Hair, Joseph F.Joseph F.HairBabin, Barry J.Barry J.BabinRingle, Christian M.Christian M.RingleSarstedt, MarkoMarkoSarstedtBecker, Jan-MichaelJan-MichaelBecker2025-11-192025-11-192025-06-07Journal of Marketing Analytics 13 (3): 709–724 (2025)https://hdl.handle.net/11420/58851Covariance-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.en2050-332620253709724Springer Science and Business Media LLChttps://creativecommons.org/licenses/by/4.0/CB-SEMCFAConfirmatory factor analysisCovariance-based structural equation modelingSEMSmartPLSSocial Sciences::300: Social SciencesNatural Sciences and Mathematics::510: MathematicsJournal Articlehttps://doi.org/10.15480/882.1617410.1057/s41270-025-00414-610.15480/882.16174Journal Article