Search Results (1,741)

This study examines the role of circular services in generating economic value within the construction sector, focusing on firms belonging to the Sustainable Habitat Cluster in the Aburrá Valley, Colombia. The research analyzes how circular business model strengthening translates into economic outcomes through the implementation of circular service portfolios. Using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the study evaluates the relationships between circular business model capabilities, circular service implementation, and circular revenue generation. The results confirm a sequential mechanism linking strategic capabilities to economic outcomes, where strengthening circular business models significantly enhances the implementation of circular services, which in turn strongly predicts the generation of circular revenues. The findings indicate that circular strategic orientation is a necessary but insufficient condition for economic value creation, as monetization occurs only when circular principles are translated into concrete service offerings. The study highlights the central role of circular services as the operational bridge between strategic readiness and economic performance, contributing to the literature on circular business models and Product–Service Systems (PSS) by providing empirical evidence of how circular strategies translate into revenue generation within the built-environment sector. Full article

As the automotive industry undergoes a green transformation, digital upgrading, and increasingly intensive supply chain collaboration, the supply chain finance credit risks faced by small and medium-sized enterprises (SMEs) in the sector exhibit characteristics such as multi-source interaction, nonlinear transmission, and class imbalance. This study uses 210 SMEs in China’s A-share automotive sector from 2020 to 2024 and constructs a credit risk evaluation system covering 56 indicators across the macro environment, financing enterprises, supply chain characteristics, and core enterprise credit support. Methodologically, DE-LightGBM is employed for feature selection to reduce redundancy and noise, while TabPFGen is introduced to generate synthetic risk-class samples. Business logic constraints and a Nearest Neighbor Distance Ratio filtering mechanism are further applied to improve the plausibility and fidelity of generated samples. Empirical results show that the TabPFN model achieves superior predictive performance after feature selection and data augmentation, and the Wilcoxon signed-rank test confirms the effectiveness and stability of sample augmentation. In addition, the ablation experiment demonstrates that green-related features provide significant incremental predictive value for supply chain finance credit risk identification. The proposed framework provides a useful reference for SME credit assessment, risk early warning, and green financial resource allocation in the automotive industry. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

This study aims to explore and evaluate the factors influencing Gen Z’s intention to choose green tourism destinations in Hanoi, Vietnam. The paper proposes a comprehensive analytical framework by integrating the Stimulus-Organism-Response (S-O-R) model and the Theory of Planned Behavior (TPB). A mixed-method approach was employed, in which quantitative data were collected from 269 Gen Z respondents in Hanoi and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique through SmartPLS. The findings reveal that external environmental stimuli, including green destination image (GDI) and social media influence (SMI), positively affect individuals’ internal psychological states, namely environmental awareness (EA), attitude toward green tourism (ATT), and subjective norms (SM). These psychological states, in turn, exert positive effects and strongly promote Gen Z’s intention to choose green tourism destinations in Hanoi. This study not only contributes to filling the theoretical gap in sustainable tourism consumption behavior in the digital era but also provides practical managerial implications for policymakers and tourism businesses in developing communication strategies and tourism products that align with the preferences and expectations of younger generations. Full article

Manual classification of breast cancer is resource-intensive, slow, and subject to inter-observer variability, motivating automated deep learning solutions. Most current methods rely on unimodal imaging data and struggle with domain generalization (DG) across varied clinical environments. We propose a Dual-Stage Multimodal Alignment approach that integrates breast ultrasound (US) imagery with clinical text reports to improve diagnostic stability. The method proceeds in two stages: (1) Local Correlation Alignment (LCA), which aligns fine-grained visual features with textual embeddings to capture localized lesion attributes, and (2) Global Attention Alignment (GAA), which applies multi-head self-attention to the joint visual–textual sequence to encourage domain-invariant representations. We evaluate the approach on a harmonized, leakage-free repository of 6880 images aggregated from six public US datasets (BUS-CoT, BrEaST, BUS-BRA, BUS-UCLM, BLUI, BUSI) under three protocols: independent benchmarking on BUS-CoT, pooled cross-dataset evaluation, and zero-shot domain generalization on unseen unimodal target domains. On the BUS-CoT benchmark, the 198M-parameter model reaches 0.8177 accuracy and 0.8852 AUC, on par with the 7-billion-parameter Qwen2.5-VL-7B with chain-of-thought reasoning (0.8064 accuracy, 0.8354 AUC) while using roughly 1/35 the parameter count. In the pooled setting, it is competitive with single-domain state-of-the-art methods on individual subsets (e.g., 0.9576 AUC on BUSI, 0.8741 accuracy on BUS-BRA). Under zero-shot transfer without clinical text, per-domain AUC ranges from 0.7360 to 0.8060 across four unseen targets, providing a lower bound under cross-scanner shift. These results indicate that task-specific multimodal alignment can rival large vision-language models in breast US diagnosis at a fraction of the parameter count. Full article

Cooperatives play a significant role within organizational models by providing essential services such as technical support, advocacy, information, knowledge, and guidance, which contribute to the production of high-quality animal products in a safe, efficient, and responsible manner. Furthermore, cooperatives aim to enhance the livelihoods of marginalized populations and address consumer needs. In this context, a study focusing on the status of women’s cooperatives in the Eastern Mediterranean offers valuable insights into women’s participation in economic and social life, as well as their challenges and expectations. This research aims to evaluate the status, perspectives, participation, activities, and expectations of women’s cooperatives in Lebanon and Turkey. The findings indicate that 90% of respondents in Lebanon and 45.5% in Turkey expressed satisfaction with their respective cooperatives. Additionally, 90% of Lebanese respondents and 59.1% of Turkish respondents would recommend that women establish their own cooperatives. The most common motivation for forming cooperatives in both countries was the belief that women are stronger when they collaborate. Furthermore, 75% of respondents in Lebanon and 45.4% in Turkey believe that cooperatives are suitable for conducting business, while those who disagreed emphasized the need for specialized traders to address specific business requirements. Respondents who expressed dissatisfaction with cooperative collaboration often mentioned difficulties in making joint decisions and challenges in group cohesion. These findings underline the importance of cooperatives in enhancing women’s roles in economic activities and the challenges they face in both Lebanon and Turkey. Despite these challenges, women’s cooperatives continue to be perceived as a valuable means of empowerment and a key strategy for fostering collaboration and economic growth. Full article

Modelling constitutes a disciplined transformation process through which heterogeneous, unstructured evidence is translated into structured representations that support reasoning and decision-making. The integration of generative artificial intelligence into such processes introduces new possibilities for automation, yet risks undermining methodological rigour, traceability, and human accountability. This paper proposes a methodology-grounded multi-agent architecture for constructing structured business ecosystem maps from unstructured document collections. The architecture decomposes the modelling lifecycle into specialised agent functions covering boundary specification, source discovery, document analysis, semantic extraction, and controlled model editing, addressing four of the five methodology stages while leaving automated completeness verification outside the current scope. A central orchestrator coordinates agents while enforcing ontological constraints derived from a formal modelling methodology. All proposed modifications are staged for human review before execution, and each map element maintains explicit provenance links to source material. To evaluate the reliability and correctness of generative modelling pipelines, a hybrid evaluation framework integrates operational metrics, semantic assessment using an LLM-based judge, and human agreement validation. Empirical evaluation across 34 generative models and 4382 experimental runs characterises capabilities across modelling tasks. In a controlled single-document extraction task, text-based extraction achieves a mean semantic match score of 0.947, whereas interaction extraction scores 0.431 and visual diagram interpretation scores 0.470, identifying relational reasoning and multimodal interpretation as principal bottlenecks. Model performance varies across agent roles, with task-aligned model selection associated with larger performance changes than hyperparameter tuning; the architecture’s causal contribution is not isolated, and comparison against monolithic or ablated baselines remains future work. Full article

South Korea is a highly import-dependent food economy and therefore offers a useful case for examining how an integrated national food control system can be built under trade openness, limited domestic agricultural capacity and changing consumer risk perceptions. This article presents a structured narrative review, rather than a causal impact evaluation, of South Korea’s transition from multi-agency food safety regulation toward an integrated, risk-based food control system. The review is organized through the FAO/WHO national food control system framework and maps Korean legal, institutional and operational evidence onto six analytical dimensions: legal foundations, institutional coordination, risk-based official controls, import supervision, traceability and recall, and risk communication. Examples of embedded risk-analysis principles include the Positive List System for pesticide residues with a default limit of 0.01 mg/kg for substances without a Korean MRL, inspection orders and risk-ranked import controls, barcode-linked recall blocking through the Hazardous Food Sales Prevention System, and public disclosure of unsafe directly purchased overseas products. Quantitative evidence is used descriptively: Korea’s agricultural and food imports reached USD 45.3 billion in 2024, hepatitis A notifications fell from 17,598 in 2019 to 3989 in 2020 after the salted-clam outbreak, and MFDS reported that 12 of 544 overseas direct-purchase products tested in the first half of 2020 contained restricted substances. These indicators suggest improvements in coordination and crisis response capacity, but they do not prove that institutional integration alone reduced foodborne disease incidence. The review finds that South Korea’s model is strongest in institutional consolidation, import-oriented technical standards and digital recall communication, while key challenges remain in small-business compliance burden, scientific independence, data transparency, cross-border e-commerce and novel foods such as cell-cultured food ingredients. Full article

While digital communication is critical for business growth, there is a notable lack of research concerning the specific digital and corporate strategies of bio-health start-ups in regional ecosystems like Andalusia. This article addresses this gap by analysing the corporate and digital strategies of the leading bio-health start-ups at the Andalusian Health Technology Park. The research focuses on innovation in the health sector and builds on the broader discourse surrounding science communication as applied to Andalusian companies. Health innovation companies are implementing their digital corporate strategies to raise their profile and reach their target audience. For Andalusian bio-health start-ups, the main focus is on their websites; this is why they are analysed here from different perspectives, with the aim of evaluating the information they share and its effectiveness. To this end, a mixed approach combining quantitative and qualitative content analysis is proposed, and data analysis tools are applied to web traffic and performance factors, as well as to the analysis of corporate culture and brand identity. The results indicate that these companies are consistent with digital communication strategies typical of B2B models, that is, emerging and highly specialised companies. In the corporate sphere, there is generally a strong focus on positioning within a framework that fosters organisational culture, employee recognition and the key elements of effective brand architecture. Full article

Renewable energy is a central component of global sustainable energy development, with solar energy experiencing substantial growth over recent decades. Solar power is widely regarded as one of the most accessible routes to clean energy generation. However, high upfront costs remain a major barrier to adoption. Many potential users are reluctant to invest in solar photovoltaic (PV) systems because of the longer payback period. To address this financial constraint, a range of business models has been developed. This study used a systematic literature review to examine existing and emerging business models for promoting Solar PV solutions. The review included peer-reviewed journal articles published in English from 2020 to 2026. In total, 39 articles were critically evaluated considering their characteristics. Nine potential business models were identified, several of which are commonly used internationally and have shown positive results that could also be applied in the UK. Importantly, Community Energy Models have shown success in Europe, Sub-Saharan and Asian regions. This has been widely supported by the government due to sustainability and climate change targets. The UK has set their target to achieve net-zero in greenhouse gas emissions by 2050. Beyond financial barriers, reliance on weather conditions and the mismatch between energy demand and supply remain substantial barriers to wider solar PV deployment. Full article

To address the insufficient consideration of load temporal characteristics, load growth, distributed generation (DG) integration, and business-expansion load connection in existing available-capacity assessment methods, this paper proposes a load-connection decision-making method for active distribution network. Firstly, considering the load temporal characteristics, load growth, DG, and the bearing capacity of transformer distribution districts, a time-series bearing capacity analysis model of transformer distribution districts is proposed. In addition, a heuristic topology search strategy considering dynamic capacity constraints is developed to identify feasible power-supply paths and evaluate the dynamically validated available capacity. Secondly, considering the integration of DG and energy storage systems (ESSs), as well as key indicators such as load balance, temporal characteristic matching and comprehensive economic performance, a business-expansion load-connection decision-making method for active distribution network is proposed. Finally, the effectiveness of the proposed model and method is validated through a case study. The results show that after DG and ESS integration, the load balancing degree and temporal characteristic matching index are improved by approximately 31.42% and 18.21%, respectively. Compared with the peak-capacity method, single-capacity-index method, and loss-priority method, the proposed method achieves the highest or jointly highest comprehensive decision value under different operating scenarios. The improved branch-and-bound method reduces the number of actual evaluations while obtaining the same optimal decision result. The proposed method can optimize load-connection schemes and provide theoretical foundation and practical decision support for active distribution network planning and business expansion. Full article

This study examines how Industry 4.0 manufacturing applications have positive associations with production system integration, supply chain agility, and operational performance. A formal VAF analysis (VAF approximately 43.5%) confirms partial mediation. It has become increasingly important for businesses in recent years to focus on digital transformation and smart manufacturing technologies. Considering this, this study examines the effects of Industry 4.0 applications on the operational capabilities of businesses and their impact on performance. Partial Least Squares Structural Equation Modeling (PLS-SEM) is also used as a means of data analysis to evaluate the predictive power of the research model using PLSpredict. A total of 300 manufacturers participates in the research, and the data obtained from 300 of them is analyzed and found to have a strong and significant effect on the integration of production systems. In addition, production system integration is found to improve supply chain agility, and supply chain agility is observed to impact operational performance positively and significantly. Industry 4.0 applications largely impact operational performance through production system integration and supply chain agility, according to the mediation results. Based on the results of PLSpredict, the research model is not only explanatory but also predictive. As a result, the research shows that Industry 4.0 production applications enhance business performance by strengthening operational integration and supply chain agility, while also serving as a key technological transformation enabler for manufacturing firms. Beyond its operational implications, this study also suggests that Industry 4.0-enabled integration and agility may contribute to more adaptive and efficiency-oriented manufacturing operations, laying a foundation for longer-term operational resilience. As such, this research contributes to both the theoretical and managerial literature on digital production technologies and operations management. Full article

The integration of renewable energy technologies into urban buildings is a key strategy in sustainable city development. This study explores the application of building-integrated photovoltaic (BIPV) systems in a selected building at Vilnius Gediminas Technical University (VGTU), aiming to identify the most balanced solution among energy efficiency, architectural quality, and operational feasibility. Using a Building Information Model (BIM) of the existing structure, five alternative design scenarios were developed by varying the number and capacity of façade-mounted photovoltaic (PV) panels and semi-transparent PV windows. Each scenario was evaluated against six criteria: (1) potential solar energy yield, (2) temporal correlation between energy generation and building consumption, (3) maintenance accessibility and associated cost, (4) architectural aesthetics, (5) installation cost, and (6) cost effectiveness. To ensure a rigorous and interdisciplinary evaluation, the Delphi-based ELECTRE Multi-Criteria Decision-Making (MCDM) method was applied. Expert panels representing disciplines of construction engineering, architecture, electrical engineering, and business management participated in determining the relative importance of each criterion. The results demonstrate the potential of combining BIM-based energy simulation with expert-driven decision analysis to optimize BIPV integration strategies in complex urban environments. The proposed framework offers a replicable methodology for guiding sustainable façade design and supporting the adoption of renewable energy in various public and administrative buildings across cities. Full article

The increasing use of large language models (LLMs) in enterprises creates a need for routing mechanisms that select models according to both technical performance and organizational preferences. This article proposes a multicriteria decision-support framework for enterprise LLM routing that combines AHP-based criterion weighting with SAW-based prompt-level model selection. The framework evaluates prompts according to criteria related to required accuracy, business risk, reasoning depth, cost sensitivity, response-time sensitivity, standardization, and creativity. The empirical evaluation was conducted on 500 heterogeneous business prompts, using GPT-5-nano as the prompt-scoring router, GPT-4o-mini as the cheaper response model, and GPT-5 as the stronger response model. Costs were calculated from actual input and output token counts, including routing overhead. Response sufficiency was assessed using a structured LLM-as-a-judge protocol with three evaluator profiles. The proposed SAW routing variant with confidence margin and risk veto achieved a sufficiency rate of 94.4%, compared with 94.6% for the always-strong strategy and 86.8% for the always-cheap strategy. Relative to always-strong routing, it reduced total cost by 37.4%, with only a 0.2 percentage-point decrease in sufficiency. The framework was also compared with keyword-risk, token-threshold, TF-IDF centroid, logistic-regression, multiplicative-SAW, and TOPSIS baselines. The results indicate that an interpretable multicriteria router can achieve near-strong-model response sufficiency at substantially lower cost while preserving auditability and alignment with enterprise decision criteria. Full article

Vehicle-to-grid (V2G) can provide flexibility and storage for low-carbon power systems while supporting sustainable mobility, yet real-world deployment remains largely confined to pilots despite substantial technical progress. This article presents a PRISMA-guided systematic review of 974 V2G/V2X studies published between 2009 and 2025 to explain why implementation lags and how it can be accelerated. Within this corpus, a total of 162 implementation-critical articles are identified and, within these, 95 studies that primarily address non-technical dimensions such as policy, markets, user behavior, and ecosystem coordination. Drawing on full-text coding, a four-domain socio-technical framework is developed that clusters recurring non-technical barriers and enablers into business–economic, governance–policy, social, and infrastructure and ecosystem domains. The analysis reveals (i) a temporal shift from technical dominance to multidisciplinary acceleration after 2021; (ii) distinct regional priorities in which Europe emphasizes regulation and business models, Asia focuses on infrastructure scaling, and the Americas on frequency services and resilience; and (iii) persistent revenue uncertainty, regulatory gaps, user resistance, and grid unreadiness as cross-cutting obstacles. For each domain, concrete transition levers and indicative deployment key performance indicators (KPIs) are derived, such as multi-actor revenue-sharing mechanisms, aggregator recognition in market rules, privacy-by-design user participation models, and targeted bidirectional charging deployment in constrained grids. Synthesizing these insights, three archetypal V2G transition pathways are proposed—regulation-led, infrastructure-first, and service-driven—that reflect regional conditions and offer alternative routes to large-scale adoption. The framework and roadmap provide researchers, policymakers, system operators, and mobility providers with an integrated basis for designing, monitoring, and evaluating V2G policies, business models, and pilots in line with energy system decarbonization goals. Full article