This web appendix has three main purposes. First, we provide a more or less 'stand-alone' technical appendix that describes the estimation algorithm for the proposed attribute model using Markov Chain Monte Carlo techniques (sections A1 and A2). The reversible jump (RJ) algorithm (Green, 1995)...

Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research we study finite mixture specifications for modeling consumer heterogeneity where each regression coefficient has its own finite mixture, that is, an...

Empirical demand functions (based on experimental studies, such as Choice Based Conjoint) are critical to many aspects of marketing, such as targeting and segmentation, setting prices and evaluating the potential of new products. While considerable work has been done on developing approaches for...

Successful product line design and development often requires balancing technical and market trade-offs. Quantitative methods for optimizing product attribute levels using preference elicitation (e.g., conjoint) data are useful for many product types. However, products with substantial...

In this research we propose a web-based adaptive self-explicated approach for multi-attribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. Our approach overcomes some of the limitations of previous self-explicated approaches. We developed a...

In this research we propose a web-based adaptive self-explicated approach for multi-attribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. In the empirical application reported here the proposed approach provides a substantial and significant...