1 Integrated mixed logit and latent variable models

A traditional discrete choice model assumes that an individual's decision-making process is based on utility maximization and that the systematic part of the utility function depends on some observable attributes and covariates. These attributes and covariates however can only explain part of the utility and a large part remains unexplained. In recent years, researchers have recognized that psychological factors such as attitudes, lifestyle and values can affect an alternative's utility and hence the individual's choices. Therefore, extending the traditional discrete choice model by incorporating those latent or unobservable factors, can help to better understand the individual's decision making process and therefore to yield more reliable part-worth estimates and market share predictions. Several integrated choice and latent variable (ICLV) models which merge the conditional logit model with a structural equation model exist in the literature. They assume homogeneity in the part-worths and use latent variables to model the heterogeneity among the respondents. The current research uses the mixed logit model that describes the heterogeneity in the part-worths and uses the latent variables to decrease the unexplained part of the heterogeneity. The empirica