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Development of a measurement instrument for pedestrians’ initial trust in automated vehicles

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journal contribution
posted on 2025-02-26, 14:27 authored by Siyuan Zhou, Xu Sun, Qingfeng Wang, Bingjian Liu, Gary BurnettGary Burnett
<p dir="ltr">Considering that a significant portion of the current pedestrian population has limited exposure to automated vehicles (AVs), it is crucial to have a reliable instrument for assessing pedestrians’ initial trust in AVs. Using a survey of 436 pedestrians, this study developed and validated a PITQA (Pedestrians’ Initial Trust Questionnaire for AVs) scale using partial least squares structural equation modeling (PLS-SEM). The proposed scale will be valuable in monitoring the progression of trust over time and considering trust-related factors during the design process. The results revealed that seven key constructs significantly contribute to predicting initial trust between pedestrians and AVs. These constructs include <i>propensity to trust, perceived statistical reliability, dependability and competence, perceived predictability, familiarity, authority/subversion, care/harm</i>, and <i>sanctity/degradation</i>. These shed light on how the trust propensity of individuals, different trust/trustworthiness attributes might constitute different aspects of initial trust in the pedestrian-AV context. The developed scale can be a potentially useful tool for future research endeavors concerning trust calibration and the design of AVs specifically tailored for vulnerable road users.</p>

Funding

Supported by the Chinese Ergonomics Society Fund of the Chinese Ministry of Education [grant number 202101042006]

2025 Key Technological Innovation Program of Ningbo City [grant number 2022Z080]

History

School

  • Design and Creative Arts

Published in

International Journal of Human-Computer Studies

Volume

191

Publisher

Elsevier Ltd

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Acceptance date

2024-07-25

Publication date

2024-07-26

Copyright date

2024

ISSN

1071-5819

Language

  • en

Depositor

Prof Gary Burnett. Deposit date: 9 August 2024

Article number

103344

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