When an online manufacturer suffers from demand reduction and product return caused by product fit uncertainty, it could display products in a retailer’s store to provide the chance for consumers to examine whether the product fits their needs. According to whether the product the retailer sells is noncompetitive or competitive, this study considers that there are cooperation and co-opetition strategies for the online manufacturer to realize an offline showroom partnership. In addition, the retailer would provide retail service for consumers who visit the store, while the service cost information is private. Thus, this study explores the optimal offline showroom partnership for an online manufacturer under information asymmetry. The results show that although partnering with a competitive retailer causes product competition, the online manufacturer would prefer the co-opetition strategy if (i) the competition intensity between the two products is low or (ii) the competition intensity is high and the cost to handle the returned product is low, and would prefer the cooperation strategy otherwise. Under the optimal strategy, the online manufacturer should design the contract according to the return handling cost. Nevertheless, the online manufacturer could set a higher price under the co-opetition strategy than that under the cooperation strategy. After comparing the online manufacturer’s optimal strategies and profits in different information environments, we find that product competition could reduce the online manufacturer’s profit loss caused by information asymmetry under certain conditions, and the online manufacturer with low return handling cost is more willing to adopt the co-opetition strategy under information asymmetry.

This paper aims to study the evolution mechanism of the third-party platform ecosystem. A multi-value chain network ecosystem composed of multiple manufacturers, multiple suppliers, several logistics providers and a third-party platform for manufacturing is considered. The system dynamics method is used to build the model, and this paper collects relevant industry and platform data to simulate the evolution of user scale and participants' revenues. Furthermore, the influence of platform subsidy and matching service level on the evolution is studied. The results show that the platform's evolution can be divided into four stages: emergence, growth, maturity and upgrade. This paper also finds that, at the emergence stage and the growth stage, the augmentation of the subsidies to manufacturers makes the manufacturers' scale expand but let their revenues decline. Meanwhile, the platform's revenues reduce at the emergence stage while increase at the growth stage. When the subsidy amount is high and continues to augment, its positive effect on the user scale is weakened while its negative effect on manufacturers' revenues is enhanced. Besides, improving the matching service level is not conducive to the platform's revenues at the emergence stage, but after entering the growth stage, it can increase user scale and the platform's revenues simultaneously.

Service-oriented third-party e-commerce platforms have emerged as a new trend in the manufacturing industry. This paper aims to investigate the platforms’ value-added service (VAS) and charging strategies with a dynamic evolution analysis. Considering the change in the user numbers and characteristics of the e-commerce industry, this paper proposes a system dynamics model composed of multi-value chains and a third-party e-commerce platform. The simulation results indicate that the platform should reduce VAS investment and appropriately increase the VAS fee in the early development period. After the number of users stabilizes, the platform should appropriately increase its VAS investment and reduce the VAS fee. When the VAS fee is low, the platform profit first increases and then decreases as the VAS level increases. Differently, the platform profit will first decrease, then increase, and finally decrease as the VAS level improves when the VAS fee is low. This paper further finds that the strong cross-network effect of manufacturers is not always beneficial to the platform.