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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

E-commerce platforms are digital marketplaces for selling goods and services online. Operational research (OR) models support business model innovation in e-commerce platforms. This issue covers eight papers that utilize OR models to examine business model innovation in e-commerce platforms. They all introduce new research problems and concepts, develop novel and rigorous OR methodologies to tackle them, and present innovative OR applications according to the real problems in e-commerce platforms. Based on the eight papers' discussions, we propose future research directions. [ABSTRACT FROM AUTHOR]

Vaccination is a well-known method to protect the public against an epidemic outbreak, e.g., COVID-19. To this end, the government of a country or region would strive to achieve its target of vaccination coverage. Limited by the total vaccine capacity of public hospitals, the government may need to cooperate with private hospitals or clinics for more vaccination. Exploring in this paper government coordination of public and private resources for vaccination, we model a vaccine system consisting of a public hospital, a profit-maximizing private clinic, and self-interested individuals, under three scenarios: (1) without information sharing (concerning vaccine inventory and vaccine price), (2) with information sharing and subsidy, and (3) with information sharing and allocation. We find that, under scenario (1), the vaccine demand is fully satisfied by the public hospital and the private clinic cannot make any profit. Under scenario (2), the private clinic is willing to enter the vaccine market with a positive profit-maximizing vaccination coverage. Under scenario (3), the socially optimal vaccination coverage may be lower than that under scenario (1). Moreover, we conduct a sensitivity analysis to generate practical implications of the research findings for vaccination policy-making. Our results provide both theoretical and managerial insights on vaccine supply decision, government intervention, and vaccination coverage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we study the electricity time-of-use (TOU) tariff for an electricity company with stochastic demand. The electricity company offers the flat rate (FR) and TOU tariffs to customers. Under the FR tariff, the customer pays a flat price for electricity consumption in both the peak and non-peak periods. Under the TOU tariff, the customer pays a high price for electricity consumption in the peak period and a low price for electricity consumption in the non-peak period. The electricity company uses two technologies, namely the base-load and peak-load technologies, to generate electricity. We derive the optimal capacity investment and pricing decisions for the electricity company. Furthermore, we use real data from a case study to validate the results and derive insights for implementing the TOU tariff. We show that in almost all the cases, the electricity company needs less capacity for both technologies under the TOU tariff than under the FR tariff, even though the expected demand in the non-peak period increases. In addition, except for some extreme cases, there is essentially no signicant reduction in the total demand of the two periods, although the TOU tariff can reduce the demand in the peak period. Under the price-cap regulation, the customer may pay a lower price on average under the TOU tariff than under the FR tariff. We conduct an extensive numerical study to assess the impacts of the model parameters on the optimal solutions and the robustness of the analytical results, and generate managerial implications of the research findings. [ABSTRACT FROM AUTHOR]