SmartPLS

PLS structural equation modeling : SmartPLS 3 includes state-of-the-art options for analyses such as importance-performance matrix analysis (IPMA), multi-group analysis (MGA), hierarchical component models second-order models), nonlinear relationships (e.g., quadratic effect), confirmatory tetrad analysis (CTA), finite mixture (FIMIX) segmentation, and prediction-oriented segmentation (POS).


References in zbMATH (referenced in 21 articles )

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  1. Fordellone, Mario; Vichi, Maurizio: Finding groups in structural equation modeling through the partial least squares algorithm (2020)
  2. Altimari, Ambra; Balzano, Simona; Zezza, Gennaro: Measuring economic vulnerability: a structural equation modeling approach (2019)
  3. Sergio Venturini, Mehmet Mehmetoglu: plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares (2019) not zbMATH
  4. Sternad Zabukovšek, Simona; Kalinic, Zoran; Bobek, Samo; Tominc, Polona: SEM-ANN based research of factors’ impact on extended use of ERP systems (2019)
  5. Hair, Joseph F. jun.; Hult, G. Tomas M.; Ringle, Christian M.; Sarstedt, Marko: A primer on partial least squares structural equation modeling (PLS-SEM) (2017)
  6. Tenenhaus, Michel; Tenenhaus, Arthur; Groenen, Patrick J. F.: Regularized generalized canonical correlation analysis: a framework for sequential multiblock component methods (2017)
  7. González-Rodríguez, M. Rosario; Díaz Fernández, M. Carmen; Simonetti, Biagio: Corporate social responsibility perception versus human values: a structural equation modeling approach (2016)
  8. Suarez, Eva; Calvo-Mora, Arturo; Roldán, José Luis: The role of strategic planning in excellence management systems (2016) ioport
  9. Backhaus, Klaus; Erichson, Bernd; Weiber, Rolf: Advanced multivariate analysis methods. An application oriented introduction (2015)
  10. Hwang, Heungsun; Takane, Yoshio: Generalized structured component analysis. A component-based approach to structural equation modeling (2015)
  11. Shanmugapriya, S.; Subramanian, K.: Structural equation model to investigate the factors influencing quality performance in Indian construction projects (2015) ioport
  12. Hair, Joseph F. jun.; Hult, G. Tomas M.; Ringle, Christian M.; Sarstedt, Marko: A primer on partial least squares structural equation modeling (PLS-SEM) (2014)
  13. Ringle, Christian M.; Sarstedt, Marko; Schlittgen, Rainer: Genetic algorithm segmentation in partial least squares structural equation modeling (2014)
  14. Henseler, Jörg; Sarstedt, Marko: Goodness-of-fit indices for partial least squares path modeling (2013)
  15. Armin Monecke; Friedrich Leisch: semPLS: Structural Equation Modeling Using Partial Least Squares (2012) not zbMATH
  16. Mateos-Aparicio, Gregoria: Partial least squares (PLS) methods: origins, evolution, and application to social sciences (2011)
  17. Henseler, Jörg: On the convergence of the partial least squares path modeling algorithm (2010)
  18. Laumer, Sven; Eckhardt, Andreas; Trunk, Natascha: Do as your parents say? - Analyzing IT adoption influencing factors for full and under age applicants (2010) ioport
  19. Sarstedt, Marko; Ringle, Christian M.: Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies (2010)
  20. Hanafi, Mohamed: PLS path modelling: computation of latent variables with the estimation mode B. (2007)

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