What drives people to accept automated vehicles? Findings from a field experiment

This field study aims at understanding the influence of direct experience of an automated vehicle (AV, Level 3) and explaining and predicting public acceptance of AVs through a psychological model. The model includes behavioral intention (BI) to use self-driving vehicles (SDVs, Level 5), willingness to re-ride (WTR) in the authors' AV (Level 3), and their four potential determinants, namely perceived usefulness (PU), perceived ease of use (PEU), trust related to SDVs, and perceived safety (PS) while riding in the authors' AV. The last two determinants are largely ignored, but the authors consider them critical in the context of AVs. Three-hundred students were invited as participants (passengers) to experience the AV. The trust, PU, PEU, and BI of the participants were recorded prior to their experiencing the AV; after this experience, all the constructs of the psychological model were recorded. The participants’ experience with the AV was found to increase their trust, PU and PEU (but not BI), the consistency between PU/PEU and BI, and the explanatory power of BI. The model explained 55% of the variance in BI and 40% in WTR. PU, trust, and PS were found to be steady and direct predictors of both the acceptance measures; PEU predicted BI only after the participants’ AV experience. Mediation analysis showed that trust also can indirectly affect AV acceptance through other determinants. Out-of-sample prediction confirmed the model’s predictive capability for AV acceptance. The theoretical contributions and practical implications of the findings are discussed.

• Record URL:
  https://doi.org/10.1016/j.trc.2018.07.024
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X18302316
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Xu, Zhigang
  • 0000-0002-8479-4973
  • Zhang, Kaifan
  • Min, Haigen
  • Wang, Zhen
  • Zhao, Xiangmo
  • Liu, Peng
  • 0000-0003-4929-0531
• Publication Date: 2018-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 320-334
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 95
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Acceptance; Attitudes; Field tests; Intelligent vehicles; Microscopic traffic flow; Origin and destination
• Uncontrolled Terms: Particle filtering
• Geographic Terms: Kunshan City (China)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01679726
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 30 2018 9:43AM