2026-10-10T09:51:11Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34027293 2026-09-30T00:59:08Z category_30388 portal_12 item_type_5 month_year_09_2026

Yalun Huang: Comparing Just-About-Right Scaling and Satisfaction-Based DFA Methods for Sensory Attribute Diagnostics in Coffee Yalun Huang (5167085) Michael J. Hautus (25138553) Danielle van Hout (25138554) Sensory processes, perception and performance Just-About-Right (JAR) scale Double-Faced Applicability (DFA) Sensory attribute diagnostics Signal Detection Theory (SDT) Penalty analysis Consumer segmentation <p dir="ltr">The Just-About-Right (JAR) scale is widely used in consumer sensory research for attribute diagnostics, but its interpretation can be compromised by individual scale-use biases and ambiguous penalty results. Satisfaction-based Double-Faced Applicability (DFA), grounded in Signal Detection Theory (SDT), offers an alternative framework combining binary satisfaction judgments, sureness ratings, and paired attribute descriptors to generate diagnostic d’ and d‘_A affect-magnitude estimates. This study compared JAR scaling and satisfaction-based DFA across diagnostic interpretability, segmentation utility, and context stability. In a within-subject, counterbalanced design (N = 61 black instant coffee consumers), participants evaluated seven roast-blend coffee samples across two sessions differing in attribute-range context (Context A: S1–S5; Context B: S3–S7). Results showed no significant context differences across overlapping samples (S3–S5) for either method. However, standard JAR penalty analysis exhibited diagnostic ambiguity with notable reversed penalties (e.g., "too low" acidity or bitterness associated with higher liking than JAR), suggesting JAR deviations can reflect consumer preference directions rather than true product defects. Furthermore, agglomerative hierarchical clustering (k = 2) revealed that JAR primarily split consumers by general scale-use level (higher- vs. middle-scale users with flat sample differentiation), whereas satisfaction-based DFA effectively separated consumers into distinct, actionable taste-preference profiles (balance-oriented vs. dark-roast-oriented). These findings demonstrate that satisfaction-based DFA provides clearer attribute-level diagnostic profiles (d’_A) and richer consumer segmentation without relying on the assumption that deviations from "just about right" inherently represent liking penalties. </p> 2026-09-30T00:59:08Z Image Poster 10.17608/k6.auckland.34027293.v2 https://figshare.com/articles/poster/Comparing_Just-About-Right_Scaling_and_Satisfaction-Based_DFA_Methods_for_Sensory_Attribute_Diagnostics_in_Coffee/34027293 CC BY 4.0