Explaining variability in tourist preferences: A Bayesian model well suited to small samples

Abstract Discrete choice experiments are becoming more popular in the tourism and travel literature. While Bayesian methods to analyze discrete choice experiment data have been used in other disciplines, they have not been used in the tourism literature. In this article, we develop a Bayesian Mixed Logit Model in which we use a little known prior distribution developed by Lewandowski, Kurowicka, and Joe (LKJ) and half Cauchy distributions as an alternative to the more traditionally used inverse Wishart distribution as a prior scheme for the covariance matrix of random parameters in mixed logit estimation. Using multiple simulated data sets, we show that use of the LKJ prior scheme improves the estimation of coefficients, especially for small data sets. Finally, we test the model with an actual small discrete choice data set examining tourist preferences for reducing glacier recession, and discuss the implications of the model for research and policy.