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This study addresses a real-world logistics problem in forestry operations: the distribution of plants from cultivation centers to planting sites under strict delivery time windows and limited depot resources. We introduce the Coordinated Truck Loading and Routing Problem (CTLRP), an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) that integrates routing decisions with truck loading schedules at a single depot with constrained capacity. To solve this NP-hard problem, we develop a metaheuristic algorithm based on Ant Colony Optimization (ACO), enhanced with a global memory system and a novel stochastic return rule that allows trucks to return to the depot when additional deliveries are suboptimal. Parameter calibration experiments are conducted to determine optimal values for the return probability and ant population size. The algorithm is tested on a real forestry dispatch scenario over six working days. The results show that an Ant Colony System (ACS–CTLRP) algorithm reduces total distance traveled by 23%, travel time by 22%, and the number of trucks used by 13 units, while increasing fleet utilization from 54% to 83%. These findings demonstrate that the proposed method significantly outperforms current company planning and offers a transferable framework for depot-constrained routing problems in time-sensitive distribution environments. Full article

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

Although cemented carbides have been manufactured by the powder metallurgy (P/M) technology for over a century now, systematic developmental efforts are still underway. In the present study, tool life improvements in metal grooving applications are the key objective. Four PVD-coated cemented carbides compositions, dedicated to groove steel, stainless steel, cast iron, and aluminium alloys, have been newly designed, along with their manufacturing conditions. Physical, mechanical and chemical characteristics—such as sintered density, modulus of elasticity, hardness, fracture toughness, WC grain size, and the chemical composition of the substrate material, as well as the chemical composition, microhardness, structure, and thickness of the coatings—have been studied. A series of grooving tests have also been conducted to assess whether modifications to the thus far marketed tool materials, tool geometries, and coatings can improve cutting performance. In order to compare the laboratory and application properties of the investigated materials with currently produced by reputable companies, commercial inserts have also been tested. The experimental results obtained indicate that the newly developed grooving inserts exhibit excellent microstructural characteristics, high hardness, fracture toughness, and wear resistance and that they show slightly longer tool life compared to the commercial ones. Full article

This study investigates the influence of corporate governance on reducing financial statement fraud (FSF) in Jordanian service and industrial companies listed on the Amman Stock Exchange from 2018 to 2022. To achieve this, the study employed the Beneish M-score model to assess the likelihood of FSF and logistic regression to examine the influence of corporate governance structure on fraud mitigation. The study identified 13 independent variables, including board size, board director’s independence, board director’s compensation, non-duality of CEO and chairman positions, board diversity, audit committee size, audit committee accounting background, number of annual audit committee meetings, external audit fees, board family business, the presence of women on the board of directors, firm size, and market listing on FSF. The study included 74 companies from both sectors—33 from the industrial sector and 41 from the service sector. Primary data was collected from financial statements and other information published in annual reports between 2018 and 2022. The results of the study revealed a total of 295 cases of fraud during the examined period. Out of the 59 companies analyzed, 21.4% demonstrated a low probability of fraud, while the remaining 78.6% (232 observations) showed a high probability of fraud. The results indicate that the following corporate governance factors significantly impact the mitigation of financial statement fraud (FSF): independent board directors, board diversity, audit committee accounting backgrounds, the number of audit committee meetings, family business involvement on the board, and firm characteristics. The study provides several recommendations, highlighting the importance for companies to diversify their boards of directors by incorporating different perspectives and experiences. Full article

Introduction: The sodium-to-potassium (Na:K) ratio in the diet is a critical biomarker for cardiovascular and metabolic health, yet global adherence to recommended levels remains poor. Objectives: The objective of this study was to identify dietary determinants of the dietary Na:K ratio and its associations with micronutrient intake and diet quality. Methods: An observational cross-sectional survey was conducted in a representative sample of manufacturing workers through a combined stratified proportional and two-stage probability sampling plan, with strata defined by company size and industrial sector from the state of Rio Grande do Norte, Brazil. Dietary intake was assessed using 24 h recalls via the Multiple Pass Method, with Na:K ratios calculated from quantified food composition data. Diet quality was assessed with the Diet Quality Index-International (DQI-I). Multiple linear regression was used to analyze associations of Na:K ratio with the study variables. Results: The survey was conducted in the state of Rio Grande do Norte, Brazil, in 921 randomly selected manufacturing workers. The sample mean age was 38.2 ± 10.7 years, 55.9% males, mean BMI 27.2 ± 4.80 kg/m². The mean Na:K ratio was 1.97 ± 0.86, with only 0.54% of participants meeting the WHO recommended target (<0.57). Fast food (+3.29 mg/mg per serving, p < 0.001), rice, bread, and red meat significantly increased the ratio, while fruits (−0.16 mg/mg), dairy, white meat, and coffee were protective. Higher Na:K ratios were associated with lower intake of calcium, magnesium, phosphorus, and vitamins C, D, and E, as well as poorer diet quality (DQI-I score: −0.026 per 1 mg/mg increase, p < 0.001). Conclusions: These findings highlight the critical role of processed foods in elevating Na:K ratios and the potential for dietary modifications to improve both electrolyte balance and micronutrient adequacy in industrial workers. The study underscores the need for workplace interventions that simultaneously address sodium reduction, potassium enhancement, and overall diet quality improvement tailored to socioeconomic and cultural contexts, a triple approach not previously tested in intervention studies. Future studies should further investigate nutritional consequences of imbalanced Na:K intake. Full article

This study aims to map the scientific knowledge of bank profitability and its determinants. It identifies trends and gaps in existing research through a bibliometric analysis. To this end, 634 documents published in the Web of Science database over the last 54 years were analyzed using the bibliometric package. The results indicate an increase in the volume of publications following the 2008 financial crisis, focusing on analyzing the factors influencing bank profitability and economic growth. The Journal of Banking and Finance is the preeminent publication in this field. The literature reviewed shows that bank profitability depends on internal factors (size, credit risk, liquidity, efficiency, and management) and external factors (such as GDP, inflation, interest rates, and unemployment). In addition to the traditional determinants, the recent literature highlights the importance of innovation and technological factors such as digitalization, mobile banking, and electronic payments as relevant to bank profitability. ESG (environmental, social, and governance) and governance indicators, which are still emerging but have been extensively researched in companies, indicate a need for evidence in this area. This paper also provides relevant insights for the formulation of monetary policy and the strategic formulation of banks, helping managers and owners to improve bank performance. It also provides directions for future empirical studies and research collaborations in this field. 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

Promoting social equity is a global issue, and common prosperity is an important goal for human society’s sustainable development. This study is the first to examine climate risks’ impacts on common prosperity from the perspective of corporate employees, providing micro-level evidence for the coordinated development of climate governance and social equity. Employing data from companies listed on the Shanghai and Shenzhen stock exchanges from 2016 to 2023, a fixed-effects model analysis was conducted, and the results showed the following: (1) Climate risks are positively associated with the common prosperity of corporate employees in a significant way, and this effect is mainly achieved through employee guarantees, rather than employee remuneration or employment. (2) Climate risk will increase corporate financing constraints, but it will also force companies to improve their ESG performance. (3) The mechanism tests show that climate risks indirectly promote improvements in employee rights and interests by forcing companies to improve the quality of internal controls and audits. (4) The results of the moderating effect analysis show that corporate size and performance have a positive moderating effect on the relationship between climate risk and the common prosperity of corporate employees. This finding may indicate the transmission path of “climate pressure—governance upgrade—social equity” and suggest that climate governance may be transformed into social value through institutional changes in enterprises. This study breaks through the limitations of traditional research on the financial perspective of the economic consequences of climate risks, incorporates employee welfare into the climate governance assessment framework for the first time, expands the micro research dimension of common prosperity, provides a new paradigm for cross-research on ESG and social equity, and offers recommendations and references for different stakeholders. Full article

Precisely deciding potential suppliers enables companies to engage with high-caliber partners that fulfill their strategic development requirements, bolster their core competitiveness, and foster sustainable market growth. To mitigate the challenges enterprises face in selecting appropriate suppliers, a recommendation method for potential suppliers tailored to a small-sized dataset is proposed. This approach employs an enhanced Graph Convolutional Neural Network (GCNN) to resolve the accuracy deficiencies in supplier recommendations within a limited dataset. Initially, a supply preference network is created to ascertain the topological relationship between the company and its suppliers. Subsequently, the GCNN is enhanced through dual-path refinements in network structure and loss function, culminating in the adaptive feature perception model. Thereafter, the adaptive feature perception model is employed to adaptively learn the topological relationship and extract the company’s procurement preference vector from the trained model. A matching approach is employed to produce a recommended supplier list for the company. A case study involving 143 publicly listed companies is presented, revealing that the proposed method markedly enhances the accuracy of potential supplier recommendations on a small-sized dataset, thereby offering a dependable and efficient approach for enterprises to effectively evaluate potential suppliers with limited data. Full article

Against the backdrop of the accelerated transition to sustainable development in global emerging markets, the synergistic mechanism between environmental governance innovation and corporate green transformation has become a key issue in realizing high-quality development. As the world’s largest emerging economy, China’s new Environmental Protection Law (EPL), implemented in 2015, has promoted green technology innovation and performance improvement of heavily polluting enterprises by strengthening environmental regulation. This paper takes Chinese A-share listed companies as samples from 2012–2023, treats the EPL as a quasi-natural experiment, and applies the DID method to explore the path of its impact on the performance of heavily polluting firms, with a focus on analyzing the mediating effect of green technological innovation and the moderating role of firm size and regional differences. The study revealed the following findings: the implementation of the EPL significantly improves the performance of heavily polluting enterprises, which verifies the applicability of “Porter’s hypothesis” in emerging markets; green technological innovation plays a partly intermediary role in the process of policy affecting enterprise performance, indicating that environmental regulation achieves win–win economic and environmental benefits by driving the innovation compensation mechanism; and there is significant heterogeneity in policy effects, with large-scale firms and firms in the eastern region experiencing more pronounced performance improvements, reflecting differences in resource endowments and institutional implementation strength within emerging markets. This study provides empirical evidence for emerging market countries to optimize their environmental governance policies and construct a “regulation–innovation–performance” synergistic mechanism, which will help green economic transformation and ecological civilization construction. Full article

What motivates industrial companies to decarbonise? While climate policy has intensified, the specific factors driving corporate decisions remain underexplored. This article addresses that gap through a mixed-methods study combining qualitative insights from a leading automotive supplier with quantitative data from over 800 manufacturing companies in Germany. The study distinguishes between internal motivators—such as risk reduction, future-proofing, and competitive positioning—and external drivers like regulation, supply chain pressure, and investor expectations. Results show that internal economic logic is the strongest trigger: companies act more ambitiously when decarbonisation aligns with their strategic interests. Positive motivators outperform external drivers in both influence and impact on ambition levels. For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. The analysis also reveals how company size, energy intensity, and supply chain position shape motivation patterns. The findings suggest a new framing for climate policy: rather than relying solely on mandates, policies should strengthen intrinsic motivators. Aligning business interests with societal goals is not only possible—it is a pathway to more ambitious, resilient, and timely decarbonisation. By turning external pressure into internal logic, companies can move from compliance to leadership in the climate transition. Full article

In Europe, corporate sustainability reporting through the double materiality assessment was formally introduced with the Corporate Sustainability Reporting Directive in response to the European Sustainability Reporting Standards. The double materiality assessment is essential not only to determine the scope of corporate sustainability reporting but also to guide companies toward an efficient allocation of resources and shape corporate sustainability strategies. However, although EFRAG represents the technical adviser of the European Commission, there are numerous “interoperable” standards related to the assessment of double materiality, including the Global Reporting Initiative (GRI), or UNI 11919-1:2023. This research intends to systematically analyze similarities and divergences between the most widespread double materiality assessment standards at the global scale, highlighting their strengths and weaknesses and trying to identify a comparable path toward the creation of a set of common guidelines. This analysis is carried out through the systematic study of seven standards and by answering nine questions ranging from generic ones, such as “what is the concept of double materiality?”, to more technical questions like “does the standard identify thresholds?”, but adding original prospects such as “does the standard refer to different types of capital?”. Findings highlight that EFRAG, UNI 11919-1:2023, and GRI represent the most complete and least-discretionary standards, but some methodological aspects need to be enhanced. In the double materiality assessment, companies must identify key stakeholders, material topics and material risks, and must develop the double materiality matrix, promoting transparent disclosure, continuous monitoring, and stakeholders’ engagement. While comparability is principally required among companies operating within the same sector and of similar size, this does not preclude the possibility of comparing firms across different sectors with respect to specific indicators, when appropriate or necessary. Full article

This study aims to explore how Corporate Digital Responsibility (CDR) influences Jordanian banks’ performance. It focuses on four CDR dimensions—“social, technological, economic, and environmental”—and examines the mediating role of firm size in these relationships. This study is the first to empirically test the mediating effect of firm size in the relationship between CDR and firm performance in the Jordanian banking sector, providing a novel perspective on how digital ethics shape organizational success. Data were collected through a structured survey from 299 bank employees in Jordan. Structural Equation Modeling (SEM) was employed to assess the direct and indirect effects of CDR dimensions on firm performance, with firm size tested as a mediating variable. All four dimensions of CDR significantly and positively affect firm performance. Additionally, firm size plays a partial mediating role in the relationship between CDR and firm performance, indicating that larger banks may better leverage digital responsibility initiatives to enhance performance. The study relies on self-reported data from a single country (Jordan), which may limit generalizability. Future studies could adopt a longitudinal design or expand to other MENA countries for comparative analysis and broader insights. The findings suggest that Jordanian banks should invest in and prioritize CDR strategies, especially in economic and technological domains, to improve their organizational outcomes and stakeholder relationships. Enhancing firm size may amplify the positive impact of CDR. The findings of this study are robust, as validated by further analysis utilizing data from a customer survey. The results derived from customer viewpoints correspond with staff data, substantiating the beneficial influence of Corporate Digital Responsibility (CDR) on banking performance and affirming the substantial mediating effect of company size. Full article