Search Results (153)

This paper systematically examines the core of new quality productive forces underpinning the sustainable development of the low-altitude economy industry. Based on the Triple Bottom Line theory, it analyzes the interrelationships among various factors of new quality productive forces and reveals the underlying mechanism through which new quality productive forces drive sustainable development in this sector. By employing an integrated Fuzzy Decision-Making Trial and Evaluation Laboratory—Interpretive Structural Modeling (Fuzzy-DEMATEL-ISM) approach, this study establishes a hierarchical structure for sustainable development in the low-altitude economy industry grounded in the logical framework of new quality productive forces. The findings indicate that: (1) the strategic origin layer represents the prerequisite and most critical task for the emergence of the low-altitude economy industry; (2) the technological breakthrough layer constitutes the core competitive strength enabling its development; (3) the resource guarantee layer provides essential resource support for operational sustainability; (4) the institutional synergy layer enhances industrial efficiency through coordinated governance; and (5) the value realization layer signifies the ultimate form of industrial evolution. These research outcomes offer significant forward-looking insights for advancing sustainable development in the low-altitude economy industry. Full article

The global transition toward net-zero emissions has led to the restructuring of labor markets and an intensification of the demand for sustainability-competent business graduates. However, higher-education curricula lack an operationalized, job-competency-based framework, and this gap in knowledge is especially acute in emerging industrial economies that are facing pressures due to the ongoing decarbonization of the global supply chain. In this context, this study addresses two interrelated gaps in the relevant research: the lack of a structured system of criteria to assess competency in sustainability that is specifically geared toward business education, and the insufficient attention that has been paid to causal interdependencies among such criteria in previously developed frameworks. The authors apply a two-stage, hybrid multiple-criteria decision-making design based on the KSAO framework, which classifies professional competency into knowledge (K), skills (S), abilities (A), and other characteristics (O). A modified Delphi method that involved 12 academic and industry experts serving as surrogate assessors of competency requirements for business and management students was first used to consolidate 142 literature-derived items into 26 initial criteria, which were then refined into 12 core competencies in sustainability, identified through cross-domain expert consensus. Following this, fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) was applied to analyze the structure of causal influence among the retained criteria. The results identified interdisciplinary work as the primary driving competency and integrated problem-solving as the central hub with the highest prominence, with the two factors forming a bidirectional feedback dynamic that anchored the competency system. The retention of four “other” criteria (O-dimension)—ethical values, normative orientation, empathy, and adaptive resilience—confirmed that competency concerning sustainability in business education extends beyond technical knowledge into deeper dispositional attributes. These findings provide business schools in Taiwan with a structurally grounded logic of sequencing for their curricula, as well as a reference framework for curriculum design that is aligned with the Association to Advance Collegiate Schools of Business (AACSB) Societal Impact standards. While the findings are grounded in Taiwan’s specific ESG regulatory and industrial context, only the methodological approach is offered as a reference for comparable settings; the substantive findings require cross-national verification. Full article

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response. Full article

Driven by China’s “dual carbon” (carbon peak and carbon neutrality) goals and the national strategy of new-type urbanization, prefabricated construction has emerged as a pivotal pathway toward industrialized and sustainable development in the construction sector—leveraging its distinctive advantages in construction efficiency, cost optimization, environmental performance, and design adaptability. Nevertheless, the inherently sequential and interdependent nature of the full construction process—encompassing off-site component manufacturing, logistics transportation, and on-site assembly—introduces pronounced cross-stage risk transmission mechanisms, with prefabricated components serving as critical risk carriers. Such transmission dynamics significantly impede the scalable and safe deployment of prefabricated construction. To date, scholarly efforts on construction safety in prefabricated buildings have predominantly addressed isolated, stage-specific risks, falling short in quantitatively modeling the coupled propagation of risks across stages, accommodating epistemic uncertainties and latent (i.e., unknown or unobserved) risks, and informing targeted, evidence-based mitigation strategies. To bridge this gap, this study develops a rigorous quantitative framework for assessing cross-stage risk transmission in prefabricated construction safety. Specifically, it aims to (i) uncover the structural patterns and driving mechanisms underlying inter-stage risk propagation; (ii) reduce the likelihood of safety incidents throughout the construction life cycle; and (iii) deliver actionable theoretical insights and methodological guidance for practitioners and policymakers. Methodologically, we first conduct a systematic identification of safety-critical risk factors and establish a hierarchical risk indicator system comprising three first-level dimensions and twenty second-level indicators. Second, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, causal relationships among risk factors are clarified, while incorporating the Leaky Noisy-or Gate (LNOG) extended model to account for unknown risks. Risk data are processed using triangular fuzzy functions, and a Bayesian network (BN) topology diagram is constructed via the GeNIe 5.0 platform, forming a DEMATEL-LNOG-BN-based model for assessing cross-phase risk transmission. Finally, applying the model to an actual project—”a prefabricated construction project in Shanghai”—the study conducts a cross-phase risk transmission analysis. Through forward probability inference, backward causality tracing, sensitivity analysis, and pathway decomposition, sensitivity comparisons are performed under different LNOG unknown risk parameters. Results are compared with those from the traditional DEMATEL-BN model to validate the stability and consistency of high-sensitivity risk factor identification, comprehensively verifying the applicability and predictive reliability of the proposed DEMATEL-LNOG-BN model. The study quantitatively reveals the progressive diffusion and amplification mechanisms of risks across the production–transportation–assembly process, providing scientific support and practical reference for precise safety risk prevention, critical node control, and the optimization of management systems in prefabricated construction sites. Full article

Scaffold-related falls remain a major safety concern in demolition projects, where temporary access systems are frequently erected, modified, used, and dismantled under changing structural and site conditions. These characteristics complicate risk prioritization because scaffold failures may involve interacting human, technical, organizational, and environmental factors. This study develops an expert-based risk prioritization framework for scaffold-related fall risks in demolition projects by integrating Failure Mode and Effects Analysis (FMEA), interval neutrosophic fuzzy (INF) theory, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Multi-Attributive Border Approximation Area Comparison (MABAC). Using the 4M1E perspective, namely Man, Machine, Material, Method, and Environment, 37 demolition-specific failure modes were identified through literature review and expert elicitation. Ten experts evaluated these failure modes using the SODE criteria, namely Severity, Occurrence, Detection difficulty, and Expected Cost impact. INF theory was used to represent uncertainty, hesitation, and judgmental variation in expert assessments. INF-DEMATEL was applied to examine interrelationships among the SODE criteria and derive interdependence-aware criterion weights, while INF-MABAC was used to rank the failure modes according to their distance from the Border Approximation Area. The framework was illustrated through an empirical application in Taiwan’s demolition industry. The results identified Severity as the most influential criterion. The highest-priority failure modes were insufficient safety awareness, improper scaffold-to-structure anchoring, and inadequate scaffold maintenance and inspection governance. Comparison with risk priority number (RPN)-based methods and sensitivity analyses using expert exclusion and Severity-weight variation showed that the ranking was generally consistent and reasonably stable under the tested conditions. The proposed framework provides a structured, uncertainty-aware decision-support procedure for identifying prevention priorities in demolition scaffold operations. Full article

Port and shipping enterprises face significant uncertainty in making effective sustainable investment decisions to meet pressing carbon reduction targets. This study addresses this challenge by developing a fuzzy multi-method framework to identify and prioritize pivotal factors that guide sustainable investments. An evolutionary game model simulates the influencing factors, while the triangular fuzzy number (TFN) and evidential reasoning (ER) algorithm assess their importance and operability. The decision-making trial and evaluation laboratory (DEMATEL) method further refines these assessments. Finally, the Bayesian probability method corrects the posteriori probability, providing a comprehensive ranking. The results reveal that low-carbon technology is the most critical driver of sustainable investment, whereas environmental factors consistently rank the lowest in importance. This methodology aids ports and shipping enterprises in making sustainable investment decisions to reduce carbon emissions. Full article

In this study, a hybrid multi-criteria decision-making (MCDM) model was developed for analyzing consumer adoption decisions regarding generative artificial intelligence (Gen AI). By extending the technology acceptance model (TAM) into a structured decision system, the proposed framework integrates ethical and risk-related criteria, including perceived cost, perceived risk, transparency, accountability, intellectual property concerns, and data privacy, into a formal causal and evaluative structure. First, a Delphi-based consensus process is employed to identify and refine key adoption criteria. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method is applied to quantify causal relationships among these criteria and to construct an influence network revealing prominence and directional effects. In total, 251 questionnaires were distributed in Taiwan, and 231 valid responses were collected. The results indicated the decision-making factors that underlie the adoption of Gen AI by consumers. The results highlighted transparency as a dominant causal factor that significantly influences multiple ethical and functional dimensions of Gen AI adoption. To address uncertainty and vagueness in human judgment, fuzzy importance–performance analysis was also incorporated. Best non-fuzzy performance values were obtained through defuzzification, enabling the classification and prioritization of critical adoption factors within a four-quadrant decision matrix. The proposed framework provides a mathematically grounded decision-support model for elucidating the structural interdependencies among adoption criteria and to facilitate strategic decision making for Gen AI system design and governance. This study contributes to the MCDM and operations research literature by transforming a behavioral acceptance model into a formal decision-analytic framework, thereby enhancing the analytical rigor and applicability of TAM-based adoption studies in complex socio-technical systems. Full article

Tangyue Village in Huizhou, China, is renowned for its monumental Bao-family archway complex and well-preserved ancestral halls, which host and memorial activities embodying rich clan traditions and regional cultural identity. However, these traditional spaces face contemporary challenges, including functional obsolescence, high energy consumption, and limited sustainability. Focusing on the memorial spaces of Tangyue Village, this study explores low-carbon design strategies for their renewal by developing a comprehensive research framework that integrates multi-stakeholder demand analysis, weighting evaluation, case-based design, and performance verification. Initially, user needs were identified through semi-structured interviews and behavioral observations, followed by the application of the Fuzzy Kano (FKANO) model to classify and filter these requirements. Subsequently, a multi-level evaluation system was established, encompassing low-carbon performance, spatial functionality, cultural continuity, and community participation. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach combined with the entropy weight method was then employed to determine the relative importance of each indicator. The results indicate that the organization of memorial spaces, the application of low-carbon materials, rainwater harvesting, and spatial accessibility represent key design priorities. Space syntax simulations conducted via DepthmapX further demonstrate that the optimized design significantly improves spatial accessibility, permeability, and vitality while enhancing the overall low-carbon performance. Ultimately, this study proposes practical low-carbon renewal strategies for memorial spaces in traditional settlements, offering a systematic approach that balances cultural heritage preservation with environmental sustainability. Full article

Human reliability analysis of the human–computer interaction process between users and systems is critical because human error can introduce significant system risks. Interaction systems designed with human reliability analysis can reduce human error. This study proposed a research methodology for analyzing human error to design interactive systems that align with users’ cognitive demands. First, the cognitive reliability and error analysis method (CREAM) is used to investigate cognitive function failures and determine the nominal cognitive failure probability. Next, fuzzy comprehensive evaluation (FCE) is used to assess the level of common performance conditions (CPCs). Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method is employed to compute the factor centrality weights of CPCs and human intrinsic factors (HIFs). The interactions among CPCs are analyzed, leading to the determination of cognitive impact weights. Then, the cognitive failure probability is calculated by combining factor centrality weights and cognitive impact weights. Finally, error causes are analyzed to propose optimization strategies and implement design improvements. An in-vehicle information system was used to validate the proposed approach. The findings revealed that this method effectively minimizes cognitive failure probability during system interaction. It also identifies the causes of human error in human–computer interactions and offers a systematic strategy to enhance human reliability in interaction design. Full article

The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces the challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. The study developed an approach to managing operational uncertainty using Industry 4.0 and 5.0 technologies. It employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms’ survey. The study employed an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The fuzzy DEMATEL results reveal that growing geopolitical tension, cost-of-living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and the entrenched power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies, with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions, can manage operational uncertainty. These technologies include artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality (AR/VR). This study advanced configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject, make meaningful changes in day-to-day manufacturing operations, and promote practical real-world problem solving. Full article

The cruise shipbuilding industry faces significant disruptions stemming from escalating trade frictions and regional conflicts which threaten its operational and economic sustainability. Enhancing supply chain resilience is thus crucial for sustainable development. This study identifies critical resilience factors and examines their interrelationships within growth-stage cruise shipbuilding supply chains. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis are integrated to explore causal linkages, hierarchical structures, and driver-dependence dynamics. The analysis reveals that customized demand responsiveness, learning organization, specialized industrial clusters, and inter-industry collaboration are fundamental causal drivers. In contrast, knowledge stock, risk culture, and final-assembly orchestration serve as critical mediators. Based on these findings, we propose distinct resource-contingent strategic pathways for managers. This study provides an actionable framework for building resilience, offering critical guidance for securing the sustainable development of the cruise shipbuilding industry amid uncertainty. Full article

In the face of mounting complexity in container terminal operations, the selection of an effective information system is paramount. The TOS (Terminal Operating System) is the most significant of all the information systems in existence for terminals. The objective of this study is to establish a set of criteria for selecting container TOS, determine the priority weights of these criteria and investigate their interactions. To the author’s knowledge, this is the first study to address this topic in such a detailed context. The hybrid FAHP (Fuzzy Analytic Hierarchy Process) and F-DEMATEL (Fuzzy Decision-Making Trial and Evaluation Laboratory) methodology was employed for the 18 criteria that were identified through the academic literature and expert views. The findings demonstrated that container terminal operators have expressed an expectation for a TOS structure that integrates complex business processes, provides effective decision support, increases traceability, works in harmony with advanced technologies, supports smart port transformation processes, enhances digital maturity and enables rapid intervention in bottlenecks. Furthermore, the fact that TOSs should support integration with external stakeholders is also critical in terms of collaboration and transparency, which are of great importance in supply chain management. It is hoped that the present study will contribute to the relevant literature and also provide a structural framework for terminal operators to select the most suitable TOS and for providers to design the most effective product. Full article

In the modern business environment, where uncertainty and complexity make decision-making difficult, the need for robust, transparent and adaptable support tools is highlighted. The proposed method, named Flexible Decision Navigator for Evaluating Trends and Strategies (F-DeNETS), offers a complementary perspective to classic Artificial Intelligence (AI), Big Data and Multi-Criteria Decision-Making (MCDM) tools. Despite their broad use, these methods frequently suffer from critical sensitivities in the weighting of criteria and the handling of uncertainty, leading to compromised reliability and limited practical utility in environments with limited data availability. To bridge this gap, F-DeNETS integrates intuition and uncertainty into a transparent and statistically grounded process. It introduces a balanced approach that combines statistical evidence with human judgment, extending the boundaries of classic AI, Big Data and MCDM methods. Classic MCDM methods, although useful, are sometimes limited by subjectivity, staticity and dependence on large volumes of data. To fill this gap, F-DeNETS, a hybrid framework combining Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Non-Asymptotic Fuzzy Estimators (NAFEs) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), transforms expert judgments into statistically sound fuzzy quantifications, incorporates dynamic adaptation to new data, reduces bias and enhances reliability. A numerical application from the shipping industry demonstrates that F-DeNETS offers a flexible and interpretable methodology for optimal decisions in environments of high uncertainty. Full article

The urgent global challenge of environmental sustainability has intensified interest in integrating Lean Management practices with environmental objectives, particularly within the automotive industry, a sector known for both innovation and high environmental impact. This study investigates the systemic relationships between 16 lean practices and three environmental performance metrics: energy consumption, CO₂ emissions, and waste generation. Using the Fuzzy Decision-Making Trial And Evaluation Laboratory (DEMATEL) methodology, data were collected from seven lean experts in the Italian automotive industry to model the cause–effect dynamics among the selected practices. The analysis revealed that certain practices, such as Total Productive Maintenance (TPM), just-in-time (JIT), and one-piece-flow, consistently act as influential drivers across all environmental objectives. Conversely, practices like Statistical Process Control (SPC) and Total Quality Management (TQM) were identified as highly dependent, delivering full benefits only when preceded by foundational practices. The results suggest a strategic three-step implementation roadmap tailored to each environmental goal, providing decision-makers with actionable guidance for sustainable transformation. This study contributes to the literature by offering a structured perspective on lean and environmental sustainability in the context of the automotive sector in Italy. The research is supported by a data-driven method to prioritize practices based on their systemic influence and contextual effectiveness. Full article

As a common coal mine disaster, a coal and gas outburst in coal mining seriously threatens the safety production of coal mines with its sudden and destructive nature. In order to accurately identify the main influencing factors of a coal and gas outburst in coal mines and assess the risk level of a coal and gas outburst, 12 indicators are established from three aspects: coal seam gas factors, coal seam physical and mechanical properties, and in situ stress state. This study introduces the fuzzy set theory on the basis of the DEMATEL and combines it with the TOPSIS to establish a fuzzy DEMATEL-TOPSIS risk assessment model. The model was applied to conduct a comprehensive evaluation of the coal and gas outburst in the 3908 working face of a coal mine in Jiangxi Province so as to determine the risk level of coal and gas outburst. The results show that, sorted by weight in descending order, the main influencing factors are gas pressure (0.105), in situ stress (0.101), gas content (0.098), burial depth (0.090), and geological structure type (0.087). The hazard grade identification of coal and gas outburst at the working face is Level II (with a relative approximation degree of 0.270), which is consistent with the actual situation. It can provide a reference for the prevention and control of coal and gas outbursts. Full article