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dc.contributor.authorCabrera-Sánchez, Juan-Pedro
dc.contributor.authorVillarejo-Ramos, Ángel F.
dc.contributor.authorLiébana-Cabanillas, Francisco
dc.contributor.authorShaikh, Aijaz A.
dc.date.accessioned2020-11-24T11:32:48Z
dc.date.available2020-11-24T11:32:48Z
dc.date.issued2021
dc.identifier.citationCabrera-Sánchez, Juan-Pedro; Villarejo-Ramos, Ángel F.; Liébana-Cabanillas, Francisco; Shaikh, Aijaz A. (2021). Identifying relevant segments of AI applications adopters : Expanding the UTAUT2’s variables. Telematics and Informatics, 58, 101529. DOI: 10.1016/j.tele.2020.101529
dc.identifier.otherCONVID_46981511
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72786
dc.description.abstractArtificial intelligence (AI) is a future-defining technology, and AI applications are becoming mainstream in the developed world. Many consumers are adopting and using AI-based apps, devices, and services in their everyday lives. However, research examining consumer behavior in using AI apps is scant. We examine critical factors in AI app adoption by extending and validating a well-established unified theory of adoption and use of technology, UTAUT2. We also explore the possibility of unobserved heterogeneity in consumers’ behavior, including potentially relevant segments of AI app adopters. To augment the knowledge of end users’ engagement and relevant segments, we have added two new antecedent variables into UTAUT2: technology fear and consumer trust. Prediction-orientated segmentation was used on 740 valid responses collected using a pre-tested survey instrument. The results show five segments with different behaviors that were influenced by the variables of the proposed model. Once known, the profiles were used to propose apps to AI developers to improve consumer engagement. The moderating effects of the added variables—technology fear and consumer trust—are also shown. Finally, we discuss the theoretical and managerial implications of our findings and propose priorities for future research.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.publisherElsevier
dc.relation.ispartofseriesTelematics and Informatics
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherartificial intelligence
dc.subject.othersegmentation
dc.subject.othertechnology fear
dc.subject.otherconsumer trust
dc.subject.otherheterogeneity
dc.subject.otherunified theory of adoption and use of technology
dc.subject.otherUTAUT2
dc.titleIdentifying relevant segments of AI applications adopters : Expanding the UTAUT2’s variables
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202011246749
dc.contributor.laitosKauppakorkeakoulufi
dc.contributor.laitosSchool of Business and Economicsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn0736-5853
dc.relation.volume58
dc.type.versionacceptedVersion
dc.rights.copyright© 2020 Elsevier
dc.rights.accesslevelembargoedAccessfi
dc.subject.ysokuluttajakäyttäytyminen
dc.subject.ysokuluttajat
dc.subject.ysotekoäly
dc.subject.ysoheterogeenisuus
dc.subject.ysosovellusohjelmat
dc.subject.ysosegmentointi
dc.subject.ysokäyttöönotto
dc.subject.ysoluottamus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p8576
jyx.subject.urihttp://www.yso.fi/onto/yso/p1397
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p19410
jyx.subject.urihttp://www.yso.fi/onto/yso/p8456
jyx.subject.urihttp://www.yso.fi/onto/yso/p18246
jyx.subject.urihttp://www.yso.fi/onto/yso/p17832
jyx.subject.urihttp://www.yso.fi/onto/yso/p1725
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.tele.2020.101529


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