Interactive Decisions Analysis in an Online Shopping Service Supply Chain Considering Reciprocal Altruism

Cooperative relationships and interactive decision behaviors between online stores and third-party logistics (TPL) have attracted increasing attention from academics as well as practitioners with the development of the online retailing industry. However, the decisions and performance of each member in a decentralized supply chain are not in line with the actual performance and decision behaviors of them, especially when taking individual altruism preference into account. Hence, without considering the truism of channel members will lead to unrealistic decisions and obtain anamorphic performance of firms and the entire system based on conventional self-interested models. This paper develops an evolutionary bilateral decision-making model to characterize the effects of all members' altruism in an online shopping service supply chain (OSSSC). The model considers different altruism of each member in the supply chain and their changes at various intervals. The decision equilibrium and interactive rules of all members are deeply analyzed based on the evolutionary bilateral decision-model as the change of altruism of each party. Our results identify that: altruism preference impacts the decisions of all participants in the OSSSC system. When the TPL and the online store have high altruism and low altruism, respectively, both retail price and logistics service price tend toward low levels and all participants have high utilities. However, this situation is more favorable for the online store. In this case, changes in the altruism level of either party will adversely impact the utilities of all parties and the performance of the whole system; specially, when the online stores are self-interested while the TPL has altruism that fluctuates within a specific range, improving logistics service level will help to lower the retail price and increase market shares. Numerical experiments are conducted to examine and verify the results and some optimal managerial insights for online retailers and TPLs are provided.