Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects

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Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects

Punel, Aymeric / Stathopoulos, Amanda

Highlights•This paper develops an empirical model of crowdshipping acceptance for package deliveries.•Control for experience-conditioning shows strong heterogeneity.•Distinct preference patterns exist for different distance classes of the shipment.•Willingness to pay and elasticity scenarios show high sensitivities for driver-performance.

AbstractCrowdshipping is a frontier in logistics systems designed to allow citizens to connect via online platforms and organize goods delivery along planned travel routes. The goal of this paper is to highlight the factors that influence the acceptability and preferences for crowdshipping. Through a survey using stated choice scenarios discrete choice models controlling for context and experience effects are specified. The results suggest that distinct preference patterns exist for distance classes of the shipment. In the local delivery setting, senders value transparency of driver performance monitoring along with speed, while longer shipments prioritize delivery conditions and driver training and experience. The model developed in this paper provides first key insights into the factors affecting preferences for goods delivery with occasional drivers.