The signal detection expectation profiling method with a two-step rating for guiding product optimization

Abstract

Understanding consumer requirements with respect to the sensory attributes of food and the sentimental consequences is critical for enhancing consumer satisfaction and achieving market success. A recent innovation, the signal detection expectation profiling method, introduced by Lee, Kim, van Hout, and Lee (2021), utilizes the two-step rating-based ‘double-faced applicability (DFA)’ test to create expectation profiles for product attributes, quantified by d′A output measures. This study aimed to demonstrate and test the usage and efficacy of the d′A expectation profiling method to provide insight for product development. The efficiency of this approach for using a two-step rating-based DFA was examined first and the information guided by the d′A expectation profiling output measures was compared to satisfaction drivers identified through partial least square (PLS) regression and landscape segmentation analysis (LSA), commonly used to link consumer satisfaction/liking and sensory perception. Consumer expectations and satisfaction/sensory evaluations of six different mayonnaise products formed the dataset. Overall, the d′A expectation profiles effectively identified the key attributes significantly impacting overall satisfaction, aligning with the results of the PLS regression and LSA. The advantage of d′A expectation profiles lies in their quantitative representation of the degree of expected sensory attributes, extending beyond the scope of actual evaluated products and offering actionable insights for product optimization. Furthermore, by incorporating custom attributes (descriptor pairs) based on the hedonic valence of target consumers, the d′A expectation profile showcases the potential for effectively addressing consumer-relevant attributes tailored to specific target consumer groups.