Search Results (3,199)

This study critically investigates the scientific literature on the intersection of management accounting and intellectual capital using a bibliometric performance analysis and science-mapping approach. Drawing on a sample of 59 publications from the Scopus and Web of Science databases, the paper maps the intellectual structure, key contributors, and thematic evolution of the field. This study conceptualizes management accounting not merely as a neutral technical system but as a socio-political mechanism that shapes how intellectual capital is rendered visible, measurable, and governable within organizations. The findings identify five dominant research clusters (intellectual capital and corporate strategy, management accounting and performance, green intellectual capital, digitalization and value creation, and management control and intangibles), revealing how accounting practices actively participate in constructing organizational realities and legitimizing particular forms of value and knowledge. The analysis highlights that measurement and reporting practices privilege certain dimensions of intellectual capital while potentially obscuring others, raising critical questions about power, visibility, and accountability in knowledge-based economies. In particular, the growing emphasis on digitalization and sustainability reflects shifting governance regimes in which accounting systems extend their influence over organizational conduct and strategic decision-making. By integrating bibliometric techniques with a critical interpretive lens, this study contributes to the literature by reframing management accounting as a key site where knowledge, control, and organizational value are negotiated. It also identifies gaps for future research, particularly regarding the ethical and political implications of accounting for intangible resources in increasingly digital and transparency-driven environments. Full article

We examine the association between Environmental, Social, and Governance (ESG) initiatives and corporate performance using a sample of 360 S&P 500 firms from 2010 to 2018. Employing MSCI ESG ratings and controlling for industry and time effects, we find that environmental initiatives positively associate with current profitability (ROA), while gender diversity correlates with long-term growth prospects (Tobin’s Q). This study moves beyond aggregated ESG metrics by providing a disaggregated analysis, revealing that different ESG dimensions affect performance through distinct financial mechanisms. To address common endogeneity concerns, we implement a rigorous empirical identification strategy, including propensity score matching, Heckman selection models, and instrumental variable approaches using industry-average instruments. Our results quantify the economic magnitude of these effects, demonstrating that a one-standard-deviation increase in environmental performance corresponds to a 0.92 percentage point increase in ROA, representing approximately $176 million in additional annual net income for the median firm. These findings provide theoretical advancement for the resource-based view and stakeholder theory by showing that specific ESG capabilities serve as valuable, inimitable resources. Ultimately, the study contributes standardized, high-resolution evidence on how specific ESG dimensions drive superior corporate performance. Through mechanism analysis, we show that environmental effects operate primarily via operational cost reduction and risk mitigation, while gender diversity creates value through enhanced innovation findings, which has direct implications for corporate ESG strategy. Full article

The global regulatory landscape is shifting from voluntary corporate social responsibility (CSR) reporting to mandatory Environmental, Social, and Governance (ESG) disclosure, yet whether this transition drives substantive corporate environmental change or merely symbolic compliance remains empirically contested. This study investigates the causal impact of mandatory ESG disclosure on firm value and operational carbon intensity, drawing on an unbalanced panel of 9682 firm-year observations for 1626 listed firms from the European Union (EU-27) and Japan covering the period 2018 to 2024. The EU serves as the treatment group, where mandatory disclosure requirements escalated substantially from 2021 onward through the Sustainable Finance Disclosure Regulation and the Corporate Sustainability Reporting Directive proposal. Japan serves as the control group, representing a developed economy with sophisticated capital markets and high ESG awareness that maintained a voluntary disclosure environment throughout the study period. A Difference-in-Differences framework with firm- and year-fixed effects is employed, and causal identification is validated through a dynamic event study analysis. Three principal findings emerge. First, mandatory ESG disclosure is not associated with a statistically significant improvement in firm value in the EU–Japan comparative context, a result that is interpreted as descriptive rather than causal given evidence of pre-existing valuation divergence between the two groups. Second, mandatory disclosure is associated with a significant and progressive reduction in Scope 1 and 2 carbon intensity, indicating substantive operational decarbonization rather than symbolic compliance. Third, this emissions-reducing effect is significantly amplified among firms with dedicated CSR sustainability committees, while the board independence policy indicator yields no significant moderating effect, a finding attributed to data limitations. These results carry direct implications for policymakers designing climate-related disclosure frameworks and for scholars examining the boundary conditions under which mandatory transparency translates into genuine environmental performance. Full article

This study examines how energy market uncertainty affects corporate financial stability and whether environmental, social, and governance (ESG) performance mitigates this relationship. Using a panel of 168 non-financial Australian firms from 2011 to 2023, we employ a two-step system generalized method of moments (GMM) with extensive robustness checks. The results reveal three central findings. First, energy market uncertainty exerts a statistically significant and economically meaningful negative effect on corporate financial stability, indicating that heightened energy price volatility amplifies firms’ financial fragility. Second, ESG performance is positively associated with financial stability, suggesting that sustainability-oriented firms exhibit superior risk management and resilience. Third, ESG performance significantly attenuates the adverse impact of energy market uncertainty, providing strong evidence that ESG functions as an effective shock-absorbing mechanism. These findings are robust to alternative measures of financial stability and energy uncertainty, different lag structures, alternative estimation methods, and a wide range of subsample analyses. Further analyses show that the moderating role of ESG is not driven by a single pillar; rather, environmental, social, and governance dimensions jointly enhance firms’ capacity to withstand energy-related shocks. The buffering effect of ESG is stronger among high-ESG firms, in knowledge- and technology-intensive sectors, and during periods of heightened systemic stress such as the COVID-19 pandemic. Overall, the study provides novel firm-level evidence that ESG performance enhances corporate resilience to energy market uncertainty. The findings have important implications for policymakers, investors, and corporate managers seeking to strengthen financial stability in an era of elevated energy volatility and accelerating sustainability transitions. Full article

An autoencoder is an unsupervised deep learning architecture designed to compress input data, extract meaningful features, and reconstruct the original input for applications such as anomaly detection and data compression. However, CPU-based implementations often suffer from limited performance and high power consumption. To address these challenges, this paper presents an FPGA-based autoencoder with a hardware-friendly neural network architecture optimized for both resource utilization and processing performance. In addition, optimization techniques such as network size reduction, quantization, and pipelining are applied to improve efficiency in real-time applications. The proposed autoencoder accelerator is integrated into a Nios II system to evaluate its effectiveness. Implemented on a Cyclone V 5CSXFC6D6F31C6 FPGA (Intel Corporation, San Jose, California, United States) at 50 MHz, the system occupies 81% of logic resources, 3% of memory blocks, and 3% of digital signal processing blocks. Experimental results show that, while an Intel Xeon CPU at 2.2 GHz requires more than 0.2 s to process a single handwritten digit from the Modified National Institute of Standards and Technology dataset, the proposed system performs the same task in approximately 4.5 milliseconds, providing a 44× speedup. This demonstrates the effectiveness of the proposed FPGA-based autoencoder accelerator. Full article

Against the backdrop of artificial intelligence technology deeply empowering the digital development of the manufacturing industry, enterprises can use AI capability as a crucial source to improve their competitiveness and play a key role in promoting high-quality corporate development. Although the existing literature has revealed the effect of AI capability on organizational performance or other factors, in-depth research remains insufficient regarding whether AI capability can effectively improve firm competitiveness through organizational learning and organizational resilience. Drawing on the resource-based view (RBV), this study constructs a relational model linking AI capability to firm competitiveness via organizational learning and organizational resilience, alongside an investigation into the moderating effect of digital innovation. Using questionnaire surveys of Chinese manufacturing firms, we obtained 304 valid samples. Regression analysis was used to analyze the effect of AI capability on firm competitiveness via organizational learning and organizational resilience. The process-bootstrap method was used to examine the sequential mediating effects of organizational learning and organizational resilience. The results show that AI capability has a direct effect on firm competitiveness, and can also influence firm competitiveness via organizational learning and organizational resilience. AI capability affects firm competitiveness sequentially through organizational learning and organizational resilience. The correlation between exploratory learning and organizational resilience gets moderated by digital innovation. Meanwhile, digital innovation presents a moderated mediating effect on the relations between AI capability and firm competitiveness through exploratory learning and organizational resilience. This paper empirically reveals the “capability-learning-resilience” mechanism through which AI capability affects firm competitiveness, thus further supplementing the study on the effect factors of firm competitiveness. The findings provide theoretical implications for manufacturing enterprises to strategically develop AI capability, implement organizational learning, actively cultivate organizational resilience, and integrate digital innovation to further enhance firm competitiveness. Full article

This bibliometric review examines the relationship between corporate governance and financial performance by synthesising evidence from a broad range of empirical studies. It also identifies key patterns in publication output, citation trends, and scholarly impact within the field. Following the PRISMA guidelines, a bibliometric review was conducted using articles indexed in the Scopus database. A total of 2095 articles published between 2020 and September 2025 were initially retrieved synthesising via a keyword search with the string “Corporate Governance” AND “Financial Performance.” After applying the inclusion criteria (full-text availability, English language, and relevance to the topic), 887 articles were retained for analysis. The findings indicate that most studies report a positive association between corporate governance practices and financial performance. The literature is primarily concentrated around themes such as corporate governance, financial performance, ESG practices, and board characteristics, with the connection between governance and a firm’s financial performance appearing generally positive, albeit context dependent. The results also reveal a growing research emphasis on sustainability-oriented governance, particularly ESG-related factors, reflecting a broader shift in the field towards long-term value creation. This review underscores the importance of nuanced corporate governance frameworks for stakeholders seeking to enhance the sustainability of financial performance, while also deepening understanding of the impact of governance on firm financial performance among both academics and practitioners. In addition, the review offers a broader perspective on the existing literature and identifies several gaps that warrant further investigation. Full article

AI, specifically Bayesian networks, was applied in this study to diagnose and predict interruptions (whether due to faults or maintenance) that most significantly impact the time of interruption per kilowatt and frequency of maintenance intervention per kilowatt indicators in electrical distribution systems. Bayesian networks were employed to identify which functional stages, such as sub-transmission lines, distribution substations, and medium-voltage networks, exert the greatest influence on these performance metrics. Additionally, the analysis was conducted to categorize the interruption catalog and contribute to these impacts. By disaggregating the service quality and maintenance indicators reported monthly by the Guayas Los Ríos Business Unit of the National Electricity Corporation to Ecuador’s electricity sector regulatory bodies, the developed framework in this study enhances service reliability, optimizes maintenance planning, and reduces interruption times. Bayesian network models generated using R illustrate relationships between interruption causes and their impact on service quality indicators. Furthermore, a comparison of several models, including Naive Bayes, Tree Augmented Naive Bayes (TAN), and Backward Sequential Elimination and Joining (BSEJ), demonstrated that TAN and BSEJ achieved the highest accuracy in predicting interruption outcomes. These insights allow for more efficient targeting of maintenance resources, ultimately reducing the most impactful categories of interruptions and improving overall technical service quality. Full article

The purpose of this paper is to investigate the firm-level determinants of the cost of debt in a bank-based emerging economy, where debt serves as the primary external financing mechanism, enabling firms to maintain operations, pursue growth opportunities, and ensure long-term financial sustainability. Using panel data from non-financial firms listed on the Casablanca Stock Exchange over the period 2018–2024, we document a robust nonlinear relationship between financial leverage and the cost of debt, whereby low and moderate debt levels reduce borrowing costs by signaling creditworthiness and financing capacity, while excessive indebtedness reverses this effect, with an optimal threshold estimated at approximately 34.8% of total assets. Firms with stronger growth prospects further benefit from more favorable financing conditions, as creditors interpret sustained asset expansion as a signal of financial strength and long-term viability. Financial performance is also found to reduce the cost of debt, although this effect is not fully robust to endogeneity controls. In contrast, asset tangibility, firm size, firm age, and liquidity do not emerge as significant determinants, suggesting that creditors in the Moroccan market adopt a financial health-oriented approach when assessing credit risk, placing greater emphasis on leverage and growth prospects than on collateral-based or reputational signals. Overall, the study highlights the coexistence of linear and nonlinear dynamics in debt pricing, thereby enriching the corporate finance literature and providing insights for managers and policymakers seeking to reduce borrowing costs, enhance access to debt financing, and support sustainable value creation. Full article

Amid growing global attention to corporate sustainability and responsible investment, the disclosure of Sustainable Development Goals (SDGs) has emerged as an important component of non-financial reporting. However, the extent to which SDG disclosure contributes to firm value remains underexplored, particularly in emerging markets. This study examines the association between SDG disclosure in corporate reports and firm value among 660 publicly listed companies across four Southeast Asian countries: Indonesia, Malaysia, Thailand, and Singapore. SDG disclosure is measured using 17 SDG indicators derived from the Refinitiv database and should be interpreted as a measure of disclosure breadth rather than disclosure quality or depth. The analysis begins with descriptive statistics to illustrate the distribution of key variables, followed by ANOVA to assess differences in SDG disclosure across countries and industries. Hypothesis testing is then conducted using multiple regression analysis with robust standard errors, with firm value proxied by price-to-book value (PBV). Several robustness checks are performed, including winsorised regression, year-by-year regressions, and regression models incorporating country and industry dummy variables. The results indicate that SDG disclosure is positively associated with firm value, although the relationship is interpreted as correlational rather than causal because of the short observation period and potential endogeneity. The findings also show that SDG disclosure is unevenly distributed across goals and countries, with SDG 8 and SDG 13 receiving the highest attention, while SDG 2 and SDG 14 remain among the least disclosed. These results highlight the importance of sustainability transparency in shaping market valuation and underscore the need for more balanced, comparable, and quality-oriented sustainability reporting frameworks across the region. Full article

This study examines the moderating effect of firm attributes on the value relevance of accounting information in Saudi Arabia. Using a sample of 630 firm-year observations from 126 Saudi listed firms over 2018–2022, the research evaluates whether audit quality, size, leverage, growth potential, board diversity, and profitability complement the valuation role of earnings per share (EPS) and book value per share (BVPS) and if so then which direction of the attribute gave greater value relevance. Results reveal that all the firm attributes tested have a significant moderating effect on value relevance. Lower leverage, higher growth potential, greater board diversity, and profitability all lead to higher predicted market value for given EPS and BVPS. Big 4 audit quality and larger firm size are found to moderate the value relevance of accounting information rather than to influence share price directly. Both attributes strengthen the value relevance of earnings per share (EPS)—the EPS coefficient is significantly higher for firms audited by a Big 4 firm and for larger firms—while weakening the value relevance of book value per share (BVPS), with the BVPS coefficient being significantly lower in both cases. The combined effect is that earnings carry greater pricing weight, and book values carry lesser pricing weight, when audit quality is high and when firms are larger. Results also reveal that cohorts with Big 4 auditor, larger size, lower leverage, higher growth potential, more diverse boards, and profitability all have greater value relevance (higher R²) than cohorts with the alternative for each attribute. Hence, tests provide evidence that these attributes strengthen the association between selective accounting figures (EPS and BVPS) and share prices. The findings contribute to agency, information asymmetry, and value-relevance theory by showing that firm attributes condition the EPS and BVPS pricing weights rather than affecting price directly. The results have implications for regulators and firms seeking to improve financial reporting credibility and usefulness amid concentrated ownership. This study contributes timely empirical evidence on the multifaceted drivers of value relevance in an under-researched Middle Eastern emerging market. Full article

The tension between corporate growth and sustainability is a common governance dilemma faced by transitional economies in their green development. This study incorporates corporate ESG performance and its potential influencing factors into the analysis framework and constructs a theoretical model to capture the relationship between China’s National Demonstration Base policy for Mass Entrepreneurship and Innovation (MEI) and corporate ESG performance, based on the framework that integrates resource enablement, reputation accumulation and information governance. Leveraging the quasi-natural experiment provided by China’s National Demonstration Program for Mass Entrepreneurship and Innovation (MEI), this study systematically evaluates the impact of China’s demonstration policy on corporate ESG performance, drawing on data from A-share listed companies spanning 2010 to 2024. The study finds that the demonstration policy significantly improves enterprise ESG performance, which remains robust after a series of robustness tests. The mechanism test reveals that the policy promotes firms’ green technology innovation by lowering innovation costs, facilitates the accumulation of social reputational capital by incentivizing charitable donations, and compels improvements in information disclosure quality by strengthening market-oriented oversight. Heterogeneity analysis shows that the policy effects are more prominent among heavy polluting industries, large-scale enterprises and firms at the mature stage. Moreover, industry competition intensity and digital transformation have a positive moderating effect on the policy effects. This paper enriches the theoretical dialogue between institutional innovation policy and enterprise sustainable development, providing empirical evidence for the development of a collaborative ESG governance mechanism characterized by an active government and an efficient market. Full article

Corporate debt crises constitute a critical source of instability in modern financial distress, rendering their early prediction essential for market regulators and investors. However, corporate debt crisis prediction is severely hindered by extreme class imbalance, as actual crisis samples are far fewer than normal ones. This issue greatly undermines the robustness and generalization ability of conventional forecasting models. To address this issue, we propose an adaptive meta weighting learning (named AMetaW) for corporate debt crisis prediction. Specifically, the model incorporates an adaptive meta weighting mechanism to alleviate class imbalance, ensuring that rare crisis samples receive sufficient attention during training. Moreover, AMetaW integrates multiple financial characteristics into a unified framework, while employing explainable machine learning techniques to reveal the heterogeneous importance of indicators across regions. Empirical analysis using firm-level data across multiple provinces in China demonstrates that: (1) AMetaW achieves superior predictive performance compared with state-of-the-art baselines under imbalanced conditions; (2) our analysis reveals that short-term benchmark interest rate, equity concentration degree, and operating profit margin are consistently the strongest predictors of debt crises; and (3) the relative importance of indicators varies across regions, with eastern firms more sensitive to equity concentration degree and cash ratio, while western firms are more exposed to risks from short-term benchmark interest rate and operating profit margin. These findings provide both methodological contributions to Corporate Debt Crises forecast model and practical insights for region-specific debt crisis prevention and offering practical guidance for group enterprises and regulators. Full article

This study provides an empirical investigation into the impact of corporate sustainability practices on financial performance in Boursa Kuwait over the period 2015 to 2025. While existing literature has largely focused on firm-level analyses of ESG practices, limited attention has been given to their aggregated effect on market-level outcomes, particularly in emerging markets such as Kuwait. Moving beyond these gaps, the research conceptualizes sustainability as a potential systemic determinant of market behavior, examining its influence on the All Share, Main Market, and Premier Market indices. The study evaluates how variations in environmental performance, governance quality, and transparency of sustainability disclosures are transmitted into various market outcomes, including index returns, volatility, and market capitalization. Employing a combination of regression analysis and time-series modeling, the framework captures both short-term fluctuations and long-term structural dynamics, enabling a nuanced understanding of the complex interplay between ESG practices and market performance. Anticipated findings suggest that improvements in governance mechanisms and sustainability disclosure standards are likely to stabilize market dynamics, mitigate volatility, and support consistent index performance in the longer term, while short term expectations are more difficult to speculate upon. Additionally, the adoption of ESG practices is hypothesized exert positive influence on investor confidence and market participation, as it’s considered to reflect a gradual alignment of Kuwait’s capital market with global sustainability norms. Full article

Environmental Management Accounting (EMA) is increasingly recognized as a vital internal tool for improving corporate environmental performance. This paper examines the hypothesis of the existence and degree of the impact of EMA on environmental performance (EP) in the Bangladesh ready-made garment (RMG) industry, the mediating factor is resource efficiency performance (REP), and the moderating boundary condition is good governance (GG). Based on the resource-based theory, dynamic capability theory, and institutional theory, the moderated mediation model is examined using partial least squares structural equation modeling (PLS-SEM) and survey data collected from 331 managers at medium- and large-scale RMG manufacturers. The findings confirm that EMA has a significant positive effect on EP, either directly or indirectly through REP, with REP accounting for about 47 percent of the overall effect. Good governance has a significant, albeit weakening, moderating effect on the EMA-REP pathway: in high-governance contexts, external regulatory pressures seem to partially replace internal EMA systems, thereby promoting resource efficiency. The results add to the literature on environmental accounting by explaining a process-based, governance-mechanism-contingent mechanism through which EMA affects environmental performance and by offering practical advice to managers and policymakers in the context of developing-economy manufacturing. Full article