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.
Dokumententyp: | Zeitschriftenartikel |
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Keywords: | Structural equation modeling; SEM; partial least squares SEM; PLS-SEM |
Fakultät: | Betriebswirtschaft > Institut für Marketing |
Themengebiete: | 300 Sozialwissenschaften > 330 Wirtschaft |
ISSN: | 1069-6679 |
Bemerkung: | Published online: 12 Apr 2022 |
Sprache: | Englisch |
Dokumenten ID: | 95611 |
Datum der Veröffentlichung auf Open Access LMU: | 03. Apr. 2023, 09:33 |
Letzte Änderungen: | 28. Aug. 2023, 10:33 |