Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1814
DC FieldValueLanguage
dc.contributor.authorSarstedt, Marko-
dc.contributor.authorHair, Joseph F.-
dc.contributor.authorRingle, Christian M.-
dc.contributor.authorThiele, Kai Oliver-
dc.contributor.authorGudergan, Siegfried-
dc.date.accessioned2018-11-07T06:32:27Z-
dc.date.available2018-11-07T06:32:27Z-
dc.date.issued2016-06-25-
dc.identifier.citationJournal of Business Research 10 (69): 3998-4010 (2016)de_DE
dc.identifier.issn0148-2963de_DE
dc.identifier.urihttp://hdl.handle.net/11420/1817-
dc.description.abstractDiscussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about themeaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective,we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based.en
dc.language.isoende_DE
dc.publisherElsevierde_DE
dc.relation.ispartofJournal of business researchde_DE
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.subjectcommon factor modelsde_DE
dc.subjectcomposite modelsde_DE
dc.subjectreflective measurementde_DE
dc.subjectformative measurementde_DE
dc.subjectstructural equation modelingde_DE
dc.subjectpartial least squaresde_DE
dc.subject.ddc330: Wirtschaftde_DE
dc.titleEstimation issues with PLS and CBSEM: where the bias lies!de_DE
dc.typeArticlede_DE
dc.identifier.urnurn:nbn:de:gbv:830-88223567-
dc.identifier.doi10.15480/882.1814-
dc.type.diniarticle-
dc.subject.ddccode330-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-88223567de_DE
tuhh.oai.showtrue-
dc.identifier.hdl11420/1817-
tuhh.abstract.englishDiscussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about themeaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective,we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based.de_DE
tuhh.publisher.doi10.1016/j.jbusres.2016.06.007-
tuhh.publication.institutePersonalwirtschaft und Arbeitsorganisation W-9de_DE
tuhh.identifier.doi10.15480/882.1814-
tuhh.type.opus(wissenschaftlicher) Artikel-
tuhh.institute.germanPersonalwirtschaft und Arbeitsorganisation W-9de
tuhh.institute.englishPersonalwirtschaft und Arbeitsorganisation W-9de_DE
tuhh.gvk.hasppnfalse-
tuhh.hasurnfalse-
openaire.rightsinfo:eu-repo/semantics/openAccessde_DE
dc.type.driverarticle-
dc.rights.ccby-nc-ndde_DE
dc.rights.ccversion4.0de_DE
dc.type.casraiJournal Article-
tuhh.container.issue10de_DE
tuhh.container.volume69de_DE
tuhh.container.startpage3998de_DE
tuhh.container.endpage4010de_DE
dc.rights.nationallicensefalsede_DE
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.creatorGNDSarstedt, Marko-
item.creatorGNDHair, Joseph F.-
item.creatorGNDRingle, Christian M.-
item.creatorGNDThiele, Kai Oliver-
item.creatorGNDGudergan, Siegfried-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorOrcidSarstedt, Marko-
item.creatorOrcidHair, Joseph F.-
item.creatorOrcidRingle, Christian M.-
item.creatorOrcidThiele, Kai Oliver-
item.creatorOrcidGudergan, Siegfried-
item.openairetypeArticle-
item.grantfulltextopen-
crisitem.author.deptPersonalwirtschaft und Arbeitsorganisation W-9-
crisitem.author.deptPersonalwirtschaft und Arbeitsorganisation W-9-
crisitem.author.orcid0000-0002-5424-4268-
crisitem.author.orcid0000-0002-7027-8804-
crisitem.author.orcid0000-0002-5493-4664-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie-
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