The fast growth of e-commerce in urban areas has led to a surge in last-mile transportation demand and an associated increase in external effects: congestion, noise, and visual pollution. A recent paper analyses a new urban freight transport service that could potentially reduce this footprint: crowd shipping.
Crowd shipping is a service where a package is delivered via a traveler already making a personal trip for other purposes. Whether or not to use crowd shipping is subject to various service, time, and price conditions, including trust in a correct delivery. The effect of trust has not been investigated explicitly, however.
Trust as variable
The researchers conducted a stated choice experiment and estimated a hybrid choice model with trust as a situation-specific latent variable. The research design allows us to explore how the relevant attributes influence service adoption via trust. We find a significant influence of established choice attributes on service adoption, except for the delivery company’s reputation and the possibility of damage. In addition, all attributes except delivery time significantly influence trust. The researchers conclude that trust partially mediates the adoption of the service except for delivery time and a fully mediating effect on adoption via reputation and damage.
Practical relevance
From a practical point of view, various recommendations can be given to provide roadmaps for crowd-shipping service providers. Firstly, our research showed that the reputation of the delivery company has the most significant impact on the level of trust towards the service choice. Even though flexible or outside service hours, parcel delivery would be possible in crowd shipping, these advantages can only be effective if the company has a good reputation. A new crowd shipping service provider in the market might have difficulty establishing a profitable demand without building a high service quality reputation. Secondly, distinguishing between market segments could be necessary as our findings also indicate significant heterogeneity in acceptance behavior between user groups.