Partial least squares path modeling: Quo vadis?

Jörg Henseler (Corresponding Author)

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Scopus)
41 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)1-8
JournalQuality and quantity
Volume52
Issue number1
DOIs
Publication statusE-pub ahead of print/First online - 22 Jan 2018

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Structural Equation Modeling
Partial Least Squares
marketing
Path
test theory
Modeling
ability
Latent Variable Models
Measurement Error
Branch
Entire

Keywords

  • UT-Hybrid-D

Cite this

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Partial least squares path modeling : Quo vadis? / Henseler, Jörg (Corresponding Author).

In: Quality and quantity, Vol. 52, No. 1, 22.01.2018, p. 1-8.

Research output: Contribution to journalArticleAcademicpeer-review

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