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Journal of the Academy of Marketing Science

, Volume 45, Issue 5, pp 616–632 | Cite as

Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods

  • Joseph F. Hair
  • G. Tomas M. Hult
  • Christian M. Ringle
  • Marko Sarstedt
  • Kai Oliver Thiele
Original Empirical Research

Abstract

Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method’s use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite-based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.

Keywords

Composite Generalized structured component analysis GSCA Partial least squares PLS SEM Simulation Structural equation modeling Sum scores regression 

Notes

Acknowledgements

Earlier versions of the manuscript have been presented at the 2015 Academy of Marketing Science Annual Conference held in Denver, Colorado, and the 2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users held in Seville, 2015. The authors would like to thank Jan-Michael Becker, University of Cologne, Jörg Henseler, University of Twente, and Rainer Schlittgen, University of Hamburg, for their support and helpful comments when developing the simulation study and its data generation in order to improve earlier versions of the manuscript. Even though this research does not explicitly refer to the use of the statistical software SmartPLS (http://www.smartpls.com), Ringle acknowledges a financial interest in SmartPLS.

Supplementary material

11747_2017_517_MOESM1_ESM.docx (704 kb)
ESM 1 (DOCX 704 kb)

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© Academy of Marketing Science 2017

Authors and Affiliations

  • Joseph F. Hair
    • 1
  • G. Tomas M. Hult
    • 2
  • Christian M. Ringle
    • 3
    • 4
  • Marko Sarstedt
    • 4
    • 5
  • Kai Oliver Thiele
    • 3
  1. 1.University of South AlabamaMobileUSA
  2. 2.Michigan State UniversityEast LansingUSA
  3. 3.Hamburg University of Technology (TUHH)HamburgGermany
  4. 4.The University of NewcastleCallaghanAustralia
  5. 5.Otto-von-Guericke-University MagdeburgMagdeburgGermany

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