How Does Information Influence Consumers’ Purchase Decisions for Environmentally Friendly Farming Produce? Evidence from China and Japan Based on Choice Experiment

Abstract

In this research, 600 Chinese and Japanese consumers were divided into four groups to analyze consumers’ marginal willingness to pay for environmentally friendly farming (EFF) produce. We found that Chinese consumers had high awareness of green foods, while Japanese consumers were more familiar with organic produce than specially cultivated produce, perhaps because the latter has not yet received uniform national certification in Japan. Choice experiments show that EFF produce prices and consumers’ income critically affect consumers’ decision to pay, especially in China. After each group read different formal definitions of EFF produce, Chinese consumers still preferred green food certification, whereas Japanese consumers chose specially cultivated carrots. Both displayed different ideological purchasing behaviors through added interaction terms with an increase in education. When no information was given, Japanese consumers’ purchasing decisions became more positive as their education levels rose. Possibly, highly educated Chinese consumers emphasize pragmatism, whereas Japanese consumers emphasize the connection between environmental protection and agriculture. Therefore, EFF messaging should be differentiated by region. For distributors committed to international trade in EFF products between developing and developed markets, we suggest lower costs, differentiated product messaging, and community initiatives events to enhance trade and marketing in both China and Japan.

1. Introduction

The 21st century has brought with it a greater awareness of the need to protect the environment. This trend has propelled the demand for environmentally friendly farming (EFF), particularly in developing economies. Organic farming, which dominates EFF, covered an area of 14.7 million hectares in the European Union in 2020, equaling 9.1% of the total arable land, indicating a 6.52% increase from the previous year [1]. The Organic Trade Association [2], the largest single market for organic produce, reported in their US Organic Industry Survey that US organic product sales climbed 12.4% in 2020, breaking the $60-billion mark for the first time and more than doubling the previous year’s growth. In Germany, the world’s second-largest consumer market for organic produce, its market share grew by 22% to €14.99 billion in 2020 [3]. In 2019, organic farms took a 12.9% share of all farmer farms [4]. The Federal Scheme for Organic Farming and Other Forms of Sustainable Agriculture projects that 30% of Germany’s arable land will be utilized for organic farming by 2030 [3]. In the face of this rapid growth, it is important to promote consumer understanding of EFF agriculture to promote consumption.

In Asia, the EFF produce market is also following a growth trajectory. China and Japan, two of Asia’s strongest powerhouse economies, actively promote trade cooperation in organic produce.

In China, since 2012, the domestic organic food consumption market, online shopping, and cold chain technology have been expanding. In large economic zones such as the Yangtze River Delta, “farm-to-customer” and “e-commerce warehouse-to-customer” direct delivery e-services for fresh food have further increased the market scale of EFF produce [5]. The system also provided assurance to the supply chain when the city was locked down during the COVID-19 pandemic [6]. Qian et al. [7] compared the establishment of food traceability systems in China and Europe and noted a positive impact of these systems on consumer preferences and international trade. Organic produce has increased consumers’ confidence in such products through their use of technologies such as certification, QR codes on packages providing information on traceability, and the identification of responsible growers or enterprise producers [8,9].

According to a report from the Japanese Ministry of Agriculture, Forestry and Fisheries (MAFF) [10], as of 2018, China, Japan’s largest trading partner, had 100 organic food exporters licensed to import into Japan. However, MAFF’s [11] report also warned that China, Korea, and Japan continue to depend heavily on fertilizers and pesticides in agriculture. Furthermore, Japan is dependent on imports for most raw materials of chemical fertilizers. For example, of the total ammonium phosphate imported into Japan in 2019, 87% was from China [12]. As the state of the environment becomes an increasingly contentious issue in academia and industry worldwide, countries such as China and Japan are facing tremendous pressure to reduce their use of pesticides and fertilizers and promote EFF markets, especially in response to new consumer demands.

The Asian organic food market has responded by expanding, catching the attention of scholars and researchers. While there is an established corpus of literature focusing on China and Japan, few studies have performed an analysis of the economics of their respective EFF markets. This lacuna inspired us to conduct this study. Existing studies analyzing EFF produce have focused on developed countries and their consumers’ perspectives. Both theory and practice can be improved by expanding such comparative studies to economies outside Europe and North America. First, given the environmental pressures facing Asia, an analysis of Asian countries will be of great value. Second, the transmission of information has an important influence on consumer perceptions of organic food. Hilverda et al. [13] state that when consumers receive an introduction to EFF produce from experts through online communication, their risk perception of the produce reduces. However, research on different attitudes toward such information in developing and developed countries is still limited. Third, the certification label as a tool of information delivery has been extensively studied [13,14,15], while ignoring the factor of knowledge of EFF produce. Based on these factors, there is a need to understand and compare how EFF is understood by consumers in Japan, a developed Asian country, and in China, a developing one.

In this study, using external information on how consumers comprehend EFF, we analyzed Chinese and Japanese consumers’ preferences and the relevant influencing factors. To this effect, we used choice experiments and divided our sample into four groups, each receiving different information. We hope that this approach will provide new perspectives on developing a sustainable organic agriculture market. In conclusion, we offer practical suggestions for EFF business and trade distributors between China and Japan.

2. Determinants of Consumers’ Purchasing Actions for EFF Produce

2.1. China-Japan EFF Agricultural Products Market Development

In China, EFF produce is divided into “organic food” and “green food,” as shown in Table 1. Unlike conventional organic farming, green food requires the China Green Food Development Center’s EFF certification [9]. In this category, restrictions on the use of pesticides and chemical fertilizers are more relaxed compared with the national standards for organic food.

In China, the area of land under certified organic farming reached 4.108 million hectares in 2018, ranking third globally [16]. Domestic sales of organic produce reached RMB 35.78 billion in 2019, fourth in the world [17,18]. As of 2018, China had 1,355,100 hectares of internationally certified organic farmland, with a total export value of US $894 million [16].

Japan divides its EFF produce as “organic” and “specially cultivated” (see Table 1). Organic produce follows uniform standards specified by the MAFF. However, specially cultivated agricultural foods with reduced pesticide and chemical fertilizers are sold only domestically and are closer to the definition of green food in China. Depending on local policies and produce type, pesticides and fertilizers can be reduced by 50% to 100%. The product must also carry information about the producer before entering the market.

As the most developed economy in Asia, Japan is witnessing growth in EFF produce, but the development of the market has been slow. For instance, between 2010 and 2018, the area of organic farmland in Japan increased from 16,700 acres to 23,700 acres, accounting for a paltry 0.2% of the total agricultural land area [10]. To hasten the pace, the MAFF [10] proposed the Green Food System Strategy on 12 May 2021, with a target to bring 25% of the total arable land in Japan under organic farming by 2050.

2.2. Social Identification and Price Sensitivity among Different Countries

Both technological and market development of organic agriculture is dominated by high-income developed countries. Additionally, since what a consumer believes about organic products is a fundamental factor that influences their behavior, it has become common to compare the consumer markets of developed countries. For instance, Bartels and Reinders [19] showed that the social identification of consumers has a positive effect on the acceptance of the new production of organic food through comparisons among British, US, and German participants. Danner and Menapace’s [20] online data analysis showed that US consumers focused on their ideological beliefs related to environmental protection, whereas German consumers focused more on the certification of organic produce and their authenticity and trustfulness. However, unlike Europe, the US does not heavily subsidize farmers who grow organic produce; instead, personal ideologies dictate the preference for environmental protection [20]. In China, increased awareness of environmental protection, corporate social responsibility, and innovation processes have contributed to consumers’ interest and trust in organic agricultural produce [21,22].

A comparative analysis of food quality preferences among consumers in Japan, Taiwan, Malaysia, and New Zealand revealed that Japanese consumers have a particular “moral concern” for food, which partly influences their trust in EFF produce [23]. Studies have compared the Japanese and the relatively mature European organic market from the perspective of consumer preferences [24,25,26]. Thøgersen et al. [15] recently revealed differences in preferences for organic and imported food between developed and developing countries. They hold that, except for China, four out of five countries (including Japan) prefer produce of domestic origin. The consumer choice of organic produce is influenced by perceptions of trust and environmental issues. Finally, Boobalan and Nachimuthu [27] claimed that Indian consumers, compared with their US counterparts, are still sensitive to the price of organic food. Together, these studies confirm that producers should fix prices reasonably to ensure that organic food is competitive in the market. The body of research offers valuable suggestions for developing organic agriculture across different countries.

2.3. Trustfulness and Information

In China, the popularity of online shopping and growing awareness about food safety have improved consumers’ trust in organic food [8,28,29]. In Japan, researchers are focusing on information delivery as a driver of consumer willingness to pay for organic food [30,31]. Empirical analyses using choice experiments to study consumer information transfer are also available. Such analyses have confirmed that the “origin” of produce—domestic or foreign—is also a critical factor influencing Japanese consumers’ choice of EFF produce [32,33]. For instance, Sato and Niiyama [34] employed choice experiments to show that consumers’ trust in information was significant in promoting the consumption of eggs. Murakami [14] designed a choice experiment and specifically defined EFF produce to assess changes in consumers’ willingness to pay for apples before and after receiving information. Nakagawa [33] showed that direct selling helped enhance the consumer perception of organic rice. Researchers have also designed choice experiments to measure consumers’ willingness to pay after providing them with information about the environmental significance of EFF produce in groups [35,36].

3. Materials and Methods

3.1. Methods

First, a short questionnaire was designed to ascertain the participants’ attributes. Second, the questionnaire was used to analyze the participants’ awareness and experience of purchasing EFF produce. Third, in line with Murakami [14] and Osakada and Fujino’s [35] research of consumer behavior based on information delivery, we examined consumers’ marginal willingness to pay (MWTP) for EFF produce using choice experiments [37,38,39]. In short, we considered the implications of receiving different categories of EFF produce knowledge based on the preferences of the Chinese and Japanese participants in this study.

3.2. Samples and Procedure

Prior to administering the questionnaire, the participants were informed that their responses would be used in academic research. We ensured that they were aware of our commitments to protecting personal data and respecting cultural differences. Owing to COVID-19 pandemic-related restrictions, an online questionnaire was used to collect data. The participants were adults aged 20–75 years from China and Japan, who purchased fresh vegetables at least once a month. Table 2 lists their personal attributes.

The questionnaire was designed for both China and Japan, but the Chinese survey had to be postponed for a year due to the pandemic. For the Japanese data, ASMARQ Co., Ltd. was commissioned to randomly sample 10,000 consumers from 31 August to 2 September 2020; 890 valid questionnaires were received from 1097 respondents (81.13% response rate). Owing to funding constraints, we randomly selected 600 of the 890 questionnaires for analysis. Given the COVID-19 pandemic as well as cultural differences (e.g., the one-child policy), four new variables were added to the questionnaire for Chinese participants. Wenjuanxing Co., Ltd. conducted the online survey on 22–23 September 2021; 886 questionnaires were obtained through random sampling, with 641 valid responses and an effective response rate of 72.35%. We randomly selected 600 of 641 samples.

The average age of the Japanese respondents was 40–60 years, while that of the Chinese respondents was 20–40 years (see Table 2). The average annual income of Japanese respondents was J¥4–5 million per year and that of Chinese respondents was RMB 110,000–150,000 (J¥2–2.6 million), showing a significant difference in per capita income.

Both Chinese and Japanese respondents indicated similarity in three variables—gender, years of education, and one family member 60 years or older. Half of the respondents answered that they had an older adult living with them at home. For the question related to change in income during the pandemic, the mean of 2.43 on a five-point Likert scale indicated that the COVID-19 pandemic had decreased the income of the Chinese respondents. Finally, the $MARRY$ and $CHILD$ variables indicated that the Chinese respondents were more often married and had children.

However, it should be noted that the online questionnaire resulted in differences in certain personal attributes compared with the initially planned offline questionnaire, such as differences in age distribution and exclusion of individuals with no internet access.

3.3. Choice Experiment

A choice experiment was used to analyze respondents’ preferences for specific goods. Choice experiment methodology has wide application across disciplines such as psychology and environmental economics. A conjoint analysis method is used chiefly for ranking preferences for goods [40,41].

First, 600 participants in Japan and China were randomly divided into four even groups: A, B, C, and D. The participants read the definition of EFF produce published by the MAFF and the China Green Food Development Center [9]. Different reading content was provided for each group. Table 3 shows the information read by the participants. Thereafter, participants choose one among multiple products based on simulated cards to achieve a simulated “purchase” based on choice experiments. The participants were asked to decide on a single choice among three options.

Three representative types were selected for the produce category: leafy vegetables (e.g., cabbage), Solanaceae vegetables (e.g., tomatoes), and root vegetables (e.g., carrots). Among these, only leafy vegetables, komatsuna, or Japanese mustard spinach (Brassica rapa var. perviridis), have been widely studied in Japan [42,43]. There are only a few studies on the sale or demand for other vegetables, in either China or Japan. To ensure the feasibility of the survey, we also took into account the number of stores selling these vegetables in China and Japan and the related production data. The 2016 MAFF survey [11] reports that 441, 66, and 42 stores sell organic carrots, tomatoes, and cabbage, respectively, in Japan. The state administration of market supervision of China in 2019 revealed that leafy vegetables, root vegetables, and Solanaceae vegetables ranked fourth, fifth, and seventh in production [16]. We therefore selected cabbage (leafy vegetable), tomatoes (Solanaceae vegetable), and carrots (root vegetable) as representative types.

For price adjustment, we referred to the market prices of the vegetables in Price and Price Ratio of Fresh and Wild Vegetables by Category in Major Cities in Japan (2015–2016) [44] and conducted a market visit survey to determine the average price of organic vegetables. For China, the average sales price of an organic product was used to adjust the price through the wholesale price index of agricultural produce published by the Chinese government. Table 4 shows the choice experiments option settings and specifications for both countries.

In the choice experiments, the participants were presented with a picture of the produce and asked to choose one selection for the item’s attributes (EFF attributes and price). The permutations of price and category revealed 15 choices (i.e., 5 [price] × 3 [EFF attributes]) for each vegetable set. According to Galesic and Bosnjak [45], too many topics can reduce the quality of the questionnaire. As the total number of options in a choice experiment is usually large, the choice experiment application utilizing R software, developed by Aizaki and Nishimura [38], was used to reduce the burden on the respondents. Eight questions for each category were selected from 15 choice options randomly, and 24 votes were set. The AlgDesign package was used in R to perform random number-based orthogonal matrix decomposition, after which, we completed the free combination of commodity bundles. Figure 1 is the screenshot of the choice experiment in group D (left: Japan; right: China).

For example, after reading the definition of organic and specially cultivated (green food in China) produce, each respondent participated in the choice experiments. Each of the questions included three options: two options containing EFF attributes with price and a “No choice” option.

4. Theory

There are two design methods for choice experiments: orthogonal design and D-efficiency. We used the orthogonal matrix design following Aizaki and Nishimura [38] and Sato et al. [46]. In the choice experiments, the following mapping relations were set.

(1)$f: \mathit{x} \left(x_1,x_2\dots ,x_n\right)\to \mathit{a} \left(a_1,a_2\dots ,a_i\right)$

where $x_n$ is the $n$ attributes that product $\mathit{x}$ has, such as price, origin, and color; and the mapping $\mathit{a}$ is the number of specific attributes in $x_n$. For example, when $x_1$ means price attribute, $=3$ means three different price settings (e.g., $1, $2, $3). Therefore, the total number of options for the choice experiments was:

(2)${{\displaystyle \prod }}_{i=1}^I\mathit{a} \left(a_1,a_2\dots ,a_i\right)$

The data obtained from the choice experiments were used in the disordered multiple-choice model. The conditional logistic (clogit) regression has close mathematical characteristics to the multinomial logistic regression. However, the clogit regression, which allows for mutual independence and heterogeneity among respondents, is often applied to unordered choices with impersonal features [47]. Based on the Independence of Irrelevant Alternative, clogit can analyze the performance of individual effects. As per Greene [47], logistic regression is more practical in disordered multiple choice compared to probit regression. This way, the model can be more effective in verifying consumer preferences in actual consumption behavior. Following Yang et al.’s [36] formula, the utility function of the clogit regression is:

(3)$U_{ni}=V_{ni}+u_{ni}=\beta x_{ni}+u_{ni}$

where $i$ is a chosen commodity attribution, $V_{ni}$ is the observable utility, $u_{ni}$ is the unobservable error term, and $x_{ni}$ is the scenario characteristic of the random variable that consumer $n$ should choose. The coefficient $\beta$ of $x_{ni}$ is used to describe individual differences in utility equal to $V_{ni}$, obeying a normal distribution to allow for mean independent analysis with individual attributes” [36] (pp. 4–5).

Under Equation (3), compared with the other choice set $j$, the consumer chooses $i$ when $U_{ni}$ satisfies the maximum utility of the probability that respondent n chooses i. c is the set of all alternatives. The probability distribution function for a consumer $n$ to choose under the fixed scenario $i$ option during an observation is:

(4)$P_{ni}=P\{V_{ni}+u_{ni}>V_{nj}+u_{nj};j\in c\left(i\ne j\right)\}=\frac{\exp \left(\mathit{\beta }{\mathit{x}}_{\mathit{n}\mathit{i}}\right)}{{{\displaystyle \sum }}_{j=1}\exp \left(\mathit{\beta }{\mathit{x}}_{\mathit{j}\mathit{i}}\right)}$

The explanatory variable ${\mathit{x}}_{\mathit{n}\mathit{i}}$ of the clogit regression contains the dummy variable selection set and personal attributes. When adding the personal attributes term as Equation (5), $I_{nh}$ is the socioeconomic variable $h$ for the respondent $n$. We constructed the interaction term between personal attributes and the dummy variable selections developed for the regression analysis. When consumer $n$ choose attributes $k$ of EFF produce, the utility is:

(5)$V_{ni}={\displaystyle \sum }_{k=1}{\beta }_k{x}_{ki}+{\displaystyle \sum }_{k=1}{\displaystyle \sum }_{h=1}{\beta }_{kh}{x}_{ki}{I}_{nh}$

Equations (5) and (6) show the specific formulation of the choice experiments regression. Three dummy variables—$ORGA$, $SPEC$/$GREEN$, and $Ordinary$ ¾ were set in the choice experiments, with each variable being equal to 1 when participants chose the corresponding attributes. $Ordinary$ refers to ordinary agricultural produce cultivated without EFF and was set as a base group. The alternative specific constant (ASC) was set to 1 for the straightforward option and 0 for the option “No choice.”

(6)$$\begin{gathered} Choic{e}_i=AS{C}_i+{\beta }_{p}PRIC{E}_i+{\beta }_{o}ORG{A}_i+{\beta }_{s/g}SPEC/GREE{N}_i+ \\ {\beta }_{i}Interaction\_term{s}_i. \end{gathered}$$

MWTP represents the price that the customers are willing to pay for a particular feature of a product. It consists of the negative ratio of the parameter $\mathit{\beta }\left({\beta }_o,{\beta }_s,{\beta }_g,{\beta }_i\right)$ in Equation (6) to the price parameter ${\beta }_P$. Equation (7) calculates the MWTP as

(7)$MWTP=-\frac{\mathit{\beta }}{{\beta }_P}$

5. Results

5.1. Awareness and Consumption of EFF Produce

The questionnaire collected information on Chinese and Japanese consumers’ purchasing experiences and awareness of EFF agricultural produce on a five-point Likert scale. Questions 3–6 collected the perceptions of Chinese and Japanese consumers about the external information access of EFF produce, as shown in Table 5.

Table 5 shows that the awareness of specially cultivated agricultural produce among Japanese consumers is still low. In contrast, Chinese consumers are more familiar with green foods, but there is a gap with respect to the knowledge of organic produce based on their response to the question, “How much do you know about the concept of EFF produce?” Furthermore, Japanese consumers have lower purchasing experience, with an average of only 1.67 for specially cultivated agricultural produce. On average, Chinese consumers have a more favorable opinion regarding access to external information, while Japanese consumers tend to choose “disagree” for specially cultivated produce. However, the difference in the age distribution of Chinese and Japanese consumers affected their perceptions. The average age of the Japanese respondents was 3.81 (40–60 years), compared with 1.81 (20–40 years) for the Chinese respondents.

Considering that the names of specially cultivated agricultural produce in Japan have not been standardized, there are differences in certification labels in different areas. Labels such as “pesticide-free” and “50% pesticide-reduced” are more widely used than the official definition of “specially cultivated.” Consumers’ unfamiliarity with the term “specially cultivated” may explain why they chose a more negative option on the SPEC side.

In China, green foods with unified markings include both agricultural produce and processed food produce. Green food production has also received tremendous government support for more than 20 years. Even if the scope of the questionnaire is limited to “green food-certified agricultural produce,” consumers may naturally associate processed green food, such as beverages, with processed meats, thus influencing their choices. This may predispose them to be more aware of green food-certified agricultural produce than organic produce. Finally, social media and China’s e-commerce industry for agricultural produce have helped young people be more aware about EFF produce, as revealed by questions 4 and 6 in Table 5. This conforms to the studies by Zeng et al. [5] and Qian et al. [7] on China’s e-commerce with the agricultural supply chain.

5.2. Choice Experiments

Considering our results related to the MWTP for the three types of agricultural produce, once the different EFF definitions of the produce were given to the consumers to peruse, we performed grouped regression by adding four personal attribute variables—age, gender, income, and education level—to the data from both China and Japan. The regression containing the interaction terms for personal attributes is presented in Table 6 and Table 7. These tables show that the price remained negatively significant for the four groups of consumers at p > 0.01 in both countries, suggesting that price remains an influential critical factor in the ordinary and EFF produce choice of customers. Then, the MWTP was calculated using Equation (7), as shown in Table 8.

By focusing on the personal attributes of Japanese consumers in the control group A, we confirmed that the information on EFF produce and the consumers’ years of formal education are significantly and positively correlated with consumers’ choices in purchasing specially cultivated produce. The more educated the consumer, the more knowledge they have, and thus, the higher their willingness to purchase EFF produce. In group A (cabbage), the individual attribute that influences consumer decisions shifted from education to income. However, consumer income is significantly positive in groups B and D for cabbage and carrots, but especially for group B. As their income increases, their propensity to consume organic food also increases. Within group B, the MWTP with the interaction term was less than ¥10, which is a small amount in real life. With the addition of the interaction term, the EFF product variable was not significant in groups A and D. After receiving different definitions as shown in Table 8, $ORGA$ was found significantly positive for group B, and $SPEC$ cabbage and carrots, significantly positive for group C.

Income shows a significant positive effect on organic produce in China except for group D carrots. A higher income engages a higher preference for organic food. Income is significantly and positively correlated with respondents’ choices, even after receiving different definitions. As shown in Table 8, consumers with higher education tended not to choose organic food in group A. After receiving information, the education variable became insignificant. The variables of gender and age did not show a common effect; however, we found that women were less inclined to choose EFF produce than men, especially in China. We argue that, while the status of women has improved considerably in the past decades, there is a tendency for women to pay more of the costs for household goods. This means that women exercise more discretion in payment decisions on inelastic agricultural produce. Finally, older consumers tended to pick ordinary produce. With the interaction term included and after receiving information about green foods, group C abandoned organic produce, significantly favoring green food.

In Japan and China, income, education, and gender had different effects across different categories of vegetables. For example, in the interaction term of education, the results were significant for group A consumers, who did not receive any information, with regard to their choice. After the consumers in other groups were provided with different EFF information, Chinese and Japanese consumers showed different preferences for three kinds of vegetables. For example, Japanese consumers did not indicate a significant preference for tomatoes in variables $ORGA$ and $SPEC$ in groups C and D, but Chinese consumers did.

Overall, the choice of produce is affected more by price and income than information for both Japanese and Chinese consumers. Additionally, in Japan, the consumers’ willingness to pay for different vegetables changes after receiving information. For Chinese consumers, income has a significant influence on their willingness to purchase organic produce. This choice perhaps reflects the developing nature of the economy and that people still do not fully understand high-value agricultural produce. We therefore conclude that it is more effective to customize messages by region rather than deliver a unified statement for EFF produce.

6. Discussion and Conclusions

This study shows that information can affect consumers’ decisions to purchase EFF produce. We derive the following results.

First, it is clear that as the education level rises, the MWTP of Chinese and Japanese consumers regarding organic food prior to receiving the definition of EFF produce shows opposite trends. The opposite coefficient indicates a difference in ideology toward EFF produce. The differences in the ideologies of global environmental protection and agricultural pragmatism between developed and developing countries must be investigated. The finding echoes those of Madani et al. [54] in terms of influence of consumer ideology on store consumption. The ideological differences between developed and developing countries in advertising campaigns are especially noticeable in trade marketing.

Second, Japanese consumers favor specially cultivated carrots; for example, organic carrots are often used to create fresh carrot juice, which Japanese consumers consider healthy food. At the same time, regardless of the definitions used, Chinese consumers mostly prefer green foods but also have a strong preference for organic tomatoes. The market management strategies for these two agricultural produces merit further attention.

Third, regardless of whether consumers have received the definition of EFF produce, the inelastic price of agricultural produce means that consumers become price-sensitive. This finding also validates Bai [55] and Boobalan and Nachimuthu [27]’s conclusions regarding the price and consumer purchase intention of organic agricultural produce. No matter the advertising strategy, suppliers should not overlook the importance of reducing the cost of EFF produce. We suggest controlling the cost of organic produce while promoting it from a food safety perspective to increase consumer awareness. The Chinese government’s support and promotion policies for green food should continue to play a vital long-term role.

As their age increases, Chinese and Japanese consumers tend to decrease their MWTP for EFF produce. With age, the consumers’ MWTP tends to decline regardless of the type of vegetable. Because of the inelastic price of agricultural produce, price remains an important influencing factor for consumer decisions. Consumers are not willing to pay excessive prices for produce, even EFF produce. Regarding the gender variable, women tend to be more cautious in choosing EFF agricultural produce, which we believe is a result of the Sino-Japanese socio-cultural structure that requires women to manage the household as economically as possible.

Uniquely, in the interaction term of education level, in group A, the Japanese consumers with higher education were found to have a higher MWTP for EFF produce, but the opposite was true for Chinese consumers. However, after receiving information, all $EDUC$ variables were no longer significant (group B, cabbage; $O\_EDUC$ near to 0). Thus, the following question becomes pertinent: “Do the opposite preferences of Chinese and Japanese consumers, with respect to their education level, mean that the intellectuals in developed and developing countries have different evaluations of environmentally friendly agriculture?” We surmise that there is an ideological difference between Japanese and Chinese consumers in the evaluation of economic development and environmental protection, and thus, in the different attitudes and MWTP toward EFF produce. However, education does not have a significant effect after consumers receive information. This also illustrates the importance of information and its differential influence on different groups of people even if they hold additional personal attributes.

By focusing on the two largest markets in Asia, we studied the differences in the attitudes of Chinese and Japanese consumers toward EFF produce and examined their MWTP through choice experiments. Once the respondents received the definitions for EFF produce, their preferences in types of vegetables and the influence of personal attributes changed. These findings can be extrapolated to developed and developing countries, but cultural differences specific to individual nations would be relevant.

Nevertheless, certain limitations exist in our results. First, the survey had a limited sample; the persistence of the COVID-19 pandemic and China’s strict vaccination policy prevented us from conducting the survey offline, as originally planned, in 2021, and we moved the questionnaire online. In this questionnaire, the assigned sample was completely random, which also caused a gap in age distribution between China and Japan. Second, the causal relationship between attention to EFF information and buying experience also deserves further analysis through latent class analysis, similar to that used by Hendriks-Hartensveld et al. [56]. Third, the information about EFF produce that the respondents perused was adopted from official documents from government websites, which did not include exaggerated descriptions used in actual marketing strategies such as “pesticide-free agricultural produce,” a technically prohibited label. In the market, distributers often use eye-catching slogans and catchphrases rather than relevant regulations or definitions, thus making our research different from real-life situations. Of course, this leads to consumers becoming unfamiliar with the definitions of EFF produce as highlighted by MAFF [10]. Therefore, one must explore different approaches to conveying the relevant regulations to consumers more efficiently through advertising. In future, we aim to analyze consumer preferences by controlling the specific age groups for EFF produce and conduct research on the transfer of information between farmers and consumers in China and Japan.

Author Contributions

R.Y. designed the study and performed the choice experiments. R.Y. wrote the paper and performed simulations in R. K.F. checked the data and revised the structure of this study. K.M. checked the language and provided suggestions for the structure of the article. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

All participants were informed that anonymity is assured, that their data will be used for academic research, that personal privacy will be protected, and that there are no risks associated with their participation. All participants provided informed consent before participation. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of 11000339*. The ethical committee number of this article about people was passed by the Tohoku University Graduate School of Agricultural Science. The document statement can be found on the official website of the Japanese Ministry of Health, Labour and Welfare, but it is only available in Japanese.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank the International Development of Study, Tohoku University, for their suggestions on the questionnaire and language revision for this study.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figure and Tables

Figure 1. EFF information for Group D before the choice experiment *. Source: Author’s creation, 2022. Note: The English version of the questionnaire is in the Supplementary Materials.

Table 1

EFF Produce Classification in China and Japan.

China	Japan
Organic & Green Food	Organic JAS & Specially Cultivated
[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]

Source: China Green Food Development Center [9] and MAFF [12].

Table 2

Variable names, definitions, and descriptive statistics.

Variable	Description	Mean	Std. Dev.	Mean	Std. Dev.
		Japan		China
AGE	Years 1 = 20–29; 2 = 30–39; 3 = 40–49; 4 = 50–59; 5 = 60–69; 6 = 70–75	3.81	1.27	1.81	0.87
GENDER	Dummy; 0 = male; 1 = female	0.45	0.50	0.52	0.50
INCOME	Annual income (yen/yuan) */**	4.50	3.23	4.41	1.78
EDUC	Education years: 1 = 9; 2 = 12; 3 = 15; 4 = 16; 5 = 18	3.34	0.93	3.80	0.72
CHILD1	Have children 12 years or under in the household.0 = no; 1 = yes	0.15	0.35	0.69	0.46
CHILD2	Have children 13 years or older in the household. 0 = no; 1 = yes	0.10	0.30	0.22	0.42
OLD	Including you, is there anyone in your household who is 60 years or older? 0 = no; 1 = yes	0.50	0.50	0.54	0.50
MARRY	Your marital status is 0 = unmarried; 1 = married.			0.80	0.40
CHILD	Do you have children: 0 = no; 1 = yes			0.78	0.41
COVID-19	Has your income changed due to the impact of the COVID-19: 1 = significant decrease; 2 = decrease; 3 = no change; 4 = increase; 5 = significant increase			2.43	0.72
WORKEXP	The number of years you have worked for (years): 1 = 0; 2 = less than 2; 3 = 3–5; 4 = 6–10; 5 = 11–20; 6 = greater than 20; 7 = retirement			3.88	1.28
Obs		600		600

Note: * yen: 1.5 = less than 2 million; 2.5 = 2–3 million; 3.5 = 3–4 million; 4.5 = 4–5 million; 5.5 = 5–6 million; 6.5 = 6–7 million; 7.5 = 7–8 million; 8.5 = 8–9 million; 9.5 = 9 = 10 million; 12.5 = 10–15 million; 17.5 = 15–20 million; 22.5 = over 20 million. ** yuan: 1.5 = less than 20 thousand; 2.5 = 20–50 thousand; 3.5 = 60–100 thousand; 4.5 = 110–150 thousand; 5.5 = 160–200 thousand; 6.5 = 210–250 thousand; 7.5 = 260–300 thousand; 8.5 = 310–400 thousand; 9.5 = 410–500 thousand; 12.5 = 510–700 thousand; 17.5 = 0.71–1 million; 22.5 = over 1 million. Source: Author’s creation, 2022.

Table 3

Reading content by groups before the choice experiment.

	Japan	China
Group A	None	None
Group B	Definition of organic agriculture, label	Definition of organic agriculture, label
Group C	Definition of SPEC produce *	Definition of green food, label
Group D	Definition of organic agriculture and SPEC produce, and organic label	Definition of organic agriculture and green food, label

* In Japan, different regions use different $SPEC$ produce label. Hence, the definition of $SPEC$ reading content was established from MAFF [12]. Source: Author’s creation, 2022.

Table 4

Choice experiments option settings and produce specifications.

Category	Cabbage	Tomato	Carrot
Price (yen)	140, 170, 200, 230, 260	170, 210, 250, 290, 330	200, 250, 300, 350, 400
Price (yuan)	2, 6, 10, 14, 18	3, 7, 11, 15, 19	2, 6, 10, 14, 18
EFF attributes	Organic/Specially cultivated (Green food)/Ordinary
Weight	500 g/package	350 g/package	500 g/package

Source: Author’s creation, 2022.

Table 5

Awareness and consumption of EFF produce.

	Mean (Std. Dev)				Reference
	Japan		China
	Organic	Spec	Organic	Green
1. Have you bought EFF produce?1 = never to 5 = always	2.99(1.08)	1.67(0.99)	3.63(0.85)	3.88(0.91)
2. How much do you know about the concept of EFF produce? 1 = not at all aware to 5 = extremely aware	3.86(0.74)	2.12 (1.09)	3.65 (0.73)	3.97 (0.74)	Huang et al. [48].
3. There are stores near me that sell EFF produce.1 = strongly disagree to 5 = strongly agree	2.55(0.78)	1.75 (0.87)	3.34 (0.95)	3.41 (0.95)	Rabadán et al. [49].
4. I see a lot of information on social media about EFF produce.1 = strongly disagree to 5 = strongly agree	2.64(0.91)	1.77 (0.88)	3.70 (0.98)	3.75 (1.04)	Liu et al. [50]; Pearson et al. [51].
5. My family members and friends regularly purchase EFF produce. 1 = strongly disagree to 5 = strongly agree	2.55(0.86)	1.76 (0.88)	3.55 (0.98)	3.71 (0.95)	Hilverda et al. [13].
6. I know EFF produce can be ordered online.1 = strongly disagree to 5 = strongly agree	2.84(1.08)	1.87 (1.05)	3.59 (1.01)	3.74 (1.02)	Castle et al. [52]; Moriuchi and Takahashi [53].
Obs	600		600

Source: Author’s creation, 2022.

Table 6

Choice experiments result based on interactions—Japan.

Dependent Variable: Choice
		A	B	C	D
Cabbage	ASC	7.583 ***	6.619 ***	8.528 ***	7.365 ***
	PRICE	−0.032 ***	−0.025 ***	−0.036 ***	−0.031 ***
	ORGA	0.460	1.729 ***	1.206 *	−0.693
	SPEC	−1.057	1.024	3.213 **	0.132
	ORGA_AGE	0.009	−0.020 ***	0.003	0.020 **
	ORGA_GENDER	−0.102	−0.050	−0.242	0.737 ***
	ORGA_INCOME	−0.032	0.120 ***	−0.014	0.079 ***
	ORGA_EDUC	0.195 *	−0.028	0.047	0.037
	SPEC_AGE	0.017	−0.022 *	−0.021	0.004
	SPEC_GENDER	−0.832 *	0.288	−0.749*	0.619 *
	SPEC_INCOME	−0.129 *	0.155 ***	−0.037	0.116 **
	SPEC_EDUC	0.683 ***	0.095	−0.054	0.009
R2		0.402	0.378	0.474	0.402
Adj-R2		0.401	0.369	0.465	0.393
LR-test		1082 ***	997.8 ***	1250 ***	1061 ***
Obs		150	150	150	150
Tomato	ASC	6.582 ***	6.443 ***	8.206 ***	6.280 ***
	PRICE	−0.023 ***	−0.021 ***	−0.026 ***	−0.021 ***
	ORGA	−0.068	1.280 **	0.733	0.095
	SPEC	−1.468 *	1.392 *	1.564	0.056
	ORGA_AGE	0.008	−0.013 *	0.003	0.008
	ORGA_GENDER	−0.239	−0.099	−0.440 *	0.237
	ORGA_INCOME	0.028	0.098 ***	0.032	0.106 ***
	ORGA_EDUC	0.145	−0.026	0.134	−0.099
	SPEC_AGE	0.017	−0.008	−0.005	−0.000
	SPEC_GENDER	0.366	0.037	−0.448	−0.001
	SPEC_INCOME	−0.045	0.056	−0.006	0.081 *
	SPEC_EDUC	0.537 ***	−0.162	0.156	0.121
R2		0.402	0.371	0.503	0.359
Adj-R2		0.393	0.362	0.494	0.350
LR-test		1061 ***	978.5 ***	1327 ***	946.9 ***
Obs		150	150	150	150
Carrot	ASC	5.512 ***	5.319 ***	7.531 ***	6.200 ***
	PRICE	−0.019 ***	−0.016 ***	−0.025 ***	−0.020 ***
	ORGA	−0.039	1.287 **	0.651	0.264
	SPEC	0.234	2.121 ***	1.591 **	0.930
	ORGA_AGE	0.001	−0.012	0.004	0.003
	ORGA_GENDER	−0.159	−0.115	−0.453	0.025
	ORGA_INCOME	0.059	0.069 **	−0.030	0.083 **
	ORGA_EDUC	0.227 *	0.066	0.209	0.036
	SPEC_AGE	0.002	−0.019 ***	0.003	−0.007
	SPEC_GENDER	−0.162	−0.255	−0.597 ***	−0.293
	SPEC_INCOME	0.036	0.109 ***	−0.009	0.090 ***
	SPEC_EDUC	0.255 ***	−0.038	0.045	0.083
R2		0.284	0.319	0.421	0.330
Adj-R2		0.275	0.310	0.412	0.321
LR-test		747.9 ***	841.7 ***	1111 ***	870.2 ***
Obs		150	150	150	150

Note: * p > 0.10, ** p > 0.05, *** p > 0.01. Source: Author’s creation in R, 2021.

Table 7

Choice experiments result based on interactions—China.

Dependent Variable: Choice
		A	B	C	D
Cabbage	ASC	2.293 ***	1.796 **	2.034 ***	1.854 ***
	PRICE	−0.173 ***	−0.139 ***	−0.157 ***	−0.143 ***
	ORGA	1.754 **	1.530 ***	0.018	0.977 **
	GREEN	2.869 ***	1.371 *	1.188 *	2.042 ***
	ORGA_AGE	−0.128	−0.408 ***	−0.075	−0.241 ***
	ORGA_GENDER	0.005	−0.324 **	−0.203	−0.188
	ORGA_INCOME	0.209 ***	0.183 ***	0.185 ***	0.115 **
	ORGA_EDUC	−0.310 **	0.007 **	0.148	0.093
	GREEN_AGE	−0.257	−0.287 **	−0.127	−0.347 ***
	GREEN_GENDER	0.143	0.171	−0.422 *	−0.646 ***
	GREEN_INCOME	0.087	0.224 ***	0.177 **	0.065
	GREEN_EDUC	−0.306	−0.004	0.099	0.087
R2		0.261	0.247	0.236	0.214
Adj-R2		0.252	0.238	0.227	0.205
LR-test		689.4 ***	652.5 ***	622.5 ***	564 ***
Obs		150	150	150	150
Tomato	ASC	2.961 ***	2.220 ***	2.599 ***	2.440 ***
	PRICE	−0.192 ***	−0.140 ***	−0.182 ***	−0.166 ***
	ORGA	3.984 ***	2.076 ***	0.678	1.705 ***
	GREEN	2.128 ***	1.545 **	1.228 *	1.378 **
	ORGA_AGE	−0.396 ***	−0.565 ***	−0.136	−0.165 *
	ORGA_GENDER	−0.092	0.257	−0.439 **	0.272
	ORGA_INCOME	0.183 ***	0.152 ***	0.158 ***	0.140 ***
	ORGA_EDUC	−0.683 ***	−0.073	0.093	−0.209 *
	GREEN_AGE	−0.525 ***	−0.192 *	0.023	−0.166
	GREEN_GENDER	0.243	0.087	−0.225	0.102
	GREEN_INCOME	−0.018	−0.016	−0.085	−0.026
	GREEN_EDUC	−0.073	0.022	0.084	0.005
R2		0.315	0.286	0.308	0.275
Adj-R2		0.306	0.277	0.299	0.266
LR-test		829.6 ***	753.2 ***	811.5 ***	726.1 ***
Obs		150	150	150	150
Carrot	ASC	1.908 ***	1.593 ***	1.749 ***	1.582 ***
	PRICE	−0.186 ***	−0.131 ***	−0.183 ***	−0.143 ***
	ORGA	2.970 ***	1.413 **	0.422	1.492 ***
	GREEN	4.172 ***	2.144 ***	2.204 ***	1.650 ***
	ORGA_AGE	−0.261 *	−0.346 ***	−0.008	−0.298 ***
	ORGA_GENDER	0.085	−0.029	−0.288	−0.028
	ORGA_INCOME	0.134 **	0.196 ***	0.132 **	0.054
	ORGA_EDUC	−0.375 **	0.029	0.210	0.106
	GREEN_AGE	−0.517 ***	−0.456 ***	−0.360 ***	−0.218 **
	GREEN_GENDER	−0.067	−0.043	−0.303	−0.210
	GREEN_INCOME	0.094	0.213 ***	0.182 ***	0.028
	GREEN_EDUC	−0.422 **	−0.110	−0.056	0.133
R2		0.321	0.292	0.304	0.271
Adj-R2		0.312	0.283	0.295	0.262
LR-test		846.5 ***	770.6 ***	801.1 ***	715 ***
Obs		150	150	150	150

Note: * p > 0.10, ** p > 0.05, *** p > 0.01. Source: Author’s creation in R, 2022.

Table 8

MWTP—Japan/China (unit: yen/yuan).

MWTP
		A		B		C		D
		Japan	China	Japan	China	Japan	China	Japan	China
Cabbage	ORGA		10.14	69.16	11.01	33.50			6.83
	SPEC/GREEN		16.58		9.86	89.25	7.57		14.28
	ORGA_AGE			−0.80	−2.94			0.65	−1.69
	ORGA_GENDER				−2.33			23.77
	ORGA_INCOME		1.21	4.80	1.32		1.18	2.55	0.80
	ORGA_EDUC	6.09	−1.79		0.05
	SPEC/GREEN_AGE			−0.88	−2.06				−2.43
	SPEC/GREEN_GENDER	−26.00				−20.81	−2.69	19.97	−4.52
	SPEC/GREEN_INCOME	−4.03		6.20	1.61		1.13	3.74
	SPEC/GREEN_EDUC	21.34
Tomato	ORGA		20.75	60.95	14.83				10.27
	SPEC/GREEN	−63.83	11.08	66.29	11.04		6.75		8.30
	ORGA_AGE		−2.06	−0.62	−4.04				−0.99
	ORGA_GENDER					−16.92	−2.41
	ORGA_INCOME		0.95	4.67	1.09		0.87	5.05	0.84
	ORGA_EDUC		−3.56						−1.26
	SPEC/GREEN_AGE		−2.73		−1.37
	SPEC/GREEN_GENDER
	SPEC/GREEN_INCOME							3.86
	SPEC/GREEN_EDUC	23.35		−7.71
Carrot	ORGA		15.97	80.44	10.79				10.43
	SPEC/GREEN		22.43	132.56	16.37	63.64	12.04		11.54
	ORGA_AGE		−1.40		−2.64				−2.08
	ORGA_GENDER
	ORGA_INCOME		0.72	4.31	1.50		0.72	4.15
	ORGA_EDUC	11.95	−2.02
	SPEC/GREEN_AGE		−2.78	−1.19	−3.48		−1.97		−1.52
	SPEC/GREEN_GENDER					−23.88
	SPEC/GREEN_INCOME			6.81	1.63		0.99	4.50
	SPEC/GREEN_EDUC	13.42	−2.27

Source: Author’s creation in R, 2022.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14159470/s1, Questionnaire China.

References

1. Eurostat. Organic Farming Statistics. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Organic_farming_statistics#Key_messages (accessed on 19 January 2022).

2. Organic Trade Association. U.S. Organic Industry Survey 2021. Available online: https://ota.com/news/press-releases/21755 (accessed on 27 November 2021).

3. Federal Ministry of Food and Agriculture-German. Strengthening Organic Farming: Strategy for the Future of Organic Farming. Available online: https://www.bmel.de/EN/topics/farming/organic-farming/strategy-future-organic-farming.html (accessed on 19 January 2022).

4. Federal Ministry of Food and Agriculture-German. Organic Farming in German. Available online: https://www.bmel.de/SharedDocs/Downloads/EN/Publications/Organic-Farming-in-Germany.html (accessed on 7 November 2021).

5. Zeng, Y.; Jia, F.; Wan, L.; Guo, H. E-commerce in agri-food sector: A systematic literature review. Int. Food Agribus. Manag. Rev.; 2017; 20, pp. 439-460. [DOI: https://dx.doi.org/10.22434/IFAMR2016.0156]

6. Lin, J.; Li, T.; Guo, J. Factors influencing consumers’ continuous purchase intention on fresh food e-commerce platforms: An organic foods-centric empirical investigation. Electron. Com. Res. Appl.; 2021; 50, pp. 101-103. [DOI: https://dx.doi.org/10.1016/j.elerap.2021.101103]

7. Qian, J.; Ruiz-Garcia, L.; Fan, B.; Robla Villalba, J.I.; McCarthy, U.; Zhang, B.; Yu, Q.; Wu, W. Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review. Trends Food Sci. Technol.; 2020; 99, pp. 402-412. [DOI: https://dx.doi.org/10.1016/j.tifs.2020.03.025]

8. Hu, Y.; Sun, J.; Zhang, Q.; Han, Y. Current status and future development proposal for Chinese agricultural product quality and safety traceability. Chin. J. Eng. Sci.; 2018; 20, pp. 57-62. [DOI: https://dx.doi.org/10.15302/J-SSCAE-2018.02.009]

9. China Green Food Development Center (CGFDC). Overview of Organic Food Development in 2010. 2011; Available online: http://www.greenfood.agri.cn/ztzl/tjnb/lssp/ (accessed on 15 April 2021). (In Chinese)

10. Ministry of Agriculture, Forestry and Fisheries (MAFF). Green Food System Strategy. 2021; Available online: https://www.maff.go.jp/j/kanbo/kankyo/seisaku/midori/ (accessed on 9 October 2021).

11. MAFF. The Pesticide Situation. 2016; Available online: https://www.maff.go.jp/j/seisan/sien/sizai/noyaku/attach/pdf/index-1.pdf (accessed on 9 December 2021). (In Japanese)

12. MAFF. The Situation of Organic Agriculture. 2019; Available online: https://www.maff.go.jp/j/seisan/kankyo/yuuki/attach/pdf/meguji-full.pdf (accessed on 9 December 2021).

13. Hilverda, F.; Kuttschreuter, M.; Giebels, E. Social media mediated interaction with peers, experts and anonymous authors: Conversation partner and message framing effects on risk perception and sense-making of organic food. Food Qual. Prefer.; 2017; 56, pp. 107-118. [DOI: https://dx.doi.org/10.1016/j.foodqual.2016.09.003]

14. Murakami, K. Consumer Understanding and Policy Effectiveness of Guidelines: A Case Study of Guidelines for Specially Cultivated Agricultural Produce; Kyoto Institute for Economic Research No. 1016 Kyoto University: Kyoto, Japan, 2011; (In Japanese)

15. Thøgersen, J.; Pedersen, S.; Aschemann-Witzel, J. The impact of organic certification and country of origin on consumer food choice in developed and emerging economies. Food Qual. Prefer.; 2019; 72, pp. 10-30. [DOI: https://dx.doi.org/10.1016/j.foodqual.2018.09.003]

16. State Administration of Market Supervision of ChinaChina Agricultural University (CAU). The Development of Organic Industry and Organic Product Certification in China, 2019; 68th ed. China Agricultural Science and Technology Press: Beijing, China, 2019; (In Chinese)

17. Forschungsinstitut für Biologischen Landbau (FiBL) Statistics. European and Global Organic Farming Statistics. 2021; pp. 210-211. Available online: https://statistics.fibl.org/ (accessed on 22 December 2021).

18. International Federation of Organic Agriculture Movements (IFOAM). Available online: https://www.ifoam.bio/sites/default/files/2021-06/Annual%20Report%202020.pdf (accessed on 18 December 2021).

19. Bartels, J.B.; Reinders, M.J. Social identification, social representations, and consumer innovativeness in an organic food context: A cross-national comparison. Food Qual. Prefer.; 2010; 21, pp. 347-352. [DOI: https://dx.doi.org/10.1016/j.foodqual.2009.08.016]

20. Danner, H.; Menapace, L. Using online comments to explore consumer beliefs regarding organic food in German-speaking countries and the United States. Food Qual. Prefer.; 2020; 83, pp. 103-912. [DOI: https://dx.doi.org/10.1016/j.foodqual.2020.103912]

21. Liu, C.; Zheng, Y.; Cao, D. An analysis of factors affecting selection of organic food: Perception of consumers in China regarding weak signals. Appetite; 2021; 161, 105145. [DOI: https://dx.doi.org/10.1016/j.appet.2021.105145] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33515620]

22. Yu, W.; Han, X.; Ding, L.; He, M. Organic food corporate image and customer co-developing behavior: The mediating role of consumer trust and purchase intention. J. Retail. Con. Serv.; 2021; 59, 102377. [DOI: https://dx.doi.org/10.1016/j.jretconser.2020.102377]

23. Prescott, J.; Young, O.; O’Neill, L.; Yau, N.J.N.; Stevens, R. Motives for food choice: A comparison of consumers from Japan, Taiwan, Malaysia and New Zealand. Food Qual. Prefer.; 2002; 13, pp. 489-495. [DOI: https://dx.doi.org/10.1016/S0950-3293(02)00010-1]

24. Willer, H.; Yussefi, M.; Sorensen, N. The World of Organic Agriculture: Statistics and Emerging Trends 2008; Earthscan Publications: London, UK, 2010.

25. Naoki, T.; Yoko, T. Characteristics of price and demand for organic vegetables in the Japanese food retailing market. Organic is Life-Knowledge for Tomorrow. Volume 2—Socio-Economy, Livestock, Food Quality, Agro-Ecology and Knowledge Dissemination, Proceedings of the Third Scientific Conference of the International Society of Organic Agriculture Research (ISOFAR), 28 September–1 October 2011, Namyangju, Korea; International Society of Organic Agricultural Research (ISOFAR): Bonn, Germany, 2011; pp. 88-91.

26. Institute of Economic ResearchKyoto University. The Report of Study on Consumer Evaluation of Food Labels. Available online: https://warp.da.ndl.go.jp/info:ndljp/pid/11539153/www.esri.go.jp/jp/prj/hou/hou057/hou057.html (accessed on 13 January 2020). (In Japanese)

27. Boobalan, K.; Nachimuthu, G.S. Organic consumerism: A comparison between India and the USA. J. Retail. Con. Serv.; 2020; 53, 101988. [DOI: https://dx.doi.org/10.1016/j.jretconser.2019.101988]

28. Yang, Y. Organic Vegetables in China: Characteristics of the Distribution System and the Direction of Future Development. Ph.D. Thesis; Tokyo University of Agriculture: Tokyo, Japan, 2014; (In Japanese)

29. Ji, H.; Ono, M. E-commerce in fresh agricultural products using SNS in China: Case of an organic agricultural producer and distributor in Qingdao, Shandong Province. J. Rural Probl.; 2018; 54, pp. 157-164. [DOI: https://dx.doi.org/10.7310/arfe.54.157]

30. Rana, J.; Paul, J. Consumer behavior and purchase intention for organic food: A review and research agenda. J. Retail. Con. Serv.; 2017; 38, pp. 157-165. [DOI: https://dx.doi.org/10.1016/j.jretconser.2017.06.004]

31. Katt, F.; Meixner, O. A systematic review of drivers influencing consumer willingness to pay for organic food. Trends Food Sci. Technol.; 2020; 100, pp. 374-388. [DOI: https://dx.doi.org/10.1016/j.tifs.2020.04.029]

32. Sato, M.; Sakagami, M.; Suzuki, Y.; Ueta, K.; Takatsuki, H. An economic analysis of consumers’ preference toward organic vegetables with special reference to the local organic material circulation and certification labels. Environ. Sci.; 2005; 18, pp. 243-255. (In Japanese)

33. Nakagawa, T. Estimation of customer value of organic produce by conjoint analysis. Jpn. J. Farm Manag.; 2012; 50, pp. 100-105. (In Japanese) [DOI: https://dx.doi.org/10.11300/fmsj.50.1_100]

34. Sato, M.; Niiyama, Y. The information quantity and consumer choice behavior at the point of food purchase. J. Food Syst. Res.; 2008; 14, pp. 13-24. (In Japanese) [DOI: https://dx.doi.org/10.5874/jfsr.14.3_13]

35. Osakada, Y.; Fujino, M. Impact of information provision on consumer preference for conservation agriculture products. Rev. Environ. Econ. Policy Stud.; 2018; 11, pp. 15-28. (In Japanese) [DOI: https://dx.doi.org/10.14927/reeps.11.2_15]

36. Yang, R.; Takashino, N.; Fuyuki, K. Japanese Consumers’ Willingness to Pay for Environmentally Friendly Farming Produce Based on Consumer Trustfulness. J. Agric. Food Ind. Organ.; 2022; 20, pp. 1-14. [DOI: https://dx.doi.org/10.1515/jafio-2020-0036]

37. Aizaki, H. Choice experiment analysis of consumers’ preference for ecologically friendly rice. Agric. Inf. Res.; 2005; 14, pp. 85-96. [DOI: https://dx.doi.org/10.3173/air.14.85]

38. Aizaki, H.; Nishimura, K. Introduction to choice experiments using R. Tech. Rep. Natl. Inst. Rural. Eng.; 2007; 206, pp. 151-173. (In Japanese)

39. Aizaki, H.; Nishimura, K. Design and analysis of choice experiments using R: A brief introduction. Agric. Inf. Res.; 2008; 17, pp. 86-94. [DOI: https://dx.doi.org/10.3173/air.17.86]

40. Tonsor, G.T.; Olynk, N.; Wolf, C. Consumer preferences for animal welfare attributes: The case of gestation crates. J. Agric. Appl. Econ.; 2009; 41, pp. 713-730. [DOI: https://dx.doi.org/10.1017/S1074070800003175]

41. Kuhfeld, W.F. Experimental design, efficiency, coding, and choice designs. Marketing research methods in SAS. MR-2010C—Experimental Design: Efficiency, Coding, and Choice Designs; SAS Institute Inc.: Cary, NC, USA, 2005; pp. 47-97.

42. Seo, Y.; Someya, Y.; Dowaki, K. Environmental impacts and consumer preference for sustainably cultivated Japanese mustard spinach, komatsuna. J. Environ. Manag.; 2019; 231, pp. 364-369. [DOI: https://dx.doi.org/10.1016/j.jenvman.2018.10.077]

43. Ujiie, K.; Matsuoka, H. Consumers’ preference heterogeneity and purchase motivation of organic vegetables. J. Food Syst. Res.; 2021; 28, pp. 54-63. (In Japanese) [DOI: https://dx.doi.org/10.5874/jfsr.28.1_54]

44. E-State Statistics of Japan. Price and Price Ratio of Fresh and Wild Vegetables by Category in Major Cities in Japan, 2015–2016. Available online: https://www.e-stat.go.jp/stat-search/files?page=1&layout=dataset&toukei=00500235&kikan=00500&stat_infid=000031556054 (accessed on 6 June 2020). (In Japanese)

45. Galesic, M.; Bosnjak, M. Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opin. Q.; 2009; 73, pp. 349-360. [DOI: https://dx.doi.org/10.1093/poq/nfp031]

46. Sato, K.; Iwamoto, H.; Demura, K. Using choice based conjoint analysis to assess competitiveness of chemical-free Hokkaido rice. J. Rural Probl.; 2001; 37, pp. 37-49. (In Japanese) [DOI: https://dx.doi.org/10.7310/arfe1965.37.37]

47. Greene, W.H. Econometric Analysis; 6th ed. Pearson Education: New York, NY, USA, 2008.

48. Huang, J.; Qiu, H.; Bai, J.; Pray, C. Awareness, acceptance of and willingness to buy genetically modified foods in urban China. Appetite; 2006; 46, pp. 144-151. [DOI: https://dx.doi.org/10.1016/j.appet.2005.11.005] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16469414]

49. Rabadán, A.; Díaz, M.; Brugarolas, M.; Bernabéu, R. Why don’t consumers buy organic lamb meat? A Spanish case study. Meat Sci.; 2020; 162, 108024. [DOI: https://dx.doi.org/10.1016/j.meatsci.2019.108024]

50. Liu, C.; Sinkovics, R.R.; Pezderka, N.; Haghirian, P. Determinants of consumer perceptions toward mobile advertising—A comparison between Japan and Austria. J. Interact. Mark.; 2012; 26, pp. 21-32. [DOI: https://dx.doi.org/10.1016/j.intmar.2011.07.002]

51. Pearson, D.; Henryks, J.; Jones, H. Organic food: What we know (and do not know) about consumers. Renew. Agric. Food Syst.; 2011; 26, pp. 171-177. [DOI: https://dx.doi.org/10.1017/S1742170510000499]

52. Franzen-Castle, L.; Colgrove, K.; Wells, C.; Henneman, A. P204 comparing website data from food newsletter subscribers with general “organic” search traffic data. J. Nutr. Educ. Behav.; 2019; 51, pp. S124-S125. [DOI: https://dx.doi.org/10.1016/j.jneb.2019.05.580]

53. Moriuchi, E.; Takahashi, I. An empirical investigation of the factors motivating Japanese repeat consumers to review their shopping experiences. J. Bus. Res.; 2018; 82, pp. 381-390. [DOI: https://dx.doi.org/10.1016/j.jbusres.2017.07.024]

54. Madani, F.; Seenivasan, S.; Junzhao, M. Determinants of store patronage: The roles of political ideology, consumer and market characteristics. J. Retail. Consum. Serv.; 2021; 63, 102691. [DOI: https://dx.doi.org/10.1016/j.jretconser.2021.102691]

55. Bai, H. The prices of organic farm products and the receptivity of consumer: A comparative analysis between California, USA. and Japan. Jpn. J. Org. Agric. Sci.; 2020; 12, pp. 56-70. Available online: https://www.jstage.jst.go.jp/article/joas/12/1/12_56/_pdf (accessed on 14 July 2021).

56. Hendriks-Hartensveld, A.E.M.; Brodock, J.L.; Hayes, J.E.; Rolls, B.J.; Keller, K.L.; Havermans, R.C. The relative importance of complexity, variety, and portion size in ice cream preference in Dutch and American participants. Food Qual. Prefer.; 2022; 99, 104523. [DOI: https://dx.doi.org/10.1016/j.foodqual.2021.104523]