Search Results (2,157)

Previous studies, constrained by the overly rigid stability requirements, often fail to adapt to complex systems and struggle to identify stable outcomes that align with the practical context of multi-agent resource allocation. To address the three-sided matching problem in complex socio-technical and business management systems, this paper proposes a fuzzy stable matching method for three-sided agents under a framework of combinatorial preference relations, integrating network and decision theory. First, we construct a membership function to measure the degree of preference satisfaction between elements of different agents, and then define the concept of fuzzy stability. By incorporating preference satisfaction, we introduce the notion of fuzzy blocking strength and derive the generation conditions for blocking triples and fuzzy stability under the fuzzy stable criterion. Furthermore, we abstract the three-sided matching problem with combined preference relations into a shortest path problem. Second, we prove the equivalence between the shortest path solution and the stable matching outcome. We adopt Dijkstra’s algorithm for problem-solving and derive the time complexity of the algorithm under the pruning strategy. Finally, we apply the proposed model and algorithm to a case study of project assignment in software companies, thereby verifying the feasibility and effectiveness of this three-sided matching method. Compared with existing approaches, the fuzzy stable matching method developed in this study demonstrates distinct advantages in handling preference uncertainty and system complexity. It provides a more universal theoretical tool and computational approach for solving flexible resource allocation problems prevalent in real-world scenarios. Full article

Driven by the national policy of total water resources control and efficiency improvement, the behavior of water resource utilization reduction by firms is widespread, which may have an impact on the value of firms. This study integrates dynamic capability theory and signaling theory to construct a dual-path analytical framework, systematically investigating the impact of water utilization reduction on firm value and its intrinsic mechanisms. Based on data from Chinese A-share listed companies spanning 2012–2023, fixed-effect models, mediation-effect tests, and heterogeneity analysis are employed for empirical verification. The results reveal that water utilization reduction exerts a significant dual-path promoting effect on firm value: it enhances financial performance (ROA) primarily through technological innovation, reflecting the process of resource orchestration and dynamic capability construction; concurrently, it boosts market performance (Tobin’s Q) mainly by improving ESG performance as a signaling channel, mirroring the capital market’s positive pricing of green signals. Further heterogeneity analysis indicates that these effects are more pronounced during the policy deepening stage, in non-water-intensive industries, and in humid/sub-humid regions. This study contributes theoretical support and empirical evidence for firms’ green transformation and the formulation of differentiated water resource policies by the government, highlighting the synergistic development of high-quality economic growth and ecological civilization construction. Full article

This study investigates the impact of city-level capital factor market integration on corporate ESG performance, using a sample of Chinese A-share listed companies from 2010 to 2024. We find that greater capital factor market integration significantly improves firms’ overall ESG performance. Mechanism analysis reveals that capital factor market integration operates through three channels: market competition, technological advancement, and attention reconstruction, enhancing both firms’ capabilities and incentives to engage in ESG activities. The positive effect is stronger for state-owned enterprises, firms in less polluting industries, and those in regions with high government environmental attention. Further analysis indicates that capital factor market integration suppresses corporate greenwashing behavior and reduces discrepancies across ESG rating agencies. Moreover, capital factor market integration exhibits asymmetric effects across ESG sub-dimensions, significantly improving environmental and governance performance while weakening social responsibility performance. This reflects firms’ preference, under competitive pressure, for environmental and governance domains characterized by shorter payback periods and more readily quantifiable outcomes, as well as their cautious stance toward the social responsibility domain where effects take considerably longer to materialize. This study contributes to understanding the micro-level mechanisms through which capital factor market integration influences corporate sustainable development, providing empirical evidence for China’s construction of a unified national market and the advancement of sustainable development strategies. Full article

Prediction of the Electric Vehicle (EV) charging demand is of great importance to charging stations, especially for newly established charging stations whose demand is difficult to predict due to the absence of past time-series transaction data. This paper develops a deep learning method to fill the literature gap to predict charging demands for the new EV charging stations in the next few days, using a transaction dataset containing over 270 charging stations in Nanjing, eastern China. Specifically, our study introduces the average transactions of neighboring stations as new time-series variables and constructs a Convolutional Neural Network (CNN) model, which is a novel deep learning method. The R-squares of the CNN model achieve an average value of 0.90, which outperforms four time-series prediction models, e.g., the Long Short-Term Memory Network (LSTM) and the Extreme Gradient Boosting (XGBoost). In addition, we visualize the areas with high predicted demand for new charging stations using the trained CNN model and achieve a recommendation accuracy rate of 0.70, providing a reference for EV charging operation companies to find the optimal location of new charging stations. Accurate prediction for new charging stations in this study can provide actionable insights to charging station operators in location selection and create a more favorable EV ecosystem. Full article

With the rapid growth of the social economy and the increasing prevalence of e-commerce, urban distribution centers (UDCs) have become vital hubs for the efficient functioning of cities. The decision regarding the location of UDCs not only impacts the operational efficiency of logistics companies but also plays a crucial role in urban sustainable development planning. Traditional location models are limited in addressing these complexities, which is why this paper introduces an innovative multi-objective location decision-making model. This model accounts for both the construction and operational costs of enterprises, and it uniquely incorporates the industrial sustainable development potential (ISDP) as a core objective function. The goal is to balance enterprise costs with the needs of urban development in location decision-making. This research adopts an interdisciplinary approach, initially using ecological theories to quantify ISDP, then employing System Dynamics to simulate the future trajectories of key industry drivers, and finally applying genetic algorithms to find solutions. The results from the numerical example demonstrate that the model and algorithm are both effective and practical. This research presents a novel approach and method for UDC location decision-making based on the long-term sustainable development of cities for logistics enterprises and urban planners. It also contributes to the related research on urban sustainable development and logistics location. Full article

Agricultural listed companies are key nodes in the agricultural industry chain, providing capital, technology and market access to upstream producers and downstream processors. When these firms face delisting risk, the resilience and sustainability of the industry chain are threatened. Using data from 897 observations of Chinese A-share listed companies in the agriculture, forestry, animal husbandry, and fishery sector over 2010–2021, this study links multidimensional firm vulnerability to subsequent delisting risk. We construct 30 internal and external indicators covering financial performance, innovation input, supply chain concentration, government support and market competitiveness. Clustering method is applied to capture heterogeneity in firms’ multidimensional structural, gradient boosting models are used to predict ST (Special Treatment) status within three years, and SHAP analysis is used to identify the main risk. The results show that a small subset of firms with high leverage, tight liquidity, weak profitability, insufficient innovation, and highly concentrated key customers and suppliers accounts for most ST cases. Strengthening capital buffers, diversifying critical supply-chain relationships, and maintaining stable innovation investment are thus crucial for reducing delisting risk and enhancing the resilience of agricultural listed companies. Full article

The performance of New Zealand’s construction companies depends on the adaptability and skills of their workforce. The soft skills of the company’s building professionals are thought to contribute to the delivery of successful construction projects. This pilot study captures the perceptions of the importance of critical soft skills in semi-structured interviews with thirteen Quantity Surveyors (QSs) and fourteen Project Managers (PMs) working in New Zealand. For both cohorts the most important skill is communication, followed by workplace ethics. An exploratory Mann–Whitney U comparison suggests a difference in their ranking of emotional intelligence in interactions with other stakeholders, with PM deeming it more important than QS. Within-cohort Spearman rank correlation shows different patterns of association among soft-skill clusters for QS and PM, offering contextual insight rather than confirmatory inference. After communication and ethics, QS prioritise dispute resolution while PM value project reasoning. A combination of individual traits and practical experience influences the successful transition from a QS role to the broader PM role. The findings are limited by the small sample size but may be useful in professional development courses and recruitment efforts, contributing to a more adaptable and flexible construction workforce. Full article

This study selects daily data from 27 fintech companies and 16 listed commercial banks between January 2015 and December 2024 as research samples. Based on complex network theory, we construct an integrated analytical framework encompassing risk measurement, regime identification, and early warning system construction through HD-TVP-VAR model coupled with the Elastic Net algorithm, MS-AR model, and dynamic Logit model. The findings reveal that the total risk spillover rate between fintech and banking ranges from 73.09% to 95.18%, demonstrating significant time-varying and event-driven characteristics in risk contagion. The risk contagion evolution is characterized by three distinct phases: net risk absorption by the banking sector, bidirectional equilibrium contagion, and net risk dominance by the fintech sector. Joint-stock commercial banks and city commercial banks exhibit higher sensitivity to fintech risks compared to state-owned large commercial banks. Key hubs for risk contagion include institutions like Yinxin Technology and Huaxia Bank, with concentrated risk contagion within industry clusters. The MS-AR model accurately delineates low-, medium-, and high-risk zones, showing strong alignment between high-risk periods and major events. The dynamic Logit model incorporating total risk correlation indices demonstrates high consistency between early warning signals and risk evolution trajectories, providing theoretical and practical references for cross-industry systemic financial risk prevention. Full article

Based on the “Future Outlook” sections of annual and semi-annual reports from Chinese A-share-listed companies (2011–2024), we construct a novel measure of managerial confidence by quantifying the intertemporal shifts in textual sentiment. Using a sample of 76,923 observations, our analysis reveals that this measure exhibits dynamic predictive power for expected stock returns. Specifically, in the short term, managerial confidence serves as a valid predictor. A long-short portfolio sorted by managerial confidence yields a 7.05% cumulative return spread over the five post-disclosure trading days. Mechanism analysis suggests that this short-term predictability stems from high managerial confidence effectively attracting investor attention. Over the medium term (six months), however, its predictive power hinges on the reliability of the confidence signal: For managers whose historical confidence has aligned with fundamental performance, high confidence predicts positive expected excess returns; for those who are chronically overoptimistic, it becomes an inverse predictor of firm value. These findings indicate that financial markets dynamically assess both the intensity and the reliability of signals within managerial disclosures, offering a new perspective on the predictive power of managerial psychological traits in capital markets. Full article

The rapid development of China’s high-speed railways (HSRs) and the implementation of revenue management policies have promoted the marketization of railway passenger transport, which is mainly reflected in the gradual transformation from a seller’s market dominated by operating companies to a buyer’s market dominated by passenger demand. Passenger travel needs are becoming increasingly diverse. In order to improve the quality of HSR services and attract more passengers, this paper starts from passenger satisfaction and considers the heterogeneity of travel preferences of passengers with different travel distances. Based on the passenger travel data of the Nanning-Guangzhou (NG) HSR line, the K-means clustering method is used to classify passengers into three categories: short-distance, medium-distance, and long-distance travel. A structural equation modeling–multinomial logit (SEM-MNL) model integrating both explicit and latent variables was constructed to analyze passenger travel origin-destination (OD) choices. Stata software was used to estimate passenger preferences for perceived satisfaction functions across different travel distances. Finally, considering constraints such as load factor, departure capacity, and spatiotemporal passenger flow demand, a line planning optimization model was constructed with the goal of minimizing train operating costs and maximizing passenger travel satisfaction. An improved subtraction optimizer algorithm was designed for the solution. Using the NG Line as a case study, the proposed method achieved a reduction in train operating costs while enhancing overall passenger satisfaction. Full article

In the context of high renewable energy penetration and increasing supply–demand imbalances, power grid companies face complex challenges in load control due to multiple constraints. Based on the actual operational context of power grid companies in China, this study systematically analyzes the key constraints on load control from an industrial chain perspective. First, a systematic analytical framework is constructed from an industrial chain perspective to identify the factors constraining load control in power enterprises. Then, by integrating in-depth qualitative insights with a rigorous quantitative analysis, we propose an analytical method for identifying key constraining factors using a novel interactive group Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. Finally, using Yunnan Power Grid Company in China as a case study, we identify specific constraining factors, including power generation costs, electricity pricing policies, distribution equipment capacity, and the level of grid intelligence. Based on the findings, this study proposes to establish a multi-dimensional coordination mechanism for Yunnan Power Grid, encompassing infrastructure-driven planning, policy–technology synergy, and cost-transmission optimization. This integrated approach will systematically enhance load control capabilities and support the transition toward a green, low-carbon power system. Full article

This study proposes and validates a comprehensive model of the determinants of online travel company success by establishing the relationships between a properly conceptualized higher-order e-service quality construct, perceived value, and satisfaction on customer loyalty and customers’ citizenship behavior. The model was tested using structural equation modeling and data collected on 257 US travelers. Results reveal that e-service quality positively influences customers’ loyalty and citizenship behavior both directly and indirectly (through perceived value and satisfaction). Perceived value also exerts a direct positive influence on satisfaction. The results provide theoretical and practical implications by helping to demystify the relationships between the tested variables, as well as by increasing our understanding of the determinants of success in online travel websites. Full article

Digital Transformation (DT) is a strategic challenge that reshapes the way companies operate. Nevertheless, its adoption in the construction industry remains slow. This paper analyzes the DT process in Brazilian construction companies through two phases. Initially, an exploratory study was conducted with 17 firms using semi-structured interviews with their Technical Directors. Second, three companies were selected for case studies involving 14 in-depth interviews, observation, and document analysis. Data underwent content analysis. In the exploratory phase, DT was found to be mainly pursued to improve construction efficiency. Barriers were strongly associated with individual aspects, especially limited knowledge about technologies and resistance to change, reinforced by difficulties in implementing organizational changes. Most problems that DT seeks to address are concentrated in the technical department and construction site. Companies adopted approaches such as technology investments, open innovation, organizational restructuring, and training, but the success of these strategies depends on top management engagement and employee acceptance. Besides cultural barriers, technological obstacles, system integration and digital delay were identified, along with process difficulties such as the complexity and costs of the DT journey. Indirect sustainability objectives also emerged, indicating that DT is perceived as both technological advancement and a means to transform the sector. Finally, based on the empirical findings, a multi-level framework comprising 12 strategies for DT in the construction industry was proposed. Overall, the empirical field investigated remains in the early stages of DT, with experimentation with technologies and a focus on efficiency, characteristics of digitization, a step prior to total transformation. The study provides a valuable diagnosis of DT to support the digital transition and informs policymakers in designing initiatives that foster DT, contributing to sector sustainability and SDG9. Full article

This study aims to explore the transformative potential of Artificial Intelligence (AI) in enhancing cost planning and control within Qatar’s construction industry. By examining both opportunities and challenges associated with the adoption of AI, it seeks to uncover that AI can lead to significant improvements in accuracy in cost estimates and optimisation of various resources. The nation faces significant cost-overruns influenced by delays, shifting market conditions, and although AI has demonstrated its benefits in cost-control management globally, there is a lack of research on its practical applications in Qatar’s construction industry. Existing practical applications are more likely to experience errors due to them requiring manual labour and limited pattern recognition. Meanwhile, this study attempts to align AI-driven advancements with Qatar’s Vision 2030, which emphasises sustainable development and economic diversification. It adopts an analysis of semi-structured interviews with a group of experienced professionals from leading construction companies in Qatar, giving a comprehensive picture of the current landscape and future prospect for AI in the construction industry. The findings of this study reveal that AI technologies can significantly mitigate common issues in the construction industry, such as cost overruns, project delays, and resource wastage. On the other hand, this study identifies various obstacles that inhibit AI adoption, including high financial costs and insufficient training data. By weaving together theoretical understandings and practical experiences, it highlights the importance of integrating AI technologies within existing workflows while addressing key concerns. Full article

Uncertainties and changes significantly shape the path of the design process, requiring situation-specific strategies and methods. Literature and practice highlight the implementation of agility as a means for companies to achieve competitive advantages in a dynamic development environment through more robust costumer integration and improved responsiveness. From a design science perspective, a key challenge remains the development of a theoretical model that explains how agility can be operationalized to realize benefits such as enhanced adaptability. Drawing on a literature review and six empirical studies on agile development of mechatronic systems in the German-speaking context, we propose a novel conceptualization of agility. Using systems thinking, we conceptualize agility as a construct and establish its relationship to agility as an attribute. Thus, the article provides a new methodological perspective on agility by explicitly linking its structural elements to established outcome perspectives from multiple domains. This work advances the methodological understanding of agility and identifies future research directions for the development of mechatronic systems, aiming to enrich the theory for its utilization. Full article