Generalized Pairwise Correlation and method comparison: Impact assessment for JAR attributes on overall liking

Abstract

In product development using JAR (Just-About-Right) scales, it is important to identify precisely, which direction of a given attribute affects hedonic scores the most. The Generalized Pairwise Correlation Method (GPCM) is a non-parametric one and it is useful to rank JAR variables according to their impact on liking. This is done using appropriate statistical tests: the McNemar’s, the Chi-square, the Conditional Fisher’s and the Williams’ t-test. As GPCM requires one-directional variables, JAR data needs to be transformed based on the dummy variable approach. GPCM gives those attributes in that order, which should be increased/decreased to gain higher consumer liking scores. An order can be created according to the impact on liking, which order determines the development of product attributes, as well. The non-parametric tests incorporated in the method are able to identify smaller differences than other statistical methods. As a result, GPCM identifies more significant product attributes; hence, it can help product development processes even if other methods cannot.