Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22832
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dc.contributor.authorSTREUKENS, Sandra-
dc.contributor.authorLEROI-WERELDS, Sara-
dc.date.accessioned2016-12-02T10:57:13Z-
dc.date.available2016-12-02T10:57:13Z-
dc.date.issued2016-
dc.identifier.citationINDUSTRIAL MANAGEMENT & DATA SYSTEMS, 116(9), p. 1922-1945-
dc.identifier.issn0263-5577-
dc.identifier.urihttp://hdl.handle.net/1942/22832-
dc.description.abstractPurpose - The purpose of this paper is to provide an illustrated step-by-step guideline of the partial least squares factorial structural equation modeling (PLS FAC-SEM) approach. This approach allows researchers to assess whether and how model relationships vary as a function of an underlying factorial design, both in terms of the design factors in isolation (i.e. main effects) as well as their joint impact (i.e. interaction effects). Design/methodology/approach - After an introduction of its building blocks as well as a comparison with related methods (i.e. n-way analysis of variance (ANOVA) and multi-group analysis (MGA)), a step-by-step guideline of the PLS FAC-SEM approach is presented. Each of the steps involved in the PLS FAC-SEM approach is illustrated using data from a customer value study. Findings - On a methodological level, the key result of this research is the presentation of a generally applicable step-by-step guideline of the PLS FAC-SEM approach. On a context-specific level, the findings demonstrate how the predictive ability of several key customer value measurement methods depends on the type of offering (feel-think), the level of customer involvement (low-high), and their interaction (feel-think offerings x low-high involvement). Originality/value - This is a first attempt to apply the factorial structural equation models (FAC- SEM) approach in a PLS-SEM context. Consistent with the general differences between PLS-SEM and covariance-based structural equation modeling (CB-SEM), the FAC-SEM approach, which was originally developed for CB-SEM, therefore becomes available for a larger amount of and different types of research situations.-
dc.description.sponsorshipThe data collection was supported by the Marketing Science Institute. The study was conducted in the period that the second author received an FWO scholarship.-
dc.language.isoen-
dc.publisherEMERALD GROUP PUBLISHING LTD-
dc.rights© Emerald Group Publishing Limited 2016-
dc.subject.otherinteraction effect; factorial design; main effect; multi-group analysis (MGA); n-way ANOVA; PLS FAC-SEM-
dc.subject.otherInteraction effect; Factorial design; Main effect; Multi-group analysis (MGA); n-way ANOVA; PLS FAC-SEM-
dc.titlePLS FAC-SEM: an illustrated step-by-step guideline to obtain a unique insight in factorial data-
dc.typeJournal Contribution-
dc.identifier.epage1945-
dc.identifier.issue9-
dc.identifier.spage1922-
dc.identifier.volume116-
local.format.pages24-
local.bibliographicCitation.jcatA1-
dc.description.notes[Streukens, Sandra; Leroi-Werelds, Sara] Hasselt Univ, Dept Mkt & Strategy, Hasselt, Belgium.-
local.publisher.placeBINGLEY-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1108/IMDS-07-2015-0318-
dc.identifier.isi000387099700006-
item.fulltextWith Fulltext-
item.validationecoom 2017-
item.accessRightsOpen Access-
item.fullcitationSTREUKENS, Sandra & LEROI-WERELDS, Sara (2016) PLS FAC-SEM: an illustrated step-by-step guideline to obtain a unique insight in factorial data. In: INDUSTRIAL MANAGEMENT & DATA SYSTEMS, 116(9), p. 1922-1945.-
crisitem.journal.issn0263-5577-
crisitem.journal.eissn1758-5783-
Appears in Collections:Research publications
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