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Conference Paper: To use a tree or a forest in behavioral intention

TitleTo use a tree or a forest in behavioral intention
Authors
Issue Date2014
PublisherAssociation for Information Systems (AIS).
Citation
The 18th Pacific Asia Conference on Information Systems (PACIS 2014), Chengdu, China, 24-28 June 2014. In the Proceedings of the 18th Pacific Asia Conference on Information Systems (PACIS), 2014, p. abstract no. 215 How to Cite?
AbstractCloud computing is a new technology that has been applied to education and has e nabled the development of cloud computing classrooms; however, student behavioral intentions toward cloud computing remain unclear. Most researchers have evaluated, integrated, or compared few (1 to 3) theories to examine user behavioral intentions and few have addressed additional theories or models. In this study, we test, compare, and unify six well -known theories, namely, service quality (SQ), self - efficacy (SE), the motivational model (MM), technology acceptance model (TAM), theory of reason action (TRA)/theory of planned behavior (TPB), and innovation diffusion theory (IDT) in the context of cloud computing classrooms. This empirical study was conducted using an online survey. The data collected from the samples (n=478) were analyzed using structural equation modeling. We independently analyzed each of the six theories, formulating a united model. The analysis yielded three valuable findings. First, comparing the explained variance and degree of freedom (df) difference, yielded the following ranking in explained variance: MM=TAM>IDT>TPB>SE=SQ (equal =; superior to>). Second, comparing the explained variance yielded the following ranking in explained variance: MM>TAM>IDT>TPB>SE=SQ. Third, based on the united model of six theories, some factors significantly affect behavioral intention and others do not. The implications of this study are critical for both researchers and practitioners.
DescriptionConference Theme: IT ubiquity and innovation
Session 9-7: IS Innovation, Adoption, and Diffusion
Persistent Identifierhttp://hdl.handle.net/10722/201497

 

DC FieldValueLanguage
dc.contributor.authorShiau, WLen_US
dc.contributor.authorChau, PYKen_US
dc.date.accessioned2014-08-21T07:28:47Z-
dc.date.available2014-08-21T07:28:47Z-
dc.date.issued2014en_US
dc.identifier.citationThe 18th Pacific Asia Conference on Information Systems (PACIS 2014), Chengdu, China, 24-28 June 2014. In the Proceedings of the 18th Pacific Asia Conference on Information Systems (PACIS), 2014, p. abstract no. 215en_US
dc.identifier.urihttp://hdl.handle.net/10722/201497-
dc.descriptionConference Theme: IT ubiquity and innovation-
dc.descriptionSession 9-7: IS Innovation, Adoption, and Diffusion-
dc.description.abstractCloud computing is a new technology that has been applied to education and has e nabled the development of cloud computing classrooms; however, student behavioral intentions toward cloud computing remain unclear. Most researchers have evaluated, integrated, or compared few (1 to 3) theories to examine user behavioral intentions and few have addressed additional theories or models. In this study, we test, compare, and unify six well -known theories, namely, service quality (SQ), self - efficacy (SE), the motivational model (MM), technology acceptance model (TAM), theory of reason action (TRA)/theory of planned behavior (TPB), and innovation diffusion theory (IDT) in the context of cloud computing classrooms. This empirical study was conducted using an online survey. The data collected from the samples (n=478) were analyzed using structural equation modeling. We independently analyzed each of the six theories, formulating a united model. The analysis yielded three valuable findings. First, comparing the explained variance and degree of freedom (df) difference, yielded the following ranking in explained variance: MM=TAM>IDT>TPB>SE=SQ (equal =; superior to>). Second, comparing the explained variance yielded the following ranking in explained variance: MM>TAM>IDT>TPB>SE=SQ. Third, based on the united model of six theories, some factors significantly affect behavioral intention and others do not. The implications of this study are critical for both researchers and practitioners.-
dc.languageengen_US
dc.publisherAssociation for Information Systems (AIS).-
dc.relation.ispartofProceedings of the 18th Pacific Asia Conference on Information Systems (PACIS 2014)en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleTo use a tree or a forest in behavioral intentionen_US
dc.typeConference_Paperen_US
dc.identifier.emailChau, PYK: pchau@business.hku.hken_US
dc.identifier.authorityChau, PYK=rp01052en_US
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros233809en_US
dc.identifier.spageabstract no. 215-
dc.identifier.epageabstract no. 215-
dc.publisher.placeUnited States-

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