Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition

Shared autonomous vehicle services (i. e., automated shuttles, AS) are being deployed globally and may improve older adults (>65 years old) mobility, independence, and participation in the community. However, AS must be user friendly and provide safety benefits if older drivers are to accept...

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Autores principales:	Sherrilene Classen, Justin R. Mason, Seung Woo Hwangbo, Virginia Sisiopiku
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	older drivers predictors acceptance automated shuttle barriers executive function Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/d391a3d340244bc7aeb19e109e4a129e
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spelling	oai:doaj.org-article:d391a3d340244bc7aeb19e109e4a129e2021-12-02T06:13:01ZPredicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition1664-229510.3389/fneur.2021.798762https://doaj.org/article/d391a3d340244bc7aeb19e109e4a129e2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fneur.2021.798762/fullhttps://doaj.org/toc/1664-2295Shared autonomous vehicle services (i. e., automated shuttles, AS) are being deployed globally and may improve older adults (>65 years old) mobility, independence, and participation in the community. However, AS must be user friendly and provide safety benefits if older drivers are to accept and adopt this technology. Current potential barriers to their acceptance of AS include a lack of trust in the systems and hesitation to adopt emerging technology. Technology readiness, perceived ease of use, perceived barriers, and intention to use the technology, are particularly important constructs to consider in older adults' acceptance and adoption practices of AS. Likewise, person factors, i.e., age, life space mobility, driving habits, and cognition predict driving safety among older drivers. However, we are not sure if and how these factors may also predict older adults' intention to use the AS. In the current study, we examined responses from 104 older drivers (Mage = 74.3, SDage = 5.9) who completed the Automated Vehicle User Perception Survey (AVUPS) before and after riding in an on-road automated shuttle (EasyMile EZ10). The study participants also provided information through the Technology Readiness Index, Technology Acceptance Measure, Life Space Questionnaire, Driving Habits Questionnaire, Trail-making Test Part A and Part B (TMT A and TMT B). Older drivers' age, cognitive scores (i.e., TMT B), driving habits (i.e., crashes and/or citations, exposure, and difficulty of driving) and life space (i.e., how far older adults venture from their primary dwelling) were entered into four models to predict their acceptance of AVs—operationalized according to the subscales (i.e., intention to use, perceived barriers, and well-being) and the total acceptance score of the AVUPS. Next, a partial least squares structural equation model (PLS-SEM) elucidated the relationships between, technology readiness, perceived ease of use, barriers to AV acceptance, life space, crashes and/or citations, driving exposure, driving difficulty, cognition, and intention to use AS. The regression models indicated that neither age nor cognition (TMT B) significantly predicted older drivers' perceptions of AVs; but their self-reported driving difficulty (p = 0.019) predicted their intention to use AVs: R2 = 6.18%, F (2,101) = 4.554, p = 0.040. Therefore, intention to use was the dependent variable in the subsequent PLS-SEM. Findings from the PLS-SEM (R2 = 0.467) indicated the only statistically significant predictors of intention to use were technology readiness (β = 0.247, CI = 0.087-0.411) and barriers to AV acceptance (β = −0.504, CI = 0.285-0.692). These novel findings provide evidence suggesting that technology readiness and barriers must be better understood if older drivers are to accept and adopt AS.Sherrilene ClassenJustin R. MasonSeung Woo HwangboVirginia SisiopikuFrontiers Media S.A.articleolder driverspredictorsacceptanceautomated shuttlebarriersexecutive functionNeurology. Diseases of the nervous systemRC346-429ENFrontiers in Neurology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	older drivers predictors acceptance automated shuttle barriers executive function Neurology. Diseases of the nervous system RC346-429
spellingShingle	older drivers predictors acceptance automated shuttle barriers executive function Neurology. Diseases of the nervous system RC346-429 Sherrilene Classen Justin R. Mason Seung Woo Hwangbo Virginia Sisiopiku Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition
description	Shared autonomous vehicle services (i. e., automated shuttles, AS) are being deployed globally and may improve older adults (>65 years old) mobility, independence, and participation in the community. However, AS must be user friendly and provide safety benefits if older drivers are to accept and adopt this technology. Current potential barriers to their acceptance of AS include a lack of trust in the systems and hesitation to adopt emerging technology. Technology readiness, perceived ease of use, perceived barriers, and intention to use the technology, are particularly important constructs to consider in older adults' acceptance and adoption practices of AS. Likewise, person factors, i.e., age, life space mobility, driving habits, and cognition predict driving safety among older drivers. However, we are not sure if and how these factors may also predict older adults' intention to use the AS. In the current study, we examined responses from 104 older drivers (Mage = 74.3, SDage = 5.9) who completed the Automated Vehicle User Perception Survey (AVUPS) before and after riding in an on-road automated shuttle (EasyMile EZ10). The study participants also provided information through the Technology Readiness Index, Technology Acceptance Measure, Life Space Questionnaire, Driving Habits Questionnaire, Trail-making Test Part A and Part B (TMT A and TMT B). Older drivers' age, cognitive scores (i.e., TMT B), driving habits (i.e., crashes and/or citations, exposure, and difficulty of driving) and life space (i.e., how far older adults venture from their primary dwelling) were entered into four models to predict their acceptance of AVs—operationalized according to the subscales (i.e., intention to use, perceived barriers, and well-being) and the total acceptance score of the AVUPS. Next, a partial least squares structural equation model (PLS-SEM) elucidated the relationships between, technology readiness, perceived ease of use, barriers to AV acceptance, life space, crashes and/or citations, driving exposure, driving difficulty, cognition, and intention to use AS. The regression models indicated that neither age nor cognition (TMT B) significantly predicted older drivers' perceptions of AVs; but their self-reported driving difficulty (p = 0.019) predicted their intention to use AVs: R2 = 6.18%, F (2,101) = 4.554, p = 0.040. Therefore, intention to use was the dependent variable in the subsequent PLS-SEM. Findings from the PLS-SEM (R2 = 0.467) indicated the only statistically significant predictors of intention to use were technology readiness (β = 0.247, CI = 0.087-0.411) and barriers to AV acceptance (β = −0.504, CI = 0.285-0.692). These novel findings provide evidence suggesting that technology readiness and barriers must be better understood if older drivers are to accept and adopt AS.
format	article
author	Sherrilene Classen Justin R. Mason Seung Woo Hwangbo Virginia Sisiopiku
author_facet	Sherrilene Classen Justin R. Mason Seung Woo Hwangbo Virginia Sisiopiku
author_sort	Sherrilene Classen
title	Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition
title_short	Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition
title_full	Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition
title_fullStr	Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition
title_full_unstemmed	Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition
title_sort	predicting autonomous shuttle acceptance in older drivers based on technology readiness/use/barriers, life space, driving habits, and cognition
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/d391a3d340244bc7aeb19e109e4a129e
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Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition

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