Reveal Preference Reversal in Consumer Preference for Sustainable Food Products

Food Quality and Preference 79 (2020) 103754

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Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual

Reveal Preference Reversal in Consumer Preference for Sustainable Food Products

T

Xuqi Chena, Zhifeng Gaob, , Brandon R. McFaddenc ⁎

a

Department of Agricultural and Resource Economics, University of Tennessee, United States Food and Resource Economics Department, University of Florida, United States c Applied Economics and Statistics, University of Delaware, United States b

ARTICLE INFO

ABSTRACT

Keywords: Stated preference elicitation Contingent ranking Contingent valuation Preference reversal Sustainable food Willingness-to-pay

This study investigates consumer preference for sustainable food products and finds the classic phenomenon of preference reversal. Using contingent ranking and contingent valuation methods, we investigate whether individuals’ rankings of a product are consistent with their willingness-to-pay (WTP). Results show that preference reversal exists for the evaluation of sustainable food products. Locally produced fresh produce receives the highest preference ranking, while organic products receive the highest price premium. The incidence of preference reversal is not uniformly distributed among different groups of respondents. In general, preference reversal is less frequent among respondents who are older and have a higher educational attainment. The results imply that preference reversal not only exists for subjects with explicit risks, such as the lottery, as indicated in the previous literature but also could occur for food products that consumers purchase on a regular basis in daily grocery shopping.

JEL classification: D120 Q13 D90

1. Introduction Obtaining consistency and accuracy of the estimates of consumer preference for food quality has been the focus of many researchers because it influences the ability of research to inform policy implementation and business strategies (Goldstein & Einhorn, 1987; Grether & Plott, 1979; MacDonald & Huth, 1989; Mowen & Gentry, 1980; Reilly, 1982; Seidl, 2002; Tversky & Thaler, 1990). The transitivity property in classic preference theory suggests that a product stated as being preferred by a person should receive a higher price bid by the same individual (MacDonald & Huth, 1989). However, preference reversal is frequently observed, which is characterized by the inconsistency in the preference elicited from two or more methods for the same individual (Cubitt, Munro, & Starmer, 2004; Lichtenstein & Slovic, 1971; Lindman, 1971). A large body of research using a lottery as the focal subject has found that many people stated that they preferred one product (e.g., A) over another (e.g., B), yet stated a lower bid price for the preferred product (e.g., bid higher for B than for A) (Grether & Plott, 1979; MacDonald & Huth, 1989; Tversky & Thaler, 1990). Similar studies on preference reversal failed to reach any concrete conclusions about the factors contributing to such phenomenon (Bostic, Herrnstein, & Luce, 1990; Grether & Plott, 1979; Lewandowski,

⁎

2014; Loomes & Sugden, 1983; Pommerehne, Schneider, & Zweifel, 1982; Slovic & Lichtenstein, 1983). The lottery is a special product in economics that is usually used for the study of consumer behavior under uncertainty or risk. However, most consumer preference studies focus on normal goods with much lower levels of risk. Thus, the fundamental question is whether preference reversal exists for products associated with less explicit risks. Answers to this question would enhance our understanding of this controversial behavior, provide better insights to preference elicitations, and bring in broader applications to the preference valuation methods. To effectively study preference reversal in a broader context, we focus on sustainable food products in the food market. One significant advantage of using food products is that consumers purchase these products on a regular basis, so the products have enough exposure for research. Although they do not have an obvious risk as in a lottery, different sustainable food products may be associated with various levels of perceived risk, which may induce different responses when consumers are stating food preferences. In addition, past research has demonstrated heterogeneous preference among consumers (Chen, Gao, Swisher, House, & Zhao, 2018; Grebitus, Lusk, & Nayga, 2013; Onken, Bernard, & Pesek, 2011; Van Loo, Caputo, Nayga, & Verbeke, 2014).

Corresponding author at: P.O. Box 110240, Gainesville, FL 32611-0240, United States. E-mail address: [email protected] (Z. Gao).

https://doi.org/10.1016/j.foodqual.2019.103754 Received 20 September 2018; Received in revised form 28 March 2019; Accepted 8 August 2019 Available online 14 August 2019 0950-3293/ © 2019 Elsevier Ltd. All rights reserved.

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Therefore, if consumer preference reversal exists for sustainable food products, the reversal may also differ for different consumers. In this study, we elicit consumer preference for three types of valueadded fresh strawberries: organic, locally produced, and naturally grown. The strawberry, a popular fruit in the U.S. consumers’ diet, almost doubled in U.S. per-capita consumption over the past two decades, reaching a consumption of 8.43 lb per capita in 2017 (Statista.com, 2019). Research has also shown that the consumption of fresh produce, such as fresh strawberries, is positively correlated with income (Dimitri, Oberholtzer, Zive, & Sandolo, 2015; Giskes, Turrell, Patterson, & Newman, 2002; Polacsek et al., 2018). Therefore, the fresh strawberry is a good focal product to study the preference reversal for the case of normal goods. In addition, sustainable products are becoming more popular in the market and have drawn a significant amount of attention in consumer preference studies (e.g., Chen, 2007; Denver & Jensen, 2014; Feldmann & Hamm, 2015; Lee & Yun, 2015). Through the estimation of consumer preference for the three chosen sustainable products, the study aims to determine 1) whether the preference reversal phenomenon exists in the food product’s content; 2) if preference reversal exists, how demographics and behavioral factors affect the intensity of preference reversal; and 3) the implications of preference reversal for future sustainable food markets. With preference reversal being extensively investigated in the context of lotteries (Alós-Ferrer, Granić, Kern, & Wagner, 2016; Grether & Plott, 1979; MacDonald & Huth, 1989; Tversky & Thaler, 1990; Yukalov & Sornette, 2015), this study contributes to the literature by examining preference reversal using food products with less explicit risks. The study also contributes to the literature by developing an index to measure the level of inconsistency and determine the factors contributing to the preference reversal. Despite the attempts by previous research, many have failed to quantify the inconsistency and disclose the influential factors behind such behavior (Merino-Castello, 2003; Mowen & Gentry, 1980; Pommerehne et al., 1982; Tversky, Slovic, & Kahneman, 1990).

the past framework for consumers’ valuation of alternatives using the concept of willingness-to-pay (WTP). The preference theory suggests that if A is stated to be preferred to B, then the WTP for A should be higher than for B (detailed proof is provided in Appendix A). For food products that do not carry explicit risks, WTP has been widely elicited by two popular stated-preference valuation methods: contingent ranking (CR) and contingent valuation (CV) (Shi, Gao, & Chen, 2014; Tecle & Yitayew, 1990). The CR allows individuals to rank alternatives from the most favorite to the least favorite, while the CV method estimates the WTP from consumers’ dichotomous choices or asks consumers to state their WTP directly for different alternatives (Bateman, Cole, Georgiou, & Hadley, 2006; Batte, Hooker, Haab, & Beaverson, 2007; Cicia, Del Giudice, & Scarpa, 2002; Foster & Mourato, 2002; Lusk & Briggeman, 2009). Despite the challenges of the CR and CV methods, such as the decreasing stability of ranking information when ranking four or more alternatives and the potential hypothetical bias, many researchers chose to use them due to their simplicity and ease of application to large data (Ben-Akiva, Morikawa, & Shiroishi, 1992; Brookshire, Eubanks, & Randall, 1983; Chen et al., 2018; Cummings, Brookshire, Schulze, Bishop, & Arrow, 1986; Hanemann, 1994). Both CR and CV have also been used in previous preference reversal studies with lotteries (Batte et al., 2007; Ben-Akiva et al., 1992; Boccaletti & Moro, 2000; Brans & Vincke, 1985; Lusk & Briggeman, 2009). However, few studies have been conducted on the preference reversal with normal goods such as sustainable food products. Therefore, we hypothesize that preference reversal would occur in the food market when the preference is elicited with CR and CV. Hypothesis 1:. Preference reversal exists in the context of the normal goods (e.g., sustainable food products) when preference is elicited by CR and CV. 2.2. Preference for the sustainable food market Due to ever-increasing awareness of and desire for more healthy eating, foods produced with sustainable practices are becoming more popular because they are believed to be safer/healthier due to the use of less hazardous or chemical inputs (Forbes, Cohen, Cullen, Wratten, & Fountain, 2009; Friel, Barosh, & Lawrence, 2014; Gao, Li, Bai, & Fu, 2017; Geissler & Powers, 2017; Losasso et al., 2012; Miller & Cassady, 2012; Reisch, Eberle, & Lorek, 2013; Tilman, 1999; Verplanken & Faes, 1999; Wang & Yue, 2017). The US organic market, which holds the largest market share in the world continues to increase (Lee & Yun, 2015). For example, total sales in the US organic market reached $43.3 billion in 2015, which was 11% higher than in 2014 (Willer & Lernoud, 2016). Food produced using sustainable practices restricts or does not allow the use of chemical fertilizers or pesticides, which may lead to reduced food risks and improved food quality (Batte et al., 2007; Feenstra, 1997; Hinrichs, 2000; Hughner, McDonagh, Prothero, Shultz, & Stanton, 2007; Winter, 2003). However, the stricter standards and additional labeling requirements increase the production cost of sustainable foods (Caswell, 1998; De Ponti, Rijk, & Van Ittersum, 2012; Golan, Kuchler, Mitchell, Greene, & Jessup, 2001; Kremen, Greene, & Hanson, 2004; Olson, Langley, & Heady, 1982). Therefore, a frequently asked question is whether sustainable practices would be compensated for its contribution to improved food quality and reduced food risks. If preference reversal exists, there is a chance that consumers may state that they “prefer” sustainable food products while placing a higher price premium for other food products. As a result, the preference for some of the sustainable food products would not be transferred into real purchase power (MacDonald & Huth, 1989). Knowing whether consumers are truly willing to pay more for sustainable food products seems to be more important than the “stated prefer” or “ranked higher” for the promotion of sustainable food and agricultural system. To examine the preference reversal in the context of sustainable food, we use the USDA certified organic (organic), Certified Naturally

2. Hypothesis development 2.1. Preference reversal, contingent ranking, and contingent valuation Building on the premises of Lichtenstein and Slovic (1971) and Lindman (1971), preference reversal has been investigated extensively in the context of lotteries (Grether & Plott, 1979; MacDonald & Huth, 1989; Tversky & Thaler, 1990). Based on classical consumer theory, if an individual chose lottery A over lottery B, then preference transitivity would anticipate that the same individual would place a higher price bid for A than for B (MacDonald & Huth, 1989). However, past research has shown that when offered the choice between two lotteries, one with a high probability of winning a moderate amount of money (a P bet) and the other with a low probability of winning a relatively large amount of money (a $ bet), many subjects chose the P bet over the $ bet in the direct paired choice (ranked P bet higher), but then put a higher price bid for the $ bet than for the P bet (MacDonald & Huth, 1989). This is the most common and predictable form of preference reversal found by previous researchers. Preference reversal has been discovered with tasks under a variety of different environments (Seidl, 2002). Early studies by Grether and Plott (1979) tried to control all the suspicious factors in a sequential experiment to test ten hypotheses related to preference reversal; however, preference reversal remained existence. Following Grether and Plott (1979), other researchers conducted similar studies (Bostic et al., 1990; Lewandowski, 2014; Pommerehne et al., 1982; Seidl, 2002; Slovic & Lichtenstein, 1983). They all concluded that while the occurrence of preference reversal exists, they could not reach any decisive conclusions about the factors contributing to such a phenomenon. Grether and Plott (1979) and Tversky and Thaler (1990) stated why preference reversal is a violation of the preference theory. We extend 2

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study also extends the application of the theoretical framework to determine the existence of preference reversal in a different context. The empirical significance comes from the essential insights that the study could provide by showing how consumers would evaluate sustainable food products, and whether the stated preference could turn into an actual purchase power. We develop an index of preference reversals that not only helps identify the presence/absence of preference reversal but also measures the intensity.

Grown (natural/CNG), and locally produced (local) terms to represent three types of sustainable food products. Although these three types of products do not have explicit risks (as with the lottery), consumers may perceive various levels of risk associated with organic, natural, and local, in descending order, from two main risk aspects (potential food risk and credibility of standards). First, from the potential food risk aspect, both organic and natural products prevent the use of some substances that are perceived to be harmful to human health or the environment, while local food products do not have these requirements. Organic products must be produced without excluded methods (e.g., genetic engineering, ionizing radiation, or sewage sludge), and only use allowed substances (United States Department of Agriculture, Agricultural Marketing Service (USDAAMS), 2019). Consumers, in general, perceive organic food to be safer and healthier and with reduced food risks (Batte et al., 2007; de Magistris & Gracia, 2008; Hughner et al., 2007; Xie, Gao, Swisher, & Zhao, 2016). Natural products must be produced following the organic standard in harmony with nature by local farmers (Grown, 2015). For local food, locality indicates only the proximity between its production origin and the marketplace, which does not imply anything related to the production methods or the substances being used (de Magistris & Gracia, 2008; Hinrichs, 2000). Studies show that consumers are willing to pay more for local food because of the perceived freshness and the intention to support the local economy. No significant evidence shows that local food could contribute to food safety or food security (Feenstra, 1997; Hinrichs, 2000; Winter, 2003). Second, from the credibility of standard aspect, organic seems to be the most credible. The organic standard is developed by the United State Department of Agriculture (USDA), and its production process is overseen by the certifying agents authorized by the USDA. In addition, the USDA monitors the use of the organic standard seal, which makes the organic certification the most credible. CNG (natural) rules and standards are developed by a group of farmers, and CNG certification is based upon a peer-review certification system (Grown, 2015). Therefore, consumers may consider the CNG certificate to be less credible than the organic certificate because it is not regulated and monitored by government agents (Darnall, Ji, & Vázquez-Brust, 2018; Sønderskov & Daugbjerg, 2011). Unlike organic and CNG (natural), local has the highest level of credibility risk because there is no consistent standards nor certification for local foods. Local food can be defined vaguely as food produced in a county or within 400 miles of the marketplace (de Magistris & Gracia, 2008; Eriksen, 2013; Feldmann & Hamm, 2015; Hinrichs, 2000). Based on the aforementioned discussion, we propose the second hypothesis below:

3. Materials and methods 3.1. Data collection A national representative sample of 2,499 respondents in the United States was collected via an online survey in June and July of 2014. The survey instruments were approved by the University of Florida IRB office. The survey was hosted on Qualtrics. An international professional market research company, Toluna, Inc., helped recruit the participants by distributing the survey to its opt-in national consumer panels. The informed consent was presented to participants before they answered the first question of the survey. Respondents could voluntarily participate in the survey by clicking the Agree button at the bottom of the informed consent. To qualify for the survey, respondents had to be aged 18 years or older, had to be the primary household grocery shopper (purchase grocery more than 50% of the time), and had to have purchased fresh strawberries in the past six months. The survey consists of several parts that ask respondents questions about their socio-demographics; general purchasing habits and purchasing behavior for fresh strawberries; and their ranking and WTP for organic, locally produced, and naturally grown strawberries using CR and CV, respectively. Prior to the CR and CV questions, respondents are informed about the definitions of organic, locally produced, and naturally grown strawberries. Following the recommendations of other researchers using CR questions, respondents are asked to rank the three types of berries based on preference from the most favorite (indicated by number three) to the least favorite (indicated by number one) (Bouyssou, 1992; Cook & Kress, 1985; di Pierro, Khu, & Savic, 2007; Furnkranz & Hullermeier, 2003). To be consistent with previous studies on preference reversal, the price is not included as one of the attributes in the CR questions (Grether & Plott, 1979; Lewandowski, 2014; MacDonald & Huth, 1989; Seidl, 2002). After the CR questions, open-ended CV questions are used to estimate consumer WTP for a 16-ounce clamshell package of fresh strawberries. The berries are presented randomly to avoid any order effect. To minimize the hypothetical bias and lower the difficulty related to the “name your own price” task, a cheap-talk script is used, and the reference price of conventional fresh strawberries is also provided (Carlsson & Martinsson, 2006; Lusk, 2003; Murphy, Stevens, & Weatherhead, 2005; Shi et al., 2014). The CV questions used in the study mimic the bidding tasks used in some of the previous preference reversal studies. The CR and CV questions are hypothetical, and the cheap-talk script is used to reduce the potential hypothetical bias, thus providing reliable information to test the preference reversal hypotheses in this study. The hypothetical nature in the current study may also help reduce the occurrence of preference reversals. Research shows that preference reversals are more common when actual monetary incentives are involved than in cases where pure hypothetical questions are asked (Grether & Plott, 1979; Lichtenstein & Slovic, 1971; Tversky & Thaler, 1990).

Hypothesis 2. In the sustainable food market, preference reversal, if it occurs, shares a similar “common” and “predictable” form as indicated in previous studies using lotteries. That is, participants would state their preference for organic food (P bet) due to its low-risk label, while willing to pay a higher price for CNG and local foods ($ bet) with relatively higher risks. Lastly, the previous literature shows that socio-demographics and purchase habits are significant factors leading to the heterogeneous preferences (Adams & Salois, 2010; Bellows, Alcaraz, & Hallman, 2010; Chen et al., 2018; James, Rickard, & Rossman, 2009; Onken et al., 2011). This implies that socio-demographics may also affect consumers’ preference reversal behavior if it exists. Therefore, the third hypothesis is set up as follows: Hypothesis 3. Participants’ socio-demographics and purchasing habits will have a significant impact on reversed preference.

3.2. Methodology and model specification

This study contributes to the literature in both theoretical and empirical aspects. The theoretical substance lies in the fact that it explores whether the two preference elicitation methods would result in consistent outcomes (as suggested by the classic preference theory) or inconsistent results (as demonstrated in previous lottery studies). The

The sample data are divided into two groups based on the consistency of responses between the CR and CV questions: consistent preference group and inconsistent preference group. To determine the group affiliation, the product of the difference in WTP and the 3

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difference in ranking of two products (e.g., organic and local, organic and natural1) is used and can be mathematically calculated by

Productorganic, local = (WTPorganic

WTPlocal ) (Rankingorganic

Table 1 Demographics of the sample (N = 2,499).

Rankinglocal ) (1)

Productorganic,natural = (WTPorganic

WTPnatural ) (Rankingorganic

Rankingnatural)

(2) If the Productorganic, i (i = local, natural) is positive, it indicates the respondent expresses a consistent preference. If the Product is negative, it indicates that the respondent is inconsistent in answering the CR and CV questions. The respondent may have ranked product A (e.g., organic) as preferable to product B (e.g., local/natural), while giving a lower WTP for A than for B; or vice versa. If the Product equals zero (Producti,j = 0 ), there are several possible cases as summarized below: Case I: Only the difference in WTP estimates equals zero (WTPi WTPj = 0) Case II: Only the difference in ranking equals zero (Ranking i Ranking j = 0) Case III: Both the difference in WTP estimates and the difference in ranking equal to zero(WTPi WTPj = 0 and Ranking i Ranking j = 0) Ranking i WTPi

WTPj = 0

WTPi

WTPj

0

Ranking j

0

Weakly Inconsistent (Case I) Consistency to be decided (Case IV)

Ranking i

WTPi Rankingi

Ranking j = 0

Consistent (Case III) Weakly Inconsistent (Case II)

Female Age Age (18–34 years old)b Age (35–49 years old) Age (50–64 years old) Age (65–79 years old) Age (> =80 years old)

56.54

51.0

26.6 26.3 28.8 16.9 1.4

23.3 19.3 19.7 10.7 3.5

Education Some High School/High School Graduate Two-year College Degree Bachelor’s Degree Post-graduate Degree

22.6 35.9 27.8 13.7

44.8 27.4 18.0 9.8

Number Kids # of Kids in the # of Kids in the # of Kids in the # of Kids in the

65.0 17.3 11.5 6.2

Household = 0 Household = 1 Household = 2 Household > = 3

10.2 12.0 13.7 15.3 22.0 12.4 9.8 2.7 1.9 $13.34 (10.58)c

12.6 11.0 10.1 13.1 17.0 11.5 13.4 5.7 5.6

Notes: a Population Data Source: US Census Bureau, Current Population Survey, Annual Social and Economic Supplement (2014). b There were 14 age categories in the survey; for reporting purposes, it was combined into a total of 5 categories. c The number in parentheses is the standard deviation.

Since there are three products in the study, the possible value for the ranking difference would be five integers: 2 , 1, 0 2, 1, and 2. The value of WTP difference, however, can be any real number. Inconsistent behavior would generate a reversed order of WTP and ranking, thus a negative sign is added to make the positive interval represent the “inconsistent” level (i.e., the larger the number, the greater the inconsistency). However, the respondents in the inconsistent group may have consistent answers for one of the two comparison groups (e.g., organic or natural), which causes the existence of negative ratios in the inconsistent group. Since the focus of the study is on the inconsistent behavior regarding ranking and WTP estimates, the regression analysis only includes respondents who are inconsistent in both groups (positive interval of the ratio). Due to the nature of how the ratios are calculated, the respondents in Case II who give the same ranking for organic and local (48 respondents, accounting for 2.54% of 1,889 respondents) and for organic and natural (55 respondents, accounting for 2.91% of 1,889 respondents) are excluded from the ratio calculation and from the regression analysis. A system of equations of ratio measurements is estimated to determine the factors contributing to the preference reversal. These equations are estimated with a multinomial tobit model to account for the potential correlations in the error terms of equations and to adjust the left-censored ratio at zero. The ratio is left-censored at zero because

WTPj Rankingj

Sample Population (% if not remark) (% if not remark)a

Annual Household Income Income (< $14,999) Income ($15,000–4,999) Income ($25,000–34,999) Income ($35,000–49,999) Income ($50,000–74,999) Income ($75,000–99,999) Income ($100,000–149,999) Income ($150,000–199,999) Income (> =200,000) Average Expenditure on Fresh Strawberry per month

In Case I, only the difference in WTP estimates equals zero, while the difference in ranking does not equal zero. This indicates the CR and CV are inconsistent in representing the preference; thus, those respondents are weakly inconsistent. The same rule applies to Case II when zerodifference exists in the ranking but not in the WTP estimates. When both the difference in ranking and WTP estimates equal zero (Case III), preferences are consistent across the CR and CV. Case III is a scenario where respondents are indifferent between the two products. In Case IV, when neither the difference in ranking nor the WTP estimates equal zero, the consistency of preference remains to be decided. For respondents in Case IV, those who give an entirely reversed order of ranking and WTP estimates (e.g., Producti, j with a negative sign ), are considered strictly inconsistent. Therefore, the inconsistent group includes both strictly inconsistent (inconsistent part of Case IV) and weakly inconsistent (Case I, II) respondents. Since there are two comparative groups of fresh strawberries (organic vs. local, and organic vs. natural) in the study, respondents are classified into the inconsistent group if any inconsistent response is found in either of the comparative groups. The consistent preference group includes those respondents in Case III where both differences in CR and CV equal zero and the consistent part in Case IV. Because there are two comparative groups, the respondents must give consistent responses in both groups to be classified into the consistent group. To quantify the level of inconsistency between ranking and WTP estimates, the negative ratio of the difference in WTP estimates and ranking is calculated:

Ratioi, j =

Variable

(3)

WTPi and Rankingi represent WTP and ranking for product i, respectively. For the products in this study, i = organic, j = local or natural. 1

The organic product is used as the benchmark since the label is nationally standardized, while the local and natural labels are not yet standardized by the government agency. To check the robustness of the results, local and CNG products are set as the base product and the results are presented in the discussion section.

2 The zero comes from the respondents who gave the same ranking for organic versus local or natural. In this study, we allowed the respondents to give the same ranking to different products.

4

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some people give the same WTP for different types of strawberries, but rank them differently, therefore resulting in a zero ratio. Particularly, the regression of the ratio for the case of organic/local and organic/ natural strawberries can be defined as

Ratioorganic, local, i =

i

+

i

Xi +

i

Ranking _Differenceorganic, local, i +

option. Around one-third (31.97%) of respondents rank naturally grown as their most favorite option and only 22.53% select organic as the most favorite choice. As for the second most favorite, half (around 50%) of the respondents choose the naturally grown, 27.93% of the respondents select locally produced, and 20.41% choose the organic. Over half (around 57%) of the respondents rank organic as the least favorite choice among the three alternatives. However, the WTP estimates from the contingent valuation in Table 2 imply a quite different pattern from the ranking results. The average WTP for organic strawberries is the highest ($3.14), followed by that for naturally grown ($3.05) and locally produced ($3.03). When the respondents are divided into preference consistent and inconsistent groups, only 24.41% (610) of the respondents belong to the consistent group. Among the consistent group, WTP for organic is $2.88, which is significantly lower than that for local ($3.16) and natural ($3.21). The inconsistent group includes 75.59% of the 1,889 respondents. The average WTP for organic is the highest at $3.23, followed by natural ($3.00) and local ($2.99). Below are two summary tables showing the number of respondents in each of the cases mentioned before. For the group of the organic vs. local case:For the group of the organic vs. natural case:

i

(4)

Ratioorganic, natural, i =

' i

+

' i

Xi +

' i

Ranking _Differenceorganic , natural, i +

' i

(5) where Ratioorganic, j, i (j = local, natural) is the ratio defined in equation (3) between organic and local/natural strawberries; Xi is a vector of demographics and purchase habit/behavior of individual i ; Ranking _Differenceorganic, j, i (j = local, natural) is the vector variable indicating the difference in ranking between organic and local/natural strawberries. 4. Results The socio-demographics of the sample and the US census statistics in the same year are summarized in Table 1. Around 56.5% of the reRankingOrg

WTPOrg

WTPLocal = 0

WTPOrg

WTPLocal

0

WTPNatural = 0

WTPOrg

WTPNatural

0

0

Weakly Inconsistent 1056 (Case I) Consistency to be decided 1368 (Case IV)

RankingOrg WTPOrg

RankingLoc

RankingNat

Weakly Inconsistent 1187 (Case I) Consistency to be decided 1228 (Case IV)

RankingOrg

RankingLoc = 0

Consistent 37 (Case III) Weakly Inconsistent 38 (Case II)

0

RankingOrg

RankingNat = 0

Consistent 44 (Case III) Weakly Inconsistent 40 (Case II)

Figs. 2 and 3 further illustrate the ranking distributions in the consistent (Case III and the consistent part of Case IV) and inconsistent groups (Case I, Case II, and the inconsistent part of Case IV). The ranking distribution of natural is similar in both groups. However, the ranking distributions of organic and local are significantly different in the two groups. The difference is particularly considerable for organic. Approximately 43% of the respondents in the consistent group rank organic as the most favorite option, while only 16% of the respondents in the inconsistent group rank organic as the most favorite option. Although only about 8% of the respondents in the consistent group rank organic as the second favorite option, this number increases substantially to almost 25% in the inconsistent group. Approximately 38% of the respondents in the consistent group rank local as the most favorite option, while over half of the respondents (around 52%) in the inconsistent group state local as the most favorite option. The results suggest that the first hypothesis (H1) is not rejected because preference reversal is found for organic vs. natural (CNG), and organic vs. local when the preference is elicited by CR and CV. However, the results suggest a rejection of the second hypothesis (H2). We found that participants would state that they prefer the organic and are willing to pay a higher price for the local and natural, which counteracts the findings in most previous studies on preference reversal.

spondents in our sample are female, which is slightly higher than the general population (51.0%). The sample is younger in age distribution compared with the general population. The median education level of the respondents is a two-year college degree (or equivalent), and the median annual household income is in the range of $35,000 to $49,999. The sample has more respondents with a higher educational attainment, with a two-year college degree or above. Over half (around 65%) of the respondents currently do not have any minor-aged children (under the age of 18) in the household. The average expenditure on fresh strawberries per month is $13.34. One of the major reasons for the sample in the current study to have some differences in the socio-demographics from the census is because the study requires the respondents to be at least 18 years or older and be the primary shopper of the household. The national census data do not have such a restriction. The fact that the sample has more females and more respondents with a higher educational attainment than the general population is consistent with what was found in previous research that also use online surveys for consumer preference studies (Chen et al., 2016; Gao, House, & Xie, 2016; Heng, Peterson, & Li, 2013). 4.1. Contingent ranking and contingent valuation

4.2. Statistics of ranking difference and ratio

The distribution of the contingent ranking of the three types of fresh strawberries is presented in Fig. 1. It shows that nearly half (around 48%) of the respondents rank local strawberries as their most favorite

The ranking difference is calculated based on the difference 5

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60% 50% 40%

30% 20% 10% 0%

Least favorite 57.06% 23.85% 18.21%

Organic Local Natural

Second most favorite 20.41% 27.93% 49.82%

Most favorite 22.53% 48.22% 31.97%

Fig. 1. Ranking distribution for three types of fresh strawberries.

between the ranking of two comparable groups of strawberries. Fig. 4 presents the distribution of the ranking difference between organic and local in the inconsistent group. The results show that in the inconsistent group, about 40% rank local as the most favorite option while ranking organic as the least favorite option, which is the case when the ranking difference has the value of 2 . Meanwhile, approximately 30% of the respondents rank local one position higher than organic. Only around 9% of the respondents rank organic as their favorite option and local as the least favorite option, which is the case when the ranking difference takes the value of 2. Fig. 5 shows the distribution of the ranking difference between organic and natural. Around 19% of the respondents rank natural as the most favorite option and organic as the least favorite option, and over half of the respondents (around 53%) rank natural (CNG) one position higher than organic. In addition, only around 6% of respondents rank

Table 2 Consumer WTP for three types of sustainable fresh strawberries. Type

Organic

Local

Natural

Average (N = 2,499) Consistent Groupa (N = 610) Inconsistent Groupb (N = 1889)

$3.14 (1.29) $2.88 (1.54) $3.23 (1.19)

$3.03 (1.03) $3.16 (0.98) $2.99 (1.04)

$3.05 (1.06) $3.21 (0.99) $3.00 (1.08)

Notes: The numbers in parentheses are standard deviations. a Consistent group included those respondents whose behaviors were strictly consistent and weakly consistent in both two comparative groups of strawberries (Case III and the consistent part of Case IV). b Inconsistent group included those respondents whose behaviors were strictly inconsistent and weakly inconsistent in either one of the two comparative groups of strawberries (Case I, Case II, and the inconsistent part of Case IV).

60% 50% 40% 30% 20% 10% 0% Organic Local Natural

Least favorite 49.84% 27.87% 20.00%

Second most favorite 7.54% 34.26% 54.75%

Most favorite 42.62% 37.87% 25.25%

Fig. 2. Ranking distribution for three types of fresh strawberries in the consistent group. 6

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70% 60% 50% 40% 30% 20%

10% 0%

Least favorite 59.40% 22.55% 17.63%

Organic Local Natural

Second most favorite 24.56% 25.89% 48.23%

Most favorite 16.04% 51.56% 34.15%

Fig. 3. Ranking distribution for three types of fresh strawberries in the inconsistent group.

45% 40%

natural (CNG) as the least favorite option and organic as the most favorite option. The ratio of the difference in WTP estimates and ranking is used as the quantitative measurement of the preference reversal. Because the study focuses on the inconsistent behavior when answering CR and CV questions, our regression analysis only uses the data from the inconsistent group. The results of the calculated ratio are presented in Table 3. The mean ratio for the organic vs. local group is 0.11, with a range from −3.99 to 4 , while the mean ratio for the organic vs. natural group is 0.15, with a range from −4 to 3.15. It indicates that the answers in the organic vs. natural group might be slightly more inconsistent than those in the organic vs. local group. Both groups have a ratio with the negative intervals, which implies the occurrence of preference consistency in the other group (according to (3)). This is possible because the respondents’ preference would be classified as inconsistent if they show preference reversal in either organic vs. local, or organic vs. natural comparison. For instance, a specific respondent might have given a consistent answer in the CR and CV questions for the organic vs. local strawberries, while giving an inconsistent answer for the organic vs. natural strawberries. That respondent would be classified into the preference inconsistent group, with a negative RatioOrganic, Local (consistent preference) and a positive RatioOrganic, Natural (preference reversal). The opposite situation could occur as well, which leads to a positive RatioOrganic, Local but a negative RatioOrganic, Natural . In the regression analysis, only the non-negative interval of the ratio is considered since the study focuses on preference reversal, meaning that respondents who are inconsistent in both groups are used for the regression analysis.

39.54%

35%

29.91%

30% 25%

19.38%

20%

15% 8.63%

10% 2.54%

5% 0%

-2

-1

0

1

2

Fig. 4. The distribution of the ranking difference between organic and local in the inconsistent group (ranking of organic minus ranking of local).

60%

52.62%

50% 40% 30% 20%

19.27%

19.11%

10% 0%

6.09%

2.91% -2

-1

0

1

4.3. Regression results The regression estimates of the factors affecting preference reversal are presented in Table 4. Variables with significant coefficients in the regression for the inconsistent ratio of organic vs. local and organic vs.

2

Fig. 5. The distribution of the ranking difference between organic and natural in the inconsistent group (ranking of organic minus ranking of natural).

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Table 3 Ratioi, j statistics in the inconsistent group. Type

Organic/Local a

Inconsistent Group (Organic vs. Local, N = 1841 )

0.11 (0.71)

Inconsistent Group (Organic vs. Natural, N = 1834b)

Organic/Natural

Range [−3.99, 4.00]

0.15 (0.65)

[−4.00, 3.15]

Notes: The numbers in parentheses are standard deviations. a The inconsistent group originally included 1,889 responses in total. Because 48 (2.54%) of them gave the same ranking for organic and local, the ratio was calculated for the remaining 1,841 respondents. b The inconsistent group originally included 1,889 responses in total. Because 55 (2.91%) of them gave the same ranking for organic and natural, the ratio was calculated for the remaining 1,834 respondents.

5. Discussion Table 4 Regression results of WTP/ranking ratio using multivariate tobit model.a Independent Variables

This study confirms the existence of preference reversal in a popular food product (sustainable fresh strawberries). This section focuses on three major issues. First, we discussed the robustness of the results by using different products as the base for comparison. Second, we discussed the implications of the results. Third, we discussed the limitations as well as potential future research directions.

Dependent Variable: (WTP/Ranking ratio for strawberries) Organic/Local

Organic/Natural

Intercept Age Gender Income Education Kids Strawberry Expenditure

−1.249*** −0.033** 0.139 −0.005 −0.059** 0.005 0.016***

−2.062*** −0.014 0.117 −0.025 −0.096*** 0.026 0.019***

Ranking _Difference −2 −1 1 2 Sigma.WTP _Rho # of Observations

Organic-Local 0.365** 0.911*** 0.162 0 1.395*** 0.863*** 1366

Organic-Natural 1.160*** 1.421*** 1.143*** 0 1.461***

5.1. Robustness check In order to check the robustness of the major results, we conduct two additional analyses by changing the base product. We separate the analysis of robustness check into two parts. The first part tests the instances of preference reversals when the base product is changed from organic to local or natural. The second part conducts the regression analyses of inconsistency behavior when the base product is changed to local or natural. With three products in the study, there are three pairs of comparative groups in total: organic vs. local, organic vs. natural, and local vs. natural. Appendix Table 1 summarizes all the groups when different products are used as the base product, as well as the consistent/inconsistent count for each case. In general, when the base product is changed to local or natural, there is no significant difference in the inconsistent frequency based on the chi-square test (pvalue = 0.29). Therefore, preference reversals still exist in a similar pattern. As for the robustness of the regression results, we run two regressions of the inconsistent levels using local and natural as the base product, respectively, and the results are presented in Table Appendix 2 and Appendix 3. The factors influencing the consumer inconsistent behavior remain the same in terms of both signs and magnitude. This also confirms the robustness of the original results when organic is used as the base product, which also proves that changing the base product to local or natural does not change the results of both inconsistent behaviors and levels of inconsistency. The prior assumptions about the riskiness of the products do not affect the preference reversal, nor the level of inconsistent behavior.

Notes: *, **, and *** indicate significant at 10%, 5% and 1% level of significance. a This regression only applies to the inconsistent group since the ratio of WTP/ranking is used as the preference reversal measurement. The organic is used as the base product.

natural include education, strawberry expenditure, and ranking difference. Respondents with a higher level of education tend to be more consistent, while respondents who spent more money on strawberries are more likely to be inconsistent. The sign of coefficients of the ranking difference is consistent with the theoretical expectation (see Appendix. B), indicating that the more distinct the ranking difference is, the more obvious the preference reversal would be (only applies to respondents in the inconsistent preference group). Results for the inconsistent ratio of organic vs. local indicate that older respondents are more consistent in stating preference in the CR and CV. The correlation coefficients of the errors of Eqs. (4) and (5) are significant and positive, implying that if a respondent was inconsistent for one pair of products (i.e., organic vs. local), the respondent would also more likely be inconsistent for the other pair of products (i.e., organic vs. natural). Therefore, the third hypothesis (H3) is not rejected as participants’ socio-demographics and purchase habits affect the behavior of preference reversal.

5.2. Key implications Our research is among the first to investigate preference reversal in normal goods, specifically in sustainable food content. Although there needs to be more investigation on whether preference reversal would

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berries as underpriced.3 The overpricing or underpricing issues would cause preferences to be intransitive, and therefore cause preference reversal between organic and local products (Lewandowski, 2014; Seidl, 2002; Tversky et al., 1990). This factor may also explain why food products in this study have more occurrences of preference reversal than the lottery case in previous studies. Compared to a lottery, respondents in the current study are more familiar with food products, so it is easier for them to sense whether certain food products are overpriced or underpriced. The findings of this study would provide essential empirical implications for the food industry, policymakers, and researchers focusing on consumer preferences. It is noticeable that most respondents in this study prefer the local and naturally grown foods, ranking these foods higher than organic foods. Even though they hope to contribute to the local economy or the development of certified naturally grown label, their higher WTP for organic products suggests stronger monetary support for the organic market (Hinrichs, 2000; Winter, 2003). This implies that being chosen as the most favorite product does not automatically guarantee a higher WTP for the same product. In fact, the order of preference elicited can be totally reversed in terms of ranking and WTP estimates. If the “stated prefer” preference cannot be transferred into actual purchase power in terms of monetary incentives, there is hardly any reason to supply these products in the market in the long run. From a broader perspective, our results imply that the food industry or policymakers should carefully use consumer preference information for product or policy development. Because rankings and WTP are not always consistent, stated preference data may not be useful unless accompanied by a WTP elicitation. For researchers in the area of consumer preferences, our results imply that the assumption of preference transitivity, in theory, should be carefully evaluated in the empirical research. What we have demonstrated here is not simply that preferences are different when estimated using different methods or different designs of the same method (Gao, House, & Yu, 2010; Gracia, Loureiro, & Nayga, 2011; Lusk & Schroeder, 2004; Shi, Xie, & Gao, 2018). Instead, we demonstrate a more serious problem for researchers to consider. That is, preferences are reversed when using different methods. This implies that researchers should meticulously select the preference elicitation methods according to the research objectives and carefully interpret the results based on the method used in their study. If possible, multiple methods, one directly estimating preference (e.g., ranking or likeliness) and another one estimating WTP should be applied, which helps to draw more robust conclusions.

occur in other normal products, this study proves the existence of this phenomenon for one popular food product. While we expect that preference reversal would occur, we do not anticipant it happens more frequently than in the previous literature that uses the lottery. However, only around 24% of the respondents in this study were able to remain consistent in answering CR and CV questions, which is much lower than the 44% in the previous literature (Grether & Plott, 1979). One possible reason may be that participants must be consistent in both group comparisons (organic vs. local and organic vs. natural) in order to be classified as consistent. From the point of the risk aspect, we found a different pattern of preference reversal compared with what was found in the previous literature. In previous studies, the most common and predictable preference reversal was when people stated that they “prefer” the low risk, moderate return product (P bet), while they stated a higher price for the high risk, high return counterpart ($ bet). The least common and less predictable preference reversal happened when respondents stated to “prefer” the high risk ($ bet) while giving a higher price for low risk (P bet). Our results show that even for products that do not involve explicit risks, consumers may still unconsciously perceive or infer the risks of the products. For instance, in this study, consumers may consider organic foods as “low risk” while local food may be perceived as associated with “high risk” because of the difference in standards and regulatory authorities (Hinrichs, 2000; Hughner et al., 2007; Michaelidou & Hassan, 2008). Therefore, what has been found in the current study is the less common and unpredictable case of preference reversal because many respondents state a preference for the local (high risk, $ bet) while giving a higher WTP for the organic (low risk, P bet). With the newly developed ratio to measure the preference reversal intensity, this study determines key factors associated with the preference reversal. The results show that respondents who are older with a higher education attainment reveal a more consistent preference in the two valuation methods. The findings are consistent with past research that concludes older, more educated consumers are more organized, and more consistent in their moral thinking (Pratt, Golding, & Hunter, 1983); tend to consider the “no harm/no foul” types of behaviors as more unethical (Vitell, Lumpkin, & Rawwas, 1991); and have more consistent endorsing behavior and select more effective strategies for interpersonal problems (Blanchard-Fields, Mienaltowski, & Seay, 2007). Research has also shown that consumers with a higher education level appear to have less discrepancy in their healthy intention and behavior (Weijzen, de Graaf, & Dijksterhuis, 2009). Higher educated individuals tend to have strong self-control, therefore enhancing the consistency between their intentions and all kinds of health behaviors (Moan & Rise, 2006; Sniehotta, Scholz, & Schwarzer, 2005; Weijzen et al., 2009). Noticeably, strawberry expenditure is positively associated with respondents’ preference reversal. The reason may be that the respondents who purchase and consume fresh strawberries at a relatively high frequency care more about the functionality and utility of the entire product rather than certain attributes (e.g., whether it is organic) that can only be used as the proxy of product quality. Therefore, respondents might become less sensitive to spending energy and time to differentiate the product attributes (e.g., labels). Their purchase decisions might be more prompt than those who seldom purchase the product or who may make more deliberate purchase decisions. Because of the less attention being paid to these attributes, more preference reversal could be observed when preferences are tested in different ways. From another perspective, the respondents who spend more on strawberries are more likely to have a better knowledge of the products and be more familiar with the market than those who seldom purchase the fruit. Then they would be more likely to view organic strawberries as overpriced and local

5.3. Limitations and future research The limitation of the current study is that it only uses one food product, which might not be representative enough to gauge the broader existence of preference reversal for food products. Another limitation is that we use only two hypothetical preference elicitation methods, which makes it difficult to generalize the conclusions to other methods that are widely used in estimating consumer preference. More research and evidence are needed to comprehensively derive both the internal and external factors explaining the phenomenon, which could provide more insights into this mysterious human behavior. First, instead of the fresh produce in the current 3 The prices of local and certified naturally grown strawberries are not available. The best data we can get are the prices of traditional and organic strawberries. Based on the USDA AMS data, the average price of conventional strawberries is $2.54 (SD=0.72); the average price of organic strawberries is $3.83 (SD= 0.89) for the years 2016–2018. The minimal and maximal prices for conventional strawberries are $0.99 and $6.29, respectively, and the minimal and maximal for organic strawberries are $0.99 to $8.49, respectively.

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study, future research could use food in different categories such as staple food (e.g., rice, corn) or processed food (e.g., chips, yogurt). Second, studies could include more methods to systematically investigate preference reversal. Other hypothetical methods such as a choice experiment or the dichotomous choice contingent valuation method could be considered to assess our findings in the hypothetical settings. Non-hypothetical methods such as experimental auctions and real choice experiments that are more incentive comparable should also be used. Lastly, it is essential to identify both the internal and external factors that can explain the preference reversal phenomenon. Other than demographics and purchase behavior variables,

future studies could include psychological factors that could help identify the moment when the rationality is not sustained while making decisions with risks (Lichtenstein & Slovic, 1971), which could be used as a powerful predictor for the behavior of preference reversal. Acknowledgement We thank the comments from the editor and two anonymous reviewers. This work is/was supported by the USDA National Institute of Food and Agriculture, [Hatch] project [FLA-FRE-005292].

Appendix A Proof of preference reversal as a violation of the preference theory: Let w = initial wealth; (x,1,0) = the state that lottery A is held with a wealth level of x; (y,0,1) = the state that lottery B is held with a wealth level of y; (z,0,0) = the state that neither of the lotteries is held with a wealth level of z; $(A) and $(B ) are the selling limit prices (reservation price) for lotteries A and B, respectively, (willingess-to-accept; WTA); P (A) and P (B ) are the price bids for lotteries A and B, respectively, (willingess-to-pay; WTP); ~ and indicate indifference and strong preferences, respectively; > indicates the ordering of monetary value; (1) (2) (3) (4) (5) (6)

(w, 1, 0) (w, 0, 1) (by the statement that lottery A is preferred to lottery B) (w, 1, 0)~(w + $(A), 0, 0) (by the definition of selling limit price of $(A)) (w, 0, 1)~(w + $(B ), 0, 0) (by the definition of selling limit price of $(B)) (w + $(A), 0, 0) (w + $(B ), 0, 0) (by the transitivity property) $(A) > $(B ) (by the fact that utility and wealth are increasing functions) P (A) > P (B ) (by the positive correlation between WTA and WTP)

Thus, the preference theory suggests that there should be a higher selling limit price and higher price bid placed on A rather than B if A is stated to be preferred to B. However, this is not what has usually been observed in previous experiments, which leads to the occurrence of preference reversal. Appendix B Proof of positive coefficients of ranking difference (e.g., organic/local): Note the dependent variables:

RatioOrganic, local =

WTPOrganic RankingOrganic

WTPLocal RankingLocal

and ranking difference:

Ranking _DifferenceOrganic, local, i = RankingOrganic

RankingLocal

Thus, take the first order derivative of ratio with respect to ranking difference would obtain:

d (RatioOrganic, local ) dRanking _DifferenceOrganic, local, i

=

(WTPOrganic

WTPLocal )

(RankingOrganic

Note in the inconsistent group, (WTPOrganic in the regression.

1

RankingLocal ) 2

=

(WTPOrganic (RankingOrganic

WTPLocal ) RankingLocal ) 2

WTPLocal ) > 0 holds generally; thus, the coefficient for ranking difference is expected to be positive

Appendix Table 1 Preference reversal count for different products used as the base. Base Product

Organic as Base

Local as Base

Natural as Base

Groups

Organic vs. Local 360 1587 Organic vs. Natural 250 1707 1405

Local vs. Organic 360 1587 Local vs. Natural 112 1793 1353

Natural vs. Organic 250 1707 Natural vs. Local 112 1793 1363

610 1889

472 2027

362 2137

2499 75.6%

2499 81.1%

2499 85.5%

Consistent Count (1) Inconsistent Count (1) Groups Consistent Count (2) Inconsistent Count (2) Inconsistent in Both Cases (3) Consistent Group (1 + 2) Inconsistent Group (1 + 2–3) Total Inconsistent Rate (%)

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Appendix Table 2 Regression results of WTP/ranking ratio using local as base product.a Independent Variables

Dependent Variable: (WTP/Ranking ratio for strawberries) Local/Organic

Local/Natural

Intercept Age Gender Income Education Kids Strawberry Expenditure

−0.695** −0.031* 0.079 0.005 −0.066*** 0.018 0.012**

−0.998*** −0.055*** 0.005 −0.003 −0.029 −0.072 0.013**

Preference_Difference −2 −1 1 2 Sigma.WTP _Rho # of Observations

Local-Organic −0.693*** −0.478*** 0.363*** 0 1.375*** 0.723*** 1321

Local-Natural −0.196 0.086 0.246 0 1.448***

Notes: *, **, and *** indicate significant at 10%, 5% and 1% level of significance. a This regression only applies to the inconsistent group since the ratio of WTP/ranking is used as the preference reversal measurement. However, the base product is changed to local instead of organic in the original manuscript. Appendix Table 3 Regression results of WTP/ranking ratio using natural as base product.a Independent Variables

Dependent Variable: (WTP/Ranking ratio for strawberries) Natural/Organic

Natural/Local

Intercept Age Gender Income Education Kids Strawberry Expenditure

−0.360 −0.031* −0.001 −0.027 −0.073*** −0.034 0.018***

−1.136*** −0.058*** −0.052 0.004 −0.026 0.019 0.015***

Preference_Difference −2 −1 1 2 Sigma.WTP _Rho # of Observations

Natural-Organic −1.243*** −0.643*** 0.107 0 1.376*** 0.623*** 1328

Natural-Local −0.059 0.254 0.379* 0 1.402***

Notes: *, **, and *** indicate significant at 10%, 5% and 1% level of significance. a This regression only applies to the inconsistent group since the ratio of WTP/ranking is used as the preference reversal measurement. However, the base product is changed to natural instead of organic in the original manuscript.

Appendix C. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodqual.2019.103754.

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