4.6 Article

Identifying drivers of liking and characterizing the ideal product thanks to Free-Comment

Journal

FOOD QUALITY AND PREFERENCE
Volume 96, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.foodqual.2021.104389

Keywords

Open-ended questions; Drivers of liking; Ideal-Free-Comment (IFC); Consumer segmentation; Cooked ham; Home-Use Test (HUT)

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This study investigates deriving drivers of consumer liking from Free-Comment and hedonic data, introducing Ideal-Free-Comment to understand consumers' descriptions of their ideal product. By grouping consumers and analyzing flavor differences, the study helps understand consumer preferences and hedonic appreciation.
Consumers' hedonic appreciation is important for the commercial success of a product. To formulate appreciated products, sensory and hedonic data of some existing products are often linked to each other. Because existing products represent only a limited sensory space of investigation, asking consumers to characterize their ideal product can provide relevant additional information to understand their preferences. First, the paper investigates whether sensory drivers of liking can be derived from linking Free-Comment (FC) and hedonic data. Second, Ideal-Free-Comment (IFC) is introduced. IFC instructs consumers to describe actual products and then their ideal product thanks to FC. IFC paired with liking scoring was used in a home-use test with 483 consumers each evaluating from 1 to 14 (5.71 on average) cooked hams from a list of 30 hams representative of the French market. Based on a mixed linear model, relevant drivers of liking were identified from FC data. The panel's average ideal product was consistent with the drivers of liking. Since descriptors with opposite meanings characterized individual ideal products, a consumer segmentation based on their ideal product was performed and resulted in two segments. The two segments' ideal products mainly differed regarding their flavor. Drivers of liking and the ideal product of the smaller segment (approximate to 15% of the consumers) were not well consistent suggesting this was a noise segment. Drivers of liking based on FC data and IFC are complementary tools to understand consumers' hedonic appreciation without the use of a pre-established list of descriptors.

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