Perceptual mapping based on idiosyncratic sets of attributes

The authors describe a compositional perceptual mapping procedure, unrestricted attribute-elicitation mapping (UAM), which allows consumers to describe and rate the brands in their own terminology and thus relaxes the restrictive assumptions of traditional compositional mapping techniques regarding the structure and interpretation of the set of attributes. They compare the performance of three estimation techniques for constructing a group space based on the idiosyncratic data, namely, INDSCAL, CANCOR, and generalized procrustes analysis (GPA). Their findings indicate that the three estimation techniques perform about equally well. UAM also is compared with traditional compositional mapping. They find that UAM is superior on fit of the solution, interpretability, and sample reliability. UAM probably holds an edge with respect to data collection. Traditional compositional mapping is superior on ease of data analysis. No major difference was found on predictive validity and structural reliability.