Abstract
The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public acceptability of automated parcel delivery options, this U.S. study explores customer preferences for four innovations: autonomous vehicles, aerial drones, sidewalk robots, and bipedal robots. We use an Integrated Nested Choice and Correlated Latent Variable (INCLV) model to reveal substitution effects among automated delivery modes in a sample of U.S. respondents. The study finds that acceptance of automated delivery modes is strongly tied to shipment price and time, underscoring the importance of careful planning and incentives to maximize the trialability of innovative logistics options. Older individuals and those with concerns about package handling exhibit a lower preference for automated modes, while individuals with higher education and technology affinity exhibit greater acceptance. These findings provide valuable insights for logistics companies and retailers looking to introduce automation technologies in their last-mile delivery operations, emphasizing the need to tailor marketing and communication strategies to meet customer preferences. Additionally, providing information about appropriate package handling by automated technologies may alleviate concerns and increase the acceptance of these modes among all customer groups.
| Original language | English (US) |
|---|---|
| Article number | 18556 |
| Journal | Scientific reports |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2023 |
Funding
This research was supported in part by the US National Science Foundation Career Award No. 1847537 and, the Northwestern Buffett Institute Global Impacts Graduate Fellowship and the Northwestern University Transportation Center terminal year fellowship. The authors would like to thank Rachel Cole, Head of the Northwestern Transportation Library, for her help with the literature review, and Emma Zajdela, Ph.D. Candidate in the Department of Engineering Sciences and Applied Mathematics at Northwestern, for her feedback on the modeling framework and the Cholesky decomposition. The study was performed in accordance with Human Subject Research Protections, informed consent was secured, and the study was approved by Northwestern’s Institutional Review Board under study number STU00212452. This research was supported in part by the US National Science Foundation Career Award No. 1847537 and, the Northwestern Buffett Institute Global Impacts Graduate Fellowship and the Northwestern University Transportation Center terminal year fellowship. The authors would like to thank Rachel Cole, Head of the Northwestern Transportation Library, for her help with the literature review, and Emma Zajdela, Ph.D. Candidate in the Department of Engineering Sciences and Applied Mathematics at Northwestern, for her feedback on the modeling framework and the Cholesky decomposition. The study was performed in accordance with Human Subject Research Protections, informed consent was secured, and the study was approved by Northwestern’s Institutional Review Board under study number STU00212452.
ASJC Scopus subject areas
- General
