Next Article in Journal
Diversity of Filamentous Fungi Associated with Dairy Processing Environments and Spoiled Products in Brazil
Previous Article in Journal
Improved Stabilization and In Vitro Digestibility of Mulberry Anthocyanins by Double Emulsion with Pea Protein Isolate and Xanthan Gum
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Foods
Volume 12
Issue 1
10.3390/foods12010152
foods-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Attribute-Driven or Green-Driven: The Impact of Subjective and Objective Knowledge on Sustainable Tea Consumption

by
Manhua Zheng
,
Decong Tang
and
Anxin Xu
*
College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China
*
Author to whom correspondence should be addressed.
Foods 2023, 12(1), 152; https://doi.org/10.3390/foods12010152
Submission received: 10 November 2022 / Revised: 11 December 2022 / Accepted: 22 December 2022 / Published: 28 December 2022
(This article belongs to the Section Sensory and Consumer Sciences)
Browse Figure
Review Reports Versions Notes

Abstract

:
The market for green agricultural products has tremendous growth potential as the pressure on resources and the environment increases and the safety of agricultural products is garnering attention. The demand for green food (tea) is also rising as tea is among the top three beverages consumed worldwide. The study attempts to propose a model of the relationship between green food (tea) customers’ product knowledge, perceived product quality, trust, purchase intention, and purchase behaviour. In addition, we will provide an analysis of the role played by age, education, income, gender, etc. The study included 700 questionnaires on green food (tea) consumers that were collected through the Credemo questionnaire platform, and data analysis was carried out using the SmartPLS software to assess the model of product knowledge on green food (tea) consumption behaviour. The findings demonstrate that, concerning differences in age, education, income, and gender; product knowledge—including attribute knowledge and green knowledge—has a positive impact on perceived product quality and trust; perceived product quality has a positive impact on trust; perceived product quality and trust have a positive impact on purchase intention; and purchase intention has a positive impact on purchase behavior.

1. Introduction

Global environmental problems (climate warming, glacier melting, soil and water pollution, etc.) frequently occur [1], triggering governments, companies, and consumers to pay attention to ecological and environmental protection [2]. Local governments have prioritized pollution control and environmental management, leading the industry toward a green transition and igniting a wave of enthusiasm for ecological environment protection [3]. A kind of environmental behavior that lessens environmental harm is purchasing environmentally friendly products [4]. Companies promote green food through media advertising to encourage consumers to purchase it, as it is a category of environmentally friendly goods that helps to safeguard the environment [2,5]. Currently, the tea industry faces issues with quality and safety, surface source pollution, and a lack of technology among tea farmers. Green tea (the green in this study refers to the sustainable characteristics of the food, not the color or variety of the food.) is a type of green food and is a safe and healthy, eco-friendly, green, and pollution-free product [6,7,8]. The tea industry’s sustainable and healthy growth depends on green food-tea development. The demand for green tea consumption is increasing as consumer consumption levels rise, government sustainability concepts mature, and consumer knowledge grows [6,9,10].
Consumers who lack knowledge of environmental issues are less likely to act in an environmentally friendly manner because knowledge is the biggest predictor of environmentally friendly behavior [11,12]. Product knowledge may be learned by consumers, and a lack of product knowledge can lead to gaps in attitudes and behaviors [13], and related research implies that product knowledge is an antecedent variable of purchase intention or purchase behavior [14,15]. However, there is considerable debate; on the one hand, some researchers contend that product knowledge has no direct effect on purchase intention or behavior [16,17]. On the other hand, several researchers have found that consumers’ intentions to make purchases or their actual behavior are not significantly influenced by their knowledge of a product [13,16,18], additionally, some studies [19,20] have come to the conclusion that knowledge is insufficient to motivate environmental behavior. There is debate on the connection between product knowledge and purchase intentions or behavior, presumably for three reasons.
First, it can be because the studies mentioned above did not focus on the same subjects (fabric softener, toilet paper, clothing, household appliances, lamps, batteries, etc.). Second, the above scholars do not define and classify green product knowledge in the same way, either as product knowledge, green firm knowledge, label knowledge and environmental knowledge, mostly as a higher-order unidimensional variable [21,22,23,24,25,26], and environmental knowledge refers to consumers’ perceptions related to climate change and the environmental impacts of production and consumption [27], which is an environmental perception of nature as a whole; and labelling knowledge, which refers to consumers’ ability to recognise labelling knowledge and understand the meaning of labels [28]. There is a gap between previous research on product knowledge and green food knowledge. This is because green food knowledge targets the food rather than the overall general environment, and green food contains not only green labels but also green attribute knowledge of overall production, transport and storage, as well as consumers’ subjective knowledge of how the overall process of green food production, transport and storage affects the environment, so it is necessary for this study to subdivide green food knowledge into two dimensions, namely attribute knowledge (objective) and green knowledge (subjective). Third, the intermediate variables between product knowledge and purchase intention or purchase behaviour are not the same (environmental attitudes, perceived value, perceived risk, perceived effectiveness, trust, environmental emotions), and there are fewer in-depth studies on purchase intention and behaviour from the respective sub-dimensions of product knowledge [15,20,26,29,30]. There are differences between green products and green food tea, with green products emphasizing the protection and improvement of the environment more [31] and green food tea having unique qualities, such as food safety attributes that offer consumers higher nutritional value to maintain their health [2,32]. A study based on the perspective of green food tea is, therefore, necessary. Taking care of this variety of difficulties can improve green consumption and therefore help to solve environmental issues.
There are two primary categories of a recent study on consumers’ purchase intentions of green products. First, from the standpoint of internal variables, researchers have found that consumers’ purchase intentions of green products are significantly influenced by attitudes, perceived value, perceived risk, perceived quality, perceived behavioural control, perceived effectiveness, and environmental concerns [14,33,34,35]. Second, from the standpoint of extraneous variables, researchers have found that brands, consumer reviews, and claims made in green advertising have a considerable positive influence on consumers’ intention to make green purchases [2,36,37]. Thus, it is clear that present researchers are less likely to examine consumer intention and behaviours related to the consumption of green foods (tea) from the standpoint of internal factors, such as product knowledge.
The study makes an effort to elaborate on the following research-related issues. Firstly, constructing a model of how product knowledge (attribute knowledge and green knowledge) affects consumers’ purchase intention and behaviour around the consumption of green food-tea. Secondly, expanding the areas in which consumer product knowledge theory can be applied. The study used green tea as its research object, and 700 green food consumer surveys were gathered to build a structural equation model with numerous clusters and to examine the model of consumer product knowledge on green tea purchase behaviour. This will serve as a guide for increasing green consumption and lowering the ecological and environmental pollution.

2. Research Hypotheses and Theoretical Framework

2.1. Research Hypotheses

2.1.1. Attribute Knowledge and Perceived Product Quality

The word “attribute knowledge” describes pertinent, precise knowledge about the characteristics, lingo, frameworks, standards, etc., of green foods [38]. According to the external cue theory, consumers can form opinions about a product’s quality based on both internal and outward cues [39]. Consumers rely strongly on outward cues in a lemon market with asymmetric knowledge, while interior cues have little impact on product quality. When consumers buy green food, the green food certification label serves as an external cue with a signalling function that can provide some information about the food’s quality. This reduces information asymmetry in the market by providing consumers with valuable reference material on the product’s quality [40].
Consumers have more faith in independent third-party certification organizations than they have in the product’s manufacturers [41]. This is because third-party certifying bodies contribute to lowering the consumer risk since consumers believe they can establish and uphold just and equal standards, regulations, procedures, etc., in the exchange of goods [42]. Consumers find it challenging to spend more time and effort researching the safety of green foods, their nutritional value, the presence of pesticide residues, and other factors when food safety incidents are frequent and raise consumer doubts about the quality of food. The food certification label is a cheap way to screen products. Consumers’ views and trust in the quality of the food they are purchasing will improve if food is authorized by a third party to certify a high-quality product [43]. Studies have revealed that consumers’ views of product standards are influenced by the information they obtain from businesses or firms [44].
The hypothesis that is generated from the analysis above is as follows.
Hypothesis 1 (H1).
Consumer attribute knowledge significantly improves the perceived product quality of green tea.

2.1.2. Attribute Knowledge and Trust

Compared to regular food, green food has qualities such as being safe, healthy, and environmentally friendly [2]. Additionally, consumers can only recognize green food at the point of purchase based on the green food label approved by a third-party certification body and the packaging promotion of the particular food [45]. This gives green food the quality of trust [46]. Third-party certification bodies reduce the opportunistic behaviour of businesses by setting rules, standards, and procedures (e.g., QS certification, HACCP certification, ISO series certification, green food certification, organic food certification, pollution-free agricultural products certification, GMO certification, etc.), ensure that qualified and compliant food products enter the market, and provide a comparatively safe and secure market for consumers to trade. As a result, via qualified, fair, and equitable transactions, consumers’ views of the qualities of green food are improved, and trust in green food firms, green food standards, and green food labelling is increased [41]. When food label information is relatively scarce, consumers will be more cautious and less trusting of the product; when label information is abundant, consumers will be more knowledgeable about the product and have higher levels of objective understanding about green food [47].
The hypothesis that is generated from the analysis above is as follows.
Hypothesis 2 (H2).
Consumer attribute knowledge significantly improves trust in green tea.

2.1.3. Green Knowledge and Perceived Product Quality

The term “green knowledge” refers to consumers’ subjective understanding of the environmental and food safety benefits of “green” foods [38]. It is also possible to define “green knowledge” as consumers’ understanding of the impacts of reducing environmental pollution by purchasing “green” items. According to the quality signalling theory, consumers evaluate a product’s perceived quality using a range of information, and since green knowledge is embedded within the product, it affects how consumers view the product’s quality.
Consumers with green knowledge are often more concerned about environmental and ecological issues, and they frequently relate products with ethics [48,49]. Consumers may believe that green food is of higher quality than ordinary food since it is healthier, more ecologically friendly, and safer due to its ability to mitigate environmental problems [10,50].
The hypothesis that is generated from the analysis above is as follows.
Hypothesis 3 (H3).
Consumer green knowledge significantly improves the perceived product quality of green tea.

2.1.4. Green Knowledge and Trust

Related research has demonstrated that as the market for green products grows and green information spreads broadly, consumers begin to feel more trust in green items [14]. Particularly, consumers are more likely to trust green food if they are more aware of it [22]. Consumers’ lack of understanding of the information on energy conservation and emission reduction provided by green products and their consequent mistrust of the information is one of the factors contributing to their difficulties in making purchasing decisions [51]. Consumers’ subjective assessments of the environmental protection efforts made by green food producers and the high level of food safety will strengthen their confidence in the capability of green food to lessen environmental pollution, supply nourishment, and sustain health.
The hypothesis that is generated from the analysis above is as follows.
Hypothesis 4 (H4).
Consumer green knowledge significantly increases trust in green tea.

2.1.5. Perceived Product Quality and Trust

As consumers base their purchasing decisions on incomplete and asymmetrical information, the perceived product quality is a signal from the company to the consumer based on which the consumer develops a sense of trust [52]. Consumers lose faith in a product when a company makes green claims that do not match how they think the product is made [53,54]. Consumers will have a greater perception of trust in a product or brand when they believe it to be of a higher quality [55,56]. Related studies have shown that perceived quality positively influences consumers’ trust [25,57,58].
The hypothesis that is generated from the analysis above is as follows.
Hypothesis 5 (H5).
Consumer-perceived product quality significantly increases trust in green tea.

2.1.6. Perceived Product Quality and Purchase Intention

By affecting the perceived advantages, which are higher than the costs when the perceived quality is higher, the perceived quality of the product or service can improve the consumer’s purchase intention [59]. When making food purchases, consumers should consider the product’s quality [60,61]. When consumers see green food as having higher quality than regular food (natural and unadulterated, free of dangerous elements, chemical fertilizers, etc.), they are more likely to purchase it [62,63]. Contrarily, agricultural products with lower perceived product quality (broken packing, deformed appearance, flaws, etc.) considerably decrease consumers’ intention to make a purchase [64].
The hypothesis that is generated from the analysis above is as follows.
Hypothesis 6 (H6).
Consumer-perceived product quality significantly increases the purchase intention of green tea.

2.1.7. Trust and Purchase Intention

Consumers’ trust in food firms’ products regarding food safety, health, and environmental protection is referred to as trust. Due to the unique characteristics of green foods and the information asymmetry in the market, consumers can only evaluate the quality of a product through personal experience and product expertise, making the intake of green food risky [41]. When consumers believe there is a significant risk involved in the consumption process, their faith in food producers, food certification organizations, marketing, green labelling, etc., helps to lower the perceived risk and thus increases their desire to acquire green food [65].
According to trust theory, a high level of trust is necessary for the establishment of a long-term, stable relationship with consumers and the creation of value in their transactional relationship. This helps consumers form a good corporate image and increases their likelihood of making a purchase [66]. Related research has demonstrated that green trust has a considerable, favourable impact on green purchase intention [33,67].
The hypothesis that is generated from the analysis above is as follows.
Hypothesis 7 (H7).
Consumer trust significantly increases the purchase intention of green tea.

2.1.8. Purchase Intention and Purchase Behaviour

According to the Theories of Planned Behaviour and Rational Behaviour, an individual’s behaviour can be inferred from behaviour intention. Behaviour intention is a measure of behaviour [17]. According to a related study, green purchase behaviour is positively influenced by green purchase intention [34,68,69,70]. The hypothesis that is generated from the analysis above is as follows.
Hypothesis 8 (H8).
Consumer purchase intention significantly increases the purchase behaviour of green tea.

2.2. Theoretical Framework

The SOR (Stimulus-Organism-Response) theoretical model has been used extensively in the field of consumer behaviour [71]. SOR theory suggests that external stimuli (products, brands, marketing, etc.) can influence consumers’ intentions and behaviour by affecting their emotions and perceptions (perceived quality, perceived value, perceived risk, etc.) [56,72,73]. In this study, attribute knowledge and green knowledge of green tea are used as external Stimulus variables, perceived product quality and trust as Organism variables, and purchase intention and purchase behaviour as response variables (Response). Constructing the theoretical model framework of this study based on the SOR model Integrating the above analysis, the theoretical model of this study is derived, as shown in Figure 1.

3. Methods

3.1. Participants and Procedure

The sample size of the study was calculated using Gpower3.1 and with an effect size f = 0.02, significance level = 0.05, test validity power = 0.80, and tested predictors = 5, the sample size was 647, therefore the study set a sample size of 700, which is sufficient to justify the adequacy of the study sample [74].
The study was based on a questionnaire collected by Credamo, an internationally recognized questionnaire collection platform, and an additional RMB 595 was spent to set the completion conditions to ensure the quality of the returned sample. Firstly, a credit score of 80 or above was set, as the higher the credit score of the sample, the higher the quality of the completed questionnaire. Secondly, we set the sample’s historical adoption rate to be greater than or equal to 80 to avoid the low quality of the questionnaire due to the unfamiliarity of the process. Thirdly, a sliding puzzle was set for intelligent human verification before answering, which helped to improve data quality and security. Fourthly, only one participant within 5 km was set to avoid sample homogeneity due to the bunching of participants.
The questionnaire was randomly distributed to all provinces in China in September 2022, and 700 questionnaires were returned, with a participant reward amount of RMB 3 per questionnaire. Two screening questions were set, the automatic screening question “20 + 40 = ?” and “How often did you buy green tea in one year (repeated question)”. During data cleaning, samples with inconsistent responses were removed to obtain the final sample for this study to ensure the reliability of the sample.

3.2. Measures

The overall questionnaire is divided into three main blocks. The first block is a brief graphic introduction to green foods. The second block contains questions related to the study variables (all borrowed from previously established scales and adapted to the actual situation of green tea, the measurement table is shown in Table 1), measured on a 7-point Likert scale. The third block contains questions on the demographic characteristics of the participants (gender, age, education, income, occupation, and household registration).

4. Results

4.1. Demographic Profile

SPSS software was applied to conduct a descriptive statistical analysis of the sample and the results were shown in Table 2. Based on the demographic characteristics, it can be seen that the majority of participants were female (63.3%), which is in line with the perception that the majority of people responsible for purchasing food in households are female [46], similar to previous related studies on green products [32], which may be due to the fact that the main population of green food purchasers in Chinese households is female [80]. The participants were mostly urban and preferred national and local green food, most likely because green food stores are mainly located in cities and Chinese consumers prefer local food, and imported food does not appear to be as fresh as local food due to long distance transportation [81]. The age is concentrated in the age group of 23–40 years old (70.3%), mostly young people, which may be due to the fact that this group has a higher spending power and is the mainstream group of consumption, similar to related studies [24,82]. The education level was high, with 70.1% of the respondents having a bachelor’s degree. The participants were mostly urban and preferred national and local green food, most likely because green food stores are mainly located in cities and Chinese consumers prefer local food, and imported food does not appear to be as fresh as local food due to long distance transportation [81] Monthly income was mainly concentrated in the range of RMB 4000–8000. Most of the participants were urban residents and favoured national and local green food.The overall sample characteristics of the study were similar to those of previous related studies on green foods in China [2,80], which are representative.

4.2. Reliability and Validity Analysis

To test the structural equation model, the study used SmartPLS3. Compared to the covariance-based structural equation modelling data analysis tool, this software considers the feasibility of all path coefficients, has no requirement for the number of question terms of the variables in the model, and is able to obtain more robust estimation results.. First, the scale’s reliability was evaluated. Referring to previous studies related to SmartPLS, this study applied three measures of Cronbach’s Alpha, Rho_A, and Composite Reliability to assess reliability [83]. The results are provided in Table 3, and the figures are higher than the critical values of 0.6, 0.7, and 0.8, respectively, suggesting that the scale has good reliability [2].
The scale’s validity was then evaluated. Validity was evaluated using the average variance extracted value (AVE), external factor loadings, FORNELL, and heterotrait-monotrait (HTMT) ratio. The external factor loadings were all more than 0.7, as shown in Table 4, indicating that all items on the scale had favourable external loadings [84]. All constructs had AVEs of more than 0.5, as shown in Table 3, indicating a variance of more than 50% and good convergent validity [85].
As can be seen in Table 5, according to the conclusion of previous studies, good discriminant validity is indicated when the square root of AVE of all the constructs is higher than the absolute value of Pearson correlation coefficient of each construct under that column [85], and the results of the present study results meet this condition. In Table 6, it is evident that the HTMT between the two constructs was less than 0.90, further supporting high discriminant validity [86] and providing circumstantial evidence that the data were not multicollinear [87].

4.3. Assessment of Structural Model

The findings of the model evaluation using a bootstrapping subsamples 5000 study are displayed in Table 7 and Table 8. According to the conclusion of previous studies, when the variance inflation factor (VIF) is less than the critical value of 5, it indicates that there is no covariance problem in the data as well as common method bias problem [88], and the results of this study meet the condition.
All constructs are higher than the crucial value of 0.25, as determined by R 2 , indicating that the model has a good predictive capacity [89]. All constructs larger than 0.15 indicate medium forecasting ability and are greater than Q 2 , indicative of out-of-sample predictive association [90]. The Standardized root means square residual (SRMR) is an absolute measure of model fit with SRMR = 0.063 < 0.080, indicating good model fit superiority [91].
The study employed PLS-SEM to assess the model’s hypotheses, and the data’s findings are displayed in Table 9. According to previous studies, it is known that the model passes the significance test when the test result t-value is greater than 1.96 or the p-value is less than 0.05 [86,92], and all hypotheses in this study passed the significance test. Attribute knowledge had a significant positive effect on perceived product quality ( β = 0.306, t = 6.408, p < 0.000 and did not cross 0 at the 5–95% confidence interval), and H1 was tested. Attribute knowledge had a significant positive effect on trust ( β = 0.116, t = 3.091, p = 0.001 and did not cross 0 at 5–95% confidence intervals), and H2 was tested. Green knowledge had a significant positive effect on perceived product quality ( β = 0.435, t = 10.795, p < 0.000 and did not cross 0 at 5–95% confidence interval), and H3 was tested. Green knowledge had a significant positive effect on trust ( β = 0.224, t = 5.746, p < 0.000 and did not cross 0 at 5–95% confidence interval), and H4 was tested. Perceived product quality had a significant positive effect on trust ( β = 0.505, t = 13.336, p < 0.000 and did not cross 0 at 5–95% confidence interval), and H5 was tested. Perceived product quality had a significant positive effect on purchase intention ( β = 0.517, t = 11.065, p < 0.000 and did not cross 0 at 5–95% confidence interval), and H6 was tested. Trust had a significant positive effect on purchase intention ( β = 0.288, t = 5.663, p < 0.000 and did not cross 0 at 5–95% confidence interval), and H7 was tested. Purchase intention had a significant positive effect on purchase behaviour ( β = 0.514, t = 18.953, p < 0.000 and did not cross 0 at 5–95% confidence interval), and H8 was tested.

4.4. Multiple Group Analysis

The outcomes of the multi-group analysis are displayed in Table 10. The results of the multi-group analysis, which divided the subjects’ educational levels into low education (undergraduate and below) and high education (undergraduate and above), show that attributing knowledge and green knowledge to trust had no significant influence on subjects with lower educational levels.
The results of the multi-group analysis, which split the respondents’ ages into middle-aged and older (30 years and older) and young (30 years and below) groups, showed that the influence of attribute knowledge on trust was not significant for older individuals. Multiple analyses of gender revealed that the relationship between attribute knowledge and trust was not significant for male individuals. From the overall results, those with relevant knowledge background, youth, and females, have higher awareness of green food labels, and label awareness further influences consumers’ label attitude evaluation (trust). This may be because groups with better knowledge of attributes are more willing to trust green food labels and make positive evaluations, building positive beliefs based on the knowledge gained [93].

5. Discussion

Previous studies have shown that knowledge is an important variable in predicting consumer pro-environmental behaviour [11]. However, existing studies are controversial about how knowledge affects consumer behaviour and the segmentation of product knowledge varies [22,23,24,26,30]. Therefore, it is necessary to develop a study of consumer behaviour by segmenting product knowledge from a green food perspective.
First, it deepens the research related to green product knowledge. Previous studies on green product knowledge have mainly been conducted from the perspectives of environmental knowledge, overall knowledge, subjective knowledge, brand knowledge, cost knowledge, and quality knowledge [15,22,24,26,51,94,95], and some of the studies have been conducted on green knowledge, but in this study green knowledge was considered as consumer knowledge related to information about product attributes [95]. However, according to previous related studies, product knowledge can be divided into subjective and objective knowledge [96], therefore, this study divides green tea product knowledge into attribute knowledge (objective knowledge) and green knowledge (subjective knowledge), which helps to study the research related to product knowledge from the perspective of lower-order segmentation.
Second, it is necessary to improve the mechanism of the influence of product knowledge on green tea consumption behaviour. There are previous studies related to product knowledge on purchase intention, purchase intention on purchase behaviour, trust on purchase intention, and knowledge on trust [14,22,25,26,67], but the findings of these studies are inconsistent, with either a direct effect or the presence of other variables between the two quantitative variables playing an indirect effect. There are fewer studies related to the relationship between the other variables knowledge on perceived product quality, perceived product quality on trust, and perceived product quality on purchase intention between the two. Therefore, this study constructs a new model framework to study the influence of product knowledge on consumption behaviour that is meaningful and enriches the related research on green tea consumption intention and behaviour.
Third, the effect of green knowledge on perceived product quality was greater than that of attribute knowledge. This finding is similar to the findings of previous studies on environmental knowledge and label knowledge on attitudes [22], but the difference is that the green food knowledge in this study targets food products rather than people’s perceptions of the general environment, and green food attribute knowledge contains not only label knowledge but also perceptions of overall production, transportation, and storage. Consumers’ product knowledge had a significant positive effect on trust, with green knowledge having a greater effect than attribute knowledge. This finding is similar to previous studies [28], but differs in that the study only concluded that label knowledge can have an effect on trust, while environmental knowledge has no effect on trust.
Fourth, perceived product quality has a significant positive effect on trust. This finding is similar to that of previous studies on millennial samples (information quality positively affects brand trust), but differs in three ways. First, this study sample is more generalizable to the entire social sample. Second, that study was on brands, while this study is on food products, which extends the scope of the study. Finally, there is a gap between information quality and perceived product quality; information quality refers to the validity, accuracy, and timeliness of information, while perceived product quality focuses on consumers’ perceptions of the product [25].
Fifth, perceived product quality has a significant positive effect on purchase intention. This finding is similar to the findings of previous studies on suboptimal agricultural products, but with the following differences. The study started the research on food products from a negative perspective, i.e., lower quality food (damaged packaging, cosmetic defects, and nearing the end date), while this study started the research on food products from a positive perspective, i.e., green tea (environmentally friendly and non-polluting, nutritious, and guaranteeing food safety), which helped to broaden the scope of application of the study [64].
Sixth, trust has a significant positive effect on purchase intention. This finding is similar to the findings of a previous study (green trust positively affects green purchase intention) for electronic products in Taiwan [33]. This suggests that the findings can be applied to the food sector, due to the fact that consumer trust is an essential factor for long-term behaviour [97], and that consumers are more likely to purchase if they trust the seller’s experience (environmental attributes of the product and health attributes of the food). If firms exaggerate the environmental performance of their products, consumers are less likely to trust the firm [98] and thus less likely to buy. Similarly, if a company exaggerates the food safety and health attributes of green tea, consumers will have a feeling of false propaganda towards the company and thus become suspicious of green tea, and thus are reluctant to buy green tea.
Seventh, purchase intention has a significant positive effect on purchase behaviour. This finding is similar to previous studies on overall green products (green purchase intention positively affects green purchase behaviour) [34], but with the following differences. On the one hand, the study was conducted for the overall green product domain and the findings may not be representative of the findings for each sub-category of green products. On the other hand, the measurement scales of green purchase intention and green purchase behaviour in the overall green product study are different from the measurement scales of the variables in this study, which suggests that the findings are consistent across quantitative variables, again confirming the generalizability of the study findings [2].

6. Limitations and Future Research

6.1. Limitations

First, the data in this study were collected at a single point in time, and there may be seasonal influences that do not accurately predict changes in green food consumer attitudes and behaviours throughout the year. Second, the data in this study were collected in China, and there may be influences from the degree of economic development, consumption habits, consumer attitudes, and national cultures of different countries. Without the influence of these boundary conditions, the findings may not accurately predict the behaviour of green food consumers in every country in the world.

6.2. Future Research

First, future research can be launched to collect data at different time points and conduct in-depth tracking studies. Data from multiple periods can be used to analyse the dynamics of consumer purchasing behaviour. It is also possible to use experimental methods for analysis, to control the main research variables, and to find the impact of variables such as time point and season. Second, the number of questionnaires distributed and the scope of distribution should be broadened so that the sample can cover all types of countries more comprehensively and make the sample more representative. Future research can distribute questionnaires to different countries, add boundary variables such as economic development level, consumption habits, culture, etc., involve more consumer groups, and conduct cross-regional comparative analysis.

7. Conclusions

Based on the SOR model, this study investigates the consumption behaviour of green tea by using green knowledge and attribute knowledge as external stimulus variables, perceived product quality and trust as mechanism variables, and purchase intention and behaviour as response variables, and draws the following conclusions. Consumers’ attribute knowledge and green knowledge have a significant positive effect on perceived product quality and trust, most likely because green knowledge, compared to attribute knowledge, is part of the quality perceptions of the product that consumers have already formed in their subjective consciousness about green tea, and thus green knowledge has a higher impact on the product quality of green tea [99]. Green knowledge is the consumers’ perception of subjective knowledge of green tea’s contribution to mitigating climate change, reducing resource consumption, and reducing environmental pollution, and there are changes in consumers’ internal attitudes in this perception process, and these are part of the functions and utilities of green tea, which make consumers more confident in making judgments about purchasing green tea [14]. In contrast, attribute knowledge is only the perception of green tea meaning, system, label, etc., which belongs to objective perception, but can be considered as an external stimulus. Therefore, changes in internal attitudes have a higher impact on consumer trust compared to external stimuli [22]. Perceived product quality has a significant positive effect on trust, most likely because consumers are in a market with information asymmetry and consumer trust is dependent on perceived product, brand, and service quality [52,57,100]. Perceived product quality has a significant positive effect on purchase intention, suggesting that perceived quality is an important factor for firms to maintain long-term relationships with consumers [101], providing a reason for consumers to purchase green tea and differentiating it from other teas [102]. Trust has a significant positive effect on purchase intention, and purchase intention has a significant positive effect on purchase behaviour. On this basis, the following recommendations are made.
Firstly, when designing the packaging of tea products, green tea information should be displayed to the greatest extent on the outer packaging so that consumers can quickly access green tea-related knowledge. For example, the green food certification can be designed in a prominent position on the outer packaging.
Secondly, when designing green tea publicity manuals, one should add various standards related to green tea from production, origin, storage, transportation, use of chemical fertilizers and pesticides, etc., in the manuals, and compare them with ordinary tea to cultivate consumers’ subjective green knowledge of green tea from objective attribute knowledge.
Thirdly, one should enhance the advertising and publicity of green tea. Green tea advertisements can be placed on TV, Taobao, JD, PDD, Tik Tok, Quick hand, WeChat, QQ, Weibo, Red, Bilibili, buses, and taxis to increase product publicity and enhance consumers’ product knowledge awareness, product quality perception, and product trust in green tea.
Fourthly, enhancing product knowledge training for green tea sales staff by enterprises is conducive to introducing consumers to the attributes of green tea such as health and pollution-free, food safety, and environmental protection before sales, thereby enhancing consumers’ product knowledge.
Fifthly, through technical innovation and service enhancement, consumer trust is enhanced. On the one hand, through technological innovation, the quality of green tea (taste, nutrition, and appearance) will be improved, and corporate self-inspection and market inspection of green tea product quality will be carried out to prevent poor-quality green tea from using green food certification to deceive consumers, thereby enhancing consumers’ trust in the product. On the other hand, one should build green tea communities to provide consumers with perfect pre-sales and after-sales services, and actively communicate with consumers, thereby enhancing the consumers’ perception of product quality and consumers’ trust in the products.
Sixth, one should target different consumer segments and conduct precision marketing. Target marketing should be aimed at consumers of different ages, education, income, and gender.

Author Contributions

Conceptualization, M.Z. and A.X.; methodology, M.Z. and D.T.; validation, D.T.; formal analysis, M.Z.; data curation, M.Z. and D.T.; writing—original draft preparation, M.Z. and A.X.; writing—review and editing, A.X.; supervision, A.X.; funding acquisition, A.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Introduction of Talents of Minjiang University Science and Technology Pre-research Project “Research on the impact of Green Advertising Appeals on consumers’ Willingness to Pay Premium for Green Agricultural Products”; Major Project of Social Science Fund of Fujian Province in 2022 “Research on The Strategy of Fujian Province Adhering to Expanding Domestic Demand” [FJ2022Z006].

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Carpinelli, C.; Einarsson Reynis, E.T.; Sigriour Islind, A.; Stefansson, H.; Oskarsdottir, M. Green Intentions: Field Research and Data-Driven Analysis of Customers’ Purchasing Patterns. Sustainability 2022, 14, 9863. [Google Scholar] [CrossRef]
  2. Zheng, M.; Tang, D.; Chen, J.; Xu, A. How Different Advertising Appeals (Green vs. Non-Green) Impact Consumers’ Willingness to Pay a Premium for Green Agricultural Products. Front. Psychol. 2022, 13, 991525. [Google Scholar] [CrossRef] [PubMed]
  3. Zhu, X.; He, M.; Li, H. Environmental Regulation, Governance Transformation and the Green Development of Chinese Iron and Steel Enterprises. J. Clean. Prod. 2021, 328, 129557. [Google Scholar] [CrossRef]
  4. Liu, P.; Teng, M.; Han, C. How Does Environmental Knowledge Translate into Pro-Environmental Behaviors?: The Mediating Role of Environmental Attitudes and Behavioral Intentions. Sci. Total Environ. 2020, 728, 138126. [Google Scholar] [CrossRef]
  5. Xu, Q.; Dhaundiyal, S.; Guan, C. Structural Conflict under the New Green Dilemma: Inequalities in Development of Renewable Energy for Emerging Economies. J. Environ. Manag. 2020, 273, 111117. [Google Scholar] [CrossRef]
  6. Bagchi, A. Organic Farming Practice for Quality Improvement of Tea and Its Anti Parkinsonism Effect on Health Defense. J. Phys. Chem. Biophys. 2015, 5. [Google Scholar] [CrossRef]
  7. Sumi, R.S.; Kabir, G. Factors Affecting the Buying Intention of Organic Tea Consumers of Bangladesh. J. Open Innov. Technol. Mark. Complex. 2018, 4, 24. [Google Scholar] [CrossRef] [Green Version]
  8. Sanders, R. A Market Road to Sustainable Agriculture? Ecological Agriculture, Green Food and Organic Agriculture in China. Dev. Chang. 2006, 37, 201–226. [Google Scholar] [CrossRef]
  9. Bu, X.; Nguyen, H.V.; Chou, T.P.; Chen, C.P. A Comprehensive Model of Consumers’ Perceptions, Attitudes and Behavioral Intention toward Organic Tea: Evidence from an Emerging Economy. Sustainability 2020, 12, 6619. [Google Scholar] [CrossRef]
  10. Wang, J.; Pham, T.L.; Dang, V.T. Environmental Consciousness and Organic Food Purchase Intention: A Moderated Mediation Model of Perceived Food Quality and Price Sensitivity. Int. J. Environ. Res. Public Health 2020, 17, 850. [Google Scholar] [CrossRef]
  11. Hines, J.M.; Hungerford, H.R.; Tomera, A.N. Analysis and Synthesis of Research on Responsible Environmental Behavior: A Meta-Analysis. J. Environ. Educ. 1987, 18, 1–8. [Google Scholar] [CrossRef]
  12. Gifford, R.; Nilsson, A. Personal and Social Factors That Influence Pro-Environmental Concern and Behaviour: A Review. Int. J. Psychol. 2014, 49, 141–157. [Google Scholar] [CrossRef]
  13. Ritter, Á.M.; Borchardt, M.; Vaccaro, G.L.R.; Pereira, G.M.; Almeida, F. Motivations for Promoting the Consumption of Green Products in an Emerging Country: Exploring Attitudes of Brazilian Consumers. J. Clean. Prod. 2015, 106, 507–520. [Google Scholar] [CrossRef]
  14. Wang, H.; Ma, B.; Bai, R. How Does Green Product Knowledge Effectively Promote Green Purchase Intention? Sustainability 2019, 11, 1193. [Google Scholar] [CrossRef] [Green Version]
  15. Wang, Q.; Wang, S.; Zhang, M.; Bu, Z.; Liu, J. Green Public Procurement as a Promoter for Green Consumption: From the Perspective of Individual’s Knowledge. Clean. Responsible Consum. 2021, 3, 100035. [Google Scholar] [CrossRef]
  16. Wheeler, M.; Sharp, A.; Nenycz-Thiel, M. The Effect of ‘Green’ Messages on Brand Purchase and Brand Rejection. Australas. Mark. J. 2013, 21, 105–110. [Google Scholar] [CrossRef]
  17. Liobikienė, G.; Mandravickaitė, J.; Bernatonienė, J. Theory of Planned Behavior Approach to Understand the Green Purchasing Behavior in the EU: A Cross-Cultural Study. Ecol. Econ. 2016, 125, 38–46. [Google Scholar] [CrossRef]
  18. Zhao, H.h.; Gao, Q.; Wu, Y.p.; Wang, Y.; Zhu, X.d. What Affects Green Consumer Behavior in China? A Case Study from Qingdao. J. Clean. Prod. 2014, 63, 143–151. [Google Scholar] [CrossRef]
  19. Wiek, A.; Withycombe, L.; Redman, C.L. Key Competencies in Sustainability: A Reference Framework for Academic Program Development. Sustain. Sci. 2011, 6, 203–218. [Google Scholar] [CrossRef] [Green Version]
  20. Paço, A.; Lavrador, T. Environmental Knowledge and Attitudes and Behaviours towards Energy Consumption. J. Environ. Manag. 2017, 197, 384–392. [Google Scholar] [CrossRef]
  21. Chaihanchanchai, P.; Anantachart, S. Encouraging Green Product Purchase: Green Value and Environmental Knowledge as Moderators of Attitude and Behavior Relationship. Bus. Strategy Environ. 2022, 31, 1249–1874. [Google Scholar] [CrossRef]
  22. Hossain, I.; Nekmahmud, M.; Fekete-Farkas, M. How Do Environmental Knowledge, Eco-Label Knowledge, and Green Trust Impact Consumers’ pro-Environmental Behaviour for Energy-Efficient Household Appliances? Sustainability 2022, 14, 6513. [Google Scholar] [CrossRef]
  23. Moslehpour, M.; Chau, K.Y.; Du, L.; Qiu, R.; Lin, C.Y.; Batbayar, B. Predictors of Green Purchase Intention toward Eco-Innovation and Green Products: Evidence from Taiwan. Econ.-Res.-Ekon. Istraz. 2022, 1–22. [Google Scholar] [CrossRef]
  24. Mohd Suki, N. Green Product Purchase Intention: Impact of Green Brands, Attitude, and Knowledge. Br. Food J. 2016, 118, 2893–2910. [Google Scholar] [CrossRef]
  25. Tian, H.; Siddik, A.B.; Masukujjaman, M. Factors Affecting the Repurchase Intention of Organic Tea among Millennial Consumers: An Empirical Study. Behav. Sci. 2022, 12, 50. [Google Scholar] [CrossRef] [PubMed]
  26. Wang, Y.; Hazen, B.T. Consumer Product Knowledge and Intention to Purchase Remanufactured Products. Int. J. Prod. Econ. 2016, 181, 460–469. [Google Scholar] [CrossRef]
  27. Pagiaslis, A.; Krontalis, A.K. Green Consumption Behavior Antecedents: Environmental Concern, Knowledge, and Beliefs. Psychol. Mark. 2014, 31, 335–348. [Google Scholar] [CrossRef]
  28. Taufique, K.M.R.; Vocino, A.; Polonsky, M.J. The Influence of Eco-Label Knowledge and Trust on pro-Environmental Consumer Behaviour in an Emerging Market. J. Strateg. Mark. 2017, 25, 511–529. [Google Scholar] [CrossRef]
  29. Carmi, N.; Arnon, S.; Orion, N. Transforming Environmental Knowledge into Behavior: The Mediating Role of Environmental Emotions. J. Environ. Educ. 2015, 46, 183–201. [Google Scholar] [CrossRef]
  30. Tan, Z.; Sadiq, B.; Bashir, T.; Mahmood, H.; Rasool, Y. Investigating the Impact of Green Marketing Components on Purchase Intention: The Mediating Role of Brand Image and Brand Trust. Sustainability 2022, 14, 5939. [Google Scholar] [CrossRef]
  31. Zhuang, W.; Luo, X.; Riaz, M.U. On the Factors Influencing Green Purchase Intention: A Meta-Analysis Approach. Front. Psychol. 2021, 12, 644020. [Google Scholar] [CrossRef] [PubMed]
  32. Zhu, Q.; Li, Y.; Geng, Y.; Qi, Y. Green Food Consumption Intention, Behaviors and Influencing Factors among Chinese Consumers. Food Qual. Prefer. 2013, 28, 279–286. [Google Scholar] [CrossRef]
  33. Chen, Y.S.; Chang, C.H. Enhance Green Purchase Intentions: The Roles of Green Perceived Value, Green Perceived Risk, and Green Trust. Manag. Decis. 2012, 50, 502–520. [Google Scholar] [CrossRef]
  34. Kamalanon, P.; Chen, J.S.; Le, T.T.Y. “Why Do We Buy Green Products?” An Extended Theory of the Planned Behavior Model for Green Product Purchase Behavior. Sustainability 2022, 14, 689. [Google Scholar] [CrossRef]
  35. Sharma, A.; Foropon, C. Green Product Attributes and Green Purchase Behavior: A Theory of Planned Behavior Perspective with Implications for Circular Economy. Manag. Decis. 2019, 57, 1018–1042. [Google Scholar] [CrossRef]
  36. Feng, Q.; Wang, Y.; Chen, C.; Dong, Z.; Shi, X. Effect of Homebuyer Comment on Green Housing Purchase Intention-Mediation Role of Psychological Distance. Front. Psychol. 2021, 12, 568451. [Google Scholar] [CrossRef] [PubMed]
  37. Li, J.; Li, G.; Sun, X. Environment and Green Brand Authenticity and Its Effects towards Brand Purchase Intention: The Case of Green Building Material. J. Environ. Prot. Ecol. 2019, 20, 1842–1851. [Google Scholar]
  38. Brucks, M. The Effects of Product Class Knowledge on Information Search Behavior. J. Consum. Res. 1985, 12, 1–16. [Google Scholar] [CrossRef]
  39. Riva, F.; Magrizos, S.; Rubel, M.R.B.; Rizomyliotis, I. Green Consumerism, Green Perceived Value, and Restaurant Revisit Intention: Millennials’ Sustainable Consumption with Moderating Effect of Green Perceived Quality. Bus. Strategy Environ. 2022, 31, 685–1247. [Google Scholar] [CrossRef]
  40. Zhao, J.; Gerasimova, K.; Peng, Y.; Sheng, J. Information Asymmetry, Third Party Certification and the Integration of Organic Food Value Chain in China. China Agric. Econ. Rev. 2020, 12, 20–38. [Google Scholar] [CrossRef]
  41. Liu, Y.; Zhou, X. A Study of Consumer Trust Formation Mechanisms Based on Food Safety. Mod. Manag. Sci. 2009, 41, 55–57. [Google Scholar]
  42. Palmer, J.W.; Bailey, J.P.; Faraj, S. The Role of Intermediaries in the Development of Trust on the WWW: The Use and Prominence of Trusted Third Parties and Privacy Statements. J. Comput.-Mediat. Commun. 2000, 5, JCMC532. [Google Scholar] [CrossRef]
  43. Truong, V.A.; Lang, B.; Conroy, D.M. When Food Governance Matters to Consumer Food Choice: Consumer Perception of and Preference for Food Quality Certifications. Appetite 2022, 168, 105688. [Google Scholar] [CrossRef] [PubMed]
  44. Barrutia, J.M.; Gilsanz, A. Electronic Service Quality and Value: Do Consumer Knowledge-Related Resources Matter? J. Serv. Res. 2013, 16, 231–246. [Google Scholar] [CrossRef]
  45. Lin, W.; Nayga, R.M. Green Identity Labeling, Environmental Information, and pro-Environmental Food Choices. Food Policy 2022, 106, 102187. [Google Scholar] [CrossRef]
  46. Nuttavuthisit, K.; Thogersen, J. The Importance of Consumer Trust for the Emergence of a Market for Green Products: The Case of Organic Food. J. Bus. Ethics 2017, 140, 323–337. [Google Scholar] [CrossRef]
  47. Chen, Z. The Impact of Traceability Information on Customer Satisfaction for Agricultural Products in the Context of Big Data—Multi-cluster Analysis of Product Knowledge. J. Commer. Econ. 2022, 41, 131–134. [Google Scholar]
  48. Lin, Y.C.; Chang, C.c.A. Double Standard: The Role of Environmental Consciousness in Green Product Usage. J. Mark. 2012, 76, 125–134. [Google Scholar] [CrossRef]
  49. Huang, H.C.; Lin, T.H.; Lai, M.C.; Lin, T.L. Environmental Consciousness and Green Customer Behavior: An Examination of Motivation Crowding Effect. Int. J. Hosp. Manag. 2014, 40, 139–149. [Google Scholar] [CrossRef]
  50. D’amico, M.; Di Vita, G.; Monaco, L. Exploring Environmental Consciousness and Consumer Preferences for Organic Wines without Sulfites. J. Clean. Prod. 2016, 120, 64–71. [Google Scholar] [CrossRef]
  51. Liu, Y.; Hong, Z.; Zhu, J.; Yan, J.; Qi, J.; Liu, P. Promoting Green Residential Buildings: Residents’ Environmental Attitude, Subjective Knowledge, and Social Trust Matter. Energy Policy 2018, 112, 152–161. [Google Scholar] [CrossRef]
  52. Kardes, F.R.; Posavac, S.S.; Cronley, M.L. Consumer Inference: A Review of Processes, Bases, and Judgment Contexts. J. Consum. Psychol. 2004, 14, 230–256. [Google Scholar] [CrossRef]
  53. Kalafatis, S.P.; Pollard, M.; East, R.; Tsogas, M.H. Green Marketing and Ajzen’s Theory of Planned Behaviour: A Cross-Market Examination. J. Consum. Mark. 1999, 16, 441–460. [Google Scholar] [CrossRef]
  54. Shao Yeh, Y.; Li, Y.M. Building Trust in M-commerce: Contributions from Quality and Satisfaction. Online Inf. Rev. 2009, 33, 1066–1086. [Google Scholar] [CrossRef] [Green Version]
  55. McKnight, D.H.; Kacmar, C.J.; Choudhury, V. Dispositional Trust and Distrust Distinctions in Predicting High-and Low-Risk Internet Expert Advice Site Perceptions. E-Service 2004, 3, 35–58. [Google Scholar] [CrossRef]
  56. Hsin Chang, H.; Wen Chen, S. The Impact of Online Store Environment Cues on Purchase Intention: Trust and Perceived Risk as a Mediator. Online Inf. Rev. 2008, 32, 818–841. [Google Scholar] [CrossRef] [Green Version]
  57. Chen, Y.S.; Chang, C.H. Towards Green Trust: The Influences of Green Perceived Quality, Green Perceived Risk, and Green Satisfaction. Manag. Decis. 2013, 51, 63–82. [Google Scholar] [CrossRef]
  58. Marakanon, L.; Panjakajornsak, V. Perceived Quality, Perceived Risk and Customer Trust Affecting Customer Loyalty of Environmentally Friendly Electronics Products. Kasetsart J.-Soc. Sci. 2017, 38, 24–30. [Google Scholar] [CrossRef]
  59. Taylor, S.A.; Baker, T.L. An Assessment of the Relationship between Service Quality and Customer Satisfaction in the Formation of Consumers’ Purchase Intentions. J. Retail. 1994, 70, 163–178. [Google Scholar] [CrossRef]
  60. Molinillo, S.; Vidal-Branco, M.; Japutra, A. Understanding the Drivers of Organic Foods Purchasing of Millennials: Evidence from Brazil and Spain. J. Retail. Consum. Serv. 2020, 52, 101926. [Google Scholar] [CrossRef]
  61. Shamsi, H.R.; Najafabadi, M.O.; Hosseini, S.J.F. Designing a Three-Phase Pattern of Organic Product Consumption Behaviour. Food Qual. Prefer. 2020, 79, 103743. [Google Scholar] [CrossRef]
  62. Hwang, J.; Chung, J.E. What Drives Consumers to Certain Retailers for Organic Food Purchase: The Role of Fit for Consumers’ Retail Store Preference. J. Retail. Consum. Serv. 2019, 47, 293–306. [Google Scholar] [CrossRef]
  63. Suciu, N.A.; Ferrari, F.; Trevisan, M. Organic and Conventional Food: Comparison and Future Research. Trends Food Sci. Technol. 2019, 84, 49–51. [Google Scholar] [CrossRef]
  64. Gimenez, A.; Aschemann-Witzel, J.; Ares, G. Exploring Barriers to Consuming Suboptimal Foods: A Consumer Perspective. Food Res. Int. 2021, 141, 110106. [Google Scholar] [CrossRef] [PubMed]
  65. Suhartanto, D.; Kartikasari, A.; Hapsari, R.; Budianto, B.S.; Najib, M.; Astor, Y. Predicting Young Customers’ Intention to Repurchase Green Plastic Products: Incorporating Trust Model into Purchase Intention Model. J. Asia Bus. Stud. 2021, 15, 441–456. [Google Scholar] [CrossRef]
  66. Zheng, M.; Huang, M.; Tang, D.; Wu, J.; Xu, A. Impact of Customer Perceived Quality on Customer Online Stickiness. Issues For. Econ. 2020, 40, 189–198. [Google Scholar] [CrossRef]
  67. Konuk, F.A.; Rahman, S.U.; Salo, J. Antecedents of Green Behavioral Intentions: A Cross-Country Study of Turkey, Finland and Pakistan. Int. J. Consum. Stud. 2015, 39, 586–596. [Google Scholar] [CrossRef]
  68. Al Mamun, A.; Mohamad, M.R.; Yaacob, M.R.B.; Mohiuddin, M. Intention and Behavior towards Green Consumption among Low-Income Households. J. Environ. Manag. 2018, 227, 73–86. [Google Scholar] [CrossRef]
  69. Chan, R.; LBY, L. Explaining Green Purchasing Behavior: A Cross-Cultural Study on American and Chinese Consumers. J. Int. Consum. Mark. 2002, 14, 9–40. [Google Scholar] [CrossRef]
  70. Wei, C.F.; Chiang, C.T.; Kou, T.C.; Lee, B.C.Y. Toward Sustainable Livelihoods: Investigating the Drivers of Purchase Behavior for Green Products. Bus. Strategy Environ. 2017, 26, 626–639. [Google Scholar] [CrossRef]
  71. Patil, A.; Malhotra, N.K.; Maity, M. Observational Learning: Influence of Regulatory Focus, Knowledge, and Summarized Observable Recommendations on Consumer Decision Outcomes in an Online Environment. Int. J. Consum. Stud. 2022, 46, 1428–1450. [Google Scholar] [CrossRef]
  72. Lin, I.Y. Evaluating a Servicescape: The Effect of Cognition and Emotion. Int. J. Hosp. Manag. 2004, 23, 163–178. [Google Scholar] [CrossRef]
  73. Turley, L.; Milliman, R. Atmospheric Effects on Shopping Behavior. J. Bus. Res. 2000, 49, 193–211. [Google Scholar] [CrossRef]
  74. Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.G. Statistical Power Analyses Using G* Power 3.1: Tests for Correlation and Regression Analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef] [Green Version]
  75. Alba, J.W.; Hutchinson, J.W. Dimensions of Consumer Expertise. J. Consum. Res. 1987, 13, 411–454. [Google Scholar] [CrossRef]
  76. Yoo, B.; Donthu, N.; Lee, S. An Examination of Selected Marketing Mix Elements and Brand Equity. J. Acad. Mark. Sci. 2000, 28, 195–211. [Google Scholar] [CrossRef]
  77. Zhao, X.; Lynch, J.G., Jr.; Chen, Q. Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. J. Consum. Res. 2010, 37, 197–206. [Google Scholar] [CrossRef]
  78. Kozup, J.C.; Creyer, E.H.; Burton, S. Making Healthful Food Choices: The Influence of Health Claims and Nutrition Information on Consumers’ Evaluations of Packaged Food Products and Restaurant Menu Items. J. Mark. 2003, 67, 19–34. [Google Scholar] [CrossRef] [Green Version]
  79. Li, W.; Li, C.; Xiao, X. A Study on Organic Food Purchasing Behavior Based on the Stimulus-Response Theory—A Case of the Consumption of Organic Pork. East China Econ. Manag. 2018, 32, 171–178. [Google Scholar] [CrossRef]
  80. Xu, A.; Wei, C.; Zheng, M.; Sun, L.; Tang, D. Influence of Perceived Value on Repurchase Intention of Green Agricultural Products: From the Perspective of Multi-Group Analysis. Sustainability 2022, 14, 15451. [Google Scholar] [CrossRef]
  81. Sirieix, L.; Kledal, P.R.; Sulitang, T. Consumer Trade-Offs between Local or Imported, Conventional or Organic Foods—A Qualitative Study in Shanghai, China. World Agric. 2014, 44, 187–191. [Google Scholar] [CrossRef]
  82. Wang, J.; Tao, J.; Chu, M. Behind the Label: Chinese Consumers’ Trust in Food Certification and the Effect of Perceived Quality on Purchase Intention. Food Control 2020, 108, 106825. [Google Scholar] [CrossRef]
  83. Dijkstra, T.K.; Henseler, J. Consistent Partial Least Squares Path Modeling. MIS Q. 2015, 39, 297–316. [Google Scholar] [CrossRef]
  84. Mela, C.F.; Kopalle, P.K. The Impact of Collinearity on Regression Analysis: The Asymmetric Effect of Negative and Positive Correlations. Appl. Econ. 2002, 34, 667–677. [Google Scholar] [CrossRef]
  85. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  86. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a Silver Bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  87. Gold, A.H.; Malhotra, A.; Segars, A.H. Knowledge Management: An Organizational Capabilities Perspective. J. Manag. Inf. Syst. 2001, 18, 185–214. [Google Scholar] [CrossRef]
  88. Kock, N. Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. Int. J. E-Collab. (IJeC) 2015, 11, 1–10. [Google Scholar] [CrossRef] [Green Version]
  89. Sarstedt, M.; Ringle, C.M.; Henseler, J.; Hair, J.F. On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Plan. 2014, 47, 154–160. [Google Scholar] [CrossRef]
  90. Geisser, S. A Predictive Approach to the Random Effect Model. Biometrika 1974, 61, 101–107. [Google Scholar] [CrossRef]
  91. Hu, L.t.; Bentler, P.M. Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification. Psychol. Methods 1998, 3, 424–453. [Google Scholar] [CrossRef]
  92. Hayes, A.F. Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium. Commun. Monogr. 2009, 76, 408–420. [Google Scholar] [CrossRef]
  93. Zhan, J.; Huang, F.; Zhai, F.; Zhang, B. Investigation on the Consumption KAP of Pre-Packaged Food among Urban Residents in China. Food Nutr. China 2015, 21, 46–48. [Google Scholar]
  94. Sun, H.; Teh, P.L.; Linton, J.D. Impact of Environmental Knowledge and Product Quality on Student Attitude toward Products with Recycled/Remanufactured Content: Implications for Environmental Education and Green Manufacturing. Bus. Strategy Environ. 2018, 27, 935–945. [Google Scholar] [CrossRef]
  95. Wang, X.; Pacho, F.; Liu, J.; Kajungiro, R. Factors Influencing Organic Food Purchase Intention in Developing Countries and the Moderating Role of Knowledge. Sustainability 2019, 11, 209. [Google Scholar] [CrossRef] [Green Version]
  96. Rao, A.R.; Monroe, K.B. The Moderating Effect of Prior Knowledge on Cue Utilization in Product Evaluations. J. Consum. Res. 1988, 15, 253–264. [Google Scholar] [CrossRef]
  97. Lee, J.; Park, D.H.; Han, I. The Different Effects of Online Consumer Reviews on Consumers’ Purchase Intentions Depending on Trust in Online Shopping Malls: An Advertising Perspective. Internet Res. 2011, 21, 187–206. [Google Scholar] [CrossRef]
  98. Schlosser, A.E.; White, T.B.; Lloyd, S.M. Converting Web Site Visitors into Buyers: How Web Site Investment Increases Consumer Trusting Beliefs and Online Purchase Intentions. J. Mark. 2006, 70, 133–148. [Google Scholar] [CrossRef]
  99. Wei, M.; Xiong, X. The Influence of Consumers’ Green Knowledge on the Purchase Intention of Green Products—Taking Brand Sustainable Perception as the Intermediary. J. Chang. Univ. Sci. Technol. Sci. Ed. 2022, 35, 135–140. [Google Scholar]
  100. Aydin, S.; Özer, G. The Analysis of Antecedents of Customer Loyalty in the Turkish Mobile Telecommunication Market. Eur. J. Mark. 2005, 39, 910–925. [Google Scholar] [CrossRef]
  101. Snoj, B.; Pisnik Korda, A.; Mumel, D. The Relationships among Perceived Quality, Perceived Risk and Perceived Product Value. J. Prod. Brand Manag. 2004, 13, 156–167. [Google Scholar] [CrossRef]
  102. Cheung, R.; Lam, A.Y.; Lau, M.M. Drivers of Green Product Adoption: The Role of Green Perceived Value, Green Trust and Perceived Quality. J. Glob. Sch. Mark. Sci. 2015, 25, 232–245. [Google Scholar] [CrossRef]
Foods 12 00152 g001 550
Figure 1. Model of the influence of consumer product knowledge on green tea consumption behaviour.
Figure 1. Model of the influence of consumer product knowledge on green tea consumption behaviour.
Foods 12 00152 g001
Table
Table 1. Measurement.
Table 1. Measurement.
VariablesItemsSources
Attribute knowledgeI have heard the term green teaAlba and Hutchinson [75]
I understand the meaning of green tea
I understand the system, standards and other information related to green tea
Green knowledgeBuying green tea can mitigate climate changeMohd Suki [24]
Buying green tea can reduce the current level of environmental pollution
Buying green tea can reduce the consumption of natural resources
Buying green tea means I am doing the right thing
Perceived product qualityI think green tea has higher nutritional valueYoo et al. [76]
I think the quality and safety of green tea is guaranteed
I think green tea is good for the environment and in line with the concept of sustainable development
Green tea meets my needs
TrustGreen certified tea is safe, non-polluting, and environmentally friendlyZhao et al. [77]
I think the green tea certification organization is more credible
I believe that the green tea certification process is fair and credible
I believe that the information shown by green tea is true
Purchase intentionI am willing to collect and learn about green teaKozup et al. [78]
I consider buying green tea
I would recommend green tea to others
I am willing to pay more for green tea
I am willing to buy green tea if I have enough time, energy and money
Purchase BehaviourFrequency of purchasing green tea in a yearLi et al. [79]
Table
Table 2. Descriptive statistics (N = 700).
Table 2. Descriptive statistics (N = 700).
ItemsFrequencyProportion
GenderFemale44363.3%
Male25736.7%
Age (in years)22 and below14320.4%
23–3029942.7%
31–4019327.6%
41–50446.3%
51–60182.6%
61 and above30.4%
EducationHigh School and below426.0%
College8412.0%
Undergraduate49170.1%
Postgraduate and above8311.9%
JobPublic institution7811.1%
Civil servants233.3%
Other172.4%
State-owned enterprises13919.9%
Foreign-funded enterprises253.6%
Students15021.4%
Private enterprises25135.9%
Freelancers172.4%
Monthly income (RMB/Yuan)Below 400018726.7%
4000–800023733.9%
8000–12,00018426.3%
12,000 and above9213.1%
Household RegistrationRural27038.6%
Urban43061.4%
Place of origin1Field436.1%
Local65793.9%
Place of origin2Foreign182.6%
National68297.4%
Table
Table 3. Reliability and validity analysis.
Table 3. Reliability and validity analysis.
ConstructsCronbach’s AlphaRho_AComposite ReliabilityAVE
Attribute knowledge0.8120.8170.8890.728
Green knowledge0.8470.8580.8960.684
Perceived product quality0.7430.7500.8380.564
Trust0.8250.8250.8840.656
Purchase intention0.8580.8590.8980.637
Purchase behavior1.0001.0001.0001.000
Table
Table 4. Factor loadings and VIF.
Table 4. Factor loadings and VIF.
ConstructsIndicatorIndicator ReliabilityVIF
Attribute KnowledgeAK10.8091.575
AK20.8731.959
AK30.8762.011
Green knowledgeGK10.7961.969
GK20.8572.277
GK30.852.165
GK40.8031.555
Perceived product qualityPPQ10.7331.431
PPQ20.7671.504
PPQ30.711.391
PPQ40.7931.488
TrustT10.7951.657
T20.7841.635
T30.8261.853
T40.8341.897
Purchase intentionPI10.7711.714
PI20.7951.832
PI30.7841.83
PI40.8132.004
PI50.8272.011
Purchase behaviorPB11
Table
Table 5. Discriminant validity (FORNELL).
Table 5. Discriminant validity (FORNELL).
(1)(2)(3)(4)(5)(6)
Attribute Knowledge (1)0.853
Green knowledge (2)0.4630.827
Perceived product quality (3)0.5070.5760.751
Trust (4)0.4760.5690.6930.81
Purchase intention (5)0.5820.5990.7170.6460.798
Purchase behavior (6)0.5350.3930.4140.3740.5141
Note: The square root of AVE is shown on the diagonal, while the absolute value of the Pearson correlation coefficient for each variable is shown below the diagonal.
Table
Table 6. Discriminant validity (HTMT).
Table 6. Discriminant validity (HTMT).
(1)(2)(3)(4)(5)
Green knowledge (1)0.551
Perceived product quality (2)0.6430.551
Trust (3)0.5800.6670.884
Purchase intention (4)0.6960.6890.8880.767
Purchase behavior (5)0.5930.4240.4720.4110.555
Table
Table 7. Internal model VIF values.
Table 7. Internal model VIF values.
(1)(2)(3)(4)
Attribute Knowledge (1)1.2721.429
Green knowledge (2)1.2721.590
Perceived product quality (3) 1.6811.923
Trust (4) 1.923
Purchase intention (5) 1
Table
Table 8. Model results.
Table 8. Model results.
R 2 Q 2 SRMR
Attribute Knowledge 0.063
Green knowledge
Perceived product quality0.4050.219
Trust0.5330.344
Purchase intention0.5570.350
Attribute Knowledge0.2640.261
Table
Table 9. Hypothesis testing.
Table 9. Hypothesis testing.
Hypotheses β LLCIULCIMeanSDTpStatus
H1: Attribute Knowledge -> Perceived Product Quality0.3060.2260.3840.3060.0486.4080Supported
H2: Attribute knowledge -> Trust0.1160.0550.1780.1170.0383.0910.001Supported
H3: Green knowledge -> Perceived product quality0.4350.370.5020.4360.0410.7950Supported
H4: Green knowledge -> Trust0.2240.1590.2880.2240.0395.7460Supported
H5: Perceived product quality -> Trust0.5050.4410.5660.5040.03813.3360Supported
H6: Perceived product quality -> Purchase intention0.5170.4370.5910.5160.04711.0650Supported
H7: Trust -> Purchase intention0.2880.2060.3730.290.0515.6630Supported
H8: Purchase intentions -> Purchase behavior0.5140.470.5590.5150.02718.9530Supported
Table
Table 10. Multigroup analysis result.
Table 10. Multigroup analysis result.
HypothesesEducationAgeGender
p (Low)p (High)p (Mid and Older)p (Young)p (Female)p (Male)
H1: Attribute Knowledge -> Perceived Product Quality0.0000.0000.0000.0000.0000.000
H2: Attribute knowledge -> Trust0.1550.0030.0560.0110.0010.153
H3: Green knowledge -> Perceived product quality0.0000.0000.0000.0000.0000.000
H4: Green knowledge -> Trust0.2930.0000.0080.0000.0000.037
H5: Perceived product quality -> Trust0.0000.0000.0000.0000.0000.000
H6: Perceived product quality -> Purchase intention0.0000.0000.0000.0000.0000.000
H7: Trust -> Purchase intention0.0370.0000.0010.0000.0000.008
H8: Purchase intention -> Purchase behavior0.0000.0000.0000.0000.0000.000
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zheng, M.; Tang, D.; Xu, A. Attribute-Driven or Green-Driven: The Impact of Subjective and Objective Knowledge on Sustainable Tea Consumption. Foods 2023, 12, 152. https://doi.org/10.3390/foods12010152

AMA Style

Zheng M, Tang D, Xu A. Attribute-Driven or Green-Driven: The Impact of Subjective and Objective Knowledge on Sustainable Tea Consumption. Foods. 2023; 12(1):152. https://doi.org/10.3390/foods12010152

Chicago/Turabian Style

Zheng, Manhua, Decong Tang, and Anxin Xu. 2023. "Attribute-Driven or Green-Driven: The Impact of Subjective and Objective Knowledge on Sustainable Tea Consumption" Foods 12, no. 1: 152. https://doi.org/10.3390/foods12010152

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop