Consumer acceptance of the autonomous robot in last-mile delivery: A combined perspective of resource-matching, perceived risk and value theories

Following the expansion of e-commerce, the need for transportation services has increased. Moreover, the pandemic has decreased in-person interaction, necessitating contactless technologies. The autonomous delivery robot (ADR) is one such contactless technology used in last-mile delivery (LMD). Hence, consumers’ acceptance of ADRs in last-mile service must be studied to promote the use of this innovative technology. This study investigates the factors influencing customers’ acceptance of ADRs in LMD and aid in resource allocation to encourage acceptance. A combination of resource-matching theory, perceived value theory, and perceived risk theory was applied to develop the theoretical model. The central premise is that customers’ intentions are motivated by the characteristics of ADRs, such as compatibility, reliability, privacy security, and convenience, through the channels of enhanced perceived value and lower perceived risk.. An online survey with 500 respondents was conducted in Singapore and structural model equation analysis was performed. The findings revealed that the effects of compatibility, convenience, privacy security, and reliability on consumer intention are fully mediated by perceived value and risk. This study enriches the literature on ADR acceptance in LMD by developing a holistic model and providing implications for promoting ADR adoption.

• Record URL:
  https://doi.org/10.1016/j.tra.2024.104008
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0965856424000569
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Koh, Le Yi
  • Xia, Zhiyang
  • Yuen, Kum Fai
• Publication Date: 2024-4

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; References;
• Pagination: 104008
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 182
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Acceptance; Autonomous vehicles; COVID-19; Delivery service; Electric vehicles; Robots
• Geographic Terms: Singapore
• Subject Areas: Data and Information Technology; Energy; Freight Transportation; Planning and Forecasting; Security and Emergencies; Society; Vehicles and Equipment;

Filing Info

• Accession Number: 01913351
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2024 10:03AM