Search Results (300)

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 study examines the impact of board characteristics on capital structure decisions in the context of a transition economy, focusing on Algeria, where governance institutions are underdeveloped and the financial market remains immature. Using the Generalized Method of Moments (GMM) on a panel dataset of 120 firms over the period 2015 to 2019, we identify a U-shaped relationship between board size and leverage, and an inverted U-shaped relationship between board size and debt maturity. Furthermore, increased nationality diversity on boards is found to significantly reduce debt maturity. These findings highlight the critical role of board composition in shaping corporate financing strategies in transition economies and provide novel insights into corporate governance dynamics in a relatively underexplored institutional context. The results are particularly relevant for national entities such as COSOB and Hawkama El Djazaïr and may guide banking sector practices by promoting the integration of board governance criteria into credit evaluation processes. Full article

The main objective of this study is to analyse the relevance of financial performance as an accurate predictor of ESG scores for French companies from 2010 to 2022. To this end, Machine Learning techniques such as linear regression, polynomial regression, Random Forest, and Support Vector Regression (SVR) were employed to provide more accurate and reliable assessments, thus informing the ESG rating attribution process. The results obtained highlight the excellent performance of the Random Forest method in predicting ESG scores from company financial variables. In addition, the approach identified specific financial variables (operating income, market capitalisation, enterprise value, etc.) that act as powerful predictors of companies’ ESG scores. This modelling approach offers a robust tool for predicting companies’ ESG scores from financial data, which can be valuable for investors and decision-makers wishing to assess and understand the impact of financial variables on corporate sustainability. Also, this allows sustainability investors to diversify their portfolios by including companies that are not currently rated by ESG rating agencies, that do not produce sustainability reports, as well as newly listed companies. It also gives companies the opportunity to identify areas where improvements are needed to enhance their ESG performance. Finally, it facilitates access to ESG ratings for interested external stakeholders. Our study focuses on using advances in artificial intelligence, exploring machine learning techniques to develop a reliable predictive model of ESG scores, which is proving to be an original and promising area of research. Full article

China’s internal control framework follows the Committee of Sponsoring Organizations (COSO) framework, emphasizing enterprise risk management and encompassing financial reporting, operations, compliance, and strategies. The authors review research that uses the COSO-based Internal Control Index to assess internal control quality among all publicly listed firms in China. Unlike the binary classification of internal control weaknesses under the Sarbanes-Oxley Act Section 404, this continuous index captures more nuanced variations in internal control effectiveness and provides two key advantages over traditional assessment of internal control over financial reporting (ICFR). First, while financial reporting can enhance a firm’s monitoring and decision-support systems, the underlying information is determined by operations. Thus, internal control over operations has a greater impact on a firm’s performance than ICFR. While U.S.-based research argues that the effects of ICFR extend to operations, the COSO-based index includes operational controls, allowing for a more direct study of internal control effects. Second, many U.S. corporations fail to report internal control weaknesses, particularly during misstatement years. In contrast, the COSO-based index, compiled by independent scholars, avoids managerial incentives to withhold negative internal control information. Covering institutional background and research evidence from China, the authors survey a wide range of internal control studies related to various aspects of enterprise risk management, such as earnings quality, crash risk, stock liquidity, resource extraction, cash holdings, mergers and acquisitions, corporate innovation, receivable management, operational efficiency, tax avoidance, and diversification strategy. Full article

This paper examines the moderating role of corporate social responsibility (CSR) on the relationship between environmental, social, and governance (ESG) dimensions and investor decision-making in Jordan. Data were collected using a structured questionnaire designed for institutional investors and financial analysts, capturing perceptions of ESG, CSR, and investment behavior. A stratified random sample of 350 professionals across the financial, industrial, and service sectors was surveyed. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings show that environmental and social dimensions have positive effects on investor decisions, with governance dimensions having a negative effect. Notably, CSR has a negative moderating effect on the governance dimensions and investor decision, with no observed statistical moderating effect for environmental or social dimensions. This research unravels the multidimensional role of CSR in building the ESG-investor decision interface and identifies a counterintuitive negative moderating impact of CSR on governance, contributing to the existing literature on sustainability alignment in emerging markets. The results offer practical implications for companies aiming to attract sustainability-oriented investors by indicating the necessity for an integrated and genuine CSR and ESG approach. Full article

This study explores the impact of growth opportunities on the capital structure of South African banks, utilising panel data from registered banking institutions covering the period from 2014 to 2023. While a substantial body of literature examines the relationship between growth prospects and corporate leverage, limited attention has been paid to this interaction within the banking sector, particularly in emerging economies. By employing the dynamic panel Generalised Method of Moments (GMM) estimator to address endogeneity concerns, the analysis reveals a statistically significant positive relationship between growth opportunities and both the total debt ratio (TDR) and the long-term debt ratio (LTDR). In contrast, a significant negative association is found between growth opportunities and the short-term debt ratio (STDR). The findings suggest that banks with stronger growth prospects are more inclined to utilise long-term financing, possibly reflecting shareholder preferences for institutions with favourable future outlooks and lower refinancing risks. These results highlight the importance of aligning capital structure decisions with an institution’s growth trajectory, while indicating that this relationship shifts depending on the maturity of the debt considered. This study contributes to the existing literature by contextualising capital structure decisions within the framework of growth opportunities. Structure theory within the context of the banking sector in a developing market offers practical insights for strategic financial planning and regulatory policy. Full article

Process anomaly detection in financial services systems is crucial for operational compliance and risk management. However, traditional process mining techniques frequently neglect the detection of significant low-frequency abnormalities due to their dependence on frequency and the inadequate incorporation of domain-specific knowledge. Therefore, we develop an enhanced process mining algorithm by incorporating a domain-specific follow-relationship matrix derived from standard operating procedures (SOPs). We empirically evaluated the effectiveness of the proposed algorithm based on real-world event logs from a corporate account-opening process conducted from January to December 2022 in a Chinese commercial bank. Additionally, we employed large language models (LLMs) for root cause analysis and process optimization recommendations. The empirical results demonstrate that the E-Heuristic Miner significantly outperforms traditional machine learning methods and process mining algorithms in process anomaly detection. Furthermore, the integration of LLMs provides promising capabilities in semantic reasoning and offers explainable optimization suggestions, enhancing decision-making support in complex financial scenarios. Our study significantly improves the precision of process anomaly detection in financial contexts by incorporating banking-specific domain knowledge into process mining algorithms. Meanwhile, it extends theoretical boundaries and the practical applicability of process mining in intelligent, semantic-aware financial service management. Full article

In the evolving landscape of China’s capital markets, the integration of Environmental, Social, and Governance (ESG) considerations has become increasingly crucial for investors and decision-makers. Traditional financial performance metrics often fall short in capturing the multidimensional and long-term impacts of ESG factors. This study introduces a novel computational framework that combines domain-adapted pre-trained language models with structured financial regression analysis, aiming to empirically assess the correlation between ESG disclosures and long-term financial performance. This approach allows for the simultaneous processing of both structured and unstructured ESG data, using graph-based modeling and reinforcement learning to guide sustainability aligned policy optimization. Our empirical results show that firms with consistent and well-structured ESG strategies exhibit significantly superior long-term financial outcomes compared to those with weak or inconsistent ESG engagement. This study not only confirms the value of ESG engagement in enhancing financial resilience but also offers practical recommendations for investors, regulators, and corporate decision-makers, emphasizing consistent disclosure, sector-aligned ESG investment, and proactive adaptation to policy shifts. Full article

Under the “Dual Carbon” strategy, collaborative governance of port atmospheric pollutants and carbon emissions is critical for low-carbon transformation. Focusing on Ningbo-Zhoushan Port (48% regional ship emissions), this study examines government, port enterprises, and public interactions. A tripartite evolutionary game model with numerical simulation reveals dynamic patterns and key factors. The results show the following: (1) A substitution effect exists between government incentive costs and penalty intensity—increased environmental governance budgets reduce the probability of government incentives, whereas higher public reporting rewards accelerate corporate emission reduction convergence. (2) Public supervision exhibits cyclical fluctuations due to conflicts between individual rationality and collective interests, with excessive reporting rewards potentially triggering free-rider behavior. (3) The system exhibits two stable equilibria: a low-efficiency equilibrium (0,0,0) and a high-efficiency equilibrium (1,1,1). The latter requires policy cost compensation, corporate emission reduction gains exceeding investments, and a supervision benefit–cost ratio greater than 1. Accordingly, the study proposes a three-dimensional “Incentive–Constraint–Collaboration” governance strategy, recommending floating penalty mechanisms, green financial instrument innovation, and community supervision network optimization to balance environmental benefits with fiscal sustainability. This research provides a dynamic decision-making framework for multi-agent collaborative emission reduction in ports, offering both methodological innovation and practical guidance value. Full article

The growing demand for sustainable business practices has led to the development of corporate sustainability assessment tools, with environmental, social, and governance (ESG) indicators becoming central to non-financial performance evaluation. These metrics increasingly influence investment decisions and corporate strategies. However, questions remain about whether sustainability practices have a measurable impact on economic value creation and distribution. This study investigates the causal relationship between corporate sustainability measured by the ISE-B3 index and stakeholder-oriented economic performance, specifically focusing on Distributed Added Value (DAV) and its main components. The analysis uses financial data from Brazilian companies listed in the ISE-B3 portfolios for the years 2022, 2023, and 2024. To address potential endogeneity, this study employs a panel data econometric approach using Instrumental Variables with Two-Stage Least Squares (IV-2SLS) as the primary estimation strategy, complemented by fixed and random effects models for robustness checks. The results indicate no statistically significant causal relationship between the ISE-B3 index and DAV or its components. The coefficient of ISE-B3 on DAV is −0.0006 (p = 0.896) in the IV-2SLS estimation, with similar non-significant results for all components. The models exhibit strong temporal dependence, with lagged dependent variable coefficients ranging from 0.8295 to 1.3578, reflecting the persistence of financial dynamics. These findings suggest that, within the Brazilian context, participation in the ISE-B3 index does not directly influence how companies create or distribute financial value to stakeholders. This study contributes to the literature by providing robust econometric evidence on the economic effects of corporate sustainability, offering a stakeholder-oriented perspective beyond the traditional shareholder-centric view. Full article

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. Results indicate that Artificial Neural Networks (ANN) and GB consistently outperform traditional models, particularly in capturing non-linear relationships and complex interactions among predictive factors. This study advances financial risk management by demonstrating the efficacy of ML-driven credit rating systems, offering a more accurate, efficient, and scalable solution. Additionally, it provides practical insights for financial institutions aiming to enhance their risk assessment frameworks. Future research should explore alternative data sources, real-time analytics, and model explainability to facilitate regulatory adoption. Full article

Nowadays, financial institutions increasingly leverage artificial intelligence to enhance decision-making and optimize investment strategies. A specific application is the automatic analysis of large volumes of unstructured textual data to extract relevant information through deep learning (DL) methods. However, the effectiveness of these methods is often limited by the scarcity of high-quality labeled data. To address this, we propose a new data augmentation technique, Concatenation Augmentation (CA). This is designed to overcome the challenges of processing unstructured text, particularly in analyzing professional profiles from corporate governance reports. Based on Mixup and Label Smoothing Regularization principles, CA generates new text samples by concatenating inputs and applying a convex additive operator, preserving its spatial and semantic coherence. Our proposal achieved hit rates between 92.4% and 99.7%, significantly outperforming other data augmentation techniques. CA improved the precision and robustness of the DL models used for extracting critical information from corporate reports. This technique offers easy integration into existing models and incurs low computational costs. Its efficiency facilitates rapid model adaptation to new data and enhances overall precision. Hence, CA would be a potential and valuable data augmentation tool for boosting DL model performance and efficiency in analyzing financial and governance textual data. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

The advancement of sustainable economic development has become a strategic imperative for enterprises aiming to combine financial development with environmental and social responsibility. In this regard, strategic enterprise management (SEM) has a critical role in incorporating the aspects of sustainability into decision making. The present paper suggests a multicriteria decision-making framework that utilizes fuzzy TOPSIS in assessing and ranking sustainability integration aspects in organizations. By considering the intrinsic vagueness of sustainability analysis, the fuzzy TOPSIS model enables the systematic analysis of environmental, social, and governance (ESG) factors by companies for ensuring their alignment to corporate strategic goals. A case study of a major international airport in Greece demonstrates how the proposed methodology assists strategic choice making, balancing economic viability and sustainable value creation. The results show primary trade-offs among human capital investment, environmental footprint reduction, and stakeholder communication, demonstrating how companies can enhance long-term resilience and competitiveness. This research adds to the existing literature by giving an integrated strategic enterprise management framework with the use of decision support instruments to foster sustainability-oriented corporate governance and strategic efficacy. The suggested model is flexible and can be applied in any industry, hence being a benchmark for sustainable business practice. This paper contributes to the literature by integrating fuzzy TOPSIS with balanced scorecard in the context of airport strategic sustainability management, offering both methodological advancement and empirical insights for transport and supply chain enterprises. Full article

Capital budgeting, particularly sophisticated decisions, is key to the financial performance and risk management of firms, yet academic studies have documented their relationship inconsistently. This study employs the fundamentals of resource-based view (RBV) and agency theories to investigate the impact of sophisticated capital budgeting decisions on financial performance and risk management of the firms of two different sizes, classified as small and medium enterprises (SMEs) and multinational corporations (MNCs). The empirical data of 590 Indonesian firms from between 2014 and 2023 were obtained and analyzed through the Generalized Method of Moments (GMM) technique. The results show that the usage of sophisticated capital budgeting decisions in investment appraisals of classified firms significantly improves their financial performance. Further analyses confirm that although sophisticated capital budgeting decisions are robust in resolving solvency issues, they appear less effective in reducing liquidity risks. The findings also elucidate that sampled firms may realize the financial benefits of sophisticated risk management. The mediation results highlighted that risk management has a significant and positive effect on the relationship between sophisticated capital budgeting decisions and financial performance. The present study contributes to corporate finance by validating the relevance of SCBDs in strategic financial planning and stable investments in firms of different sizes. Full article