Search Results (52)

This research aims to investigate financing decisions of capital-constrained small and medium-sized enterprise (SME) manufacturers and distributors under a Green Supply Chain (GSC) framework. By evaluating the impact of Supply Chain Finance (SCF) instruments, this study utilizes Stackelberg game model to explore a decentralized decision-making system. To our knowledge, this investigation represents the first exploration of game models that uniquely compares financing through trade credit, where the manufacturer offers zero-interest credit without discounts with reverse factoring, while also considering distributor’s efforts on sustainable marketing under the impact of supportive government policies. Our study suggests that manufacturers should adopt reverse factoring for optimal profits and actively participate in distributors’ financing decisions to address inefficiencies in decentralized systems. Furthermore, the distributor’s demand quantity, profits and sustainable marketing efforts show significant increase under reverse factoring, aided by favorable policies. Finally, the results are validated through Python 3.8.8 simulations in the Anaconda distribution, offering meaningful insights for policymakers and supply chain managers. Full article

As ad-supported subscription models proliferate across over-the-top (OTT) media platforms, understanding the psychological mechanisms and perceptual factors that underlie consumers’ transition decisions becomes increasingly consequential. This study integrates the Uses and Gratifications framework with a contemporary motivation-based perspective to examine how users’ media consumption motivations and advertising attitudes predict intentions to adopt ad-supported OTT plans. Data were collected via a nationally representative online survey in South Korea (N = 813). The sample included both premium subscribers (n = 708) and non-subscribers (n = 105). The findings reveal distinct segmentation in decision-making patterns. Among premium subscribers, switching intentions were predominantly driven by intrinsic motivations—particularly identity alignment with content—and by the perceived informational value of advertisements. These individuals are more likely to consider ad-supported plans when ad content is personally relevant and cognitively enriching. Conversely, non-subscribers exhibited greater sensitivity to extrinsic cues such as the entertainment value of ads and the presence of tangible incentives (e.g., discounts), suggesting a hedonic-reward orientation. By advancing a dual-pathway explanatory model, this study contributes to the theoretical discourse on digital subscription behavior and offers actionable insights for OTT service providers. The results underscore the necessity of segment-specific advertising strategies: premium subscribers may be engaged through informative and identity-consistent advertising, while non-subscribers respond more favorably to enjoyable and benefit-laden ad experiences. These insights inform platform monetization efforts amid the evolving dynamics of consumer attention and subscription fatigue. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

Key core technology innovation has become an important strategic path for countries to maintain industrial security amid intensifying global technological competition. As an important innovation paradigm, R&D collaboration is generally regarded as an effective way to achieve such innovation. However, the key issue of which collaborative mechanism is most effective at promoting key core technology innovation remains insufficiently explored. Therefore, systematically comparing the effectiveness of different mechanisms of collaborative innovation is of great strategic significance for achieving key core technology innovation and overcoming Western technological blockades. In this study, the R&D level and market share of key core technology were incorporated into an analytical framework and applied to a differential game focused on the innovation behaviors of leading enterprises, supporting enterprises, and academic research institutions under Nash non-collaborative, cost-sharing, and collaborative mechanisms. A simulation analysis was conducted using the MATLAB 2020a software. The results show that the optimal strategies for the key core technology innovation of innovation entities are negatively correlated with the cost coefficient, discount rate, technology, and market recession coefficient. Meanwhile, they are positively correlated with the sensitivity coefficient of technology R&D and market promotion. Furthermore, the R&D levels and market shares of key core technology are highest under the collaborative mechanism. In this scenario, the revenues of the innovation entity and the overall system reach Pareto optimality. Within a threshold range, the cost-sharing mechanism significantly improves innovative efforts, the R&D level, and the market share of key core technology, leading to a Pareto improvement for both the participants’ and overall system’s revenues compared to the non-collaborative mechanism. This study not only contributes to theoretical results of differential games but also provides valuable suggestions for policymakers and innovation entities to foster key core technology innovation from the perspective of collaboration. Full article

Traditionally, catering enterprises were restricted to exclusive agreements with food delivery platforms. However, recent regulatory changes in various countries addressing the “one out of two” practices of these platforms have facilitated the ability of catering businesses to engage with multiple platforms simultaneously. The existing body of literature primarily focuses on the competitive interactions between bilateral platforms or the optimal strategies for exclusive trading platform supply chains, yet it does not adequately investigate the differentiated impacts of merchant marketing strategies within this competitive framework. This study seeks to evaluate the effects of diverse strategies on the performance of supply chain participants, concentrating on four distinct strategies employed by catering businesses in their selection of service platforms and the execution of marketing initiatives. By employing game theory and inverse solving methodologies, the research determines the optimal profits for each member of the supply chain under various strategies, as well as the influence of valuation discount factors on member profits. The results reveal that the marketing efforts of service providers across different platforms significantly affect the performance of supply chain participants. Additionally, the profits of both service providers and platforms are correlated with the cost coefficient related to the marketing efforts of service providers, and these marketing initiatives also impact the perceived value of platform coupons. This research provides evidence for the positive influence of marketing decisions made by service providers. Full article

In financial markets, irrational behaviors such as hyperbolic discounting and panic selling are prevalent. However, their widespread empirical associations remain unexplored. Numerous behavioral theories discuss how cognitive biases exacerbate panic selling through the lens of immediate loss aversion, a phenomenon in which individuals exhibit impulsive decision-making tendencies due to an intense fear of financial loss during market upheaval. Despite the theoretical elucidation, empirical investigations of these dynamics are lacking. Using a robust dataset comprising 121,293 active investors sourced from a collaborative effort between Hiroshima University and Rakuten Securities Inc., this study used mean comparison tests and probit regression to analyze hyperbolic discounting’s role in panic selling behavior on the global COVID-19 financial crisis. The findings reveal that hyperbolic discounting plays a central role in triggering investors’ impulsive panic selling behavior, which is driven primarily by fear of potential losses. Other factors that influence panic selling behavior include age, male gender, low education level, financial literacy, household income, household assets, risk aversion, and overconfidence in financial knowledge. Our study explicates the need to address cognitive biases in financial decision making during market crises through strategies such as targeted financial education, regulatory interventions against market manipulation, and the provision of professional advice to investors. Full article

Despite Parvovirus B19 (B19V) generally causing mild or asymptomatic infections, and only certain high-risk groups such as hematological or immunocompromised patients and pregnant women tending to develop complications, several factors challenge the assumption of a “benign” clinical course in immunocompetent adults and adolescents. A significant proportion of the population may harbor undiagnosed health conditions or genetic predispositions that could render them more susceptible to severe B19V complications. These could include mild hematological disorders, immune dysregulation not resulting in overt immunodeficiency, or underlying cardiac conditions. Concurrent infections with other pathogens, even seemingly minor ones, could synergistically increase the severity of B19V infection, leading to more pronounced clinical manifestations. While not definitively proven, the possibility of emerging B19V strains with increased virulence or altered tissue tropism cannot be entirely discounted. Additionally, the period of pandemic-related restrictions likely led to reduced B19V circulation, potentially resulting in a cohort of young adults with limited natural immunity, making them more vulnerable to infection. Potential clinical consequences include atypical and severe presentations, even in individuals without known risk factors. The traditional focus on B19V primarily as a pediatric concern might lead to underdiagnosis or delayed diagnosis in adults, potentially hindering timely intervention and management. A surge in B19V-related complications, even if individually mild, could collectively strain healthcare resources, particularly in settings with limited capacity or pre-existing pressures. Possible recommendations are to heighten clinical awareness with a high index of suspicion for B19V infection in adults and adolescents presenting with compatible symptoms, even in the absence of classic risk factors. Additionally, expanding testing criteria and enhancing public health surveillance efforts would be prudent. Full article

Globally, the integration of electric vehicles (EVs) in the transportation sector represents a significant step towards achieving environmental decarbonization. This shift also introduces a new demand for electric power within the utility grid network. This study focuses on the design and development of a grid-connected renewable energy system tailored to meet the EV load demands in Taif, Kingdom of Saudi Arabia (KSA). The integration of renewable energy sources, specifically solar photovoltaic (SPV) and wind turbines (WT), is explored within the context of economic feasibility and system reliability. Key considerations include optimizing the system to efficiently handle the fluctuating demands of EV charging while minimizing reliance on conventional grid power. Economic analyses and reliability assessments are conducted to evaluate the feasibility and performance of the proposed renewable energy system. This article discusses the technical sizing of hybrid systems, energy reduction, and net present cost for the selected location. A rigorous sensitivity analysis is performed to determine the impact of major variables such as inflation rate, real discount rate, solar irradiation, and Lack of Power Supply Probability (LPSP) on system performance. The results demonstrate that the Pufferfish Optimization Algorithm (PFO) significantly outperforms other metaheuristic algorithms documented in the literature, as well as the HOMER software. The study found that the grid-connected renewable energy system is the best option for operating EV charging stations at the selected location. The findings underscore the potential for sustainable energy solutions in urban environments like Taif, highlighting the importance of integrating renewable energy technologies to meet growing energy demands with enhanced economic efficiency and system reliability. This initiative seeks to pave the way for a greener and more resilient energy infrastructure, aligning with global efforts towards sustainable development and clean transportation solutions. Full article

With the development of artificial intelligence, reinforcement-learning-based intelligent control algorithms, which generally learn control strategies through trial and error, have received more attention in the automation equipment and manufacturing fields. Although they can intelligently adjust their control strategy without the need for human effort, the most relevant algorithms for servo motors only consider the overall performance, while ignoring the risks in special cases. Therefore, overcurrent problems are often triggered in the training process of the reinforcement learning agent. This can damage the motors’ service life and even burn it out directly. To solve this problem, in this study we propose a risk-sensitive intelligent control algorithm based on value distribution, which uses the quantile function to model the probability distribution of cumulative discount returns and employs the condition value at risk to measure the loss caused by overcurrent. The agent can accordingly learn a control strategy that is more sensitive to environmental restrictions and avoid the overcurrent problem. The performance is verified on three different servo motors with six control tasks, and the experimental results show that the proposed method can achieve fewer overcurrent occurrences than others in most cases. Full article

Building-integrated photovoltaics (BIPVs) entail the use of photovoltaics as building materials, such as windows, roofs, and walls. Owing to their electricity-generation ability, BIPVs have become popular building materials for green buildings. This study involves an economic feasibility analysis of BIPVs for an agricultural research and development center in Fiji. The computerized relative allocation of facilities technique (CRAFT) is extended for cost-efficient facility design, and the levelized cost of electricity (LCOE) of BIPVs is evaluated to identify feasible alternatives from among three options, namely roof-integrated, window, and rooftop BIPVs. From the experiments, the LCOE values of the roof-integrated, window, and rooftop BIPVs were USD 0.13/kWh, 0.17/kWh, and 0.09/kWh, respectively. Moreover, the profit for the window BIPV was negative when the discount rate was greater than 0.08 due to its inefficient productivity. Contrarily, the roof-integrated BIPV showed a reasonable LCOE even though it required the highest investment cost. Nevertheless, further efforts are needed to reduce the cost of BIPVs for practical implementation. Full article

Environmental, social, and governance (ESG) integration is an increasingly popular and innovative investing strategy that requires companies to be transparent about their ESG practices to facilitate investors’ decisions. In the palm oil sector, companies are addressing ESG risks by adopting and disclosing ESG efforts to improve access to financing. This study seeks to broaden existing research on ESG transparency and firms’ financial indicators by using firm valuation as a financial indicator and investigating the moderating role of firm size in the palm oil sector. It first investigates whether ESG transparency has a direct positive or negative effect on firm valuation. Transparency is measured using the Zoological Society of London’s (ZSL) Sustainability Policy Transparency Toolkit (SPOTT) 2021 assessment, which provides scores for palm oil companies’ total, environmental, social, and governance disclosures. Firm valuation is measured by the price-to-earnings ratio (P/E), a widely used ratio calculated by dividing the share price by earnings per share. The study also explores the moderating role of firm size, using accounting-based measures such as revenue and assets, in strengthening the relationship between ESG transparency and firm valuation. The results show statistically significant negative relationships between ESG transparency and firm valuation. Companies with stronger ESG transparency are valued at a discount relative to companies with weaker ESG transparency. Additionally, the results find that firm size plays a moderating role such that larger firms strengthen the negative relationships between all transparency measures and firm valuation. These findings encourage constructive action for various stakeholders and provide implications for future research to support mainstreaming sustainable palm oil. Full article

The use of renewable energy sources has become strategic in the production of electricity worldwide due to global efforts to increase energy efficiency and achieve a net zero carbon footprint. Hybrid systems can maximize stability and reduce costs by combining multiple energy sources. A conventional metric, such as the levelized cost of energy (LCOE), that is appropriate for assessing the cost-effectiveness of an option may not be appropriate when evaluating the economic feasibility of hybrid systems. This study proposes a stochastic discounted cash flow model (DCF) to assess the economic viability of a hybrid renewable energy system (HRES) in Brazil. The objective is to determine the combinations that will provide the highest 50th percentile internal rate of return (IRR) and the lowest coefficient of variation (CV). Model variables include capital expenditures (CAPEX), operation and maintenance (O&M) costs, sectoral charges, taxes, and long-term energy production metrics. The results demonstrate that the synergies modeled contributed to the higher economic outcomes for the HRES obtained by combining both energy sources rather than opting for a stand-alone configuration. A wind-dominant combination of 60% wind was able to increase the 50th percentile of the IRR, while a solar-dominant combination of 65% solar minimized the CV. Full article

In the modern catering business model, restaurants usually use established platforms to promote their food and use two channels to sell their food: online and offline sales. We construct demand functions for online and offline, considering promotion and substitution relationships by a revised Bertrand model. We first consider three classic models: the decentralized decision model, the equilibrium decision model, and the centralized decision model. In the decentralized decision model, the platform decides both the promotional effort and the online discount; in the equilibrium decision model, the platform decides the online discount, while the food service provider decides the promotional effort. In the centralized decision model, the takeaway platform and the food service provider have maximized the overall profit as the decisive goal. We find that the online discount decreases in price when the impact factor of the online promotion is high but increases in price when the impact factor of the online promotion is low. Then, we analyze and compare the results under three models. We find that when the substitution factor is low enough, or the impactor factor of online promotion is low enough, the global optimal platform discount is higher than the equilibrium platform discount and the decentralized online discount; otherwise, the results are the opposite. In addition, the global optimal promotional effort is always higher than the optimal promotional effort in the decentralized model. When the substitution factor is low enough, or the impactor factor of online promotion is low enough, the global optimal promotional effort is higher than the equilibrium optimal promotional effort; otherwise, the result is the opposite. Full article

Although procrastination has been extensively studied, precrastination remains an unsolved puzzle. Precrastination is the tendency to start tasks as soon as possible, even at the cost of extra effort. Using the near bucket paradigm with 81 undergraduate students, this study examined the relationship between precrastination and time perspective, proactive personality, and subjects’ differential performance in intertemporal decision-making. The results confirmed the cognitive-load-reduction (CLEAR) hypothesis. Precrastination was found to be positively predicted by the future time dimension of time perspective and negatively predicted by proactive personality. In addition, there is a significant positive correlation between precrastination and delay discounting of intertemporal decision-making, which exists only for the loss situation. Full article

Climate change threatens economic and environmental stability and requires immediate action to prevent and counteract its impacts. As large investments are already going into mitigation efforts, it is crucial to know how to best allocate them in time and among the alternatives. In this work, we tackle this problem using optimal control methods to obtain the temporal profiles of investments and their allocation to either clean energy development or carbon removal technologies expansion. The optimal allocation aims to minimize both the abatement and damage costs for various scenarios and mitigation policies, considering the optimization time horizon. The results show that early investments and a larger share of demand satisfied by clean energy should be priorities for any economically successful mitigation plan. Moreover, less stringent constraints on abatement budgets and reduced discounting of future utility are needed for a more economically and environmentally sustainable mitigation pathway. Full article