Tourism research contains a large share of consumer behavior-orientated studies using multidimensional constructs (exogenous/endogenous). Accordingly, scholars have mainly made use of a two-step approach that can be referred to as PCA-MLR (principal component analysis and then ordinary least squares multiple linear regression analysis) to examine the relationships among exogenous and endogenous constructs in a statistical model. Although this two-step approach has contributed to the advancement of tourism research, it still suffers from a number of drawbacks which can readily be overcome by a so-called second-generation statistical tool, namely, partial least squares approach to structural equation modeling (PLS-SEM). The current chapter explains and illustrates (with an application to tourism data) the advantages (e.g., several layers of estimations, suiting small sample sizes, robustness to multicollinearity, model-based clustering, etc.) of PLS-SEM both from a statistical and practical point of view. Finally, an elucidation is also provided for suggesting PLS-SEM as an alternative to PCA-MLR instead of COV-SEM (covariance-based structural equation modeling). The chapter concludes by proposing that PLS-SEM is a reliable and flexible statistical approach that is of high value, in particular, for applied research.
Mehmetoglu, M. (2012), "Partial Least Squares Approach to Structural Equation Modeling for Tourism Research", Chen, J.S. (Ed.) Advances in Hospitality and Leisure (Advances in Hospitality and Leisure, Vol. 8), Emerald Group Publishing Limited, Bingley, pp. 43-61. https://doi.org/10.1108/S1745-3542(2012)0000008007
Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited