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Expectations and Trust in Automated Vehicles
dc.contributor.authorZhang, Qiaoning
dc.contributor.authorYang, X. Jessie
dc.contributor.authorRobert, Lionel + "Jr."
dc.date.accessioned2020-02-16T22:38:47Z
dc.date.available2020-02-16T22:38:47Z
dc.date.issued2020-02-16
dc.identifier.citationZhang, Q., Yang, X. J. and Robert, L. P. (2020). Expectations and Trust in Automated Vehicles, In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, April 25-30, 2020, Honolulu, Hawaii, USA.en_US
dc.identifier.urihttp://dx.doi.org/10.1145/3334480.3382986
dc.identifier.urihttps://hdl.handle.net/2027.42/153795
dc.description.abstractA lack of trust is a major barrier to the adoptions of Automated Vehicles (AVs). Given the ties between expectation and trust, this study employs the expectation-confirmation theory to investigate in trust in AVs. An online survey was used to collect data including expectation, perceived performance, and trust in AVs from 443 participants which represent U.S. driver population. Using the polynomial regression and response surface methodology, we found that higher trust is engendered when perceived performance is higher than expectation, and perceived risk can moderate the relationship between expectation confirmation and trust in AVs. Results have important theoretical and practical implicationsen_US
dc.description.sponsorshipUniversity of Michigan Mcityen_US
dc.language.isoen_USen_US
dc.publisherCHI 2020en_US
dc.subjectAutomated Vehiclesen_US
dc.subjectexpectation confirmationen_US
dc.subjectExpectation Confirmation Theoryen_US
dc.subjectautomated drivingen_US
dc.subjectself-driving carsen_US
dc.subjectAV trusten_US
dc.subjectautonomous vehiclesen_US
dc.subjectvehiclesen_US
dc.subjectautomotive vehiclesen_US
dc.subjectAutomated Vehicle trusten_US
dc.subjectautonomous vehicles expectationsen_US
dc.subjectautonomous vehicles acceptanceen_US
dc.subjectself-driving car acceptanceen_US
dc.titleExpectations and Trust in Automated Vehiclesen_US
dc.typeConference Paperen_US
dc.subject.hlbsecondlevelInformation and Library Science
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumInformation, School ofen_US
dc.contributor.affiliationumRobotics Instituteen_US
dc.contributor.affiliationumCollege of Engineeringen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/153795/1/Zhang et al. 2020.pdf
dc.identifier.doi10.1145/3334480.3382986
dc.identifier.sourceExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, April 25-30, 2020, Honolulu, Hawaii, USA.en_US
dc.identifier.orcid0000-0002-1410-2601en_US
dc.description.filedescriptionDescription of Zhang et al. 2020.pdf : Main file
dc.identifier.name-orcidRobert, Lionel P.; 0000-0002-1410-2601en_US
dc.owningcollnameInformation, School of (SI)


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