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Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery

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...

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Detalles Bibliográficos
Autores principales: Said, Maher, Aeschliman, Spencer, Stathopoulos, Amanda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613628/
https://www.ncbi.nlm.nih.gov/pubmed/37899375
http://dx.doi.org/10.1038/s41598-023-45371-1
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author Said, Maher
Aeschliman, Spencer
Stathopoulos, Amanda
author_facet Said, Maher
Aeschliman, Spencer
Stathopoulos, Amanda
author_sort Said, Maher
collection PubMed
description 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.
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spelling pubmed-106136282023-10-31 Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery Said, Maher Aeschliman, Spencer Stathopoulos, Amanda Sci Rep Article 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. Nature Publishing Group UK 2023-10-29 /pmc/articles/PMC10613628/ /pubmed/37899375 http://dx.doi.org/10.1038/s41598-023-45371-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Said, Maher
Aeschliman, Spencer
Stathopoulos, Amanda
Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
title Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
title_full Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
title_fullStr Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
title_full_unstemmed Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
title_short Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
title_sort robots at your doorstep: acceptance of near-future technologies for automated parcel delivery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613628/
https://www.ncbi.nlm.nih.gov/pubmed/37899375
http://dx.doi.org/10.1038/s41598-023-45371-1
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