Growing concerns over global climate change, net-zero targets and the urgency to fulfill Sustainable Development Goals (SDGs) have intensified the need for effective decarbonization strategies. Collaborative efforts among manufacturers and retailers can accelerate the transition from high-carbon products (HCPs) to decarbonization products (DCPs). This paper analyzes a two-period supply chain with two leadership strategies for guiding the decarbonization level: the manufacturer-led decarbonization (M-DCPs) strategy and the retailer-led decarbonization (R-DCPs) strategy. Supported by a shared responsibility mechanism, these strategies influence both the supply chain's long-term profitability and its overall emission reductions. Our findings show that when either firm adopts a long-term perspective and assumes the leadership role in the decarbonization level, the leader can enhance its own profitability. In contrast, focusing on a short-term perspective may cause firms to bypass the decarbonization transition, suggesting that such a transition does not necessarily offer immediate profitability. Moreover, we find that although DCPs may exhibit a lower second-period price and demand after the decarbonization transition relative to the pre-transition period, from a profitability perspective, the M-DCPs strategy yields higher overall profitability for the supply chain; while from an environmental perspective, the R-DCPs strategy achieves more emission reductions. Notably, only the M-DCPs strategy achieves a Pareto improvement while this outcome cannot achieve under the R-DCPs strategy. Our findings emphasize the need for a forward-looking commitment to decarbonization, supported by an effective shared responsibility mechanism, to advance environmental goals, enhance supply chain profitability and guide industry professionals in fostering collective decarbonization transitions in multi-period contexts.

Advances in artificial intelligence (AI) have enabled firms to deploy sophisticated digital humans in livestream commerce, creating viable alternatives to traditional human streamer partnerships. In this paper, we develop a game-theoretic model to examine how firms choose between human and AI-powered virtual streamers. We characterise streamers along two dimensions: entertaining capability, which determines consumer base, and informing capability, which shapes consumer product beliefs. Within this framework, we investigate how optimal strategies differ across streamer types under varying technological and market conditions. Our analysis yields several insights. First, when collaborating with human streamers, pricing depends on the streamer's capability: Low entertaining capability leads to more aggressive pricing that compresses firm profit, whereas high entertaining capability enables more balanced pricing and value sharing. Second, with virtual streamers, firms adjust anthropomorphism investment based on the imitated human streamer's characteristics. Investment is higher when imitating low-appeal or top streamers, lower at moderate levels, and stronger informing capability boosts investment incentives. Third, firms prefer human streamers when their capabilities create sufficient value, whereas virtual streamers become more attractive when human streamers are highly entertaining but costly, especially under high technology efficiency.

The dynamic pricing mechanisms of firms and search techniques for historical prices have spurred strategic behaviors and price expectations among customers. These customers predict future markdowns and delay their purchases based on price expectations (i.e., reference prices), where joint pricing and ordering decisions should be considered carefully to counteract or soften the negative impact of customers’ strategic behaviors and their reference prices on the seller’s profitability. Moreover, relevant literature neglects general and important features in the actual market (e.g., nonzero price thresholds and randomness in the formation of reference prices). In this study, we establish a Markov game between the retailer and strategic customers over a multi-period horizon, and set the reference price as a three-regime linear piecewise model with loss and gain thresholds. Then, we propose an adaptive multi-agent deep deterministic policy gradient (MA2DDPG) algorithm containing the experience replay buffer separation mechanism and delayed actor updates. Experimental results with synthetic and real-world datasets reveal that our algorithm converges to optimal policies in terms of convergence and rationality, and that it vastly outperforms the benchmark algorithms. Moreover, valuable managerial insights are obtained through an extensive numerical analysis for practitioners, particularly when they must consider the complicated characteristics of heterogeneous customer populations, such as disappointment behaviors, price thresholds, and strategic customer proportions. Our results pave the way for future research aimed at using multi-agent reinforcement learning to improve operations management with behavioral factors.

ESG (Environmental, Social, Governance) has emerged as a critical role for evaluating corporate long-term resilience. Meanwhile, artificial intelligence (AI) is reshaping the underlying logic of industrial operations. Thus, whether and how AI improves ESG performance is necessary to be explored. This study, grounded in the Schumpeter's Innovation Theory, systematically examines the two main underlying influence mechanisms (i.e., green technology innovation and process improvement) and the heterogeneity of enterprise resource structure (i. e., labor, asset, and technology intensity) by conducting the longitudinal data from manufacturing firms. Empirical findings reveal that AI achieves greater ESG performance through such dual mechanisms. Additionally, AI can improve ESG performance in different enterprise resource structure; however, their underlying influence mechanism is different. These findings enrich the relevant literature and the empirical studies of Schumpeter's Innovation Theory, and practically provide actionable insights for firms to design AI strategies to improve ESG performance.

Supplier greenwashing behaviors in corporate social responsibility (CSR) have become increasingly notable, often involving exaggerated claims about CSR initiatives or diverting resources to conceal violations. In this article, we develop a game-theoretic model to investigate the motivation of greenwashing and its intricate economic and social consequences, and explore whether blockchain technology can effectively enhance CSR efforts. We find that the tradeoff between exaggeration and concealment inherent in greenwashing plays a significant role in CSR strategy selection and corresponding performances. Specifically, if the exaggerating effect dominates greenwashing, 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 greenwashing coefficient has polarized impacts on the optimal CSR effort level, retailer's profit, and consumer surplus. We unveil that blockchain implementation achieves the highest social welfare when the exaggeration of greenwashing plays a more prominent role, particularly when the CSR violation penalty is relatively subtle for both firms. Interestingly, we observe that greenwashing, 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.

New technology innovations and operational research (OR) models can enhance the efficiency and competitiveness of e-commerce platforms. The rapid expansion of e-commerce has led to increased complexity in operations, including dynamic pricing, inventory management, last-mile delivery, and fraud detection. This special issue features nine articles exploring how advanced OR models, such as game-theory-based approaches, machine learning, and big data analytics, offer solutions to these challenges by enabling platforms to make data-driven decisions. As e-commerce evolves, these technological advancements, combined with OR models, will continue to drive platform innovation, improve efficiency, and strengthen resilience in the face of disruptions and fluctuating market demands.

New technology innovations and operational research (OR) models can enhance the efficiency and competitiveness of e-commerce platforms. The rapid expansion of e-commerce has led to increased complexity in operations, including dynamic pricing, inventory management, last-mile delivery, and fraud detection. This special issue features nine articles exploring how advanced OR models, such as game-theory-based approaches, machine learning, and big data analytics, offer solutions to these challenges by enabling platforms to make data-driven decisions. As e-commerce evolves, these technological advancements, combined with OR models, will continue to drive platform innovation, improve efficiency, and strengthen resilience in the face of disruptions and fluctuating market demands.

Global warming has escalated the frequency of extreme climate events. Considering the escalating severity of air pollution, investigating its potential to cause operational problems is of value. Some studies have explored air pollution’s effects from individual perspectives, including emotions, attitudes, and decision-making behaviors. Yet, the influence of heavy haze weather on firm performance from an operations management viewpoint remains uninvestigated. This study seeks to address this research gap. Utilizing a longitudinal dataset from 2,255 manufacturing firms between 2014 and 2020, this study empirically examines heavy haze weather’s impact on operational efficiency. Three contingency categories are identified: strategic positioning, structural contingency, and institutional factors. The findings suggest that heavy haze weather adversely impacts operational efficiency, notably for firms with cost leadership strategies and high labor productivity, whereas such effect can weaken in the firms with process standardization and governmental environment inspection. Enhancing the understanding about the effect of heavy haze weather on operational performance, these findings offer strategic insights for firms to mitigate heavy haze weather and provide empirical support for government to make environmental policy-making.

Modular integrated construction (MiC) becomes a promising solution to improve production efficiency in the construction industry. However, the off-site production and on-site installation processes of MiC pose challenges to collaboration efficiency among the multiple stakeholders involved, including subcontractors, contractors, and consumers. These challenges stem from information dispersion, which impedes effective collaboration and communication. Such problems can be solved by introducing a blockchain-based cyber-physical service platform, which can facilitate information sharing and collaboration across the supply chain. In this paper, we study the impacts of MiC and blockchain technology on construction supply chains and reveal several important insights. First, we find that there exists a first-mover advantage in the traditional construction supply chain, where subcontractors engage in a sequential game, and the subcontractor who produces first obtains more profit than the counterparts. Moreover, the contractor's ability to increase profits by reducing the unit cost of construction time is limited, but it can improve the effectiveness of the time gap due to early delivery. Second, we show that MiC should not be introduced if it significantly reduces collaboration efficiency in the supply chain. Interestingly, increasing the unit cost of construction for subcontractors can actually result in greater profits for all members of the supply chain. Regarding adopting blockchain technology, our findings suggest that supply chain members generally hold similar attitudes. Specifically, when the value of blockchain in improving collaboration efficiency is below a certain threshold, its adoption may not be beneficial, despite its potential to enable rapid production and early delivery.

The rapid growth of the commercial satellite industry is hindered by its vulnerability to launch failures, demanding the adoption of effective risk mitigation strategies. This research investigates such strategies, including the adoption of launch insurance, government subsidies, and blockchain technology integration within satellite launch supply chains. Utilizing Stackelberg games, we model scenarios with launch insurance (Model I), insurance plus government subsidies (Model IG), blockchain-embedded insurance (Model B), and blockchain-embedded insurance with government subsidies (Model BG), to investigate optimal launch and retail pricing strategies and to enhance launch success probabilities. Our findings demonstrate that government-subsidized launch insurance can create a win-win scenario, while the incorporation of blockchain technology fosters an all-win situation, benefiting all stakeholders, including consumers. Notably, the study reveals a synergistic relationship between government subsidies and blockchain technology, significantly enhancing supply chain efficiency and leading to positive spillover effects on profits and social welfare. This research contributes significantly to the understanding of managerial implications of these strategies within the commercial space launch market.

In this paper, we study the logistics outsourcing strategy of a manufacturer with regard to green logistics, greenwashing, and blockchain. The manufacturer sells its products through an e-commerce platform, which has self-built logistics service. To transport the products to the customers, the manufacturer can choose to outsource this logistics service to the e-commerce platform or a third-party logistics (3PL) firm. The e-commerce platform and 3PL firm are called logistics firms in this paper. Both logistics firms consider investing in green logistics activities and may involve in greenwashing behavior. We show that outsourcing to the e-commerce platform is more beneficial for the manufacturer, no matter whether the logistics firms pursue greenwashing. Moreover, whether the logistics firms pursue greenwashing depends on both the probability that the greenwashing behavior is exposed and the corresponding penalty for greenwashing. Although adopting blockchain has the potential to prevent greenwashing, the logistics firms may still take risks to pursue greenwashing when the cost of blockchain adoption is too large. Interestingly, we find that the logistics firms will set the same green levels in logistics activities, independent of the outsourcing strategy of the manufacturer, and greenwashing decisions and blockchain adoption decisions of the logistics firms.

The COVID-19 pandemic led to crises in global supply chains and surges in consumer demand for personal protective equipment (PPE). At that time, some companies dealt with the crisis by conducting supply chain innovation, which meant searching for new suppliers to switch production lines and producing alternative products (in this case, personal protective products). Such innovation activities can improve firms’ economic and social performance, thereby ensuring the sustainability of the supply chain. Some companies adopted a process switching strategy, under which companies initially produced the original products at their highest production capacity, then implemented a production change plan to change over their production lines to produce PPE, and then, finally, produced their original product according to demand. This study explores four production change plans for implementing a switching strategy to help firms achieve sustainability by adjusting their production lines promptly to cope with a global supply chain disruption. The investigation simulates supply and demand for two types of PPE products, masks and protective clothing under each of the four plans using a life cycle uncertainty model. We further tackle the problem of the changeover timeline for the production line and propose a method that combines simulation and machine learning to benefit curve fitting in modeling the time series. Our results indicate that types of PPE and the production capacity of firms are critical factors in determining when and how a firm changes its production plan. In addition, the version of the production plan with high value and low inventory cost can increase economic performance and improve the social value for the firms. Moreover, our findings reveal that switching the production line in the later stage can alleviate the price disadvantage of low-value products for the firm by adjusting the stockout cost and production capacity, further influencing the economic performance of the whole supply chain.

The literature has recognized that the consumers' purchase behaviour and retailers' policies are significantly influenced by reference price effects, but has yet to provide flexible operational decisions on the threshold-based reference price. In this study, we consider an infinite-horizon joint pricing and inventory control problem in which the customers' psychological behaviour is modelled as reference price effects and price thresholds. Spe-cifically, there is a region of price insensitivity allowing for threshold effects around the reference price, which is formulated as a three-regime piecewise function with the consideration of gain, loss, and indifference. A reso-lution approach based on proximal policy optimization is proposed to address the combinatorial complexity of continuous domains in action-state spaces. Tested on eight different market environments, we demonstrate how the deep reinforcement learning (DRL) approaches the ground-truth algorithm and outperforms three other al-gorithms. Moreover, near-optimal strategies and convergence results are obtained with regard to initial states and price thresholds of the system. The results show that the order-up-to level is increasing in price thresholds and the sales price is decreasing in them, while the retailer's profits suffer from the increase in thresholds. Be-sides, the sales price is increasing in the reference price. In the steady state of the environment, the average profits are higher than those in which the retailer ignores reference price effects with thresholds. Our study provides evidence that black-box DRL algorithms can effectively solve joint pricing and inventory control problems with psychologic and behavioural concerns.

The coronavirus pandemic (COVID-19) threatens people's health. During the COVID-19 outbreak, people are encouraged to wear masks to reduce the spread of the virus. With the strong demand for masks, it has come a boom in counterfeit production. Combating counterfeit masks is vital and urgent to reduce the risks for public health. Motivated by the actual practices during the COVID-19, we examine how quality inspection and blockchain adoption help combat counterfeit masks. We find that quality inspection may not be always effective, as the government will tolerate the presence of counterfeit masks if the presence of the counterfeits is not significant. Comparing quality inspection with blockchain adoption, when the spread of COVID-19 is mild, authentic mask sellers may be encouraged to use the blockchain technology, which can increase their profits and reduce the social health risk. Furthermore, we extend our model to investigate the impacts of endogenous quality. Both quality inspection and blockchain adoption can induce low-quality mask sellers to enhance thequality level. When the number of counterfeit masks is increasing, encouraging the high-quality mask sellers to adopt the blockchain technology is effective to reduce social health risk when the spread of the coronavirus is rapid.

Some emerge technologies (e.g., blockchain) and new business models (e.g., e-commerce platform) are utilized to promote the digitalization of supply chains. In this paper, we study the channel selection (direct channel/ecommerce platform) and pricing (wholesale pricing/agency pricing) strategies for a capital constrained supplier in the digital supply chain with the consideration of supply chain finance and blockchain. The supplier can sell through the direct channel or the e-commerce platform. When selling through the e-commerce platform, it can choose agency pricing or wholesale pricing mode, as well as whether to obtain financial supports through supply chain finance with blockchain technology. We show that the application of supply chain finance is not always beneficial for the supplier. The initial capital of the supplier plays an important role in channel selection. Besides, the adoption of blockchain technology can promote the development of supply chain finance. Regardless of wholesale pricing or agency pricing, the adoption of blockchain technology is always beneficial to suppliers. However, for the e-commerce platform, the adoption of blockchain technology can bring an increase in the interest and increase the profits of the e-commerce platform, only when the initial capital of the supplier is relatively low.

The phenomenon of copycats is common in a wide range of industries. Recently, to indicate product authenticity and combat copycats, many brand name companies (BNCs) have started selling products through retailers. These BNCs deploy a scalable protocol that is integrated into a permissioned blockchain technology (PBT) platform. We examine how PBT combats copycats in the supply chain and how it benefits BNCs. Although PBT implementation helps novice customers identify product authenticity and the real quality of products, that is, to take advantage of a quality disclosure effect, we show that, if and only if the number of novice customers is large enough, then selling through a PBT retailer can effectively combat copycats. Thus, PBT increases the profit of the BNC, consumer surplus, social welfare, and reduces the profit of a copycat. Moreover, conventional wisdom tells us that PBT ensures supply chain transparency and motivates a firm to improve its product quality. However, the BNC reduces the quality of its products when using PBT, because an improvement in product quality is not profitable if consumers can distinguish between genuine and imitation products. Furthermore, we extend the model by considering the case where the BNC itself implements PBT. Without the double marginalization effect, even if the number of novice customers is small, blockchain technology may exist in the market (the BNC self‐implements). In addition, if the unit production cost of a genuine product is large enough, social welfare increases when production cost increases.

For infectious diseases that occurs recurringly or periodically, e.g., influenza, humans have tried to develop vaccines to effectively prevent infection. However, vaccination coverage, which is the most effective way to prevent infections, is undesirably low. Existing epidemiology studies have consistently shown that there is association between the vaccination decisions in different flu seasons (epidemic periods), but related research in operations management mainly focuses on the single-period model. In this paper we construct a multi-period vaccine demand model to study multi-period vaccine supply decisions and government interventions. We consider that members of the public make vaccination decisions at the beginning of an epidemic period, given the information of the last epidemic period. Both the manufacturer and government make multi-period decisions in our model. The vaccination coverage is determined by the minimum between the supply and demand for the vaccine. We derive the multi-period profit-maximizing coverage and compare it with the socially optimal coverage. In addition, we show that, besides supply uncertainty, vaccine demand may decrease or increase with the vaccination coverage in the last epidemic period, depending on the vaccine effectiveness. Furthermore, the coverage convergence depends on the vaccine effectiveness and infection loss distribution. Accordingly, the multi-period profit-maximizing coverage and government intervention depend on the vaccine effectiveness and coverage convergence. We also conduct numerical studies to generate practical implications of the analytical findings. Our results provide management insights on vaccine supply decisions, government interventions, and vaccination coverage.