CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting

•Both methods are equally effective for developing and analyzing the structural relationship.•CB-SEM demands a lot from the data, whereas PLS-SEM is quite lenient.•For a factor-based model, CB-SEM should be used.•For a composite-based model, PLS-SEM should be considered.•CB-SEM and PLSc-SEM methods...

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Bibliographic Details
Published in:Technological forecasting & social change Vol. 173; p. 121092
Main Authors: Dash, Ganesh, Paul, Justin
Format: Journal Article
Language:English
Published: New York Elsevier Inc 01.12.2021
Elsevier Science Ltd
Subjects:
Algorithms
Analysis of covariance
Covariance
Covariance based structural equation modeling (CB-SEM)
Forecasting
Indexes
Least squares
Mathematical models
Modelling
Multivariate statistical analysis
Partial least squares (consistent) based structural equation modeling (PLSc-SEM)
Partial least squares based structural equation modeling (PLS-SEM)
Reliability
Respondents
Social research
Social sciences
Structural equation modeling
Structural equation modeling (SEM)
Structural models
Technological forecasting
Technology
Variance
Covariance based structural equation modeling (CB-SEM)
Partial least squares (consistent) based structural equation modeling (PLSc-SEM)
Partial least squares based structural equation modeling (PLS-SEM)
Forecasting
Structural equation modeling (SEM)
ISSN:0040-1625, 1873-5509
Online Access:Get full text
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Summary:•Both methods are equally effective for developing and analyzing the structural relationship.•CB-SEM demands a lot from the data, whereas PLS-SEM is quite lenient.•For a factor-based model, CB-SEM should be used.•For a composite-based model, PLS-SEM should be considered.•CB-SEM and PLSc-SEM methods provide almost similar results. This study compares the two widely used methods of Structural Equation Modeling (SEM): Covariance based Structural Equation Modeling (CB-SEM) and Partial Least Squares based Structural Equation Modeling (PLS-SEM). The first approach is based on covariance, and the second one is based on variance (partial least squares). It further assesses the difference between PLS and Consistent PLS algorithms. To assess the same, empirical data is used. Four hundred sixty-six respondents from India, Saudi Arabia, South Africa, the USA, and few other countries are considered. The structural model is tested with the help of both approaches. Findings indicate that the item loadings are usually higher in PLS-SEM than CB-SEM. The structural relationship is closer to CB-SEM if a consistent PLS algorithm is undertaken in PLS-SEM. It is also found that average variance extracted (AVE) and composite reliability (CR) values are higher in the PLS-SEM method, indicating better construct reliability and validity. CB-SEM is better in providing model fit indices, whereas PLS-SEM fit indices are still evolving. CB-SEM models are better for factor-based models like ours, whereas composite-based models provide excellent outcomes in PLS-SEM. This study contributes to the existing literature significantly by providing an empirical comparison of all the three methods for predictive research domains. The multi-national context makes the study relevant and replicable universally. We call for researchers to revisit the widely used SEM approaches, especially using appropriate SEM methods for factor-based and composite-based models.
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ISSN:0040-1625
1873-5509
DOI:10.1016/j.techfore.2021.121092