Search Results (16,205)

This paper examines the impact of the reference price effect on pricing decisions in a risk-averse supply chain with a dual-sourcing procurement strategy, particularly during single-sourcing supply disruption. To analyze supply chain pricing decisions under non-disrupted and disrupted scenarios, we innovatively use semivariance as a risk measure to effectively avoid the limitations of the traditional variance approach and integrate it into Stackelberg game models. Based on these models, we analyze the impact of the reference price effect, risk aversion, and single-sourcing supply disruption on supply chain members’ pricing decisions. The main findings include the following: the single-sourcing supply disruption degree may increase the price of non-disrupted products and then increase the non-disrupted supplier’s utility; the strength of the reference price effect positively influences retailer utility but negatively impacts product pricing for supply chain members; the pricing decisions and utility of supply chain members are influenced by their risk aversion, and supply chain members with higher risk aversion adopt more conservative pricing strategies and consequently obtain lower utility; and equilibrium decisions generally demonstrate a degree of robustness. These insights may help supply chain managers respond rationally to supply disruptions and properly develop pricing strategies by taking into account the reference price effect. Full article

This study, within the framework of uncertainty theory, employs an uncertain differential equation with jumps to model the asset value process of a company, establishing a structured model of uncertain credit risk that incorporates jumps. This model is applied to the pricing of two types of credit derivatives, yielding pricing formulas for corporate zero-coupon bonds and Credit Default Swap (CDS). Through numerical analysis, we examine the impact of asset value volatility and jump magnitude on corporate default uncertainty, as well as the influence of jump magnitude on the pricing of zero-coupon bonds and CDS. The results indicate that an increase in volatility levels significantly enhances default uncertainty, and an expansion in the magnitude of negative jumps not only directly elevates default risk but also leads to a significant increase in the value of zero-coupon bonds and the price of CDS through a risk premium adjustment mechanism. Therefore, when assessing corporate default risk and pricing credit derivatives, the disturbance of asset value jumps must be considered a crucial factor. Full article

The increasing penetration of photovoltaic (PV) systems and the need for reliable backup power solutions have led to the development of hybrid uninterruptible power supply (UPS) systems. These systems integrate PV energy storage with battery backup and grid power to optimize real-time energy management. This paper proposes an advanced energy management strategy and an artificial neural network (ANN)-based control method for PV-integrated hybrid UPS systems. The proposed strategy dynamically determines the optimal power-sharing ratio between battery storage and the grid based on real-time economic parameters, load demand, and battery state of charge (SoC). A centralized ANN-based controller ensures precise control of the LLC converter and rectifier, achieving stable and efficient power distribution. Additionally, a genetic algorithm is implemented to optimize the power sharing ratio, minimizing the LCOE under varying load and electricity pricing conditions. The proposed approach is validated through simulations, demonstrating significant improvements in cost-effectiveness, system stability, and dynamic adaptability compared to conventional control methods. These findings suggest that integrating ANN-based control with optimized energy management can enhance the efficiency and sustainability of hybrid UPS systems, particularly in fluctuating energy markets. Full article

This study investigates the relationship between news delivered via the QUICK terminal and stock market behavior. Specifically, through an evaluation of the performance of investment strategies that utilize news index created based on its scores indicating positive or negative sentiment, we examine whether index construction that takes into account the content of individual news items contributes to improved predictive power with regard to stock prices. We verify the performance of this investment strategy based on signal indicators derived from news indices focusing on short-term trends using time-series decomposition. After refining the news indicators based on news categories, we observe an improvement in the strategy’s performance, demonstrating that the value of news varies across different categories and the importance of considering the content and meaning of text news. Full article

Inductive Power Transfer (IPT) is evolving fast in many domains, but its efficiency, its extensive resource requirements, and its cost remain crucial problems for its development. Although the inverter is mainly responsible for its cost and material consumption, a considerable quantity of conductors is required for the coupling realization. Therefore, A drastic cost reduction is possible when comparing the traditional most efficient copper Litz wire with aluminum conductors for a similar volume and a lighter embedded system. However, alternative ribbon wire solutions are also characterized and seem promising as substitutes for such applications. First, standard electrical efficiency is evaluated for all cases, before the price and weight. To complement the results and as the alternative couplers imply different materials and production processes, a Life Cycle Assessment is performed. A comparison is carried out on copper and aluminum litz wires and copper and aluminum ribbons. Results demonstrate the promising interest in industrial application of such study, furthermore for systems requiring many couplers as Dynamic IPT (DIPT). Full article

Genetically modified (GM) foods have existed for decades, and governments internationally have legislated packaging disclosure statement language that typically incorporates the words genetic, modified, and organism. In 2018, the United States implemented the National Bioengineered Food Disclosure Standard (NBFDS) and introduced the term Bioengineered (BE) into GM disclosure language to help clarify consumer uncertainty regarding GM foods. Since then, the US consumer attitudes, perceptions, and knowledge of genetically modified foods remain negative, reflecting a contaminated interaction. Current mandated disclosure labels, utilizing engineering and science-based signifiers, are associated with this negative interaction. This research assesses whether food disclosure labels based on the signifier Design, unassociated with current contaminations, can positively impact the consumer perception of GM foods compared to the negatively contaminated science and engineering signifiers currently used. Two online studies of 1931 participants analyzed GM/BE food disclosure labels comparing four existing and six newly created engineering and science-based signifiers against four new design-based signifiers across fifteen attributes, including Price, Purchase Likelihood, Environmental Impact, Fair Trade, Safety, Nutrition, Healthfulness, Quality, Eating Experience, Comforting, Inviting, Frightening, Understandable, Ethical, and Sustainable. Across both studies, design-related labels consistently outperformed traditional engineering/science-based terms in fostering positive perceptions. However, even the best-performing labels did not fully overcome the entrenched skepticism associated with GM foods, underscoring the need for complementary strategies beyond linguistic changes. Full article

The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be susceptible to outliers. To account for potential model violations, a maximum trimmed likelihood estimation (MTLE) approach is utilized to derive a system of nonlinear estimating equations, and an iterative procedure is developed to solve the latter. In addition to robustness, our new technique allows for reliable recovery of the long-term mean, unlike existing methodologies. A set of simulation studies across multiple dimensions, sample sizes and robustness configurations are performed. MTLE outcomes are compared to those of multivariate least trimmed squares (MLTS), MLE and MLS. Empirical results suggest that MTLE not only maintains good relative efficiency for uncontaminated data but significantly improves overall estimation quality in the presence of data irregularities. Additionally, real data examples containing daily log-volatilities of six common assets (commodities and currencies) and US/Euro short rates are also analyzed. The results indicate that MTLE provides an attractive instrument for interest rate forecasting, stochastic volatility modeling, risk management and other applications requiring statistical robustness in complex economic and financial environments. Full article

In the context of new retail, the development of omni-channels is flourishing. The entry threshold for the clothing industry is low, and the popularity of online shopping has, to some extent, reduced consumers’ perception of the authenticity of clothing. As a result, returns are a serious issue in the clothing industry. This article focuses on a clothing retailer while addressing retail and return issues in the clothing industry. It develops and analyzes models for an online single-channel strategy and two omni-channel showroom strategies: “Experience in Store and Buy Online (ESBO)” with an experience store and “Buy Online and Return in Store (BORS)” with a physical store. These models are used to examine the pricing and return decisions of the retailer in the three strategic scenarios. Additionally, this study considers the impact of fashion trends on demand. It explores pricing and return strategies in two showroom models under the influence of the fashion trend decay factor. Moreover, sensitivity analyses and numerical analyses of the important parameters are performed. This research demonstrates the following: (1) In the case of high return transportation costs and online return hassle costs, clothing retailers can attract consumers to increase profits through establishing offline channels; (2) extending the sales time of fashionable clothing has a positive effect on profits, but blindly prolonging the continuation of the sales time will lead to a decrease in profits; (3) the larger the initial fashion level or the smaller the fashion level decay factor, the greater the optimal retailer profits. The impacts of the initial fashion level and fashion level decay factor on profits are more significant in omni-channel operations. This article aims to identify optimal strategies for retailers utilizing omni-channel operations and offer managerial insights for the sale of fashionable apparel. Full article

Prior studies have shown mixed findings on the role of financial slack. This study examines how environmental factors such as munificence, dynamism, and complexity moderate the relationship between financial slack and innovation activity. Using data from Compustat and the Center for Research in Security Prices (CRSP) database on 578 computer-processing firms in innovation-intensive industries in the United States, our results reaffirm that financial slack is a strategic asset that enhances R&D investment. Further, we find that the positive consequences of financially abundant firms pursuing innovation are attenuated in munificent environments where firms increasingly rely on external resources. Similarly, in dynamic environments, unpredictable market changes divert slack resources from long-term R&D investments, further weakening the effect. However, there is no significant difference in complex environments. Our study contributes to the existing literature by integrating different environments and highlighting the importance of balancing internal resources with external environments in shaping innovation strategies. For managers, these findings provide practical guidance for resource allocation strategies to effectively support innovation in varying external environments. Full article

Various Higher Education Institutions (HEIs) set themselves goals to become carbon neutral through the implementation of different reduction strategies such as the replacement of fossil-fueled vehicles with electric cars. However, even if all reduction measures are taken, residual GHG emissions will still remain. Therefore, most HEIs have to compensate for the remaining emissions by, for example, buying carbon credits. However, due to growing criticism of carbon credit purchases, HEIs need to explore options for establishing carbon sinks on their own premises to offset their remaining, unavoidable emissions. This study aimed to assess the CO₂ footprint of Hochschule Geisenheim University (HGU) as an exemplary HEI, identify emission hot-spots, and investigate the potential of biomass utilization for achieving carbon neutrality or even negative emissions. The analysis found that HGU’s main emissions were scope 1 emissions, primarily caused by on-site heat supply. The research determined that conversion to a wood chip-based heating system alone was insufficient to achieve climate neutrality, but this goal could be achieved through additional carbon dioxide removal (CDR). By operating a pyrolysis-based bivalent heating system, the study demonstrated that heat demand could be covered while producing sufficient C-sink certificates to transform HGU into the first carbon-negative HEI, at a comparable price to conventional combustion systems. Surplus C-sink certificates could be made available to other authorities or ministries. The results showed that bivalent heating systems can play an important role in HEI transitions to CO₂ neutrality by contributing significantly to the most urgent challenge of the coming decades: removing CO₂ from the atmosphere to limit global warming to as far below 2 °C as possible at nearly no extra costs. Full article

This study examines the out-of-sample predictability of expected skewness of oil price returns, which serves as a metric for global future risks, as we show statistically through the association with crises of different nature, for stock returns of 10 (8 advanced plus two emerging) countries using long-range monthly data of over a century for each country. Using a distributed lag predictive econometric model, which controls for endogeneity, persistence, and conditional heteroscedasticity, we provide evidence of the strong statistical significance of the predictive impact of the third moment of oil price returns for equity returns for all the countries across various forecast horizons and the length of out-of-sample periods. These findings also hold for the shorter sample periods of 3 other emerging markets: Brazil, China, and Russia. Our findings have important implications for academics, investors, and policymakers. Full article

The global energy sector has been profoundly reshaped by the COVID-19 pandemic, triggering diverse reactions in energy demand patterns, accelerating the transition toward renewable energy sources, and amplifying concerns over global energy security and the digital safety of energy infrastructure. Five years after the pandemic’s onset, this study provides a taxonomy-based lesson-learned analysis, offering a comprehensive examination of the pandemic’s enduring effects on energy systems. It employs a detailed analytical framework to map short-, medium-, and long-term transformations across various energy-related sectors. Specifically, the study investigates significant shifts in the global energy landscape, including the electric and conventional vehicle markets, the upstream energy industry (oil, coal, and natural gas), conventional and renewable energy generation, aerial transportation, and the broader implications for global and continental energy security. Additionally, it highlights the growing importance of cybersecurity in the context of digital evolution and remote operations, which became critical during the pandemic. The study is structured to dissect the initial shock to energy supply and demand, the environmental consequences of reduced fossil fuel consumption, and the subsequent pivot toward sustainable recovery pathways. It also evaluates the strategic actions and policy measures implemented globally, providing a comparative analysis of recovery efforts and the evolving patterns of energy consumption. In the face of a global reduction in energy demand, the analysis reveals both spatial and temporal disparities, underscoring the complexity of the pandemic’s impact on the energy sector. Drawing on the lessons of COVID-19, this work emphasizes the need for flexible, forward-thinking strategies and deeper international collaboration to build energy systems that are both resilient and sustainable in the face of uncertainties. Full article

This study investigated market efficiency across 20 major commodity assets, including crude oil, utilizing fractal analysis. Additionally, a rolling window approach was employed to capture the time-varying nature of efficiency in these markets. A Granger causality test was applied to assess the influence of crude oil on other commodities. Key findings revealed significant inefficiencies in RBOB(Reformulated Blendstock for Oxygenated Blending) Gasoline, Palladium, and Brent Crude Oil, largely driven by geopolitical risks that exacerbated supply–demand imbalances. By contrast, Copper, Kansas Wheat, and Soybeans exhibited greater efficiency because of their stable market dynamics. The COVID-19 pandemic underscored the time-varying nature of efficiency, with short-term volatility causing price fluctuations. Geopolitical events such as the Russia–Ukraine War exposed some commodities to shocks, while others remained resilient. Brent Crude Oil was a key driver of market inefficiency. Our findings align with Fractal Fractional (FF) concepts. The MF-DFA method revealed self-similarity in market prices, while inefficient markets exhibited long-memory effects, challenging the Efficient Market Hypothesis. Additionally, rolling window analysis captured evolving market efficiency, influenced by external shocks, reinforcing the relevance of fractal fractional models in financial analysis. Furthermore, these findings can help traders, policymakers, and researchers, by highlighting Brent Crude Oil as a key market indicator and emphasizing the need for risk management and regulatory measures. Full article

Historically, innovation has been a milestone in achieving sustainable agriculture for small-scale producers. For several centuries, innovation has improved agricultural activity. However, there is still the challenge of introducing technologies pertinent to the knowledge and practices of small producers to achieve sustainability. Therefore, the present study explores the traditional knowledge embedded in the activities of Planting–Harvest and First Disposal circuit (PHFDc) of beans (Phaseolus vulgaris L.) for its innovation involving the social, economic, and environmental context. Applying the methodology of roadmapping technology to 73 small-scale producers in Guanajuato, Mexico, combining the SDGs catalogue, in addition to statistical analysis, the results show access to government financial support; improving sales price, production, area, and profitability; having accessible tools; creating their inputs; in addition to having more excellent knowledge for plant care and advice as strategies to develop within economic sustainability. In this sense, based on the assertion that social and productive conditions are directly related to innovation, the proposal for reorientation is towards the creation of word credit, improving bean varieties, sustainable practices, mechanical seeders, bean corridors, and the connection with associations and institutes as the most pertinent ones that are developing in similar contexts. This research can be significant for small producers and the general population regarding food security, zero hunger, and the fight against climate change, as well as for researchers and politicians who support continuing new studies. Full article

This research identified the optimal scenarios to produce three bioenergy outputs: dual generation (electricity and heat), electricity, and heat in two regions located in the northern part of Costa Rica. Two biomass conversion technologies—boilers and gasification—with 2, 5, and 10 MW production capacities were assessed to ascertain the most suitable technology-capacity pairing for each bioproduct. To this end, a comprehensive financial model was developed to maximize the net present value. Following this, the equilibrium point for biomass supply and demand was ascertained, alongside estimations of the associated costs and energy utility. The findings indicated that the three bioenergy products could be completed within the local energy market at prices below 0.14 USD/kWh, with maximum supply distances of 90 km. The boiler and turbine technology proved most suitable for dual and electricity generation, with capacities ranging between 2 MW and 5 MW, where differentiation was influenced by biomass transportation. Furthermore, heat generation demonstrated financial viability at a capacity of 2 MW. In the evaluation of supply-demand break-even points, a maximum benefit of 26% was observed, with dual production yielding the highest benefits and heat production being the least favorable option due to the costs linked to biomass transportation and the low efficiency of energy transformation. Full article