Browsing by Author "Loudon M"

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    Comparing traditional check-all-that-apply (CATA) and implicit response time Go/No-go approaches for profiling consumer emotional response when tasting food
    (Elsevier Ltd, 2023-12) Weerawarna N.R.P. M; Godfrey AJR; Loudon M; Foster M; Hort J
    Adapting implicit approaches to capture consumer responses when tasting foods is of recent interest in sensory and consumer science. Implicit consumer responses are reported to be more spontaneous than explicitly gathered data. Traditionally, emotional response to foods is captured using explicit methods like check-all-that-apply (CATA). The present study aimed to compare an implicit response time (IRT) Go/No-go approach with traditional CATA to profile consumer emotional responses. Participants (n = 104) were consumers of, or willing to consume, cow's milk and plant-based milk alternatives (PBMA). Emotional responses for two cow milk and five PBMA products were evaluated across two sessions using IRT and CATA. The cow milk products were replicated across the sessions to allow consistency of response across sessions to be evaluated for each method. Data were collected using a bespoke single page web application (JavaScript, ECMA 2015). Data consistency across sessions (Spearman correlation (ρ)), emotion selection frequency (ρ and generalised linear models) and product discrimination (linear mixed models and correspondence analysis) were compared across the IRT and CATA approaches. Results showed high data consistency from both IRT and CATA across the two sessions (ρ > 0.89). Emotion selection frequency was also comparable across IRT and CATA. Interestingly, CATA was differentiating more between cow's milk products and IRT within the PBMA space. However, further investigations showed that fewer participants provided different responses in CATA than when under time pressure in the IRT Go/No-go. Additional investigations on the performance of explicit versus implicit methods, or their combination, are required across different product matrices to identify the optimum approach to capture consumer product experience.