Search Results (59,564)

As a pillar industry of the national economy for many countries, the construction sector has long faced challenges in workplace safety. Unsafe behaviors among construction workers are the core cause of safety incidents, and controlling these behaviors is key to enhancing safety management. Numerous studies confirm that unsafe behaviors are closely linked to cognitive biases and decision-making errors. However, existing research still has theoretical gaps in analyzing the multi-factor interaction mechanisms from a cognitive perspective. This study constructs a three-stage theoretical model to reveal the formation mechanism of unsafe behaviors, which is validated by structural equation modeling based on the data collected by a questionnaire from ongoing construction projects in Jiangxi Province, China. It is found that (1) Organizational environment (safety atmosphere, safety culture, and safety management) exerts a negative influence on unsafe behavior; (2) While safety atmosphere has no direct impact on safety motivation, the overall organizational environment positively affects individual cognition; (3) Individual cognitive factors exert a negative influence on unsafe behavior, with the following hierarchical order: safety motivation > safety competence > safety values. (4) While safety motivation does not mediate the relationship between safety atmosphere and unsafe behavior, individual cognitive factors overall mediate the relationship between organizational environment and unsafe behavior. This study theoretically enriches the knowledge system of safety behavior and provides a theoretical foundation for optimizing enterprise unsafe behavior management and formulating differentiated management policies. Full article

This research focuses on the effects of leadership styles on the implementation of Green Human Resource Management (Green HRM) initiatives in hotels in Greece by staff members, and it recognizes the lack of sustainability research in the Mediterranean hospitality sector. Employing the Full-Range Leadership Model, we explore the impact of transformational, transactional, and passive leadership on the implementation of environmental HR practices. The data for this study were obtained from 216 employees in 29 hotels in Greece, who completed the Multifactor Leadership Questionnaire (MLQ-5x) and a Green HRM instrument. Several regression analyses showed that transformational leadership was the most robust positive predictor of Green HRM practices, followed by leadership outcomes and transactional leadership. On the other hand, passive leadership was significantly inversely associated with Green HRM implementation. Demographic variables, such as gender, age, and experience, had a substantial impact on both perceptions of leadership and involvement in Green HRM as well. The results offer significant theoretical implications and practical directions for improving environmental performance in hospitality organizations through the strategic use of leadership development and human resource management ‍‌‍‍‌intervention. Full article

In the context of tightening sustainability regulations and rising demands for transparent and responsible capital allocation, understanding how digital financial innovations influence market efficiency has become increasingly important. This study examines the impact of Financial Technology (FinTech) solutions and crowdfunding platforms on sustainable market efficiency, volatility dynamics, and risk structures in the United Kingdom. Using weekly data for the Financial Times Stock Exchange 100 (FTSE 100) index from January 2010 to June 2025, the analysis applies the Lo–MacKinlay variance ratio test to assess compliance with the Random Walk Hypothesis as a proxy for informational efficiency. Firm-level proxies for FinTech and crowdfunding activity are constructed using the Nomenclature of Economic Activities (NACE) and Standard Industrial Classification (SIC) systems. The empirical results indicate substantial deviations from random-walk behavior in crowdfunding-related market segments, where persistent positive autocorrelation and elevated volatility reflect liquidity constraints and informational frictions. By contrast, FinTech-dominated segments display milder inefficiencies and faster information absorption, pointing to more stable price-adjustment mechanisms. After controlling for structural distortions through heteroskedasticity-consistent corrections and volatility adjustments, variance ratios converge toward unity, suggesting a restoration of informational efficiency. The results provide relevant insights for investors, regulators, and policymakers seeking to align financial innovation with the objectives of sustainable financial systems. Full article

The rapidly evolving business landscape, driven by stringent energy conservation policies, compels construction firms to adopt energy-efficient project-centric structures, particularly in modern construction projects. These firms face a complex, multi-mode, resource-constrained, multi-project scheduling problem characterized by dynamic project arrivals and multiple resource constraints, including global, local, and non-renewable capacities. This environment pressures managers to simultaneously optimize the conflicting objectives of minimizing total project duration and total energy consumption. To address this challenge, we propose a novel multi-objective Smart Raccoon Family Optimization (SRFO) algorithm. The SRFO, a hybrid evolutionary approach, is designed to enhance global exploration and local exploitation. Its performance is boosted by integrating a non-dominated sorting mechanism, a dedicated energy-efficient search strategy, and enhanced genetic operators. The SRFO simultaneously optimizes two conflicting objectives: minimizing the total project duration and total energy consumption. This approach effectively integrates the unique constraint of off-site component production and on-site assembly within an intelligent scheduling framework. Empirical validation across benchmark problems and a real-world case study is conducted, comparing the SRFO with existing multi-objective approaches, such as NSGA-III, MOABC, and MOSMO. Performance is assessed using convergence and distribution metrics, augmented by TOPSIS-based multi-criteria decision-making. Results conclusively demonstrate that the proposed SRFO significantly outperforms existing approaches and offers a robust, high-quality solution for project management in energy-constrained environments. Full article

Taking climate funds (e.g., the Green Climate Fund) as the main financial mechanism for providing funding to developing countries, this paper examines a long-term climate funding relationship between two parties—the rich country and the poor country. Conflicts between the rich and poor countries arise when determining (1) the size of climate funding that the rich country contributes to the poor country and (2) the funding allocation between climate adaptation and mitigation projects in the poor country. In addition, the rich country cannot be forced to commit contractual contributions to the poor country, and in each period, there is a probability that the countries can renegotiate the contract. This paper derives two main dynamic comparative–static results: (1) climate funds converge to the first-best in the long run, both in the size of climate funding in adaptation and mitigation projects, if and only if climate damage becomes sufficiently severe; (2) fewer renegotiations between the rich and poor countries make climate funding contracts more efficient, remedying inequality between the poor and rich countries. These results highlight how increasing climate damages and reducing the frequency of renegotiation can push climate funds closer to a first-best allocation, suggesting design principles for climate funding mechanisms like the Green Climate Fund. Full article

Two main perspectives exist regarding the interaction between fiscal deficits and expansionary monetary policy. The first perspective argues that fiscal deficits raise interest rates, thereby increasing interest payments and complicating monetary stabilization efforts. The second posits that expansionary monetary policy enhances nominal GDP growth, which in turn reduces the government debt-to-GDP ratio and strengthens the fiscal position. Using panel data from the IMF World Economic Outlook covering advanced economies between 1980 and 2025, this study empirically evaluates which perspective is more consistent with observed data, while accounting for the dynamics of tax revenues, government expenditures, interest rates, and nominal GDP growth. Empirical evidence indicates that moderate monetary expansion—raising nominal GDP—tends to stabilize budget deficits, as government revenues generally outpace expenditures and interest rates do not increase proportionally with nominal growth. These results are further illustrated through case studies of Greece, Italy, Portugal, Spain, Japan, the United Kingdom, and the United States. Full article

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem. Full article

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article

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

As global production networks become increasingly regionalized, diversified, and resilience-oriented, metropolitan areas (MAs) have emerged as important spatial platforms for industrial development. This study examines whether China’s national-level metropolitan area integration policies promote urban industrial structure upgrading and, if so, through which channels. We first develop a set of conceptual mechanisms and hypotheses, and then test them using panel data for 281 prefecture-level cities in China from 2012 to 2022. A staggered difference-in-differences (DID) model, complemented by a series of robustness checks, is employed to identify the policy effects. The baseline estimates indicate that the industrial structure of MA member cities is, on average, about 2.43 percentage points more advanced than that of non-MA cities. Mechanism analysis shows that the policies foster urban industrial upgrading through unified market formation, technological improvement, and optimization of factor endowments. However, the policies have only a very limited impact on breakthroughs in cutting-edge or frontier technologies. Based on these findings, we propose targeted policy recommendations to address the identified shortcomings. Full article

AI technologies are increasingly applied to optimize operations in both port and urban logistics systems, yet integration across the full maritime city chain remains limited. The objective of this study is to assess, using a simulation-based experiment, the impact of an AI-orchestrated control policy on the performance of port–city logistics relative to a baseline scheduler. The study proposes an AI-orchestrated approach that connects autonomous ships, smart ports, central warehouses, and multimodal urban networks via a shared cloud control layer. This approach is designed to enable real-time, cross-domain coordination using federated sensing and adaptive control policies. To evaluate its impact, a simulation-based experiment was conducted comparing a traditional scheduler with an AI-orchestrated policy across 20 paired runs under identical conditions. The orchestrator dynamically coordinated container dispatching, vehicle assignment, and gate operations based on capacity-aware logic. Results show that the AI policy substantially reduced the total completion time, lowered truck idle time and estimated emissions, and improved system throughput and predictability without modifying physical resources. These findings support the expectation that integrated, data-driven decision-making can significantly enhance logistics performance and sustainability in port–city contexts. The study provides a replicable pathway from conceptual architecture to quantifiable evidence and lays the groundwork for future extensions involving learning controllers, richer environmental modeling, and real-world deployment in digitally connected logistics corridors. Full article

In this paper, we propose a generalized odd log-logistic standard Laplace model and study some of its main properties. The novelty of this model is based on classical and risk-based measures to effectively analyze the body mass index (BMI) data. The analysis underscores the importance of a multidisciplinary approach in addressing challenges related to health, performance, and risk management. The proposed methodology not only is helpful to understand the variability of BMI measurements, but also prove how common statistical models considered in financial field can be effectively adapted to other ones, offering insights that drive informed decision-making and strategic planning. Full article

Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish between deterministic astronomical cycles, and random meteorological variability. The objective of this study was to develop and apply a new Physics-Aware Deep Learning Framework that identifies and utilizes physical attributes of solar irradiance via Fourier-based signal decomposition. The proposed method decomposes the time-series into polynomial trend, Fourier-based seasonal component and stochastic residual, each of which are processed within different neural network paths. A wide variety of architectures were tested (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)), at multiple historical window sizes and forecast horizons on a diverse dataset from a three-year span. All of the architectures tested demonstrated improved accuracy and robustness when using the physics aware decomposition as opposed to all other methods. Of the architectures tested, the GRU architecture was the most accurate and performed well in terms of overall evaluation. The GRU model had an RMSE of 78.63 W/m² and an $R^2$ value of 0.9281 for 15 min ahead forecasting. Additionally, the Fourier-based methodology was able to reduce the maximum absolute error by approximately 15% to 20%, depending upon the architecture used, and therefore it provided a way to reduce the impact of the larger errors in forecasting during periods of unstable weather. Overall, this framework represents a viable option for both physically interpretive and computationally efficient real-time solar forecasting that provides a bridge between Physical Modeling and Data-Driven Intelligence. Full article

The Lunar Gateway is a planned small space station that will orbit the Moon and serve as a central hub for NASA’s Artemis program to return humans to the lunar surface and to prepare for Mars missions. This work presents a hybrid optimization strategy for designing minimum-fuel transfers from an Earth orbit to a Lunar Near-Rectilinear Halo Orbit. The corresponding optimal control problem—crucial for missions to NASA’s Lunar Gateway—is characterized by a high-dimensional, non-convex solution space due to the multi-body gravitational environment. To tackle this challenge, a two-stage hybrid optimization scheme is employed. The first stage uses a Genetic Algorithm heuristic as a global search strategy, to identify promising feasible trajectory solutions. Subsequently, the initial solution guess (or guesses) produced by GA are improved by a local optimizer based on a Sequential Quadratic Programming method: from a suitable initial guess, SQP rapidly converges to a high-precision feasible solution. The proposed methodology is applied to a representative cargo mission case study, demonstrating its efficiency. Our numerical results confirm that the hybrid optimization strategy can reliably generate mission-grade quality trajectories that satisfy stringent constraints while minimizing propellant consumption. Our analysis validates the combined GA-SQP optimization approach as a robust and efficient tool for space mission design in the cislunar environment. Full article

This study examines how Artificial Intelligence (AI) is reshaping the role of university teachers and transforming the foundations of academic work in the digital age. Building on the Dynamic Capabilities Theory (sensing–seizing–transforming), the article proposes a theoretical reframing of university teachers’ perceptions of AI. This approach allows us to bridge micro-level emotions with meso-level HR policies and macro-level sustainability goals (SDGs 4, 8, and 9). The empirical foundation includes a survey of 453 Ukrainian university teachers (2023–2025) and statistics, supplemented by a bibliometric analysis of 26,425 Scopus-indexed documents. The results indicate that teachers do not anticipate a large-scale replacement by AI within the next five years. However, their fear of losing control over AI technologies is stronger than the fear of job displacement. This divergence, interpreted through the lens of dynamic capabilities, reveals weak sensing signals regarding professional replacement but stronger signals requiring managerial seizing and institutional transformation. The bibliometric analysis further demonstrates a theoretical evolution of the university teacher’s role: from a technological adopter (2021–2022) to a mediator of ethics and integrity (2023–2024), and, finally, to a designer and architect of AI-enhanced learning environments (2025). The study contributes to theory by extending the application of Dynamic Capabilities Theory to higher education governance and by demonstrating that teachers’ perceptions of AI serve as indicators of institutional resilience. Based on Dynamic Capabilities Theory, the managerial recommendations are divided into three levels: government, institutional, and scientific-didactic (academic). Full article