This paper presents a multidimensional scaling model that is estimated on pick any/N choice data, and accommodates a broad range of context effects. The methodology estimates a set of parameters capturing the direction and magnitude of the context effects, as well as the locations of brands and...

In multidimensional unfolding multidimensional scaling (MDS) procedures, the predicted utility of a brand for a consumer is inversely related to the distance between that consumer's ideal point and the brand position in the derived space. Most MDS models treat all brands the same regardless of...

This paper presents a multidimensional scaling (MDS) methodology (vector model) for the spatial analysis of preference data that explicitly models the effects of unfamiliarity on evoked preferences. Our objective is to derive a joint space map of brand locations and consumer preference vectors...

The authors propose a methodology for determining the segment-level impact of explanatory variables on multiple criterion measures obtained on a constant-sum scale. These explanatory variables could characterize different product, situation, or person related conditions that either occur...

This paper presents a multidimensional scaling (MDS) methodology (vector model) for the spatial analysis of preference data that explicitly models the effects of unfamiliarity on evoked preferences. Our objective is to derive a joint space map of brand locations and consumer preference vectors...

We propose an approach for deriving joint space maps of bundle compositions and market segments from three-way (e.g., consumers x product options/benefits/features x usage situations/scenarios/time periods) pick-any/J data. The proposed latent structure multidimensional scaling procedure...