Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33961
DC FieldValueLanguage
dc.contributor.authorPani, Agnivesh-
dc.contributor.authorMishra, Sabyasachee-
dc.contributor.authorGolias, Mihalis-
dc.contributor.authorFigliozzi, Miguel-
dc.date.accessioned2025-01-29T08:43:38Z-
dc.date.available2025-01-29T08:43:38Z-
dc.date.issued2020-12-01-
dc.identifier.citationTransportation Research Part D: Transport and Environment, vol.89, 2020en_US
dc.identifier.issn13619209-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/33961-
dc.description.abstractAutonomous delivery robot (ADR) technology for last-mile freight deliveries is a valuable step towards low-carbon logistics. The ongoing COVID-19 pandemic has put a global spotlight on ADRs for contactless package deliveries, and tremendous market interest has been pushing ADR developers to provide large-scale operation in several US cities. The deployment and penetration of ADR technology in this emerging marketplace calls for collection and analysis of consumer preference data on ADRs. This study addresses the need for research on public acceptance of ADRs and offers a detailed analysis of consumer preferences, trust, attitudes, and willingness to pay (WTP) using a representative sample of 483 consumers in Portland. The results reveal six underlying consumer segments: Direct Shoppers, E-Shopping Lovers, COVID Converts, Omnichannel Consumers, E-Shopping Skeptics, and Indifferent Consumers. By identifying the WTP determinants of these latent classes, this study provides actionable guidance for fostering mass adoption of low-carbon deliveries in the last-mile.en_US
dc.language.isoenen_US
dc.relation.ispartofTransportation Research Part D: Transport and Environmenten_US
dc.subjectLow-carbon deliveryen_US
dc.subjectConsumer acceptanceen_US
dc.subjectAttitude-based segmentationen_US
dc.subjectWillingness to payen_US
dc.subjectLatent class analysisen_US
dc.subjectCOVID-19en_US
dc.titleEvaluating public acceptance of autonomous delivery robots during COVID-19 pandemicen_US
dc.typeArticleen_US
dc.collaborationUniversity of Memphisen_US
dc.collaborationPortland State Universityen_US
dc.subject.categoryENGINEERING AND TECHNOLOGYen_US
dc.journalsSubscriptionen_US
dc.countryUSAen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.trd.2020.102600en_US
dc.identifier.scopus2-s2.0-85094882110en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85094882110en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.relation.volume89en_US
cut.common.academicyear2020-2021en_US
item.openairetypearticle-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
crisitem.journal.journalissn1361-9209-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Shipping-
crisitem.author.facultyFaculty of Management and Economics-
crisitem.author.orcid0000-0001-9402-2996-
crisitem.author.parentorgFaculty of Management and Economics-
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