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

•A new way of Just-About-Right data analysis is introduced.•The nonparametric Generalized Pairwise Correlation Method (GPCM) is applied.•GPCM identifies small differences and it is more sensitive than other methods.•Two new visualization tools are presented (line plot and bubble plot).•GPCM Excel 97/2003 macro is free to download and use.

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.