Alharbi, A., Cantarelli, C. and Brint, A. orcid.org/0000-0002-8863-407X (2022) Crowd models for last mile delivery in an emerging economy. Sustainability, 14 (3). 1401.
Abstract
The dramatic rise in online shopping means that the last mile delivery (LMD) task is becoming extremely important. However, last mile delivery faces many economic, social, and environmental challenges. A fast-growing innovative solution is Crowd Logistics Delivery (CLD). This study investigates how CLD is meeting these challenges in a rapidly emerging economy (Saudi Arabia). It uses semi-structured interviews to analyse CLD from the perspectives of multiple stakeholders, focusing on its implementation, benefits to different stakeholders, and its limitations. While the findings of this study broadly support the work of other studies in this area, it provides several new insights. It observed three different business models being used for CLD: B2B, B2C, and C2C. It identified the internal success factors of each business model, including registration, assigning orders, compensation, and the payment model. It revealed the motivations for stakeholders to use CLD as a last mile delivery solution, such as LMD-related benefits and the social impact on society. In addition, the study highlighted the four main challenges these CLD implementations face that impede their success: legislation, availability of supply/drivers, trust, and culture. These results add to the rapidly expanding field of CLD.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | last mile delivery; crowd logistics; business model; stakeholder analysis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Feb 2022 11:30 |
Last Modified: | 07 Feb 2022 11:30 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/su14031401 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183295 |