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How old are you really? Cognitive age in technology acceptance

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dc.contributor.authorHong, Se-Joon-
dc.contributor.authorLui, Carrie Siu Man-
dc.contributor.authorHahn, Jungpil-
dc.contributor.authorMoon, Jae Yun-
dc.contributor.authorKim, Tai Gyu-
dc.date.accessioned2021-09-05T18:45:41Z-
dc.date.available2021-09-05T18:45:41Z-
dc.date.created2021-06-15-
dc.date.issued2013-12-
dc.identifier.issn0167-9236-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/101520-
dc.description.abstractWith increasing trends toward global aging and accompanying tendencies of (older) individuals to feel younger than they actually are, an important research question to ask is whether factors influencing IT acceptance are the same across individuals who perceive themselves to be as old as they actually are (i.e., cognitive age = chronological age) and those that perceive themselves to be younger than they actually are (i.e., cognitive age < chronological age). We conduct an empirical analysis comparing these two groups in the context of mobile data services (MDS). Our results show that for the "young at heart," perceived usefulness, perceived ease of use and perceived enjoyment play significant roles in their IT acceptance decisions, whereas for those who perceive themselves to be as old as they actually are, perceived ease of use and subjective norms were significant. Practical implications regarding use of cognitive age as a basis for customer segmentation in IT industries as well as theoretical implications about meaningful age in human computer interaction research are offered and discussed. (C) 2013 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectINFORMATION-TECHNOLOGY-
dc.subjectMODELING APPROACH-
dc.subjectPLANNED BEHAVIOR-
dc.subjectINTERNET BANKING-
dc.subjectUSER ACCEPTANCE-
dc.subjectDIGITAL NATIVES-
dc.subjectADOPTION-
dc.subjectDECISIONS-
dc.subjectCONSUMERS-
dc.subjectGENDER-
dc.titleHow old are you really? Cognitive age in technology acceptance-
dc.typeArticle-
dc.contributor.affiliatedAuthorHong, Se-Joon-
dc.contributor.affiliatedAuthorMoon, Jae Yun-
dc.contributor.affiliatedAuthorKim, Tai Gyu-
dc.identifier.doi10.1016/j.dss.2013.05.008-
dc.identifier.scopusid2-s2.0-84889100457-
dc.identifier.wosid000329005000013-
dc.identifier.bibliographicCitationDECISION SUPPORT SYSTEMS, v.56, pp.122 - 130-
dc.relation.isPartOfDECISION SUPPORT SYSTEMS-
dc.citation.titleDECISION SUPPORT SYSTEMS-
dc.citation.volume56-
dc.citation.startPage122-
dc.citation.endPage130-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.subject.keywordPlusINFORMATION-TECHNOLOGY-
dc.subject.keywordPlusMODELING APPROACH-
dc.subject.keywordPlusPLANNED BEHAVIOR-
dc.subject.keywordPlusINTERNET BANKING-
dc.subject.keywordPlusUSER ACCEPTANCE-
dc.subject.keywordPlusDIGITAL NATIVES-
dc.subject.keywordPlusADOPTION-
dc.subject.keywordPlusDECISIONS-
dc.subject.keywordPlusCONSUMERS-
dc.subject.keywordPlusGENDER-
dc.subject.keywordAuthorAge-
dc.subject.keywordAuthorChronological age-
dc.subject.keywordAuthorCognitive age-
dc.subject.keywordAuthorTechnology acceptance-
dc.subject.keywordAuthorMobile data services-
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