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Software Packages for Partial Least Squares Structural Equation Modeling: An Updated Review

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Partial Least Squares Path Modeling

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Abstract

As a result of its ability to deal with situations that are difficult to address using other SEM methods, the partial least squares (PLS) approach to structural equation modeling (SEM) has attracted a lot of attention in recent years from applied researchers and practitioners in various fields. One reason for this growth in interest is represented by the many theoretical contributions emerging from the PLS-SEM research community, which have allowed us to deepen our knowledge of the method and extend its capabilities into new contexts. However, these contributions would have remained confined to academic journals if not for a parallel and similar development in the software packages available to implement these methodological innovations. Indeed, it is a well-known fact in the history of PLS-SEM that the lack of advanced and user-friendly software has been the main reason for the delay in the diffusion of this method in the applied sciences. Fortunately, we find ourselves nowadays in the opposite situation, as many high-quality packages for performing all varieties of PLS-SEM analyses have become available. In this chapter we present an updated review of the most popular commercial and open-source software packages for PLS-SEM. In particular, we discuss and compare ADANCO, SmartPLS, WarpPLS, XLSTAT-PLSPM, the plssem package for Stata, and the cSEM and SEMinR packages for R. Using a publicly available data set, we briefly illustrate the main features of each of these software packages and examine their corresponding strengths and weaknesses.

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Notes

  1. 1.

    https://www.chapsvision.com/softwares-data/data-mining-machine-learning/.

  2. 2.

    https://www.stata.com.

  3. 3.

    https://www.r-project.org.

  4. 4.

    https://www.composite-modeling.com.

  5. 5.

    The path weighting scheme Lohmöller (1989) is not available because it requires a specific direction of causal structure and thus does not support non-recursive models and unanalyzed relationships [Henseler 2021, p. 91].

  6. 6.

    For a review of CCA see also Hubona et al. (2021).

  7. 7.

    https://scriptwarp.com/warppls/.

  8. 8.

    Factor-based methods, as implemented in WarpPLS, combine elements of PLS-SEM methods and covariance-based SEM and provide estimates of the composites and correlation-preserving factors in a path model. Differently from the PLSc approach, which is a parameter correction technique, factor-based methods estimate prototypical elements, such as factors, which are then used in the production of parameters, thus requiring no corrections. For more details see Kock (2019).

  9. 9.

    https://www.mathworks.com/products/matlab.html.

  10. 10.

    https://www.xlstat.com/en/.

  11. 11.

    We remind that, despite the name similarity, PLS regression must not be confused with PLS-SEM. They share a common origin, but they have different aims.

  12. 12.

    For more information about dynamic documents in Stata, execute the command help reporting.

  13. 13.

    As we already stated in the introduction, there are other R packages for PLS-SEM, but they are older and have been superseded by the more modern packages we discuss here.

  14. 14.

    https://cran.r-project.org/web/packages/cSEM/index.html.

  15. 15.

    https://m-e-rademaker.github.io/cSEM/.

  16. 16.

    A further useful resource is the personal webpage of one of the cSEM developers, http://florianschuberth.com/csem-2/ where one can find many tutorials regarding specific analyses available in the package.

  17. 17.

    For the complete list of fit measures returned by cSEM and the corresponding definitions, see the package webpage at https://m-e-rademaker.github.io/cSEM/articles/Using-assess.html and the references therein.

  18. 18.

    https://cran.r-project.org/web/packages/seminr.

  19. 19.

    https://github.com/sem-in-r/seminr.

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Acknowledgements

We would like to thank Jörg Henseler (University of Twente, Netherlands) and the sales teams of SmartPLS, WarpPLS and XLSTAT for their support and collaboration.

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Authors and Affiliations

  1. Università Cattolica del Sacro Cuore, Cremona, Italy

    Sergio Venturini

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Mehmet Mehmetoglu

  3. FTD Institute, Jl. Ir. Putuhena Ambon, 97234, Indonesia

    Hengky Latan

  4. HLC Consulting, Jl. Kertanegara Selatan V No 5B, Semarang, 50241, Indonesia

    Hengky Latan

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  1. Sergio Venturini
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  2. Mehmet Mehmetoglu
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Correspondence to Sergio Venturini .

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Editors and Affiliations

  1. HLC Consulting, Semarang, Indonesia

    Hengky Latan

  2. Mitchell College of Business, University of South Alabama, Mobile, AL, USA

    Joseph F. Hair, Jr.

  3. Institute of International Education, Stockholm University, Stockholm, Sweden

    Richard Noonan

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Venturini, S., Mehmetoglu, M., Latan, H. (2023). Software Packages for Partial Least Squares Structural Equation Modeling: An Updated Review. In: Latan, H., Hair, Jr., J.F., Noonan, R. (eds) Partial Least Squares Path Modeling. Springer, Cham. https://doi.org/10.1007/978-3-031-37772-3_5

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