A comparison of CB-SEM and PLS-SEM in validating online casino e-servicescape theory

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Conference Proceeding

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Publication Title

Proceedings of the Global Conference on Services Management



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Structural equation modeling (SEM) is a multivariate statistical analysis technique for analyzing structural relationships. This technique is a combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs. The commonly used approach to structural equation modeling involves specifying a model and estimating the parameters so that the distance between the model’s implied population covariance matrix and the sample covariance matrix S is minimized; this approach is referred to as the covariance based SEM (CB-SEM). In the Partial Least Squares approach to SEM (PLSSEM), the explained variance of the endogenous latent variables is maximized. CB-SEM has been widely used by hospitality researchers, but not the soft-modeling approach of PLS-SEM. The CBSEM method requires hard distributional assumptions on the data, whereas PLS-SEM is more flexible. This article compares the results of CB-SEM model with results derived from the PLSSEM method to test the same hypothesis on a dataset from online casino gaming; the results show that PLS-SEM is more accurate than CB-SEM for this dataset. Literature also suggests the use PLS-SEM over CB-SEM since multivariate normality of the sample is not required, and it generally works well even with smaller sample sizes.


online gambling; atmospherics; servicescape; user experience; partial least squares; structural equation modeling, bootstrap


Gaming and Casino Operations Management

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