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The Relationship Between Exposure to and Trust in Automated Transport Technologies and Intention to Use a Shared Autonomous Vehicle.

Authors :
Farmer, Devon
Kim, Hyun
Lee, Jinwoo
Source :
International Journal of Human-Computer Interaction. Oct2024, Vol. 40 Issue 19, p5897-5909. 13p.
Publication Year :
2024

Abstract

It is crucial to understand the mechanism of how people perceive and accept Shared Autonomous Vehicles (SAVs) to realize the benefits that they will bring to transport networks, including increased safety on the roads. Experience with semi-automated cars and Autonomous Vehicle (AV) demonstration projects has been shown to positively affect acceptance of AVs, however automated transport technologies have been adopted in practice for years such as automated railways and aircraft autopilot; whether there are any connections between exposure to automated railways and aircraft autopilot and acceptance of SAVs is not well known. We explored the connections between trust in safety, knowledge of, and experience with automated railways, aircraft autopilot, and ADAS and intention to use a SAV. We surveyed individuals from Korea National University of Transportation (KNUT) in Chungju, Korea (n = 226), who will soon be able to use a SAV service, and adopted a model based on the Technology Acceptance Model (TAM). We found there to indeed be a connection between exposure to and trust in the safety of existing automated transport modes and acceptance of a SAV. Constructs with the strongest effects were found to be trust in safety of automated transport, hedonic motivation, perceived ease-of-use, perceived usefulness, and knowledge of automated transport. Experience with automated railways, but not semi-automated cars, was found to negatively moderate the relationship between hedonic motivation and intention to use a SAV. We discuss the policy implications of these results. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10447318
Volume :
40
Issue :
19
Database :
Academic Search Index
Journal :
International Journal of Human-Computer Interaction
Publication Type :
Academic Journal
Accession number :
179995547
Full Text :
https://doi.org/10.1080/10447318.2023.2247553