Jiajia Nie

This paper examines the impact of a potential entrant’s entry into the complementary goods market on the technology innovation of the basic product. We focus on an e-commerce platform supply chain comprising a manufacturer, an e-commerce platform, and a potential entrant. The manufacturer distributes a basic product and a complementary product through the e-commerce platform. The manufacturer has the option to improve the quality of the basic product through technology innovation. The determination of whether the potential entrant enters the market for complementary goods is contingent upon its observation of the manufacturer’s technology innovation strategy. Upon the potential entrant’s entry into this market, competition ensues between the potential entrant and the manufacturer in the realm of the complementary goods market. Our findings reveal a trade-off: on the one hand, the manufacturer’s decision to forgo technology innovation results in reduced profit, while, on the other hand, technology innovation attracts the potential competitor to enter the complementary goods market, which also leads to decreased profit. Under certain circumstances, the loss incurred by the former is less than that incurred by the latter. Consequently, the manufacturer may choose to forego technology innovation to prevent the entry of the potential entrant in the complementary goods market. Furthermore, our study demonstrates that the entry of the potential entrant can yield a mutually beneficial outcome for the manufacturer, the platform, and the potential entrant, creating a win–win–win scenario. In addition, our results suggest that as the production cost of innovative products rises, the manufacturer may have a stronger incentive to intensify its innovation efforts in response to the potential entrant’s exit from the market. Finally, we establish that our main results remain robust even when some of our initial assumptions are relaxed.

In a modern supply chain, the quality of the final product is not determined by a single firm but depends on the joint efforts of multiple firms. When a quality defect happens, it is usually cost‐inefficient to trace the source of quality problems and all firms in the supply chain might incur a loss. New technologies such as blockchain can enable supply chain traceability at an affordable cost. In this paper, we develop a two‐echelon supply chain model including one supplier and one buyer in a competitive market. The quality of the final product depends on the two firms’ quality efforts. The buyer as a leader can set a wholesale price to stimulate the supplier's effort. In the absence (presence) of traceability, the payment of wholesale price is based on the realization of the final product's quality (the supplier's individual quality). Interestingly, we find that supply chain traceability may cause a decrease of the supplier's quality effort, thereby reducing the quality of the final product when market competition is sufficiently intensive. Furthermore, traceability weakly increases the supplier's profit but weakly decreases the buyer's profit. However, the total profit of the supply chain can be increased in the presence of traceability.

Sustainable operations management will appeal to the post-pandemic world. As the economy recovers, the surging demand for low-carbon bike-sharing has led to exacerbated mismatch in urban transportation. It is a serious challenge to optimize the reallocation schedule of sharing bikes among multiple positions in a network. To address the problem, we develop a novel predict-then-optimize method consisting of a data-driven robust optimization model and a branch-and-price algorithm. The optimization model derives the predicted demand surplus of each position based on historical data, enabling the optimal reallocation schedule in the network at minimum operational costs. Based on the prediction, the branch-and-price algorithm can find out the best routes of assigning bikes to specific positions that further improves transportation efficiency. Finally, we deploy the predict-then-optimize method to a realistic bike-sharing network in one major city of China. The computational results demonstrate that our method can significantly save the cost of operations and reduce the waste of resources. Therefore, the novel predict-then-optimize method has a great potential to facilitate the sustainable development of bike-sharing systems in urban transportation.

To reduce carbon emissions in the electric vehicle (EV) market, the Chinese government has issued a subsidy scheme involving a subsidy threshold that is quite different from the fixed subsidy. This study aims to investigate competing EV manufacturers’ decisions on carbon-reduction (CR) technology investment under the subsidy threshold. We construct a Cournot game model involving two competitive EV manufacturers. The results show that decisions of the two EV manufacturers on investing in CR technology vary under different subsidy thresholds. Particularly, they would make the same investment decisions when the subsidy threshold is low or large, but achieve the opposite investment decisions when the subsidy threshold is intermediate. In the benchmark model where there is only one EV manufacturer, an EV manufacturer could always increase its profit from investing in CR technology when the subsidy threshold is low. In the Cournot model where there are two competing manufacturers, however, they may get involved in the Prisoner’s Dilemma and both manufacturers could suffer economic losses from CR technology investment.

With the rapid development of the internet, many WEEE (Waste Electrical and Electronic Equipment) online recyclers have emerged and developed. They improve recycling efficiency significantly and bring hope for sustainable development. Unfortunately, few works in the literature are concerned about it, with many questions still unanswered. The WEEE online recyclers always face a challenge with regard to how to strategically design their reselling channel structure in fact. In this paper, we examined this challenge by two decision models: agency and self-run. We derived the performance of the self-run model and agency model by backward induction and then examined the desirability from different stakeholders’ perspective. The results suggested that WEEE online recyclers prefer the self-run model to the agency model when the unit recycling cost is low and a conflict exists between the WEEE online recycler and e-retailer for the resale channel preference. Interestingly, the manufacturer shows consistent resale channel preference with the WEEE online recycler when the unit recycling cost is low. Our further analysis revealed that all players can agree on the agency model in a certain case. In addition, we found that the consumer surplus and the recovery ratio in the self-run model are always better than that in the agency model. Our work provides insight into the promotion of the WEEE online recycling industry and into the resale channel selection for online recyclers. The study also generates managerial insights for environmental sustainability.

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