Partial Least Squares Structural Equation Modeling

Marko Sarstedt (), Christian M. Ringle () and Joseph F. Hair ()
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Marko Sarstedt: Otto-von-Guericke University
Christian M. Ringle: University of Newcastle
Joseph F. Hair: University of South Alabama

A chapter in Handbook of Market Research, 2022, pp 587-632 from Springer

Abstract: Abstract Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

Keywords: Partial least squares structural equation modeling; PLS-SEM; Path model analysis; Composite modeling; Results evaluation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-57413-4_15

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