The authors propose a new Bayesian latent structure regression model with variable selection to solve various commonly encountered marketing problems related to market segmentation and heterogeneity. The proposed procedure simultaneously performs segmentation and regression analysis within the...

Over the last decade, Chinese authorities have pressed foreign multinational companies to recognize official trade unions. Employing cross-classified multilevel modeling on a large data set (10,108 foreign-owned firms cross-embedded in 32 home countries and 755 Chinese cities), this study...

A new Bayesian multinomial probit model is proposed for the analysis of panel choice data. Using a parameter expansion technique, we are able to devise a Markov Chain Monte Carlo algorithm to compute our Bayesian estimates efficiently. We also show that the proposed procedure enables the...

Multidimensional scaling (MDS) represents a family of various spatial geometric models for the multidimensional representation of the structure in data as well as the corresponding set of methods for fitting such spatial models. Its major uses in Marketing include positioning, market...

Kim, Fong, and DeSarbo (2012) recently introduced a finite mixture Bayesian regression model to simultaneously identify market segments of consumers (heterogeneity) and determine how such segments differ with respect to active regression coefficients (variable selection). The current manuscript...