TY - JOUR
T1 - Partial least squares is an estimator for structural equation models: A comment on Evermann and Rönkkö (2021)
AU - Schuberth, Florian
AU - Zaza, Sam
AU - Henseler, Jörg
N1 - Funding Information:
[*Note: Jörg Henseler acknowledges a financial interest in the composite-based SEM software ADANCO and its distributor, Composite Modeling. Moreover, he gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020).]
Publisher Copyright:
© 2023 by the Association for Information Systems.
PY - 2023/6/19
Y1 - 2023/6/19
N2 - In 2012 and 2013, several critical publications questioned many alleged PLS properties. As a consequence, PLS benefited from a boost of developments. It is, therefore, a good time to review these developments. Evermann and Rönkkö (2023) devote their paper to this task and formulate guidelines in the form of 14 recommendations. Yet, while they identified the major developments, they overlook a fundamental change, maybe because it is so subtle: the view on PLS. As mentioned by Evermann and Rönkkö (2023, p. 1), “[PLS] is a statistical method used to estimate linear structural equation models” and consequently should not be regarded as a standalone SEM technique following its own assessment criteria. Against this background, we explain which models can be estimated by PLS and PLSc. Moreover, we present the Henseler-Ogasawara specification to estimate composite models by common SEM estimators. Additionally, we review Evermann and Rönkkö's (2023) 14 recommendations one by one and suggest updates and improvements where necessary. Further, we address their comments about the latest advancement in composite models and show that PLS is a viable estimator for confirmatory composite analysis. Finally, we conclude that there is little value in distinguishing between covariance-based and variance-based SEM—there is only SEM.
AB - In 2012 and 2013, several critical publications questioned many alleged PLS properties. As a consequence, PLS benefited from a boost of developments. It is, therefore, a good time to review these developments. Evermann and Rönkkö (2023) devote their paper to this task and formulate guidelines in the form of 14 recommendations. Yet, while they identified the major developments, they overlook a fundamental change, maybe because it is so subtle: the view on PLS. As mentioned by Evermann and Rönkkö (2023, p. 1), “[PLS] is a statistical method used to estimate linear structural equation models” and consequently should not be regarded as a standalone SEM technique following its own assessment criteria. Against this background, we explain which models can be estimated by PLS and PLSc. Moreover, we present the Henseler-Ogasawara specification to estimate composite models by common SEM estimators. Additionally, we review Evermann and Rönkkö's (2023) 14 recommendations one by one and suggest updates and improvements where necessary. Further, we address their comments about the latest advancement in composite models and show that PLS is a viable estimator for confirmatory composite analysis. Finally, we conclude that there is little value in distinguishing between covariance-based and variance-based SEM—there is only SEM.
KW - 2023 OA procedure
U2 - 10.17705/1CAIS.05232
DO - 10.17705/1CAIS.05232
M3 - Comment/Letter to the editor
SN - 1529-3181
VL - 52
SP - 711
EP - 729
JO - Communications of the Association for Information Systems
JF - Communications of the Association for Information Systems
ER -