TY - JOUR
T1 - The choice of structural equation modeling technique matters
T2 - A comment on Dash and Paul (2021)
AU - Schuberth, Florian
AU - Hubona, Geoffrey S.
AU - Roemer, Ellen
AU - Zaza, Sam
AU - Schamberger, Tamara Svenja
AU - Chuah, Francis
AU - Cepeda-Carrión, Gabriel
AU - Henseler, Jörg
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
Schuberth, F., Hubona, G. S., Roemer, E., Zaza, S., Schamberger, T. S., Chuah, F., Cepeda-Carrión, G., & Henseler, J. (2023). The choice of structural equation modeling technique matters: A comment on Dash and Paul (2021). Technological Forecasting and Social Change, 194(September), 1-11. [122665]. https://doi.org/10.1016/j.techfore.2023.122665---The last author gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal) national funding through a research grant from the Information Management Research Center – MagIC/NOVA IMS ( UIDB/04152/2020 ). He also acknowledges a financial interest in the composite-based SEM software ADANCO and its distributor, Composite Modeling. The authors thank Alexandra Elbakyan for her efforts in making science accessible. Last but not least, the authors especially thank the editor of Technological Forecasting and Social Change, Scott Cunningham, for granting the opportunity to write this commentary.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Ganesh Dash and Justin Paul authored an article titled “CB-SEM vs. PLS-SEM methods for research in social science and technological forecasting” in a special issue of Technological Forecasting and Social Change, co-edited by Justin Paul. Unfortunately, the article’s central conclusion – “CB or PLS or PLSc do not matter” – is misleading and at odds with practically all extant conceptual and empirical research on this subject. This commentary identifies an unsuitable research design to be the major cause of the erroneous conclusion and aims to set the record straight. A Monte Carlo simulation demonstrates that the choice of the approach to structural equation modeling can have a substantial impact on the results and their validity. In general, analysts should choose a structural equation modeling approach that fits their conceptual model.
AB - Ganesh Dash and Justin Paul authored an article titled “CB-SEM vs. PLS-SEM methods for research in social science and technological forecasting” in a special issue of Technological Forecasting and Social Change, co-edited by Justin Paul. Unfortunately, the article’s central conclusion – “CB or PLS or PLSc do not matter” – is misleading and at odds with practically all extant conceptual and empirical research on this subject. This commentary identifies an unsuitable research design to be the major cause of the erroneous conclusion and aims to set the record straight. A Monte Carlo simulation demonstrates that the choice of the approach to structural equation modeling can have a substantial impact on the results and their validity. In general, analysts should choose a structural equation modeling approach that fits their conceptual model.
KW - Structural equation modeling (SEM)
KW - Covariance-based structural equation modeling (CB-SEM)
KW - Partial least squares based structural equation modeling (PLS-SEM)
KW - Consistent partial least squares (PLSc)
UR - https://osf.io/z2h7v/?view_only=52e0d1e574dc42e2a22b92192d87c16a
UR - http://www.scopus.com/inward/record.url?scp=85162148049&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2023.122665
DO - 10.1016/j.techfore.2023.122665
M3 - Comment/debate
SN - 0040-1625
VL - 194
SP - 1
EP - 11
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
IS - September
M1 - 122665
ER -