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Multigroup Analysis in Information Systems Research using PLS-PM

2022-08-01Artigo científico Acesso aberto
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dc.contributor.authorKlesel, Michael
dc.contributor.authorSchuberth, Florian
dc.contributor.authorNiehaves, Björn
dc.contributor.authorHenseler, Jörg
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblACM - Association for Computing Machinery
dc.date.accessioned2022-08-24T22:18:42Z
dc.date.available2022-08-24T22:18:42Z
dc.date.issued2022-08-01
dc.descriptionKlesel, M., Schuberth, F., Niehaves, B., & Henseler, J. (2022). Multigroup Analysis in Information Systems Research using PLS-PM. Data Base for Advances in Information Systems - ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 53(3), 26-48. https://doi.org/10.1145/3551783.3551787 ---%ABS2%---Funding Information: Jörg Henseler gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020). Moreover, he acknowledges a financial interest in ADANCO and its distributor, Composite Modeling.
dc.description.abstractHeterogeneity is a pertinent issue in Information Systems (IS) research because human behavior often differs across groups. In the partial least squares path modeling (PLS-PM) context, several approaches have been proposed to investigate potential group differences. Despite the availability of numerous approaches, literature that compares their efficacy is sparse. Consequently, IS researchers lack guidance on which approach is best suited to detect group differences. We address this issue by presenting the results of an extensive Monte Carlo simulation study that juxtaposes the various approaches' behavior under numerous conditions. In doing so, we first provide an overview on existing approaches proposed for multigroup analysis (MGA) in the PLS-PM context. Moreover, we derive important implications for applied research: Firstly, we show that the omnibus test of group differences (OTG) and approaches based on the comparison of confidence intervals are not recommendable for MGA. Secondly, we provide detailed information as to which approaches are suitable for comparing one specific path coefficient and which are recommended if the complete structural model is compared across groups. Finally, we show that approaches which are designed to compare a single parameter require an adjustment for multiple comparisons when used to compare more than two groups.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent23
dc.format.extent341731
dc.identifier.doi10.1145/3551783.3551787
dc.identifier.issn0095-0033
dc.identifier.otherPURE: 46126719
dc.identifier.otherPURE UUID: 3cf5d579-461e-439c-b48a-ccc932d6aa0a
dc.identifier.otherScopus: 85135048060
dc.identifier.otherWOS: 000836062500003
dc.identifier.urihttp://hdl.handle.net/10362/143278
dc.identifier.urlhttps://www.scopus.com/pages/publications/85135048060
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000836062500003
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectdistance-based permutation test
dc.subjectmonte carlo simulation
dc.subjectmultigroup analysis
dc.subjectomnibus test of group differences (otg)
dc.subjectpartial least squares path modeling
dc.subjectManagement Information Systems
dc.subjectComputer Networks and Communications
dc.titleMultigroup Analysis in Information Systems Research using PLS-PMen
dc.typejournal article
degois.publication.firstPage26
degois.publication.issue3
degois.publication.lastPage48
degois.publication.titleData Base for Advances in Information Systems
degois.publication.volume53
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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