Employee willingness to collaborate with autonomous delivery robots: a cross-cultural perspective

Purpose This study investigates the factors influencing delivery workers’ willingness to collaborate with autonomous delivery robots (ADRs). As ADRs become more prevalent in Logistics 4.0 environments, understanding human–technology collaboration is critical for supporting both operational efficiency and decent work. Design/methodology/approach We draw from the technology acceptance model (TAM) and service robot acceptance model (sRAM) to develop a model and examine the impact of functional, social and relational factors on delivery workers’ willingness to collaborate with ADRs. A field survey with a sample size of 483 and an online survey with a sample size of 292 were conducted to test the relationships of interest. Findings The results indicate that perceived usefulness, social influence and anthropomorphism have a positive influence on willingness to collaborate with ADRs, with procedural fairness acting as a significant mediator. While the overall model holds across both samples, differences in the strength of relationships suggest that cultural context shapes how employees perceive and respond to ADRs. Originality/value This study contributes to the literature by extending TAM and sRAM to the logistics sector and providing a cross-cultural perspective on employee–ADR collaboration. It addresses a critical gap in logistics and supply chain research, providing practical approaches to technology integration that support decent work in the Logistics 4.0 era.

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Publication Details

Journal
International Journal of Physical Distribution & Logistics Management
Published
2025-11-03
DOI
https://doi.org/10.1108/ijpdlm-01-2025-0019
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48
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article

Employee willingness to collaborate with autonomous delivery robots: a cross-cultural perspective

J. J. Wen, Ginnah Lee, V. Myles Landers, Jinghua Huang et al.
1 citations
International Journal of Physical Distribution & Logistics Management
AI in Service Interactions
2.48
article

Employee willingness to collaborate with autonomous delivery robots: a cross-cultural perspective

J. J. Wen, Ginnah Lee, V. Myles Landers, Jinghua Huang, Christopher A. Boone, Xinwei Liu, Zhao Liu, Jason Shin
article en
1 citations

Abstract

Purpose This study investigates the factors influencing delivery workers’ willingness to collaborate with autonomous delivery robots (ADRs). As ADRs become more prevalent in Logistics 4.0 environments, understanding human–technology collaboration is critical for supporting both operational efficiency and decent work. Design/methodology/approach We draw from the technology acceptance model (TAM) and service robot acceptance model (sRAM) to develop a model and examine the impact of functional, social and relational factors on delivery workers’ willingness to collaborate with ADRs. A field survey with a sample size of 483 and an online survey with a sample size of 292 were conducted to test the relationships of interest. Findings The results indicate that perceived usefulness, social influence and anthropomorphism have a positive influence on willingness to collaborate with ADRs, with procedural fairness acting as a significant mediator. While the overall model holds across both samples, differences in the strength of relationships suggest that cultural context shapes how employees perceive and respond to ADRs. Originality/value This study contributes to the literature by extending TAM and sRAM to the logistics sector and providing a cross-cultural perspective on employee–ADR collaboration. It addresses a critical gap in logistics and supply chain research, providing practical approaches to technology integration that support decent work in the Logistics 4.0 era.

International Journal of Physical Distribution & Logistics ManagementVol. 56(2)
Farmingdale State College (US), RetiVue (United States) (US), Drexel University (US), Mississippi State University (US)
Openalex Percentile: Top 8%
AI in Service Interactions
2.48
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