PERCEPTUAL MAPPING BASED ON IDIOSYNCRATIC SETS OF ATTRIBUTES

The authors describe a compositional perceptual mapping procedure, unr estricted attribute-elicitation mapping (UAM), which allows consumers to describe and rate the brands in their own terminology and thus rela xes the restrictive assumptions of traditional compositional mapping t echniques regarding the structure and interpretation of the set of att ributes. They compare the performance of three estimation techniques f or constructing a group space based on the idiosyncratic data, namely, INDSCAL, CANCOR, and generalized procrustes analysis (GPA). Their fin dings indicate that the three estimation techniques perform about equa lly well. UAM also is compared with traditional compositional mapping. They find that UAM is superior on fit of the solution, interpretabili ty, and sample reliability. UAM probably holds an edge with respect to data collection. Traditional compositional mapping is superior on eas e of data analysis. No major difference was found on predictive validi ty and structural reliability.