Search Results (1,269)

In recent years, the operational performance of agricultural enterprises has been influenced by both natural conditions and market environments, resulting in high uncertainty and volatility. When performance falls below expectations, agricultural enterprises consciously engage in strategic change and proactive risk-taking to alleviate performance pressures. Based on Firm Behavioral Theory, Performance Feedback Theory, and Prospect Theory, we examine how performance expectation gap affects risk-taking of agricultural enterprises by using panel data of Chinese A-share listed agricultural firms from 2007 to 2023. The results show that performance expectation gap has a positive effect on risk-taking, which means the greater the gap, the higher the level of risk-taking. And the better developed the institutional environment, the greater the tendency for risk-taking. Further analysis shows that performance expectation gap promotes risk-taking by driving strategic change within agricultural enterprises. This research enriches the study on the influencing factors of risk-taking in agricultural enterprises, offering decision-making insights for them to prudently assess and manage risks. Full article

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework for early-warning forecasting of corporate financial distress using lagged accounting-based financial information. The proposed framework integrates heterogeneous base learners, including Decision Tree, Neural Network, and k-Nearest Neighbors models, and is evaluated using financial statement data from 752 publicly listed firms in Thailand, comprising sixteen financial ratios across six dimensions: liquidity, operating efficiency, debt management, profitability, earnings quality, and solvency. To ensure robustness under imbalanced and rare-event conditions, the study employs feature selection, data normalization, stratified cross-validation, resampling techniques, and repeated validation procedures. Empirical results demonstrate that the proposed Voting Ensemble delivers a precision-oriented and decision-relevant forecasting profile, outperforming classical classifiers and maintaining greater early-warning reliability when benchmarked against advanced tree-based ensemble models. Probability-based evaluation further confirms the robustness and calibration stability of the proposed framework under repeated cross-validation. By adopting a forward-looking, early-warning perspective and integrating ensemble learning with explainable machine learning principles, this study offers a transparent and scalable approach to financial distress forecasting. The findings offer practical implications for auditors, investors, and regulators seeking reliable early-warning tools for corporate risk assessment, particularly in emerging market environments characterized by data imbalance and heightened uncertainty. Full article

Under the background of accelerating global transitions towards low-carbon development, digital intelligence transformation (DIT) has become a critical force that helps companies overcome green technological constraints and translate external green pressures into substantive green innovation. Taking the establishment of China’s NAIIDTZs as a quasi-natural experiment, this study investigates the impact of DIT on corporate green innovation (CGI) from an internal decision-making perspective. Based on a panel dataset of 19,440 samples from Chinese A-share listed companies during 2012–2023, our findings show that DIT significantly enhances both the quantity and quality of CGI. Mechanism analyses indicate that DIT promotes CGI’s quantity through increased R&D human capital input, while improving CGI’s quality through managerial myopia reduction. Heterogeneity analyses further reveal that the positive effects of DIT on CGI are particularly pronounced in firms operating under fierce market competition, in high industrial technological intensity, and in eastern regions. Furthermore, we find that CGI exerts a lagged effect on carbon emission reduction performance, while the effect of CGI’s quality is stronger than that of CGI’s quantity. These findings extend the dynamic capacity theory to digitalization and provide practical and policy implications for promoting CGI through digital intelligence development. Full article

This study examines the relationship between publicly traded Thai companies’ ESG performance and value as well as how board structures moderate this. In the Thai context, there is a limited number of empirical studies that employ the board of directors’ structure as a moderating variable, despite the importance of the board’s role in corporate management. This study aims to address this research gap. A panel GMM regression model is employed to address endogeneity issues, and our sample consists of 94 Thai listed companies with available ESG data from 2019 to 2023, resulting in 470 firm-year observations. The results demonstrate positive direct impact of ESG score on corporate value. In addition, board independence is positively significant and relates to company value. However, this research found negative moderating effect of board independence on the relationship between ESG score and corporate value. Furthermore, the empirical results indicate that board size does not have a significant direct and moderate impact on corporate value. Moreover, firm size and leverage are not related to corporate value. The results confirm the agency theory and stakeholder theory. Based on the findings, company executives should integrate ESG practices into their strategic plans. Moreover, regulatory authorities should promote expertise diversity and independence within the board and promote ESG standards and disclosure, as well as offer tax incentives for companies with outstanding ESG. This would enable investors to consider ESG performance in their decision-making. This study represents a new contribution to literature, especially in the context of emerging markets. Full article

We have developed a comprehensive mathematical framework that delineates the complete transition of a firm from private to public ownership. This framework explicitly formalizes the endogenous decision to list, pre-listing restructuring, regulatory feasibility constraints, information production, pricing and allocation mechanisms, and post-listing market dynamics. A unified structure is employed to represent traditional IPOs, direct listings, and de-SPAC mergers. The proposed framework integrates the concepts of information asymmetry, free-float constraints, and market impact with equilibrium offer prices, first-day returns, and post-listing volatility. This integration enables the formulation of testable predictions across a range of listing mechanisms. Full article

Background/Objectives: Adult spinal deformity (ASD) management often requires pelvic fixation, with S2 alar–iliac (S2AI) screws emerging as an alternative to traditional iliac screws. Despite multiple meta-analyses comparing these techniques, the methodological quality of these syntheses and technical heterogeneity across primary studies significantly impact their conclusions and subsequent clinical decision-making. This systematic review evaluates the evidence quality of meta-analyses comparing S2AI with traditional iliac screws for ASD management, focusing on methodological rigor, primary study overlap, and clinical heterogeneity. Methods: PubMed, Cochrane, and Epistemonikos were searched for meta-analyses comparing S2AI with iliac screws for patients with ASD. The Quality of Reporting of Meta-analyses (QUOROM) checklist and the revised Assessment of Multiple Systematic Reviews (AMSTAR 2) tool were adopted to assess the methodological quality. Primary study overlap was evaluated using the Corrected Covered Area (CCA) method. Clinical heterogeneity was assessed by examining characteristics of studies included in ≥67% of meta-analyses. Results: From a total of 29 publications, six meta-analyses met the inclusion criteria (4807 patients; mean age: 59 years; 33% female). All included meta-analyses exhibited critically low methodological quality per AMSTAR-2, with common flaws including failure to provide lists of excluded studies and lack of a priori protocols. Very high primary study overlap was observed (CCA: 31%), with only 11% (2 of 19) primary studies included in all meta-analyses, whereas 42% (8 of 19) primary studies were included by only a single meta-analysis. Substantial clinical heterogeneity existed regarding patient characteristics, surgical techniques, and outcome definitions. Conclusions: This systematic review of meta-analyses identified critically low methodological quality, high primary study overlap, and substantial clinical heterogeneity in the existing evidence comparing pelvic fixation techniques for ASD. While published meta-analyses generally favor S2AI screws, these significant limitations prevent drawing definitive conclusions about superiority. Future research should prioritize high-quality prospective studies with standardized reporting to generate more reliable evidence for improving surgical outcomes in ASD management. Full article

The mobility of data factors and the adoption of a collaborative innovation framework are key drivers influencing the gaps in total factor productivity (TFP) among enterprises in the digital economy. Using panel data from Chinese A-share listed companies between 2006 and 2022, this study empirically demonstrates how data factor flow reduces TFP gaps. The findings reveal that data factor flow enhances TFP convergence by facilitating knowledge diffusion, improving information transmission, and boosting innovation efficiency. However, the heterogeneity in enterprise RD efforts limits this convergence effect, highlighting the importance of collaborative innovation. The study further shows that the impact of data factor flow is more significant in smaller, privately owned enterprises in the eastern regions and in industries with low to high technology intensity and high market concentration. Key insights include (1) a positive synergy between government data openness policies and enterprise data flow, which reinforces the narrowing of TFP gaps; (2) a nonlinear relationship between data flow and TFP gaps, suggesting an optimal range for its maximum impact. The study concludes that an integrated framework optimizing both data governance and collaborative innovation ecosystems can foster innovation diffusion and support productivity-based competition. These findings provide valuable insights for innovation policy formulation and strategic decision-making in the digital economy. Full article

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study utilizes a sample comprising Chinese A-share listed enterprises over the period from 2012 to 2024 to construct an enterprise climate transition risk index using text analysis methods. It empirically investigates this index’s impact on enterprise green innovation by adopting panel data analysis method to construct a fixed effects model and further examines the moderating roles of institutional investors’ shareholding and enterprise environmental uncertainties in response to climate transition risks. The research findings indicate the following: First, climate transition risks significantly enhance enterprise green innovation. The validity of this conclusion persists following a series of robustness and endogeneity tests, including replacing the explained variable, lagging the explanatory variable, controlling for city-level fixed effects, and applying instrumental variable methods. Second, both institutional investors’ shareholding and enterprise environmental uncertainties exert a significant positive regulatory effect on the relationship between climate transition risk and green innovation, indicating that external monitoring and heightened risk perception jointly enhance enterprises’ responsiveness in driving green innovation. Thirdly, heterogeneity analysis indicates that the positive impact of climate transition risks on green innovation is notably amplified within non-state-owned enterprises and manufacturing enterprises. By examining the dual regulatory mechanisms of ‘external monitoring’ and ‘risk perception’, this study broadens the study framework on the relationship between climate risks and enterprise green innovation, offering new empirical evidence supporting the applicability of the ‘Porter Hypothesis’ within the context of climate-related challenges. Furthermore, it provides valuable implications for policymakers in refining climate information disclosure policies and assists enterprises in developing forward-looking green innovation strategies. Full article

This study examines the impact of tax avoidance practices on investment efficiency in Egypt, with particular emphasis on the moderating role of earnings management by exploring whether these tactics reflect managerial opportunism or serve as a mechanism to ease financial constraints. We employ panel data regression to analyze a sample of 58 non-financial firms listed on the Egyptian Exchange (EGX) over the period 2017–2024, yielding 464 firm-year observations. Data are collected from official corporate websites, EGX, and Egypt for Information Dissemination (EGID). Grounded in agency theory, signaling theory, and pecking order theory, this study reveals how conflicts of interest and information asymmetry between managers and stakeholders lead to managerial opportunism. The findings show that tax avoidance undermines the investment efficiency in the Egyptian market. Earnings manipulation further intensified this effect due to the financial statements’ opacity. A closer examination reveals that earnings management exacerbates overinvestment by masking managerial decisions. Conversely, for financially constrained firms with a tendency to underinvest, tax avoidance and earnings management may contribute to improved efficiency by generating internal liquidity and alleviating external financing constraints. These results provide valuable insights for regulators, highlighting that policy should be directed against managerial opportunism and improving transparency, instead of focusing solely on curbing tax avoidance. From an investor perspective, they should closely monitor and understand the tax-planning strategies to ensure they enhance the firm’s value. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

The essay delves into the impact of environmental uncertainty on asymmetric mispricing, utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improving the efficiency of emerging capital markets. Full article

Over the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary “cocktails” of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenarios. At the toxicological level, the field is now anchored in concentration/dose addition as the default model for similarly acting chemicals, supported by extensive experimental evidence that most environmental mixtures behave approximately dose-additively at low effect levels. Building on this paradigm, a portfolio of quantitative metrics has been developed to operationalize component-based mixture assessment: HI as a conservative screening anchor; Relative Potency Factors (RPF) and Toxic Equivalents (TEQ) to express doses within cumulative assessment groups; the Maximum Cumulative Ratio (MCR) to diagnose whether risk is dominated by one or several components; and the combined Margin of Exposure (MOET) as a point-of-departure-based integrator that avoids compounding uncertainty factors. Regulatory frameworks developed by EFSA, the U.S. EPA and FAO/WHO converge on tiered assessment schemes, biologically informed grouping of chemicals and dose addition as the default model for similarly acting substances, while differing in scope, data infrastructure and legal embedding. Implementation in food safety critically depends on robust exposure data streams. Total Diet Studies provide population-level, “as eaten” exposure estimates through harmonized food-list construction, home-style preparation and composite sampling, and are increasingly combined with conventional monitoring. In parallel, human biomonitoring quantifies internal exposure to diet-related xenobiotics such as PFAS, phthalates, bisphenols and mycotoxins, embedding mixture assessment within a dietary-exposome perspective. Across these developments, structured uncertainty analysis and decision-oriented communication have become indispensable. By integrating advances in toxicology, exposure science and regulatory practice, this review outlines a coherent, tiered and uncertainty-aware framework for assessing real-world dietary mixtures of xenobiotics, and identifies priorities for future work, including mechanistically and data-driven grouping strategies, expanded use of physiologically based pharmacokinetic modelling and refined mixture-sensitive indicators to support public-health decision-making. Full article

This study empirically investigates the relationship between Environmental, Social, and Governance (ESG) scores and the financial performance of Mexican companies traded at Bolsa Mexicana de Valores (BMV), based on firm value and profitability. The study used a quantitative method of correlational research. Using data from the Refinitiv, the study analyzes 103 companies operating in 37 different industries listed on the BMV over five years (2019–2023), excluding financial institutions. Ordinary least squares (OLS) regressions revealed a statistically significant, positive correlation between ESG scores associated with higher return on assets (ROA) and market value measured by Tobin’s Q). Stakeholder theory serves as the theoretical foundation, as ESG initiatives may enhance long-term value for stakeholders. The study found that ESG efforts contribute positively to ROA and Tobin’s Q of public companies in Mexico. This study focuses exclusively on Mexican companies, expanding the existing literature. Corporate decision makers and investors can gain insights into ESG’s role in Mexican companies’ financial strategy and stakeholder value creation. Full article

This study examines the determinants of goodwill impairment recognition under IFRS 3 in the context of Moroccan listed firms. Using an unbalanced panel covering the period of 2006–2024 and comprising 862 firm-year observations, we employ a three-stage empirical strategy that integrates a Probit model to estimate the likelihood of impairment, a Tobit model to assess the magnitude of the loss, and a Heckman two-step procedure to correct for potential self-selection. The results show that goodwill impairment reflects key economic and financial fundamentals, including revenue growth, book-to-market ratios, and operating performance. However, both real and accrual-based earnings management significantly influence the probability and intensity of impairment, particularly through abnormal cash flows and income-smoothing behavior. Discretionary accruals become significant only after correcting for selection bias, indicating that they do not drive the recognition decision but contribute to determining the size of the impairment once it has been recorded. The findings are robust across multiple specifications and contribute to the broader literature on financial reporting quality under IAS/IFRS, while enriching empirical evidence on managerial discretion and earnings management in emerging-market environments. Full article