In recent years, the literature on PLS has increased tremendously. We strongly recommend the following paper as an introduction to PLS path modeling and variance-based structural equation modeling in general:

Henseler, Jörg; Hubona, Geoffrey; Ray, Pauline Ash (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116 (1), 2-20, doi:10.1108/IMDS-09-2015-0382. [download]


In order to get a more comprehensive overview of what PLS is, what research problems it can help solve, and how to apply it, we recommend the following textbook:

Henseler, Jörg (2021). Composite-based structural equation modeling: Analyzing latent and emergent variables, New York: Guilford Press.


In our seminars, we present and discuss extant literature on PLS. Some of it is freely available:

  • Benitez, Jose; Henseler, Jörg; Castillo, Ana; Schuberth, Florian (2019). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, electronic pre-print available, doi:10.1016/ [download]
  • Müller, Tobias; Schuberth, Florian; Henseler, Jörg (2018). PLS path modeling: a confirmatory approach to study tourism technology and tourist behavior. Journal of Hospitality and Tourism Technology, 9 (3), 249-266. [download]
  • Schuberth, Florian; Henseler, Jörg; Dijkstra, Theo K. (2018). Confirmatory Composite Analysis. Frontiers in Psychology, 9 (December), Article 2541. [download]
  • Henseler, Jörg (2017). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46 (1), 178-192. [download]
  • Riel, Allard C. R. van; Henseler, Jörg; Kemény, Ildikó; Sasovova, Zuzana (2017). Estimating hierarchical constructs using consistent partial least squares: The case of second-order composites of common factors. Industrial Management & Data Systems, 117 (3), 459-477. [download]
  • Henseler, Jörg (2016). New developments in PLS path modeling. Industrial Management & Data Systems, 116 (9), 1842-1848. [download]
  • Dijkstra, T.K. and Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly (39:2). [download]

  • Henseler, J., Ringle, C.M., and Sarstedt, M. (forthcoming), "A new criterion for assessing discriminant validity in variance-based structural equation modeling," Journal of the Academy of Marketing Science (43:1), 115-135.  [download]
  • Dijkstra, T.K. and Henseler, J. (2015), "Consistent and asymptotically normal PLS estimators for linear structural equations," Computational Statistics & Data Analysis (81:1), 10-23. [download]
  • Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., Ketchen, D.J., Jr., Hair, J.F., Hult, G.T.M., and Calantone, R.J. (2014), "Common beliefs and reality about PLS: Comments on Rönkkö & Evermann (2013)," Organizational Research Methods (17:2), 182-209. [download]
  • Henseler, J. and Sarstedt, M. (2013), "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics (28:2), 565-580. [download]
  • Ringle, C.M., Sarstedt, M., and Straub, D.W. (2012), "A critical look at the use of PLS-SEM in MIS Quarterly," MIS Quarterly (36:1), iii-xiv. [download]
  • Sarstedt, M., Becker, J.-M., Ringle, C.M., and Schwaiger, M. (2011), "Uncovering and treating unobserved heterogeneity with FIMIX-PLS: which model selection criterion provides an appropriate number of segments?," Schmalenbach Business Review (63:1), 34-62. [download]
  • Henseler, J. (2010), "On the convergence of the partial least squares path modeling algorithm," Computational Statistics (25:1), 107-120. [download]
  • Henseler, J., Ringle, C.M., and Sinkovics, R.R. (2009), "The use of partial least squares path modeling in international marketing," Advances in International Marketing (20:1), 277-320. [download]


We have selected some technical papers that we regard as particularly worth reading:

  • Hair, J.F., Sarstedt, M., Ringle, C.M., and Mena, J.A. (2012), "An assessment of the use of partial least squares structural equation modeling in marketing research," Journal of the Academy of Marketing Science (40:3), 414-433, DOI: 10.1007/s11747-011-0261-6.
  • Henseler, J., Fassott, G., Dijkstra, T.K., and Wilson, B. (2012), "Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling," European Journal of Information Systems (21:1), 99-112, DOI: 10.1057/ejis.2011.36.
  • Hair, J.F., Ringle, C.M., and Sarstedt, M. (2011), "PLS-SEM: Indeed a Silver Bullet," Journal of Marketing Theory and Practice (19:2), 139-151, DOI: 10.2753/MTP1069-6679190202.
  • Henseler, J. and Chin, W.W. (2010), "A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling," Structural Equation Modeling (17:1), 82-109, DOI: 10.1080/10705510903439003.
  • Reinartz, W.J., Haenlein, M., and Henseler, J. (2009), "An empirical comparison of the efficacy of covariance-based and variance-based SEM," International Journal of Research in Marketing (26:4), 332-344, DOI: 10.1016/j.ijresmar.2009.08.001.