Complementary evaluation approaches for sensory acceptance: Monadic liking vs. paired satisfaction in relation to bias sensitivity, data outputs, and consumer segmentation

Sensory acceptance is commonly evaluated using monadic liking tests, such as the 9-point hedonic scale. However, when a meaningful reference product is available, paired comparative approaches like the Degree of Satisfaction Difference (DOSD) method may offer enhanced interpretability and evaluation stability. This study compared two structurally distinct methods—monadic direct scaling for hedonic liking and paired indirect scaling with a reference sample for satisfaction—to examine how differences in evaluation format influence bias sensitivity, output measures, and consumer segmentation. The analysis considered sample presentation order and consumer thinking style as potential factors of bias in controlled experimental contexts involving multi-sample testing. The DOSD d' estimate was also introduced as a relative satisfaction index to explore underlying preference structures. A total of 180 consumers evaluated six cucumber varieties using both methods. The cognitive reflection test classified participants as high (HRT) or low (LRT) reflection thinkers. Mixed-model ANOVA revealed that only monadic hedonic ratings were significantly affected by sample presentation order, particularly among LRT with greater response variability. DOSD ratings, based on paired comparative design, were unaffected by these factors. Moreover, DOSD-based clusters revealed clearer preference directions and greater response consistency, complementing the hedonic-based clusters, which primarily reflected variations in scale usage. These findings demonstrate that the DOSD method methodologically complements monadic hedonic scaling by providing reference-based comparative insights. Together, they offer a more stable and informative framework for interpreting consumer acceptance, particularly in benchmarking contexts. Further research is needed to validate these findings across diverse product categories and testing conditions.