Modelling the acceptance of fully autonomous vehicles: A media-based perception and adoption model

As the technology matures, fully autonomous vehicles (AVs) are on the corner. This calls for exploring the factors that might influence potential users’ perception and acceptance of AVs. Limited existing studies related to acceptance modeling investigated the effects of media and human on fully AVs’ beliefs. Hence, a media-based perception and adoption model (MPAM) is developed to investigate how information and opinion (from mass media and social media) affect human self-perception (including self-efficacy and subjective norms) and product value perception (including perceived usefulness and risks), which in turn drive users’ adoption intention to private AVs and public AVs as well. Through a questionnaire survey, 355 samples from two universities were collected in Beijing. The structural equation model results confirm that media channels have salient effects on consumer and product with different emphases. Mass media enhances potential users’ self-efficacy of fully AVs, while social media strengthens subjective norms. Both usefulness and risks of AVs are perceived simultaneously via mass media, whereas risks perception can be significantly eliminated by social media. All constructs of user’s self-perception and product perception are verified to drive users’ intention to using AVs and public AVs. Besides the theoretical and modeling contributions, practical implications are provided for the marketers and stakeholders in the early stages of AVs launch.

• Record URL:
  https://doi.org/10.1016/j.trf.2020.06.004
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1369847820304411
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhu, Ge
  • Chen, Yuche
  • Zheng, Jiali
• Publication Date: 2020-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 80-91
• Serial:
  • Transportation Research Part F: Traffic Psychology and Behaviour
  • Volume: 73
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1369-8478
  • Serial URL: http://www.sciencedirect.com/science/journal/13698478

Subject/Index Terms

• TRT Terms: Acceptance; Autonomous vehicles; Mass media; Questionnaires; Risk; Social media; Structural equation modeling
• Geographic Terms: Beijing (China)
• Subject Areas: Administration and Management; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01746666
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 27 2020 9:36AM