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The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

The increased participation of economies in regional and bilateral free trade agreements (FTAs) has resulted in welfare effects. This chapter attempts to determine the welfare implications of preferential reductions in tariffs and free-trade zones on Afghan imports by adopting the Magee (2016) framework. This approach separates the consequences of tariff hikes triggered by FTAs from the general equilibrium effects (GEEs) caused by unknown variables impacting the country's imports (historical links, shared language and culture, landlockedness, etc.). This method evaluates whether preferential tariff reductions favouring partner countries would benefit or harm member countries. The results indicate that the magnitude of the effects of trade creation (TC) is significantly higher than those of trade diversion. TC resulting from GEEs unexpectedly surpasses TC resulting from tariff preferences extended to member nations. Afghanistan's FTAs are not harmful but enhance living conditions. This chapter recognises South Asian Free Trade Area’s (SAFTA's) potential for trade expansion by focusing on commitment to regional integration and increasing liberalisation by implementing a more easily upgraded tariff framework and trade facilitation system. The findings are relevant since World Trade Organisation (WTO) members are often sceptical about regional trade agreements (RTAs) or Bilateral Free Trade Agreements (BFTAs) as agents harming well-being.

This paper aims to explore blockchain integration strategies within a three-level livestock meat supply chain in which consumers have a preference for quality trust in livestock meat products. The paper investigates three questions: First, how does consumers’ preference for quality trust affect blockchain integration and transaction decisions among supply chain participants? Second, under what circumstances will retailers choose to participate in the blockchain? Finally, how can other factors such as blockchain costs and supplier–retailer partnership value affect integration decisions?

This paper formulates a supply chain network equilibrium model and employs the logarithmic-quadratic proximal prediction-correction method to obtain equilibrium decisions. Extensive numerical studies are conducted using a pork supply chain network to analyze the implications of blockchain integration for different supply chain participants.

The results reveal several key insights: First, suppliers’ increased blockchain integration, driven by higher quality trust preference, can negatively affect their profits, particularly, with excessive trust preferences and high blockchain costs. Second, an increase in consumers’ preference for quality trust expands the range of unit operating costs for retailers engaging in blockchain. Finally, the supplier–retailer partnership drives retailer blockchain participation, facilitating enhanced information sharing to benefit the entire supply chain.

This study provides original insights into blockchain integration strategies in an agricultural supply chain through the application of the supply chain network equilibrium model. The investigation of several key factors on equilibrium decisions provides important managerial implications for different supply chain participants to address consumers’ preference for quality trust and enhance overall supply chain performance.

Financial institutions actively seek to leverage the capabilities of artificial intelligence (AI) across diverse operations in the field. Especially, the adoption of AI advisors has a significant impact on trading and investing in the stock market. The purpose of this paper is to test whether AI advisors are less preferred compared to human advisors for investing and whether this algorithm aversion diminishes for trading.

The four hypotheses regarding the direct and indirect relationships between variables are tested in five experiments that collect data from Prolific.

The results of the five experiments reveal that, for investing, consumers are less likely to use AI advisors in comparison to human advisors. However, this reluctance to AI advisors decreases for trading. The author identifies the perceived importance of careful decision-making for investing and trading as the psychological mechanism. Specifically, the greater emphasis on careful decision-making in investing, as compared to trading, leads to consumers’ tendency to avoid AI advisors.

This research is the first to investigate whether algorithm aversion varies based on whether one’s approach to the stock market is investing or trading. Furthermore, it contributes to the literature on carefulness by exploring the interaction between a stock market approach and the lay belief that algorithms lack the capability to deliberate carefully.

In this study, we investigate what drives the MAX effect in the South Korean stock market. We find that the MAX effect is significant only for overpriced stocks categorized by the composite mispricing index. Our results suggest that investors' demand for the lottery and the arbitrage risk effect of MAX may overlap and negate each other. Furthermore, MAX itself has independent information apart from idiosyncratic volatility (IVOL), which assures that the high positive correlation between IVOL and MAX does not directly cause our empirical findings. Finally, by analyzing the direct trading behavior of investors, our results suggest that investors' buying pressure for lottery-like stocks is concentrated among overpriced stocks.

This paper intends to address the decision-making and coordination of green supply chain (GSC) considering risk-averse manufacturers under mixed carbon policy.

This paper focuses on a GSC consisting of a manufacturer and a retailer, in which the manufacturer is risk-averse (R-A). This paper employs Stackelberg game theory and mean variance analysis to assess the pricing decision-making process under various scenarios. Furthermore, cost-sharing contracts are introduced to coordinate the GSC.

The research results suggest that the green level of the product and the profit of the GSC under a centralized scenario are higher than those under a decentralized scenario, while the retail price is lower. Under the decentralized scenario, the green level of product, wholesale price and manufacturer’s profit in the R-A scenario are lower than the values in the risk-neutrality scenario, while retailer's profit is higher. In addition, when a cost-sharing contract is utilized for coordination in the GSC, it can lead to Pareto improvement, regardless of whether the manufacturer makes risk-neutrality or R-A decisions.

This research provides a deeper understanding of GSC decision-making and coordination strategy under mixed carbon policy with consideration of R-A from a theoretical perspective and provides decision support for enterprises to choose strategies in practice.

The management issues of this article, and the author is attempting to address these issues, are as follows: What is the optimal decision of each entity in the closed-loop supply chain for the cascading utilization of power batteries under three government measures: no subsidies, subsidies and rewards and punishments? How do different measures affect the process of cascading the utilization of power batteries? Which measures will help incentivize cascading utilization and battery recycling efforts?

The paper uses game analysis methods to study the optimal decisions of various stakeholders in the supply chain under the conditions of subsidies, non-subsidies and reward and punishment policies. The impact of various parameters on the returns of game entities is tested through Matlab numerical simulation.

The analysis discovered that each party in the supply chain will see an increase in earnings if the government boosts trade-in subsidies, which means that the degree of recycling efforts of each entity will also increase; under the condition with subsidies, the recycling efforts and echelon utilization rates of each stakeholder are higher than those under the incentive and punishment measure. In terms of the power battery echelon’s closed-loop supply chain incentive, the subsidy policy exceeds the reward and punishment policy.

The article takes the perspective of differential games and considers the dynamic process of exchanging old for new, providing important value for the practice of using old for new behavior in the closed-loop supply chain of power battery cascading utilization.

As a response to challenges that globalization poses, governments often utilize an expansionary fiscal policy, a mix of increased compensation spending and capital tax cuts. To account for these policy measures that are consistent with neither the compensation nor the efficiency hypothesis, this study examines government fractionalization as the key conditional factor.

We test our hypothesis with a country-year data covering 24 OECD countries from 1980 to 2011. To examine how a single country juggles compensation spending and capital taxation policies jointly, we employ a research strategy that classifies governments into four categories based on their implementation of the two policies and examine the link between imports and fiscal policy choices conditioned on government fractionalization.

This study shows that highly fractionalized governments are more likely to implement an expansionary fiscal policy than marginally fractionalized governments as a policy response to economic globalization and import shock.

Our findings imply that fractionalized governments are likely to face budget deficits and debt crises, as the expansionary fiscal policy persists over time.

By examining government fractionalization as one of the critical factors that constrain the fiscal policy choice, this study enhances our understanding of the relationship between economic globalization and compensation or efficiency policies. The arguments and findings in this study explain why governments utilize the seeming incompatible policy preferences over increased compensation spending and reduced capital tax burdens as a response to globalization, potentially subsuming both hypotheses.

By comparing and contrasting the two scenarios of power producers investing in renewable energy and electricity sellers investing in renewable energy, we explore the conditions under which profit growth and carbon emission reduction can be realized, and provide a theoretical basis for decision-making on renewable energy investment by electric power companies as well as for government policy formulation.

This paper constructs a game model of a grid supply chain consisting of a leader generator and a follower seller in the context of the C&T mechanism, considering two scenarios in which the generator and the seller invest in renewable energy. Conclusions are drawn by comparing and analyzing the equilibrium solutions in different scenarios.

The scenario where electricity sellers invest in renewable energy exhibits a higher investment volume compared to the scenario involving power generators. In scenarios where power producers invest in renewable energy, electricity sellers achieve lower profits than power generators, while scenarios with electricity seller' investments yield higher profits for them. Increasing the cost coefficient of renewable energy investment reduces investment volume, electricity prices and electricity demand, leading to decreased profits for electricity seller but increased profits for power generator. A rise in the preference coefficient for renewable energy results in increased profits for electricity seller but decreased profits for power generator.

Addressing a literature gap in the context of low carbon, this study examines the investment scenario of electricity sellers in low carbon technologies, complementing existing research focused on power generators and consumers. The findings enrich knowledge in low carbon investment. By analyzing the investment decisions of both power producers and electricity sellers, this study explores the practical implications of renewable energy investments on the decision-making and operational dynamics of power supply chain enterprises. It sheds light on their profitability and investment strategies.

Properties with specific orientations are preferred in South Korea, depending on the real estate market. This preference is usually considered during property transactions and in designing buildings. Despite the importance of property orientation, the magnitude of preference for favored orientation has rarely been empirically estimated in the literature. This study attempts to estimate the value of favored orientation in a quantitative manner and interpret the results.

Using a geographically weighted regression model, this study obtains nationwide property price data and estimates the strength of orientation preference, that is, the premium for favored orientation. Among the various property types, residential sites and forests were investigated because the orientation of these two property types is known to influence their sales prices in the Korean real estate market.

The results show that premiums for south-facing residential sites exist in the market, varying locally and ranging from zero to 13.2%, over residential sites with non-south orientations. The results for forests are mixed in that a south-facing forest commands a maximum of 33.1% premium in a certain region, over a forest with a non-south direction, while it also commands a maximum of 33.8% negative premium (discount) in another region, indicating significant local variations in premiums.

These findings are expected to be utilized in fields such as property valuation, house architecture and design.