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An assessment of the use of partial least squares structural equation modeling in marketing research

Abstract

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.

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Notes

  1. 1.

    It is important to note that the SEM-related literature does not always employ the same terminology when referring to elements of the model. Publications addressing CB-SEM (e.g., Hair et al. 2010) often refer to “structural model” and “measurement models,” whereas those focusing on PLS-SEM (e.g., Lohmöller 1989) use the terms “inner model” and “outer models.” As this paper deals with PLS-SEM, related terminology is used.

  2. 2.

    The search process was completed on January 31, 2011.

  3. 3.

    In the following, we consistently use the term “studies” when referring to the 204 journal articles and the term “models” when referring to the 311 PLS-SEM applications in these articles.

  4. 4.

    The same holds for comparative scales such as rank order, paired comparison, or constant sum scales.

  5. 5.

    It has to be noted that, contrary to Bergkvist and Rossiter’s (2007, 2009) findings, several studies have shown that single-item measures do not necessarily match multi-item measures in terms of psychometric properties (e.g., Gardner et al. 1989; Kwon and Trail 2005; Sarstedt and Wilczynski 2009).

  6. 6.

    We also computed the means, medians, and ranges of statistics related to outer (and inner) model assessment (e.g., composite reliability, indicator weights). These are available from the authors upon request.

  7. 7.

    This analysis includes only those models specified as including only reflective and a mixture of reflective and formative latent variables. Models in which the measurement mode was not specified are not included.

  8. 8.

    In addition, we contrasted PLS-SEM use between studies that appeared in the five highest ranked and the five lowest ranked journals (20 studies with 23 model estimations) considered in this specific analysis. These analyses produced highly similar results compared to those reported here and are available from the authors upon request.

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Acknowledgments

The authors would like to thank three anonymous reviewers, Jörg Henseler (University of Nijmegen), and Edward E. Rigdon (Georgia State University) for their helpful remarks on earlier versions of this article.

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Hair, J.F., Sarstedt, M., Ringle, C.M. et al. An assessment of the use of partial least squares structural equation modeling in marketing research. J. of the Acad. Mark. Sci. 40, 414–433 (2012). https://doi.org/10.1007/s11747-011-0261-6

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Keywords

  • Empirical research methods
  • Partial least squares
  • Path modeling
  • Structural equation modeling