Search Results (985)

Construction management, from the contractor’s perspective, is led by the Construction Manager (CM). The work motivation and leadership style of the CM are critical variables for the successful execution of construction projects. The scientific literature identifies participative leadership as the most effective style for mitigating conflicts among various stakeholders. However, analyzing the specific variables that influence a CM’s conflict resolution capacity remains an underexplored area. Furthermore, while the CM must act as a leader for their team (subcontractors, suppliers, etc.), they remain accountable to the contractor’s senior management. Therefore, this study aims to analyze the mediating role of CM motivation in the relationship between leadership and conflict resolution capacity using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the construction industry, conflict resolution is not merely a situational fix but a critical process of capturing and externalizing tacit knowledge. Knowledge management and the ability to resolve conflicts in the construction sector are directly linked, critical, and strategic in nature. Construction is an industry characterized by fragmentation, the temporary nature of its projects, diversity of stakeholders (developers, builders, subcontractors, engineering firms) and a high level of uncertainty. In this environment, conflict is virtually inevitable. However, the way in which a CM handles a conflict determines whether it becomes a destructive dispute or an opportunity for improvement. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

In the digital era, intelligent applications play an increasingly pivotal role in restructuring supply chain cost management. Using panel data from Chinese-listed firms between 2010 and 2024, this study examines the impact of customers’ Artificial Intelligence (AI) adoption on the cost stickiness of their suppliers. The findings indicate that customers’ AI adoption mitigates suppliers’ cost stickiness. This effect is more pronounced for larger suppliers, those with shorter geographic distance to customers, and those in highly competitive industries. Furthermore, customers’ AI adoption alleviates suppliers’ cost stickiness by promoting flexible production modes, enhancing production information flexibility, and raising production efficiency. Moreover, a two-stage model suggests that this alleviation of cost stickiness enhances suppliers’ corporate resilience, offering directional insights for transmitting within supply chain systems. In summary, this paper expands the theoretical understanding of intelligent applications in supply chain systems, by substantiating cross-firm spillover effects and interactive behaviors among supply chain stakeholders. Full article

The construction sector operates under conditions of high capital intensity, project complexity, cost uncertainty, fragmented supply chains, and increasing pressure to improve efficiency, sustainability, and long-term competitiveness. Although prior research has emphasized the importance of organizational resources and knowledge-based capabilities for competitive advantage, fewer empirical studies have examined how internal capacities, intellectual capital, and knowledge sharing jointly explain sustainable competitive advantage in construction companies. Drawing on the resource-based view, the knowledge-based view, and the dynamic capabilities perspective, this study examines the effects of marketing capacity, financial capacity, innovative capacity, management capacity, human capacity, human capital, structural capital, relational capital, and knowledge sharing on sustainable competitive advantage in the construction sector. Survey data were collected from 306 employees working in construction companies in the Republic of Serbia and analyzed using confirmatory factor analysis and covariance-based structural equation modeling. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. The structural results indicate that financial capacity is the only significant internal capacity predicting sustainable competitive advantage, while relational capital is the only significant dimension of intellectual capital. Marketing capacity, innovative capacity, management capacity, human capacity, human capital, structural capital, and knowledge sharing did not show significant direct effects. The study contributes to research on sustainable competitive advantage by showing that, in construction companies, competitiveness is most strongly associated with financial robustness and stakeholder-based relational strength. For managers, the findings highlight the importance of strengthening liquidity, investment capacity, risk absorption, and long-term relationships with clients, suppliers, subcontractors, and institutional stakeholders. Full article

Acquisition costs account for a significant share of total construction project costs, underscoring the importance of purchasing and supply management for organizational success. Supplier selection and evaluation are particularly critical because they involve multiple criteria, qualitative and quantitative attributes, and several decision-makers. In the construction industry, these activities become even more complex due to sector-specific characteristics such as convergent material flows, temporary facilities, buyer–supplier conflicts, price-oriented decisions, and the volatility of project-based markets. This paper investigates the supplier evaluation process in a construction company and identifies the company’s requirements and decision-makers’ expectations. Based on the collected data, this research proposes a model aligned with the company’s characteristics and the decision-makers’ expectations. The model combines two methods: the Intuitionistic Fuzzy approach to aggregate decision-makers’ opinions and ELECTRE TRI to classify suppliers based on predefined criteria and thresholds. The proposed model handles different weights assigned to each decision-maker for each criterion without allowing compensation among criteria. This model also explores the role of symmetry in multicriteria decision-making by combining Intuitionistic Fuzzy Sets with the ELECTRE TRI method. Decision-makers validated the proposal and emphasized its simplicity and flexibility, which allow future adjustments to both the criteria weights and the decision-makers’ assigned weights. Full article

Environmental supply-chain governance increasingly requires firms to trace climate accountability across buyer–supplier relationships. This study examines whether downstream customer climate pressure is associated with suppliers’ green supply-chain management and value-chain carbon accountability among Chinese listed firms. We construct an exposure-weighted customer pressure measure by combining disclosed top-customer relationships with customer climate-accountability signals, and we decompose this measure into disclosure-based and non-disclosure-based components so that symbolic and substantive accountability can be separated. We then link this measure to supplier green supply-chain indicators, value-chain carbon-disclosure components, Scope 3 disclosure, environmental investment, and reported environmental performance indicators, including air emissions, water pollutant discharge, resource consumption, and environmental tax. Using firm-year panel regressions with fixed effects, alternative pressure measures, selection corrections, and extended outcome tests, we find an association between customer climate pressure and supplier value-chain disclosure. The depth of the association is concentrated where customer carbon-disclosure visibility is observed and is not separately identified in the smaller climate-only subsample, while the value-chain interaction association is positive but imprecisely estimated there. The value-chain disclosure associations are robust to a year-stratified randomization-inference placebo test. We do not find evidence that customer pressure is associated with supplier emissions, resource use, environmental investment, or environmental tax in the available matched samples. The pattern is consistent with symbolic compliance in supply-chain carbon accountability: customer disclosure visibility maps into supplier disclosure visibility, while we do not observe parallel movement in substantive environmental outcomes. The central finding is therefore that downstream customer climate pressure is associated with what suppliers disclose rather than with what they emit, shaping supplier disclosure behavior rather than substantive emission reduction. The estimates apply to supplier-year observations with disclosed and mappable listed-customer links, which we treat as the scope condition of the study rather than as an incidental data limitation. Full article

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain. Full article

Background/Objectives: Technology transfer (TT) has been identified as a global health priority due to its impact on improving access to vaccines, including for pandemic influenza preparedness and response through bilateral and multilateral mechanisms. This study aimed to (1) characterize examples of influenza vaccine TT (IVTT) and (2) identify key lessons learned that may inform future activities relevant for next-generation influenza vaccine technologies. Methods: Using a contingent effectiveness model, a convergent mixed-methods study was conducted with vaccine manufacturers and global experts to capture quantitative survey data on IVTT activities and enablers and qualitative data on successes, challenges, and opportunities for IVTT through interviews, complemented by secondary data from peer-reviewed and grey literature to characterize additional IVTT observations. Results: This study included 24 participants, including 14 representatives from 13 vaccine manufacturers and 10 experts. Interviews were conducted with representatives from eight manufacturers and seven experts. Eighteen IVTT observations were identified through the surveys and interviews, of which 15 IVTT transfers were completed and 13 resulted in an approved vaccine. Secondary data provided additional evidence on eight IVTT recipients and one supplier, expanding the range of institutional and programmatic contexts assessed. Shorter IVTT completion and vaccine approval timelines were observed in association with prior TT experience and private management structures for manufacturers, for pre-pandemic/pandemic influenza vaccines versus seasonal influenza vaccines, and among bilateral transfer mechanisms (versus multilateral mechanisms) and fill/finish transfer methods. Manufacturers also described spillover benefits, including the use of IVTT-related know-how for the development of COVID-19 and routine vaccines. Both manufacturers and experts generally agreed on a list of 17 enablers for successful IVTT and ranked government commitment to vaccine production and procurement as the top enabler. Findings from the literature-based observations were consistent with primary data and included additional public sector recipient experiences, evidence of widespread human capital development, and a commentary on the importance of the demand environment. Conclusions: Assessed IVTT activities across primary and secondary data sources yielded commercial and spillover benefits as described in the contingent effectiveness model and provided a triangulated analysis of IVTT experiences across manufacturers, experts, and documented cases. Participants agreed that effective technology transfer is contingent upon a host of determinants. Using a systematic application of the contingent effectiveness model to IVTT, this study provided an exploratory analysis of past activities among vaccine manufacturers and experts. While certain nuances for influenza were identified, the lessons learned from this study may be applicable for other TT activities, including those to support pandemic preparedness. The contingent effectiveness model is a useful tool to inform and evaluate future TT activities. Full article

Recently, there has been an increasing prevalence of software supply chain attacks on software component suppliers. These attacks have targeted suppliers with relatively weak security or they have exploited vulnerabilities in open-source software. The software bill of materials (SBOM) has gained significant attention as a mechanism for improving software supply chain transparency and traceability. In this study, we propose an SBOM distribution architecture based on Hyperledger Fabric, which is a permissioned blockchain platform, to facilitate secure SBOM management. This approach utilizes Hyperledger Fabric private data collections (PDCs) to separate SBOM metadata from sensitive component information, thereby enabling confidential data sharing while reducing the blockchain storage overhead compared to a fully on-chain approach. The proposed PDC-based architecture achieves lower latency and higher throughput than the fully on-chain approach under the evaluated workload conditions, while supporting integrity verification and controlled sharing of sensitive component data. Full article

Although corporate social responsibility (CSR) research in hospitality has grown substantially, most evidence comes from large corporations in high-income countries, leaving food-service micro and small enterprises (MSEs) in emerging economies largely unexplored. This study investigated which sociodemographic and organizational factors predict operational CSR practices in 150 formal restaurants in Piura, Peru, using a quantitative, cross-sectional, associative-predictive design. Data were analyzed using IBM SPSS Statistics v.28 for descriptive, bivariate, and regression analyses, and IBM SPSS AMOS v.27 for confirmatory factor analysis. Grounded in an integrative framework combining human capital theory, institutional theory, and stakeholder theory, the study operationalized CSR through three dimensions validated for the Peruvian context: supplier relations, customer relations, and food safety. Multiple regression analysis revealed that manager academic education, certifications, and monthly sales were significant predictors, jointly explaining 23.9% of CSR variance, while firm size and service mode were not significant. Nearly all establishments scored at an intermediate CSR level, with none reaching the optimal category. Theoretically, these findings demonstrate that managerial cognitive capabilities and institutional routinization mechanisms are more powerful drivers of sustainability adoption than firm size in resource-constrained contexts. Practically, the results suggest that subsidized certification programs and targeted management training represent more efficient sustainability levers than generic business growth loans for food-service MSEs, contributing to SDG targets 8.3, 12.3, and 12.6. Full article

This study examines how entrepreneurial ecosystem constraints shape MSME resilience in the Jakarta–Bogor–Depok Indonesia corridor using a qualitative multiple-case design. Drawing on 20 MSME case reports across food and beverage, retail, services, and small-scale manufacturing, the study addresses two questions: (1) which ecosystem domains are the most binding constraints, and (2) how MSMEs convert ecosystem resources into resilience outcomes. The analysis shows that market pressure is the most universal constraint (20/20 cases), followed by digital-managerial support infrastructure gaps (18/20), supply chain volatility (13/20), and finance, human capital, and institutional constraints (each 12/20 cases). Cross-case evidence identifies four recurrent mechanisms: market pressure is managed through digital channel orchestration and customer engagement; capital constraints are managed through internal cash discipline and partnership-based financing; input volatility is managed through supplier diversification, local sourcing, and inventory control; and skill gaps are managed through internal training and process standardization. Building on these mechanisms, the study develops a threefold resilience typology: Adaptive Leaders, Operational Survivors, and Vulnerable Traditionalists. The main theoretical contribution is to show that MSME resilience is configurational and depends on inter-domain alignment rather than on isolated ecosystem components or entrepreneur-level grit alone. The practical contribution is a typology-based policy logic that prioritizes integrated intervention bundles, which are finance, digital capability, operations, supply chain, and managerial upgrading, over fragmented support programs. Full article

Demand-side management plays a critical role in the secure and efficient operation of smart grids. Traditional real-time pricing generally takes social welfare maximization as the only objective, while ignoring the benefit balance among electricity suppliers, grid company and users. This will lead to uneven benefit distribution among stakeholders and impair the long-term stable operation of power systems. To solve this problem, a bilevel real-time pricing strategy based on tripartite welfare equilibrium is proposed in this paper. The upper-level model minimizes the welfare differences among electricity suppliers, grid company and users to ensure fair benefit allocation, and the lower-level model maximizes the total social welfare so as to guarantee the economic efficiency of the system. The model adopts different utility functions for residential and industrial users to describe user heterogeneity. By using the Karush–Kuhn–Tucker conditions, the original bilevel model is transformed into a single-level optimization problem with complementarity constraints. The CHKS smoothing function and pseudo-Huber function are introduced to deal with complementarity constraints and absolute-value objective functions respectively. Combined with the central difference method, a modified rolling penalty function algorithm is developed for numerical solution. The 24 h simulation results show that the prices of four time periods converge steadily to equilibrium values as iterations proceed. Compared with the total social welfare maximization model, the proposed bilevel model effectively reduces the peak-to-average load ratio. It reduces the welfare disparities among the three stakeholders while maintaining the total social welfare at a stable level. Furthermore, it still maintains excellent applicability and robustness when the user scale is expanded. Full article

Sustainable aviation supply chains (SCs) are increasingly exposed to risks arising from environmental regulations, social responsibility pressures, and economic uncertainties. These risks are associated with different SC members and may propagate through operational dependencies among suppliers, maintenance service providers, and airline operators. To support systematic risk assessment, this study proposes a hybrid Analytical Hierarchy Process-Bayesian network (AHP-BN) framework for sustainable aviation SC risk management. The intended contribution is a contextual and structural extension of existing AHP-BN logic to member-level sustainability risk propagation in aviation SCs, rather than a claim that AHP-BN integration itself is fundamentally new. The proposed framework first classifies sustainability risks into environmental, social, and economic dimensions and identifies the risk exposure relationship between SC members and risk factors. For the weighting component, Analytical Hierarchy Process (AHP) is used to derive relative importance weights from specified illustrative pairwise comparison matrices in the numerical experiment. Bayesian network (BN) is employed to model probabilistic dependencies among nodes defined by SC members and risk factors. The two methods are coupled through a weighted expected risk index, which integrates AHP-derived weights, member-specific exposure intensities, probabilities inferred by BN, and losses associated with different risk states. A numerical illustration based on a synthetic aviation SC with suppliers, maintenance service providers, and airline operators is conducted to demonstrate the computational procedure and diagnostic use of the proposed framework rather than to validate an empirical risk profile of the aviation industry. Within this illustrative setting, cost volatility, supplier reliability, emissions regulation, and sustainable aviation fuel availability emerge as the major contributors to the overall risk index under the assumed inputs. The analysis further indicates that the proposed framework can identify critical active pairs of SC members and risk factors, reveal vulnerabilities at the levels of SC members and sustainability dimensions, and provide a transparent decision-support tool for sustainable aviation SC risk assessment, while the resulting rankings should be interpreted as conditional outputs under the assumed input parameters. Full article

Large inland waterway infrastructure projects are increasingly exposed to supply disruptions, logistics uncertainty, carbon-control pressure, and dredged-material management challenges. Although resilient supplier selection, closed-loop supply chains, and ESG-oriented optimization have been widely studied, existing models rarely integrate resilient sourcing, hub configuration, forward material supply, reverse dredged-material resourceization, and social externality penalties within a unified maritime infrastructure decision framework. To fill this gap, this study proposes an ESG-endogenous closed-loop supply-chain optimization model for construction of an inland waterway navigation hub. The model jointly optimizes resilient supplier selection, transshipment/resourceization hub activation, equipment deployment, forward material flows, and reverse dredged-material flows. Three objectives are considered: minimizing economic cost, minimizing carbon emissions, and maximizing net social benefit. In particular, a social benefit and ecological-debt penalty function is introduced to quantify the transition from beneficial reuse to disposal-related negative externalities. NSGA-II is adopted as a multi-objective solver, with parameter calibration, convergence analysis, and benchmark comparison used to evaluate computational performance. The Pinglu Canal project is used as a case study. The results produce 14 Pareto-optimal solutions and show that the lowest-cost and lowest-emission configurations may still generate negative social benefits. A low-cost ESG transition region around 197.3–197.8 million CNY is identified, where limited additional investment can activate resourceization pathways and shift the system from ecological debt to near-saturated social benefit. These findings suggest that sustainable infrastructure planning should move beyond isolated cost or carbon minimization and instead identify balanced supplier–hub–equipment–flow configurations that jointly support resilience, circularity, and ESG performance. Full article

Background: Nutrient-enriched sports foods can support efficient nutrient intake in specific circumstances in athletic nutrition management, such as during competition, when training away from the usual environment, or during periods of weight management. Despite their widespread availability, sports foods are not always used appropriately, necessitating tools to support informed product selection. Objective: This study aimed to characterize sports foods consumed by Japanese athletes and to develop a Japanese sports food exchange list to facilitate product selection based on target nutrient requirements. Methods: Seven sports food categories commonly used in Japanese sports settings were examined: sports drinks, energy jellies, energy bars, energy gels, protein drinks, protein bars, and protein powders. Following the methodology of Spain’s sports food exchange list, development proceeded in two stages. First, suppliers were selected based on INFORMED CHOICE certification or listing on the Japan Anti-Doping Agency’s product information website, with input from experienced sports dietitians. Subsequently, 523 products were classified into subcategories based on nutrient content per unit using established statistical criteria, including the mean, standard deviation, coefficient of variation, and z-values. Results: After excluding products with z-values outside ±2 or compositions deemed unsuitable for carbohydrate or protein supplementation, 498 products from 36 suppliers were classified into 24 subcategories. Japanese sports foods exhibited broad distributions in nutrient composition, variability derived from ingredient differences, and a high proportion of plant-based protein powders. Conclusions: This study developed a Japanese sports food exchange list comprising 498 products across 24 subcategories, enabling evidence-based product selection aligned with the nutrient intake goals of Japanese athletes. Full article