Exploration of a new consumer test method based on metacognitive certainty

Journal Pre-proofs Exploration of a new consumer test method based on metacognitive certainty In-Ah Kim, Ha-Yeon Cho, Michael J. Hautus, Hye-Seong Lee PII: DOI: Reference:

S0950-3293(19)30623-8 https://doi.org/10.1016/j.foodqual.2019.103857 FQAP 103857

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Food Quality and Preference

Received Date: Revised Date: Accepted Date:

9 August 2019 20 November 2019 20 November 2019

Please cite this article as: Kim, I-A., Cho, H-Y., Hautus, M.J., Lee, H-S., Exploration of a new consumer test method based on metacognitive certainty, Food Quality and Preference (2019), doi: https://doi.org/10.1016/j.foodqual. 2019.103857

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Exploration of a new consumer test method based on metacognitive certainty

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IN-AH KIM1, HA-YEON CHO1, MICHAEL J. HAUTUS2, and HYE-SEONG LEE1*

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1Department

of Food Science and Technology, College of Engineering,

Ewha Womans University, Seoul 03760, South Korea 2School

of Psychology, The University of Auckland, New Zealand

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* Corresponding Author: HYE-SEONG LEE

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Department of Food Science and Technology, College of Engineering

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Ewha Womans University, 03760, South Korea

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Email: [email protected]

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Tel.: 82-2-3277-6687

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Fax: 82-2-3277-6687

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Abstract

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Successful product development and marketing necessitate a study of the consumer concept

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of culture-specific or deep-positioned branded food. In this study, a new consumer test

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method was designed based on an authenticity test and used as a reference frame for the

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target concept without an upsetting story. The response format of this method included the

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metacognitive certainty response following the sensory authenticity response using the A-Not

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A test procedure. The method was applied to study the concept of goso flavor, as perceived

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by 91 female consumers with three commercial soymilk products, having each consumer

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evaluate each product 45 times over three days. The repeated responses of sensory

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authenticity were analyzed as mean scores and signal detection theory (SDT) d-prime (d')

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values of the product difference. From the metacognitive certainty responses after the sensory

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authenticity response, a new quantitative group measure of d-prime metacognition (d'MC) was

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calculated in the SDT context and compared with the other outputs. The measure ranged from

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negative to positive values, indicating a mismatch to a match for the concept of each product.

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Data analyses were conducted on both pooled data and segmented data, which was driven

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from the results of cluster analyses using the mean sensory authenticity scores and SDT C

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values (estimates of response bias about the concept tested). The results showed that d'MC of

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each product corresponded to the mean scores and d' with the advantage of easy

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interpretation. Overall, d'MC can be a useful group measure for studying the consumer concept

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towards food and beverages.

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Keywords: authenticity test; concept measurement; metacognition; signal detection theory

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(SDT); consumer research

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1. Introduction

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Measurements of the consumer concept are important for achieving a range of

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business objectives, such as product development, quality control, and marketing in the food

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and beverage industry (Lee & O’Mahony, 2005). Consumer concepts can be defined as the

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constructions and associations between the identity of a particular object and other conceptual

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associations that allow people to interpret, understand, and assign meaning to a perceptual

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space (Thomson, Crocker, & Marketo, 2010; Stolzenbach, Bredie, Christensen, & Byrne,

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2016). Consumers who consume local or ethnic cuisine in their everyday life create a concept

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of its typical taste or brand. Such sensory concepts of a typical taste of certain food can be

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obtained without the consumers’ awareness or effort and remain in implicit memories

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(Frandsen et al., 2003). As heavy users also have built the concept of their most loved brand,

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the quantitative consumer concept measurement is becoming more critical for a range of

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well-branded processed foods and foods with strong nationality.

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The authenticity test has been applied to discriminate different brands or treatments

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of foods as an affect-based discrimination test method (Mojet & Köster, 1986; Köster, 1998;

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Frandsen et al., 2003, 2007; Boutrolle et al., 2009). Mojet and Köster (1986) conducted an

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authenticity test on consumers of a particular Dutch brand beer with different brands of beer

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by applying an emotional story for the beer brewing process to increase the consumers’

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involvement in and attention to the test. They found that consumers improved their ability to

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discriminate among different brands of beer with an implicitly learned reference in the

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authenticity tests, while consumers did not discriminate among the same set of beers in the

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repeated preference-ranking test. In the authenticity test (Köster, 1998), there is no right or

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wrong answer; the consumers’ implicitly learned reference was used to evaluate the subtle

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differences among the products. The outcome of the authenticity tests, where consumers have

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to report whether the given product is ‘authentic’ (‘real’ or ‘original’) or not by a comparison

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with their internal reference, indicates whether the consumers have a stable internal concept

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for a typical taste or brand of the particular product. Therefore, the authenticity test can be

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expanded to evaluate the sensory authenticity of national or local foods and beverages.

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In authenticity tests, an upsetting story regarding the products is presented to

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consumers to evoke the consumers’ emotional mindset (i.e., a negative affective attitude

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towards inauthentic products) as emotional priming. Frandsen et al. (2003, 2007) applied

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authenticity tests for Danish consumers by asking them to identify milk samples as either

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Danish and foreign from among a set of milk samples that had undergone different treatments

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(i.e., animal feed types and storage length). Consumers were told the following upsetting

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story to provoke a suspicious mindset before the test: “We want to investigate the possibility

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of selling fresh, foreign milk on the Danish market. This will only be possible if the

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consumers accept the product, and if it tastes like Danish milk. Practically, there are no

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barriers to get fresh, foreign milk in the Danish shops. Even though the purchase of the

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product will be cheaper for the retailer, the price will be the same for the consumers”. They

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reported that after applying the authenticity tests, which relies on affective responses, the

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consumers’ ability to perceive subtle flavor differences were sharpened.

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Another application of using a sensory evaluation methodology for measuring the

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consumer concept about the sensory authenticity may include affective discrimination tests

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used to test the consumer discriminability of re-formulated products from the original using

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variants of the reminder designs, including duo-trio tests (Kim, Chae, van Hout, & Lee, 2014;

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Jeong, Kang, et al., 2016; Kim & Lee, 2015; Kim, Yoon, & Lee, 2015; Kim, Dessirier, van

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Hout, & Lee, 2015; Kim, Sim, & Lee, 2015, 2016; Jeong, van Hout, Groeneschild, & Lee,

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2017; Kim, van Hout, Dessirier, & Lee, 2018). In the applications of overall difference tests,

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subtle sensory differences between products were measured by applying affective reference

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frames for the original product to evoke the consumers’ internal concept regarding the

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original product. Therefore, although these methods did not incorporate the negative

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emotional involvement of the subjects, they were similar to the authenticity tests in terms of

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an evaluation scheme for comparing the test product to the given original (i.e., reference)

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product. These sensory discrimination methods using reminder designs were more sensitive

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to other overall difference test methods using balanced-reference designs, such as same-

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different and triangle and tetrad methods (Kim, et al., 2014; Jeong, Kang, et al., 2016; Jeong,

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van Hout, et al., 2017). When the reference does not need to be presented physically but only

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retained in the affective state of mind with positive framing, the A-Not A test format, which

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is procedurally the same as authenticity tests, could be used as an alternative format for

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sensory authenticity tests to measure the consumer concept. On the other hand, in addition to the authenticity tests and A-Not A reminder designs,

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the general test design to study the consumer concept has been to use a range of formats of

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single-step simple scaling (Lee & O’Mahony, 2005). Simple scaling provides a numerical

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estimation for each product and allows quantitative comparisons to be made across different

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products. Thus, it is used widely to study various concepts regarding food and beverages.

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However, when applied to consumer tests, the pooled data generated by consumers who are

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not trained would be expected to yield a large boundary variance, leading to lower

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discrimination sensitivity (Lee & O’Mahony, 2005). In this regard, for both authenticity tests

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and sensory discrimination tests, Signal Detection Theory (SDT; Green & Swets, 1966;

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Macmillan & Creelman, 2005) and Thurstonian modeling (TM; Thurstone, 1927; Ennis, 1990)

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have been applied broadly (i.e., Frandsen et al., 2007; Jeong et al., 2016; Stocks et al., 2017)

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to measure the discrimination sensitivity independently from the response bias. Therefore,

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these approaches have been extended to measure various consumer concepts and sensory

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perceptions, such as softness (Seo, 2013), taste fullness (Kim, 2015; Lee, 2017), product-

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benefit (Kim, Dessirier, van Hout, & Lee, 2015; Kim, van Hout, Dessirier, & Lee, 2018), and

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holistic product usage experience (Kim, Hopkinson, van Hout, & Lee, 2017a,b). One of the

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SDT and TM parameters, the d-prime (d') estimate, can be obtained from the equal-variance

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normal-distribution model, and it has been accepted broadly as a sensory distance measure.

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This indicates the difference in the perceived strengths of the sensory or conceptual

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properties between different products when assuming one product as the signal level and the

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other as the noise level. Therefore, it always necessitates responses to the reference, which

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does not always exist in practice, to set a noise distribution, and it cannot provide an

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independent index for each product like the mean scores of scaling (Kim, Hopkinson, et al.,

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2017b). Extending the SDT approach to drive quantitative outputs, such as d' measures, based

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solely on the certainty and solidity of the consumer’s internally built concept regarding the

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sensory authenticity and computed for each product without the need to provide a reference

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would be very useful.

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Therefore, a new consumer test method was designed to measure the consumer

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concepts, including sensory authenticity, using the A-Not A test design with a reference

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frame for the target concept without an upsetting story. This test design was modified further

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by adding the metacognitive certainty response following the sensory authenticity response to

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explore a means of calculating the consumer group measure for each product in the SDT and

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TM context. In the present study, the output index proposed as a new quantitative group

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measure driven from an analysis of the metacognitive certainty responses was called d-prime

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metacognition (d'MC), and compared with the mean scores for each product and d' estimates

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for the sensory differences between products, which were calculated from the repeated

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responses of the sensory authenticity only. Metacognition is the ability to think about our

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own thoughts and knowledge and to recognize our own cognitive processing, i.e., people can

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assess retrospectively whether they provide the right decision or not, in perceptual or memory

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tasks (Fleming & Lau, 2014; Massoni, Gajdos, & Vergnaud, 2014; Grimaldi, Lau, & Basso,

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2015). Thus, in the present study, it was investigated whether the pooled metacognitive

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certainty responses, which are a group of consumers’ confidence judgements on the sensory

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authenticity response to a given sensory concept, can represent the certainty or solidity of

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consumer concept for each product.

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The chosen concept studied in the present study was the goso flavor; three

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commercial soymilk products were evaluated. The goso flavor is one of the representative

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flavoring ingredients in Korean cuisine (Cha et al., 2010). Therefore, Korean consumers who

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consume soymilk habitually have built an internal concept of goso flavor for soymilk. As the

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segmentation issue is very important in a consumer study, data analyses were conducted to

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analyze pooled and segmented data, which was driven from the results of cluster analyses

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using the mean sensory authenticity scores and estimates of the response bias about the

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concept tested (SDT C values).

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2. Materials and Methods

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2.1. Test products

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Seven soymilk products commercially available at local supermarkets in South Korea

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were used. Among these seven products, three (coded as P1, P2, and P3), which represent the

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salient sensory characteristics of commercial soymilk products, were selected for use in the

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main test. The remaining four were used in the practice test. The products were presented in

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counterbalanced order to eliminate any carry-over effects and were served monadically in a

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random order. All products were stored at room temperature (22.5 ± 0.5°C) and served in 20

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ml aliquots in disposable plastic cups (3-oz, Lotte Aluminum Co. Ltd., Seoul, South Korea)

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with 3-digit random numbers on their labels. The plastic cups were wrapped in opaque paper

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to prevent any bias from the color of the product. All tests were conducted under fluorescent

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lighting conditions (light intensity 650-720 Lux) in separate sensory booths.

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2.2. Consumers

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Ninety-one female consumers (age range: 20-34 years) were recruited through an

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online posting at Ewha Womans University (Seoul, South Korea). The consumers usually

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consumed soybean products, including soymilk, and had a concept as to what constitutes the

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typical goso flavor of fresh soybean. All consumers were instructed not to gargle or eat any

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food except for water for at least one hour before the test.

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The Institutional Review Board of Ewha Womans University approved the ethical

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and safety aspects of this study. All consumers provided informed consent after being

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explained the test procedures, and they received 20,000 won (~US$17) for their participation

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after completing the test.

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2.3. Experimental design and test procedures

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The experiment consisted of three stages: 1) delivering the cover story, 2) practice

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test, and 3) main test. First, to evoke their affective state of mind, all consumers received the

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following cover story: “We will present three soymilk products that were manufactured by

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different processes. Thus, you will be able to perceive the goso flavor differently. Please

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evaluate the degree of perceived goso flavor by recalling the flavor driven by the freshness of

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soybean”. A practice test was next conducted with the four practice products to enable the

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consumers to experience a wide range of goso flavors and reinforce their evaluative criteria

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relevant to the goso flavor, as well as to gain familiarity with the test procedures for the main

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test. The main test was conducted over three days separated by a one-week interval. On each

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day, the test was split into five sessions, with five-minute breaks between sessions. In each

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session, all three products were evaluated three times per consumer. Therefore, from the main

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test, each consumer provided 45 rating responses (three days x five sessions/day x three times

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= 45) for each product, as shown in Table 1.

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Table 1 SHOULD BE ABOUT HERE (across two columns in a portrait layout)

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A new consumer test method, the metacognitive authenticity test method, which

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included the metacognitive certainty response following the sensory authenticity response,

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was first designed, as shown in Fig. 1. To discriminate samples with subtle differences better,

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the metacognitive authenticity test method was applied to a series of two sequential rating

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options and used in the practice and main tests (Fig. 2). First, the consumers were instructed

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to rate the degree of the perceived goso flavor of each product using a 6-point category scale

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(“not goso”, “slightly not goso”, “unsure but probably not goso”, “unsure but probably goso”,

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“slightly goso”, and “goso”), which was regarded as the sensory authenticity responses.

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Subsequently, they were asked to rate the degree of certainty to their response on the previous

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sensory authenticity response to the goso flavor of each product using a 4-point category

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scale (“uncertain”, “slightly uncertain”, “slightly certain”, and “certain”), which was regarded

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as the metacognitive certainty responses. These rating responses were collected on a

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computer using E-prime 2.0 software (Psychology Software Tools, Inc.).

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During the tests, the consumers were instructed to rinse their mouths with de-ionized

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water (22.5 ± 0.5°C) before starting each set of sub-sessions and with lemon-added de-

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ionized water (22.5 ± 0.5°C) after every sub-session. Re-tasting and changing the response

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were not permitted. All consumers were asked to swallow the first sip of the product, and

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they were allowed to expectorate the rest of the stimulus as desired.

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After completing the evaluation of the main test, a socio-demographic survey was

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also administered (data were not published). The experiment lasted for approximately one

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hour.

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Fig. 1 SHOULD BE ABOUT HERE (across two columns in a portrait layout) Fig. 2 SHOULD BE ABOUT HERE (across two columns in a portrait layout)

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2.4. Data analysis

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2.4.1. Data analysis of pooled data

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Two sequential rating responses were collected from the metacognitive authenticity

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test method: sensory authenticity, and metacognitive certainty responses (Fig. 2). The mean

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scores and d' were calculated using the pooled data of sensory authenticity responses. In

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addition, a new quantitative group measure of d'MC was calculated using the pooled data of

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the metacognitive certainty responses.

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ANOVA was conducted to analyze the sensory authenticity responses by treating the

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products as a fixed factor and consumers as a random effect, and a Tukey’s test was

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conducted for multiple pairwise comparisons at a significance level of 5%. XLSTAT

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software (Addinsoft, 2019) was used for data analysis.

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Next, a standard SDT approach was also applied to measure the sensory differences

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between a pair of stimuli using the sensory authenticity responses. The degree of difference

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in the perceived goso flavor between the two stimuli was calculated in terms of the d'

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estimate. As shown in Fig. 2a, the signal, and noise distributions were decided based on the

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response frequency for the authenticity ratings: S1 has higher response frequencies for

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‘goso(merging the responses to “unsure but probably goso”, “slightly goso” and “goso”)’

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than S2 in a pair. Thus, S1 was considered a signal distribution, whereas S2 was considered a

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noise distribution. Therefore, three product pairs were constructed and are denoted as P2 ‒ P1,

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P2 ‒ P3, and P1 ‒ P3, suggesting that the first and second products were assumed to be the

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signal and noise distributions, respectively (signal ‒ noise). From this, the d' estimates for

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each of the three product pairs were calculated assuming normal distributions with equal

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variance, as described by O’Mahony (1992) or Lee and van Hout (2009).

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To analyze the metacognitive certainty responses, a new quantitative group measure

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of d'MC was applied in the context of SDT and TM approaches. In Fig. 2b, for each stimulus,

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the responses to ‘goso’ were treated as a signal, whereas those to ‘not goso’ were treated as a

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noise distribution. This is based on the idea of Type 2 sensitivity, where the metacognition is

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calculated from the consumers’ confidence judgments on both correct and incorrect responses

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(Galvin, Podd, Drga, & Whitmore, 2003; Higham, Perfect, & Bruno, 2009; Macmillan &

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Creelman, 2005; Maniscalco & Lau, 2012). Compared to the d' estimates, constructing the

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response data matrix does not require any physical noise level. Therefore, it can be computed

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for each stimulus, not for each pair of stimuli. Thus, the d'MC estimates for each soymilk

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product (P1, P2, and P3) were computed, as were the mean scores.

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Both d' and d'MC represent a group mean measure calculated from the pooled

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responses of a group of consumers. A nonparametric approach was used for their calculation

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because it does not require any assumptions regarding the consumers’ response distributions,

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and it is applicable for relatively small sample sizes (Macmillan & Creelman, 2005; Lee &

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van Hout, 2009; Bi et al., 2013; Kim et al., 2017b). Moreover, d' and d'MC were used instead

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of R-index or other types of nonparametric measure because the concept of d' estimates has

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recently been accepted widely as a measure of difference in the psychological and sensory

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science field (Ennis, Rousseau, & Ennis, 2014; Macmillan & Creelman, 2005; Kim et al.,

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2017b). Based on Green’s area theorem (Green and Swets, 1966), R-index was converted

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to d' and d'MC using the equation shown in Fig. 2, where Φ-1(.) denotes the inverse standard

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normal transform. The theorem states that the R-index in the A-Not A test (a monadic

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test) equals the proportion of correct responses (correctly identifying the signal) by an

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unbiased observer in a hypothetical paired comparison task. The SDT Assistant program

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(Hautus, 2014) was used to calculate the d' and d'MC estimates, as well as their estimates of

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the variance and standard errors through the bootstrap method (bootstrap replications: 10000).

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The R/S-Plus code, i.e., dstest, developed by the Sensometrics Research and Service and

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authorized by Bi (2006) was used to test the significance of the differences in multiple d' and

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d'MC estimates.

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2.4.2. Data analysis of segmentation and segmented data

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Consumer segments were established based on the concept of the sensory

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authenticity of the goso flavor of soymilk products using cluster analysis with the following

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two different types of data of segmentation variables:

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(1) mean sensory authenticity scores – the repeated sensory authenticity responses of each consumer were averaged into the mean sensory authenticity scores

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(2) SDT C values – estimates of the response bias about the concept tested were

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calculated into C values using a cumulative link model function in the R-package ordinal

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(Christensen, Cleaver, & Brockhoff, 2011; Christensen, 2015). Data correction was applied to

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avoid the problem of zero responses (empty cells) in the middle two categories by adding one

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to each of two categories in the middle of the response categories, similar to the log-linear

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correction in the estimation of d' (Hautus, 1995).

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These two data sets for cluster analysis were arranged in 91 rows and three columns

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regarding the consumers and products. Agglomerative hierarchical clustering (dissimilarity

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proximity type, Ward’s method, and Euclidean distance) was firstly applied, and three

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clusters were established based on the dendrogram plot. K-means cluster analysis was then

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conducted to classify the consumers based on the criterion of the Determinant (W). XLSTAT

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software (Addinsoft, 2019) was used for cluster analysis.

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For the segmented data driven from the results of cluster analysis using the mean

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sensory authenticity scores, the mean scores were calculated, while for the segmented data

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driven from the results of the cluster analysis using SDT C values, the d' and d'MC estimates

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were calculated according to the data analysis process mentioned above.

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3. Results

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3.1. Comparison of the results obtained from pooled data collected from the metacognitive

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authenticity test method

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The mean scores for each product and the d' estimates for each product pair were

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calculated using the sensory authenticity responses, and are listed in Tables 2 and 3,

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respectively. The results showed that P2 had the highest score of the three products. P2 ‒ P3

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had the largest difference compared to the other product pairs, but it was less than one (Table

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2).

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The degree of differences in the perceived goso flavor between two products was

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calculated in terms of the d' estimates, and all three d' estimates were significantly greater

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than zero (Table 3). This suggests that the product assumed to be signal was perceived to

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have more goso flavor than the product assumed to be noise based on the 95% confidence

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interval. For example, d' estimates P2 ‒ P1 and P2 ‒ P3 were significantly greater than zero,

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suggesting that P2 can be more perceived to have goso flavor than P1 and P3. A comparison

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of the differences between the d' estimates revealed no significant difference (Table 3). On

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the other hand, the results showed that the difference between P2 and P3 (P2 ‒ P3) was the

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largest of the three product pairs and was the same as the results shown in Table 2.

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A new quantitative group measure of d'MC was also applied to analyze the

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metacognitive certainty responses for each product, and the results are listed in Table 4. The

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d'MC estimate can be interpreted differently depending on the significance testing with zero: 1)

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significantly lower than zero (negative value), indicating a mismatch between sensory

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perception and concept; or 2) significantly greater than zero, indicating that sensory

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perception is matched to the concept. Therefore, the results shown in Table 4a suggest that

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the sensory perception of P2, but not P3, was matched to the concept. One of the advantages

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of d'MC is that it can be calculated for each product like the mean scores because it does not

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require a physical noise level. Therefore, when comparing with the results of mean scores

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shown in Table 2, the d'MC provided similar results to the mean score including the significant

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differences between products. Moreover, as shown in Table 4b, the differences in the d'MC

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estimates of the two products were also calculated to compare with the results of the d'

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estimates (Table 3). The results showed that the difference between P2 and P3 (P2 ‒ P3) was

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largest among the three product pairs, as shown in Table 3, indicating that the d'MC estimates

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can predict the results of the difference between two products.

330 331 332

Table 2 SHOULD BE ABOUT HERE (in a single column in a portrait layout)

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Table 3 SHOULD BE ABOUT HERE (in a single column in a portrait layout)

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Table 4 SHOULD BE ABOUT HERE (across two columns in a portrait layout)

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3.2. Consumer segmentation based on differences in consumers’ concept perception and

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comparison of results obtained from the segmented data

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From cluster analysis, three different consumer segments were identified based on

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the different sensory concepts of goso flavor of three soymilk products. Two different cluster

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analysis approaches using the mean sensory authenticity scores and SDT C values resulted in

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identical results, except that only one consumer who classified Group 1 when using the mean

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sensory authenticity scores and Group 3 when using the C value for cluster analysis. From the

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results of cluster analysis, the mean scores for the groups driven from the results of cluster

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analysis were calculated using the mean sensory authenticity scores, as listed in Table 5. The

349

d' and d'MC estimates were also calculated for the groups driven from the results of cluster

350

analysis using the SDT C values and are listed in Tables 6 and 7, respectively.

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As shown in Table 5, the overall pattern of scores for the consumer segments was

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different from the results of the overall consumers shown in Table 2, highlighting the

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importance of consumer segmentation when studying consumer perception on sensory

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concepts. In addition, P2 had the highest scores of the three products in Group 1 and 3,

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whereas the consumers in Group 2 gave high scores for P1 and low scores for P2. The

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product with the lowest score was different in all three groups.

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The d' estimates for P1 ‒ P3 in Group 1 and P2 ‒ P1 in Group 2 were significantly

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lower than zero, indicating that the sensory perceptions of the products assumed to be a noise

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level were perceived to have more goso flavor than those assumed to be a signal level (Table

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6). Moreover, the d' estimates for each group showed that the order of the size of the

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differences among the three product pairs was different from the results shown in Table 3.

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For example, in the case of P2 ‒ P1, the d' estimate was 0.311 in Table 3, suggesting that

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these two products had only a slight difference in the degree of sensory perception. After

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cluster analysis, however, the d' estimates of P2 ‒ P1 were more likely to increase in all three

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groups (Table 6). A comparison with the mean scores in Table 5 revealed some differences.

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For example, the d' estimates of P2 ‒ P3 in Group 2 were approximately zero, indicating that

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there was no significant difference between these two products (Table 6), whereas P3 had

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significantly higher scores than P2 (Table 5).

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An inspection of the d'MC estimates for each consumer group (Table 7a) revealed the

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sensory perception of P2 to be matched to the concept in the Groups 1 and 3, whereas it was

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not matched to the concept in Group 2. On the other hand, P1 was perceived as being

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matched to the concept by the consumers in Groups 2 and 3, not in Group 1. The results for

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the d'MC estimates were also compared with the results of mean scores shown in Table 5. The

374

d'MC estimates provided identical results only in the case of Group 1. A comparison of the

375

difference in the d'MC estimates for two products (Table 7b) with the d' estimates in each

376

consumer group (Table 6) revealed similar results to those of d' estimates in terms of the

377

order and direction of size of the difference between the product pairs, as in the pooled data.

378 379 380

Table 5 SHOULD BE ABOUT HERE (in a single column in a portrait layout)

381 382 383

Table 6 SHOULD BE ABOUT HERE (in a single column in a portrait layout)

384 385 386 387

Table 7 SHOULD BE ABOUT HERE (across two columns in a portrait layout)

388

4. Discussion

389

In the present study, as a new consumer concept test method, the metacognitive

390

authenticity test method was designed to include the metacognitive certainty response

391

following the authenticity response from which an SDT-based quantitative consumer concept

392

measure for an individual product could be calculated. This method was then applied

393

successfully to the consumer concept of perceived goso flavor using three commercial

394

soymilk products. It was demonstrated that the pooled metacognitive certainty responses,

395

which are a group of consumers’ confidence judgements on the sensory authenticity response

396

to the concept of goso flavor, could represent the certainty or solidity of such concept for

397

each product. The method differs from the original authenticity test in two aspects: cover

398

story delivery stage, and data collection and data analysis.

399

Regarding the cover story delivery stage, unlike in previous authenticity tests

400

(Frandsen et al., 2003, 2007; Boutrolle et al, 2009), where an upsetting story was used to

401

elicit an emotional response from the subjects, in the new authenticity test method, the cover

402

story was designed using only a reference frame for the target concept without an upsetting

403

story. Stocks et al. (2017) reported that the effect of an authenticity test with an upsetting

404

story on the performance of product discrimination varies according to the product

405

differences (small vs. large). Therefore, a cover story was used only to remind the consumers

406

what they thought of the sensory authenticity of the target concept and did not evoke a

407

negative affective attitude. This was to develop a test procedure capable of being applied to

408

broader business cases and be a more general consumer concept test method. As the present

409

study was the first exploration of the new consumer test method for studying the consumer

410

concept about the perceived goso flavor of commercial soymilk, validation in follow-up

411

studies will be necessary. In future research, more effective methods and test designs that can

412

evoke the consumers’ thinking and attitude for the target concept should be studied for a

413

range of concepts with diverse food and beverage products. The effects of negative versus

414

positive framing of the concept in the test instruction on the sensitivity of the test should also

415

be studied further.

416

Regarding data collection and data analysis, two sequential rating response options

417

were used: sensory authenticity and metacognitive certainty responses, as shown in Fig. 2.

418

More conventional approaches of mean scores and d' estimates were calculated to analyze the

419

sensory authenticity responses. To examine the metacognitive certainty responses, d'MC

420

estimates were calculated in the context of SDT and TM approaches, and the results were

421

compared to find a more applicable group measure of the consumer concept. As shown in

422

Tables 3 and 6, the d' estimates were computed to measure the degree of difference in the

423

perceived goso flavor for two products, indicating that one product was perceived to have

424

more goso flavor than the other. Because it was based on a pair-wise comparison, only the

425

relative distance/difference between two products could be calculated, and a physical noise

426

level was always required. In contrast, the mean scores and d'MC estimate could be calculated

427

for each product. Hence, these two scores allow quantitative comparisons to be made across

428

different products. Previously, Kim et al. (2017b) proposed a novel nonparametric estimate of

429

a group mean output measure of the affect magnitude (d'A) to measure the affect magnitude

430

for each attribute and for each product. This novel group measure can be calculated

431

independently for each product because it does not require a physical noise level. Therefore,

432

it was applied to develop product usage experience profiles of a household surface cleaner

433

(Kim et al., 2017b) and extend it for use as an alternative approach to conventional

434

descriptive analysis (Kim et al., 2018). Like the d'A estimate, a physical noise level is not

435

needed to calculate the d'MC estimate, which can be calculated independently for each product

436

as an absolute measure based on the idea of Type 2 sensitivity. In addition, its range from

437

negative (‒) to positive (+) values for each product indicates ‘mismatch with the concept’ to

438

‘match with the concept’. This provides a clear boundary for the certain consumer concept,

439

goso flavor in this case, by testing the significant difference from zero across a range of

440

product categories, and this boundary can be used as a business action standard. This is one

441

of the main advantages of this new measure compared to the mean scores, which would be

442

more arbitrary and dependent on the product types and categories.

443

Boutrolle et al. (2009) emphasized the importance of performing subgroup analysis

444

(i.e., cluster analysis) to consider the individual response patterns because an analysis with

445

overall data sometimes does not produce sufficient results. Therefore, in their study, the

446

participants received products monadically, but not the reference product, because they had to

447

compare the products with their internal reference. They were then asked to answer whether

448

the product was original or not (i.e., copy). Four different responses, ‘original’ and ‘copy’

449

responses concerning two products (X, Y), were obtained to conduct cluster analysis based

450

on the SDT matrix of consumers’ individual responses. The purpose was to examine not only

451

how the consumers’ response patterns differed among the different consumer groups but also

452

how their responses regarding authenticity differed when the decision criterion they used was

453

measured dependently. In this regard, Kim, van Hout, and Lee (2018) calculated the C values

454

to reflect the extent to which the subjects’ responses were skewed towards satisfaction or

455

dissatisfaction by applying the cumulative link model, which can be applied to obtain the TM

456

parameters, including the C values from the responses of A-Not A with rating categories

457

(Christensen, Cleaver, & Brockhoff, 2011). They highlighted the importance of considering

458

the subjective response bias in terms of providing meaningful information related to a study

459

of consumer perception and behavior.

460

Regarding the importance of the evaluative criterion, the C values, which represent

461

the consumer’s response bias towards the concept of goso flavor, were used for cluster

462

analysis. The results showed that these C values could be used to reveal the consumer

463

segments similar to the mean scores. The resulting study findings also showed that a

464

consideration of the individual response pattern is very important for a better understanding

465

of the consumers’ responses compared to the results of cluster analysis of the overall

466

consumers. These results are consistent with Boutrolle et al. (2009)’s suggestion in that

467

individual response patterns need to be reviewed because different conclusions can be drawn

468

depending on the type of data (pooled vs. subgroup).

469

After clustering the consumer groups based on their response pattern, the d'MC

470

estimates for each group were also calculated to indicate the degree of the perceived goso

471

flavor of each product for each group of consumers. As with the results of pooled data, it

472

provided similar results to the mean scores because it can be calculated for each product

473

individually. In addition, the product that is perceived to be well matched to the concept

474

could be interpreted easily for individuals in a certain consumer group. Moreover, a

475

comparison of the d' estimates results proved that the d'MC estimates could also predict the

476

product differences. Therefore, the use of d'MC estimates avoids the need to measure the

477

relative difference between two products. As a result, d'MC estimates driven from

478

metacognitive certainty responses might be an appropriate concept measure to provide

479

valuable information on the individual scores for each product as well as the relative

480

difference between products with the advantage of data interpretation. Although it is beyond

481

the scope of this paper, further validation of a new concept measure of d'MC is necessary,

482

focusing on the computation/calculation process of d'MC estimate and practical application of

483

the new measure (i.e., number of trials (sample size), application to the predictive model

484

development). Therefore, more theoretical examination and discussion from a statistical

485

perspective for such an approach is required in future studies.

486

Regarding the consumer concept measurement, the general measurement approach

487

has been the scaling method. Previous studies have measured how well consumers fitted a

488

concept statement to given products and their attributes, using a 9-point category scale with

489

labels (Carr, Craig-Petsinger, & Hadlich, 2001). The conceptual attributes of different

490

cheeses were also measured using a 7-point category scale (Bárcenas, Pérez de San Roman,

491

Pérez Elortondo, & Albisu, 2001), and a consumer concept of the refreshingness of

492

toothpaste was measured using a ranking task for a group of consumers (Lee & O’Mahony,

493

2005). Chae, Lee and Lee (2010) examined consumer discrimination between milks with

494

subtle differences using the same-different rating. Although such measures have been used to

495

investigate the consumer concept, response bias arises because of the different uses of the

496

response categories. Such response bias caused by the measurement tool should be separated

497

from the consumers’ natural response bias (response pattern) towards certain or uncertain

498

categories for the consumer concept. In addition, the scaling method produces scores that are

499

strongly dependent on the experimental context (i.e., a range of products and use of a

500

category of scale), producing relative scores (Kim, van Hout, Dessirier, & Lee, 2018). This

501

also yields a large boundary variance in the scores (Lee & O’Mahony, 2005;), making it

502

difficult to interpret the results.

503

For this reason, the SDT C values were used for consumer segmentation, and a novel

504

approach of computing d'MC was applied to measure the consumer concept without needing to

505

determine a reference or noise level product. These results suggest that the novel measure of

506

d'MC driven from the metacognitive certainty response following the authenticity response can

507

be an efficient alternative to the standard difference measure and mean scores for studying

508

the sensory and conceptual attribute of food and beverages. This novel approach of the

509

consumer test method using the repeated two-step ratings and computation of d'MC as a

510

measure of an abstract concept or authentic feel has the potential for a range of consumer

511

measurements and consumer segmentations. The applicability of this model warrants further

512

investigation using various abstract and synthetic consumer concepts under different

513

experimental situations with more realistic constraints.

514

515

Acknowledgements

516

This work was supported by the National Research Foundation of Korea(NRF) grants funded

517

by the Korea government(MSIT)(No. 2012R1A1A1001768; 2015R1A1A1A05001170) and

518

the

Ministry

of

Education(No.

2018R1A6A3A01012357).

519

References

520

Bárcenas, P., Pérez de San Roman, R., Pérez Elortondo, F. J., & Albisu, M. (2001).

521

Consumer preference structures for traditional Spanish cheeses and their relationship

522

with sensory properties. Food Quality and Preference, 12, 269–279.

523 524

Bi, J. (2006). Sensory discrimination tests and measurements: Statistical principles, procedures and tables. Iowa: Blackwell Publishing (Chapter 10).

525

Bi, J., Lee, H.-S., & O’Mahony, M. (2013). Statistical analysis of receiver operating

526

characteristic (ROC) curves for the ratings of the A-Not A and the same-different

527

methods. Journal of Sensory Studies, 28, 34–46.

528

Boutrolle, I., Julien D., Köster, E. P., Aranz, D., & Danzart, M. (2009). Use of a test of

529

perceived authenticity to trigger affective responses when testing food. Food Quality and

530

Preference, 20, 418–426.

531

Carr, B. T., Craig-Petsinger, D., & Hadlich, S. (2001). A case study in relating sensory

532

descriptive data to product concept fit and consumer vocabulary. Food Quality and

533

Preference, 12, 407–412.

534

Cha, S.-M., Chung, L., Chung, S.-J., Kim, K.-O., Han, G.-J., & Lee, S.-R. (2010). Exploring

535

Korean typical tastes, flavors and foods using Delphi technique. Korean Journal of Food

536

and Cookery Science, 26, 155–164.

537

Chae, J.-E., Lee, Y.-M., & Lee, H.-S. (2010). Affective same-different discrimination tests

538

for assessing consumer discriminability between milks with subtle differences. Food

539

Quality and Preference, 21, 427–438.

540

Christensen, R. H. B., Cleaver, G., & Brockhoff, P. B. (2011). Statistical and Thurstonian

541

models for the A-not A protocol with and without sureness. Food Quality and Preference,

542

22, 542–549.

543 544 545 546 547 548 549 550

Christensen, R. H. B. (2015). Ordinal – Regression models for ordinal data. R package version 2015.6–28.. Ennis, D. M. (1990). Relative power of difference testing methods in sensory evaluation. Food Technology, 44, 114–118. Ennis, J. M., Rousseau, B., & Ennis, D. M. (2014). Sensory difference tests as measurement instruments: A review of recent advances. Journal of Sensory Studies, 29, 89–102. Fleming, S. M., & Lau, H. C. (2014). How to measure metacognition. Frontiers in human neuroscience, 8, 443.

551

Frandsen, L., Dijksterhuis, G., Brockhoff, P., Nielsen, J., & Martens, M. (2003). Subtle

552

differences in milk: Comparison of an analytical and an affective test. Food Quality and

553

Preference, 14, 515–526.

554

Frandsen, L., Dijksterhuis, G., Brockhoff, P., Nielsen, J., & Martens, M. (2007). Feelings as a

555

basis for discrimination: Comparison of a modified authenticity test with the same-

556

different test for slightly different types of milk. Food Quality and Preference, 18, 97–

557

105.

558

Galvin, S. J., Podd, J. V., Drga, V., & Whitmore, J. (2003). Type 2 tasks in the theory of

559

signal detectability: Discrimination between correct and incorrect decisions.

560

Psychonomic Bulletin & Review, 10, 843–876.

561

562 563 564

Green, D.M., & Swets, J.A. (1966). Signal detection theory and psychophysics. New York: John Wiley & Sons Ltd. Grimaldi, P., Lau, H., & Basso, M. A. (2015). There are things that we know that we know,

565

and there are things that we do not know we do not know: Confidence in decision-

566

making. Neuroscience & Biobehavioral Reviews, 55, 88-97.

567

Hautus, M. J. (1995). Corrections for extreme proportions and their biasing effects on

568

estimated values of d′. Behavior Research Methods, Instruments, & Computers, 27, 46–

569

51.

570 571

Hautus, M. J. (2014). SDT Assistant [Computer software]. Auckland, New Zealand. Retrieved April 1, 2019. Available from http://www.hautus.org/sdt-assistant.php.

572

Higham, P. A., Perfect, T. J., & Bruno, D. (2009). Investigating strength and frequency

573

effects in recognition memory using Type-2 signal detection theory. Journal of

574

Experimental Psychology: Learning, Memory, and Cognition, 35, 57–80.

575

Jeong, Y.-N., Kang, B.-A., Jeong, M.-J., Song, M.-J., & Lee, H.-S. (2016). Sensory

576

discrimination by consumers of multiple stimuli from a reference: Stimulus configuration

577

in A-Not AR and constant-ref. duo-trio superior to triangle and unspecified tetrad? Food

578

Quality and Preference, 47, 10-22.

579

Jeong, Y.-N., van Hout, D., Groeneschild, C., & Lee, H. S. (2017). Comparative

580

categorization method: Using 2-AFC strategy in constant-reference duo-trio for

581

discrimination of multiple stimuli from a reference. Food Quality and Preference, 62,

582

284-295.

583

584

Kim, H.-J. (2015). Investigation of consumer acceptance measure using two different SDT

585

indirect scaling approaches for sodium reduction. Master’s thesis. Ewha Womans

586

University, Seoul, South Korea.

587

Kim, I.-A., den-Hollander, E., & Lee, H.-S. (2018). Two-step rating-based ‘double-faced

588

applicability’ test for sensory analysis of spread products as an alternative to descriptive

589

analysis with trained panel. Food Research International, 105, 250-260.

590

Kim, I.-A., Hopkinson, A., van Hout, D., & Lee, H.-S. (2017a). A novel two-step rating-

591

based ‘double-faced applicability’ test. Part 1: Its performance in sample discrimination

592

in comparison to simple one-step applicability rating. Food Quality and Preference, 56,

593

189-200.

594

Kim, I.-A., Hopkinson, A., van Hout, D., & Lee, H.-S. (2017b). A novel two-step rating-

595

based ‘double-faced applicability’ test. Part 2: Introducing a novel measure of affect

596

magnitude (d′A) for profiling consumers’ product usage experience based on Signal

597

Detection Theory. Food Quality and Preference, 59, 141-149.

598

Kim, I-A., Yoon, J.-Y., & Lee, H-S. (2015). Measurement of Consumers’ Sensory

599

Discrimination and Preference: Efficiency of Preference-Difference Test Method

600

Utilizing 3-Point Preference Test and Same-Different Test. Food Science and

601

Biotechnology, 24, 1355-1366.

602

Kim, M.-A, Chae, J.-E., van Hout, D., & Lee, H.-S. (2014). Higher performance of constant-

603

reference duo–trio test incorporating affective reference framing in comparison with

604

triangle test. Food Quality and Preference, 32, 113–125.

605

606

Kim, M.-A., Dessirier, J.-M, van Hout, D., & Lee, H.-S. (2015). Consumer context-specific

607

sensory acceptance tests: Effects of a cognitive warm-up on affective product

608

discrimination. Food Quality and Preference 41, 163-171.

609

Kim, M.-A., & Lee, H.-S. (2015). Duo-trio difference-preference test with two replications:

610

Use of psychological biases for measuring meaningful preference. Journal of sensory

611

studies 30, 211-224.

612

Kim, M.-A., Sim, H.-M., & Lee, H.-S. (2015). Affective discrimination methodology:

613

Determination and use of a consumer-relevant sensory difference for food quality

614

maintenance. Food Research International 70, 47-54.

615

Kim, M.-A., Sim, H.-M., & Lee, H.-S. (2016). Idiographic duo–trio tests using a constant-

616

reference based on preference of each consumer: Sample presentation sequence in

617

difference test can be customized for individual consumers to reduce error. Food

618

Research International, 89, 454-462.

619

Kim, M.-A., van Hout, D., Dessirier, J.-M, & Lee, H.-S. (2018). Degree of satisfaction-

620

difference (DOSD) method for measuring consumer acceptance: A signal detection

621

measurement with higher reliability than hedonic scaling. Food Quality and Preference,

622

63, 28-37.

623

Kim, M.-A., van Hout, D., & Lee, H.-S. (2018). Degree of satisfaction-difference (DOSD)

624

method for measuring consumer acceptance: Comparative and absolute measures of

625

satisfaction based on signal detection theory. Food quality and preference, 68, 167-172.

626

Köster, E. P. (1998). Épreuves hédoniques. In F. Depledt, & F. Strigler (Eds.), Évaluation

627

sensorielle: manuel méthodologique (2ème éd., pp. 182–207). Paris, London, New York:

628

Lavoisier Tec & Doc.

629

Lee, H.-S., & O’Mahony, M. (2005). Sensory evaluation and marketing: measurement of a

630 631 632

consumer concept. Food Quality and Preference, 16, 227–235. Lee, H.-S., & van Hout, D. (2009). Quantification of sensory and food quality: the R-index analysis. Journal of Food Science, 74, R57–R64.

633

Lee, S.-Y. (2015). Develeopment of sensory measures for the perceived kokumi of chicken

634

bouillon products (Unpublished master’s thesis). Ewha Womans University, Seoul,

635

South Korea.

636 637

Macmillan, N. A., & Creelman, C. D. (2005). Detection theory: A user’s guide. (2nd ed.). Mahwah, New Jersey: Lawrence Erlbaum Associates.

638

Maniscalco, B., & Lau, H. (2012). A signal detection theoretic approach for estimating

639

metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21,

640

422–430.

641 642

Massoni, S., Gajdos, T., & Vergnaud, J. C. (2014). Confidence measurement in the light of signal detection theory. Frontiers in psychology, 5, 1455.

643

Mojet, J., & Köster, E. P. (1986). Investigation into the appreciation of three low-alcohol

644

beers. Utrecht University: Report Psychological Laboratory (Confidential research

645

report), Utrecht, Netherlands.

646

O’Mahony, M. (1992). Understanding discrimination tests: A user‐friendly treatment of

647

response bias, rating and ranking R‐index tests and their relationship to signal detection.

648

Journal of Sensory Studies, 7, 1–47.

649

650 651 652

Seo, J.-E. (2013). Development of a measure for the perceived holistic softness of personal products. Master’s thesis. Ewha Womans University, Seoul, South Korea. Stocks, M., Shepherd, D., Lee, H.-S., van Hout, D., & Hautus, M. J. (2017). Cognitive

653

decision strategies adopted by consumers in reminder difference tests: Influence of the

654

authenticity test. Food Research International, 97, 265-271.

655

Stolzenbach, S., Bredie, W. L., Christensen, R. H. B., & Byrne, D. V. (2016). Understanding

656

liking in relation to sensory characteristics, consumer concept associations, arousal

657

potential and “appropriateness for use” using apple juice as an application. Journal of

658

Sensory Studies, 31, 135-142.

659

Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273–286.

660

Thomson, D. M. H., Crocker, C., & Marketo, C. G. (2010). Linking sensory characteristics to

661

emotions: An example using dark chocolate. Food Quality and Preference, 21, 1117-

662

1125.

663

Research Highlights

664



A new consumer test method, the metacognitive authenticity test, was designed.

665



It included the metacognitive certainty response following sensory authenticity response.

666



A new group measure of d'MC was calculated and compared with the mean scores and d'.

667



These comparisons were performed based on both pooled data and segmented data.

668



d'MC corresponded to the mean scores and d' and could be interpreted easily.

669

670

Fig. 1. Illustration of the response classification of the metacognitive authenticity test

671

method for the computation of d' and d'MC estimations based on signal detection theory

672

(SDT): SDT response matrix for computation of (a) d' for S1 and S2 pair, (b) d'MC for S1

673

and (c) d'MC for S2.

674 675

Fig. 2. Illustration of data analysis for computation of d' and d'MC estimations: (a) d' from

676

the sensory authenticity response, and (b) d'MC from the metacognitive certainty response.

677 678

Table 1

679

Number of responses and tasting products for each consumer and across all consumers.

Main test

Type of

Total No. of responses for each product

responses For each consumer

For all consumers (pooled data using data analysis)

: 3 days with a week interval for

Sensory

15 responses x 3 days

authenticity

= 45 responses

45 sensory authenticity responses x 91 consumers = 4,095 responses

response

31

Calculating the mean scores and d' estimates

each product (5 sessions/day)

Metacogniti ve certainty response

15 responses x 3 days

45 metacognitive certainty responses x 91 consumers = 4,095 responses

= 45 responses

: 3 products

Calculating the d'MC estimates

were evaluated Total number

45

responses

of tastings

products

x

3

4095 responses x 3 products = 12,285 tasting

= 135 tasting

680

32

681

Table 2

682

Mean scores for each product (N=91, n=4,095)* Product

Mean score (SD)

P1

3.491b (1.784)

P2

4.048c (1.517)

P3

3.091a (1.513)

683

*N:

684

consumers) for each product

685

a-c

Total No. of consumers, n: Total No. of responses (45 sensory authenticity responses x Total No. of

Different superscripts indicate a significant difference between the mean scores in a column (p < 0.05).

686

33

687

Table 3

688

d' estimates for each product pair (N=91, n=8,190)* Product pair (Signal

‒

d' (95% CI)**

Noise) P2 ‒ P1

0.311a (0.268, 0.355)

P2 ‒ P3

0.629a (0.584, 0.673)

P1 ‒ P3

0.234a (0.190, 0.278)

689

*N:

690

consumers x Two products) for each product pair

691

**

692

a-c

693

test, p < 0.05).

Total No. of consumers, n: Total No. of responses (45 sensory authenticity responses x Total No. of

Values in bold represent a significant difference from zero. Different superscripts indicate a significant difference between the d' estimates in a column (chi-square

694 695 696

34

697

Table 4

698

d'MC estimates for each product (N=91, n=4,095)* (a) For each product

(b) For each product pair Difference

Product

d'MC (95% CI)**

d'MC

between products

P1

0.048b (-0.011, 0.106)

P2 ‒ P1

0.205

P2

0.253c (0.191, 0.314)

P2 ‒ P3

0.471

P1 ‒ P3

0.266

-0.218a

P3

(-0.278,

0.158)

699

*N:

700

consumers) for each product

701

**

702

a-c

703

test, p < 0.05).

-

Total No. of consumers, n: Total No. of responses (45 metacognitive certainty responses x Total No. of

Values in bold represent a significant difference from zero. Different superscripts indicate a significant difference between the d'

704

35

MC

estimates in a column (chi-square

705

Table 5

706

Mean scores for each product for each consumer group Group

Group 1 (N=38, n=1,710)*

Group 2 (N=35, n=1,575)

Group 3 (N=18, n=810)

Product

Mean score (SD)

P1

2.140a (1.306)

P2

4.716c (1.245)

P3

3.406b (1.530)

P1

4.685c (1.311)

P2

2.979a (1.332)

P3

3.097b (1.490)

P1

4.021b (1.497)

P2

4.716c (1.141)

P3

2.415a (1.290)

707

*N:

708

consumers) for each product

709

a-c

710

group (p < 0.05).

Total No. of consumers, n: Total No. of responses (45 sensory authenticity responses x Total No. of

Different superscripts indicate a significant difference between the mean scores in a column within each

711

36

712

Table 6

713

d' estimates for each product pair for each consumer group Product pair Group

(Signal ‒

d' (95% CI)**

Noise) P2 ‒ P1

1.786c (1.705, 1.867)

P2 ‒ P3

0.864b (0.795, 0.934)

Group 1 (N=37, n=3,330)*

P1 ‒ P3

P2 ‒ P1 Group 2 (N=35,

P2 ‒ P3

n=3,150)

Group 3 (N=19, n=1,710)

-0.945a

(-1.107,

-

(-1.313,

-

0.874) -1.236a 1.159) -0.042b

(-0.111,

0.028)

P1 ‒ P3

1.096c (1.021, 1.171)

P2 ‒ P1

0.542a (0.446, 0.638)

P2 ‒ P3

1.860c (1.741, 1.979)

P1 ‒ P3

1.088b (0.985, 1.191)

714

*N:

715

consumers x Two products) for each product pair

716

**

717

a-c

718

group (chi-square test, p < 0.05).

Total No. of consumers, n: Total No. of responses (45 sensory authenticity responses x Total No. of

Values in bold represent a significant difference from zero. Different superscripts indicate a significant difference between the d' estimates in a column within each

719 720

37

721

Table 7

722

d'MC estimates for each product for each consumer group (a) For each product

(b) For each product pair Difference

Group Product

d'MC (95% CI)**

between

d'MC

products

P1 Group 1 (N=37,

P2

n=1,665)* P3

P1 Group 2 P2 (N=35, n=1,575) P3

-0.716a

(-0.844,

-

0.587) 0.578c (0.448, 0.708) -0.091b

(-0.186,

0.003) 0.644b (0.502, 0.786) -0.176a

(-0.275,

-

(-0.236,

-

0.076) -0.140a 0.044)

P2 ‒ P1

1.294

P2 ‒ P3

0.669

P1 ‒ P3

-0.624

P2 ‒ P1

-0.820

P2 ‒ P3

-0.036

P1 ‒ P3

0.784

P1

0.239b (0.104, 0.373)

P2 ‒ P1

0.225

P2

0.463b (0.266, 0.661)

P2 ‒ P3

0.831

P1 ‒ P3

0.606

Group 3 (N=19, n=855)

P3

-0.367a

(-0.541,

-

0.193)

723

*N:

724

consumers) for each product

725

**

726

a-c

727

group (chi-square test, p < 0.05).

Total No. of consumers, n: Total No. of responses (45 metacognitive certainty responses x Total No. of

Values in bold represent a significant difference from zero. Different superscripts indicate a significant difference between the d'

728

38

MC

estimates in a column within each

729 730

Author Statement

731 732

In-Ah Kim: Conceptualization, Methodology, Investigation, Writing - Original Draft,

733

Writing- Reviewing and Editing. Ha-Yeon Cho: Conceptualization, Methodology,

734

Investigation, Writing - Original Draft. Michael J. Hautus: Software, Writing - Reviewing

735

and Editing. Hye-Seong Lee: Supervision, Conceptualization, Methodology, Writing -

736

Original Draft, Writing- Reviewing and Editing.

737

39