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Partial Least Squares Structural Equation Modeling

In: Handbook of Market Research

Author

Listed:
  • Marko Sarstedt

    (Otto-von-Guericke University
    University of Newcastle)

  • Christian M. Ringle

    (University of Newcastle
    Hamburg University of Technology (TUHH))

  • Joseph F. Hair

    (University of South Alabama)

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.

Suggested Citation

  • Marko Sarstedt & Christian M. Ringle & Joseph F. Hair, 2022. "Partial Least Squares Structural Equation Modeling," Springer Books, in: Christian Homburg & Martin Klarmann & Arnd Vomberg (ed.), Handbook of Market Research, pages 587-632, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-57413-4_15
    DOI: 10.1007/978-3-319-57413-4_15
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