{"title":"信号检测期望值剖析法采用两步评级法指导产品优化","authors":"Yeon-Joo Lee , Danielle van Hout , Hye-Seong Lee","doi":"10.1016/j.foodqual.2024.105170","DOIUrl":null,"url":null,"abstract":"<div><p>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 <span>Lee, Kim, van Hout, and Lee (2021)</span>, utilizes the two-step rating-based ‘double-faced applicability (DFA)’ test to create expectation profiles for product attributes, quantified by <em>d′</em><sub><em>A</em></sub> output measures. This study aimed to demonstrate and test the usage and efficacy of the <em>d′</em><sub><em>A</em></sub> 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 <em>d′</em><sub><em>A</em></sub> 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 <em>d′</em><sub><em>A</em></sub> expectation profiles effectively identified the key attributes significantly impacting overall satisfaction, aligning with the results of the PLS regression and LSA. The advantage of <em>d′</em><sub><em>A</em></sub> 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 <em>d′</em><sub><em>A</em></sub> expectation profile showcases the potential for effectively addressing consumer-relevant attributes tailored to specific target consumer groups.</p></div>","PeriodicalId":322,"journal":{"name":"Food Quality and Preference","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The signal detection expectation profiling method with a two-step rating for guiding product optimization\",\"authors\":\"Yeon-Joo Lee , Danielle van Hout , Hye-Seong Lee\",\"doi\":\"10.1016/j.foodqual.2024.105170\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <span>Lee, Kim, van Hout, and Lee (2021)</span>, utilizes the two-step rating-based ‘double-faced applicability (DFA)’ test to create expectation profiles for product attributes, quantified by <em>d′</em><sub><em>A</em></sub> output measures. This study aimed to demonstrate and test the usage and efficacy of the <em>d′</em><sub><em>A</em></sub> 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 <em>d′</em><sub><em>A</em></sub> 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 <em>d′</em><sub><em>A</em></sub> expectation profiles effectively identified the key attributes significantly impacting overall satisfaction, aligning with the results of the PLS regression and LSA. The advantage of <em>d′</em><sub><em>A</em></sub> 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 <em>d′</em><sub><em>A</em></sub> expectation profile showcases the potential for effectively addressing consumer-relevant attributes tailored to specific target consumer groups.</p></div>\",\"PeriodicalId\":322,\"journal\":{\"name\":\"Food Quality and Preference\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Quality and Preference\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950329324000727\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Quality and Preference","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950329324000727","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
The signal detection expectation profiling method with a two-step rating for guiding product optimization
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.
期刊介绍:
Food Quality and Preference is a journal devoted to sensory, consumer and behavioural research in food and non-food products. It publishes original research, critical reviews, and short communications in sensory and consumer science, and sensometrics. In addition, the journal publishes special invited issues on important timely topics and from relevant conferences. These are aimed at bridging the gap between research and application, bringing together authors and readers in consumer and market research, sensory science, sensometrics and sensory evaluation, nutrition and food choice, as well as food research, product development and sensory quality assurance. Submissions to Food Quality and Preference are limited to papers that include some form of human measurement; papers that are limited to physical/chemical measures or the routine application of sensory, consumer or econometric analysis will not be considered unless they specifically make a novel scientific contribution in line with the journal''s coverage as outlined below.