Xiutian Shi

This paper investigates how AI-enabled ESG management reshapes operational decisions in a supplier–retailer supply chain characterized by responsibility investment, information distortion, and consumer privacy concerns. We develop a game-theoretic model in which the supplier determines responsible input with a certain disclosure strategy, while the retailer decides whether to adopt AI for ESG reporting. The analysis shows that the supplier’s greenwashing or brownwashing behavior critically moderates the impact of AI adoption: when disclosure distortion is mild, AI enhances transparency, stimulates demand, and increases responsible input, whereas strong greenwashing weakens or even reverses these effects. The retailer’s AI adoption incentive depends on the interaction between disclosure distortion and penalty allocation, generating distinct adoption regions under different regulatory exposures. From the stakeholder perspective, consumer privacy sensitivity and information distortion jointly affect the impacts of AI on consumer surplus and social welfare. Overall, the results highlight the contingent value of AI-enabled ESG and offer managerial guidance on technology adoption, penalty design, and data-governance strategies in sustainable supply chain management.

Retailers incur price-adjustment costs called menu costs when changing the prices of their products over time. These products are often sourced from upstream suppliers, who endogenously determine wholesale prices, which have cascading effects on retail prices. In this paper, we consider the pricing strategies of supply chain members in the presence of menu costs. Our analysis suggests that even a minuscule menu cost can substantially affect pricing strategies and the equilibrium outcome. Upstream manufacturers always benefit from a menu cost whereas downstream retailers benefit from only a moderate menu cost and are hurt by both high and low menu costs. Meanwhile, menu costs always alleviate the issue of double marginalization, thereby improving supply chain profits, consumer surplus, and social welfare. This study provides channel members with guidance on how to set prices in the presence of menu costs. It also recommends retailers to optimally maintain a moderate menu cost.

1. IntroductionCharacterized by short product lifecycles and highly volatile demand, fashion supply chains face challenges from large-scale systemic shocks and risk, which amplify uncertainties rem...

Supplier green washing behaviors in corporate social responsibility (CSR) have become increasingly notable, often involving exaggerated claims about CSR initiatives or diverting resources to conceal violations. We develop a game-theoretic model to investigate the motivation of green washing and its intricate economic and social consequences, and explore whether blockchain technology can effectively enhance CSR efforts. We find that the trade-off between exaggeration and concealment inherent in green washing plays a significant role in CSR strategy selection and corresponding performances. Specifically, if the exaggerating effect dominates green washing, the supplier has an incentive to adopt blockchain for self-certification in CSR activities. Conversely, when the concealment effect prevails, opportunistic behavior is more likely to occur. However, the integrative green washing coefficient has polarized impacts on the optimal CSR effort level, retailer's profit, and consumer surplus. We unveil that block chain implementation achieves the highest social welfare when the exaggeration of green washing plays a more prominent role, particularly when the CSR violation penalty is relatively subtle for both firms. Interestingly, we observe that green washing, despite its negative connotations, can eventually stimulate CSR efforts due to the incremental demand induced by exaggerated disclosures, ultimately leading to the highest social welfare.

With the rapid development of internet and mobile technologies, we have witnessed a boom in on-demand service platform operations. Acting as an intermediary between consumers and service providers, the platform needs to consider the attributes of both the supply side and demand side, and match them efficiently. However, multiple controversies and challenges exist in on-demand service platform operations, making it urgent to identify associated risks and mitigate them. In this paper, we first combine a systematic literature review, real-world practices, and industrial discussions to examine the key risks and propose potential risk management strategies involved in on-demand service platform operations. We divide our discussions into three areas: Demand and pricing, supply and incentives, and matching mechanisms. Based on the derived insights, we establish an innovative and interdisciplinary framework based on related theories to depict the relationships among key risks, system members' key decisions, risk management “SET” strategies, and systems performances. The proposed framework could help on-demand service platforms alleviate risks, optimize decisions, achieve operational innovation, and improve operations performances. Finally, we establish a future research agenda. Managerial Relevance Statement: By combining the literature, real-world practices, and industrial discussions, this paper establishes a framework which proposes insights to improve on-demand service platform operations. This innovative framework depicts the relationships among key risks, system members' decisions, risk management “SET” strategies, and systems performances. The focus of the proposed “SET” strategies is on risk management, which would provide valuable guidance to engineering managers of on-demand service platforms.

Supply disruption is one of the crucial risks faced by supply chains in the changing external environment. To mitigate supply disruption risk and enhance resilience, product upgrading with Research and Development (R&D) investment is widely adopted by firms. In this paper, we establish a game-theoretic model to study the operation decisions and product upgrading strategies for a supply chain. Specifically, Firm 1 faces supply disruption risks when selling a normal product with a key component sourced from an unreliable supplier. To achieve supply resilience, the firm has an incentive to replace the unreliable supplier with product upgrading supported by R&D. It has an option to make high or low investments in R&D projects, indicating a high or low success rate. Our findings reveal that Firm 1 prefers to make investments in R&D for the upgraded product at a high investment level when the additional production cost of the upgraded product is relatively low or both the upgrading cost and supply disruption risk are relatively high. Extending our model to a duopoly scenario, we investigate the impact of competition on the product upgrading strategy selection. It is shown that a firm with a sufficiently large market share can drive its competitor out of the market by making R&D investments. Compared to the monopoly scenario, Firm 1 has a stronger incentive to upgrade the product under the duopoly scenario if its market share is relatively low. Finally, we identify the conditions where consumers get benefits from product upgrading. Our findings shed light on the importance of product upgrading strategy in a volatile business environment and provide insights into how firms can effectively mitigate supply disruption risk through R&D investments.

With the emergence of digital technologies, we have witnessed a rapid growth of the sharing economy in recent years. In fashion industry, sharing economy concept is also widely applied. Instances can be found all over the world, like Rent the Runway, Golden Tote, Stitch Fix, and Le Tote in the US, Air Closet in Japan, Y Closet, Ms Paris, and Dora’s Dream in China, etc. In this paper, motivated by the industrial practices of fashion sharing platforms, we first conduct a literature review on business operations of sharing platforms, including the sharing platform operations, digital and information technologies employed in sharing economy, business models, and sustainability and circularity in business operations. Afterwards, we analyze the challenges faced by fashion sharing platforms by exploring the two leading fashion sharing platforms in the US and in China, respectively. At last, we propose future research directions on fashion sharing platform operations and conclude the paper.

During the recent COVID-19 (CoV) global outbreak, there is a sharp decline of revenue of on-demand ride-hailing (ODR) platforms because people have serious worries of infection in the shared vehicles. Blockchain, which supports cryptocurrency and creates full traceability of the service history of each car and driver, may come to rescue by allowing the platform to offer only the “safe cars” to consumers. Motivated by the real world challenges associated with the CoV outbreak for the ODR platform, we build game-theoretical models based on the M/M/n queuing system to explore if and how blockchain can help. In the basic model, the ODR platform decides the service price and special hygiene level. Comparing between the cases with and without blockchain, we find that blockchain implementation increases both the service price and hygiene level. In addition, when the consumers’ inherent worry of infection is substantially large, implementing blockchain achieves all-win for the ODR platform, drivers and consumers. In the extended models, we first consider the case when the special hygiene level is determined by the drivers under a mixed-leadership game and then explore the case when customers are risk averse. The main findings about blockchain adoption remain valid in both cases. However, when the drivers take charge of the special hygiene level, both optimal decisions are lower in most cases. It is also important to make efforts to reduce consumers’ feeling volatility toward service valuation for improving the value of blockchain adoption and related performances.