Recently, Live Streaming Selling (LSS) has become increasingly prevalent. Numerous omnichannel retailers are striving to introduce live streaming channel to absorb additional demand. However, it is challenging to investigate robust pricing and inventory strategies that consider the characteristics of omnichannel operations and LSS with uncertain demand. We consider a joint optimization of ordering, replenishment, order fulfillment, and pricing, where customers are sensitive to prices and delivery times. LSS can influence demand and benefit other channels to take free-riding. Furthermore, service level requirements are formulated as joint chance constraints to guarantee adequate performance. The Worst-case Mean Quantile-Deviation (WMQD) is employed to measure risks. The Wasserstein metric is adopted to design the data-driven ambiguity set. Accordingly, a data-driven Distributionally Robust Joint Chance Constrained Programming (DRJCCP) based on WMQD is constructed. Leveraging the dual theory, Conditional Value-at-Risk (CVaR) approximation, and linearization techniques, the developed model can be transformed into tractable formulations, which can be solved by commercial solvers. We further conduct numerical experiments to demonstrate the efficiency and practicality of our developed model. The comparative results reveal that the DRJCCP model based on WMQD has superior out-of-sample performance and is capable of effectively managing uncertainty, thereby ensuring more robust service levels. Furthermore, the sensitivity analyses are performed to verify the effects of some key parameters on the decision-making. The results indicate that introducing live streaming channel is not always profitable for the retailer and increasing the level of LSS effort can enhance free-riding effect without necessarily improving retailer's profits.

How should supply chain partners design and govern blockchain-enabled anti-counterfeiting when consumer psychology alters demand incentives? We address this question in a supply chain where deceptive counterfeits create authenticity uncertainty and consumers may anticipate regret after choosing an authentici ty-uncertain purchase over a more reliable alternative. We develop Stackelberg game models under two blockchain implementation leadership structures: a manufacturer-led scenario (Scenario MB) and a retailer-led scenario (Scenario RB). We derive the equilibrium blockchain-enabled verification effort and pricing decisions under both structures. Key findings include: First, a higher block chain-enabled verification effort is not always better. When authentic product quality is low, a moderate verification effort is optimal because the incremental benefit from stronger verification may not offset the additional cost, whereas when authentic product quality is sufficiently high, maximizing verification effort becomes optimal. Second, the retailer generally prefers manufacturer-led implementation due to free-riding. However, interestingly, this preference is not always shared by the manufacturer. Specifically, the manufacturer prefers to lead blockchain implementation and bear the associated verification cost when authentic product quali ty is low. Third, while conventional wisdom suggests that CAR reduces consumer willingness to pay, which is detrimental to supply chain stakeholders, we reveal a counterintuitive outcome: under certain conditions, CAR can actually benefit the stakeholder that does not lead blockchain implementation. Practice based nu merical analysis shows that residual counterfeit infiltration is reduced by 73.58% and 29.95% under manufacturer and retailer leadership, respectively, relative to conventional verification. We further examine four extensions to assess robustness.

Live streaming (LS) sales are increasingly popular, leading many manufacturers to adopt the LS channel to encroach on the online market. Three characteristics of the LS channel are identified: (1) Real-time interaction reduces product matching uncertainty. (2) The increase in product matching probability mitigates consumer disappointment aversion (CDA). (3) Increased entertainment and trust enhance consumers valuation of products. Considering a supply chain that includes a manufacturer and an online retailer, we investigate the manufacturer’s LS channel encroachment strategies for both centralized and decentralized scenarios. We find that LS channel encroachment always occurs in the centralized scenario but may not necessarily take place in the decentralized scenario. Specifically, if CDA intensity is strong and product matching probability is small, the manufacturer adopts replacement encroachment. If product matching probability is moderate, the manufacturer employs competition encroachment. However, if product matching probability is large and LS’s valuation enhancement effect is low, the manufacturer avoids encroachment in the centralized scenario but chooses threatening encroachment in the decentralized scenario. Finally, although CDA can promote encroachment, it always makes the manufacturer worse off. Intuitively, CDA weakens the online retail channel’s competitiveness, thereby harming the retailer. However, our study reveals an interesting finding: When product matching probability is sufficiently large and LS’s valuation enhancement effect is low, CDA makes the retailer better off.

Original products (sold by a brand-owner) are exposed to the dual challenge of imitation products (sold by a copycat) and deceptive counterfeit products (provided by a counterfeiter). In such cases, consumers face higher shopping risks and are more likely to experience post-purchase regret. Blockchain can prevent the infiltration of deceptive counterfeits and the occurrence of regret, but its adoption can also raise consumer privacy concern. Hence, considering the above factors, we focus on the brand-owner's blockchain adoption strategy. First, we define two types of regret: counterfeit-purchasing regret (CPR) and low-cost-effective regret (LCR). Second, we consider three scenarios: scenario NR: without blockchain and considering only CPR, scenario NT: without blockchain and considering only LCR, and Scenario B: with blockchain. Finally, we compare the equilibrium results under scenarios NR and NT with those under scenario B, respectively. We interestingly find that blockchain adoption benefits the brand-owner when (1) privacy concern is low or (2) privacy concern is moderate and the sensitivity factor of CPR is large or the sensitivity factor of LCR is small. Therefore, we advise brand-owners to intervene in consumers' sensitivity to regret in order to make blockchain adoption work in their favor.

This study discusses blockchain adoption strategy and coordination of a poverty alleviation supply chain (PASC) consisting of one retailer with altruistic preference and one farmer cooperative. Four decentralized models are considered, including scenario AN (with altruistic preference and without blockchain), scenario AB (with altruistic preference and with blockchain), scenario DN (without altruistic preference and without blockchain), and scenario DB (without altruistic preference and with blockchain). We solve the game models in these four scenarios based on backward induction and obtain the optimal solutions. Additionally, two centralized models, namely scenario CN (without blockchain) and scenario CB (with blockchain), are used as benchmarks for coordinating the PASC. Through comparative analysis, we find that (1) the retailer does not necessarily benefit from blockchain adoption. The retailer has an incentive to adopt blockchain when the fixed and unit operating costs of blockchain adoption are low. Interestingly, even when the unit operating cost of blockchain adoption is high, the retailer can still increase its profits by adopting blockchain when consumers' acceptance level of agricultural products is low. (2) Under certain conditions, blockchain adoption can lead to a win-win situation for the retailer and the farmer cooperative. (3) The retailer's altruistic preference increases the profits of the farmer cooperative and the PASC, but at the cost of reducing its own profits. (4) The two proposed contracts, the altruistic preference joint two-part tariff contract and the altruistic preference joint profit-sharing contract, can effectively coordinate the PASC. (5) Three extensions illustrate that the findings regarding the retailer's blockchain adoption strategy remain robust under strict conditions.

This paper mainly discusses the joint optimization of product assortment, inventory and pricing for an omnichannel retailer under uncertain demand, seeking to maximize the retailer's Worst-case Conditional Value-atRisk (WCVaR) based profit and customer's expected utility. The risk aversion of decision-maker and the time preference of customers are depicted by the WCVaR and quasi-hyperbolic discounting function, respectively. A bi-objective stochastic optimization model is developed. To alleviate the computational burden, a linearization method is applied to convert the problem into a mixed integer linear programming that proves to be solvable within reasonable CPU times using the augmented epsilon-constraint method. Numerical studies are conducted to investigate the applicability of the proposed model and the efficiency of the solution approach. Our experimental results show that offering customers with high expected utility typically requires low prices and small assortments. We also make other counterintuitive observation that the price of products sometimes increases when the expected utility increases. Furthermore, WCVaR has superiority compared with the risk-neutral model in terms of robustness and stability. The time preference of customers has non-negligible impacts on decision-making results. In addition, the further sensitivity analyses investigate the impacts of various key parameters on the performance of omnichannel retailing.

Blockchain has been widely adopted in the field of product traceability owing to its powerful information tracing function. In this study, considering consumers' anticipated regret (e.g., high-price regret and misfit regret), we investigate a brand owner's (BO's) blockchain adoption strategy under competition between new and secondhand products. Moreover, we identify the uncertainty mitigation effect and brand image improvement effect of blockchain adoption. Four scenarios are involved: Scenario NN (without blockchain and no anticipated regret), Scenario NH (without blockchain considering only high-price regret), Scenario NL (without blockchain considering only misfit regret), and Scenario B (with blockchain). We construct Stackelberg games to explore the optimal pricing decisions for BO and secondhand supplier (SS). The results indicate that, without blockchain adoption, alleviating high-price regret (or stimulating misfit regret) can enhance the profits of both BO and SS. BO does not necessarily benefit from blockchain adoption. Specifically, blockchain adoption increases BO's profits if (1) the brand image improvement level is large, or (2) the brand image improvement level is moderate, and the high-price regret intensity is strong (or the misfit regret intensity is weak). Furthermore, under certain conditions, SS is able to become a free rider of BO's blockchain adoption. Three extensions are analyzed to demonstrate that key findings are robust.

An omnichannel retail network includes an omnichannel retailer (o-retailer) determining which products among the candidate set are offered via an online or offline channel, and then procuring these products from the supplier and distributing them from a distribution center (DC) to physical stores for serving customers. The o-retailer adopts either the distribution center or physical stores to fulfill online orders. This study considers an integrated approach to manage assortment planning, inventory control and e-fulfillment problems and develops a distributionally robust optimization model, in which the distribution of uncertain demand is only partially available in advance. In the proposed model, the worst-case mean-Conditional Value-at-Risk (WMCVaR) is formulated as the objective function that makes a trade-off between the expected profit and the risk, and a quasi-hyperbolic discounting function is adopted to denote the customers’ time preference. Furthermore, to overcome model solvability obstacle caused by imprecise probability distributions, the box ambiguity set is applied to derive computationally tractable counterparts. Numerical studies are conducted to investigate the validity and efficiency of our proposed model. Some useful managerial insights and implications for o-retailers are generated through the analyses of computational results.

In this study, we investigate pricing decisions with different decision sequences in an e-commerce supply chain. The e-commerce platform sells blockchain-compatible products through a self-operated model, while the retailer operating on the platform sells general products. Furthermore, we take into account the impact of counterfeits infiltrating the general product market. This can trigger consumers' anticipated regrets, including high-price regret and fake purchase regret. We design three scenarios: Scenario ES (the e-commerce platform makes the decision first), Scenario RS (the retailer makes the decision first), and Scenario VN (they make decisions simultaneously). We study the impact of consumers' anticipated regrets and the decision sequence on pricing strategies, as well as the profits of the e-commerce platform and the retailer. Our results show that the optimal retail prices of the two versions of the products and the profits of the e-commerce platform and the retailer are negatively correlated with the relative regret intensity. The profits of the e-commerce platform and the retailer are the lowest under Scenario VN due to the fiercest competition. The e-commerce platform prefers Scenario RS to Scenario ES. If the counterfeit penetration rate is small and the commission rate is large, the retailer prefers Scenario RS to Scenario ES. However, if the counterfeit penetration rate is high, or if both the counterfeit penetration rate and the commission rate are low, the retailer prefers Scenario ES to Scenario RS. Furthermore, if government penalties (or consumer claims) exist, the retailer should combat counterfeiting appropriately for better financial performance.

Manufacturers often make their green innovation decisions discreetly, considering the potential disappointment of failing to reach expectations caused by high uncertainty in the process. This article examines manufacturer's disappointment-aversion behavior and incorporates it into a two-echelon green supply chain decision-making framework. We first investigate and analyze a firm's equilibrium strategies with a Wholesale Price contract as a benchmark, and then we propose collaborative mechanisms under either a noncooperative game structure or under a cooperative game structure. The results show that manufacturer's disappointment aversion will reduce its interest in implementing green innovation, and that both types of collaborative mechanisms could realize Pareto improvements. Moreover, our results imply that the contract under a noncooperative structure can offset the negative effect caused by the manufacturer's disappointment-aversion, and the advantage of the contract under a cooperative structure lies in that it can eliminate the "double-marginalization" effect. However, interestingly and counterintuitively, our analysis indicates that we may not get the best results under a cooperative decision-making structure when the green innovation decision-maker is averse to disappointment.

以一个电商平台和一个制造商所构成的闭环供应链为研究对象,分别在电商平台未开辟旧货市场和开辟旧货市场的情形下构建闭环供应链模型,并考虑开辟旧货市场后消费者参考价格效应对供应链成员决策的影响,探讨了单位佣金、参考价格效应强度、参考价格、二手产品偏好等参数对平台广告水平、新产品与二手产品定价以及供应链成员利润的影响.研究发现:电商平台开辟旧货市场可以降低新产品的销售价格,提高旧产品的回收价格,大幅度提高电商平台的收益.参考价格效应和消费者二手产品偏好的增强会使二手产品的销售量增加,并提高电商平台对回收旧产品的广告水平,增加旧产品的回收量.但旧货市场的开辟与参考价格效应的存在均会降低制造商的利润.

Considering the impact of consumer fairness concerns on the pricing decisions of an ecommerce supply chain,the single-channel and dual-channel supply chain models were established when the manufacturer set online platform store selling price and the e-commerce platform set online platform store selling price. The results show that the increase in the weight of fairness concerns reduces prices and the profits of the manufacturer and the e-commerce platform. In the dual-channel supply chain where the e-commerce platform sets online platform store selling price,the price is positively correlated with the fairness concern coefficient. The profit of the manufacturer and the ecommerce platform in the single-channel supply chain is negatively correlated with the fairness concern coefficient. The e-commerce platform does not always benefit from increasing the commission rate.The profit of the e-commerce platform first increases and then decreases as the commission rate increases in the dual-channel supply chain where the manufacturer has pricing power.

Returns and consumer fairness concerns put a huge pressure on manufacturers who sell their products online. The optimal selling format and return freight strategy become particularly important for manufacturers in an e-commerce supply chain. Therefore, we build game models for the following scenarios under different selling formats: the seller bearing the return freight costs or the return-freight insurance premiums, consumers buying insurance for themselves, and no one buying insurance. By comparing the optimal solutions of the game models under reselling and agency formats, several conclusions are derived. In the agency format, if the return-freight insurance premium is higher than a particular threshold, then the optimal strategy of the manufacturer is to decrease the selling price to encourage consumers to purchase the insurance, otherwise, the manufacturer should purchase the insurance. As the level of consumer fairness concerns and platform commission rates increase, the manufacturer should gradually move from the agency to reselling format. In the reselling format, if the return-freight insurance premium is lower than a particular threshold, then consumers will purchase insurance and this will make the platform more profitable, and conversely, the platform should bear the return freight costs.

基于部分授权、全权委托和双向竞争三种合作模式,构建补贴政策下电商闭环供应链的博弈模型.探索制造商进行产品绿色创新的关键因素,并分析政府补贴和创新成本系数对产品绿色创新与定价决策的影响.研究发现:只有当政府补贴或绿色创新成本节约率足够大时,制造商才会进行产品绿色创新.部分授权模式下政府补贴最高,制造商进行绿色创新的意愿也最高.双向竞争模式下政府补贴最低,全权委托模式下制造商绿色创新意愿最低.补贴政策下绿色创新会降低产品销售价格,提高回收价格,并显著提高制造商收益.

分别在垄断与双寡头竞争情形下,研究不同市场结构下的低碳供应链运营决策问题,考虑减排技术投资的不确定性会引起制造商的失望规避行为,在两种情形下构建相应的博弈模型,探讨供应链的最优减排技术投资水平与定价决策问题.研究表明:垄断情形下,均衡结果与失望规避系数负相关,与减排技术投资成本效率正相关;竞争情形下,无论是低碳制造商主导还是双方权力对等,均衡结果与失望规避系数负相关、与消费者单位旅行成本正相关,低碳制造商主导时的低碳产品零售价格高于权力对等时,低碳制造商主导时的减排技术投资水平小于双方权力对等时,低碳制造商的期望利润和效用与减排技术投资的成本系数负相关,而普通制造商恰恰相反.

考虑人的社会属性与绿色产品的象征性价值,针对绿色产品市场进入及两竞争性企业的定价策略问题,建立基于社会比较行为的消费者效用函数,通过构建传统制造商领导的Stackelberg博弈模型,分析消费者社会比较行为对市场均衡结果及环境产生的影响.研究表明:消费者社会比较程度越高,绿色产品市场进入的可能性越大,同时传统制造商越倾向于采取高定价策略,以最大限度攫取普通消费者的剩余价值;当考虑消费社会比较行为时,绿色产品市场进入对环境并不总是有利的,某些情形下,绿色产品市场进入反而会加剧环境的负担.

基于多项式Logit(multinomial logit,MNL)选择模型,本文研究风险规避型全渠道零售商多周期产品组合与库存联合优化问题,建立了具有服务水平约束的多周期随机优化模型并提出相应的求解方法,通过数值算例验证了所构建模型和求解方法的有效性.研究结果表明,基于均值-CVaR的随机优化模型,能够使全渠道零售商更好地权衡利润与风险,并且使利润表现出更好的鲁棒性,进而满足零售商的风险规避需求.针对全渠道零售商实体店容量约束以及履单中心容量约束,零售商的相关决策会随着约束的改变而变化.两种渠道的存在在保证产品多样性方面具有一定优势.通过探究消费者的选择行为发现,当消费者群的考虑集完全不重叠时产品被选率是最高的.采用混合订单履行策略优于单一履行策略,且有助于实现产品的多样化,保证产品组合决策的稳定性与灵活性.

This article examines a low-carbon supply chain composed of a disappointment-averse manufacturer and a completely rational retailer. Considering the uncertainty of low-carbon technology innovation investment, this article constructs game models under three channel power structures-manufacturer-led（MS）, retailer-led（RS）, and the manufacturer and the retailer have equal power（Nash）, and addresses decision-making issues such as the optimal emission reduction level and pricing of the low-carbon supply chain. We then analyze and compare the influence of the manufacturer’s disappointment aversion coefficient on its optimal emission reduction level and utility under these three power structures. The results show that the manufacturer’s disappointment aversion coefficient is negatively correlated with the manufacturer’s emission reduction level and utility under all three conditions. When the manufacturer is the leader, the manufacturer has the greatest utility but the lowest emission reduction level. The relationship between the emission reduction level（or the manufacturer’s utility） under cases of retailer-led and the manufacturer and the retailer have equal power as a threshold effect of the emission reduction cost coefficient and disappointment aversion level. In addition, through numerical analyses, we find that the retailer’s expected profit is the highest when the retailer dominates, second when the powers of the two parties are equal, and the lowest when the manufacturer dominates. At the same time, the total expected profit of the entire supply chain is the lowest when the manufacturer dominates. In the other two power structures, there is also a threshold effect of the emission reduction cost coefficient and the disappointment aversion level.

将制造商风险规避行为特征考虑到不同权力结构闭环供应链博弈模型中,利用均值-方差理论探讨风险规避行为和权力结构对闭环供应链决策的影响.研究表明:随着制造商风险规避程度的提高,各权力结构下的批发价、绿色度、零售价和产品性价比逐渐降低;市场中领导者的先动优势总会使其制定出更加有利于自身的决策.对于制造商而言,在零售商主导和无权力结构的市场中,适度的规避风险有助于其效用的提高,而在自身主导的市场中,风险规避行为反而对其不利;当消费者对产品绿色品质的追求较低时,处于无权力结构的市场最有利,否则处于零售商主导的市场会更有利,然而无论消费者处于何种权力结构的市场,制造商风险规避行为都会对其产生负面影响.

以由直播平台、网红团队及电商卖家组成的供应链为研究对象,考虑需求受网红直播带货的影响,分别构建电商卖家主导、网红团队主导以及双方权力对等的3种权力结构下的供应链决策模型,并进行模型间的比较分析与重点参数的敏感性分析.研究表明:①电商卖家、网红团队及直播平台利润、消费者总效用、社会福利与网红个人影响力及直播营销成本效率正相关;②3种情形下,电商卖家与网红团队利润间的大小关系与直播营销成本效率密切相关;③消费者总效用及社会福利在电商卖家与网红团队权力对等时最大,在电商卖家主导时次之,在网红团队主导时最小.