Investigating the impact of late deliveries on the operations of the crowd-shipping platform: A mean-variance analysis

Abstract

Accompanying the booming of e-commerce, crowd-shipping (CS) service has gained much attention recently. It outsources shipping tasks to the crowd with app-based platform technologies, which largely increases shipping capacities. Despite its merits in providing flexible options for consignees, CS services often face difficulties in delivering packages on time due to several reasons such as crowdshippers’ unprofessional skills, which can be regarded as one of the risks in the CS platform’s operations. Motivated by this, we adopt a mean–variance (MV) approach to characterize the CS platform’s behaviors towards late deliveries, in which two kinds of risk-related behaviors, i.e., risk-neutral and risk-averse attitudes, are incorporated. To identify the impact of late deliveries on the CS platform’s operations, we propose two MV-based risk models, i.e., the risk-neutral and risk-averse models. Equilibrium results concerning the shipping price, the service level, the platform’s expected profit, the consignees’ surplus, and social welfare can be derived from the two models. Results show that late deliveries will negatively affect the CS platform’s profit but positively affect the CS market demand. Policy implications concerning offsetting the negative impact of late deliveries are further proposed and discussed.