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. 2022 Dec 6:1–23. Online ahead of print. doi: 10.1007/s10668-022-02819-0

How does risk perception of the COVID-19 pandemic affect the consumption behavior of green food?

Houjian Li 1, Andi Cao 1, Si Chen 1, Lili Guo 1,✉
PMCID: PMC9734953  PMID: 36530362

Abstract

Attention to health is on the rise with the global pandemic of COVID-19, especially in food security. Green food is viewed as a healthy, safe, and nutritious food, which plays a significant role in enhancing immunity. This study aimed to investigate how risk perception affects the consumption behavior of green food. Risk perception and health awareness were added to the original model based on the extended theory of planned behavior. And an online survey about the influence of COVID-19 on consumers' green food consumption behavior was conducted with 612 valid respondents recruited. The results indicate that risk perception has a positive effect on both consumption intention and behavior. The mediating effect analysis shows that risk perception influences green food consumption intention by improving people’s attitudes, subjective norms, and health awareness. These findings can not only help clarify the relationship between green food consumption behavior and the risk perception of COVID-19 but also provide some valuable implications for policymakers and marketers in promoting green food.

Keywords: Risk perception, Health awareness, COVID-19, Green food consumption behavior, Theory of planned behavior

Introduction

In the Green Food Mark Management Measures released by China in 2012, green food was defined as safe, high-quality edible agricultural products and related products produced in an excellent ecological environment, produced under the green food standard, with full quality control and the right to use the green food mark. Over the past ten years, green food in China has developed rapidly, and green food products have been marketed successively. Data provided by the Center for Quality and Safety of Agricultural Products of the Ministry of Agriculture and Rural Affairs showed that the number of certified products in the green food industry in China reached 21,638 in 2021, an increase of 28.32% from 2020. However, green food only accounts for approximately 8% of the total number of major food products, indicating a low market share for green food. Despite the awareness of the health benefits of green food, the higher price of green food compared to regular food led to a gap between green food consumption intention and behavior (Qi et al., 2020; Zhang et al., 2018; Zhu et al., 2013).

The coronavirus disease identified in 2019 (COVID-19) has been playing a huge negative impact on people's lives and production activities (WHO, 2020). During the COVID-19 spread, people perceive a strong risk of infection and the whole society has been full of tension, anxiety, and fear (Cori et al., 2020; Sun et al., 2021). Against this background, people began to choose their food more cautiously (Laguna et al., 2020). This paper focuses on whether and how the risk perception derived from the COVID-19 pandemic affects green food consumption. However, existing studies do not provide a definite answer to this question. To our knowledge, some studies have observed changes in people's green food consumption behavior during the COVID-19 pandemic, but the investigation of potential mechanisms is still limited. For example, Sun et al. (2021) found that fear, anxiety, and powerlessness induced by COVID-19 motivated people to pay more attention to their safety and health, thereby promoting green food consumption. Risk aversion, furthermore, played a mediating role between negative awe and green consumption behavior. Qi et al. (2020) found that health consciousness, environmental consciousness, social influence, perceived attributes, family structure, and shopping experiences were the main factors driving consumers' willingness to purchase green food. Still, green food consumption intention was also hindered by high price, unavailability, distrust, and lack of knowledge. Although COVID-19 increases consumers' willingness to purchase green food, there is still a gap between purchase intention and purchase behavior. And Qi et al. (2021) incorporated three factors such as moral attitude, health consciousness, and the impact of COVID-19 into the original theory of planned behavior and used the structural equation model (SEM) to explain consumers' intention to purchase green food in the post-COVID-19 pandemic period. They found that attitude, perceived behavioral control, moral attitude, health consciousness, and the impact of COVID-19 enhanced the consumption intention of green food.

In contrast to the above studies, this paper attempts to explore the relationship and underlying mechanisms between the risk perception of the COVID-19 pandemic and green food consumption behavior in the framework of the extended theory of planned behavior, which fills the research gap. The theory of planned behavior (TPB) is one of the best theoretical frameworks for studying consumption intention and behavior and has been widely used. In psychology, this theory links one’s beliefs and behavior (Ajzen, 1985) and improves the predictive power of the theory of reasoned action (TRA) by including perceived behavioral control (Ajzen, 1991). It covers people's non-volitional behavior, which cannot be explained by the TRA. The intention is not the only determinant of behavior where the individual does not have complete control over their behavior. Adding more factors, the TPB can better explain the relationship between intention and actual behavior. And the growing literature found that the TPB helps in predicting health-related behavioral intention better than the TRA, given that the TPB has improved the predictability of intention in various health-related fields and even nutrition intervention (Ajzen, 1989; Conner et al., 2003; Nguyen et al., 1997; Sweitzer et al., 2011). Currently, the outbreak of COVID-19 is viewed as an external health risk factor that affects consumers' decision-making when purchasing green food. To estimate the potential influence of COVID-19 on green food consumption in China, this study added risk perception and health awareness to the TPB. Our study aims to construct an integrated framework concerning the effect of consumers' risk perception of COVID-19 on green food consumption behavior. Specifically, this study is based on the extended TPB, using the structural equation model (SEM) to fit 612 valid samples collected online in 2020, and found that risk perception has a positive effect on both consumption intention and behavior. Furthermore, we also found that risk perception affects consumption intention by improving people's attitudes, subjective norms, and health awareness.

This study contributes to several aspects. First, this paper constructs an analytical framework for the risk perception of COVID-19 to influence green food consumption behavior. This paper adds risk perception and health awareness into the original TPB framework, which contributes to the relevant literature. Second, we collected a larger sample size than those involved in existing studies, and therefore the results would be more reliable when estimating the effect of COVID-19 on green food consumption intention and behavior using the SEM. Third, this paper also examines how the risk perception of COVID-19 influences green food consumption intention. Compared to existing studies, this paper is more in-depth in this aspect. Revealing the internal mechanism could help us deeply understand the interrelationship between them. Fourth, the findings of this paper could also provide some useful empirical evidence for green food policymakers and marketing promoters.

The remainder of this study is as follows: Sect. 2 describes the theoretical framework and poses hypotheses; Sect. 3 introduces the data collection and empirical methods; Sect. 4 provides a detailed discussion of findings; and Sects. 5–7 discuss and summarize the results, illustrate the limitations of this study, and suggest future research directions, respectively.

Theoretical framework and hypothesis development

Theory of planned behavior (TPB)

The TPB illustrates that three factors determine intention––attitude, subjective norms, and perceived behavior control, further substantially impacting behavior (Ajzen, 1985). The theory has been frequently applied in the research field of individual behavior. It has been used to explain people's behavior and their decision-making process in different disciplines, such as family recycling (Kaiser & Gutscher, 2003), family waste of dairy products (Toma et al., 2020), food quality, and preference (Sultan et al., 2020). Both personal and external factors can determine green food consumption (Tanner & Kast, 2003). Considering changes in individual psychology and behavior, COVID-19 can influence green food consumption. Moreover, it has been proved in the existing literature that it is appropriate to use the TPB to explain the consumption of green food (Wang & Wang, 2016; Zhu et al., 2013). Under the background of the COVID-19 pandemic, this paper uses the TPB to explore green food consumption, which could help us better understand green food consumption behavior.

Green food consumption intention and behavior

The intention is the probability that a person will take specific action for a certain intention and purpose, which is the primary factor in predicting behavior (Russell & Fielding, 2010). And behavior refers to an individual taking action. Consumption intention is determined by attitude, subjective norms, and perceived behavior control. Meanwhile, consumption intention has a positive effect on consumption behavior. These conclusions have been confirmed by numerous studies (Dunn et al., 2011; Luo et al., 2009; Michaelidou & Hassan, 2014; Paul et al., 2016; Yazdanpanah & Forouzani, 2015; Zhu et al., 2013). Thus, hypothesis 1 is presented as follows:

Hypothesis 1 (H1)

Consumption intention of green food has a positive effect on consumption behavior.

Risk perception and health awareness

In addition to the general applicability, many studies tried to improve the explanatory ability of the model by adding variables. For example, Paul et al. (2016) confirmed that the extended TPB model was more effective in predicting the consumption intention of green products by adding environmental awareness variables into the model. Qi and Ploeger (2019) added confidence and personal characteristics into the TPB framework to study green food consumption behavior. However, most of the literature variables are unique, but few studies included external risk factors. The intention might be affected by various external factors, thereby determining people's behavior. To identify the impact of COVID-19 on consumers, this paper introduced risk perception and health awareness to discuss the relationship between the risk brought by COVID-19 and green food consumption.

Risk perception refers to the perception of uncertainty or the possible negative consequences of a specific event (Jacobs & Worthley, 1999), namely the subjective judgment of the severity of the risk (Slovic et al., 1982). Risk perception is influenced by various factors, such as the severity of perceived outcomes, cautious behaviors, and concerns caused by risk (Champion & Skinner, 2008). During the COVID-19 spread, the perceived health risk was very high, increasing anxiety and quickly spilling throughout society (Cori et al., 2020). Since consumers are risk-averse, they make consumption decisions to minimize risk. In terms of food consumption, if consumers perceive a product as high-risk, they would consider choosing an alternative to reduce the risk (Ha et al., 2020; Lee, 2020). Green food is considered less risky than traditional food (Yu et al., 2014) and can reduce foodborne diseases (Sanders, 2006; Sirieix et al., 2011), which might prompt consumers to buy it. For instance, Sun et al. (2021) pointed out that more and more people chose to buy green food to protect their families during the COVID-19 pandemic. As alluded to above, hypotheses 2 and 3 are proposed as follows:

Hypothesis 2 (H2)

The risk perception of COVID-19 has a positive effect on green food consumption intention.

Hypothesis 3 (H3)

The risk perception of COVID-19 has a positive effect on green food consumption behavior.

When people recognize the risk of COVID-19, their health awareness is also awakened. The mortality following COVID-19 infection reaches up to 6%, and it is easier to lead to severe sequelae such as heart, brain, and lung diseases (Baud et al., 2020). Epidemic diseases can reshape the public’s awareness of life, especially the perception of health risks that can trigger individuals to think about health, body, and life in multiple dimensions (Commodari et al., 2020; Ferrer & Klein, 2015). For example, people subconsciously wash their hands frequently, maintain physical distance, avoid public places and wear masks to prevent infection (Dryhurst et al., 2020). Health awareness indicates an individual’s willingness to be healthier and take the necessary actions to achieve the goal (Sun & Liang, 2020). Furthermore, health awareness can strengthen people's intentions to respond to public health crisis events actively and encourage health-protective behaviors during the COVID-19 pandemic (Paakkari & Okan, 2020). People perceive green food as a healthy food with a higher consumption value, richer nutrition, and lower contamination risk (Sivapalan et al., 2021; Zhu et al., 2013). And results of Kriwy and Mecking (2012) and Qi et al. (2021) confirmed that consumers' health awareness could strengthen green food consumption intention. Given this, health awareness is added to the model, and hypotheses 4 and 4a are posed as:

Hypothesis 4 (H4)

Health awareness has a positive effect on green food consumption intention.

Hypothesis 4a (H4a)

Health awareness mediates the effect of risk perception of COVID-19 on green food consumption intention.

Attitude

Attitude refers to the positive or negative feelings of the individual toward behavior (Chen, 2016), which evaluates people's behavior (Ajzen, 1991). Consumers' attitude toward green food reflects consumers' recognition and trust in green food. Ajzen (1985) pointed out that consumers would have a positive attitude when believing that the product is beneficial. Attitude plays a significant role in encouraging consumers to buy healthy food (Voon et al., 2011). Consumers prefer to understand healthy products when optimistic about them (Golnaz et al., 2012; Kim and Chung, 2011; Paul et al., 2016). When consumers hold a positive attitude toward green food and think it is healthy, their consumption intention will be increased. Based on the relationship between attitude and intention, hypothesis 5 is proposed as follows:

Hypothesis 5 (H5)

Attitude toward green food has a positive effect on green food consumption intention.

Earlier studies found that attitude is negatively correlated with risk perception (Jarvenpaa et al., 2000; Shimp & Bearden, 1982; White & Truly, 1989). Using the structural equation model (SEM), Xu et al. (2020) analyzed the impact factors of consumers' risk perception of transgenic. They found that consumers' attitudes toward transgenic technology and risk perception of transgenic food are opposite. In this study, green food is a safe and healthy alternative to traditional food. Health concerns significantly impact consumers' attitudes toward purchasing healthy food (Husic-Mehmedovic et al., 2017). The increase in consumers' risk perception of COVID-19 may lead to a higher positive attitude toward green food consumption. For instance, Hsu et al. (2016) found that food safety problems had a positive impact on both consumers' attitudes toward healthy food and consumption intention. Consumers pay more attention to food health and have more positive attitudes toward healthy food. Therefore, we suppose that consumers would pay more attention to health and change their attitude toward green food due to infection concerns. Then hypothesis 5a is put forward as follows:

Hypothesis 5a (H5a)

Attitude toward green food mediates the effect of risk perception of COVID-19 on green food consumption intention.

Subjective norms

Subjective norms refer to the social pressure on individuals to behave and reflect the influence on other people’s opinions as well as personal decision-making. Scholars believe subjective norms can represent social influence (Malhotra & Galletta, 1999), including the effects of alternatives, people, and opinion leaders. In this paper, subjective norms are measured by the threat of substitutes for usual food and the attitude of the media and the public. If the public and organizations think highly of the importance of green food, consumers would be more likely to purchase green food (Park, 2000). Most studies supported that subjective norms can significantly affect consumption intention (Åstrøm & Masalu, 2001; Bianchi & Mortimer, 2015; Kim & Chung, 2011; Pomsanam et al., 2014). Because of the great influence of COVID-19 and the frequent media coverage, the pandemic would have a certain impact on subjective norms. Moreover, consumers mainly obtain information about COVID-19 through the government, media, and surrounding people. News reports, media communication, and the preventive measures adopted by surrounding people impact consumers' risk perception, then affect their subjective norms, and finally affect their consumption intention (Fuentes & Fuentes, 2015; Huynh, 2020) (Fig. 1). Therefore, hypotheses 6 and 6a are proposed as follows:

Fig. 1.

Fig. 1

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Conceptual framework of green food consumption behavior based on the original structure of TPB

Hypothesis 6 (H6)

Subjective norms have a positive effect on green food consumption intention.

Hypothesis 6a (H6a)

Subjective norms of green food mediate the effect of risk perception of COVID-19 on green food consumption intention.

Perceived behavior control

Perceived behavioral control (PBC) usually reflects the impediment of personal experience or second-hand information on consumption intention and behavior (Ajzen, 1991). In this paper, PBC contains the costs (including money, time, and energy), convenience, and purchasing experience. Cranfield (2020) found that consumers' purchasing habits, places, and ways changed during the COVID-19 pandemic. Ajzen (1991) held that the perceived behavioral control of a certain behavior positively affects a person's intention. For example, Kavaliauske and Ubartaite (2014) argued that consumers prefer fresh and easily accessible healthy food and are not sensitive to the price. Considering the factors of limited movement and reduced income during COVID-19, we assumed that consumers with rich green food knowledge and more convenient purchase conditions would have a stronger purchasing intention:

Hypothesis 7

Perceived behavior control has a positive effect on green food consumption intention.

Data and method

Questionnaire design

We designed the questionnaire concerning existing literature and well-established scales (Ajzen, 1991; Anvar & Venter, 2014; Gerhold, 2020; Prentice et al., 2019; Siegrist et al., 2021; Voon et al., 2011; Zhu et al., 2013), then distributed and collected the questionnaire through an online survey. The questionnaire consists of two parts: the demographic section (including gender, age, education level, salary, etc.) and the measurements of the extended TPB model (behavior, intention, risk perception, health awareness, attitude, subjective norms, and PBC). About 4 to 6 questions are set under each construct. All questions are formulated using the five-point Likert Scale and rated from strongly disagree to strongly agree. Each construct is modified according to its implication in this study and adapted from previous literature. A detailed design is given as follows:

(1) Consumption behavior of green food (CBGF) is used to measure the actual behavior of consumers to buy green food, including four questions: the preference and frequency of purchasing green food and whether consumers recommend it to others; (2) consumption intention of green food (CIGF) involves six questions: the possibility and purchasing motivation (for environmental protection, requirements for health and high-quality life, etc.); (3) risk perception (RP) is measured by five questions, which describe consumers' worry and fear of being infected; (4) health awareness (HA) is also measured by five questions about washing hands, cleaning rooms, wearing masks, and opening doors; (5) attitude (ATGF) contains six questions to measure how consumers think green food is environmentally friendly, healthy, and safe; (6) subjective norms (SN) include four questions, reflecting the influence of a vital person's advice, reliability, and the feeling when purchasing substitutes; (7) perceived behavior control (PBC) includes questions about the available conditions for purchasing green food (time, purchasing experience, and purchasing power, etc.). Table 5 in Appendix refers to the details of the questionnaire set.

Table 5.

Survey Instrument

Code Questions Sources
Risk Perception (1. Strongly Disagree to 5. Strongly Agree)
RP-1 I am afraid to contact other strangers during the pandemic Gerhold (2020); Siegrist et al. (2021)
RP-2 I am afraid to go out during the pandemic
RP-3 I will wash the vegetables I bought outside many times during the pandemic
RP-4 I am afraid that the food in my family was not well cooked during the pandemic
RP-5 I often worry that I will infect with SARS-CoV-2 during the pandemic
Health awareness (1. Strongly Disagree to 5. Strongly Agree)
HA-1 I am aware that my family should wash their hands frequently during the pandemic Qi et al. (2021); Naveed and Shaukat (2022)
HA-2 I am aware that my house should be often cleaned during the pandemic
HA-3 I am aware that the door should be often opened for ventilation during the pandemic
HA-4 I am aware that my hands should be washed frequently during the pandemic
HA-5 I am aware that one should wear a mask at home during the pandemic
Attitude toward Green Food (1. Strongly Disagree to 5. Strongly Agree)
ATGF-1 Compared with ordinary food, I prefer to buy similar green food Voon et al. (2011); Prentice et al. (2019); Ajzen (1991)
ATGF-2 Eating green food is very necessary to prevent foodborne diseases
ATGF-3 I think it is a good idea to buy green food
ATGF-4 I think buying green food is a good thing for family health
ATGF-5 I think green food is safe
ATGF-6 I think buying green food is a good thing for environmental protection
Subjective Norms (1. Strongly Disagree to 5. Strongly Agree)
SN-1 Those who are very important to me suggest I buy green food Anvar and Venter (2014); Ajzen (1991)
SN-2 Those who are important to me want me to buy green food
SN-3 Everyone has the responsibility to protect the environment by purchasing green food
SN-4 I feel terrible when I replace green food with common food of the same kind
Perceived Behavior Control (1. Strongly Disagree to 5. Strongly Agree)
PBC-1 It's convenient for me to buy green food Ajzen (1991)
PBC-2 I have enough time to choose and buy green food
PBC-3 I have enough economic strength to buy green food
PBC-4 I have enough experience to distinguish similar green food from common food
Consumption Intention of Greed Food (1. Strongly Disagree to 5. Strongly Agree)
CIGF-1 I have a good chance to buy green food Zhu et al. (2013); Grankvist and Biel (2001); Ajzen (1991)
CIGF-2 I want to reduce the damage to the environment by buying green food
CIGF-3 For the sake of health, I have a strong desire to buy green food
CIGF-4 As a responsible individual, I will buy green food
CIGF-5 To improve life quality, I will spend more money to buy green food
CIGF-6 I plan to continue to buy green food
Consumption Behavior of Green Food (1. Strongly Disagree to 5. Strongly Agree)
CBGF-1 When purchasing food, I try to buy food with a green food logo Zhu et al. (2013); Ajzen (1991)
CBGF-2 Even though the price is high, I still buy green food
CBGF-3 I will recommend relatives and friends buy green food
CBGF-4 I often buy green food
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Data collection

The survey data come from an internet questionnaire called The Impact of COVID-19 on Consumers' Green Food Consumption Behavior designed by us and distributed randomly on one of China's largest online questionnaire platforms. The sample contains consumers' ages, incomes, regions, and education levels. Most respondents were from Sichuan Province. Between February 20, 2020, and May 10, 2020, 738 samples were collected, and 126 invalid questionnaires were deleted, with a valid response rate of 82.9%. Respondents aged between 16 and 25, 26 and 35, and 36 and 45 accounted for 51.8, 24.5, and 14.2%, respectively. In addition, 76.1% of participants had a bachelor's or a higher education degree. Moreover, 46.2% of respondents were students, only 12 respondents were unemployed, and the rest had a job. And 94.1% of respondents thought they were healthy or above; only 4 thought they were not in good health. When asked about income, 54.8% of respondents declared their monthly income is less than 3000 yuan, and the income of 14.2% is between 3001 and 5000 yuan.

Method

Partial least squares-structural equation modeling (PLS-SEM) was used to test the correlation between the risk perception of the COVID-19 pandemic and green food consumption. The PLS-SEM is a causal modeling method that focuses on the relationship between the potential variables in the estimation and analysis model. The estimation of the structural model will be more robust when all path coefficients are valid simultaneously (Hoyle, 1995). The PLS-SEM proposed by Hair et al. (2012b) includes effect size, internal consistency reliability, average variance extraction (AVE), discrimination validity, convergence validity, and path coefficient estimation. It can simplify the complicated decision-making process. For example, PLS minimizes the deviation caused by the minimum sample size and would not lead to unreasonable estimates or model identification problems. Moreover, it can also maximize the ability to predict.

In the previous study, the conceptual framework of green food consumption behavior based on the original structure of TPB is established in Sect. 2. Based on this conceptual framework, we build a structural equation model in the Smart-PLS, which contains seven latent variables such as attitude, subjective norms, perceived behavior control, risk perception, health awareness, consumption intention, and consumption behavior. Latent variables above have been measured by several indicators shown in Table 5. Then we use the confirmatory factor analysis to optimize the scale. Confirmatory factor analysis (CFA) is a research method used to test whether the relationship between a factor and the corresponding measure term conforms to the theoretical relationship designed by the researcher. The main purpose of confirmatory factor analysis (CFA) is to verify the validity of CFA and to analyze the common method deviation CMV. The reliability of each index should be considered in the verification, and the absolute standardized load of each index should be higher than 0.7. The indicator demonstrating a load of 0.4 or less should be removed from the reflection scale. In the first CFA calculation result, the load value of HA5 (I am aware that one should wear a mask at home during the pandemic) is less than 0.7, so it should be deleted. In the second operation result, the load values of RP3 (I will wash the vegetables I bought outside many times during the pandemic) and RP5 (I often worry that I will infect with SARS-CoV-2 during the pandemic) are also less than 0.7. After deleting them, the confirmatory analysis is carried out again. Finally, the absolute standardization compliance of each index is higher than 0.7. The effective and reliable scale of each structure was established, and the influence relationships among every factor were confirmed.

Subsequent sections examine the reliability and validity of the latent variables contained in the structural equation model to ensure the accuracy and reliability of path coefficient estimation and mediating effect identification.

Survey results

Reliability and validity

In this study, Smart-PLS software was used to detect the mean value, standard deviation, and reliability of each variable. Table 1 presents the results of the descriptive statistics. It demonstrates that the mean values of overall indicators are below four except for health awareness. At the same time, health awareness had a relatively low standard deviation. The higher mean and lower standard deviation indicate that most consumers will take corresponding measures to deal with infectious diseases.

Table 1.

Reliability and validity

Variables No. items Means SD CA CR AVE
SN 4 3.288 1.088 0.898 0.930 0.768
HA 4 4.379 0.620 0.735 0.832 0.555
RP 3 3.805 1.164 0.733 0.843 0.641
PBC 4 3.240 1.106 0.888 0.897 0.922
ATGF 6 3.843 0.907 0.913 0.932 0.698
CIGF 6 3.788 0.848 0.956 0.961 0.806
CBGF 4 3.516 0.977 0.928 0.949 0.822
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RP Risk perception, HA Health awareness, ATGF Attitude toward green food, SN Subjective norms, PBC Perceived behavioral control, CIGF Consumption intention of green food, CBGF Consumption behavior of green food, SD Standard deviation, CA Cronbach’s alpha, CR Composite reliability, AVE Average variance extracted

In contrast, PBC had a lower mean and a higher standard deviation, signifying that the overall level of PBC is low, and consumers' experience, time, and economic situation have large differences. In addition, the standard deviation of risk perception is 1.164, indicating that the scattered distribution of risk perception is high overall. To evaluate the internal consistency of each construct, Cronbach’s alpha is used to test internal consistency and reliability. When the CA value is greater than 0.7, the construct is reliable. According to Table 1, the CA values of all variables are above 0.7, implying good consistency and reliability.

Additionally, the CR method is used to test internal consistency. The threshold of composite reliability was 0.7 (Hair et al., 2012a). As shown in Table 1, all CR values are greater than 0.8, implying that all indicators are reliable. Convergence validity represents potential structures (Carmines & Zeller, 1979) and is measured by the AVE (Average Variance Extracted) value. The AVE represents the average variance of structure in its index variable relative to its total index variance. According to Table 1, the convergence validity of all indexes is over 0.5. The average value of more than 0.5 indicates sufficient convergence validity (Fornell & Larcker, 1981). It means that the potential variables account for more than half of the variances, so they all have a good convergence validity.

Discriminant validity can be used to test the non-correlation between constructs. Fornell–Larcker criterion, cross-loading analysis, and heterotrait–monotrait ratio (HTMT) approach are commonly used in the evaluation. Hair et al.’s (2012a) marketing study involved a certain type of discriminant validity assessment, using cross-loadings (7.79%) and the Fornell–Larcker criterion (72.08%). The AVE square should be greater than the correlation coefficient between other constructs and itself. According to Table 2, all indicators meet the conditions. The second method is more convenient for testing validity: The loadings of each indicator should be greater than that of cross-loadings. Table 6 in Appendix reports the load and cross load of all indexes in the model. The construct load of its structure was relatively higher than that of other structures, confirming the discrimination validity. Since PLS overestimates the factor loadings and underestimates the relationship between the constructs, the HTMT method can reduce this error. The HTMT is used to estimate the correlation between each variable, and the threshold of HTMT is 0.9. According to the results, all of them are below 0.9, implying no clear evidence of a lack of discriminant validity. Therefore, the above three methods all verified the validity of the discrimination.

Table 2.

Discriminant validity

Variables Fornell–Larcker criterion Heterotrait–monotrait ratio (HTMT)
AVE SN HA RP PBC ATGF IGF CGF SN HA RP PBC ATGF IGF
SN 0.768 0.876 SN
HA 0.555 0.336 0.745 HA 0.398
RP 0.641 0.401 0.397 0.801 RP 0.464 0.521
PBC 0.922 0.688 0.267 0.229 0.865 PBC 0.767 0.313 0.256
ATGF 0.698 0.606 0.354 0.461 0.553 0.835 ATGF 0.777 0.413 0.533 0.606
CIGF 0.806 0.654 0.383 0.406 0.587 0.845 0.898 CIGF 0.813 0.442 0.461 0.633 0.886
CBGF 0.822 0.617 0.377 0.403 0.688 0.570 0.634 0.907 CBGF 0.885 0.437 0.454 0.755 0.835 0.856
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RP Risk perception, HA Health awareness, ATGF Attitude toward green food, SN Subjective norms, PBC Perceived behavioral control, IGF Consumption intention of green food, CGF Consumption behavior of green food. The square root of Average Variance Extracted (AVE) is marked in bold

Table 6.

Loadings and cross-loadings

SN HA RP PBC ATGF IGF CGF
SN1 0.918 0.285 0.356 0.560 0.527 0.568 0.541
SN2 0.915 0.290 0.376 0.557 0.515 0.555 0.528
SN3 0.846 0.312 0.336 0.434 0.561 0.510 0.505
SN4 0.824 0.288 0.338 0.459 0.565 0.501 0.488
HA1 0.287 0.818 0.351 0.209 0.330 0.328 0.315
HA2 0.307 0.732 0.319 0.287 0.298 0.339 0.360
HA3 0.175 0.660 0.259 0.102 0.170 0.188 0.176
HA4 0.197 0.759 0.225 0.160 0.219 0.249 0.229
RP1 0.246 0.304 0.797 0.130 0.304 0.267 0.226
RP2 0.244 0.305 0.815 0.113 0.306 0.259 0.247
RP4 0.420 0.334 0.790 0.263 0.452 0.406 0.436
PBC1 0.494 0.240 0.206 0.897 0.528 0.543 0.519
PBC2 0.533 0.239 0.229 0.898 0.512 0.551 0.519
PBC3 0.513 0.214 0.153 0.834 0.374 0.419 0.528
PBC4 0.529 0.229 0.194 0.829 0.478 0.502 0.504
ATGF1 0.501 0.368 0.472 0.494 0.827 0.433 0.595
ATGF2 0.521 0.274 0.405 0.458 0.836 0.464 0.545
ATGF3 0.524 0.307 0.394 0.491 0.899 0.525 0.588
ATGF4 0.586 0.288 0.358 0.439 0.865 0.523 0.535
ATGF5 0.493 0.235 0.297 0.398 0.750 0.430 0.455
ATGF6 0.501 0.289 0.367 0.479 0.826 0.547 0.428
CIGF1 0.538 0.371 0.376 0.504 0.577 0.899 0.521
CIGF2 0.472 0.367 0.376 0.466 0.555 0.871 0.493
CIGF3 0.465 0.338 0.380 0.490 0.556 0.911 0.538
CIGF4 0.497 0.343 0.409 0.528 0.586 0.925 0.564
CIGF5 0.465 0.290 0.307 0.562 0.575 0.849 0.572
CIGF6 0.521 0.353 0.342 0.507 0.599 0.931 0.598
CBGF1 0.495 0.361 0.373 0.542 0.507 0.573 0.896
CBGF2 0.504 0.318 0.326 0.539 0.559 0.527 0.912
CBGF3 0.592 0.351 0.401 0.499 0.598 0.552 0.911
CBGF4 0.572 0.335 0.362 0.514 0.528 0.569 0.908
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The bold values in the matrix above are the item loadings and others are cross-loadings

Path analysis

We used SEM to construct the structural model and test the relationship between each variable (Table 3). The coefficient value of intention to behavior (H1) is 0.802 and significant at a 1% level, indicating that consumption intention has a positive impact on behavior. The f2 value of 1.789 implies that the effect of intention on behavior is great. The influence coefficient value (H2) of risk perception on green food consumption intention is 0.025 with a p value of 0.0348, indicating that risk perception has a positive impact on people's consumption intention significantly. The f2 value of 0.002 demonstrates that the effect is negligible. And the coefficient value of risk perception toward consumption behavior (H3) is 0.078 with a p value of 0.0173, indicating that risk perception of the COVID-19 pandemic has a positive impact on green food consumption behavior. The standardized regression coefficient illustrates that the effect of risk perception on behavior is smaller than that of intention. The f2 value of 0.017 shows that the impact of risk perception on behavior is weak. R2 value indicates that 70% of green food consumption changes are explained by risk perception and intention. Finally, the Q2 value is 0.539, suggesting that risk perception and intention have a robust predictive relevance toward green food consumption.

Table 3.

Path coefficient

Hypothesis Beta p R2 f2 Q2 Decision
H1 CIGF-CBGF 0.802 0.000 0.700 1.789 0.539 Accept
H2 RP-CIGF 0.025 0.035 0.002 Accept
H3 RP-CBGF 0.078 0.017 0.769 0.017 0.578 Accept
H4 HA-CIGF 0.071 0.039 0.017 Accept
H5 ATGF-CIGF 0.610 0.000 0.723 Accept
H6 SN-CIGF 0.278 0.000 0.121 Accept
H7 PBC-CIGF 0.046 0.632 0.005 Reject
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RP Risk perception, HA Health awareness, ATGF Attitude toward green food, SN Subjective norms, PBC Perceived behavioral control, CIGF Consumption intention toward green food, CBGF Consumption behavior of green food

In addition, the coefficient of health awareness (H4) is 0.071 with a p value of 0.0386 (less than 5% significance level). The standardized regression coefficient indicates that health awareness has a positive impact on consumption intention, which is slightly higher than that of risk perception. Meanwhile, the coefficient of attitude on intention (H5) is 0.610 and significant at the 1% level, showing that attitude has a positive and significant effect on intention. Moreover, the coefficient value (H6) of the influence of subjective norms on green food consumption intention is 0.278 with a p value of 0.000, suggesting that subjective norms have a positive impact on green food consumption intention. The value of f2 is 0.121, indicating that the influence of subjective criteria on green food consumption intention is weak. Finally, the coefficient value (H7) of the influence of PBC on the intention of green food is 0.046, but not significant. The result shows that the PBC has no significant positive effect on green food consumption intention. In general, the R2 value demonstrates that 76.9% of the changes in green food consumption intention are jointly explained by risk perception, health awareness, attitude, subjective norms, and the PBC. Moreover, the Q2 value is 0.578, which indicates that risk perception and health awareness have a medium predictive relevance for green food consumption intention (Fig. 2).

Fig. 2.

Fig. 2

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Path coefficients followed by p-values. Note ***, **, and * indicate 1, 5, and 10% significance levels, respectively; The dotted line is not significant

Mediating effects

Three mediating analyses are performed: RP → HA → CIGF, RP → ATGF → CIGF, and RP → SN → CIGF. In this paper, bootstrap (5000 samples) is utilized to test whether the mediating effect is significant (Table 4). The confidence interval includes zero, meaning that it is not significant. Results show that the confidence interval from risk perception to health awareness (H4a) does not include zero, indicating that the impact is also significant (95% CI 0.320–0.474). While the confidence interval of risk perception (H5a) has a significant indirect effect on green food consumption intention (95% CI 0.386–0.537), confirming that attitude can mediate the relationship between risk perception and green food consumption intention. The effect of risk perception (H6a) on subjective norms is also statistically significant (95% CI 0.325–0.477). Therefore, health awareness, attitude, and subjective norms mediate the relationship between the risk perception of COVID-19 and green food consumption intention.

Table 4.

Mediating models

Hypothesis Beta LLCI ULCI Result
H4a: HA mediates the relationship between RP and CIGF 0.397 0.320 0.474 Mediation
H5a: ATGF mediates the relationship between RP and CIGF 0.461 0.386 0.537 Mediation
H6a: SN mediates the relationship between RP and CIGF 0.401 0.325 0.477 Mediation
Open in a new tab

RP Risk perception, HA Health awareness, ATGF Attitude toward green food, SN Subjective norms

Discussion

We proposed seven direct and three mediating hypotheses, all of which have been tested. Consistent with most current research results, consumption intention has a significant effect on consumption behavior (H1). If people buy green food with strong possibility, intention, and purpose, they will be more likely to have corresponding consumption behavior. Therefore, marketers can promote green food through in-store promotion, online marketing, media publicity, and other ways to improve their perceived value of green food and encourage them to buy it. By publicizing the characteristics of safety, social benefits, and environmental benefits, consumers would take action in consideration of personal health and a sense of social responsibility. Furthermore, adding risk perception into the framework improves explanatory power. H2 and H3 support that risk perception impacts both consumption intention and behavior. Risk perception plays a vital role in strengthening green food consumption behavior. On the one hand, risk perception can directly affect consumption behavior. On the other hand, it can conduct the effect of consumer behavior through intention. Additionally, H4 is also confirmed with the significant influence of health awareness on intention. Under the influence of COVID-19, there is a higher demand for high-quality and safe food. As a high-quality and nutritious food, green food is conducive to enhancing human immunity and reducing the diet risk to the human body (Arshad et al., 2020; Qin et al., 1998). Therefore, it is necessary to promote the benefits of green food and related knowledge.

These findings also show that attitude and subjective norms have strong and significant path coefficients on green food consumption intention. Among them, attitude is the key to understanding behavior and intention, contributing to predicting food consumption behavior (Yadav & Pathak, 2017). A positive attitude will encourage consumers to buy green food (H5 is accepted), which is consistent with other research (Kim & Chung, 2011; Paul et al., 2016). Besides, the results show that subjective norms are a significant determinant of consumption intention, and the H6 is proved. The influence of important people or organizations and people's feelings about using substitutes would affect their consumption intention. This is also in line with relevant research (Jackson et al., 1993; Paul et al., 2016). Therefore, society can increase the role of subjective norms by utilizing the influence of public figures and institutions. However, this study also demonstrated that the effect of PBC on intention was not significant, rejecting H7. The result is interpretable in common sense. Although the impact of PBC on consumption intention is not significant at the 10% level, the path coefficient is still positive. Our results suggest that during the COVID-19 pandemic, green food consumption intention is mainly affected by attitude and subjective norms, while consumers' purchasing power, energy, and purchasing experience of green food have little to do with their consumption intention. This could be attributed to the introduction of risk perception in the TPB framework. That is, there may be a substitution relationship between PBC and risk perception (Luo et al., 2009), but we cannot deny the effect of PBC on consumption intention.

Three mediating hypotheses are all confirmed in this study. H4a is accepted, which means health awareness mediated the impact of risk perception on consumption intention. In this paper, the potential risk of COVID-19 to human health may affect their health awareness, which is consistent with the results of Gullette et al. (2009), Kaba et al. (2017), and Tran et al. (2013). The frequency and intensity of preventive measures will increase when the pandemic becomes more serious. In H5a, consumers' high risk of COVID-19 magnifies their attitude toward green food and promotes their consumption intention. A strong sense of risk for COVID-19 will make a better attitude toward green food. Additionally, the public health event formed an external pressure, which impacted subjective norms. Immunity plays an important role in fighting SARS-CoV-2, and people can reduce the infection rate by advising others on a healthy diet. Finally, this paper found that attitude played a more critical role in regulating perception than subjective norms, which is consistent with the results of Chen (2016).

Conclusion

In the context of the COVID-19 pandemic, this study explored the influence of consumers' risk perception on green food consumption behavior. Based on the original TPB, external factors such as risk perception and health awareness were added for a comprehensive analysis. According to the results, COVID-19 was confirmed to affect green food consumption behavior in China. Furthermore, the stronger the risk perception is, the higher the intention to purchase green food.

This paper provides some new study aspects. On the one hand, consumer behavior is often affected by external factors. We considered the change in the external environment and added risk perception of COVID-19 as the key factor. On the other hand, the emergence of COVID-19 threatened people's health and their decision for food, so they have to take measures to prevent being infected. As a more healthy and safe food alternative, green food is a better choice with the increasing food quality requirements. Therefore, this study introduced risk perception and health awareness in the original model and proposed three mediating hypotheses. This study can enrich the research on green food consumption, and provide a valuable reference for marketers to comprehend consumers' psychology and make targeted strategies to expand the market.

Based on these findings, attention should be paid to improving consumers' knowledge about green food and enhancing its perceived value. Risk perception is a key factor affecting green food consumption intention. The government and marketers should attach importance to the benefits of green food as it can enhance human immunity and reduce the risk of illness. Attitude has the largest influence among seven direct effects and three mediating effects, and it also promotes the impact of risk perception on consumption intention. Some scholars found that consumers' knowledge positively impacts their risk perception (Liu, 2008; Martinez-Poveda et al., 2009). Therefore, strengthening the propaganda of epidemic prevention and improving people's knowledge of green food is the key to expanding the green food market. By emphasizing the importance of healthy food as well as the threat brought by external risks, green food consumption intention will be enhanced.

Limitations and future research

Although the hypotheses proposed in this study have been verified, there are still some limitations. First, the survey area is concentrated in southwest China, which is affected by the local green food industry, climate, customs, etc. The green food market is mainly concentrated in large and medium-sized cities, especially in the eastern coastal areas (Yin et al., 2010). Additionally, this study only focused on the impact of green food intention on behavior but neglected the gap between them. The results showed that the effect of risk perception on behavior is greater than that on intention. Although intention and behavior are significantly related, the gap between them may not be neglected.

Future studies can expand the sample size and distribute it to each population characteristic group and region, as well as conduct a detailed survey of the eastern coastal areas with the most active green food market. Second, the sample size can be narrowed to a certain income group and occupation type group, which is helpful in exploring the specific consumption behavior. Third, as an essential factor in predicting green consumption, risk perception affects consumption behavior differently. Future studies can consider the influence of external events and change the research object. Finally, since COVID-19 has a huge impact on the world, extensive research can be done on other countries worldwide.

Acknowledgements

Funding for this research from National Social Science Foundation of China with ratification number 20CMZ037 is gratefully acknowledged. We would like to thank the anonymous reviewers for their kind comments and valuable suggestions. We confirm that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The datasets generated for this study are available on request to the corresponding author.

Appendix

See Tables 5 and 6.

Data availability

The datasets generated for this study are available on request to the corresponding author.

Declarations

Conflict of interest

We confirm that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Houjian Li, Email: 14159@sicau.edu.cn.

Andi Cao, Email: caoandi@stu.sicau.edu.cn.

Si Chen, Email: chensi@stu.sicau.edu.cn.

Lili Guo, Email: 14453@sicau.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

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