Search Results (29)

This study examines the economic consequences associated with the mandatory adoption of International Financial Reporting Standards (IFRS) in the Iraqi banking sector. Motivated by growing evidence that the outcomes of IFRS adoption depend on institutional and market conditions, the study focuses on a bank-based emerging economy characterized by relatively underdeveloped capital markets and evolving enforcement mechanisms. Using a balanced panel of 24 banks listed on the Iraq Stock Exchange over the period 2014–2018, the analysis exploits the mandatory IFRS adoption in 2016 within a before–after regulatory framework. Panel regression techniques are employed to examine the associations between IFRS adoption and stock market liquidity, firm value, information asymmetry, and the cost of debt, while controlling for bank-specific characteristics and macroeconomic conditions. The results indicate that IFRS adoption is positively significantly associated with stock market liquidity, and negatively significantly associated with information asymmetry, consistent with improvements in the informational environment of Iraqi banks following enhanced disclosure and comparability. The findings also reveal a positive and significant relationship between IFRS adoption and the cost of debt, suggesting higher perceived financial risk by creditors. In contrast, no statistically significant association is observed between IFRS adoption and bank market valuation, highlighting the limited sensitivity of equity prices to accounting reforms in thin and institutionally constrained markets. Overall, the study contributes to the literature on the economic consequences of IFRS adoption by providing evidence from an underexplored emerging market and a highly regulated banking sector. The findings underscore the role of institutional context in shaping the outcomes of accounting standard convergence and offer policy-relevant insights for regulators and standard-setters in bank-oriented financial systems. Full article

This research examines market reactions to financial distress announcements by small privately held Canadian oil firms operating in the upstream sector between 2015 and 2021, employing an event study methodology, with daily spot prices for Brent and WTI crude oil serving as market benchmarks. The sample includes 11 firms that filed for insolvency, giving 99 observations for analysis. Data were collected from the publicly available Haynes Boone repository, ensuring transparency and verifiability. Abnormal returns were computed using market-adjusted returns to control for general market movements, isolating event-specific effects. The findings reveal statistically significant yet modest abnormal returns around the announcement day, indicating a measured market reaction. These results indicate that investors may partially anticipate such events and interpret them as potential restructuring opportunities rather than indicators of sector-wide collapse. The study underscores the importance of transparent disclosure and structured legal frameworks in moderating market volatility during financial distress. While the analysis is confined to short-term effects and small firms, it provides valuable insights into how financial distress in small upstream oil firms influences commodity markets, contributing new evidence to the literature on event studies and financial distress in energy markets, and offers implications for policymakers aiming to enhance market stability. Full article

Diagnostic disclosure is a complex communication task that requires learners to integrate interpersonal attunement, structured information delivery, and condition-specific reasoning in real time. We conducted a randomized controlled trial comparing conventional diagnostic communication training with the same training supplemented by an AI-supported adaptive virtual patient simulation designed to provide additional deliberate practice and individualized, just-in-time feedback. Eighty undergraduate medical students were randomized 1:1 and completed standardized-patient encounters involving disclosure of a new diagnosis of type 2 diabetes mellitus before and after training. Performance was assessed by blinded physician raters using an adapted Kalamazoo rubric. Among students with complete pre–post data (conventional training, n = 25; AI-supported training, n = 26), both groups showed substantial improvement. Mean gains were numerically larger in the AI-supported group, with small-to-moderate standardized effects across selected communication domains; however, baseline-adjusted group-by-time interactions did not reach conventional thresholds for statistical significance, indicating that any added mean effects beyond conventional training remain uncertain. Exploratory person-level analyses suggested greater heterogeneity of improvement in the AI-supported condition, including a higher density of large gains in higher-order communication components. These findings should therefore be interpreted as exploratory rather than confirmatory. AI-supported adaptive simulation appears feasible as an adjunct to communication training, but adequately powered studies are needed to clarify effect magnitude, mechanisms, and generalizability across training contexts. Full article

This study examined the influence of Environmental, Social, and Governance factors on the financial performance of companies listed on the Saudi Stock Exchange (Tadawul). Employing a panel data approach, the analysis covers 450 firm observations collected annually during the period 2018–2023. Financial performance is measured using Return on Assets (ROA) and Return on Equity (ROE), while ESG disclosure scores are disaggregated into their three constituent pillars. Firm size, revenue per share, and leverage are incorporated as control variables. The fixed effects regression results reveal that social factors demonstrate statistically significant positive relationships with both ROA and ROE, supporting the stakeholder theory-based perspective that strong social practices enhance operational efficiency and investor confidence. Conversely, environmental and governance factors exhibit no significant association with either financial performance metric within the study period. Leverage shows a significant negative relationship with ROA but not with ROE, while revenue per share consistently demonstrates strong positive associations with both performance measures. These findings contribute to the limited literature on ESG–performance linkages in Gulf Cooperation Council markets and offer important implications for corporate managers, investors, and policymakers seeking to advance sustainability objectives within the framework of Saudi Vision 2030. Full article

This study examines campus affinity card agreements under the Credit Card Accountability, Responsibility, and Disclosure (CARD) Act, focusing on how portfolio size, new account openings, and institutional governance affect issuer payments. Using cross sectional data from 6145 issuer institution agreements reported to the Consumer Financial Protection Bureau (CFPB) in 2022, the analysis employs descriptive statistics, correlation tests, and multivariate regression models to identify predictors of payment volume. Results show that total open accounts strongly predict issuer payments, while new account openings exhibit a weak positive bivariate correlation but exert a modest negative effect in multivariate models once portfolio scale and institution type are controlled for. Foundations received the highest average issuer payments, followed by hybrid organizations and universities. This pattern reflects differences in governance structure, administrative capacity, and bargaining leverage. Transparency requirements under the CARD Act reveal broad patterns but omit incentive timing and interchange revenue, limiting full accountability. This is among the first large-scale empirical analyses of CFPB’s affinity card dataset, advancing understanding of equity in campus credit markets and offering policy relevant insights for regulators and administrators. Full article

In the context of China’s “dual-carbon” strategy, the Beijing 2022 Winter Olympics provides a critical case for examining whether carbon-neutral commitments can be translated into measurable and lasting environmental outcomes through a closed-loop governance mechanism. This study develops an integrated analytical framework linking institutional design, policy tools, monitoring–reporting–verification (MRV), and environmental legacy, and evaluates full life-cycle carbon-neutral governance and post-event environmental performance using officially verified carbon accounting materials, governmental disclosures, and publicly available statistical data from 2016–2022. We synthesize the emission structure across preparation and Games-time phases, examine key mitigation and offset portfolios, and assess multi-dimensional environmental indicators in Beijing and Zhangjiakou, including atmospheric quality, energy structure transition, ecological restoration, and low-carbon transport systems. The results suggest that an MRV-centered governance chain strengthened accounting transparency and compliance-oriented implementation, while environmental indicators in the competition zones exhibited sustained improvement over the study period. To reduce over-attribution under concurrent national clean-air policies and macro-level environmental governance trends, we benchmarked host-zone indicators against external reference statistics and interpret the observed improvements as an “acceleration effect” under bounded inference rather than a strict net causal contribution. The findings highlight the importance of hotspot-oriented asset-chain governance (transport infrastructure and venue construction), robust MRV disclosure, and quality-controlled offsets in shaping credible environmental legacies, and provide policy implications for future mega-events seeking to balance carbon neutrality with long-term regional sustainability. Full article

The revision and implementation of the Environmental Protection Law signaled a major transformation in China’s environmental regulatory paradigm—from a traditional command-and-control model to a more diversified and market-oriented approach. This shift has raised critical questions regarding the actual impact of regulation on green technological innovation. Using panel data from A-share listed firms in China between 2011 and 2022, this study employs a propensity score matching–difference-in-differences (PSM-DID) model to identify the causal effect of environmental regulation on green innovation. Results reveal that the enactment of the law significantly enhances firms’ green innovation capacity. Robustness tests confirm the stability of these findings. Further analysis identifies several potential transmission mechanisms. Specifically, we find robust empirical evidence that environmental regulation exerts its effects through elevated R&D investment levels and strengthened executives’ environmental awareness, while the financing constraint and environmental information disclosure channels yield suggestive yet less statistically robust results in indirect effect tests. Moreover, heterogeneous effects are more evident among non-state-owned enterprises, firms in the eastern region, and those in highly market-oriented provinces. This study contributes empirical evidence to the literature on environmental regulation and green innovation, and offers policy insights for improving environmental governance in emerging economies. Full article

Background: The increasing integration of Environmental, Social, and Governance (ESG) factors into financial decision-making has prompted debate over their impact on corporate credit risk. While many studies suggest that ESG performance may enhance firms’ resilience, empirical evidence remains mixed due to data inconsistency and methodological heterogeneity and differences in time horizons over which ESG effects materialise. Methods: The study investigates the relationship between ESG performance and credit risk using a panel of European firms from 2020 to 2024, a phase highly characterised by substantial macroeconomic shocks. The Altman Z-score serves as a proxy for default risk, while ESG data are sourced from Refinitiv Eikon. Four fixed-effects panel regressions are estimated: a baseline model using aggregate ESG scores, an extended model with financial controls, and disaggregated and sector-specific models. Results: The findings indicate that ESG scores—either aggregated or by pillar—show limited statistical significance in explaining variations in the Z-score. In contrast, financial variables such as solvency, liquidity, and cash flow ratios display strong, positive, and significant effects on credit stability. Some heterogeneous sectoral effects emerge: social factors are positive in technology, while governance has a negative impact in basic materials. Conclusions: ESG initiatives may not yield immediate improvements in default risk metrics, particularly over short and crisis-dominated periods, but could enhance financial resilience over time. Combining ESG information with traditional financial ratios remains essential; the results underscore the importance of consistent and high-quality ESG disclosure to reduce measurement error and enhance comparability across firms. Full article

The rapid proliferation of Electric Vehicle (EV) infrastructures has led to the massive generation of high-frequency streaming data uploaded to cloud platforms for real-time analysis, while such data supports intelligent energy management and behavioral analytics, it also encapsulates sensitive user information, the disclosure or misuse of which can lead to significant privacy and security threats. This work addresses these challenges by developing a secure and scalable scheme for protecting and verifying streaming data during storage and collaborative analysis. The proposed scheme ensures end-to-end confidentiality, forward security, and integrity verification while supporting efficient encrypted aggregation and fine-grained, time-based authorization. It introduces a lightweight mechanism that hierarchically organizes cryptographic keys and ciphertexts over time, enabling privacy-preserving queries without decrypting individual data points. Building on this foundation, an electric vehicle key management and query system is further designed to integrate the proposed encryption and verification scheme into practical V2X environments. The system supports privacy-preserving data sharing, verifiable statistical analytics, and flexible access control across heterogeneous cloud and edge infrastructures. Analytical and experimental evidence show that the designed system attains rigorous security guarantees alongside excellent efficiency and scalability, rendering it ideal for large-scale electric vehicle data protection and analysis tasks. Full article

Background: The US Food and Drug Administration (FDA) authorized over 690 machine learning (ML)-enabled medical devices between 1995 and 2023. In 2024, new guidance enabled the inclusion of Predetermined Change Control Plans (PCCPs), raising expectations for transparency, equity, and safety under the Good Machine Learning Practice (GMLP) framework. Objective: The objective was to assess regulatory pathways, predicate lineage, demographic transparency, performance reporting, and PCCP uptake among ML-enabled devices approved by the FDA in 2024. Methods: We conducted a cross-sectional analysis of all FDA-authorized ML-enabled devices in 2024. Data extracted from FDA summaries included regulatory pathway, predicate genealogy, performance metrics, demographic disclosures, PCCPs, and cybersecurity statements. Descriptive and nonparametric statistics were used. Results: The FDA authorized 168 ML-enabled Class II devices in 2024. Most (94.6%) were cleared via 510(k); 5.4% were cleared via De Novo. Radiology dominated (74.4%), followed by cardiovascular (6.5%) and neurology (6.0%). Non-US sponsors accounted for 57.7% of clearances. Among 159 510(k) devices, 97.5% cited an identifiable predicate; the median predicate age was 2.2 years (IQR 1.2–4.1), and 64.5% ML-enabled. Predicate reuse remained uncommon (9.9%). Median review time was 162 days (151 days for 510(k) vs. 372 days De Novo; p < 0.001). A total of 49 devices (29.2%) reported both sensitivity and specificity; 15.5% provided demographic data. PCCPs appeared in 16.7% of summaries, and cybersecurity considerations appeared in 54.2%. Conclusions: While 2024 marked a record year for ML-enabled device approvals and internationalization, uptake of PCCPs and transparent performance and demographic reporting remained limited. Policy efforts to standardize disclosures and strengthen post market oversight are critical for realizing the promises of GMLP. Full article

This study examines the association between Saudi banks’ internal adoption of AI-enabled FinTech-related digital tools and their financial performance, sustainability performance, and financial stability over the period 2015–2024. Using a panel dataset of 10 banks, the analysis investigates how the adoption of AI-driven technologies—such as machine-learning credit assessment, robo-advisory systems, and automated compliance tools—is related to market performance (Tobin’s Q), accounting performance (ROA and ROE), financial stability (Z-Score), and sustainability outcomes measured by both Bloomberg ESG Disclosure Score and the LSEG ESG performance-oriented score. To ensure robust inference and reduce simultaneity concerns, the empirical strategy employs Pooled OLS and Fixed Effects Models with Driscoll–Kraay standard errors, as well as a dynamic Fixed Effects Models incorporating lagged dependent variables, lagged independent variables, and shock-interaction terms. Bank-specific characteristics—including size, age, leverage, liquidity, loan-to-deposit ratio, non-performing loans, net interest margin, market capitalization, and board size—are included as controls. The findings indicate a positive and statistically significant relationship between banks’ internal adoption of AI-enabled digital/FinTech-related technologies and their financial performance, sustainability performance, and financial stability. These relationships remain robust across estimation approaches, providing insights for policymakers, regulators, and bank managers seeking to advance digital transformation while safeguarding financial soundness and supporting sustainable development in the Saudi banking sector. Full article

Excessive corporate use of fossil fuels has significantly worsened global air quality. In response, many governments, including China’s, have implemented tax incentives to promote sustainable development, though their effectiveness at the firm level remains unclear. This study empirically examines the relationship between tax incentives and corporate green transition using a panel of 30,483 firm-year observations from Chinese A-share non-financial listed firms spanning 2009–2023. We construct a Green Sustainable Development Performance (GSDP) index based on green patent applications and environmental disclosure and identify innovation investment as the main transmission mechanism. The results show that stronger tax incentives are associated with higher GSDP scores. This relationship is largely driven by innovation: after controlling R&D input, the direct effect of tax incentives declines, while the indirect effect through innovation remains both statistically and economically significant. The effect is more evident in large firms and those in eastern provinces, but weaker in regions with higher financial constraints with limited time lags. The findings offer practical implications for designing targeted, verifiable, and innovation-oriented tax instruments to foster high-quality, sustainable corporate development. Full article

This study investigates the impact of financial performance and corporate governance mechanisms on environmental, social, and governance (ESG) disclosure in Saudi Arabia, a country undergoing significant institutional transformation under Saudi Vision 2030 and Tadawul’s 2021 ESG reporting reforms. While ESG research has gained traction globally, studies in emerging economies, particularly in the Gulf region, remain limited. This paper addresses this gap by examining whether profitability, measured by return on assets (ROA), and board size influence ESG disclosure. This study analyzes 260 firm-year observations of Saudi non-financial listed companies from 2009 to 2023. Using multiple regression analysis, including ordinary least squares (OLS), fixed effects (FE), and generalized method of moments (GMM), the analysis controls for endogeneity and ensures robust results. Findings indicate that board size had a negative and statistically significant relationship with ESG disclosure. The robustness tests confirm the inverse relationship between board size and ESG. ROA showed no correlation with ESG disclosure in the main findings; however, robustness tests revealed a negative and significant correlation. This study is the first to explore these impacts post Tadawul’s 2021 ESG guidelines. It also offers novel insights into ESG practices aligned with Saudi Vision 2030. This study contributes to the literature by situating ESG disclosure within the Saudi context, highlighting the unique role of governance dynamics in shaping sustainability practices in emerging markets. The results carry practical implications for policymakers, regulators, and corporate boards by recommending stronger governance frameworks, such as board-level ESG committees, executive compensation linked to ESG, and sector-specific disclosure standards. 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

Climate change is a critical sustainability issue that influences investors’ decisions. Numerous organizations have implemented climate-related policies and established governance structures to address this challenge. This study examines the extent to which climate change management performance affects firm value. This research utilizes 13 climate change management performance indicators from the Refinitiv Eikon Database. Firm value was measured using the price-to-book value (PBV) ratio, with firm size, profitability, and cost of debt included as control variables. This study examines 531 public companies in three Southeast Asian countries. Quantitative data were analyzed using descriptive statistics, ANOVA, and path analysis. The results indicate that robust climate change management performance positively affects firm value. However, significant variations exist across countries and industries regarding climate change management practices. These findings highlight the necessity for organizations to strengthen their climate change management efforts by preparing comprehensive performance disclosures. Enhanced transparency can provide clearer insights for environmentally conscious investors, potentially fostering positive market reactions toward the company. Full article