical survey. The mean age was 50 years. In total, 40% of women had not been to a gynecological check-up in 3 or more years, and more than 56% had never been screened. Only four were vaccinated against HPV, and none remembered which type of vaccine was dispensed or how many doses were utilized. The primary hindrances to accessing health services or immunization programs were language barriers, financial burdens, and a lack of information. Respondents’ general distrust of health systems and healthcare workforces were recurrent themes. Relationship status, living arrangements, and previous engagement in screening practices influenced immunization rates. Perceiving the healthcare officials as proactive concerning optional vaccination programs such as HPV immunization and actively receiving recommendations drove respondents to pursue vaccination. Conclusions: This analysis offers a foundational insight into the specific needs of refugee women. It can guide the development of effective public health interventions to improve health outcomes and vaccination rates among Ukrainian refugees in Romania. Tailored preventive campaigns with adequate native language information and prompts from medical experts in designated centers should be deployed to ensure inclusive tactics for vulnerable populations. Full article

Background: Medication adherence is essential for managing chronic conditions, while non-adherence remains a widespread issue, leading to poorer health outcomes and higher healthcare costs. This study aimed to identify key adherence barriers, explore their relationship with patient satisfaction, and assess their impact on overall well-being among ambulatory patients in Albania. Methods: A cross-sectional study was conducted in three public urban health centers in Vlora, Albania, between November 2024 and January 2025. A total of 80 ambulatory patients were recruited using convenience sampling. Data were collected through face-to-face interviews using validated questionnaires, including the Adherence Barriers Questionnaire (ABQ), the Patient Satisfaction with Nursing Care Quality Questionnaire (PSNCQQ), and the Patient Health Questionnaire (PHQ-9) for depression screening. Results: The study included 80 ambulatory patients (mean age 66.7 years; 48.7% female), predominantly diagnosed with diabetes (42.5%) and rheumatic diseases (36.3%). All participants reported at least one adherence barrier, with 92.5% experiencing multiple barriers. The most common were financial burden (91.3%) and fear of side effects (77.5%). A significant positive correlation was found between adherence barriers and depression severity (ρ = 0.518, p < 0.0001), while patient satisfaction did not significantly influence adherence barriers (ρ = −0.217, p = 0.053) or depression severity (ρ = −0.004, p = 0.969). Multiple regression analysis showed that higher depression severity (p = 0.0049) was significantly associated with greater adherence barriers, while postgraduate education was associated with fewer barriers (p = 0.0175). Conclusions: Financial burden, fear of side effects, and psychological distress are key barriers to adherence among Albanian ambulatory patients. Although there are limitations inherent to the cross-sectional design and modest sample size, our findings highlight the potential benefit of routine mental health screening, targeted financial support, and improved patient education on medication management within primary care. These insights may help inform future research and interventions aimed at enhancing adherence and overall well-being. Patient satisfaction did not significantly impact adherence or depression. Targeted interventions focusing on financial support, mental health care, and patient education are needed to improve adherence and patient well-being. These findings underscore the need for integrated mental health and adherence support strategies within routine primary care services. Full article

The summarized content of one or more extensive text documents helps users extract only the most important key information, instead of reviewing and reading hundreds of pages of text. This study uses extractive and abstractive mechanisms to automatically extract and summarize information retrieved from various web documents on the same topic. The research aims to develop a methodology for designing and developing an information system for pre- and post-processing natural language obtained through web content search and web scraping, and for the automatic generation of a summary of the retrieved text. The research outlines two subtasks. As a first step, the system is designed to collect and process up-to-date information based on specific criteria from diverse web resources related to project funding, initiated by various organizations such as startups, sustainable companies, municipalities, government bodies, schools, the NGO sector, and others. As a second step, the collected extensive textual information about current projects and programs, which is typically intended for financial professionals, is to be summarized into a shorter version and transformed into a suitable format for a wide range of non-specialist users. The automated AI software tool, which will be developed using the proposed methodology, will be able to crawl and read project funding information from various web documents, select, process, and prepare a shortened version containing only the most important key information for its clients. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article

In this work, we examine the correlation between financial inclusion and the Environmental, Social, and Governance (ESG) factors of sustainable development with the assistance of an exhaustive panel dataset of 103 emerging and developing economies spanning 2011 to 2022. The “Account Age” variable, standing for financial inclusion, is the share of adults owning accounts with formal financial institutions or with the providers of mobile money services, inclusive of both conventional and digital entry points. Methodologically, the article follows an econometric approach with panel data regressions, supplemented by Two-Stage Least Squares (2SLS) with instrumental variables in order to control endogeneity biases. ESG-specific instruments like climate resilience indicators and digital penetration measures are utilized for the purpose of robustness. As a companion approach, the paper follows machine learning techniques, applying a set of algorithms either for regression or for clustering for the purpose of detecting non-linearities and discerning ESG-inclusion typologies for the sample of countries. Results reflect that financial inclusion is, in the Environmental pillar, significantly associated with contemporary sustainability activity such as consumption of green energy, extent of protected area, and value added by agriculture, while reliance on traditional agriculture, measured by land use and value added by agriculture, decreases inclusion. For the Social pillar, expenditure on education, internet, sanitation, and gender equity are prominent inclusion facilitators, while engagement with the informal labor market exhibits a suppressing function. For the Governance pillar, anti-corruption activity and patent filing activity are inclusive, while diminishing regulatory quality, possibly by way of digital governance gaps, has a negative correlation. Policy implications are substantial: the research suggests that development dividends from a multi-dimensional approach can be had through enhancing financial inclusion. Policies that intersect financial access with upgrading the environment, social expenditure, and institutional reconstitution can simultaneously support sustainability targets. These are the most applicable lessons for the policy-makers and development professionals concerned with the attainment of the SDGs, specifically over the regions of the Global South, where the trinity of climate resilience, social fairness, and institutional renovation most significantly manifests. Full article

This study examines the impact of capital market openness on corporate green total factor productivity (GTFP) using a quasi-natural experiment based on the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect policies. Employing a multi-period difference-in-differences (DID) approach, the findings reveal that capital market openness significantly enhances corporate GTFP through two primary mechanisms: strengthening firms’ green financial resources and technological innovation (green “hard strength”) and improving corporate environmental governance, green information disclosure, and managerial green expertise (green “soft strength”). Further heterogeneity analysis suggests that firms with greater institutional investor engagement, higher market competition, and non-state ownership exhibit stronger responses. These results provide policy insights into leveraging financial liberalization to drive corporate sustainability and green economic growth. This study highlights the role of financial markets in supporting global carbon neutrality and sustainable development goals. Full article

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions. Full article