Shaoling Fu
Xiaodan Liu
Hua Liu
Yatao Huang- College of Economics and Management, South China Agricultural University, Guangzhou, China
The “lemon effect,” which is the result of information asymmetry and barriers to trust, poses serious challenges to the sustainable development of green agricultural products. Therefore, enhancing consumers’ trust is critical to maintain sustainable purchasing behavior. Information transparency has been widely attention as a marketing tool, and previous research related to agricultural products has focused on the visible information. Based on signaling theory, this study takes an invisible information perspective and empirically investigates how production information transparency of green agricultural products affects consumer trust and online purchasing behavior. The results of structural equation modeling analysis show that production technology information transparency and production means information transparency have different effects on the dimensions of consumer trust (in competence and benevolence). Moreover, trust in competence has a significant positive impact on trust in benevolence; they both have significant impacts on consumers’ online green purchase behavior. The results of this study contribute to signaling theory and the product transparency literature, and offer significant implications to practitioners of the green agricultural sectors.
Introduction
Sustainability is an emerging paradigm in the circular economy. It is a concept that can provide long-term vision, making it possible to achieve environmental and social goals in line with the UN’s sustainable development requirements. In recent years, there has been growing interest in sustainable development and circular economy research (Aman et al., 2021; Li et al., 2022; Awan and Sroufe, 2022; Li et al., 2022; Geng et al., 2022). Nevertheless, studies in this area are largely scattered and research on sustainable development and environmental protection in agriculture is still in its infancy. The COVID-19 epidemic increased public concerns about health (Li et al., 2021) and environment (He et al., 2022; Singh et al., 2022; Wang et al., 2022), thereby the consumers’ willingness to buy green or environmentally friendly products has grown rapidly in both developed and developing countries (Kumar et al., 2021; Chen et al., 2022). The COVID-19 pandemic developed new challenges for global consumers by leading to online shopping (Al Halbusi et al., 2022). Against this background, online consumption of green agricultural products has flourished in China, especially through e-commerce platforms. Consumers can easily choose a variety of green, healthy agricultural products, from natural, pollution-free organic fruits, vegetables, fresh meat, poultry, seafood, green grain, oil, and dry goods. However, the “lemon principle,” resulting from information asymmetry and betrayal of trust has seriously damaged consumers’ trust and inhibited their purchase intentions in the online shopping environment. Therefore, enhancing consumer trust and stimulating online purchase behavior remain important subjects for both academia and the industry.
Previous studies have reported that information transparency can reduce information asymmetry between consumers and sellers, improve consumer trust, and promote purchase behaviors (Kraft et al., 2020; Mohan et al., 2020). However, in practice, information transparency is a double-edged sword (Zhu, 2004), and sellers may not always benefit from it (Zhu, 2002; Li and Zhu, 2021). Therefore, further exploration of the mechanisms through which information transparency affects consumer purchase behavior is necessary. In addition, the degree and effect of information transparency are closely related to the quality of the information disclosed. Generally, information transparency regarding the production of green agricultural products can be divided into visible information and invisible information. In China, sellers are legally required to disclose information related to the origin of the product and all ingredients; this is one form of visible information. However, disclosure of invisible information such as the production process is left up to the seller. Compared with other agricultural products, green agricultural products have a premium of 20%–47% (Abraben et al., 2017; McFadden and Huffman, 2017; Lang and Rodrigues, 2022), and that for organic agricultural products is even higher at 109%–210% (Gschwandtner, 2018; Ha et al., 2019). To justify the higher price of such products, consumers must be given more information about them, including whether the production process meets green production standards.
Thus, information transparency needs further investigation in the context of green agricultural products, especially how it can be used to encourage consumer purchases. Drawing upon signaling theory, this study aims to build a conceptual framework of “green production information transparency–online green trust–online green purchase behavior” and empirically validate it through a large-scale survey of Chinese consumers. Specifically, our study seeks to answer three research questions: 1) What are the key elements of production information transparency and how do they affect online green trust? 2) How does online green trust affect online green purchase behavior? 3) What are the interrelations between the dimensions of online green trust? Through answering these questions, we contribute to the signaling theory literature by extending its application to the emerging field of online shopping for green agricultural products. Our study also clarifies the dimensions of production information transparency and provides empirical evidence on how they affect consumer trust and purchase behavior from a developing country perspective. Our findings also offer important insights for practitioners.
Literature review and research hypotheses
Signaling theory
Signaling theory grows out of the problem of information asymmetry. In a transactional market, the buyer is often in a more advantageous position in terms of access to information and relies heavily on any signals sent out by the seller when making purchase decisions (Kirmani and Rao, 2000). A thorough review of the recent literature on signaling theory indicates that it has rarely been applied to the context of green agricultural products. There are some exceptions: for instance, Berger. (2019) found that signals can increase consumers’ willingness to purchase green products. Khan et al. (2022) confirmed that consumer trust can be enhanced by signals regarding green product attributes. Chang et al. (2021) argued that environmental signals such as the avoidance of excessive packaging significantly motivate consumers’ green purchase behavior. Therefore, ensuring the sustainable development of green agricultural products and gaining long-term support from consumers requires continuous signaling of the products’ value by the producer and seller. In the e-commerce context, where the producer and consumers are typically distant from each other, green production information transparency is a key method of signaling that is central to consumers’ online trust and decision-making.
Green production information transparency
Information transparency is a central topic in the customer relationship management literature, as it is a key tool for companies to secure a competitive advantage. From information delivery perspective, it is defined as the level of availability and accessibility of market information to its participants (Zhu, 2004; Granados et al., 2010). From the product perspective, it refers to the degree to which consumers can easily access and understand the information needed to assess a product’s performance, including information on its price, quality, and characteristics (Zhou et al., 2018). From a production process perspective, information transparency is the extent to which information about product quality and sustainability is disclosed throughout the whole process (Mol, 2014). The three dimensions of information transparency are product transparency, supply chain transparency, and transaction transparency (Zhou et al., 2018). Studies of information transparency focus on information disclosure in capital markets (Zadeh et al., 2021), information sharing in supply chains (Rao et al., 2021), corporate social responsibility disclosure (Sendlhofer and Tolstoy, 2022), environmental information disclosure (Teubner et al., 2020; Du et al., 2022; Lin, 2022), and product information disclosure (Peschel and Aschemann-Witzel, 2020).
In recent years, with the rise of online shopping, studies have begun to address information transparency issues in online marketplaces (Liu et al., 2022). While theoretically, information transparency benefits all parties involved in the online transaction, a number of scholars question whether sellers are always beneficiaries (Li and Zhu, 2021). It is widely accepted that transparency can increase consumer trust and subsequent willingness to buy (Kang and Hustvedt, 2014). However, the cost associated with disclosing more information and the potential negative effect of information disclosure require more in-depth investigation (Peschel and Aschemann-Witzel, 2020). Therefore, by exploring the context of online shopping for green agricultural products, this study aims to shed light on how green production information transparency in the form of information on production means and production technology affects consumer online purchase behavior through their online green trust.
To achieve sustainable production, it is necessary to improve transparency in all segments, as a lack of information is associated with uncertainty and doubt. However, lack of transparency will be one of the most important factors affecting consumers’ purchasing decisions (Wiederhold and Martinez, 2018). Further, some scholars indicate that cost transparency promotes trust, and trust in turn enhances consumers’ willingness to purchase goods (Mohan et al., 2020). Buell and Kalkanci. (2021) found that operational transparency increases sales. And Shao and Ünal. (2019) showed through structural equation modeling results that environmental impact information has a significant influence on green purchasing. In addition, consumers are even willing to pay premium prices for products with complete product information transparency. Cheung and To. (2019) expressed that green product information is a crucial determinant of consumers’ green purchase behavior.
Online green trust
Researchers identified customer trust as a key determinant of customer purchase intentions (Schlosser et al., 2006). Because of exaggerations and vagueness in marketing communications, consumers tend to lack trust in green products (Kalafatis et al., 1999). Green trust is defined as “the willingness to rely on beliefs or expectations based on trustworthiness, benevolence, and competence in environmental performance of a product or service,” which is generally believed to influence customers’ green purchase intentions and behavior (Chen, 2010, p. 309). Empirically, especially in the context of green agricultural products, studies focus on the antecedents of green trust and its outcomes. For instance, Chen and Huang. (2021) found that consumer-oriented drivers are critical in forming consumer trust in green products. Ahmad et al. (2022) confirmed a positive effect of green trust on green product purchase intention and behavior. However, whether these relationships and effects hold in an online shopping setting are unclear. Trust is believed to be decisive for consumers’ decision-making in uncertain environments such as online marketplaces (McKnight et al., 2002); however, more empirical evidence is needed, and providing such evidence is one goal of our study. Based on studies by Singh and Sirdeshmukh. (2000) and Ba and Pavlou. (2002), we divided green trust into competence and benevolence. Green trust competence refers to consumers’ confidence in the online seller’s ability to execute the green production process effectively and reliably, and green trust benevolence is consumers’ belief that the online seller has the intention and motivation to benefit the consumer.
Research hypotheses
Transparency has been observed to be a crucial parameter for creating trust (Robinson, 2020; Sukma and Leelasantitham, 2022). As a signal that conveys information related to the quality of green agricultural products, production information transparency provides consumers with rich information on how the product is made and what measures have been taken to ensure its quality, which causes consumers to form a positive image of the product. This positive image will increase trust in the seller and the product will ultimately lead to greater willingness to purchase the product and actual purchase behavior.
Consumers increasingly prioritize quality of products over other factors such as price, functionality, and availability. Therefore, there is high demand for production information transparency (Zhou et al., 2018). The use of means of production is one of the factors affecting the quality of green agricultural products. When the online seller fully discloses detailed information about how a green agricultural product is made (e.g., the use of fertilizers, pesticides, or growth regulators), consumers’ perception of product quality will be greatly enhanced, as this shows the producer’s professionalism and expertise. During information exchange about the production process, consumers learn more about green products and develop trust in the producer and seller. Therefore, we propose the following:
H1a Green production means information transparency has a significant positive impact on consumers’ green trust competence.
Making the production process more transparent, potentially enhances consumers’ awareness of the wider aspects relevant to the value of products or services (Montecchi et al., 2021). The production process of green agricultural products is directly related to consumers’ health and wellbeing. Consumers are especially concerned about whether legal and industrial standards are strictly followed during the production process. When consumers can easily access such information, they tend to regard the company as a trustworthy one that sincerely cares about their wellbeing. Accordingly, we propose the following:
H1b Green production means information transparency has a significant positive impact on consumers’ green trust benevolence.
Companies can earn consumers’ trust by proactively disclosing information to consumers (Kalkanci et al., 2016). In the context of green agricultural products, disclosure of details about the information technologies used in the production process, from the purchase of seeds and other inputs to the harvest of primary products, reassures consumers about product quality and the producer’s technological capabilities. Effective application of technologies requires support from skilled workers and other resources, and consumers tend to trust producers who have such support. Centobelli et al. (2022) reported that technology is a bridge to building trust. Therefore, we propose the following:
H2a Green production technology information transparency has a significant positive impact on green trust competence.
Production technology is one of the factors affecting product quality. If disclose information on the production technology of green agricultural products, indicates that the technical quality of green agricultural products in the production process meets the relevant green production standards, it will make consumers believe that the online sellers will strictly follow the production technology and quality standards of green agricultural products, which will in turn enhance consumers’ trust benevolence of online sellers. And information sharing can enhance benevolent trust with customers (Barry et al., 2021). For instance, Wang et al. (2018) found empirical evidence that sharing green attribute information, such as emissions reductions and energy savings, and green certification information enhances consumer trust in the producer. Therefore, we propose the following:
H2b Green production technology information transparency has a significant positive impact on green trust benevolence.
The literature suggests two possible outcomes of consumers’ trust in online shopping, namely purchase intention and purchase behavior, and points out that the intention to buy does not necessarily result in actual purchase behavior. Therefore, our study focuses on the purchase behavior of consumers in business-to-customer (B2C) e-commerce as a result of trust.
Online transactions generally involve a high degree of product uncertainty because buyers are concerned about potential product defects. Therefore, consumers tend to evaluate the seller’s competence, which is regarded as a prerequisite for their purchase decision-making. In an online shopping environment, when consumers’ trust in the producer’s competence is established, they tend to believe that the producer’s ability to provide safe, high-quality agricultural products and reliable product information; these beliefs are likely to result in online purchase behavior. The empirical results of Kim and Song. (2020) supported that consumers’ trust in sellers’ competence had a significant positive impact on consumers’ purchase intention. Therefore, we propose the following:
H3a Green trust competence has a significant positive impact on online green purchase behavior.
When consumers have high trust in the producer’s competent delivery of green products, they are more likely to believe that the producer/seller shares the same values, such as environmental protection and a healthy lifestyle, and to see them as a responsible organization. Based on this belief, consumers expect the seller/producer to put the consumers’ interests first and voluntarily protect their rights and interests during and after the transaction. Di Battista et al. (2021) confirmed that competence trust promotes benevolent trust. Therefore, we propose the following:
H3b Green trust competence has a significant positive impact on green trust benevolence.
Consumers’ trust in the producer’s/seller’s benevolence can reduce transaction uncertainties and establish a positive emotional connection, thereby stimulating consumers’ purchase behavior. In an empirical study of Australian companies, Mohan et al. (2021) found that benevolent trust is significantly related to consumers’ purchase intention, which is a crucial antecedent of purchase behavior (Wang et al., 2021; Xu et al., 2022). On this basis, we propose the following:
H4 Green trust benevolence has a significant positive impact on online green purchase behavior.
The conceptual framework including all of the hypotheses is depicted in Figure 1.
FIGURE 1. Conceptual framework and hypothetical relationships.
Research methodology
Questionnaire design and data collection
To validate the conceptual framework, our study uses a survey research method targeting potential consumers of online green agricultural products. The measurement items adopted in this study are based on established scales in the literature. To ensure the validity and reliability of the questionnaire, we invited potential respondents and academic experts, including university professors and research students in the field of agricultural product marketing and e-commerce, to take a pretest before we officially launched the survey. Based on their feedback, minor changes were made to the wording of some questions.
The questionnaire comprises three parts. The first part explains the purpose of the survey and key concepts such as green agricultural products. The second part gathers general information about the respondent’s demographic characteristics (Yoosefi Lebni et al., 2020; Geng et al., 2022), including gender, age, highest education level, monthly income level, and occupation. The third part is the main part of the survey, presenting questions on the key constructs of the research model. A 7-point Likert scale is used for the questions in Part Three, with 1 = “completely disagree” and 7 = “completely agree.” The full questionnaire is provided in Supplementary Appendix A.
Because of the ongoing COVID-19 pandemic (Fu et al., 2021; Zhou et al., 2021; Farzadfar et al., 2022), the survey was launched online using two popular platforms in China. With the support of online shops selling green agricultural products, we collected 498 valid responses, a response rate of 87.3%. The administration of the survey fulfills the research ethics requirements of the lead researcher’s institution.
The demographic characteristics of the sample are shown in Table 1. The survey targeted consumers who had experience with purchasing green agricultural products online. More than 60.8% of the respondents were female. There are two possible explanations for the disproportionate female–male ratio in the sample. First, women generally have a stronger preference for online shopping than men. While women often see shopping as fun activity, men usually view it as a chore (https://ecommerce-platforms.com/zh-CN/articles/male-shopping-habits-versus-female-shopping-habits). Second, during the COVID-19 epidemic, women’s share of household income greatly increased after they engaged in work to cope with the risk (Ge et al., 2022). Further, women are more likely than men to spend money on household needs (Nichter and Goldmark, 2009). Therefore, it is reasonable for there to be a larger proportion of female respondents to our survey on online shopping for agricultural products.
TABLE 1. Basic characteristics of respondents.
In terms of age, people between 25 and 34 represent the largest proportion of the sample (48%). 79.5% of respondents hold an undergraduate degree or above. The sample covers a wide range of monthly income levels, of which the largest groups were 5,001–8,000 CNY (21.9%), 10,001–15,000 CNY (15.4%), and more than 20,000 CNY (3.8%). Thus, the sample is reasonably representative.
We used Harman’s single factor test to detect potential common method bias (Podsakoff et al., 2003). Common method bias is not a serious issue in this study as shows in Table 2.
TABLE 2. Common method bias detection.
Measures
Production information transparency refers to the degree of disclosure of information about products, their quality, and the sustainability of the production process (Mol, 2014). In this study, two dimensions of production information transparency are captured, namely, production means information transparency and production technology information transparency; the measurement scales for these dimensions are adapted from Liu. (2013).
Online green trust is divided into two dimensions: trust in competence and trust in benevolence. In B2C e-commerce, trust in competence refers to consumers’ belief that the seller can ensure the smooth progress of online transactions. Trust in benevolence refers to consumers’ belief that the seller cares about their interests in online transactions, rather than focusing completely on their own economic interest. The scales used to measure both dimensions of consumer trust were adapted from McKnight et al. (2002), Mohan et al. (2021), and Xu et al. (2016) and consist of three items for competence and four for benevolence.
Online green purchase behavior for green agricultural products can be understood as consumers’ prioritizing of green agricultural products over others when making online purchase decisions, and the measurement scale is adapted from Kang and Hustvedt. (2014), Suki and Suki. (2019), and Talwar et al. (2021). All of the measurement scales and items in the questionnaire are shown in Table 3.
TABLE 3. Reliability and convergence validity.
Results
Reliability and validity analyses
SPSS 26.0 software was used for data analysis. The first step was to test the reliability of the scales, for which we used the widely accepted indicator of Cronbach’s alpha. According to Table 3, the Cronbach’s alpha values of all constructs are between 0.735 and 0.815, indicating high internal consistency of measurement items within each construct, and further analysis can therefore be carried out.
Following the reliability assessment, confirmatory factor analysis (CFA) was conducted using AMOS 26.0 to test the convergent validity and discriminant validity of the constructs. Convergent validity is established for a scale if the average variance extracted (AVE) is more than 0.5. As can be seen in Table 3, the AVE values of all of the constructs are in the range of 0.543–0.66, indicating sufficient convergent validity. In addition, the composite reliability (CR) values of all of the constructs are greater than 0.6, between 0.777 and 0.854. Therefore, the internal consistency of each construct is confirmed. To assess the discriminant validity of the constructs, the square root of each construct’s AVE value is extracted and compared with the correlation coefficient with other constructs. As Table 4 shows, discriminant validity is established for all variables, as the square roots of the AVE values of the five constructs are greater than the correlation coefficients between all of the constructs.
TABLE 4. Analysis of discriminant validity.
Structural equation modeling and results
SmartPLS 3.3.2 was used to test the hypotheses; the results are presented in Figure 2 and Table 5.
FIGURE 2. Result of Structural equation model analysis.
TABLE 5. Results of the hypothesis tests.
As shown in Table 6, a bootstrap procedure shows that all of the indirect paths are statistically significant except for “green production means information transparency → green trust benevolence → online green purchase behavior,” where the lower and the upper bounds of the 95% confidence interval include zero.
TABLE 6. Analysis of mediation effects.
Discussion
The effect of green production information transparency on online green trust
Figure 2 shows the different effects of the two dimensions of green production information transparency on the two dimensions of online green trust.
(1) Green production means information transparency has a significant positive impact on trust competence (β = 0.366, p < 0.001); thus, H1a is supported. According to signaling theory, making the production means transparent reflects the producer’s/seller’s competence and confidence, which creates a positive image in consumers’ minds. This is consistent with the research of Sukma and Leelasantitham. (2022), transparency promotes consumer confidence in the organization. Similarly (Arshad and Khurram, 2020), government agencies provision of quality information on social media is positively related to citizens’ trust in the agency. It may be that transparency allows citizens to see the work and efforts of the government. As a result, citizens trust their government to translate these efforts into practical results. Therefore, consumers tend to trust that the seller/producer can deliver what they promise.
Interestingly, the effect of green production means information transparency on trust benevolence was not found to be significant (β = −0.149, p > 0.05); thus, H1b was not supported. This result is consistent with Garbarino and Lee. (2003), who found that consumers’ perceptions of the dynamic pricing of companies under information transparency reduced their trust in the companies benevolence. A possible reason is that trust in sellers’ benevolence is primarily a result of the seller’s genuine concerns, knowledge, and interest in consumer welfare, which are difficult to strengthen directly by disclosing how a product is made. Peschel and Aschemann-Witzel. (2020) showed that a higher degree of transparency increases product choice only to a minor degree or even affects it negatively. Therefore, it is reasonable that production means information transparency does not directly impact on consumer trust in the benevolence of the organization.
(2) Green production technology information transparency has a significant positive effect on online green trust; thus, H2a (β = 0.412, p < 0.001) and H2b (β = 0.298, p < 0.001) are both supported. The results show that the high availability of green production technology information can promote both dimensions of online trust. Effective sharing of production technology information with consumers is conducive to increasing their trust in the producers’ production technology capacity. The use of clean technologies also shows the organization’s commitment to the environment. Therefore, production technology information transparency can promote consumer trust in both the capability and benevolence of the organization. However, despite clear evidence that transparency can enhance consumer trust, more information on the production process also enhances consumers’ expectations of the product, which plays a key role in their perceptions of product quality and subsequent satisfaction (Al Sulaiti et al., 2005; Al-Sulaiti et al., 2021). Moreover, Azadi et al. (2021) indicated that effective information sharing could enhance the perception of the other party while potentially promoting positive behavior.
Improving online purchase behavior in connection with green agricultural products through online green trust
As shown in Figure 2, green trust in competence has a significant positive effect on online green purchase behavior (β = 0.985, p < 0.001), and green trust in benevolence has a significant positive effect on online green purchase behavior (β = 0.193, p < 0.05); thus, H3a and H4 are supported. The results indicate that consumers’ online trust in both the competence and benevolence of an organization can effectively improve their online purchase behavior. This is consistent with Ahmad and Zhang. (2020) reported that green trust has a significant positive effect on green online purchase intention. In addition, trust in benevolence has a stronger effect on online purchase behavior than trust in competence. A possible reason is that benevolence focuses on the organization’s attentiveness to consumers’ interests, which can lead to an emotional connection with consumers. Therefore, when consumers believe that an organization has high benevolence, they are more likely to make positive purchase decisions.
The survey also revealed a direct effect of trust in competence on trust in benevolence (β = 0.733, p < 0.001), which supports H3b. This agrees with the results of Di Battista et al. (2021), participants perceived that more competent professors were also caring. This is reasonable, as if the organization is believed to be capable of producing and delivering green products, it is also likely to be seen as a responsible organization concerned about consumer health and welfare. Also, competence is a more important symbolic organizational attribute than benevolence (Wilhelmy et al., 2019). Although we acknowledge that all types of organizations can have high benevolence, those with more capabilities are more likely to actually obtain goodwill. Therefore, consumers’ trust in benevolence can be enhanced by their trust in competence.
Conclusion
This study empirically explores the mechanisms through which production information transparency can affect consumers’ online purchase behavior for agricultural products. A conceptual model linking transparency, trust, and purchase behavior is constructed based on signaling theory and validated through structural equation modeling. The results showed that the two dimensions of green production information transparency (means and technology) have different effects on online green trust, which comprises trust in competence and trust in benevolence. While production transparency in means was found to be positively related to trust in competence although, its effect on trust in benevolence was non significant. However, production transparency in technology has a significant effect on both dimensions of trust. Both dimensions of online green trust were found to positively correlated to consumer online green purchase behavior. Further, the two dimensions of trust were found to be interrelated, i.e., trust in competence can lead to trust in benevolence.
Theoretical contributions
This study makes two theoretical contributions.
First, our study applies signaling theory to the context of consumers’ online purchases of green agricultural products. Regarding production information transparency as a key signal that producers send to potential consumers, we confirm that it has a positive impact on consumers’ online purchase decision-making for green agricultural products. Our study also contributes to the literature on the country-of-origin effect, which posits that the country where a product is produced affects consumers’ perception of product quality (Al-Sulaiti and Baker, 1998). The result showed that consumers’ trust and confidence in products made in China have been significantly enhanced through increased transparency of the production process. Thus, our study validates and extends the application of the signaling theory to the emerging field of e-commerce and green agricultural products in China.
Second, this study focuses on the role of invisible information disclosure of green agricultural products in the Chinese context. It is a complement to previous research on the value of visible information on green products and provides new insights into green product information concerns. Further, it enriches the research on the transparency of information on green agricultural products in developing countries. Previous studies on production information transparency have mainly focused on non-agricultural products. This study aims to explore the transparency of information on green agricultural products, thus complementing the existing studies on production information transparency. Production information transparency is divided into production means information transparency and production technology information transparency, which is an expansion of the production information transparency dimension. This extension facilitates better understanding of which dimensions of production information transparency of green agricultural products have a positive impact on consumers’ behavior. The results also show that different types of green production information transparency have different effects on different dimensions of online green trust, thus providing theoretical evidence for understanding the mechanisms behind influencing the online purchasing behavior of green agricultural products. This study provides new insights into how to promote green agricultural products to Chinese consumers from the perspective of product transparency.
Managerial implications
For the government, it is crucial to further promote agricultural enterprises’ green and low-carbon “Internet +” development. The implications for the policy include the following.
The government should encourage agricultural enterprises to disclose information on green agricultural products, which can cultivate consumers’ trust of green agricultural products. For example, by providing corresponding subsidies, project financial support, and establishing the signpost of key enterprises, etc. The government can lead enterprises to actively build and develop infrastructures and platforms for product information transparency, and provide financial support and technical guidance. Moreover, by government media, which publicizes the advantages of green agricultural production information transparency, typical enterprise representatives, etc., would guide enterprises to disclose related information actively.
In areas where green agricultural products are grown on a large scale, the government can take the lead in establishing a traceability platform for green agricultural products, and connect with agricultural production enterprises to monitor the production process of green agricultural products throughout the process, to make production information transparent, maintain the order of the green agricultural product market, and promote the sustainable development of the online consumer market for green agricultural products.
The management implications for agricultural enterprises to promote sustainable consumption of green agricultural products are as follows.
Strengthening consumer trust is vital for maintaining the sustainable consumption of green agricultural products. Trust significantly motivates consumers to buy green products online. Therefore, online shops are encouraged to disclose more information about how their products are made, such as the production process and the technologies used. Information about the green product should also be made accessible to consumers. In addition, shops can also share their values and missions to establish a strong emotional connection with potential consumers who hold the same values. With the help of modern digital technologies, online shops can establish communication channels where consumers can interact with them. Through frequent exchange of ideas, information asymmetry is reduced, and consumers’ trust in the shop’s competence and benevolence is strengthened.
Consumers with higher trust in an online shop are more likely to engage in continuous transactions with that shop. Therefore, after trust is established through transparency, it should be properly managed and maintained. Therefore, online shops should have channels for consumer reviews and feedback, and consider a proactive attitude toward improving information sharing and product and service quality. At a macro level, government interventions such as subsidies to producers of green agricultural products are needed to help organizations through difficult times such as the COVID-19 pandemic.
Limitations and future research directions
Although the findings in this study are helpful for increasing the sustainable consumption of green agricultural products, there are some limitations. 1) Cross-sectional data are used to verify the proposed theoretical model. Longitudinal data could provide a more in-depth perspective on the relationship between production information transparency and online purchase behavior, which would make the results more reliable. Consumer demand is constantly changing; thus, in the future, a follow-up survey could be considered, and new findings and conclusions could be obtained. 2) The empirical results show that information transparency can promote online purchase behavior by cultivating consumer trust. However, whether the production information shared online is always true remains questionable. A comprehensive study of whether blockchain technology will affect consumers’ purchase behavior could be conducted. Such a study would have strong academic and practical value by accelerating the development of agricultural blockchain core technology, strengthening research on agricultural blockchain standardization, and promoting the innovative application of blockchain technology to ensure the quality, safety, traceability, and transparency of the supply chain.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
Author contributions
Conception and design: SF. The provision of materials (i.e., questionnaires): XL. Data analysis and hypotheses: HL, YH. Article revision and proofreading: AL and YH.
Funding
This research was supported by the National Planning of Philosophy and Social Science Foundation of China (16BGL128) and the Social Science Foundation of Guangdong Province (#D22CGL18).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2022.985101/full#supplementary-material
References
Abraben, L. A., Grogan, K. A., and Gao, Z. (2017). Organic price premium or penalty? A comparative market analysis of organic wines from tuscany. Food Policy 69, 154–165. doi:10.1016/j.foodpol.2017.04.005
Ahmad, F. S., Rosli, N. T., and Quoquab, F. (2022). Environmental quality awareness, green trust, green self-efficacy and environmental attitude in influencing green purchase behaviour. Int. J. Ethics. Syst. 38 (1), 68–90. doi:10.1108/IJOES-05-2020-0072
Ahmad, W., and Zhang, Q. (2020). Green purchase intention: Effects of electronic service quality and customer green psychology. J. Clean. Prod. 267, 122053. doi:10.1016/j.jclepro.2020.122053
Al Halbusi, H., Al-Sulaiti, K., Abbas, J., and Al-Sulaiti, I. (2022). Assessing factors influencing technology adoption for online purchasing amid COVID-19 in Qatar: Moderating role of word of mouth. Front. Environ. Sci. 1039, 942527. doi:10.3389/fenvs.2022.942527
Al Sulaiti, K., Al Khulaifi, A., and Al Khatib, F. (2005). Banking services and customer’s satisfaction in Qatar: A statistical analysis. Stud. Bus. Econ. 11 (1), 130–154. doi:10.29117/sbe.2005.0009
Al-Sulaiti, K. I., Abaalzamat, K. H., Khawaldah, H., and Alzboun, N. (2021). Evaluation of katara cultural village events and services: A visitors’ perspective. Event Manag. 25 (6), 653–664. doi:10.3727/152599521x16106577965099
Al-Sulaiti, K. I., and Baker, M. (1998). Country of origin effects: A literature review. Mark. Intell. Plan. 16, 150–199. doi:10.1108/02634509810217309
Aman, J., Abbas, J., Shi, G., Ain, N. U., and Gu, L. (2021). Community wellbeing under China–Pakistan economic corridor: Role of social, economic, cultural, and educational factors in improving residents’ quality of life. Front. Psychol. 12, 816592. doi:10.3389/fpsyg.2021.816592
Arshad, S., and Khurram, S. (2020). Can government’s presence on social media stimulate citizens’ online political participation? Investigating the influence of transparency, trust, and responsiveness. Gov. Inf. Q. 37 (3), 101486. doi:10.1016/j.giq.2020.101486
Awan, U., and Sroufe, R. (2022). Sustainability in the circular economy: Insights and dynamics of designing circular business models. Appl. Sci. 12 (3), 1521. doi:10.3390/app12031521
Azadi, N. A., Ziapour, A., Lebni, J. Y., Irandoost, S. F., and Chaboksavar, F. (2021). The effect of education based on health belief model on promoting preventive behaviors of hypertensive disease in staff of the Iran University of Medical Sciences. Arch. Public Health 79 (1), 69. doi:10.1186/s13690-021-00594-4
Ba, S., and Pavlou, P. A. (2002). Evidence of the effect of trust building technology in electronic markets: Price premiums and buyer behavior. MIS Q. 26 (3), 243–268. doi:10.2307/4132332
Barry, J. M., Graça, S. S., Kharé, V. P., and Yurova, Y. V. (2021). Examining institutional effects on B2B relationships through the lens of transitioning economies. Ind. Mark. Manag. 93, 221–234. doi:10.1016/j.indmarman.2020.09.012
Berger, J. (2019). Signaling can increase consumers’ willingness to pay for green products: Theoretical model and experimental evidence. J. Consum. Behav. 18 (3), 233–246. doi:10.1002/cb.1760
Buell, R. W., and Kalkanci, B. (2021). How transparency into internal and external responsibility initiatives influences consumer choice. Manag. Sci. 67 (2), 932–950. doi:10.1287/mnsc.2020.3588
Centobelli, P., Cerchione, R., Del Vecchio, P., Oropallo, E., and Secundo, G. (2022). Blockchain technology for bridging trust, traceability and transparency in circular supply chain. Inf. Manag. 59 (7), 103508. doi:10.1016/j.im.2021.103508
Chang, T. W., Chen, Y. S., Yeh, Y. L., and Li, H. X. (2021). Sustainable consumption models for customers: Investigating the significant antecedents of green purchase behavior from the perspective of information asymmetry. J. Environ. Plan. Manag. 64 (9), 1668–1688. doi:10.1080/09640568.2020.1837087
Chen, T. Y., and Huang, C. J. (2021). The drivers of the green trust formation: Evidence from Taiwan and Japan. J. Bus. Adm. 46 (1), 1–31. doi:10.3966/102596272021030461001
Chen, X., Rahman, M. K., Rana, M., Gazi, M., Issa, A., Rahaman, M., et al. (2022). Predicting consumer green product purchase attitudes and behavioral intention during COVID-19 pandemic. Front. Psychol. 6352, 760051. doi:10.3389/fpsyg.2021.760051
Chen, Y. S. (2010). The drivers of green brand equity: Green brand image, green satisfaction, and green trust. J. Bus. Ethics 93 (2), 307–319. doi:10.1007/s10551-009-0223-9
Cheung, M. F., and To, W. M. (2019). An extended model of value-attitude-behavior to explain Chinese consumers’ green purchase behavior. J. Retail. Consumer Serv. 50, 145–153. doi:10.1016/j.jretconser.2019.04.006
Di Battista, S., Smith, H. J., Berti, C., and Pivetti, M. (2021). Trustworthiness in higher education: The role of professor benevolence and competence. Soc. Sci. 10 (1), 18. doi:10.3390/socsci10010018
Du, L., Wang, F., and Tian, M. (2022). Environmental information disclosure and green energy efficiency: A spatial econometric analysis of 113 prefecture-level cities in China. Front. Environ. Sci. 1232, 966580. doi:10.3389/fenvs.2022.966580
Farzadfar, F., Naghavi, M., Sepanlou, S. G., Saeedi Moghaddam, S., Dangel, W. J., Davis Weaver, N., et al. (2022). Health system performance in Iran: A systematic analysis for the global burden of disease study 2019. Lancet 399, 1625–1645. doi:10.1016/s0140-6736(21)02751-3
Fu, Q., Abbas, J., and Sultan, S. (2021). Reset the industry redux through corporate social responsibility: The COVID-19 tourism impact on hospitality firms through business model innovation. Front. Psychol. 12, 6686. doi:10.3389/fpsyg.2021.795345
Garbarino, E., and Lee, O. F. (2003). Dynamic pricing in internet retail: Effects on consumer trust. Psychol. Mark. 20 (6), 495–513. doi:10.1002/mar.10084
Ge, T., Abbas, J., Ullah, R., Abbas, A., Sadiq, I., and Zhang, R. (2022). Women’s entrepreneurial contribution to family income: Innovative technologies promote females’ entrepreneurship amid COVID-19 crisis. Front. Psychol. 13, 828040. doi:10.3389/fpsyg.2022.828040
Geng, J., Jaffar, A., Haq, S. U., Ye, H., Abbas, A., Cai, Y., et al. (2022). Survival in pandemic times: Managing energy efficiency, food diversity, and sustainable practices of nutrient intake amid COVID-19 crisis. Front. Environ. Sci. 861, 945774. doi:10.3389/fenvs.2022.945774
Granados, N., Gupta, A., and Kauffman, R. J. (2010). Research commentary—information transparency in business-to-consumer markets: Concepts, framework, and research agenda. Inf. Syst. Res. 21 (2), 207–226. doi:10.1287/isre.1090.0249
Gschwandtner, A. (2018). The organic food premium: A local assessment in the UK. Int. J. Econ. Bus. 25 (2), 313–338. doi:10.1080/13571516.2017.1389842
Ha, T. M., Shakur, S., and Do, K. H. P. (2019). Rural-urban differences in willingness to pay for organic vegetables: Evidence from Vietnam. Appetite 141, 104273–104278. doi:10.1016/j.appet.2019.05.004
He, L., Hu, Q., Liu, Z., and Zhang, Y. (2022). Editorial: COVID-19: Mitigation strategies and their implications for the global environment. Front. Environ. Sci. 128, 858607. doi:10.3389/fenvs.2022.858607
Kalafatis, S. P., Pollard, M., East, R., and Tsogas, M. H. (1999). Green marketing and ajzen’s theory of planned behaviour: A cross-market examination. J. Consum. Mark. 16 (5), 441–460. doi:10.1108/07363769910289550
Kalkanci, B., Ang, E., and Plambeck, E. L. (2016). “Strategic disclosure of social and environmental impacts in a supply chain,” in Environmentally responsible supply chains (Cham: Springer), 223–239. doi:10.1007/978-3-319-30094-8_13
Kang, J., and Hustvedt, G. (2014). Building trust between consumers and corporations: The role of consumer perceptions of transparency and social responsibility. J. Bus. Ethics 125 (2), 253–265. doi:10.1007/s10551-013-1916-7
Khan, K. U., Atlas, F., Arshad, M. Z., Akhtar, S., and Khan, F. (2022). Signaling green: Impact of green product attributes on consumers trust and the mediating role of green marketing. Front. Psychol. 3891, 790272. doi:10.3389/fpsyg.2022.790272
Kim, J. H., and Song, H. (2020). The influence of perceived credibility on purchase intention via competence and authenticity. Int. J. Hosp. Manag. 90, 102617. doi:10.1016/j.ijhm.2020.102617
Kirmani, A., and Rao, A. R. (2000). No pain, no gain: A critical review of the literature on signaling unobservable product quality. J. Mark. 64 (2), 66–79. doi:10.1509/jmkg.64.2.66.18000
Kraft, T., Valdés, L., and Zheng, Y. (2020). Consumer trust in social responsibility communications: The role of supply chain visibility. Prod. Operations Manag., 1–32. doi:10.2139/ssrn.3407617
Kumar, A., Prakash, G., and Kumar, G. (2021). Does environmentally responsible purchase intention matter for consumers? A predictive sustainable model developed through an empirical study. J. Retail. Consumer Serv. 58, 102270–102316. doi:10.1016/j.jretconser.2020.102270
Lang, M., and Rodrigues, A. C. (2022). A comparison of organic-certified versus non-certified natural foods: Perceptions and motives and their influence on purchase behaviors. Appetite 168, 105698. doi:10.1016/j.appet.2021.105698
Li, H., and Zhu, F. (2021). Information transparency, multihoming, and platform competition: A natural experiment in the daily deals market. Manag. Sci. 67 (7), 4384–4407. doi:10.1287/mnsc.2020.3718
Li, X., Dongling, W., Baig, N. U. A., and Zhang, R. (2022a). From cultural tourism to social entrepreneurship: Role of social value creation for environmental sustainability. Front. Psychol. 13, 925768. doi:10.3389/fpsyg.2022.925768
Li, Y., Al-Sulaiti, K., Dongling, W., Abbas, J., and Al-Sulaiti, I. (2022b). Tax avoidance culture and employees’ behavior affect sustainable business performance: The moderating role of corporate social responsibility. Front. Environ. Sci. 1081, 964410. doi:10.3389/fenvs.2022.964410
Li, Z., Wang, D., Hassan, S., and Mubeen, R. (2021). Tourists’ health risk threats amid COVID-19 era: Role of technology innovation, transformation, and recovery implications for sustainable tourism. Front. Psychol. 12, 769175. doi:10.3389/fpsyg.2021.769175
Lin, S. (2022). Can environmental information disclosure improve urban green economic efficiency? New evidence from the mediating effects model. Front. Environ. Sci. 747, 920879. doi:10.3389/fenvs.2022.920879
Liu, S., Wei, K., and Gao, B. (2022). Power of information transparency: How online reviews change the effect of agglomeration density on firm revenue. Decis. Support Syst. 153, 113681. doi:10.1016/j.dss.2021.113681
Liu, Y. (2013). The role of transparency in consumer brand relationships. London: Imperial College London. Doctoral dissertation. doi:10.25560/17840
McFadden, J. R., and Huffman, W. E. (2017). Willingness-to-pay for natural, organic, and conventional foods: The effects of information and meaningful labels. Food Policy 68, 214–232. doi:10.1016/j.foodpol.2017.02.007
McKnight, D. H., Choudhury, V., and Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Inf. Syst. Res. 13 (3), 334–359. doi:10.1287/isre.13.3.334.81
Mohan, B., Buell, R. W., and John, L. K. (2020). Lifting the veil: The benefits of cost transparency. SSRN J. 39 (6), 1105–1121. doi:10.2139/ssrn.2498174
Mohan, M., Nyadzayo, M. W., and Casidy, R. (2021). Customer identification: The missing link between relationship quality and supplier performance. Ind. Mark. Manag. 97, 220–232. doi:10.1016/j.indmarman.2021.07.012
Mol, A. P. J. (2014). Governing China’s food quality through transparency: A review. Food control. 43, 49–56. doi:10.1016/j.foodcont.2014.02.034
Montecchi, M., Plangger, K., and West, D. C. (2021). Supply chain transparency: A bibliometric review and research agenda. Int. J. Prod. Econ. 238, 108152. doi:10.1016/j.ijpe.2021.108152
Nichter, S., and Goldmark, L. (2009). Small firm growth in developing countries. World Dev. 37 (9), 1453–1464. doi:10.1016/j.worlddev.2009.01.013
Peschel, A. O., and Aschemann-Witzel, J. (2020). Sell more for less or less for more? The role of transparency in consumer response to upcycled food products. J. Clean. Prod. 273, 122884. doi:10.1016/j.jclepro.2020.122884
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 88 (5), 879–903. doi:10.1037/0021-9010.88.5.879
Rao, S., Gulley, A., Russell, M., and Patton, J. (2021). On the quest for supply chain transparency through blockchain: Lessons learned from two serialized data projects. J. Bus. Logist. 42 (1), 88–100. doi:10.1111/jbl.12272
Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technol. Soc. 63, 101421. doi:10.1016/j.techsoc.2020.101421
Schlosser, A. E., White, T. B., and Lloyd, S. M. (2006). Converting web site visitors into buyers: How web site investment increases consumer trusting beliefs and online purchase intentions. J. Mark. 70 (2), 133–148. doi:10.1509/jmkg.70.2.133
Sendlhofer, T., and Tolstoy, D. (2022). How employees shape csr transparency: A sensemaking perspective. J. Bus. Res. 150, 268–278. doi:10.1016/j.jbusres.2022.05.074
Shao, J., and Ünal, E. (2019). What do consumers value more in green purchasing? Assessing the sustainability practices from demand side of business. J. Clean. Prod. 209, 1473–1483. doi:10.1016/j.jclepro.2018.11.022
Singh, B. P., Rana, P., Mittal, N., Kumar, S., Athar, M., Abduljaleel, Z., et al. (2022). Variations in the yamuna river water quality during the COVID-19 lockdowns. Front. Environ. Sci. 1089, 940640. doi:10.3389/fenvs.2022.940640
Singh, J., and Sirdeshmukh, D. (2000). Agency and trust mechanisms in consumer satisfaction and loyalty judgments. J. Acad. Mark. Sci. 28 (1), 150–167. doi:10.1177/0092070300281014
Suki, N. M., and Suki, N. M. (2019). Examination of peer influence as a moderator and predictor in explaining green purchase behaviour in a developing country. J. Clean. Prod. 228, 833–844. doi:10.1016/j.jclepro.2019.04.218
Sukma, N., and Leelasantitham, A. (2022). The influence and continuance intention of the E-government system: A case study of community water supply business. Front. Env. Sci. 770, 91898. doi:10.3389/fenvs.2022.91898
Talwar, S., Jabeen, F., Tandon, A., Sakashita, M., and Dhir, A. (2021). What drives willingness to purchase and stated buying behavior toward organic food? A stimulus-organism-behavior-consequence (SOBC) perspective. J. Clean. Prod. 293, 125882. doi:10.1016/j.jclepro.2021.125882
Teubner, K., Teubner, I., Pall, K., Kabas, W., Tolotti, M., Ofenböck, T., et al. (2020). New emphasis on water transparency as socio-ecological indicator for urban water: Bridging ecosystem service supply and sustainable ecosystem health. Front. Environ. Sci. 8, 573724. doi:10.3389/fenvs.2020.573724
Wang, Q., Zhang, W., Tseng, C. P. M. L., Sun, Y., and Zhang, Y. (2021). Intention in use recyclable express packaging in consumers’ behavior: An empirical study. Resour. Conservation Recycl. 164, 105115. doi:10.1016/j.resconrec.2020.105115
Wang, T., Du, H., Zhao, Z., Zhang, J., and Zhou, C. (2022). Impact of meteorological conditions and human activities on air quality during the COVID-19 lockdown in northeast China. Front. Environ. Sci. 454, 877268. doi:10.3389/fenvs.2022.877268
Wang, Y., Huscroft, J. R., Hazen, B. T., and Zhang, M. (2018). Green information, green certification and consumer perceptions of remanufctured automobile parts. Resour. Conservation Recycl. 128, 187–196. doi:10.1016/j.resconrec.2016.07.015
Wiederhold, M., and Martinez, L. F. (2018). Ethical consumer behaviour in Germany: The attitude-behaviour gap in the green apparel industry. Int. J. Consum. Stud. 42 (4), 419–429. doi:10.1111/ijcs.12435
Wilhelmy, A., Kleinmann, M., Melchers, K. G., and Lievens, F. (2019). What do consistency and personableness in the interview signal to applicants? Investigating indirect effects on organizational attractiveness through symbolic organizational attributes. J. Bus. Psychol. 34 (5), 671–684. doi:10.1007/s10869-018-9600-7
Xu, J. D., Cenfetelli, R. T., and Aquino, K. (2016). Do different kinds of trust matter? An examination of the three trusting beliefs on satisfaction and purchase behavior in the buyer-seller context. J. Strategic Inf. Syst. 25 (1), 15–31. doi:10.1016/j.jsis.2015.10.004
Xu, Y., Du, J., Khan, M. A. S., Jin, S., Altaf, M., Anwar, F., et al. (2022). Effects of subjective norms and environmental mechanism on green purchase behavior: An extended model of theory of planned behavior. Front. Environ. Sci. 39. doi:10.3389/fenvs.2022.779629
Yoosefi Lebni, J., Abbas, J., Khorami, F., Khosravi, B., Jalali, A., and Ziapour, A. (2020). Challenges facing women survivors of self-immolation in the Kurdish regions of Iran: A qualitative study. Front. Psychiatry 11, 778. doi:10.3389/fpsyt.2020.00778
Zadeh, M. H., Magnan, M., Cormier, D., and Hammami, A. (2021). Environmental and social transparency and investment efficiency: The mediating effect of analysts’ monitoring. J. Clean. Prod. 322, 128991. doi:10.1016/j.jclepro.2021.128991
Zhou, L., Wang, W., Xu, J., Liu, T., and Gu, J. (2018). Perceived information transparency in B2C e-commerce: An empirical investigation. Inf. Manag. 55 (7), 912–927. doi:10.1016/j.im.2018.04.005
Zhou, Y., Draghici, A., Mubeen, R., Boatca, M. E., and Salam, M. A. (2021). Social media efficacy in crisis management: Effectiveness of non-pharmaceutical interventions to manage COVID-19 challenges. Front. Psychiatry 12, 626134. doi:10.3389/fpsyt.2021.626134
Zhu, K. (2002). Information transparency in electronic marketplaces: Why data transparency may hinder the adoption of B2B exchanges. Electron. Mark. 12 (2), 92–99. doi:10.1080/10196780252844535
Keywords: green production information transparency, online green trust, online green purchase behavior, green agricultural products, sustainable consumption, signaling theory
Citation: Fu S, Liu X, Lamrabet A, Liu H and Huang Y (2022) Green production information transparency and online purchase behavior: Evidence from green agricultural products in China. Front. Environ. Sci. 10:985101. doi: 10.3389/fenvs.2022.985101
Received: 03 July 2022; Accepted: 07 November 2022;
Published: 22 November 2022.
Edited by:
Fu-Sheng Tsai, Cheng Shiu University, TaiwanReviewed by:
S. Ale Raza Shah, Xi’an Jiaotong University, ChinaGulnaz Muneer, Bahauddin Zakariya University, Pakistan
Kashif Ullah Khan, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan
Jun (Justin) Li, South China Normal University, China
Eduardo Moraes Sarmento, University of Lisbon, Portugal
Copyright © 2022 Fu, Liu, Lamrabet, Liu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hua Liu, liu199325@stu.scau.edu.cn