Search Results (109)

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

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

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 investigates whether and how the complexity of derivative use influences the stock price crash risk in China’s capital market, a critical question given the growing use of derivatives in emerging economies where governance structures and disclosure standards vary widely. While prior research has examined the binary effects of derivative usage, limited attention has been paid to the multidimensional complexity of such instruments and its informational consequences. Using a novel hand-collected dataset of annual reports from Chinese A-share-listed firms between 2010 and 2023, we develop and implement new indicators that capture both the economic complexity (diversity and scale) and accounting complexity (reporting dispersion and fair-value hierarchy) of derivative use. Our analysis shows that higher complexity is associated with a significantly lower likelihood of stock price crashes. This effect is especially pronounced in non-state-owned firms and those with weaker internal-control systems, suggesting that derivative complexity can enhance information transparency and serve as a substitute for other governance mechanisms. These findings challenge the conventional view that complexity necessarily increases opacity and highlight the importance of disclosure quality and institutional context in shaping the market consequences of financial innovation. Full article

Background/Objectives: Breast reconstruction (BR) following mastectomy plays a critical role in post-cancer care by offering both physical and psychological benefits. Despite advancements in techniques and shared decision-making (SDM), BR uptake remains inconsistent. This systematic review aims to synthesize evidence on objective (medical and socioeconomic) and subjective (psychological and personal) factors influencing BR decision-making among women undergoing mastectomy for breast cancer. Methods: A systematic search was conducted across PubMed, ScienceDirect, OVID, and Google Scholar, identifying peer-reviewed studies published between January 2013 and 25 July 2024. Eligible studies examined determinants of BR decisions in women undergoing therapeutic mastectomy, excluding perspectives of non-patient stakeholders and post-decision outcomes. The risk of bias and study quality were assessed using the Quality Appraisal for Diverse Studies (QuADS) tool. This review was registered in PROSPERO (CRD42023456198) and followed PRISMA guidelines. Results: Twenty-seven studies comprising 994,528 participants across 16 countries met the inclusion criteria. The objective factors included age, comorbidities, insurance coverage, physician recommendations, and healthcare access. The subjective factors encompassed body image concerns, self-esteem, fear of recurrence, and emotional readiness. Younger age, private insurance, and active physician counseling were associated with increased BR uptake, while older age, lack of information, and financial or logistical barriers reduced uptake. Regional disparities were noted across healthcare systems. Conclusions: BR decisions are influenced by complex, interrelated clinical, psychological, and systemic factors. Integrating SDM tools, enhancing patient education, and addressing healthcare inequities are essential for supporting informed and equitable BR decision-making. Future research should prioritize longitudinal studies and policy interventions to improve access to and patient satisfaction with BR outcomes. Full article

The Industry–Finance Collaboration Pilot (IFCP) integrates governmental green guidance with digital collaboration platforms to promote non-equity-based cooperation between industrial and financial sectors. Using a Difference-in-Differences (DID) approach and a sample of A-share listed industrial firms on the Shanghai and Shenzhen Stock Exchanges from 2011 to 2023, this study examines the IFCP’s impact on corporate green innovation (GI). Results show that the IFCP increases the number of green patent applications by 7.5% on average, indicating its effect in stimulating GI. This effect operates through two main mechanisms. First, under governmental green guidance, the IFCP encourages local green fiscal subsidies, increases green investor participation, improves environmental information disclosure, and lowers agency costs. Second, through digital finance empowerment, it mitigates information asymmetry and transaction costs in financial activities, thereby reducing credit costs and enhancing firms’ access to green credit. The effect of the IFCP on GI is more pronounced in regions with stricter environmental regulation, in pollution-intensive industries, and among firms with smaller asset sizes. Further analysis indicates that the IFCP primarily stimulates tactical, low-value GI driven by compliance or opportunistic motives, rather than promoting substantive, high-quality innovation. This study provides empirical evidence and policy insights into how governmental green guidance and digital finance empowerment can jointly promote green industrial development. Full article

This study explores how corporate reputation and regulation influence the quantity and quality of non-financial information (NFI) disclosure. While internal drivers of NFI reporting are well-studied, external pressures remain underexplored. Analyzing Ibex35 firms (2015–2019) during Spain’s adoption of Directive 2014/95/EU, the study uses panel data analysis to assess the impact of reputation and regulation on NFI reporting. The findings show that highly reputed firms disclose more extensive and higher-quality NFI, while regulatory changes significantly improve both variables of NFI reporting. Thus, firms go beyond mere compliance. By distinguishing between quality and quantity, the study clarifies conflicting prior findings and highlights the complementary roles of reputation and regulation in fostering transparency. The results offer valuable insights for managers and policymakers, enhancing stakeholder trust and the effectiveness of regulation in promoting corporate transparency. Full article

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes. Full article

The purpose of this research is to determine, first, whether an auditor, by conducting a statutory audit of the company’s financial statements, can improve the company’s non-quantitative information quality, and, second, whether the six leading audit firms in Greece improve the non-quantitative information quality more than other, smaller audit firms. The data are primary, arising from the published financial statements for the period from 2019 to 2022 of 84 companies operating in the agricultural supplies sector. These financial statements were retrieved in January 2024 from the General Commercial Registry of Greece. We have reviewed the management reports of these companies to examine their compliance with Greek legislation requirements on non-quantitative information reporting, i.e., on the entity’s performance, business risk management, and environmental and labor issues. We note that non-quantitative information reporting is improving during the period 2019–2022, regardless of the auditor’s involvement. The average reporting scores of audited companies are higher than the corresponding scores of non-audited companies, so the auditors have significantly improved the non-quantitative information reporting. In addition, the reporting scores of companies audited by six leading audit firms are higher than the corresponding scores of companies audited by the other, smaller audit firms. Full article

This systematic review analyzes and compares the predictive power between traditional financial ratios and cash flow-based ratios in estimating performance. Although traditional ratios of return on assets and debt to equity have received extensive application, cash flow ratios are increasingly valued by their dynamic insights into both liquidity and financial health. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this review systematically analyzes 21 studies spread across various industries and regions. The results reveal that cash flow ratios usually dominate the traditional metrics during forecasting financial performance, especially in the presence of the use of machine learning models. Among the identified variables of the logistic regression model and gradient boosting model predictors, key indicators are those showing the return on investment, the current ratio, and the debt-to-asset ratio. The bottom line of the findings is that a combination of cash flow and traditional ratios gives a better understanding of a company’s financial stability. These results may serve as a starting point for investors, regulators, and entrepreneurs and may further facilitate informed decisions with a reduced chance of miscalculations that enhance proactive financial planning. In addition, future prediction models should integrate non-financial factors such as governance quality and market conditions to enhance financial health assessments. Additionally, longitudinal studies examining the evolution of financial ratios over time, along with hybrid statistical and machine learning approaches, can improve forecasting accuracy. Integrating cutting-edge analytical tools with the strength of financial metrics gives this study actionable insights that allow stakeholders to understand financial performance in a more nuanced sense. Full article

This article examines whether the presentation of integrated reports (IRs), the external assurance of non-financial information, and the use of auditing standards affect market liquidity and the accuracy of earnings per share forecasts in the Chilean market following the publication of the International IR Framework. Using ordinary least squares estimations, results show that IRs significantly reduce information asymmetry, thereby improving market liquidity. This effect is reinforced when non-financial information is externally assured, particularly under the ISAE3000 standard. However, neither IRs nor external assurance significantly impact financial analysts’ earnings forecast accuracy, suggesting that such information serves a complementary role in their evaluations. This study contributes to the literature by providing empirical evidence on the role of IRs and assurance in emerging economies, emphasizing their effectiveness in enhancing transparency and liquidity. The findings have direct implications for companies, as they suggest that adopting IRs and obtaining external assurance can strengthen market perceptions and investor confidence, particularly when using the ISAE3000 standard. For regulators, the results highlight the potential benefits of promoting standardized sustainability disclosures and assurance mechanisms to foster transparency in capital markets. Investors, in turn, can use IR quality and assurance as signals of corporate credibility and long-term value creation. Full article

This article explores the intricate relationship between digital transformation and non-financial performance in Central and Eastern European (CEE) countries. As these nations navigate the complexities of post-communist economic landscapes, the role of digitalization emerges as a pivotal factor influencing various dimensions of organizational performance beyond mere financial outcomes. In this framework, our research aims to analyze the ways in which digital transformation (as proxied by DESI) impacts a range of non-financial performance metrics (ESG) in order to furnish a thorough comprehension of the intricate interplay within the specific context of CEE countries. With data collected over an 11-year timeframe, we performed a panel data analysis, relying on a robust regression. The main findings indicate that digital transformation profoundly impacts the environmental (CO₂ emissions, renewable energy consumption), social (ratio of female-to-male labor force participation rate, unemployment) and governance (government effectiveness) performance of CEE countries, although the effects vary significantly across different regions. The panel data highlight potential areas for policy emphasis, particularly in relation to reducing CO₂ emissions, improving regulatory quality, and advancing digital integration and connectivity. The disparities identified may inform targeted strategies aimed at uplifting underperforming regions, thereby contributing to enhanced economic growth and sustainability. Full article

Accurate disclosure and proactive engagement in ESG practices are essential for achieving high-quality economic development, particularly as China addresses significant challenges during its reform journey. The Classified Reform of State-Owned Enterprises (CRSOE) is a strategic initiative by the Chinese government aimed at fostering this development. Our study leverages the implementation of the CRSOE as an exogenous shock, employing the difference-in-differences approach to assess the policy’s governance impact on ESG decoupling from the perspective of ownership heterogeneity. The policy was found to alleviate ESG decoupling, particularly pronounced among SOEs with special functions. The governance effect is achieved by reducing the aspiration–performance gap. Specifically, the policy effectively narrows the disparity between a company’s actual performance and the expected performance based on the industry average, thereby mitigating ESG decoupling. However, the policy’s impact can be weakened by factors such as political connections among executives and media attention. Furthermore, the CRSOE effectively addresses greenwashing practices within ESG decoupling, with a particularly strong effect on SOEs that fail to disclose ESG information in alignment with Global Reporting Initiative (GRI) standards. These findings highlight the importance of understanding the broader implications and underlying mechanisms of the policy. Therefore, building on the assessment of how the CRSOE policy impacts ESG decoupling, we also examine the mechanisms through which this policy operates and how its effectiveness varies under different conditions of heterogeneity. By extending the application of principal-agent theory and performance feedback theory, our research suggests that policymakers should prioritize market-driven reforms for fully competitive SOEs and promote a stronger emphasis on non-financial goals. Additionally, it is essential to mitigate the undue influence of political promotions on the management of all SOEs. Full article

(1) Background: Facing the challenges of a post-pandemic period and the Ukraine War and recognising the gap in scientific research on the application of revenue management (RM) in the Portuguese hotel industry, the main objective of this study is to identify the most effective and least appropriate RM practices for use in periods of low demand and crises, reflecting the financial sustainability perspective. The theoretical framework of this study focuses on the main RM practices, grouping them into price and non-price strategies. (2) Methods: A quantitative methodology was employed, collecting information from Portuguese hotels through an online questionnaire, and statistical analysis using Mann–Whitney and Chi-square tests was conducted. (3) Results: Hotels offered discounts during the pandemic, but room rates were reduced during the recovery period. These findings also revealed that commonly used techniques were the best available rate (BAR) and rate fences, particularly during the pandemic. Quality, brand image, strategic partnerships, and marketing actions are recognised as essential. However, loyalty programs, length of stay (LOS) control, rate parity, and bundled services are not commonly implemented despite their importance during periods of low demand. Larger hotels, five-star hotels, and members of international chains applied more RM practices than smaller four-star independent hotels. (4) Originality: This study provides original and valuable insights into increasing hotel revenues and occupancy rates during future periods of low demand, which benefit financial sustainability. Full article