Search Results (551)

Internationalisation can profoundly reshape corporate social responsibility (CSR). As companies expand into global markets, they not only seek to boost revenues but also to strengthen their social impact. This study explores the influence of internationalisation on the CSR performance of Peruvian manufacturing firms between 2019 and 2022, drawing on the theoretical frameworks of Johanson and Vahlne, the Uppsala model, and Merubia’s theory of international networks. Using panel data econometric models, this study analyses a sample of 21 manufacturing companies based in Lima. It examines how CSR performance relates to key dimensions of internationalisation, including foreign sales, geographic diversification, and cultural diversification. The findings reveal a significant relationship between internationalisation and CSR, suggesting that firms enhance their operations by integrating responsible and sustainable practices throughout their international expansion. Full article

As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate under either an agency or a resale mode. However, the relative effectiveness of these strategies remains unclear. This study investigates an e-commerce supply chain comprising a single brand and platform, examining how AI and KOL livestream influence supply chain decisions across different sales modes and identifying optimal strategies for the brand and platform. Results show that when the platform’s revenue sharing rate is low, the agency mode consistently yields a Pareto improvement over resale, regardless of the livestream scheme. Moreover, when the KOL promotion fee rate is low, KOL livestream outperforms AI livestream under both sales modes. When the revenue sharing rate is high, the brand’s optimal strategy is “resale mode and KOL livestream”, while the platform prefers “agency mode and KOL livestream”. Conversely, when the revenue sharing rate is low, the platform’s best strategy is “resale mode and KOL livestream”, while the brand favors the agency mode, with livestream preferences shaped by KOL promotion fee rate. Full article

The bullwhip effect is the phenomenon of distorted information that causes the amplification of variability of demand in supply chains. We examine the relationship between the bullwhip effect and cost behavior using a large sample of U.S. public firms from 1980 to 2019. Our empirical results show that the costs of firms with a higher intensity of bullwhip effect are significantly more responsive to changes in sales, suggesting that firms facing higher amplification of demand will adopt a less rigid short-term cost structure with lower fixed and higher variable costs. Furthermore, the bullwhip effect is associated with a higher elasticity of number of employees, operating leases, and rental expenses with respect to sales. The findings of mediation analyses suggest that firms are likely to lease capacity resources to increase the flexibility and manage the operating risk associated with the bullwhip effect. The results are robust to alternative model specifications. This study contributes to both the cost accounting and supply chain management literature, and documents large sample evidence on whether and how the bullwhip effect affects a firm’s choice of cost structure. Full article

Artificial intelligence (AI) has significant potential to transform small- and medium-sized enterprises (SMEs), yet its adoption is often hindered by challenges such as limited financial and human resources. This study addresses this issue by investigating the core AI technologies adopted by SMEs, their broad range of applications across business functions, and the strategies required for successful implementation. Through a systematic literature review of 50 studies published between 2016 and 2025, we identify prominent AI technologies, including machine learning, natural language processing, and generative AI, and their applications in enhancing efficiency, decision-making, and innovation across sales and marketing, operations and logistics, finance and other business functions. The findings emphasize the importance of workforce training, robust technological infrastructure, data-driven cultures, and strategic partnerships for SMEs. Furthermore, the review highlights methods for measuring and optimizing AI’s value, such as tracking key performance indicators and improving customer satisfaction. While acknowledging challenges like financial constraints and ethical considerations, this research provides practical guidance for SMEs to effectively leverage AI for sustainable growth and provides a foundation for future studies to explore customized AI strategies for diverse SME contexts. Full article

Energy communities led by local citizens are vital for achieving the European energy transition goals. This study examines the design of a regional energy community in a rural area of Spain, aiming to address the pressing issue of rural depopulation. Seven villages were selected based on criteria such as size, energy demand, population, and proximity to infrastructure. Three energy valorization scenarios, generating eight subscenarios, were analyzed: (1) self-consumption, including direct sale (1A), net billing (1B), and selling to other consumers (1C); (2) battery storage, including storing for self-consumption (2A), battery-to-grid (2B), and electric vehicle recharging points (2C); and (3) advanced options such as hydrogen refueling stations (3A) and hydrogen-based fertilizer production (3B). The findings underscore that designing rural energy communities with a focus on social impact—especially in relation to depopulation—requires an innovative approach to both their design and operation. Although none of the scenarios alone can fully reverse depopulation trends or drive systemic change, they can significantly mitigate the issue if social impact is embedded as a core principle. For rural energy communities to effectively tackle depopulation, strategies such as acting as an energy retailer or aggregating individual villages into a single, unified energy community structure are crucial. These approaches align with the primary objective of revitalizing rural communities through the energy transition. Full article

The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the calibration of hybrid operating strategies. This paper presents a dynamic closed-loop validation of a hardware-in-the-loop testbench designed for the virtual calibration of hybrid operating strategies for a plug-in hybrid electric vehicle. Requirements regarding the hardware-in-the-loop testbench accuracy are defined based on the investigated use case. From this, a dedicated hardware-in-the-loop testbench setup is derived, including an electrical setup as well as a plant simulation model. The model is then operated in a closed loop with a series production hybrid control unit. The closed-loop validation results demonstrate that the chassis simulation reproduces driving resistance closely aligning with the reference data. The driver model follows target speed profiles within acceptable limits, achieving an R² = 0.9993, comparable to the R² reached by trained human drivers. The transmission model replicates the gear ratios, maintaining rotational speed deviations below 30 min⁻¹. Furthermore, the shift strategy is implemented in a virtual control unit, resulting in a gear selection comparable to reference measurements. The energy flow simulation in the complete powertrain achieves high accuracy. Deviations in the high-voltage battery state of charge remain below 50 Wh in a WLTC charge-sustaining drive cycle and are thus within the acceptable error margin. The net energy change criterion is satisfied with the hardware-in-the-loop testbench, achieving a net energy change of 0.202%, closely matching the reference measurement of 0.159%. Maximum deviations in cumulative high-voltage battery energy are proven to be below 10% in both the charging and discharging directions. Fuel consumption and CO₂ emissions are modeled with deviations below 3%, validating the simulation’s representation of vehicle efficiency. Real-time capability is achieved under all investigated operating conditions and test scenarios. The testbench achieves a real-time factor of at least 1.104, ensuring execution within the hard real-time criterion. In conclusion, the closed-loop validation confirms that the developed hardware-in-the-loop testbench satisfies all predefined requirements, accurately simulating the behavior of the reference vehicle. Full article

The energy market in the European Union is dominated by large energy companies. However, the liberalization of this market, the removal of market barriers, and the encouragement of small companies to enter the market are creating new conditions and changing the structure of companies. In addition to large energy companies, a significant number of small entities are also emerging. The aim of this research is to analyze the relationship between the size of energy companies and their technical efficiency. This analysis was carried out for the period 2019–2023. In order to assess the efficiency of the researched energy companies, the Data Envelopment Analysis (DEA) method was employed. The analyzed enterprises were divided into three groups: small (IA), medium (IB), and large (II). The following economic categories were adopted as the division criteria: 1. net sales revenue; 2. operating costs; 3. fixed assets. The findings of our study suggest that small and medium-sized energy companies can exhibit levels of efficiency that are comparable to those of larger enterprises. This result suggests that companies of different sizes can coexist in the energy market. The results obtained are not completely conclusive, as statistically significant differences in technical efficiency (TE) were recorded in 2021 and 2022 but only between small enterprises (IA) and medium-sized enterprises (IB). This study highlights the potential of small energy companies to contribute effectively to Poland’s energy sector and suggests that supporting their development could enhance energy security and market competition. However, many energy companies—regardless of size—exhibited low levels of efficiency, underlining the need for deeper investigation into the sources of inefficiency. Full article

The volatility and uncertainty of wind power output pose significant challenges to the safe and stable operation of power systems. To enhance the economic efficiency and reliability of day-ahead scheduling in wind farms, this paper proposes a day-ahead planning and scheduling method for wind/storage systems based on multi-scenario generation and Conditional Value-at-Risk (CVaR). First, based on the statistical characteristics of historical wind power forecasting errors, a kernel density estimation method is used to fit the error distribution. A Copula-based correlation model is then constructed to generate multi-scenario wind power output sequences that account for spatial correlation, from which representative scenarios are selected via K-means clustering. An objective function is subsequently formulated, incorporating electricity sales revenue, energy storage operation and maintenance cost, initial state-of-charge (SOC) cost, peak–valley arbitrage income, and penalties for schedule deviations. The initial SOC of the storage system is introduced as a decision variable to enable flexible and efficient coordinated scheduling of the wind/storage system. The storage system is implemented using a 1500 kWh/700 kW lithium iron phosphate (LiFePO₄) battery to enhance operational flexibility and reliability. To mitigate severe profit fluctuations under extreme scenarios, the model incorporates a CVaR-based risk constraint, thereby enhancing the reliability of the day-ahead plan. Finally, simulation experiments under various initial SOC levels and confidence levels are conducted to validate the effectiveness of the proposed method in improving economic performance and risk management capability. Full article

This study investigates inventory management and pricing techniques in a two-tier supply chain where newborn items are grown, slaughtered, and transported to retailers for consumer sale. This study assesses how certain carbon regulations can enhance or hinder profitability for suppliers and retailers, demonstrating the interdependence of their financial performance in connection to environmental regulations. A mathematical model considers demand as impacted by unit weight, selling price, and storage duration, with consumption patterns as a power function of these variables. This paper examines demand dynamics and proposes a solution for optimizing crucial factors such as the number of newborn items, the retailer’s selling price, and operating cycle time to increase profitability while maintaining excellent customer service. Full article

In recent years, as an important alternative to traditional gasoline-powered vehicles, new electric vehicles (NEVs) have gained widespread attention and rapid development globally. In the traditional automotive industry chain, downstream vehicle manufacturers need to master core technologies, such as engines, chassis, and transmissions. In contrast to the traditional automotive industry chain, where downstream vehicle manufacturers must master core technologies, like engines, chassis, and transmissions, the electric vehicle industry chain has evolved in a way that the development of core components is gradually separated from the vehicle manufacturers. Downstream vehicle manufacturers can now outsource key components, such as batteries, electric controls, and motors. Additionally, in terms of sales models, the electric vehicle industry chain can adopt either the traditional 4S dealership model or a direct-sales model. As the research and development of core components are increasingly separated from vehicle manufacturers, the downstream vehicle manufacturers can source components, like batteries, electric controls, and motors, externally. At the same time, they can choose to use either the traditional 4S dealership model or the direct-sales model. The underlying mechanisms and channel selection in this context require further exploration. Based on this, a mathematical model is established by incorporating terminal marketing input, product competitiveness, and after-sales service levels from the literature to solve for the optimal pricing under centralized and decentralized pricing strategies. Using numerical examples, the pricing and profit performance under different market structures are analyzed to systematically examine the impact of the electric vehicle supply chain on business operations, as well as the changes in various elements across different channels. We will focus on how after-sales services (including the spare part supply) influence the pricing strategy and profit distribution in the supply chain, aiming to provide insights into advanced manufacturing system management for manufacturing enterprises and improve the efficiency of intelligent logistics management. The research indicates that (1) The direct-sales model helps to improve the terminal marketing input, after-sales service quality, and product competitiveness for supply chain stakeholders; (2) It is noteworthy that the manufacturer’s direct-sales model also significantly contributes to lowering prices, highlighting that the direct-sales model has substantial impacts on both supply chain stakeholders and, importantly, consumers. Full article

In an increasingly competitive construction industry, quality management systems (QMSs) play a critical role in improving operational performance and customer satisfaction. Despite growing interest, limited research has examined how customer-oriented QMSs affect financial and non-financial outcomes in construction firms, particularly in emerging markets such as Kazakhstan. This study investigates the relationship between QMS implementation and company performance by analyzing data from 23 Kazakhstani construction companies. The methodology combines regression analysis, multivariate analysis, and k-means clustering to assess the impact of QMSs on sales volume, product profitability, quality management costs, and customer satisfaction. Regression analysis revealed that customer satisfaction (CSL), product profitability (PP), and economic efficiency of the QMS (EEQMS) have a statistically significant positive effect on sales volume (SV), while excessive quality costs (QMC) may negatively influence performance if not optimized. Cluster analysis further identified distinct groups of companies with varying levels of QMS effectiveness and profitability. This study offers empirical evidence on the financial value of customer-oriented QMSs in the construction sector. It contributes to the literature by highlighting performance drivers in QMS implementation and provides practical recommendations for managers and policymakers to improve quality strategies in similar regional contexts. Full article

Selecting grid loss reduction strategies is crucial for energy-saving transformations, particularly in the context of electricity transmission and distribution pricing reforms. The optimization of strategic selection is not easy due to the vast number of grid devices, which leads to a multitude of possible strategy combinations. This paper presents an optimal model for selecting loss reduction strategies, aiming to minimize the sum of comprehensive investment costs and energy loss costs over the life cycle of the strategies. The energy loss costs include both direct expenses due to energy loss and indirect costs, namely, carbon emission penalties. The constraints include allowable voltage deviations, branch power transmission, the number of loss reduction measures, loss rates, and total investment limits. The model comprehensively considers both economic benefits and the social benefits of reduced carbon emissions. It can help companies better adapt to electricity transmission and distribution pricing reforms, reduce operational costs, and contribute to low-carbon development. Finally, the model is validated using the data provided by one provincial power grid company in China. The results show that the loss reduction reaches 13.9 MW and the reduced carbon emission per hour is 10.425 t. The proposed method is also compared with the enumeration method, which demonstrates its effectiveness and efficiency. Further research will be conducted on establishing functional relationships between electricity sales prices and line losses to incentivize companies to apply loss reduction measures under different pricing functions. Full article

The automotive industry is a key driver of global economic growth. However, traditional management approaches, relying on statistical tools and continuous improvement methodologies from the 1990s, lack the agility needed to address today’s complex challenges. This study proposes the Agile Viable Model (AVM), which integrates Viable System Model (VSM), Soft Systems Methodology (SSM), and Agile Methodologies to enhance responsiveness, resilience, and strategic decision-making in quality management. Applied to the Problem Resolution Process in Quality Control, the study identified low network connectivity, confirming that the system operates under a Supercritical Regime, limiting efficiency. Analyst involved and his leader emerged as critical nodes, with 68% of interactions being conflictual, revealing the need for improved communication and collaboration. To address these challenges, agile tools such as virtual boards, product logs, and daily meetings were integrated, optimizing process flexibility. CATWOE analysis facilitated the development of conceptual models aligned with organizational objectives. The Problem Resolution Process is vital both pre- and post-sale, influencing customer satisfaction and warranty costs. Strengthening connectivity and collaboration will lead to faster problem resolution, reduced costs, and enhanced reputation. The AVM offers a structured yet adaptive solution, improving operational efficiency and decision-making in automotive quality management. Full article

Facing the widespread cooperation among different agribusiness entities in China, this study explores the impact mechanism of land scale on farmers’ cooperation with new agricultural business entities (abbreviated as NABEs), including family farms, cooperatives, and agribusinesses. The effects of income within the cooperation mechanism are further analyzed. Based on survey data from 1558 farmers in 10 provinces, applying binary Logit regression and mediation effect models, the study finds the following: (1) The current land area, past growth of land, and future willingness to expand land all positively affect farmers’ cooperation with new agricultural business entities; (2) An inverted U-shaped relationship exists between land size and the proportion of farmers joining new agricultural business entities. The probabilities of joining family farms, cooperatives, and agribusinesses peak at land sizes of 2.65, 6.82, and 7.04 acres, respectively; (3) The current income situation has an intermediary effect on the cooperation between farmers and family farms, while the future income expectation has an intermediary effect on the cooperation between farmers and cooperatives and agribusinesses; (4) The effect of land scale on cooperation is more significant for farmers of village officials or agricultural organization members, full-time farmers, and those with green production and modern sales. This study proposes a development growth curve of farmers, which can be divided into “self-development–cooperation–transformation” stages, and gives solutions for each stage, to facilitate moderate-scale operations and long-term cooperation among various entities in the context of market reforms and social division of labor. Full article

As China’s power market reforms deepen, the scale of market operations and the number of participants have reached new highs, introducing increasingly complex threats and heightened risk scenarios. Traditional risk early warning systems for electricity sales companies are heavily influenced by subjective factors, incomplete data, and poor real-time performance, which cannot meet the requirements of sustainable development. To achieve efficient, full-chain, and sustainable risk control, this paper proposes a data-driven risk warning method for electricity sales companies, encompassing the entire sales process. Firstly, based on data correlations across the electricity sales process, appropriate data sources for risk warnings are identified. Key elements are then extracted using Principal Component Analysis (PCA), while historical business data is adaptively clustered, with risk warning levels classified using the Adaptive Sparrow Optimization Density Peak Clustering Algorithm (DPC-SSA). Lastly, dynamic risk warnings are generated through the stacking identification model. The effectiveness and practicality of the proposed method are validated through an analysis using real data from a provincial power trading management platform. Full article