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Partial Least Squares Path Modeling

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Advanced Methods for Modeling Markets

Part of the book series: International Series in Quantitative Marketing ((ISQM))

Abstract

Structural equation modeling (SEM) is a family of statistical techniques that has become very popular in marketing. Its ability to model latent variables, to take various forms of measurement error into account, and to test entire theories makes it useful for a plethora of research questions. It does not come as a surprise that some of the most cited scholarly articles in the marketing domain are about SEM (e.g., Bagozzi and Yi 1988; Fornell and Larcker 1981), and that SEM is covered by two contributions within this volume. The need for two contributions arises from the SEM family tree having two major branches (Reinartz et al. 2009): covariance-based SEM (which is presented in Chap. 11) and variance-based SEM, which is presented in this chapter.

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Notes

  1. 1.

    This assumption should be relaxed in the case of non-recursive models (Dijkstra and Henseler 2015a).

  2. 2.

    For an application of the NFI, see Ziggers and Henseler (2016).

  3. 3.

    Interestingly, the methodological literature on factor models hardly mentions what to do if the test rejects a factor model. Some researchers suggest considering a composite model as an alternative, because it is less restrictive (Henseler et al. 2014) and not subject to factor indeterminacy (Rigdon 2012). Others suggest allowing small deviations without principally questioning the factor model (see Asparouhov et al. 2015).

  4. 4.

    The AVE must be calculated based on consistent loadings, otherwise the assessment of convergent and discriminant validity based on the AVE is meaningless.

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Acknowledgments

Major parts of this paper are taken from Henseler et al. (2016). The author acknowledges a financial interest in ADANCO and its distributor, Composite Modeling.

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  1. Department of Design, Production and Management, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The Netherlands

    Jörg Henseler

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Editors and Affiliations

  1. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Peter S. H. Leeflang

  2. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Jaap E. Wieringa

  3. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Tammo H.A Bijmolt

  4. Department of Marketing, D’Amore-McKim School of Business, Northeastern University, Boston, USA

    Koen H. Pauwels

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Henseler, J. (2017). Partial Least Squares Path Modeling. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds) Advanced Methods for Modeling Markets. International Series in Quantitative Marketing. Springer, Cham. https://doi.org/10.1007/978-3-319-53469-5_12

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