Abstract
The fast growth of e-commerce in urban areas has led to a surge in last-mile transportation demand and an associated increase of external effects: congestion, noise and visual pollution. This paper analyses a new urban freight transport service that has a potential to reduce this footprint: crowdshipping. Crowdshipping is a service where a package is delivered via a traveller who is already making a personal trip for other purposes. The decision of whether or not to use crowdshipping is known to be subject to various service, time and price conditions, including trust in a correct delivery. The effect of trust has not been investigated explicitly, however. We conduct a stated choice experiment and estimate 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 have a significant influence on trust. We conclude that trust has a partially mediating effect on the adoption of the service except delivery time, and a fully mediating effect on adoption via reputation and damage.
Original language | English |
---|---|
Article number | 103622 |
Number of pages | 14 |
Journal | Transportation Research. Part A: Policy & Practice |
Volume | 170 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- Crowdshipping
- Trust
- Stated Preference
- Hybrid Choice Model
Fingerprint
Dive into the research topics of 'The effect of trust on the choice for crowdshipping services'. Together they form a unique fingerprint.Datasets
-
Dataset underlying the publication: The effect of trust on the choice for crowdshipping services
Cebeci, M. S. (Creator), Tapia, R. J. (Creator), Kroesen, M. (Creator), de Bok, M. A. (Creator) & Tavasszy, L. A. (Creator), TU Delft - 4TU.ResearchData, 23 Mar 2023
DOI: 10.4121/B1CD5E55-C3C2-4D5B-8EC7-58F071F53B6E
Dataset/Software: Dataset