Multinational tourism research involves analysis of multiple origins, multiple tourist destinations, and multiple attributes for destination selections. For example, tourist perceptions of a destination may vary across countries of origin. This can have important implications for which countries...

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 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...

We present a new Bayesian formulation of a vector multidimensional scaling procedure for the spatial analysis of binary choice data. The Gibbs sampler is gainfully employed to estimate the posterior distribution of the specified scalar products, bilinear model parameters. The computational...

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...

This paper presents a new multidimensional scaling (MDS) methodology which operationalizes the Krumhansl (1978) distance-density model for the analysis of asymmetric proximity data. In Krumhansl's conceptualization, the symmetric Euclidean interbrand distances typically associated with the...