Genuine representation of brands: a new method of representing unbiased brand-by-attribute perceptions

Abstract

In this study we propose a new method of representing unbiased perceptions of brands. Specifically a likelihood-based model simultaneously disentangles a major class of psychological bias affecting attribute-based perceptions, and obtains a dimensionality reduction of the problem, resulting in a two-dimensional map. Extant research, while recognizing the importance of halo effect, has mainly focused on the identification and measurement of this bias. Conversely, in this study we explicitly address how to obtain and to graphically represent genuine brand-by-attribute ratings. Our perceptual representation offers a better understanding of the idiosyncratic impact of each attribute on brands, which ultimately helps managers to delve into the nature of brand differentiation. The proposed approach is exemplified through an empirical application using the BrandAsset® Valuator scale in a high-involvement product category.