Search Results (835)

Dynamic multi-criteria group decision-making (MCGDM) is widely applied in complex real-world settings where multiple experts evaluate alternatives across diverse criteria under uncertain and evolving conditions. This study proposes a transparent and interpretable linguistic (L-) framework for dynamic MCGDM grounded in hedge algebras (HA), a mathematical formalism that provides explicit algebraic and semantic structures for L-domains. A novel binary L-aggregation operator is developed using the 4-tuple semantic representation of HA, ensuring closure, commutativity, monotonicity, partial associativity, the existence of an identity element, and semantic consistency throughout the aggregation process. Using this operator, a two-stage dynamic decision-making model is developed—(i) L-FAHP for determining the criterion weights in dynamic environments, and (ii) L-FTOPSIS for ranking alternatives—where both methods are formulated on HA L-scales. To address temporal dynamics, a dynamic L-aggregation mechanism is further proposed to integrate current expert judgments with historical evaluations through a semantic decay factor, enabling the controlled attenuation of outdated information. A case study on enterprise digital transformation readiness illustrates that the proposed framework enhances semantic interpretability, maintains stability under uncertainty, and more accurately captures the temporal evolution of expert assessments. These results underscore the practical value and applicability of the HA-based dynamic L-approach in complex decision environments where expert knowledge and temporal variability are critical. Full article

In typical executive compensation structures, higher corporate ranks are associated with greater pay. However, the reform of state-owned enterprises (SOEs) in China introduced strict salary caps for top executives, while lower-tier managers continued to receive market-based compensation, resulting in a phenomenon of pay-rank inversion—where subordinates earn more than their superiors. Leveraging this anomaly as a quasi-natural experiment, this study investigates the specific impact and underlying mechanism of pay-rank inversion on mergers and acquisitions (M&A) decisions and subsequent value realization within Chinese SOEs, thereby addressing the broad academic discourse on optimal executive compensation design. Employing a difference-in-differences (DID) approach with panel data spanning from 2007 to 2022, our analysis reveals that pay-rank inversion significantly reduces firms’ M&A intentions. Mechanistic analysis suggests that this negative effect arises primarily from diminished executive risk-taking. Furthermore, we find that the adverse impact is attenuated when CEOs possess longer tenures or receive equity-based incentives, but it ultimately undermines the realization of value post-M&A. These findings highlight the unintended consequences of high-level compensation reforms and emphasize the critical role of a well-structured pay hierarchy in sustaining executive incentives for strategic decision-making. Despite providing robust evidence, this study is subject to limitations, including its focus on measuring inversion only between the first and second management tiers. Future research should extend the analysis to the pay inversion between the listed firm and its controlling SOE group and explore alternative causal pathways beyond risk-taking, such as CEO work motivation, to deepen the understanding of high-level executive behavior. Full article

Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R&D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development. Full article

The effectiveness of national energy transitions increasingly depends on public support for state-led measures; yet little is known about societal expectations regarding government assistance for renewable energy-oriented businesses. This study examines public expectations regarding state support for renewable-energy-oriented enterprises in Poland. Using a nationwide survey of 1000 adults (N = 974 valid responses), we developed a latent construct measuring attitudes toward pro-RES business policies. Overall public support is high (M = 3.73; median = 3.86). Women express significantly stronger support than men (median 3.86 vs. 3.71), and Baby Boomers score higher than younger generations (median 4.00 vs. 3.57–3.71). The most notable differences relate to respondents’ experience with RES: Individuals already using renewable energy at home report substantially higher support (M = 4.03) than non-users (M = 3.67). Similarly, those planning to adopt RES within three years show stronger approval (M = 4.07) compared with those not planning adoption (M = 3.43). Education, income, and place of residence do not significantly differentiate attitudes. The findings indicate broadly favorable public sentiment toward state-led support for green entrepreneurship, especially among specific demographic groups and those personally engaged in RES. These insights provide actionable guidance for designing socially legitimate and politically robust sustainability policies. Full article

Amid China’s push for digital transformation, green technology innovation has become a vital pathway to achieving its carbon neutrality goals. Using panel data from Chinese A-share-listed companies between 2012 and 2023, sourced from the CNRDS and CSMAR databases, this study employs a two-way fixed effects model to examine how digital transformation affects green innovation. In this model, carbon information disclosure serves as a mediator and is measured through text analysis and entropy weighting, while media attention is included as a moderator. The results show that: (1) Digital transformation significantly promotes green technology innovation, with a one-unit increase in the digitalization index raising green patent applications by 4.45%; upon controlling for potential path dependence, the effect remains stable at 3.76%. (2) Carbon information disclosure plays a partial mediating role. (3) Media attention moderates both the direct effect of digital transformation and the first stage of the indirect effect through carbon information disclosure. (4) Heterogeneity analyses, supplemented by inter-group difference tests, reveal stronger effects in state-owned enterprises, firms in western China, and larger firms. The study concludes with practical recommendations for corporate practice and public policy. Full article

In the context of economic globalization, robust corporate social responsibility (CSR) serves as a critical source of legitimacy and competitive advantage for multinational enterprises (MNEs). However, institutional and competitive disparities between host and home countries frequently lead overseas subsidiaries of MNEs to deviate from parent company standards by substituting symbolic for substantive CSR practices and thereby creating potential threats to MNEs’ group-wide reputation. Although external stakeholder monitoring is widely recognized, most studies adopt static, dyadic perspectives and thus rarely examine the dynamic interplay between external monitoring and MNEs’ CSR governance. To address this gap, this study constructs a tripartite evolutionary game model involving the parent company, overseas subsidiaries, and external stakeholders, systematically analyzes the evolutionary pathways and the stability of their strategic interactions and uses numerical simulations to identify the conditions for system equilibriums and the influence of key parameters. The findings demonstrate that moderate incentives and penalties from the parent company and active monitoring by external stakeholders significantly promote overseas subsidiaries’ adoption of substantive CSR. This equilibrium becomes more stable when the benefits of substantive CSR increase or its costs decrease for overseas subsidiaries. However, excessive incentive expenditures may weaken the parent company’s willingness to implement strict supervision. Furthermore, information synergies and collaborative governance between the parent company and external stakeholders reduce cross-border supervision and coordination costs, thereby increasing the likelihood of an equilibrium with strict supervision and substantive CSR. By moving beyond conventional static and binary analytical frameworks, this study proposes governance pathways, including optimizing incentive mechanisms, strengthening external stakeholder monitoring, and fostering information synergies, thereby offering new theoretical perspectives and managerial implications for understanding the evolution of CSR behavior in MNEs. Full article

Vocational education and training (VET) is a strategic driver of national education and skills development systems. It covers both Initial VET (IVET), which provides young people with vocational qualifications before they enter the labor market, and Continuing VET (CVET), which supports adults in updating or expanding their skills throughout their working lives. VET provides individuals with essential skills for employment and supports economies in adapting to technological, labor market, and social changes. Within the European Union (EU), VET plays a central role in addressing labor market transformation, the green and digital transitions, the rise of artificial intelligence, and the pursuit of social equity. This paper presents a data-driven analysis of VET in the EU countries. It reviews the relevant literature and outlines the role of Cedefop, the European Centre for the Development of Vocational Training, together with its main VET performance indicators. The analysis draws on publicly available Cedefop data on key VET indicators, filtered for reliability and systematically processed to ensure robust results. This research focuses on a selected set of key indicators covering participation in IVET at upper- and post-secondary levels, adult participation in both formal and non-formal learning, government and enterprise expenditure on training, the gender employment gap, and adult employment rates. These indicators are derived from Cedefop data spanning the period 2010–2024, with coverage varying across indicators. This study applies descriptive analysis to identify outlier countries, correlation analysis to explore relationships between indicators, and cluster analysis to group countries with similar VET profiles. It also compares the largest EU countries using common indicators. The results suggest key patterns, differences, and connections in VET performance across EU countries, offering insights for policy development and future research in VET. Full article

Financial literacy, defined as the set of knowledge, skills, and attitudes that enable individuals to make informed economic decisions and manage resources efficiently, is fundamental for social inclusion and the reduction of inequalities. This study, through a systematic review of the scientific literature using the PRISMA methodology, selected 120 primary studies that met the inclusion and exclusion criteria and presented a low risk of bias. These studies examined aspects related to financial literacy programs, the populations benefited, their effects, the challenges encountered, and the lessons that can guide the replication of these initiatives. The results show that the most frequent programs include training in basic financial concepts—savings, budgeting, access to banking services and microfinance—as well as workshops, seminars, and group training sessions. The populations most benefited were rural communities and women, although informal workers, migrants, and refugees could also significantly improve their financial inclusion and economic resilience. Among the positive effects, improvements were observed in income and expense management, increased savings, investment planning, preparation for emergencies and retirement, and the strengthening of economic empowerment and the sustainability of microenterprises and small enterprises. These findings highlight the importance of implementing financial literacy programs adapted to specific contexts to promote inclusion and economic well-being. Full article

This work was motivated by the premise that new competitive advantages in the international economy are increasingly enabled by the collaborative industrial system rather than working alone. Construction firms are transforming from contractors to integration service providers. However, existing studies on collaborative processes ignore the value attributes of the firm. This study aims to explore a comprehensive framework by complementing the value attribute perspective and empirically reveals the impact of six necessary collaboration factors on competitive advantage. Data of 192 respondents from seven leading Chinese construction Groups based in China are collected. The results show that the two macro elements (i.e., Value Reconfiguration and Strategy Congruence) act together on the remaining four endogenous variables of Resource Sharing, Information Sharing, Organizational Integration and External Integration. The realization of enterprise collaboration has a significant positive impact on the improvement of its competitive advantage, and 13 critical paths are identified in this paper. This paper provides a new perspective on the theoretical system of collaboration and practical guidance for enterprise to provide a higher-quality package of services. Full article

Enterprise document management faces a significant challenge: traditional clustering methods focus solely on content similarity while ignoring organizational context, such as priority, workflow status, and temporal relevance. This paper introduces FLACON (Flag-Aware Context-sensitive Clustering), an information-theoretic approach that captures multi-dimensional document context through a six-dimensional flag system encompassing Type, Domain, Priority, Status, Relationship, and Temporal dimensions. FLACON formalizes document clustering as an entropy minimization problem, where the objective is to group documents with similar contextual characteristics. The approach combines a composite distance function—integrating semantic content, contextual flags, and temporal factors—with adaptive hierarchical clustering and efficient incremental updates. This design addresses key limitations of existing solutions, including context-aware systems that lack domain-specific intelligence and LLM-based methods that require prohibitive computational resources. Evaluation across nine dataset variations demonstrates notable improvements over traditional methods, including a 7.8-fold improvement in clustering quality (Silhouette Score: 0.311 vs. 0.040) and performance comparable to GPT-4 (89% of quality) while being ~7× faster (60 s vs. 420 s for 10 K documents). FLACON achieves $O(m log n)$ complexity for incremental updates affecting $m$ documents and provides deterministic behavior, which is suitable for compliance requirements. Consistent performance across business emails, technical discussions, and financial news confirms the practical viability of this approach for large-scale enterprise document organization. Full article

With the acceleration of the Fourth Industrial Revolution, the South Korean government has promoted the Smart Factory Construction Support Project as a core strategy for the digital transformation of manufacturing, particularly targeting small and medium-sized enterprises (SMEs). While more than 35,000 smart factories had been established by 2024, systematic evidence on the policy’s economic impact remains limited. This study evaluates the effectiveness of government support for smart factories by analyzing SME financial performance between 2018 and 2021. Specifically, it investigates whether smart factory adoption—supported by government subsidies—led to improvements in sales growth and compound annual growth rate (CAGR). To address potential selection bias, propensity score matching (PSM) was employed to construct a comparable control group of non-recipient firms. The findings show that, after accounting for confounding variables, supported firms demonstrated improvements in sales-based growth metrics compared to their non-supported counterparts, thereby confirming a positive policy effect. These results provide empirical justification for sustained public investment in digital transformation initiatives, while also highlighting the importance of employing rigorous causal inference methods in policy evaluation. Full article

Cities are complex systems with socioeconomic activities exhibiting diverse spatial distributions, where selecting an appropriate observation scale is vital for understanding urban complexity. However, the traditional methods for this task are often limited, either because they rely on subjective judgments or lack generalizability before being applied across the diverse functions of a city. To address this issue, we introduce a complexity–heterogeneity balancing method, which employs renormalization group techniques to generate distribution matrices across different scales, striking a balance between complexity and heterogeneity to objectively identify appropriate observation scales. We implement this method on freight, enterprise and restaurant distribution data derived from major Chinese cities to identify their appropriate spatial scales. The results properly reflect the characteristic spatial organization structure of each urban function, meaning that the method provides a robust framework for determining appropriate scales in urban spatial analysis tasks. Our study has potential applications in enhancing the logistics optimization, industrial zoning and commercial planning processes and identifying urban functions and morphological features, thereby contributing to sustainable urban development. Full article

Effective management of village funds is central to financing sustainable and equitable rural futures, particularly in underdeveloped and resource-diverse regions such as Papua, Indonesia. This study explores the governance factors that shape the sustainability of village fund management (VFM) by examining institutional, financial, and socio-cultural dimensions across 212 villages. Primary data from village heads and secondary data on village-owned enterprises (BUMDes) and 2024 village fund allocations were analyzed using exploratory factor analysis (EFA), partial least squares structural equation modeling (PLS-SEM), and multi-group analysis (MGA). Seven key governance constructs emerged, with ethical governance, implementation capacity, mandatory disclosure and reporting, community participation, and financial management capacity demonstrating significant positive effects on sustainable VFM outcomes. In contrast, perceived social and economic impacts were negatively associated with performance, and planning quality exerted an influence only under specific contextual conditions. These relationships proved highly context-dependent, varying by geography, natural resource availability, transport accessibility, and demographic composition. The findings underscore the need for adaptive and context-sensitive governance strategies to strengthen institutional resilience, enhance fiscal equity, and maximize the developmental impact of village funds in underdeveloped rural regions. Full article

Using big data for risk analysis of construction projects is a largely unexplored area. In this traditional industry, risk identification is often based either on so-called domain expert knowledge, in other words on experience, or on different statistical and quantitative analysis of individual past projects. The motivation of this research is based on the implemented and evaluated data-driven and AI-based DARIA approach to identify financial risks in the execution phase of transportation infrastructure construction projects that shows exceptional results at an early stage of the project execution phase and has already been deployed into enterprise-wide production within the STRABAG group. Due to DARIA’s productive use, concern and doubts about the trustworthiness of its ML algorithm are certainly possible, especially when DARIA identifies risky projects while all conventional metrics within the STRABAG controlling system do not identify any problems. “If AI systems do not prove to be worthy of trust, their widespread acceptance and adoption will be hindered, and the potentially vast societal and economic benefits will not be fully realized”. Thus, and based on the results of a user study during DARIA’s successful deployment into enterprise-wide production, this paper focuses on the identification of suitable indicators to measure the trustworthiness of the DARIA ML algorithm in the interaction between individuals and systems as well as on the modeling of the reproducibility of the internal state of DARIA’s ML model. Full article

Liuyang, the primary fireworks manufacturing base in the world, is demonstrating potential metals pollution risks. In this study, 163 soil samples were collected in Liuyang City, China, for source apportionment, pollution assessment and health risk evaluation using self-organizing map, positive matrix factorization and statistical methods. Geostatistical analysis confirmed high contamination risks from Hg, Cd, Pb, and As. Samples were classified into four groups based on contamination characteristics. Pollution sources included irrigation water, fireworks enterprises, and fireworks packaging material. Cluster 1 exhibited uniformly low metals concentrations, with sampling points widely distributed across the study area. Cluster 2 samples were concentrated in the central and northern regions. The average concentration of Cr was the highest, with irrigation water contributing the most to Cr at 74%. The contribution of fireworks companies and packaging materials was 14% and 12%, respectively. Cluster 3 displayed elevated Hg and Pb levels with distinct spatial banding, where fireworks enterprises contributed 49% (Hg) and 47% (Pb), while packaging materials accounted for 37% (Hg) and 39% (Pb). Cluster 4, gathered in the southeast, showed the highest Cd and As concentrations, with fireworks companies contributing the most with 73% and 82%, respectively. Risk assessment demonstrated that children experienced greater non-carcinogenic risks from oral and dermal exposure to As, Hg, Pb, Cr, and Cd, while adults faced higher inhalation risks for Cr and Cd. Carcinogenic risks exceeded safety thresholds, with children (4.1 × 10⁻⁹–2.0 × 10⁻⁴) more vulnerable than adults (2.9 × 10⁻¹²–1.4 × 10⁻⁴). Asdult carcinogenic risks via ingestion dominated, whereas Cr posed greater risks for children through inhalation. Full article