Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22832
Title: PLS FAC-SEM: an illustrated step-by-step guideline to obtain a unique insight in factorial data
Authors: Streukens, Sandra 
Leroi-Werelds, Sara 
Issue Date: 2016
Publisher: EMERALD GROUP PUBLISHING LTD
Source: INDUSTRIAL MANAGEMENT & DATA SYSTEMS, 116(9), p. 1922-1945
Abstract: Purpose - 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.
Notes: [Streukens, Sandra; Leroi-Werelds, Sara] Hasselt Univ, Dept Mkt & Strategy, Hasselt, Belgium.
Keywords: interaction effect; factorial design; main effect; multi-group analysis (MGA); n-way ANOVA; PLS FAC-SEM;Interaction effect; Factorial design; Main effect; Multi-group analysis (MGA); n-way ANOVA; PLS FAC-SEM
Document URI: http://hdl.handle.net/1942/22832
ISSN: 0263-5577
e-ISSN: 1758-5783
DOI: 10.1108/IMDS-07-2015-0318
ISI #: 000387099700006
Rights: © Emerald Group Publishing Limited 2016
Category: A1
Type: Journal Contribution
Validations: ecoom 2017
Appears in Collections:Research publications

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