# Ethnocentrism, Trust, and the Willingness to Pay of Chinese Consumers for Organic Labels from Different Countries and Certifiers

Authors: Shijiu Yin; Fei Han; Yiqin Wang; Wuyang Hu; Shanshan Lv
Journal: Journal of Food Quality
 (2019)

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## Abstract

Although numerous studies have examined consumer preference for organic foods, few have focused on consumer willingness to pay (WTP) for organic labels from dissimilar countries or certifiers. We conducted a choice experiment to examine how Chinese consumer ethnocentrism and trust on organic labels and certifiers may affect their WTP for organic labels from different countries as well as for different certifiers. Chinese consumers did not show a high level of ethnocentrism, and this may lead to inconsistencies in their WTP for organic labels. Significantly, consumer preferences for certifiers did not change remarkably with the increase in consumer ethnocentrism. Chinese consumers generally preferred organic labels from developed countries (or US-invested organic certifiers). With increases in the trust in labels, consumer WTP for each type of organic label increased in general, but the difference between WTPs for organic labels from different countries decreased. Similar results were observed in consumer WTP for certifiers. Determining distinct preferences for organic labels from various sources and countries can be a valuable reference for manufacturers or international certification service providers to choose target markets and for governments to establish their certification systems.


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## Body

## 1. Introduction

Food safety concerns are a global issue, especially in developing countries. Due to profound economic reforms and changes throughout the country, food risks and food safety incidents have become particularly prevalent in China [1]. From an economic point of view, market failure led by information asymmetry is an important cause of food safety problems [2]. Authoritative third-party certifiers can gain greater consumer trust than producers [3], and organic certification by third-parties has become an important tool for mitigating food quality information asymmetry [4]. Organic food labeling is, therefore, a common method for manufacturers to demonstrate food quality to consumers and is an important policy in government food safety management [5].Since the late twentieth century, China has developed diversified organic certification policies in which domestic and foreign organic labeling play joint lead roles. (According to the Certification and Accreditation Administration of the PRC, there were 83 organic food certifiers in Mainland China in 2016. Several different organic labeling schemes developed by China or the US and European countries coexist in the Chinese market, with 13,000 valid certificates having been issued, such as the China organic certification, EU organic certification, and Japan Agricultural Standard (JAS) certification. Organic food sales have reached over 8.1 billion US dollars. China has become an important component of the emerging global organic food market (CNCA news,http://food.cnca.cn/cnca/spncp/sy/index.shtml, accessed January 15, 2015).) Organic food sold in Mainland China can be branded with organic labels from China or countries such as the US, Japan, and Argentina. In addition, these organic labels can be issued by different certifiers, e.g., the China Organic Food Certification Center under the Chinese Ministry of Agriculture or the foreign-funded CERES (Shanghai) Certification Co., Ltd. However, the relatively immature Chinese organic food market continues to experience problems, such as deficiency of supervision, unclear certificate issuers, lack of manufacturer self-discipline, and unfair competition [6]. Constant food safety scares such as fake organic food (for instance, in 2013, the media reported that the China Kweichow Moutai Co. was suspected of using fake organic raw ingredients in the production of organic maotai, a well known high-profile liquor brand, causing widespread concern) have led to market chaos in the Chinese organic food industry, resulting in consumer distrust of organic food labeled by domestic certifiers [7]. Organic labels as well as organic certifiers from other countries have gained popularity in China. Currently, there are 83 certifiers in China and more than 20 of them are foreign-funded. Most of these certifiers can issue several labels from different countries. However, there has been few research on consumer preference for labels from diverse countries or certifiers. This triggers several questions. First, do Chinese consumers prefer organic labels from certain countries? Second, do these preferences interact with certifiers issuing the labels or are they affected by consumer ethnocentrism? Third, if consumers have more trust in organic labels from certain countries or certifiers, how does their trust affects consumer willingness to pay (WTP)? These questions are the primary focus points of this paper.In this study, we focused on organic tomatoes. China is both a major vegetable producing and consuming country, with vegetable production of 76,005.48 million tons in 2014 and household per capita purchase of fresh vegetables of 112.3 kg in 2012 [8]. Tomatoes are one of the most commonly produced and consumed vegetables in China. (Technically, tomatoes are a type of fruit (berry) but following the conventions of the Chinese grocery listings, they are referred to as a type of vegetable in this study.) In 2012, Chinese tomato production was 50 million tons, accounting for approximately 30.9% of world tomato production [9]. In addition, tomato producers in China are generally small-sized, with dispersed production and without any major brand names. These traits have reduced the influence of unnecessary brand affiliation on consumer choice in our study.


## 2. Literature Review

Being organic is a credence attribute, which is difficult to assess at the time of purchase and even after consumption. An organic certification label is an important basis for consumers to identify organic food [10]. Many reviews have dealt with the topic of consumer preference for organic food [11, 12]. Previous studies have examined consumer WTP for organic food(s) (such as apples), or compared consumer WTP for organic food with other food attributes, such as local production or animal welfare labels. Gil et al. found that consumer WTP for organic food ranged between 111.33% and 125.29% [13]. Tranter et al. revealed that consumers were prepared to pay a premium for conversion-grade produce of around half the premium for organic produce, with vegetables attracting a higher premium than meat [14]. Soler et al. investigated Spanish consumer WTP for organic food and showed that 70% of respondents expressed a willingness to pay a premium for organic olive oil [13]. Rousseau and Vranken found that Flemish consumers were willing to pay a premium for organic labeled apples [15]. Napolitano and Braghieri investigated the WTP of Italian consumers for conventional and organic beef and found there was a significant relationship between personal preference and WTP for organic food [16]. Hempel and Hamm combined a consumer survey with an in-store choice experiment to study consumer preferences for organic and local foods and determined that organic-minded consumers (i.e., those who regarded organic food production as important) had stronger preferences for organic and local products [17]. Olesen et al. used a choice experiment to find that Norwegian consumers preferred organic and animal welfare-labeled salmon to otherwise identical salmon from conventional farms [18].To the best of our knowledge, however, few studies have compared consumer WTP for different organic labels. Janssen and Hamm investigated different organic certification labels in six European countries and found that consumer WTP for different labels varied greatly [5]. Van Loo et al. analyzed chicken breast that carried a United States Department of Agriculture (USDA) organic label or a general organic certification label, with consumer WTP for the former found to be much higher [19]. Wu et al. assessed consumer WTP for organic labels from the EU, US, and China and found that Chinese consumers show a higher WTP for EU than for Chinese organic labels [7]. Built upon these studies, we further assess the effect of different organic certifiers while considering the impact of consumer trust and ethnocentrism.The ability of organic labels to provide food quality assurance to consumers and thus reducing information asymmetry between suppliers and buyers depends on consumer trust in organic labels [7, 12]. Roitner-Schobesberger et al. conducted a sample survey in Thailand and found consumer WTP was affected by trust [20]. Vittersø and Tangeland discussed challenges towards the purchase of organic food among Norwegian consumers and demonstrated that trust in the labeling system was significant [21]. Yin et al. focused on consumer trust in Chinese organically labeled milk and found that consumers generally did not trust this product [22]. As organic labels are issued by specific organic certifiers, consumer trust in organic labels is inevitably related to their trust in the label issuers. However, consumer trust in different organic certifiers and its impact on WTP have not yet been studied.Similar to that, in many other countries, organic food labels on the Chinese market can come from different countries or from certifiers from different countries. Thus, consumer WTP for these organic labels might be affected by consumer ethnocentrism. Empirical studies have indicated that consumers with high ethnocentrism tend to prefer to buy domestic products and have a prejudice against foreign products [23–26]. The purchase of foreign products is thought to threaten national enterprises and, in some cases, is considered immoral, with resulting ethical conflicts [25, 27, 28]. Historically, Chinese consumer ethnocentrism has resulted from the strong impact of foreign capital on national capital. Since the 1980s, with increasing integration of China to the world economy, Chinese attitudes towards products from overseas have become increasingly complex. Due to numerous interacting factors, there are varying degrees of heterogeneity in consumer ethnocentrism among different products and consumer groups. Nevertheless, Chinese consumers generally do not show high ethnocentrism [29, 30]. Some studies have examined the impact of ethnocentrism on Chinese consumer preferences for products or brands from different countries [23, 29]. He and Wang reported that Chinese consumer ethnocentrism has a negative impact on relative preference for import brands but not on actual buying of domestic or import brands [31]. Liu et al. found that country of origin could moderate the impact of consumer ethnocentrism on foreign brand evaluations, significantly so for a US brand but insignificant when the country of origin was Australia [29]. However, such studies have primarily focused on physical products, such as automobiles and bottled water [23, 31], rather than product attributes such as certification.In the present study, we investigate consumer trust in organic labels from different countries and different types of organic certifiers. Consumer WTP for organic labels from different countries and different types of organic certifiers, as well as the interactions between them were evaluated and compared. The possible impacts of consumer trust and ethnocentrism on consumer preferences were also analyzed.


## 3. Methodology

### 3.1. Choice Experiment

We examined consumer WTP for organic labels from different countries and certifiers through a consumer survey featuring a choice experiment. Five different labels were considered: US organic label (USORG), Japanese organic label (JPORG), Argentine organic label (ARORG), Chinese organic label (CNORG), and no organic label (NOORG). The reasons for setting such attribute levels are as follows: (1) A previous empirical study and focus group interview showed that Chinese consumers are relatively familiar with USORG, JPORG, ARORG, and CNORG6. (In the focus group interview, organic labels from 15 different countries/regions were presented. The participants were then asked to mark the labels they knew. Organic labels known by more than 30% of participants included: Chinese organic labels (76%), US organic labels (47%), EU organic labels (46%), Japanese organic labels (42%), New Zealand organic labels (41%), Australian organic labels (37%), Argentine organic labels (18%), and Brazilian organic labels (17%). The statistical results from the formal survey were substantially similar to the above results.) (2) Both the US and Japan are important trading partners with China. Japan is geographically close to China and is the only Asian country in the Group of Seven (G7). Although China and Japan have a long history of exchange, Chinese consumers have boycotted Japanese goods due to historical and territorial issues28. Therefore, USORG and JPORG were selected as representatives of certification services from developed countries/regions with different types of association with China. (3) Although ARORG is not as well-known to Chinese consumers, it is still one of the most well-known organic labels from developing countries/regions. Moreover, Argentina is second only to the US in land devoted to organic agricultural production [32] and is a leader among developing countries. ARORG was thus chosen as a representative of the developing world.Because organic labels must be approved and issued by specific organic certifiers, organic certifier was introduced as an attribute in the choice experiment to examine organic label and certifier interaction. The reason for examining this interaction is that, according to Dawes and Corrigan, 70–90% of consumer utility variance is explained by the main effects of an attribute and as much as 5–15% by two-way interactions [33]. Therefore, considering two-way interactions can not only provide insights into understanding the interaction effects between the attributes but also help reduce errors in estimating the main effect of an attribute [34]. Similar to that of most countries, there are many (approximately 20) organic certifiers in China, which can be divided into four types: (1) certifiers established under the relevant government departments, e.g., the China Organic Food Certification Center under the Chinese Ministry of Agriculture; (2) certifiers established under academic research institutions, e.g., Northwest A&F University Certifier under the Northwest Agriculture and Forestry University; (3) private certifiers, e.g., Hangzhou Wantai Certification Limited; and (4) foreign-invested certifiers approved by the Certification and Accreditation Administration of the PRC. For instance the CERES (Shanghai) Certification Co., Ltd. is a joint venture established in China by the CERES Certification of Environmental Standards GmbH, Germany. All four types of certifiers can provide organic certification services in China or other countries on a cooperative or commission basis. (According to the Regulations of the People’s Republic of China on Certification and Accreditation, foreign certifiers are not allowed to provide organic certification services in China without preapproval.) Therefore, the certifier attribute was set at five levels: Chinese government-supported (GOVCERT), Chinese private (PRICERT), Chinese academic institution-supported (ACACERT), developed country-invested, and developing country-invested certifiers. To facilitate participant selection, US-invested certifiers (USCERT) and Argentine-invested certifiers (ARCERT) were used to represent developed and developing country-invested certifiers, respectively.For the attribute price (PRICE), high (1.4 $/kg), medium (1.1 $/kg), and low (0.8 $/kg) levels were set per the actual market price of tomatoes in supermarkets and organic food stores. (To facilitate understanding, we converted the original RMB yuan currency unit into US dollars in accordance with the exchange rate on November 1, 2015 (1 US dollar = 6.3171 RMB)).The final attributes and their levels, which are shown in Table1, resulted in 5×5×3=75 virtual tomato product options. Each choice task contained two virtual tomato product options, as well as an opt-out option. If a full factorial design was used, the participants had to complete C752=2775 choice tasks, which is unrealistic. Therefore, a fractional factorial design (FFD) was used and the first-order interaction effect was considered in order to reduce the number of tasks, while ensuring efficiency. Five versions were generated using SAS software, each with 15 tasks for a design D-efficiency of 100% (see Figure 1 for an example of a choice task).Table 1
Attributes and attribute levels used in the choice experiment design.
Attribute
Level
Organic label
US organic label, Japanese organic label, Argentine organic label, Chinese organic label, no label
Certifiers
Chinese government-supported certifiers, Chinese private certifiers, Chinese academic institution-supported certifiers, US-invested certifiers, Argentine-invested certifiers
Price
US$ 1.4/kg (high), US$ 1.1/kg (medium), US$ 0.8/kg (low)Figure 1
Sample choice task.


### 3.2. Consumer Ethnocentrism and Trust in Labels from Different Countries

Consumer ethnocentrism and consumer trust in different labels or different certifiers were also collected in the consumer survey.


#### 3.2.1. Consumer Ethnocentrism

To quantify consumer ethnocentrism, survey participants indicated their agreement with six items in the questionnaire using a Likert scale from 1 (totally disagree) to 7 (totally agree). The items were selected based on consumer ethnocentrism scales and surveys used by previous researchers [25, 35]. Only one factor was extracted after applying factor analysis of the collected answers. Following Zhuang et al., and after removing one item with the lowest item-total correlation, a five-item consumer ethnocentrism scale (α = 0.727) was obtained [25]. Table 2 lists the items and their mean scores. The consumer ethnocentrism propensity score (ETH) was calculated for each survey participant by adding the scores of each item and dividing by five.Table 2
Consumer ethnocentrism scale.
Item description
Mean scores
Chinese people should give priority to domestic products in their purchasing decisions to protect the national industry
3.756
I am proud of the development of the Chinese national enterprises
4.364
I am worried about the threat of foreign products to China’s related industries and markets
3.757
I am uneasy about the expansion of foreign brands or products in China
3.752
Chinese people should use Chinese products as much as possible
4.080
Acceptance of Chinese products by consumers is directly related to the sustainable development of national enterprises and the Chinese economya
3.851
α value
0.727
Explanation power of the overall scale
48.76%
aThis item was not used in the calculation of the ethnocentrism propensity score because of its low correlation coefficient with the overall scale.


#### 3.2.2. Consumer Trust in Different Labels and Certifiers

Consumer trust in organic labels from each country was measured using two seven-point Likert scale (α = 0.733) questions as per Delgado-Ballester and Munuera-Aleman, who allowed respondents to provide a self-judgment using a seven-point Likert scale (the agree/disagree anchor points) [36]. (In our survey, we did not want to impose a definition of food quality upon respondents. Rather, it was treated as a broad concept that could encompass many components including food safety. The two questions are as follows: (a) I think that the tomato I buy with this label can be trusted for its high quality; (b) I think that the tomato I buy with this label has reliable quality. The mean scores of the two items are 4.962 and 4.938, respectively.) The index of participants’ trust in organic labels (LTRUST) was calculated by averaging the scores of the two items.Consumer trust in each organic certifier (CTRUST) was measured using the similar two-item scale (α = 0.723) previously to investigate respondents’ trust in food suppliers [37]. (The two questions are as follows: (a) in general, I can rely on organic certifiers to supervise organic suppliers to provide high quality tomatoes; (b) in general, I think that organic certifiers can be trusted to ensure that tomatoes are of high quality. The mean scores of the two items are 4.786 and 4.934, respectively.) Participants indicated their agreement with seven items in the questionnaire using a Likert scale from 1 (totally disagree) to 7 (totally agree). The index of participants’ trust in organic certifiers (CTRUST) was calculated by averaging the scores of these two items.


### 3.3. Econometric Modelling

The econometric analysis of the choice data was based on random utility theory [38]. Assuming individual consumers are rational, they will choose the product (i.e., tomato) option with the maximum utility. Utility Uni obtained by consumer n choosing tomato option i can be defined as follows:(1)Uni=Vni+εni,Vni=Xniβ,where Vni and εni are the deterministic and stochastic terms of utility, respectively, indicating that the true consumer utility is unobservable from the perspective of researchers. If consumer n maximizes his/her utility by choosing tomato option ia in choice set C, the probability of consumer n choosing tomato option i can be defined as follows:(2)Pni=ProbUni>Unj;j=1,…,J;j≠i,Pni=Probεni−εnj>Unj−Uni;j=1,…,J;j≠i.Assumingεnii=1,…,J follows an independent and identically distributed type I maximum extreme-value distribution, the probability of consumer n choosing tomato option i can be expressed as follows [39]:(3)Pni=eXiβ∑j=1JeXiβ,i=1,…,J.This is the conditional logit model (MNL). The MNL model assumes that consumer preferences are homogeneous, although this can be inconsistent with the actual situation. A more realistic assumption is that consumer preferences are heterogeneous. In other words,β may not be fixed, but stochastic, and follows a certain distribution. Given this assumption, the utility obtained by consumer i choosing option m in situation t can be defined as follows [40]:(4)Uimt=βi′Ximt+eimt,where Ximt is the vector of observable variables associated with the option and decision maker; βi is the coefficient vector of consumer i with respect to these variables; and eimt is a stochastic term. This utility function can be further expressed as follows:(5)Uimt=β+Ω′ηi′Ximt+eimt,where β is the mean of vector βi, Ω′ηi represents taste heterogeneity, and Ω is the Cholesky matrix considering the correlation between the random coefficients βi. An appropriate distribution assumption can be made for random deviation ηi. The unconditional probability is the integral of the logit probability over the density function of βi:(6)Pimt=∫eβ′Ximt∑jeβ′Xijtfβdβ,where fβ is the multivariate probability density function. Equation (6) is a generalized form of the MNL model, known as the mixed logit (ML) model (or random parameter logit model). Assuming that the consumer makes a choice at T different times and that the choice sequence is I=i1,...,iT, the probability for the consumer choosing the sequence is as follows:(7)LiTβ=∏t=1Teβi′Xiitt∑j=1Jeβi′Xijt.The unconditional probability is the integral overβ:(8)PiT=∫LiTβfβdβ.Due to the random parameter specification, the ML model relaxes the restrictive assumption of independence of irrelevant alternatives associated with the MNL model.


## 3.1. Choice Experiment

We examined consumer WTP for organic labels from different countries and certifiers through a consumer survey featuring a choice experiment. Five different labels were considered: US organic label (USORG), Japanese organic label (JPORG), Argentine organic label (ARORG), Chinese organic label (CNORG), and no organic label (NOORG). The reasons for setting such attribute levels are as follows: (1) A previous empirical study and focus group interview showed that Chinese consumers are relatively familiar with USORG, JPORG, ARORG, and CNORG6. (In the focus group interview, organic labels from 15 different countries/regions were presented. The participants were then asked to mark the labels they knew. Organic labels known by more than 30% of participants included: Chinese organic labels (76%), US organic labels (47%), EU organic labels (46%), Japanese organic labels (42%), New Zealand organic labels (41%), Australian organic labels (37%), Argentine organic labels (18%), and Brazilian organic labels (17%). The statistical results from the formal survey were substantially similar to the above results.) (2) Both the US and Japan are important trading partners with China. Japan is geographically close to China and is the only Asian country in the Group of Seven (G7). Although China and Japan have a long history of exchange, Chinese consumers have boycotted Japanese goods due to historical and territorial issues28. Therefore, USORG and JPORG were selected as representatives of certification services from developed countries/regions with different types of association with China. (3) Although ARORG is not as well-known to Chinese consumers, it is still one of the most well-known organic labels from developing countries/regions. Moreover, Argentina is second only to the US in land devoted to organic agricultural production [32] and is a leader among developing countries. ARORG was thus chosen as a representative of the developing world.Because organic labels must be approved and issued by specific organic certifiers, organic certifier was introduced as an attribute in the choice experiment to examine organic label and certifier interaction. The reason for examining this interaction is that, according to Dawes and Corrigan, 70–90% of consumer utility variance is explained by the main effects of an attribute and as much as 5–15% by two-way interactions [33]. Therefore, considering two-way interactions can not only provide insights into understanding the interaction effects between the attributes but also help reduce errors in estimating the main effect of an attribute [34]. Similar to that of most countries, there are many (approximately 20) organic certifiers in China, which can be divided into four types: (1) certifiers established under the relevant government departments, e.g., the China Organic Food Certification Center under the Chinese Ministry of Agriculture; (2) certifiers established under academic research institutions, e.g., Northwest A&F University Certifier under the Northwest Agriculture and Forestry University; (3) private certifiers, e.g., Hangzhou Wantai Certification Limited; and (4) foreign-invested certifiers approved by the Certification and Accreditation Administration of the PRC. For instance the CERES (Shanghai) Certification Co., Ltd. is a joint venture established in China by the CERES Certification of Environmental Standards GmbH, Germany. All four types of certifiers can provide organic certification services in China or other countries on a cooperative or commission basis. (According to the Regulations of the People’s Republic of China on Certification and Accreditation, foreign certifiers are not allowed to provide organic certification services in China without preapproval.) Therefore, the certifier attribute was set at five levels: Chinese government-supported (GOVCERT), Chinese private (PRICERT), Chinese academic institution-supported (ACACERT), developed country-invested, and developing country-invested certifiers. To facilitate participant selection, US-invested certifiers (USCERT) and Argentine-invested certifiers (ARCERT) were used to represent developed and developing country-invested certifiers, respectively.For the attribute price (PRICE), high (1.4 $/kg), medium (1.1 $/kg), and low (0.8 $/kg) levels were set per the actual market price of tomatoes in supermarkets and organic food stores. (To facilitate understanding, we converted the original RMB yuan currency unit into US dollars in accordance with the exchange rate on November 1, 2015 (1 US dollar = 6.3171 RMB)).The final attributes and their levels, which are shown in Table1, resulted in 5×5×3=75 virtual tomato product options. Each choice task contained two virtual tomato product options, as well as an opt-out option. If a full factorial design was used, the participants had to complete C752=2775 choice tasks, which is unrealistic. Therefore, a fractional factorial design (FFD) was used and the first-order interaction effect was considered in order to reduce the number of tasks, while ensuring efficiency. Five versions were generated using SAS software, each with 15 tasks for a design D-efficiency of 100% (see Figure 1 for an example of a choice task).Table 1
Attributes and attribute levels used in the choice experiment design.
Attribute
Level
Organic label
US organic label, Japanese organic label, Argentine organic label, Chinese organic label, no label
Certifiers
Chinese government-supported certifiers, Chinese private certifiers, Chinese academic institution-supported certifiers, US-invested certifiers, Argentine-invested certifiers
Price
US$ 1.4/kg (high), US$ 1.1/kg (medium), US$ 0.8/kg (low)Figure 1
Sample choice task.


## 3.2. Consumer Ethnocentrism and Trust in Labels from Different Countries

Consumer ethnocentrism and consumer trust in different labels or different certifiers were also collected in the consumer survey.


### 3.2.1. Consumer Ethnocentrism

To quantify consumer ethnocentrism, survey participants indicated their agreement with six items in the questionnaire using a Likert scale from 1 (totally disagree) to 7 (totally agree). The items were selected based on consumer ethnocentrism scales and surveys used by previous researchers [25, 35]. Only one factor was extracted after applying factor analysis of the collected answers. Following Zhuang et al., and after removing one item with the lowest item-total correlation, a five-item consumer ethnocentrism scale (α = 0.727) was obtained [25]. Table 2 lists the items and their mean scores. The consumer ethnocentrism propensity score (ETH) was calculated for each survey participant by adding the scores of each item and dividing by five.Table 2
Consumer ethnocentrism scale.
Item description
Mean scores
Chinese people should give priority to domestic products in their purchasing decisions to protect the national industry
3.756
I am proud of the development of the Chinese national enterprises
4.364
I am worried about the threat of foreign products to China’s related industries and markets
3.757
I am uneasy about the expansion of foreign brands or products in China
3.752
Chinese people should use Chinese products as much as possible
4.080
Acceptance of Chinese products by consumers is directly related to the sustainable development of national enterprises and the Chinese economya
3.851
α value
0.727
Explanation power of the overall scale
48.76%
aThis item was not used in the calculation of the ethnocentrism propensity score because of its low correlation coefficient with the overall scale.


### 3.2.2. Consumer Trust in Different Labels and Certifiers

Consumer trust in organic labels from each country was measured using two seven-point Likert scale (α = 0.733) questions as per Delgado-Ballester and Munuera-Aleman, who allowed respondents to provide a self-judgment using a seven-point Likert scale (the agree/disagree anchor points) [36]. (In our survey, we did not want to impose a definition of food quality upon respondents. Rather, it was treated as a broad concept that could encompass many components including food safety. The two questions are as follows: (a) I think that the tomato I buy with this label can be trusted for its high quality; (b) I think that the tomato I buy with this label has reliable quality. The mean scores of the two items are 4.962 and 4.938, respectively.) The index of participants’ trust in organic labels (LTRUST) was calculated by averaging the scores of the two items.Consumer trust in each organic certifier (CTRUST) was measured using the similar two-item scale (α = 0.723) previously to investigate respondents’ trust in food suppliers [37]. (The two questions are as follows: (a) in general, I can rely on organic certifiers to supervise organic suppliers to provide high quality tomatoes; (b) in general, I think that organic certifiers can be trusted to ensure that tomatoes are of high quality. The mean scores of the two items are 4.786 and 4.934, respectively.) Participants indicated their agreement with seven items in the questionnaire using a Likert scale from 1 (totally disagree) to 7 (totally agree). The index of participants’ trust in organic certifiers (CTRUST) was calculated by averaging the scores of these two items.


## 3.2.1. Consumer Ethnocentrism

To quantify consumer ethnocentrism, survey participants indicated their agreement with six items in the questionnaire using a Likert scale from 1 (totally disagree) to 7 (totally agree). The items were selected based on consumer ethnocentrism scales and surveys used by previous researchers [25, 35]. Only one factor was extracted after applying factor analysis of the collected answers. Following Zhuang et al., and after removing one item with the lowest item-total correlation, a five-item consumer ethnocentrism scale (α = 0.727) was obtained [25]. Table 2 lists the items and their mean scores. The consumer ethnocentrism propensity score (ETH) was calculated for each survey participant by adding the scores of each item and dividing by five.Table 2
Consumer ethnocentrism scale.
Item description
Mean scores
Chinese people should give priority to domestic products in their purchasing decisions to protect the national industry
3.756
I am proud of the development of the Chinese national enterprises
4.364
I am worried about the threat of foreign products to China’s related industries and markets
3.757
I am uneasy about the expansion of foreign brands or products in China
3.752
Chinese people should use Chinese products as much as possible
4.080
Acceptance of Chinese products by consumers is directly related to the sustainable development of national enterprises and the Chinese economya
3.851
α value
0.727
Explanation power of the overall scale
48.76%
aThis item was not used in the calculation of the ethnocentrism propensity score because of its low correlation coefficient with the overall scale.


## 3.2.2. Consumer Trust in Different Labels and Certifiers

Consumer trust in organic labels from each country was measured using two seven-point Likert scale (α = 0.733) questions as per Delgado-Ballester and Munuera-Aleman, who allowed respondents to provide a self-judgment using a seven-point Likert scale (the agree/disagree anchor points) [36]. (In our survey, we did not want to impose a definition of food quality upon respondents. Rather, it was treated as a broad concept that could encompass many components including food safety. The two questions are as follows: (a) I think that the tomato I buy with this label can be trusted for its high quality; (b) I think that the tomato I buy with this label has reliable quality. The mean scores of the two items are 4.962 and 4.938, respectively.) The index of participants’ trust in organic labels (LTRUST) was calculated by averaging the scores of the two items.Consumer trust in each organic certifier (CTRUST) was measured using the similar two-item scale (α = 0.723) previously to investigate respondents’ trust in food suppliers [37]. (The two questions are as follows: (a) in general, I can rely on organic certifiers to supervise organic suppliers to provide high quality tomatoes; (b) in general, I think that organic certifiers can be trusted to ensure that tomatoes are of high quality. The mean scores of the two items are 4.786 and 4.934, respectively.) Participants indicated their agreement with seven items in the questionnaire using a Likert scale from 1 (totally disagree) to 7 (totally agree). The index of participants’ trust in organic certifiers (CTRUST) was calculated by averaging the scores of these two items.


## 3.3. Econometric Modelling

The econometric analysis of the choice data was based on random utility theory [38]. Assuming individual consumers are rational, they will choose the product (i.e., tomato) option with the maximum utility. Utility Uni obtained by consumer n choosing tomato option i can be defined as follows:(1)Uni=Vni+εni,Vni=Xniβ,where Vni and εni are the deterministic and stochastic terms of utility, respectively, indicating that the true consumer utility is unobservable from the perspective of researchers. If consumer n maximizes his/her utility by choosing tomato option ia in choice set C, the probability of consumer n choosing tomato option i can be defined as follows:(2)Pni=ProbUni>Unj;j=1,…,J;j≠i,Pni=Probεni−εnj>Unj−Uni;j=1,…,J;j≠i.Assumingεnii=1,…,J follows an independent and identically distributed type I maximum extreme-value distribution, the probability of consumer n choosing tomato option i can be expressed as follows [39]:(3)Pni=eXiβ∑j=1JeXiβ,i=1,…,J.This is the conditional logit model (MNL). The MNL model assumes that consumer preferences are homogeneous, although this can be inconsistent with the actual situation. A more realistic assumption is that consumer preferences are heterogeneous. In other words,β may not be fixed, but stochastic, and follows a certain distribution. Given this assumption, the utility obtained by consumer i choosing option m in situation t can be defined as follows [40]:(4)Uimt=βi′Ximt+eimt,where Ximt is the vector of observable variables associated with the option and decision maker; βi is the coefficient vector of consumer i with respect to these variables; and eimt is a stochastic term. This utility function can be further expressed as follows:(5)Uimt=β+Ω′ηi′Ximt+eimt,where β is the mean of vector βi, Ω′ηi represents taste heterogeneity, and Ω is the Cholesky matrix considering the correlation between the random coefficients βi. An appropriate distribution assumption can be made for random deviation ηi. The unconditional probability is the integral of the logit probability over the density function of βi:(6)Pimt=∫eβ′Ximt∑jeβ′Xijtfβdβ,where fβ is the multivariate probability density function. Equation (6) is a generalized form of the MNL model, known as the mixed logit (ML) model (or random parameter logit model). Assuming that the consumer makes a choice at T different times and that the choice sequence is I=i1,...,iT, the probability for the consumer choosing the sequence is as follows:(7)LiTβ=∏t=1Teβi′Xiitt∑j=1Jeβi′Xijt.The unconditional probability is the integral overβ:(8)PiT=∫LiTβfβdβ.Due to the random parameter specification, the ML model relaxes the restrictive assumption of independence of irrelevant alternatives associated with the MNL model.


## 4. Survey Design and Data

This study was conducted in Shandong Province, a populous province located in the eastern coastal area of China. The gross value of agricultural production in Shandong was 11,942 million US dollars in 2014, including vegetable production of 4,324 million US dollars, making it the top agricultural province among the 31 provinces of Mainland China8. The economic development within Shandong is not balanced, with remarkable differences between the eastern coastal and western inland areas. This is a close reflection of the developmental differences between the eastern coastal and the central and western inland areas of China. To this regard, Shandong is representative of China. In this study, three representative cities were selected from each of the eastern (Qingdao, Weihai, and Rizhao), central (Laiwu, Zibo, and Tai’an) and western regions (Liaocheng, Dezhou, and Heze) of Shandong, respectively.The survey included two phases. In the first phase, about 10 participants were selected in each city using a random sampling method to conduct focus group interviews. The purpose was to obtain basic information about consumers and their knowledge of organic labels and provide a basis for defining the attributes and their levels. Focus group discussions are a suitable way to gain understanding of specific topics or product categories [41]. From April to July 2015, a one-hour focus group discussion was conducted in each of the nine cities, respectively. Each discussion group included 8–10 participants (81 in total). Each participant was the family member who most frequently purchased food and was aged between 18 and 65. Each interview consisted of two segments. The first segment assesses participants’ knowledge, trust, buying habits, buying motives, reasons for purchase, and key attributes with respect to organic food. The second segment examines consumer ethnocentrism and attitudes, such as attitudes towards products or brands from different sources. To improve and finalize the survey questionnaire, a preliminary pilot study was first carried out with 100 consumers selected from Rizhao, Shandong province, in October 2015. Only minor adjustments were made to the questionnaire after the pilot testing.The second phase involves implementing the actual survey from October to December 2015. Participants were recruited at random near supermarkets or shopping centers to take part in an in-person survey in each of the nine cities. Although supermarkets and farmers’ markets are the main places for Chinese residents to buy vegetables, organic vegetables are mainly sold in supermarkets and organic food stores in business districts6, which was also verified by the focus group interviews. The survey was administered by trained investigators to ensure consistency. Every third individual approaching our booth was intercepted as a possible respondent to ensure the randomness of the sample. A total of 907 consumers (around 100 in each city) participated in the survey with an estimated response rate of 74.36%; among these, a total of 853 respondents completed the questionnaire. The sample included 480 women (56.28%), which is consistent with the fact that most household food buyers in China are female [7]. Survey sample demographics are shown in Table 3.Table 3
Sociodemographic characteristics of the respondents.
Variables
Categories
Number of respondents
Percentage of sample
Gender
Female
480
56.28
Male
373
43.72
Agea
18–45 years
404
47.39
46–60 years
294
34.47
>60 years
155
18.14
Educationb
High education
260
30.45
Medium education
340
39.86
Low education
253
29.70
Family income (per year)c
<8,015 US$
264
31.32
8,015–16,030 US$
358
42.47
>16,030 US$
221
26.21
aAccording to the China Statistical Yearbook, individuals aged 18–45 years are classified as young, 46–60 years are classified as middle-aged, and over the age of 60 are classified as old. Respondents were classified per the same criteria in this study. The age structure of respondents was consistent with that of the Chinese population reported in the China Statistical Yearbook, as well as with that of Shandong Province reported in the Shandong Statistical Yearbook. bBased on the level of education in Mainland China, as well as the criteria used in the China and Shandong Statistical Yearbooks, low education denotes primary school or lower, medium education denotes junior high or high school, and high education denotes university or higher. The education level of respondents was consistent with that of urban residents in Shandong Province, as reported in the Shandong Statistical Yearbook, and slightly higher than that of Chinese urban residents, as reported in the China Statistical Yearbook. cBased on the economic development and consumer income levels in Mainland China, an annual family income of less than 8,015 US$ is defined as low income, 8,015–16,030 US$ is defined as medium income, and greater than 16,030 US$ is defined as high income. As this study aimed to understand the willingness of consumers to pay for organic food, participants were recruited from urban supermarkets and business districts. The income level of the participants was slightly higher than the per capita income of the Chinese and Shandong populations reported in the China and Shandong Statistical Yearbooks, respectively.


## 5. Results

### 5.1. Chinese Consumer Ethnocentrism and Trust Scores

The mean Chinese consumer ethnocentrism (ETH) score was 3.9417, with a standard deviation of 1.0341. This result is consistent with the conclusions of Rašković et al. and Liu et al., who noted that Chinese consumers generally do not show high ethnocentrism2930. It could be attributed to the increasing integration of China in the world economy over the last 30 years of reform.Based on consumer trust in the four organic labels, as shown in Table4, mean consumer trust was 4.75, with some differences found among labels. Mean trust for the four organic labels was, in descending order, USORG (5.42), JPORG (5.34), ARORG (4.73), and CNORG (4.32). Further analysis of mean consumer trust by dependent-sample t-test revealed significant differences in mean consumer trust among the four organic labels (all p<0.01). Mean consumer trust in USORG and JPORG were similar and much higher than that in ARORG and CNORG. Consumers had more trust in organic labels from developed countries/regions. This is consistent with previous research indicating that products from developed countries are often more popular than those from developing countries [42].Table 4
Participants' trust in different organic labels and certifiers.
Label
Mean
Standard deviation
Certifiers
Mean
Standard deviation
USORG
5.42
1.3547
GOVCERT
4.96
1.0357
JPORG
5.34
1.1357
PRICERT
3.94
0.7586
ARORG
4.73
0.9834
ACACERT
5.24
0.8674
CNORG
4.32
0.7589
USCERT
5.68
1.2457
—
—
—
ARCERT
5.02
1.1567
AVERAGEa
4.95
1.2498
AVERAGEa
4.86
1.0247
aMean consumer trust in all organic labels or certifiers.As seen in Table4, differences in trust were also found among different certifiers. Dependent-sample t-test showed significant differences in mean consumer trust (all p<0.01). Consumers had the highest trust in USCERT, followed by ACACERT, then ARCERT and GOVCERT, and lastly PRICERT. Consumers showed lower trust in private than government certification and lower trust in domestic than foreign certifiers, especially certifiers from developed countries. This coincides with the findings of Wu et al. [7].


### 5.2. ML Model Estimates

Effect coding was used in the analysis. Assumptions for the relevant parameter distributions are as follows: first, the coefficients of the price (PRICE) and opt-out variables were fixed; and secondly, the attribute levels followed normal distribution [43]. Table 5 shows the ML model estimation results using NLOGIT 5.0. As seen in Table 5, the ML model estimates showed significantly positive two-way interactions (and thus complementary relationships) between USCERT and USORG and between ARCERT and ARORG. Certification of USORG by USCERT and certification of ARORG by ARCERT both generated positive effects on the combined main effects of the two attributes. For foreign certifiers, certification of organic labels from their respective countries improved Chinese consumer preferences. In addition, ACACERT exhibited significantly positive interactions with USORG, JPORG, and ARORG. Thus, certification of USORG, JPORG, and ARORG by academic institutions also generated positive effects on the combined main effects of the two attributes. This might be because consumers assumed that the ability of academic institutions to provide certification services for foreign organic labels highlighted their professional competence, thereby increasing consumer preference. This supports earlier findings that showed complementary or substitutional relationships between different attributes of food [7, 42].Table 5
Mixed logit model result on consumers' preferences for attributes of organic tomatoes.
Coef. of estimates
SE
t-value
95% CI
Main effects
PRICE
−1.978∗∗∗
0.081
−23.83
[−2.137, −1.819]
Opt out
−1.489∗∗
0.087
−15.12
[−1.660, −1.318]
USORG
1.071∗∗∗
0.144
7.54
[0.789, 1.353]
JPORG
0.718∗∗
0.377
1.91
[−0.021, 1.457]
ARORG
0.759∗
0.394
1.95
[−0.013, 1.531]
CNORG
0.425∗∗∗
0.231
1.81
[−0.028, 0.878]
USCERT
1.158∗∗∗
0.057
17.28
[1.046, 1.270]
ARCERT
0.678∗
0.047
13.79
[0.586, 0.770]
GOVCERT
0.454∗
0.520
0.86
[−0.565, 1.473]
ACACERT
0.767∗∗∗
0.316
2.38
[0.148, 1.386]
Interaction term
USORG × USCERT
−0.521∗∗∗
0.027
−17.85
[−0.574, −0.468]
JPORG × USCERT
0.031∗∗∗
0.011
3.14
[0.009, 0.053]
ARORG × USCERT
0.203∗∗∗
0.061
3.36
[0.083, 0.323]
CNORG × USCERT
0.146∗∗∗
0.047
3.12
[0.054, 0.238]
USORG × ARCERT
0.440∗∗∗
0.105
4.31
[0.234, 0.646]
JPORG × ARCERT
0.151∗∗∗
0.034
3.89
[0.084, 0.218]
ARORG × ARCERT
0.131∗∗∗
0.024
5.20
[0.084, 0.178]
CNORG × ARCERT
0.161∗∗∗
0.026
6.01
[0.110, 0.212]
USORG × GOVCERT
0.035∗∗∗
0.007
5.42
[0.021, 0.049]
JPORG × GOVCERT
0.231∗∗∗
0.009
8.87
[0.213, 0.249]
ARORG × GOVCERT
0.048∗∗∗
0.013
3.46
[0.023, 0.073]
CNORG × GOVCERT
0.074∗∗∗
0.017
4.41
[0.041, 0.107]
USORG × ACACERT
0.078∗∗∗
0.025
3.09
[0.029, 0.127]
JPORG × ACACERT
0.067∗∗∗
0.021
3.25
[0.026, 0.108]
ARORG × ACACERT
0.111∗∗∗
0.031
3.60
[0.050, 0.172]
CNORG × ACACERT
0.215∗∗∗
0.037
5.60
[0.142, 0.289]
ETH × USORG
0.052∗∗∗
0.013
3.73
[0.027, 0.077]
ETH × JPORG
0.175∗∗∗
0.030
4.88
[0,116, 0.234]
ETH × ARORG
0.154∗∗
0.024
4.71
[0.107, 0.201]
ETH × CNORG
0.148∗
0.044
3.58
[0.062, 0.234]
ETH × USCERT
0.129∗∗∗
0.044
3.21
[0.043, 0.215]
ETH × ARCERT
0.199∗∗∗
0.105
2.67
[0.007, 0.405]
ETH × GOVCERT
0.242∗∗∗
0.016
14.13
[0.211, 0.273]
ETH × ACACERT
0.171∗
0.049
3.42
[0.075, 0.267]
LTRUST × USORG
0.217∗∗
0.074
2.88
[0.072, 0.362]
LTRUST × JPORG
0.105∗∗
0.0212
3.56
[0.063, 0.147]
LTRUST × ARORG
0.148∗∗∗
0.053
2.76
[0.044, 0.252]
LTRUST × CNORG
0.281∗∗∗
0.111
3.09
[0.063, 0.499]
LTRUST × USCERT
0.214∗∗
0.036
6.42
[0.143, 0.285]
LTRUST × ARCERT
0.175∗∗∗
0.016
6.38
[0.144, 0.206]
LTRUST × GOVCERT
0.060∗∗
0.018
4.35
[0.025, 0.095]
LTRUST × ACACERT
0.178∗∗
0.042
3.21
[0.096, 0.260]
CTRUST × USORG
0.247∗∗∗
0.081
3.08
[0.088, 0.406]
CTRUST × JPORG
0.106∗∗∗
0.0212
4.51
[0.064, 0.148]
CTRUST × ARORG
0.150∗∗
0.055
2.46
[0.042, 0.258]
CTRUST × CNORG
0.302∗∗
0.110
2.78
[0.086, 0.518]
CTRUST × USCERT
0.204∗∗∗
0.029
5.25
[0.147, 0.261]
CTRUST × ARCERT
0.265∗∗
0.023
5.87
[0.220, 0.310]
CTRUST × GOVCERT
0.055∗∗∗
0.024
6.22
[0.008, 0.102]
CTRUST × ACACERT
0.173∗∗
0.050
3.56
[0.075, 0.271]
Diagonal values in Cholesky matrix
USORG
0.899∗∗∗
0.047
20.41
[0.807, 0.991]
JPRORG
0.523∗∗∗
0.028
13.12
[0.468, 0.578]
ARORG
0.047∗∗
0.021
3.24
[0.006, 0.088]
CNORG
0.153∗∗∗
0.044
2.75
[0.067, 0.239]
USCERT
0.069∗∗∗
0.021
1.68
[0.027, 0.110]
ARCERT
0.529∗∗
0.040
12.02
[0.451, 0.607]
GOVCERT
0.064∗∗∗
0.018
4.81
[0.029, 0.099]
ACACERT
0.264∗∗∗
0.037
7.05
[0.191, 0.337]
Log likelihood
−3604.36
McFadden R2
0.252
AIC
7236.68
—
—
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively; SE: standard error


### 5.3. Estimates of WTP

Based on the estimates in Table5, as well as the characteristics of the ordinal utilities of the main effects of an attribute, WTP was calculated as follows:(9)WTPk=−2×βk+βETH×ak×ETH+βLTRUST×ak×LTRUST+βCTRUST×ak×CTRUST+∑jβk×j×ajβprice,where WTPk is the WTP for level k of the attribute of interest; βk is the coefficient of the main effect of level k of this attribute; βETH×ak is the coefficient of the interaction between ETH and level k of the attribute; βLTRUST×ak and βCTRUST×ak are the coefficients of the interaction between LTRUST and CTRUST and level k of the attribute, respectively; βk×j is the coefficient of the interaction between level k of the attribute and other attributes; and βprice is the estimated price coefficient. Because effect coding was used in the analysis, the WTP should be multiplied by 2 in the calculation [44]. The confidence interval of WTP was estimated using the parametric bootstrapping technique (PBT) [45]. Table 6 presents the mean WTP estimates for different organic labels and certifiers and the corresponding confidence intervals. Consumer ethnocentrism and trust variables were held at the sample average level. Attribute variables entered as interactions were held at −1.Table 6
Consumers' willingness to pay measured at the sample average level of ethnocentrism and trust.
Attribute level
Coef.
SE
95% CI
USORG
1.589∗∗∗
0.529
[0.552, 2.626]
JPORG
1.027∗∗∗
0.374
[0.294, 1.760]
ARORG
1.116∗∗∗
0.286
[0.555, 1.677]
CNORG
0.729∗∗∗
0.307
[0.127, 1.331]
USCERT
1.768∗∗∗
0.299
[1.182, 2.354]
ARCERT
1.042∗∗∗
0.304
[0.446, 1.638]
GOVCERT
0.837∗∗∗
0.234
[0.378, 1.296]
ACACERT
1.216∗∗∗
0.295
[0.638, 1.794]
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively.As shown in Table6, there were considerable differences in consumer WTP for different organic labels. The order of consumer preference for the organic labels was not consistent with that of consumer trust shown in Table 7. Among the four organic labels, the highest WTP was expressed for USORG (US$ 1.589), followed by ARORG (US$ 1.116), JPORG (US$ 1.027), and lastly by CNORG (US$ 0.729). In terms of WTP for different certifiers, consumers had the highest WTP for USCERT (US$ 1.768), followed by ACACERT (US$ 1.216), ARCERT (US$ 1.042), and finally GOVCERT (US$ 0.837). This is the same as the order of mean trust in different certifiers shown in Table 4.Table 7
Consumers' willingness to pay for organic labels based on different levels of trust in organic labels#.
Label
LTRUST = 1
LTRUST = 2
LTRUST = 3
LTRUST = 4
LTRUST = 5
LTRUST = 6
LTRUST = 7
USORG
1.531∗∗∗ (0.583)
1.511∗∗∗ (0.579)
1.576∗∗∗ (0.548)
1.588∗∗∗ (0.573)
1.592∗∗∗ (0.566)
1.607∗∗∗ (0.602)
1.608∗∗∗ (0.598)
JPORG
0.903∗∗∗ (0.341)
0.923∗∗∗ (0.349)
0.955∗∗∗ (0.362)
1.056∗∗∗ (0.359)
1.109∗∗∗ (0.403)
1.111∗∗∗ (0.412)
1.114∗∗∗ (0.409)
ARORG
1.112∗∗∗ (0.409)
1.108∗∗∗ (0.396)
1.134∗∗∗ (0.421)
1.197∗∗∗ (0.430)
1.211∗∗∗ (0.462)
1.257∗∗∗ (0.473)
1.266∗∗∗ (0.471)
CNORG
0.599∗∗∗ (0.212)
0.605∗∗∗ (0.227)
0.710∗∗∗ (0.221)
0.715∗∗∗ (0.247)
0.813∗∗∗ (0.289)
0.907∗∗∗ (0.301)
0.915∗∗∗ (0.315)
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. Standard errors of consumer WTPs are reported within the brackets. #Consumer ethnocentrism and trust in certifiers are held at the sample average level.Consumers tend to assume that the US, as a developed country, has stricter food quality management and organic certification supervision7. Therefore, a much higher WTP was expressed for USORG and USCERT. This agrees with studies on country-of-origin effects that products (or services) from developed countries are always more popular than those from developing countries [42]. Of note, a much lower WTP was expressed for organic labels from Japan, a developed country, and was even slightly lower than the WTP for organic labels from Argentina, a developing country. By contrast, the mean trust in USORG and JPORG was similar, and much higher than that in ARORG or CNORG, as shown in Table 7. The deviation between trust and WTP, i.e., relatively low WTP for JPORG, might be based on several reasons: First, consumers might consider that Japan, an island country with a small territory, does not have competitive advantages in agricultural production; second, it might be related to consumer ethnocentrism and the complex Sino-Japanese relationship. Another surprising finding was that a relatively high WTP was expressed for organic labels from Argentina, a country that is relatively similar to China in economic development. A possible reason is that Argentina is a large agricultural country, with a competitive advantage in agricultural production and the second largest organic farmland area in the world. It is also a large developing country like China, which might generate positive country-of-origin effects on the products (or services) of Argentina. As shown in Table 6, the lowest WTP was expressed for CNORG. In contrast, Alphonce and Alfnes and Lusk et al. argued that consumers have greater loyalty to their own country and discriminate against other countries to some extent [46, 47]. The possible reasons for this inconsistency are as follows: (1) Many Chinese consumers believe that agricultural science and technology and management levels in China fall far behind those of developed countries, with organic certification no exception. In addition, some consumers exhibit blind faith in foreign products, which is a belief primarily based on purchase experience. Consumers can believe that products from developed countries are of a higher quality than those made in China, even when quality is the same [7]. (2) The misuse and even falsification of organic labels has jeopardized the development of this emerging market in China. (After inspecting the authenticity of organic certification in 2014, the Certification and Accreditation Administration of the PRC declared a remarkable misuse rate of 5.8% in certification labels. Of them, 50% were due to the prolonged use of certificates, 28.6% were suspected of lacking organic codes, 14.3% were suspected of using counterfeit organic codes, and 7.1% did not use certification labels as specified. Source: Sohu, http://mt.sohu.com/20160621/n455577833.shtml.) Numerous certification fraud incidents have occurred in recent years. The “Chongqing Wal-Mart green pork” incident in 2011 and the “Guizhou organic Maotai” incident in 2013 both impacted the authority of Chinese food safety certification and reduced consumer trust in Chinese organic labels. Thus, consumers believe that the reliability of organic labels from other developing countries, such as Argentina, is higher than that of China.The WTP estimates for certifiers in Table6 showed that consumers were willing to pay higher premiums for GOVCERT than PRICERT. Janssen and Hamm found that consumers in some countries have a higher WTP for government-certified labels than private ones, whereas the opposite is true in other countries; furthermore, consumers in some countries do not show significantly different WTP between the two labels, and consumer preferences for labels can vary with the type of organic product [5].


### 5.4. Consumer Ethnocentrism and WTP for Organic Food

To further examine the sensitivity of consumer WTP based on different levels of ethnocentrism, using the parametric bootstrapping technique outlined in Hole, we calculated WTP measures for each type of organic label and certifier based on different levels of ETH [45]. In this calculation, the levels of trust in labels (LTRUST) and trust in certifiers (CTRUST) were held at sample average while ETH was allowed to change from 1 to 7, taking only integer values. Table 8 shows the result.Table 8
Consumers' willingness to pay based on different levels of ethnocentrism#.
Level
ETH = 1
ETH = 2
ETH = 3
ETH = 4
ETH = 5
ETH = 6
ETH = 7
USORG
1.638∗∗∗
1.618∗∗∗
1.597∗∗∗
1.582∗∗∗
1.501∗∗∗
1.428∗∗∗
1.431∗∗∗
JPORG
1.321∗∗∗
1.317∗∗∗
1.205∗∗∗
1.113∗∗∗
0.954∗∗∗
0.886∗∗∗
0.884∗∗∗
ARORG
1.129∗∗∗
1.125∗∗∗
1.125∗∗∗
1.121∗∗∗
1.003∗∗∗
0.922∗∗∗
0.829∗∗∗
CNORG
0.685∗∗∗
0.698∗∗∗
0.738∗∗∗
0.754∗∗∗
0.893∗∗∗
1.029∗∗∗
1.253∗∗∗
USCERT
1.508∗∗∗
1.504∗∗∗
1.496∗∗∗
1.486∗∗∗
1.395∗∗∗
1.370∗∗∗
1.371∗∗∗
ARCERT
1.460∗∗∗
1.451∗∗∗
1.448∗∗∗
1.436∗∗∗
1.431∗∗∗
1.434∗∗∗
1.433∗∗∗
GOVCERT
1.080∗∗∗
1.085∗∗∗
1.107∗∗∗
1.115∗∗∗
1.311∗∗∗
1.308∗∗∗
1.319∗∗∗
ACACERT
0.891∗∗∗
0.882∗∗∗
0.921∗∗∗
0.936∗∗∗
1.086∗∗∗
1.165∗∗∗
1.172∗∗∗
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. #Variables LTRUST and CTRUST are held at the sample average level.As seen in Table8, there were considerable differences in the order of preference for the organic labels among the ETH levels. The greatest difference was observed in their WTP for Japanese and Chinese organic labels. When the level of ETH is low, the order of WTP for organic labels from different countries was USORG > JPORG > ARORG > CNORG. Their WTP for USORG and JPORG was much higher than that for ARORG, which was, in turn, much higher than that for CNORG. With increases in ETH, the WTP for USORG and ARORG decreased, especially for JPORG, but the WTP for CNORG increased. When the level of ETH is relatively high, there is a different WTP order for organic labels from different countries, that is, USORG > CNORG > ARORG > JPORG. They showed a much higher WTP for CNORG, and although it was lower than the WTP for USORG, it was higher than that for ARORG and JPORG (which had the lowest WTP). Overall, with the increase in ETH, consumers showed a lower WTP for foreign organic labels, especially for those from Japan, and a significantly higher WTP for organic labels from China. This indicated that ETH had a considerable impact on consumer preferences.Table8 also shows that, with the increase in ETH, consumer WTP slightly increased for Chinese certifiers, slightly decreased for USCERT, and remained substantially unchanged for ARCERT. Moreover, the order of preferences for certifiers remains unchanged. This indicated that the preferences of Chinese consumers for domestic and foreign certifiers were not affected by ethnocentrism to any great extent. Since its reformation, the Chinese government has generally adopted a policy of encouraging direct foreign investment. In this context, Chinese consumers have also welcomed direct foreign investment and view its contribution to domestic employment and economic growth positively. However, this speculation needs further verification.


### 5.5. Trust Effects on Preferences and WTP

For a similar reason, WTP measured at different levels of trust for each type of organic label and certifier was calculated while allowing the trust measures to change from 1 to 7, taking every integer values in between. When calculating the impact of trust in organic labels (LTRUST), consumer ethnocentrism (ETH) and trust in certifiers (CTRUST) were held at sample average. Variables ETH and LTRUST were held at the sample average while calculating the impact of CTRUST. The results are shown in Tables7 and 9.Table 9
Consumers' willingness to pay for certifiers based on different levels of trust in certifiers#.
Certifiers
CTRUST = 1
CTRUST = 2
CTRUST = 3
CTRUST = 4
CTRUST = 5
CTRUST = 6
CTRUST = 7
USCERT
1.473∗∗∗ (0.521)
1.481∗∗∗ (0.499)
1.521∗∗∗ (0.532)
1.632∗∗∗ (0.578)
1.669∗∗∗ (0.592)
1.717∗∗∗ (0.623)
1.785∗∗∗ (0.637)
ARCERT
0.756∗∗∗ (0.213)
0.803∗∗∗ (0.201)
0.956∗∗∗ (0.266)
1.127∗∗∗ (0.242)
1.151∗∗∗ (0.308)
1.286∗∗∗ (0.403)
1.301∗∗∗ (0.424)
GOVCERT
0.974∗∗∗ (0.359)
1.006∗∗∗ (0.362)
1.128∗∗∗ (0.412)
1.315∗∗∗ (0.437)
1.346∗∗∗ (0.464)
1.385∗∗∗ (0.502)
1.402∗∗∗ (0.521)
ACACERT
0.713∗∗∗ (0.245)
0.721∗∗∗ (0.257)
0.974∗∗∗ (0.291)
1.102∗∗∗ (0.286)
1.142∗∗∗ (0.331)
1.213∗∗∗ (0.392)
1.291∗∗∗ (0.378)
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. Standard errors of consumers' WTPs are reported within the brackets. #Consumer ethnocentrism and trust in organic labels are held at the sample average level.Based on results in Table7, with the increase in consumer trust (the respondents were grouped in five different ways per their trust in the four organic labels, as well as average trust. The WTP for each type of organic label was then calculated for the different groups. However, only the WTP of respondents grouped by average trust (AVERAGE) are discussed in this paper. Based on the survey data, the correlation coefficients between the trust scores for the different organic labels were all greater than 0.8, indicating a positive correlation between trust scores for each type of organic label. Although consumer trust varied among the organic labels, consumers with a higher trust in a certain organic label were more inclined to trust the other three types of organic labels. Our calculation results also showed no significant differences in WTP among the trust groups when the respondents were grouped as mentioned above. Given space limitations, the calculation results for all groups are not presented. The same is true for the grouping of respondents by their trust in different certifiers), the order of preference for the organic labels remained the same: USORG > ARORG > JPORG > CNORG. Similarly, the order of preference for the certifiers remained the same: USCERT > ACACERT > ARCERT > GOVCERT. However, with the increase in trust, consumer WTP for each type of organic label and certifier generally increased, which corroborates previous research on consumer WTP for organic food12. It is worth noting that, in general, the difference in WTP among organic labels decreased with the increase in trust. For example, according to Table 7, when consumer trust in organic labels increased from 1 to 7, the difference between their WTP for a US label and a Chinese label decreased from US$ 0.932 to $ 0.693, respectively. Consumer WTP for other labels showed a similar trend.Similarly, the data in Table9 indicate that the differences in WTP among certifiers also decreased with the increase in trust (Table 9). Due to the frequent food safety incidents and scandals in China in recent years, consumers in the low trust groups had lower levels of trust in Chinese organic labels and certifiers, but relatively higher levels of trust in foreign organic labels and foreign-invested certifiers, thus resulting in greater differences in their WTP. This suggests that an increase in consumer trust in organic certification will help to enhance WTP, especially for Chinese organic labels and certifiers.


## 5.1. Chinese Consumer Ethnocentrism and Trust Scores

The mean Chinese consumer ethnocentrism (ETH) score was 3.9417, with a standard deviation of 1.0341. This result is consistent with the conclusions of Rašković et al. and Liu et al., who noted that Chinese consumers generally do not show high ethnocentrism2930. It could be attributed to the increasing integration of China in the world economy over the last 30 years of reform.Based on consumer trust in the four organic labels, as shown in Table4, mean consumer trust was 4.75, with some differences found among labels. Mean trust for the four organic labels was, in descending order, USORG (5.42), JPORG (5.34), ARORG (4.73), and CNORG (4.32). Further analysis of mean consumer trust by dependent-sample t-test revealed significant differences in mean consumer trust among the four organic labels (all p<0.01). Mean consumer trust in USORG and JPORG were similar and much higher than that in ARORG and CNORG. Consumers had more trust in organic labels from developed countries/regions. This is consistent with previous research indicating that products from developed countries are often more popular than those from developing countries [42].Table 4
Participants' trust in different organic labels and certifiers.
Label
Mean
Standard deviation
Certifiers
Mean
Standard deviation
USORG
5.42
1.3547
GOVCERT
4.96
1.0357
JPORG
5.34
1.1357
PRICERT
3.94
0.7586
ARORG
4.73
0.9834
ACACERT
5.24
0.8674
CNORG
4.32
0.7589
USCERT
5.68
1.2457
—
—
—
ARCERT
5.02
1.1567
AVERAGEa
4.95
1.2498
AVERAGEa
4.86
1.0247
aMean consumer trust in all organic labels or certifiers.As seen in Table4, differences in trust were also found among different certifiers. Dependent-sample t-test showed significant differences in mean consumer trust (all p<0.01). Consumers had the highest trust in USCERT, followed by ACACERT, then ARCERT and GOVCERT, and lastly PRICERT. Consumers showed lower trust in private than government certification and lower trust in domestic than foreign certifiers, especially certifiers from developed countries. This coincides with the findings of Wu et al. [7].


## 5.2. ML Model Estimates

Effect coding was used in the analysis. Assumptions for the relevant parameter distributions are as follows: first, the coefficients of the price (PRICE) and opt-out variables were fixed; and secondly, the attribute levels followed normal distribution [43]. Table 5 shows the ML model estimation results using NLOGIT 5.0. As seen in Table 5, the ML model estimates showed significantly positive two-way interactions (and thus complementary relationships) between USCERT and USORG and between ARCERT and ARORG. Certification of USORG by USCERT and certification of ARORG by ARCERT both generated positive effects on the combined main effects of the two attributes. For foreign certifiers, certification of organic labels from their respective countries improved Chinese consumer preferences. In addition, ACACERT exhibited significantly positive interactions with USORG, JPORG, and ARORG. Thus, certification of USORG, JPORG, and ARORG by academic institutions also generated positive effects on the combined main effects of the two attributes. This might be because consumers assumed that the ability of academic institutions to provide certification services for foreign organic labels highlighted their professional competence, thereby increasing consumer preference. This supports earlier findings that showed complementary or substitutional relationships between different attributes of food [7, 42].Table 5
Mixed logit model result on consumers' preferences for attributes of organic tomatoes.
Coef. of estimates
SE
t-value
95% CI
Main effects
PRICE
−1.978∗∗∗
0.081
−23.83
[−2.137, −1.819]
Opt out
−1.489∗∗
0.087
−15.12
[−1.660, −1.318]
USORG
1.071∗∗∗
0.144
7.54
[0.789, 1.353]
JPORG
0.718∗∗
0.377
1.91
[−0.021, 1.457]
ARORG
0.759∗
0.394
1.95
[−0.013, 1.531]
CNORG
0.425∗∗∗
0.231
1.81
[−0.028, 0.878]
USCERT
1.158∗∗∗
0.057
17.28
[1.046, 1.270]
ARCERT
0.678∗
0.047
13.79
[0.586, 0.770]
GOVCERT
0.454∗
0.520
0.86
[−0.565, 1.473]
ACACERT
0.767∗∗∗
0.316
2.38
[0.148, 1.386]
Interaction term
USORG × USCERT
−0.521∗∗∗
0.027
−17.85
[−0.574, −0.468]
JPORG × USCERT
0.031∗∗∗
0.011
3.14
[0.009, 0.053]
ARORG × USCERT
0.203∗∗∗
0.061
3.36
[0.083, 0.323]
CNORG × USCERT
0.146∗∗∗
0.047
3.12
[0.054, 0.238]
USORG × ARCERT
0.440∗∗∗
0.105
4.31
[0.234, 0.646]
JPORG × ARCERT
0.151∗∗∗
0.034
3.89
[0.084, 0.218]
ARORG × ARCERT
0.131∗∗∗
0.024
5.20
[0.084, 0.178]
CNORG × ARCERT
0.161∗∗∗
0.026
6.01
[0.110, 0.212]
USORG × GOVCERT
0.035∗∗∗
0.007
5.42
[0.021, 0.049]
JPORG × GOVCERT
0.231∗∗∗
0.009
8.87
[0.213, 0.249]
ARORG × GOVCERT
0.048∗∗∗
0.013
3.46
[0.023, 0.073]
CNORG × GOVCERT
0.074∗∗∗
0.017
4.41
[0.041, 0.107]
USORG × ACACERT
0.078∗∗∗
0.025
3.09
[0.029, 0.127]
JPORG × ACACERT
0.067∗∗∗
0.021
3.25
[0.026, 0.108]
ARORG × ACACERT
0.111∗∗∗
0.031
3.60
[0.050, 0.172]
CNORG × ACACERT
0.215∗∗∗
0.037
5.60
[0.142, 0.289]
ETH × USORG
0.052∗∗∗
0.013
3.73
[0.027, 0.077]
ETH × JPORG
0.175∗∗∗
0.030
4.88
[0,116, 0.234]
ETH × ARORG
0.154∗∗
0.024
4.71
[0.107, 0.201]
ETH × CNORG
0.148∗
0.044
3.58
[0.062, 0.234]
ETH × USCERT
0.129∗∗∗
0.044
3.21
[0.043, 0.215]
ETH × ARCERT
0.199∗∗∗
0.105
2.67
[0.007, 0.405]
ETH × GOVCERT
0.242∗∗∗
0.016
14.13
[0.211, 0.273]
ETH × ACACERT
0.171∗
0.049
3.42
[0.075, 0.267]
LTRUST × USORG
0.217∗∗
0.074
2.88
[0.072, 0.362]
LTRUST × JPORG
0.105∗∗
0.0212
3.56
[0.063, 0.147]
LTRUST × ARORG
0.148∗∗∗
0.053
2.76
[0.044, 0.252]
LTRUST × CNORG
0.281∗∗∗
0.111
3.09
[0.063, 0.499]
LTRUST × USCERT
0.214∗∗
0.036
6.42
[0.143, 0.285]
LTRUST × ARCERT
0.175∗∗∗
0.016
6.38
[0.144, 0.206]
LTRUST × GOVCERT
0.060∗∗
0.018
4.35
[0.025, 0.095]
LTRUST × ACACERT
0.178∗∗
0.042
3.21
[0.096, 0.260]
CTRUST × USORG
0.247∗∗∗
0.081
3.08
[0.088, 0.406]
CTRUST × JPORG
0.106∗∗∗
0.0212
4.51
[0.064, 0.148]
CTRUST × ARORG
0.150∗∗
0.055
2.46
[0.042, 0.258]
CTRUST × CNORG
0.302∗∗
0.110
2.78
[0.086, 0.518]
CTRUST × USCERT
0.204∗∗∗
0.029
5.25
[0.147, 0.261]
CTRUST × ARCERT
0.265∗∗
0.023
5.87
[0.220, 0.310]
CTRUST × GOVCERT
0.055∗∗∗
0.024
6.22
[0.008, 0.102]
CTRUST × ACACERT
0.173∗∗
0.050
3.56
[0.075, 0.271]
Diagonal values in Cholesky matrix
USORG
0.899∗∗∗
0.047
20.41
[0.807, 0.991]
JPRORG
0.523∗∗∗
0.028
13.12
[0.468, 0.578]
ARORG
0.047∗∗
0.021
3.24
[0.006, 0.088]
CNORG
0.153∗∗∗
0.044
2.75
[0.067, 0.239]
USCERT
0.069∗∗∗
0.021
1.68
[0.027, 0.110]
ARCERT
0.529∗∗
0.040
12.02
[0.451, 0.607]
GOVCERT
0.064∗∗∗
0.018
4.81
[0.029, 0.099]
ACACERT
0.264∗∗∗
0.037
7.05
[0.191, 0.337]
Log likelihood
−3604.36
McFadden R2
0.252
AIC
7236.68
—
—
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively; SE: standard error


## 5.3. Estimates of WTP

Based on the estimates in Table5, as well as the characteristics of the ordinal utilities of the main effects of an attribute, WTP was calculated as follows:(9)WTPk=−2×βk+βETH×ak×ETH+βLTRUST×ak×LTRUST+βCTRUST×ak×CTRUST+∑jβk×j×ajβprice,where WTPk is the WTP for level k of the attribute of interest; βk is the coefficient of the main effect of level k of this attribute; βETH×ak is the coefficient of the interaction between ETH and level k of the attribute; βLTRUST×ak and βCTRUST×ak are the coefficients of the interaction between LTRUST and CTRUST and level k of the attribute, respectively; βk×j is the coefficient of the interaction between level k of the attribute and other attributes; and βprice is the estimated price coefficient. Because effect coding was used in the analysis, the WTP should be multiplied by 2 in the calculation [44]. The confidence interval of WTP was estimated using the parametric bootstrapping technique (PBT) [45]. Table 6 presents the mean WTP estimates for different organic labels and certifiers and the corresponding confidence intervals. Consumer ethnocentrism and trust variables were held at the sample average level. Attribute variables entered as interactions were held at −1.Table 6
Consumers' willingness to pay measured at the sample average level of ethnocentrism and trust.
Attribute level
Coef.
SE
95% CI
USORG
1.589∗∗∗
0.529
[0.552, 2.626]
JPORG
1.027∗∗∗
0.374
[0.294, 1.760]
ARORG
1.116∗∗∗
0.286
[0.555, 1.677]
CNORG
0.729∗∗∗
0.307
[0.127, 1.331]
USCERT
1.768∗∗∗
0.299
[1.182, 2.354]
ARCERT
1.042∗∗∗
0.304
[0.446, 1.638]
GOVCERT
0.837∗∗∗
0.234
[0.378, 1.296]
ACACERT
1.216∗∗∗
0.295
[0.638, 1.794]
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively.As shown in Table6, there were considerable differences in consumer WTP for different organic labels. The order of consumer preference for the organic labels was not consistent with that of consumer trust shown in Table 7. Among the four organic labels, the highest WTP was expressed for USORG (US$ 1.589), followed by ARORG (US$ 1.116), JPORG (US$ 1.027), and lastly by CNORG (US$ 0.729). In terms of WTP for different certifiers, consumers had the highest WTP for USCERT (US$ 1.768), followed by ACACERT (US$ 1.216), ARCERT (US$ 1.042), and finally GOVCERT (US$ 0.837). This is the same as the order of mean trust in different certifiers shown in Table 4.Table 7
Consumers' willingness to pay for organic labels based on different levels of trust in organic labels#.
Label
LTRUST = 1
LTRUST = 2
LTRUST = 3
LTRUST = 4
LTRUST = 5
LTRUST = 6
LTRUST = 7
USORG
1.531∗∗∗ (0.583)
1.511∗∗∗ (0.579)
1.576∗∗∗ (0.548)
1.588∗∗∗ (0.573)
1.592∗∗∗ (0.566)
1.607∗∗∗ (0.602)
1.608∗∗∗ (0.598)
JPORG
0.903∗∗∗ (0.341)
0.923∗∗∗ (0.349)
0.955∗∗∗ (0.362)
1.056∗∗∗ (0.359)
1.109∗∗∗ (0.403)
1.111∗∗∗ (0.412)
1.114∗∗∗ (0.409)
ARORG
1.112∗∗∗ (0.409)
1.108∗∗∗ (0.396)
1.134∗∗∗ (0.421)
1.197∗∗∗ (0.430)
1.211∗∗∗ (0.462)
1.257∗∗∗ (0.473)
1.266∗∗∗ (0.471)
CNORG
0.599∗∗∗ (0.212)
0.605∗∗∗ (0.227)
0.710∗∗∗ (0.221)
0.715∗∗∗ (0.247)
0.813∗∗∗ (0.289)
0.907∗∗∗ (0.301)
0.915∗∗∗ (0.315)
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. Standard errors of consumer WTPs are reported within the brackets. #Consumer ethnocentrism and trust in certifiers are held at the sample average level.Consumers tend to assume that the US, as a developed country, has stricter food quality management and organic certification supervision7. Therefore, a much higher WTP was expressed for USORG and USCERT. This agrees with studies on country-of-origin effects that products (or services) from developed countries are always more popular than those from developing countries [42]. Of note, a much lower WTP was expressed for organic labels from Japan, a developed country, and was even slightly lower than the WTP for organic labels from Argentina, a developing country. By contrast, the mean trust in USORG and JPORG was similar, and much higher than that in ARORG or CNORG, as shown in Table 7. The deviation between trust and WTP, i.e., relatively low WTP for JPORG, might be based on several reasons: First, consumers might consider that Japan, an island country with a small territory, does not have competitive advantages in agricultural production; second, it might be related to consumer ethnocentrism and the complex Sino-Japanese relationship. Another surprising finding was that a relatively high WTP was expressed for organic labels from Argentina, a country that is relatively similar to China in economic development. A possible reason is that Argentina is a large agricultural country, with a competitive advantage in agricultural production and the second largest organic farmland area in the world. It is also a large developing country like China, which might generate positive country-of-origin effects on the products (or services) of Argentina. As shown in Table 6, the lowest WTP was expressed for CNORG. In contrast, Alphonce and Alfnes and Lusk et al. argued that consumers have greater loyalty to their own country and discriminate against other countries to some extent [46, 47]. The possible reasons for this inconsistency are as follows: (1) Many Chinese consumers believe that agricultural science and technology and management levels in China fall far behind those of developed countries, with organic certification no exception. In addition, some consumers exhibit blind faith in foreign products, which is a belief primarily based on purchase experience. Consumers can believe that products from developed countries are of a higher quality than those made in China, even when quality is the same [7]. (2) The misuse and even falsification of organic labels has jeopardized the development of this emerging market in China. (After inspecting the authenticity of organic certification in 2014, the Certification and Accreditation Administration of the PRC declared a remarkable misuse rate of 5.8% in certification labels. Of them, 50% were due to the prolonged use of certificates, 28.6% were suspected of lacking organic codes, 14.3% were suspected of using counterfeit organic codes, and 7.1% did not use certification labels as specified. Source: Sohu, http://mt.sohu.com/20160621/n455577833.shtml.) Numerous certification fraud incidents have occurred in recent years. The “Chongqing Wal-Mart green pork” incident in 2011 and the “Guizhou organic Maotai” incident in 2013 both impacted the authority of Chinese food safety certification and reduced consumer trust in Chinese organic labels. Thus, consumers believe that the reliability of organic labels from other developing countries, such as Argentina, is higher than that of China.The WTP estimates for certifiers in Table6 showed that consumers were willing to pay higher premiums for GOVCERT than PRICERT. Janssen and Hamm found that consumers in some countries have a higher WTP for government-certified labels than private ones, whereas the opposite is true in other countries; furthermore, consumers in some countries do not show significantly different WTP between the two labels, and consumer preferences for labels can vary with the type of organic product [5].


## 5.4. Consumer Ethnocentrism and WTP for Organic Food

To further examine the sensitivity of consumer WTP based on different levels of ethnocentrism, using the parametric bootstrapping technique outlined in Hole, we calculated WTP measures for each type of organic label and certifier based on different levels of ETH [45]. In this calculation, the levels of trust in labels (LTRUST) and trust in certifiers (CTRUST) were held at sample average while ETH was allowed to change from 1 to 7, taking only integer values. Table 8 shows the result.Table 8
Consumers' willingness to pay based on different levels of ethnocentrism#.
Level
ETH = 1
ETH = 2
ETH = 3
ETH = 4
ETH = 5
ETH = 6
ETH = 7
USORG
1.638∗∗∗
1.618∗∗∗
1.597∗∗∗
1.582∗∗∗
1.501∗∗∗
1.428∗∗∗
1.431∗∗∗
JPORG
1.321∗∗∗
1.317∗∗∗
1.205∗∗∗
1.113∗∗∗
0.954∗∗∗
0.886∗∗∗
0.884∗∗∗
ARORG
1.129∗∗∗
1.125∗∗∗
1.125∗∗∗
1.121∗∗∗
1.003∗∗∗
0.922∗∗∗
0.829∗∗∗
CNORG
0.685∗∗∗
0.698∗∗∗
0.738∗∗∗
0.754∗∗∗
0.893∗∗∗
1.029∗∗∗
1.253∗∗∗
USCERT
1.508∗∗∗
1.504∗∗∗
1.496∗∗∗
1.486∗∗∗
1.395∗∗∗
1.370∗∗∗
1.371∗∗∗
ARCERT
1.460∗∗∗
1.451∗∗∗
1.448∗∗∗
1.436∗∗∗
1.431∗∗∗
1.434∗∗∗
1.433∗∗∗
GOVCERT
1.080∗∗∗
1.085∗∗∗
1.107∗∗∗
1.115∗∗∗
1.311∗∗∗
1.308∗∗∗
1.319∗∗∗
ACACERT
0.891∗∗∗
0.882∗∗∗
0.921∗∗∗
0.936∗∗∗
1.086∗∗∗
1.165∗∗∗
1.172∗∗∗
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. #Variables LTRUST and CTRUST are held at the sample average level.As seen in Table8, there were considerable differences in the order of preference for the organic labels among the ETH levels. The greatest difference was observed in their WTP for Japanese and Chinese organic labels. When the level of ETH is low, the order of WTP for organic labels from different countries was USORG > JPORG > ARORG > CNORG. Their WTP for USORG and JPORG was much higher than that for ARORG, which was, in turn, much higher than that for CNORG. With increases in ETH, the WTP for USORG and ARORG decreased, especially for JPORG, but the WTP for CNORG increased. When the level of ETH is relatively high, there is a different WTP order for organic labels from different countries, that is, USORG > CNORG > ARORG > JPORG. They showed a much higher WTP for CNORG, and although it was lower than the WTP for USORG, it was higher than that for ARORG and JPORG (which had the lowest WTP). Overall, with the increase in ETH, consumers showed a lower WTP for foreign organic labels, especially for those from Japan, and a significantly higher WTP for organic labels from China. This indicated that ETH had a considerable impact on consumer preferences.Table8 also shows that, with the increase in ETH, consumer WTP slightly increased for Chinese certifiers, slightly decreased for USCERT, and remained substantially unchanged for ARCERT. Moreover, the order of preferences for certifiers remains unchanged. This indicated that the preferences of Chinese consumers for domestic and foreign certifiers were not affected by ethnocentrism to any great extent. Since its reformation, the Chinese government has generally adopted a policy of encouraging direct foreign investment. In this context, Chinese consumers have also welcomed direct foreign investment and view its contribution to domestic employment and economic growth positively. However, this speculation needs further verification.


## 5.5. Trust Effects on Preferences and WTP

For a similar reason, WTP measured at different levels of trust for each type of organic label and certifier was calculated while allowing the trust measures to change from 1 to 7, taking every integer values in between. When calculating the impact of trust in organic labels (LTRUST), consumer ethnocentrism (ETH) and trust in certifiers (CTRUST) were held at sample average. Variables ETH and LTRUST were held at the sample average while calculating the impact of CTRUST. The results are shown in Tables7 and 9.Table 9
Consumers' willingness to pay for certifiers based on different levels of trust in certifiers#.
Certifiers
CTRUST = 1
CTRUST = 2
CTRUST = 3
CTRUST = 4
CTRUST = 5
CTRUST = 6
CTRUST = 7
USCERT
1.473∗∗∗ (0.521)
1.481∗∗∗ (0.499)
1.521∗∗∗ (0.532)
1.632∗∗∗ (0.578)
1.669∗∗∗ (0.592)
1.717∗∗∗ (0.623)
1.785∗∗∗ (0.637)
ARCERT
0.756∗∗∗ (0.213)
0.803∗∗∗ (0.201)
0.956∗∗∗ (0.266)
1.127∗∗∗ (0.242)
1.151∗∗∗ (0.308)
1.286∗∗∗ (0.403)
1.301∗∗∗ (0.424)
GOVCERT
0.974∗∗∗ (0.359)
1.006∗∗∗ (0.362)
1.128∗∗∗ (0.412)
1.315∗∗∗ (0.437)
1.346∗∗∗ (0.464)
1.385∗∗∗ (0.502)
1.402∗∗∗ (0.521)
ACACERT
0.713∗∗∗ (0.245)
0.721∗∗∗ (0.257)
0.974∗∗∗ (0.291)
1.102∗∗∗ (0.286)
1.142∗∗∗ (0.331)
1.213∗∗∗ (0.392)
1.291∗∗∗ (0.378)
∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, and 1% levels, respectively. Standard errors of consumers' WTPs are reported within the brackets. #Consumer ethnocentrism and trust in organic labels are held at the sample average level.Based on results in Table7, with the increase in consumer trust (the respondents were grouped in five different ways per their trust in the four organic labels, as well as average trust. The WTP for each type of organic label was then calculated for the different groups. However, only the WTP of respondents grouped by average trust (AVERAGE) are discussed in this paper. Based on the survey data, the correlation coefficients between the trust scores for the different organic labels were all greater than 0.8, indicating a positive correlation between trust scores for each type of organic label. Although consumer trust varied among the organic labels, consumers with a higher trust in a certain organic label were more inclined to trust the other three types of organic labels. Our calculation results also showed no significant differences in WTP among the trust groups when the respondents were grouped as mentioned above. Given space limitations, the calculation results for all groups are not presented. The same is true for the grouping of respondents by their trust in different certifiers), the order of preference for the organic labels remained the same: USORG > ARORG > JPORG > CNORG. Similarly, the order of preference for the certifiers remained the same: USCERT > ACACERT > ARCERT > GOVCERT. However, with the increase in trust, consumer WTP for each type of organic label and certifier generally increased, which corroborates previous research on consumer WTP for organic food12. It is worth noting that, in general, the difference in WTP among organic labels decreased with the increase in trust. For example, according to Table 7, when consumer trust in organic labels increased from 1 to 7, the difference between their WTP for a US label and a Chinese label decreased from US$ 0.932 to $ 0.693, respectively. Consumer WTP for other labels showed a similar trend.Similarly, the data in Table9 indicate that the differences in WTP among certifiers also decreased with the increase in trust (Table 9). Due to the frequent food safety incidents and scandals in China in recent years, consumers in the low trust groups had lower levels of trust in Chinese organic labels and certifiers, but relatively higher levels of trust in foreign organic labels and foreign-invested certifiers, thus resulting in greater differences in their WTP. This suggests that an increase in consumer trust in organic certification will help to enhance WTP, especially for Chinese organic labels and certifiers.


## 6. Conclusions and Limitations

### 6.1. Conclusions

In this paper, consumer WTP for organic labels from different sources was examined using the choice experiment method and mixed logit model. Furthermore, the impacts of consumer ethnocentrism and trust on consumer preferences were analyzed. The main conclusions are as follows: (1) Mean consumer trust in organic labels varied with the country of origin. The highest level of trust was found in US organic labels, followed by Japanese, Argentine, and Chinese organic labels. Consumer trust in certifiers also significantly differed with the country of origin. The highest level of trust was found in US-invested certifiers, followed by Chinese academic institution-supported, Argentine-invested, and Chinese government-supported certifiers, and lastly in Chinese private certifiers. (2) Consumer WTP varied greatly among the organic labels. The highest WTP was expressed for US organic labels, followed by Argentine and Japanese organic labels, and lastly Chinese organic labels. The order of WTP was not consistent with that of consumer trust, mainly due to the relatively low WTP for Japanese organic labels. Chinese consumers not only showed strong preference for US organic labels but also the highest WTP for US-invested certifiers, followed by Chinese academic institution-supported, Argentine-invested, and Chinese government-supported certifiers. This is the same as the order of mean trust in different certifiers. (3) Certification of organic labels from their respective countries improved Chinese consumer preferences. In addition, academic institution-supported certifier exhibited significantly positive interactions with foreign organic labels. (4) Chinese consumers did not show high ethnocentrism. However, there was a considerable discrepancy in the order of preference for the organic labels among the ETH levels. With the increase in consumer ethnocentrism, consumers showed a substantially lower WTP for Japanese organic labels and a significantly higher WTP for Chinese organic labels. Consumer WTP for certifiers did not change significantly with the increase in ETH, with a slight increase for Chinese certifiers, slight decrease for US-invested certifiers, and stability for Argentine-invested certifiers. (5) The order of preference for different organic labels and that for various certifiers did not change with the increase in consumer trust. However, consumer WTP for each type of organic label generally increased with the increase in trust. It is worth noting that the differences in consumer WTP among organic labels and among certifiers decreased with the increase in trust.Overall, Chinese consumers showed different WTPs for organic labels from different sources due to consumer ethnocentrism and trust. These conclusions should serve as a vital basis for food suppliers in their selection of certification services and target market positioning. Moreover, this research can not only serve as a guidance for the reformation of the certification system and development of the certified food market in China, but also provide information to foreign certification service providers when entering the Chinese market.


### 6.2. Limitations

This study had the following limitations: (1) Our research was conducted during the period when Sino-Japanese economic and trade relations had cooled due to the Diaoyu Islands dispute and other issues. This historical background inevitably affected consumer attitudes towards Japanese products or services to varying degrees. Thus, consumer WTP for Japanese organic labels should be tracked over a longer period. (2) Countries-of-origins were limited to the US, Japan, Argentina, and China. The WTPs of Chinese consumers for organic labels from other countries/regions, such as Canada and Brazil, need to be further investigated. (3) Consumer trust in different organic labels and certifiers were examined separately; however, trust was not examined in regards to a specific type of organic label from a specific type of certifier, such as a US organic label issued by a Chinese government-supported certifier, which should be examined in future research.


## 6.1. Conclusions

In this paper, consumer WTP for organic labels from different sources was examined using the choice experiment method and mixed logit model. Furthermore, the impacts of consumer ethnocentrism and trust on consumer preferences were analyzed. The main conclusions are as follows: (1) Mean consumer trust in organic labels varied with the country of origin. The highest level of trust was found in US organic labels, followed by Japanese, Argentine, and Chinese organic labels. Consumer trust in certifiers also significantly differed with the country of origin. The highest level of trust was found in US-invested certifiers, followed by Chinese academic institution-supported, Argentine-invested, and Chinese government-supported certifiers, and lastly in Chinese private certifiers. (2) Consumer WTP varied greatly among the organic labels. The highest WTP was expressed for US organic labels, followed by Argentine and Japanese organic labels, and lastly Chinese organic labels. The order of WTP was not consistent with that of consumer trust, mainly due to the relatively low WTP for Japanese organic labels. Chinese consumers not only showed strong preference for US organic labels but also the highest WTP for US-invested certifiers, followed by Chinese academic institution-supported, Argentine-invested, and Chinese government-supported certifiers. This is the same as the order of mean trust in different certifiers. (3) Certification of organic labels from their respective countries improved Chinese consumer preferences. In addition, academic institution-supported certifier exhibited significantly positive interactions with foreign organic labels. (4) Chinese consumers did not show high ethnocentrism. However, there was a considerable discrepancy in the order of preference for the organic labels among the ETH levels. With the increase in consumer ethnocentrism, consumers showed a substantially lower WTP for Japanese organic labels and a significantly higher WTP for Chinese organic labels. Consumer WTP for certifiers did not change significantly with the increase in ETH, with a slight increase for Chinese certifiers, slight decrease for US-invested certifiers, and stability for Argentine-invested certifiers. (5) The order of preference for different organic labels and that for various certifiers did not change with the increase in consumer trust. However, consumer WTP for each type of organic label generally increased with the increase in trust. It is worth noting that the differences in consumer WTP among organic labels and among certifiers decreased with the increase in trust.Overall, Chinese consumers showed different WTPs for organic labels from different sources due to consumer ethnocentrism and trust. These conclusions should serve as a vital basis for food suppliers in their selection of certification services and target market positioning. Moreover, this research can not only serve as a guidance for the reformation of the certification system and development of the certified food market in China, but also provide information to foreign certification service providers when entering the Chinese market.


## 6.2. Limitations

This study had the following limitations: (1) Our research was conducted during the period when Sino-Japanese economic and trade relations had cooled due to the Diaoyu Islands dispute and other issues. This historical background inevitably affected consumer attitudes towards Japanese products or services to varying degrees. Thus, consumer WTP for Japanese organic labels should be tracked over a longer period. (2) Countries-of-origins were limited to the US, Japan, Argentina, and China. The WTPs of Chinese consumers for organic labels from other countries/regions, such as Canada and Brazil, need to be further investigated. (3) Consumer trust in different organic labels and certifiers were examined separately; however, trust was not examined in regards to a specific type of organic label from a specific type of certifier, such as a US organic label issued by a Chinese government-supported certifier, which should be examined in future research.



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*Source: 8173808-2019-02-24.xml*