Search Results (771)

The article examines the persistent stability of the weighted average cost of capital (WACC) disclosed by German DAX40 companies despite substantial declines in risk-free interest rates between 2004 and 2021. While theory suggests that WACC should reflect lower risk-free interest rates and decline as well with falling government bond yields, empirical evidence reveals minimal adjustment in reported WACC figures. Disclosed WACC of DAX40 companies remains between 7% and 8% as the yield of the ten-year German government bond fell from 4.1% to −0.2%. This study employs quantitative analyses to investigate whether systematic increases in risk exposure can explain this phenomenon. Using capital market data spanning from 2000 to 2023, we analyze five risk dimensions: systematic risk (beta factors), overall market volatility, risk aversion (lambda factors), earnings risk, and financial structure risk. Bootstrap analyses reveal a 41.5% reduction in beta factor variance, while volatility analyses demonstrate declining market risk exposure. The market price of risk analysis does not reveal definite findings. Earnings risk measures indicate improved financial stability, and debt ratios show modest declines. These findings suggest that observable risk parameters cannot explain persistent WACC levels, indicating a disconnect between theoretical WACC calculations and practitioner applications in investment project decision-making following value-based management principles. Full article

In practice, the emerging shared electric bicycles battery-swapping systems face weather disturbances and time-window lateness, which can reduce travel efficiency and degrade operational reliability. To facilitate the operation reliability and management robustness, this study builds a scenario-based location–routing optimization model that links station siting with replenishment routing under two weather scenarios, no rain and rain. The first stage selects sites and determines battery-swapping station construction decisions before scenario realization. The second stage reacts through scenario-dependent depot assignment and routing and scheduling decisions. The objective functions are to minimize average cost while restraining tail risk through an explicit worst-case term, yielding an adjustable efficiency–resilience balance. The modeling constraints impose a minimum service level, preserve route feasibility under scenario travel times, and prevent structural shortcuts. An improved genetic algorithm is proposed to solve the model. The algorithm adopts construction encoding and scenario-wise assignment encoding, applies feasibility repair before evaluation, and constructs executable routes during decoding with local improvement. Experiments demonstrate that the proposed method achieves better objective values than benchmark methods and exhibits stable convergence. Case study shows that rain increases transportation and lateness-related costs. The System Resilience Analysis shows that the robust penalty term reduces variable operating loss under rain by 5.33% and cuts the cost shock from no rain to rain by 32.82%, demonstrating improved resilience under adverse weather. Full article

The rapid integration of AI customer service in e-commerce raises an important managerial question: Can AI effectively reduce product-related information asymmetry and improve sales performance across different product types? While prior research highlights both the uncertainty-reducing benefits of information and the risks of algorithm aversion, little is known about how AI customer service performs under varying levels of product uncertainty and information asymmetry. Using a difference-in-differences design with fixed effects across time, products, shops, and categories, we examine the impact of replacing customer service with AI on sales outcomes, distinguishing between search and experience goods. We further test how the depth and breadth of product information moderate these effects. Our findings indicate that AI customer service reduces sales for experience goods but not for search goods, unless accompanied by sufficient informational depth and breadth. We argue that this effect arises because AI technically inherits and amplifies the information asymmetry inherent in experience products, while greater informational depth and breadth of product information can mitigate this amplified asymmetry. Additionally, we find that this mitigating effect is more pronounced among products with high return rate. These findings clarify when AI-generated information mitigates product uncertainty and when it exacerbates it. Our results provide actionable guidance for firms seeking to deploy AI strategically in digital commerce environments. Full article

Grey plastic is a representative traditional architectural decoration craft in the Lingnan region in China, carrying rich historical and cultural values as well as distinctive regional artistic characteristics. However, the grey plastic craft is currently facing problems such as inheritance gaps and a shortage of craftsmen, and its restoration projects impose extremely high professional requirements on contractors. Existing contractor selection methods are mostly applicable to ordinary construction projects and are difficult to adapt to its particularity, which may easily lead to risks such as substandard restoration quality. Therefore, this paper proposes a contractor selection method for grey plastic decoration projects of cultural relic buildings based on the BWM-TODIM method. Firstly, an evaluation system covering six core criteria is constructed; secondly, the BWM is adopted to determine the criteria weights; thirdly, the TODIM method is used to characterize the decision-makers’ loss aversion psychology and rank the candidate contractors; finally, an empirical analysis is conducted with a grey plastic restoration project in Lingnan as a case to verify the feasibility and effectiveness of the method. This study can provide decision support for the scientific selection of contractors for grey plastic decoration projects and contribute to the sustainable protection of cultural heritage. The scope of this study is limited to contractor selection for grey plaster decoration engineering of cultural relic buildings. Full article

During pandemic emergencies, demand for relief supplies in affected areas surges abruptly and evolves randomly and dynamically, resulting in highly asymmetric supply and demand. Ensuring timely and reliable supply requires robust decision-making under risk. This study addresses a stochastic multi-objective location-routing problem (LRP) that simultaneously considers demand uncertainty and travel time variability. A multi-scenario stochastic programming model is developed with three objectives: minimizing total system cost, minimizing total waiting time, and minimizing the composite conditional value at risk (CVaR–Rcomp) to capture tail risks under extreme scenarios. A novel regret-based risk mechanism is introduced to unify temporal and cost dimensions, enabling joint evaluation of uncertainties within a single framework. To solve this challenging high-dimensional problem, a reinforcement learning-enhanced NSGA-III (RL-NSGAIII) is proposed. Specifically, Q-learning generates high-quality initial solutions, which accelerate convergence and improve population diversity for NSGA-III. Case studies demonstrate that the proposed method outperforms traditional evolutionary algorithms in convergence efficiency and Pareto solution quality, while effectively revealing potential risk blind spots. The results provide quantitative decision support and robust optimization insights for emergency logistics networks operating under uncertain conditions. Full article

Against the backdrop of China’s rural revitalization, understanding the factors influencing farmers’ agricultural technology adoption behavior is crucial for enhancing such adoption. Therefore, exploring the decision-making logic behind farmers’ agricultural technology adoption behavior is of paramount importance. This study, conducted among 482 typical farming households in the Chengdu Plain of Sichuan Province, China, introduced the Random Forest (RF) algorithm into an Improved TAM. Combined with SHAP and PDP techniques, it identified 21 influencing factors and their nonlinear interaction mechanisms. Key findings include the following: (1) Adoption rates stood at only 14.3%, exhibiting a pronounced “advantage-oriented” pattern favoring male farmers, middle-aged/young adults, and higher-income groups; (2) Level of agricultural production tools and technology (C1) and Agricultural product sales channels (C2) emerged as core drivers, with C1 presenting a significant “technology threshold effect”—adoption probability surged from 0.1 to over 0.35 during intelligent technology transitions; (3) Monthly household income level (B4) effectively mitigates risk aversion among elderly farmers, revealing the critical role of Age (A2) in decision-making and enabling a complementary relationship between experience and technology; (4) Self-learning and training proficiency in agricultural technology (F1) reflects that excessive technological complexity triggers resistance and blocks adoption, while Highest educational attainment in the household (B1) and Number of educated family members (B2) exhibit nonlinear peak characteristics influenced by “brain drain” due to labor migration. These findings not only expand the theoretical application of machine learning in studying farmer behavior but also provide granular insights for overcoming the “last mile” bottleneck in agricultural technology dissemination. Full article

Financial data analysis remains a significant challenge due to the inherent stochasticity, non-stationarity, and low signal-to-noise ratio of market data. Conventional methods often struggle to disentangle intrinsic trends from noise and frequently overlook the critical influence of investor sentiment on price dynamics. To address these issues, we propose an adaptive trading model named CBAM-BiLSTM-DDQN, which integrates signal decomposition, multi-source feature fusion, and deep reinforcement learning. First, we construct a comprehensive heterogeneous feature set by combining price signals decomposed via Variational Mode Decomposition (VMD) and investor sentiment indices extracted from financial texts. Subsequently, a Genetic Algorithm (GA) is employed to identify the most significant feature subset, effectively reducing dimensionality and redundancy. Finally, these optimized features are input into a Double Deep Q-Network (DDQN) agent equipped with a Convolutional Block Attention Module (CBAM) and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture complex spatiotemporal dependencies. We evaluated this approach through simulated trading on three major Chinese stock indices—the Shanghai Stock Exchange Composite (SSEC), the Shenzhen Stock Exchange Component (SZSE), and the China Securities 300 (CSI 300). Experimental results demonstrate the superiority of our method over traditional strategies and standard baselines; specifically, the trading agent achieved robust cumulative returns across the SSEC and CSI 300 indices, confirming the model’s exceptional capability in balancing profitability and risk aversion in complex financial environments. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Full article

Background/Objectives: Patients with food allergy (FA) may exhibit dysfunctional eating behaviours and/or food aversions that extend beyond the necessary allergen elimination diet and may result in avoidant/restrictive food intake disorder (ARFID) or food neophobia (FN); however, no data are available on adults. This study aimed to explore ARFID, FN, FA anxiety, and eating styles in adults with FA, analysing influences of sociodemographic and clinical factors. Methods: This cross-sectional preliminary study involved 79 adults with FA, who completed the Nine Item ARFID screen (NIAS), Food Neophobia Scale (FNS), Scale of Food Allergy Anxiety (SOFAA), and Dutch Eating Behaviour Questionnaire (DEBQ—brief). Pearson and Spearman correlation coefficients and multiple linear regressions were performed (p < 0.05). Results: In total, 25% and 73% of participants scored positively for ARFID and FN, respectively. A positive correlation was observed between FN and ARFID levels (p < 0.006), and between FN and FA anxiety (p < 0.001). Current psychological problems positively correlated with ARFID (p = 0.004), FN (p = 0.006), and FA anxiety scores (p = 0.03). Restrained eating was positively associated with female gender (p < 0.001), and ARFID (p = 0.002) and FN scores (p = 0.028). External eating was negatively correlated with ARFID (p = 0.004). Adrenaline auto-injector (AAI) prescription was negatively associated with ARFID (p < 001) and restrained eating (p = 0.006), while previous anaphylaxis was negatively associated with ARFID (p = 0.020) and positively associated with external eating (p = 0.021). Multiple logistic regression models confirmed that restrained eating was associated with a higher probability of both ARFID (p = 0.031) and FN (p = 0.074). Conclusions: Clinicians should be aware of the risk of ARFID and FN among adult patients with FA and recommend appropriate psychological and dietary support. Further studies are needed to better understand the protective and precipitating factors of ARFID and FN to develop effective prevention and treatment strategies. Full article

To enhance the low-carbon operational performance of integrated energy systems (IESs) under multi-source uncertainties, this study proposes a bilevel stochastic optimization framework incorporating a dynamic mean–CVaR risk model and a tiered carbon pricing mechanism. The upper level adopts an improved NSGA-II to jointly optimize economic cost, carbon emissions, and system flexibility through capacity planning decisions. The lower level performs scenario-based operation evaluation with a time-varying risk aversion coefficient, enabling differentiated risk responses across operating periods. A stepwise carbon price function and a capped carbon revenue mechanism are introduced to represent real carbon market regulations and avoid excessive emission reduction benefits. Multidimensional uncertainty scenarios—covering renewable variability, load fluctuations, and market price disturbances—are generated for risk-aware evaluation. Simulation results show that the proposed approach effectively reduces cost and emission volatility and achieves a more balanced trade-off between economy and low-carbon performance compared with conventional static-risk models. Sensitivity analyses further reveal that increased risk aversion shifts system operation strategies from economy-oriented to robustness-oriented modes, highlighting the importance of dynamic risk modeling and carbon policy design for future low-carbon multi-energy systems. Full article

Investment Risk Appetite (IRA) is a pivotal concept in risk management, reflecting an investor’s willingness to tolerate financial risks within acceptable thresholds. As empirical investigations into this construct gain momentum, there is a growing need for a scientifically validated tool to facilitate in-depth examinations of risk appetite. There are no scales to measure risk appetite. The present study addresses this gap by developing and validating a scale on risk appetite. Leveraging data collected from 405 respondents and employing established methodologies, the study introduces the Investment Risk Appetite (IRA) Scale. The questionnaire had a five-point scale. The scale consists of two factors: risk tolerance (α = 0.837, composite reliability = 0.836) and risk aversion (α = 0.905, composite reliability = 0.906). The validation was done by exploratory and confirmatory factor analysis (EFA and CFA). The loadings for EFA and CFA exceeded the threshold limit of 0.40. The scale demonstrates robust internal consistency, content, and construct validity. Hence, this scale has all the required validity. Overall, this scale demonstrates robust validity and reliability. In addition, this study examined the differences based on the demographics of the respondents. The scale, poised to make a significant contribution to the literature on risk appetite, will provide a theoretical foundation for future in-depth investigations. This study is expected to inspire future empirical examinations of this compelling construct. Full article

This paper examines how e-commerce retailers should structure return freight insurance (RFI) partnerships—exclusive single-insurer or competitive multi-insurer—to maximize consumer surplus. Using a game-theoretic model with heterogeneous consumers and vertically differentiated insurance products, we find that monopolistic RFI provision can paradoxically enhance consumer welfare over competition when markets exhibit high heterogeneity, limited loss aversion, and low compensation levels. The monopoly’s advantage stems from unified risk pooling, preventing adverse selection, cross-subsidization maintaining universal coverage, and quality preservation avoiding competitive price degradation. Optimal premiums show non-monotonic relationships with market parameters, creating distinct pricing regimes. Our results suggest retailers should match market structure to consumer characteristics: exclusive arrangements for heterogeneous, price-sensitive markets, while competitive provision for homogeneous, loss-averse segments. These insights advance our understanding of platform economics and inform tailored market design for internet insurance. Full article

Modern financial systems do not exist in isolation but form part of a complex global network of interconnected financial systems. This globalization of financial systems significantly increases the risk of contagion in financial markets, impacting asset prices and other important economic factors, including interest rates and market volatility. This phenomenon informs not only investors’ investment strategies but also the prices of contingent claims. In this article, we present a derivative pricing model in an incomplete and globalized financial market. To appreciate the dynamics and impact of some important market factors, particularly default risks due to contagion, we consider two different financial markets with defaultable assets: in one market, we consider a stock whose price process follows a Heston stochastic volatility model, and in the other, a stock that follows a Hawkes-type jump diffusion model whose intensity is subjected to external systemic shocks. In both markets, we derive an indifference price for a contingent claim that is subject to the risk of default and show the impacts the investor’s risk aversion and external shocks on the price of the contingent claim. Full article

Forest ecosystems are increasingly degraded by natural disasters and human activities, intensifying the need for large-scale restoration. Because restoration outcomes are long-term, uncertain, and largely non-market, understanding how environmental attitudes relate to public preferences and willingness to pay (WTP) is important for socially acceptable and financially feasible policy design. Using a discrete choice experiment in Korea, this study applies a hybrid choice framework that incorporates latent attitudinal variables into a mixed logit structure, allowing attitudes to interact with preference heterogeneity across restoration attributes. Results show significant heterogeneity in choices and WTP. The model identifies two segments with distinct trade-off patterns: one is more sensitive to risk and payment burden, while the other places relatively greater value on restoration and access-related improvements. Although attitudinal indicators are statistically relevant, segment differentiation is more strongly associated with risk sensitivity and cost aversion than with attitudes alone. Compared with conventional choice models, the latent-attitude specification improves behavioural interpretability and model fit, and yields policy-relevant WTP estimates. Overall, the findings indicate that attitudinal information is complementary to economic and risk-related factors, supporting more targeted and publicly aligned forest-restoration policies. Full article

Urban contemporary living has increasingly shifted emotional regulation inward, away from wider social networks and into tightly managed daily life. Within this landscape, dogs can become regulatory partners whose presence reshapes human rhythms, attention, and everyday sociability. This review examines how urban conditions—including risk-averse caregiving, dense living, and reduced opportunities for sustained social contact—reconfigure emotional co-regulation within human–dog relationships and, in turn, human emotional environments. Drawing on research from behavioural science, physiology, comparative ethology, psychology, and the social sciences (2010–2025), it treats attachment, synchrony, and social buffering as interconnected processes. Across disciplines, evidence suggests that dogs and humans often settle emotionally together, showing coupled dynamics in behaviour and physiology. Such coupling can support stress buffering and recovery, yet under chronic human stress or heightened control it may stabilise shared vigilance and dependence, concentrating regulatory work within the dyad. These effects are conditional: when dogs lack stable, reciprocal social buffering—especially with conspecifics—the dyad may be less able to support recovery, and synchrony may tilt toward vigilance rather than calm. Seen this way, human–dog bonds function as part of the emotional infrastructure of contemporary cities, shaping how calm, uncertainty, and social contact are organised. Full article

The aim of this article is to analyze the motivations and barriers that influence the pursuit of non-agricultural activities by residents of rural areas in Poland and to assess the extent to which entrepreneurial activity contributes to the diversification of household income and strengthening the economic stability of these households. The study was conducted among households located in rural areas using a diagnostic survey method. The empirical analysis was based on comparative tests, correlation analysis, and linear regression models. The results indicated that the main motivations for engaging in non-agricultural activities are the desire to increase income, the need for financial stability, and the desire to utilize local resources, while limited access to capital, risk aversion, and insufficient business knowledge remain significant barriers. The analyses confirmed that households conducting non-agricultural activities are characterized by a higher level of income diversification and greater resilience to economic fluctuations. From this perspective, income diversification based on non-agricultural activities can be considered an important mechanism for strengthening the economic sustainability of households and supporting the sustainable development of rural areas by reducing their vulnerability to economic risk. The obtained results indicate the importance of activities supporting the development of entrepreneurial competencies of rural residents and improving the availability of financial instruments as factors conducive to income diversification. Future research could deepen the analysis by considering the diversity of non-agricultural activities, the intermediary mechanisms between income diversification and economic resilience, and a dynamic approach to changes in household income strategies. Full article