Search Results (3,086)

Previous survey research has documented that consumers place value on socially responsible firms. This support includes the intention to be more loyal to these firms and also the willingness to pay higher prices for their products. Our study connects the customer intentions documented in survey research with actual measures of financial performance from published financial statements. The study uses gross profits scaled by total assets as a proxy for customers’ willingness to pay higher prices and sales increases as a proxy for loyalty. Additionally, the study examines differences in the aforementioned measures between customers in the business-to-business (B2B) and business-to-consumer (B2C) segments. These differences have been documented in studies that suggest customers in these segments value different characteristics of suppliers when making their purchases. Finally, customers must be made aware of a firm’s sustainability practices; therefore, the study looks at three different approaches firms use to communicate the quality of their sustainability practices. These approaches include external assurance of the social responsibility report, the auditor’s review of the firm’s internal controls, and the firm’s advertising intensity. Data used in this study includes financial performance measures of North American firms and corporate social responsibility data from disclosures collected by the Global Reporting Initiative. Using ordinary least squares, the results suggest that customers require some sort of assurance of a company’s socially responsible disclosures when making decisions about whether to support the company. Full article

This study investigates the relationship between capital intensity, debt financing, and profitability in non-financial firms in Oman over the period 2012–2022. Using a robust panel dataset of 76 firms, the research explores how capital structure dynamics influence firm performance across different firm sizes and industries. The findings reveal that capital intensity significantly enhances profitability, and debt financing further strengthens this effect, with variations observed across firm size and sector. The analysis also identifies a non-linear (concave) relationship between capital intensity and profitability, indicating that while moderate capital investment improves firm performance, excessive capital accumulation may lead to diminishing returns. Larger firms, with better access to financial resources, exhibit a stronger positive relationship between debt financing and profitability, while smaller firms face more challenges due to limited access to capital. Industry-specific results indicate that capital-intensive sectors, such as Energy and Industrials, demonstrate a more pronounced effect of capital intensity on profitability compared to less capital-intensive sectors. The study also incorporates the effects of the COVID-19 pandemic, showing its significant influence on firm performance, particularly in sectors with high debt exposure. By integrating non-linear effects, firm size, industry heterogeneity, and pandemic shocks, this study provides novel insights into capital structure management in emerging economies, offering implications for both corporate decision-makers and policymakers aiming to enhance financial access and optimize debt strategies across sectors. Full article

The optimization of distillation columns is critically important due to their substantial contribution to operational costs in the petrochemical industry. This paper introduces a computationally efficient surrogate-based optimization framework designed explicitly for prefractionation columns. To address the challenges of high computational cost and model accuracy in model-based optimization, a self-adaptive Kriging model, which features automated hyperparameter tuning via Bayesian optimization, is implemented and trained using Latin hypercube sampling of historical process data. By integrating a self-adaptive Kriging model with a modified firefly algorithm, the framework efficiently identifies optimal operating conditions that maximize economic profit while adhering to operational constraints. Case studies demonstrate that the proposed framework achieves superior economic performance, increasing the average final profit by 0.17–0.31% compared to non-adaptive surrogate benchmarks. Furthermore, it is exceptionally stable, achieving a minimal relative standard deviation of only 0.037% in the final profit across 30 independent runs, significantly lower than the 0.266% and 0.237% achieved by the benchmark methods. This study provides a practical and efficient tool to optimize complex distillation columns with limited computational resources. Full article

This study examines the determinants of financial sustainability in Turkish maritime industry by analyzing firm-level panel data from 190 ship and boat maintenance firms and 208 coastal shipping companies for the 2010–2022 period, comprising 5174 firm-year observations. Fixed-effects models with Driscoll–Kraay robust standard errors are employed to evaluate how asset structure, liquidity, and energy efficiency jointly affect firm profitability across subsectors, using the Operating Return on Assets (OROA) as the principal indicator of operational performance. The empirical results indicate substantial heterogeneity between maintenance and shipping firms. For maintenance firms, OROA shows a positive association with the Non-Current Assets to Total Assets ratio (NCATA) and the Economic Efficiency Ratio (EER) but a negative association with the Current Ratio (CR), suggesting that capital deepening and operational efficiency tend to correlate with stronger performance, whereas excess liquidity is associated with weaker outcomes. For shipping firms, OROA is positively associated with EER and Total Asset Turnover (TATR) but negatively associated with Fixed Asset Turnover (FATR) and CR, indicating relationships consistent with efficiency gains from energy management and asset utilization but linkages suggesting challenges from fleet aging and liquidity mismanagement. Overall, the findings suggest that the drivers of financial sustainability are associated with different structural conditions across maritime subsectors, highlighting the importance of targeted modernization, port efficiency, and energy-transition investment strategies. Full article

Modern electrical vehicles (EVs) are equipped with sizable batteries that possess significant potential as energy prosumers. EVs are poised to be transformative assets and pivotal contributors to the virtual power plant (VPP), enhancing the performance and profitability of VPPs. The number of household EVs is increasing yearly, and this poses new challenges to the optimization of VPP operations. The computational cost increases exponentially as the number of decision variables rises with the increasing participation of EVs. This paper explores the role of a large number of EVs as prosumers, interacting with a VPP consisting of a photovoltaic system and battery energy storage system. To accommodate the large quantity of EVs in the modeling, this research adopts the decentralized control structure. It optimizes EV operations by regulating their charging and discharging behavior in response to pricing signals from the VPP. A two-stage optimization framework is proposed for VPP-EV operation using a reinforcement algorithm and gradient-based programming. Action masking for reinforcement learning is explored to eliminate invalid actions, reducing ineffective exploration, thereby accelerating the convergence of the algorithm. The proposed approach is capable of handling a substantial number of EVs and addressing the stochastic characteristics of EV charging and discharging behaviors. Simulation results demonstrate that the VPP-EV operation optimization increases the revenue of the VPP and significantly reduces the electricity costs for EV owners. Through the optimization of EV operations, the charging cost of 1000 EVs participating in the V2G services is reduced by 26.38% compared to those that opt out of the scheme, and VPP revenue increases by 27.83% accordingly. Full article

The conversion of CO₂ into valuable chemicals such as formic acid offers a promising approach to reducing CO₂ emissions. This study presents a techno-economic assessment of two continuous catalytic processes for formic acid production via carbon dioxide (CO₂) hydrogenation. The processes differ in the type of nitrogenous base used, operating under either homogeneous or heterogeneous catalytic conditions. Process simulations and techno-economic evaluations were performed in SuperPro Designer^TM for a medium-scale facility with an annual CO₂ processing capacity of around 14 kMT. The homogeneous catalysis pathway demonstrated superior plant performance, producing 13.03 kMT of formic acid per year at 99.78% purity. In contrast, the heterogeneous pathway required higher capital investment and exhibited lower overall efficiency. The techno-economic analysis confirmed the economic viability of the homogeneous process, with a production cost of $1.18/kg and favorable investment indicators, whereas the heterogeneous route proved economically unattractive under the evaluated assumptions. Sensitivity analysis identified the selling price of formic acid as the most critical profitability parameter, with the homogeneous process remaining robust across varying conditions. Overall, homogeneous catalytic CO₂ hydrogenation demonstrates a technically efficient and economically promising process for the chemical transformation of CO₂, contributing to carbon management. Full article

In the context of the growing adoption of alternative gas separation processes, combined with the interest in hydrogen as a fuel and energy carrier, the use of membrane technology in H₂/CH₄ purification is analyzed in this work, focusing on the techno-economic aspects. In particular, the separation and economic performance of three Pd–Ag/Si-CHA membrane plants are simulated, aiming to achieve high degrees of purity and recovery paired with cost-effective configurations. A single Pd–Ag membrane stage operating at 20 atm and 350 °C can theoretically guarantee a CH₄ concentration of 95%, while a completely pure H₂ stream leaves the plant as a permeate product. The choice of a less selective Si-CHA membrane allows a temperature reduction but implies the use of more stages to achieve the desired CH₄ target. In addition, H₂ purity does not exceed 98%. A two-stage hybrid process, in which the retentate gas leaving the Pd–Ag membrane is cooled and fed to the Si-CHA unit, is also a cost-effective solution, as feed pressure can be reduced to 10 atm with significant compression cost savings. All the configurations are able to provide positive values of economic potential (EP); however, the single Pd–Ag membrane plant is the best option since it guarantees the highest EP, net profit and net present value (NPV). Full article

Agrivoltaic (AV) systems are increasingly recognized as a strategy to enhance sustainable land management, yet their application in post-conflict settings remains underexplored. This study addresses this gap by evaluating AV deployment in two Colombian municipalities located in PDET/ZOMAC regions, using an integrated framework that expands the conventional Water–Energy–Food (WEF) nexus into the Water–Energy–Food–Soil–Climate–Communities (WEFSCC) nexus. The research combined GIS-based site characterization, crop yield and water balance modeling (contrasting traditional irrigation with hydroponics), and photovoltaic performance simulations for 30 kW systems, under conservative and moderate scenarios. Economic analyses included Net Present Value (NPV), Internal Rate of Return (IRR), and Free Cash Flow (FCL), with sensitivity tests for crop prices, yields, tariffs, and costs. Results indicate that AV can reduce crop irrigation demand by up to 40%, while generating 17 MWh/month of electricity per site. Cabrera exhibited higher profitability than Pisba, explained by yield differences and site-specific energy outputs. Comparative analysis confirmed consistency with experiences in Africa and Europe, while emphasizing local socio-environmental benefits. Conclusions highlight AV systems as resilient tools for sustainable land management in Colombia’s post-conflict regions, with actionable implications for land-use regulation, fiscal incentives, and international cooperation programs targeting rural development. Full article

This study examines the relationship between corporate cash holdings and firm performance within the IT industry, which is characterized by intense competition and rapid technological advancements. We propose an integrated framework that combines principal component analysis (PCA), machine learning (ML) algorithms, and Shapley additive explanation (SHAP) values to estimate and interpret model outcomes. Based on 21,051 corporate financial statement data items from 2004 and 2023, the empirical evidence supports an inverted U-shaped relationship between cash holdings and profitability, suggesting that holding either too little or too much cash is suboptimal. Among the tested models, the random forest demonstrates the highest explanatory power (R²) and the lowest prediction errors (RMSE), outperforming the traditional ordinary least squares (OLS) regression by explaining 47% more variance. Our findings provide practical implications for researchers and stakeholders interested in enhancing corporate risk management and performance. Full article

To optimize operations and raise their performance, many industries have used the Theory of Constraints (TOC) methodology. The effective management of the constraints that prevent businesses from achieving their goals is described in the Theory of Constraints, which Goldratt developed in the 1980s. Increased restricted contribution margin, decreased inventory levels, and lower operating costs are the key points of the Theory of Constraints. Therefore, by effectively allocating enterprise resources, profitability can be increased. This study’s goal is to locate and eliminate the significant constraints standing in the way of a company’s accomplishment of their objectives, raising productivity and, consequently, firm profitability. A furniture company in the Maharashtra Region undertook the Theory of Constraints practice for this reason. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

Cinema can be conceptualized as a socio-technical system in which scripts encode intended emotions, production processes transform them into multimodal experiences, and audiences generate emergent responses through reviews and ratings. This study investigates the emotional fidelity between designed affective trajectories in film scripts and perceived emotions expressed in audience reviews. A system-oriented computational framework was developed, integrating large-scale script and review data with transformer-based natural language processing models fine-tuned on the GoEmotions dataset. By applying a unified classification pipeline, we compare emotional distributions across scripts and reviews, analyze temporal and genre-specific patterns, and examine correlations with film success metrics such as profit and ratings. The results reveal both convergence and divergence between scripted intentions and audience responses, with genres functioning as semi-autonomous subsystems and historical trends reflecting context-dependent adaptation. Emotional fidelity—defined as the degree to which intended emotional expressions are preserved, transformed, or inverted in audience interpretation—is introduced as a system-level performance indicator. These findings advance theoretical perspectives on narrative communication as a feedback-driven socio-technical process and demonstrate how emotion mining can function as affective monitoring infrastructure for complex adaptive systems. The study contributes actionable insights for screenwriters, producers, and system designers seeking to enhance affective engagement. Full article

In inventory management, business organizations gradually face challenges due to the complexities of managing perishable goods whose value diminishes over time. In such circumstances, interval’s bounds estimated business policy can be adopted to study a non-deterministic inventory model incorporating decay, preservation technology, and financial incentives, viz. advanced payments and fixed discounts. This study explores an interval Economic Order Quantity (EOQ) model incorporating advanced payment with discount options under preservation technology framework in interval environment. In this model, the demand rate is expressed as a convex combination of linear and power patterns of the selling price. The present model is formulated mathematically using interval differential equations and interval mathematics. Then, the corresponding interval-valued average profit of the model is obtained. In order to optimize the corresponding interval optimization problem, C-U optimization technique is developed. Employing the C-U optimization technique, the said interval optimization problem is converted into crisp optimization problems. Then, these problems are solved numerically by Wolfrom MATHEMATICA-11.0 software and validated with the help of two numerical examples. Finally, sensitivity analyses have been performed to study the impact of known inventory parameters on optimal policy. Full article

This study investigates the complex relationship between corporate ethics and financial performance in the context of sustainable management. By subdividing corporate social responsibility (CSR) into corporate ethical levels and ethical efforts, it analyzes how these factors, along with their interaction, influence corporate financial performance. The analysis employs ordinary least squares (OLS) regression with year fixed effects (year dummy variables), using twelve years (2012–2023) of ESG evaluation data from 384 publicly listed Korean manufacturing companies. The study empirically tests whether highly ethical firms may fall into a corporate moral licensing phenomenon, whereby past ethical achievements are used to justify socially irresponsible decisions in pursuit of short-term profit. The findings reveal that while higher corporate ethical levels generally improve financial performance, sustained ethical efforts at a high level can paradoxically reduce short-term profitability, thereby exposing firms to risks that undermine sustainability. Taken together, these results highlight the dual nature of CSR as both an enabler and a potential constraint for sustainable business practices. Overall, the study contributes to sustainability research by offering novel evidence of moral licensing at the corporate level and providing actionable insights for managers and policymakers seeking to balance ethical commitments with long-term sustainable performance. Full article

This study examines how supply chain structure diversification drives high-quality enterprise development in the digital economy. Using panel data from Chinese listed non-financial firms (2009–2023), we find that diversification of both suppliers and customers significantly improves firms’ total factor productivity (TFP), and the results remain robust after controlling for endogeneity. Mechanism analyses show that diversification enhances innovation capability, sustainability performance, and risk resilience, while digital supply chains strengthen these effects by improving information flow and coordination. Heterogeneity tests reveal that the impact is greater for firms with higher operational efficiency, cultural synergy, and information transparency. Overall, the findings highlight that diversified and digitally integrated supply chains are essential for innovation-driven, resilient, and sustainable enterprise growth. Full article