While the Bayesian SEM approach is now receiving a strong attention in the literature, tourism studies still heavily rely on the covariance-based approach for SEM estimation. In a recent special issue dedicated to the topic, Zyphur and Oswald (2013) used the term “Bayesian revolution” to describe the rapid growth of the Bayesian approach across multiple social science disciplines. The method introduces several advantages that make SEM estimation more flexible and powerful. We aim in this paper to introduce tourism researchers to the power of the Bayesian approach and discuss its unique advantages over the covariance-based approach. We provide first some foundations of Bayesian estimation and inference. We then present an illustration of the method using a tourism application. The paper also conducts a Monte Carlo simulation to illustrate the performance of the Bayesian approach in small samples and discuss several complicated SEM contexts where the Bayesian approach provides unique advantages.