Bayesian Structural Equation Modelling for Research in Retailing

This paper discusses the Bayesian structural equation modelling (SEM) with the purpose of popularising the Bayesian method among researchers in the retail domain. We attempt to explain every Bayesian concept in a lucid manner. In order to explain, we collected data from 160 respondents and analyzed using AMOS software, which has a graphical user interface and is so easy to use for researchers. We performed the Bayesian confirmatory factor analysis, Bayesian SEM, and Bayesian mediation analysis. The paper discusses the various concepts of Bayesian SEM, such as the MCMC algorithm, posterior predictive probability (PPP), deviance information criterion (DIC), etc. This is the first paper to discuss the Bayesian structural equation modelling in the retailing domain.