Finite mixture and genetic algorithm segmentation in partial least aquares path modeling: Identification of multiple segments in complex path models

UTSePress Research/Manakin Repository

Search UTSePress Research


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Ringle, Christian en_US
dc.contributor.author Sarstedt, Marko en_US
dc.contributor.author Schlittgen, Rainer en_US
dc.contributor.editor Fink, A; Lausen, B; Seidel, W; Ultsch, A en_US
dc.date.accessioned 2012-02-02T02:02:44Z
dc.date.available 2012-02-02T02:02:44Z
dc.date.issued 2010 en_US
dc.identifier 2009000659 en_US
dc.identifier.citation Ringle Christian, Sarstedt Marko, and Schlittgen Rainer 2010, 'Finite mixture and genetic algorithm segmentation in partial least aquares path modeling: Identification of multiple segments in complex path models', in http://dx.doi.org/10.1007/978-3-642-01044-6_15 (ed.), Springer, Berlin, Germany, pp. 167-176. en_US
dc.identifier.issn 978-3-642-01042-9 en_US
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/14247
dc.description.abstract When applying structural equation modeling methods, such as partial least squares (PLS) path modeling, in empirical studies, the assumption that the data have been collected from a single homogeneous population is often unrealistic. Unobserved heterogeneity in the PLS estimates on the aggregate data level may result in misleading interpretations. Finite mixture partial least squares (FIMIX-PLS) and PLS genetic algorithm segmentation (PLS-GAS) allow the classification of data in variance-based structural equation modeling. This research presents an initial application and comparison of these two methods in a computational experiment in respect of a path model which includes multiple endogenous latent variables. The results of this analysis reveal particular advantages and disadvantages of the approaches. This study further substantiates the effectiveness of FIMIX-PLS and PLS-GAS and provides researchers and practitioners with additional information they need to proficiently evaluate their PLS path modeling results by applying a systematic means of analysis. If significant heterogeneity were to be uncovered by the procedures, the analysis may result in group-specific path modeling outcomes, thus allowing further differentiated and more precise conclusions to be formed. en_US
dc.language en_US
dc.publisher Springer en_US
dc.relation.isbasedon http://dx.doi.org/10.1007/978-3-642-01044-6_15 en_US
dc.title Finite mixture and genetic algorithm segmentation in partial least aquares path modeling: Identification of multiple segments in complex path models en_US
dc.parent Advances in Data Analysis, Data Handling and Business Intelligence en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 167 en_US
dc.identifier.endpage 176 en_US
dc.cauo.name BUS.School of Marketing en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 104474 en_US
dc.personcode 0000054637 en_US
dc.personcode 0000054649 en_US
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Finite mixture - Genetic algorithm - Heterogeneity - PLS path modeling - Segmentation en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record