Abstract

Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.

Keywords

Structural equation modelingPartial least squares regressionSilver bulletLatent variableComputer scienceScanning electron microscopeMathematicsManagement scienceArtificial intelligenceMaterials scienceEngineeringMachine learningComposite material

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Publication Info

Year
2011
Type
article
Volume
19
Issue
2
Pages
139-152
Citations
19600
Access
Closed

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Joe F. Hair, Christian M. Ringle, Marko Sarstedt (2011). PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice , 19 (2) , 139-152. https://doi.org/10.2753/mtp1069-6679190202

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DOI
10.2753/mtp1069-6679190202