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Covariance-based structural equation modeling (CB-SEM): a SmartPLS 4 software tutorial

Author

Listed:
  • Joseph F. Hair

    (University of South Alabama)

  • Barry J. Babin

    (The University of Mississippi)

  • Christian M. Ringle

    (Hamburg University of Technology
    James Cook University)

  • Marko Sarstedt

    (Ludwig-Maximilians-University Munich
    Babes-Bolyai University)

  • Jan-Michael Becker

    (BI Norwegian Business School)

Abstract

Covariance-based structural equation modeling (CB-SEM) enables researchers to estimate models with hypothesized cause-effect relationships between latent variables (i.e., constructs), each of which is operationalized by several items (i.e., indicators). To conduct CB-SEM analyses, researchers can rely on a range of software applications. However, many of these applications require researchers to engage in sometimes complicated and error-prone programming tasks. While IBM SPSS AMOS provides a graphical user interface (GUI), it does not fully meet the expectations of contemporary software. In order to address these challenges, the statistical SmartPLS 4 software has recently introduced a new CB-SEM module, which improves the user experience through a modern and intuitive graphical interface and comprehensive result reports. This tutorial describes the key CB-SEM analysis steps (i.e., model setup, estimation, and results evaluation) using the SmartPLS software.

Suggested Citation

  • Joseph F. Hair & Barry J. Babin & Christian M. Ringle & Marko Sarstedt & Jan-Michael Becker, 2025. "Covariance-based structural equation modeling (CB-SEM): a SmartPLS 4 software tutorial," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(3), pages 709-724, September.
  • Handle: RePEc:pal:jmarka:v:13:y:2025:i:3:d:10.1057_s41270-025-00414-6
    DOI: 10.1057/s41270-025-00414-6
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