Behavioral Intention to Resist the Consumption of Wild Animals in China: Netizen Survey

Abstract

Since the beginning of 2020, China has banned the consumption of wild animals to combat the spread of zoonoses. Most existing studies focus on the intention and behavior of wildlife consumption and their causes; however, few have looked at public willingness to resist wildlife consumption, as well as the cause and effects of such actions. In this study, a framework for an extended theory of planned behavior was constructed. Based on a 7-point Likert scale, a sample of 1194 respondents from eight provinces across China was obtained through an online survey. Structural equation modeling was used to analyze netizen behavioral intention to resist consuming wild animals and their causes to provide a reference for the implementation and optimization of relevant policies. The study model passed the goodness-of-fit test, confirming the robustness of the results. The results showed that Chinese netizens’ intention to resist consuming wild animals was moderate, with 55.19% willing to participate in activities against it, i.e., it is important to resist eating wild animals as a standard. Attitude, subjective norm, perceived behavioral control, and past experience of the Chinese netizen had significant positive effects on resistance intention, i.e., (1) netizens’ current living area with severe outbreaks were more likely to resist wildlife consumption, (2) highly knowledge level netizens were more likely to resist wildlife consumption than less knowledgeable ones, and (3) lower income level had higher behavioral intentions of netizens. The findings suggest that the government must take a lead role in wildlife protection and strengthen its restrictions, laws, and regulations. The media should also be used to promote conservation and popularize a protective message in favor of wild animals. Public quality and assurance of wildlife protection should be culturally reinforced to effectively ban the illegal trade of wild animals and their products.

1. Introduction

In late 2019 and early 2020, the outbreak of COVID-19 became a worldwide disaster. Current epidemiological studies mostly point to the association between the source of the virus and wild animals [1]. Bats (Chiroptera spp.) are likely to be the storage hosts of COVID-19 [2]. Sunda pangolin (Manis javanica) and other wild animals may be intermediate hosts in the process of virus transmission and mutation [3,4,5,6]. The prevention of zoonotic diseases has received unprecedented attention. In addition to COVID-19, existing studies have shown that many diseases originate in wild animals [7,8,9]. In the past, severe acute respiratory syndrome, Middle East respiratory syndrome, and Ebola hemorrhagic fever have been confirmed to be related to animals or wild animals, whose pathogens jumped to human beings from animal hosts [10,11,12,13]. It was found that 60.3% of the 335 new infectious diseases worldwide from 1940 to 2004 were caused by zoonotic pathogens, and 71.8% of the pathogens were from wild animals [14,15]. To avoid the public health risks caused by improper consumption of wild animals, some other countries choose to formulate a “Blacklist” for the consumption of wild animals with regulatory laws. For example, in the Lacey Act, the import and interstate transportation of species that may be harmful to humans, agriculture, horticulture, forestry, wildlife, or wildlife resources in the United States are prohibited, and the specific list is authorized to be formulated by the Secretary of the Interior [16].

Eating wild animals is a common way to obtain animal protein around the world [17]. Due to the intense conflict between biodiversity conservation and economic development, the consumption of animals and other types of animal use activities have become the main factors endangering wildlife populations. Some traditional Chinese diets view dishes made with wild animals as a symbol of nobility; over time, some Chinese people have formed the habit of eating wild animals and regard their consumption as a part of China’s dietary culture [18], particularly in the south [19]. This activity has led to the poaching and smuggling of endangered species and damage to biodiversity [20,21]. In 1988, the Wildlife Conservation Law was promulgated, and wild animals were regarded as a usable resource [22]. Article 1 of the Wildlife Protection Law stipulated to protect and save precious and endangered wild animals, develop, and rationally use wild animal resources, maintain ecological balance, and formulate law-abiding action [23]. The Wildlife Conservation Law has so far gone through three minor amendments (i.e., 2004, 2009, and 2018), and one major amendment (i.e., 2016) [24]. Chinese researchers have explored wildlife conservation, seeking to understand its consumption level in China, publicizing the Wildlife Conservation Law—to crackdown on immoderate and disordered hunting, illegal transportation, and illegal management of wildlife resources—to promote the sustainable development of wildlife resources and to guide the public to set up a corrective diet concept wild animal free.

A study in 1999 surveyed 21 representative cities across China [25], including Beijing, Shanghai, and Guangzhou, on restaurants, non-staple food stores, markets, and citizens that manage wild animals, and showed that people mistakenly believed that eating wild animals had the effect of better nourishment, strengthening the body, which resulted in their excess consumption, destruction of wildlife resources, and occurrence of zoonoses. The China Wildlife Conservation Association and the American Wildlife Rescue Association jointly released the “2005 National Survey of Edible Wild Animals” in Beijing, i.e., the second survey conducted after 1999. The comparison showed that the proportion of the public eating of wild animals had declined because the public’s awareness of wildlife protection had been greatly improved, and government departments were actively taking measures to stop the spread of indiscriminate eating of wild animals [26]. The 2008 TRAFFIC survey results show that the consumption of wild animals, especially the consumption of wild food, medicinal materials, and supplements, is widespread in many cities, and most respondents held a neutral or accepting attitude toward the consumption of wild animals [27]. In 2016, the Wildlife Conservation Law explicitly banned the consumption of wildlife under national key protection but did not prohibit the consumption of wild animals with legal sources. To a certain extent, the production and management of wild animals with legal sources have also been allowed [28]. The Wildlife Conservation Law is aimed at the current situation of controlling the consumption of wild animals—in an attempt to cut the demand, combat illegal hunting, and better manage wild animal reserves [23].

In China, the edible utilization of artificially farmed wild animals has great economic and social benefits. Under the guidance of the poverty alleviation policy, some local governments have supported the development of the artificial breeding wild animal industry in accordance with local conditions [29]. According to the 2017 Chinese Academy of Engineering’s survey, data from the domestication and breeding industry showed that the number of direct employees in the national edible wild animal breeding industry reached 6,263,400, creating an output value of RMB (Chinese Yuan) 125.54 billion [30]. According to a recent study [31], during the period of COVID-19, concern about zoonosis has been at an all-time high, with more than 90% of Chinese people agreeing with the legislation of banning the consumption of wild animals. Existing wildlife consumption groups tend to no longer consume them due to the outbreak. However, some stakeholders of wildlife breeding and utilization still disagree with a complete outright ban. Another study used a convenient sampling method to conduct an online questionnaire survey of 419 people in 30 provinces across China. Only 0.48% of people expressed support for eating wild animals, and only 4.36% of those had eaten wild animals more than ten times in their lifetime [32]. On 24 February 2020, China introduced the Decision on Completely Prohibiting Illegal Wildlife Trade (hereinafter referred to as: the Decision), stating: (1) it prohibits hunting, trading, transportation, and consumption of wild animals in the Wildlife Conservation Law and other relevant laws must be strictly prohibited; (2) it is fully prohibited to consume “terrestrial wildlife with important ecological, scientific, and social value” and other terrestrial wildlife, including artificial breeding and raised terrestrial wildlife; and (3) it is strictly prohibited to hunt, trade, and transport terrestrial wild animals that naturally grow and breed in the wild environment for the purpose of eating. The purpose of the Decision is to comprehensively prohibit and punish illegal wildlife trading, eliminate the bad habits of overeating wildlife, maintain biological and ecological safety, and effectively prevent major public health risks [33]. Breaking the traditional value balance between the protection and utilization of wild animals has led to a shift in the concept of “protective utilization” in the current system, and the scope of utilization of wild animals has been further reduced [16]. Overall, the behavior and causes of public wildlife consumption have received much attention in the last year. This notion lays out the foundation for the public study of behavioral willingness to resist the consumption of wild animals in China, as well as to understand the cause and effect of such motivation.

This study examines the resistance of consumption of wild animals as an example and proposes research hypotheses by expanding on the theory of planned behavior (TPB) theoretical analysis framework, combined with online research to obtain first-hand data that objectively presents the netizens’ willingness to resist the consumption of wild animals, their behavioral psychology, and other main characteristics. To provide decision-making reference to that resistance, this study adopts a structural equation model (SEM) to verify the research hypotheses on the factors affecting resistance to the consumption of wild animals. Compared with the few existing studies on public resistance to wildlife consumption, this study features the first social psychological perspective on netizens’ resistance to wildlife consumption, i.e., behavior that has not occurred or cannot be observed. TPB is suitable since it predicts behaviors in this manner via behavioral intention.

2. Materials and Methods

2.1. Theoretical Framework

TPB explains the general decision-making process of individual behavior from the perspective of information processing, with the theory of expected value as the starting point [34]. This process has been widely applied in Chinese public garbage classification, forest tourism, green production, green consumption, and other applications [35,36,37,38]. In classical TPB, people make decisions through rational thinking after integrating their own value judgments, estimating the views others may produce, and considering social norms. Behavioral intention is then mainly affected by attitude, subjective norm, and perceived behavioral control [39]. As the public’s decision to resist wildlife consumption tends to be rational [31], and TPB weakens the influence of emotion on behavior [40]; therefore, TPB is selected as the basic theoretical framework of this study. The theoretical framework of planned behavior is an open analytical framework, and scholars continue to expand the model from both theoretical and application perspectives [34,41]. This study reviewed the existing literature, expanding on the theoretical framework of TPB, and constructed an analytical framework (Figure 1) to study Chinese public intention to resist consuming wild animals and its influencing factors.

2.2. Factors

2.2.1. Attitude

Attitude is an evaluation, positive or negative, of an object, person, or activity, and refers to an individual’s overall evaluation of a behavior and the degree of liking or disliking of the execution of a specific behavior [39,42]. It is also an evaluation of whether the object of the behavior, the behavior itself, and the results of the behavior are important [43]. In this study, the attitude of the public toward resisting the consumption of wild animals was measured from three aspects (i.e., moral standard, the value of public health safety, and responsibility consciousness).

2.2.2. Subjective Norm

Subjective norms refer to the social constraints to conduct a certain behavior, reflecting the external influence on the individual behavior [34]. In most studies, subjective norms have the weakest influence on behavioral intentions [44]. Cialdini et al. [45] divided subjective norms into personal norms, descriptive norms, and imperative norms, among which personal norms can be equivalent to self-identity or moral norms. Harrison [46] found that adding ethics to some behavioral studies improved the explanatory power of the theory. Based on this, this research merges personal ethics and subjective norms into norms. Personal ethics refers to the individual’s sense of responsibility for resisting the consumption of wild animals. Subjective norms include imperative norms and descriptive norms, among which imperative norms refer to the public perception of other types of pressure to participate in the resistance of wild animals, and descriptive norms refer to the individual’s perception of whether others have participated in the activities of the resistance.

2.2.3. Perceived Behavioral Control

Perceived behavioral control refers to the individual’s perception of how easy it is to perform a certain behavior [39]. Resisting consuming wild animals is based on the public’s judgement on their own ability, i.e., if they believe they can easily participate in resisting the consumption of wild animals and their perceived behavioral control, and if their behavioral intention is strong. This factor confirms whether the public’s participation in resisting consuming wild animals is based on their understanding of their own resource endowments, which consist of time, economy, legal knowledge, and relevant approaches.

2.2.4. Past Experience

Existing research has confirmed that without considering expectational and emotional factors, the frequency of the behavior or past experience affected behavioral intention to a large extent [47]. Incorporating past experience into the TPB model is an effective extension of the classic model, which can more accurately evaluate behavioral wishes and predict behavioral responses [48,49]. In this study, past experience refers to the behavior of resisting the consumption of wild animals in the past. This is measured through the frequency of performing behaviors from three aspects: consuming wild animals, participating in the resistance of consuming wild animals, and participating in propaganda against consuming wild animals.

2.2.5. Socioeconomic Characteristics

Socioeconomic characteristics can also have a significant effect on behavioral intentions; it is widely used in expanding the TPB model [50]. Studies on wildlife conservation also recognized knowledge as an influencing factor of the intention to conserve the species [51,52]. Howe et al. (2012) also highlighted the importance of understanding the demography and sociocultural aspects of the focal audience in saiga antelope conservation [53]. Therefore, this paper adds a personal knowledge level about wildlife, income level, and current living area to the overall interpretation of the results [48,54,55].

2.3. Hypotheses

Based on the above factors, the following hypotheses are proposed:

Hypothesis 1 (H1).

Past experience positively affects netizens’ behavioral intention to resist consuming wild animals.

Hypothesis 2 (H2).

Attitude positively affects netizens’ behavioral intention to resist consuming wild animals.

Hypothesis 3 (H3).

Subjective norm positively affects netizens’ behavioral intention to resist consuming wild animals.

Hypothesis 4 (H4).

Perceivedexperience control positively netizens’ public behavioral intention to resist consuming wild animals.

Hypothesis 5 (H5).

Socioeconomic characteristics positively affects netizens’ behavioral intention to resist consuming wild animals. This article selects knowledge level about wildlife, income level, and current livingarea to do research.

2.4. Questionnaire Design and Data Collection

This study determined the feasibility of the proposition and the operability of the survey through relevant literature and expert interviews. First, according to the framework of expanded TPB, the scale was designed, which consisted of five parts (i.e., attitude, subjective norm, perceived behavioral control, past experience, and behavioral intention) [56,57]. The scale was revised and improved by experts in the field of wildlife protection. A pilot survey was carried out in February 2020, and a total of 200 questionnaires were collected, of which 153 were valid. On the one hand, it aided in ensuring that the designed questionnaire and the research topic fit and were appropriate to the timing of the study due to the COVID-19 pandemic. Testing ensured that the questionnaire contained the questions needed to supply the structure and content for data collection. On the other hand, revisions to the questionnaire were carefully applied, and a total of 4 questions describing the problem were deleted to reduce question redundancy (i.e., not to mislead or vaguely ask a question) [58]. After the pilot survey, a formal questionnaire was administered in April 2020.

We were informed by the Ethical Committee of Beijing Forestry University that we did not need special approval for the research, since the research was anonymously conducted and had no invasion of individual privacy. The formal questionnaire included the scale and demographic characteristics of the respondents (Supplementary Materials). A 7-point Likert scale [59] was adopted, which was designed from 1 (i.e., very infrequent) to 7 (i.e., very frequent) for past experience, and from 1 (i.e., strongly disagree) to 7 (i.e., strongly agree) for attitude, subjective norm, perceived behavioral control, and behavioral intention to indicate the degree of public cognition on these factors. Demographic characteristics included gender, age, educational background, current living area, income, and knowledge about wildlife. Among them, income was assigned a value of 1–5 from low to high. Living areas were assigned a value of 1–7 from low to high according to the severity of the outbreak. Relevant knowledge was set with 7 questions, and if each question was answered correctly, it was assigned one; otherwise, if it was incorrect, zero. The total knowledge level of all questions was from 0–7.

Due to the COVID-19 situation, data collection was completed through an online questionnaire survey. After comparing existing online questionnaire platforms, this study chose to use the Tencent questionnaire platform (i.e., https://wj.qq.com, accessed on 1 April 2020) that can identify the geographical locations of respondents for data collection. Compared with offline interviews, the advantages and disadvantages of online surveys are more obvious. The advantages are low cost, low rejection rate, and timeliness, while the disadvantage is that the sample is difficult to review (i.e., in a general overall sense) and the sample of netizen represents a specific group of Chinese residents [31,60,61]. To reflect the intention of people from different pandemic areas to resist consuming wild animals, according to the severity of the situation, six selected provinces, one municipality directly under the central government, and an autonomous region in China comprised of the study area, i.e., Hubei (i.e., 68,135 infected cases), Heilongjiang (i.e., 1612 infected cases), Zhejiang (i.e., 1386 infected cases), Henan (i.e., 1317 infected cases), Beijing (i.e., 1078 infected cases), Jiangxi (i.e., 937 infected cases), Yunnan (i.e., 427 infected cases), and Ningxia (i.e., 76 infected cases), with severity from high to low (i.e., real-time data provided by the National Health Commission of the People’s Republic of China (i.e., http://www.nhc.gov.cn, accessed on 21 June 2020). The distribution of the surveyed provinces is shown in Figure 2.

To ensure the validity of the online survey data, this study designed a logical verification question to test the data quality, and judge whether the questionnaires were valid (i.e., using the total answering time of not less than 180 s and the same option repetition rate not exceeding 70%). The questionnaires that did not pass the quality test were eliminated. To ensure that the survey data reflected the real situation of respondents, at the beginning of the questionnaire for the purposes of this survey, a brief description of the work being asked of them was included to eliminate respondent concern. A total of 1497 questionnaires were collected, of which 1194 were valid, with an effective rate of 79.76%.

2.5. Characteristics of the Sample

The sample characteristics are illustrated in

Table 1. Demographic characteristics of the sample.

Variable	Frequency	Proportion in the Sample	Proportion in Chinese Netizens a
Gender
Male	631	52.85%	52.40%
Female	563	47.15%	47.60%
Age (years old)
18–28	466	39.03%	21.50%
29–40	460	38.53%	20.80%
>40	268	22.45%	34.50%
Educational background
Primary school and below	6	0.50%	17.20%
Junior high school	55	4.61%	41.10%
Junior college or high school	507	42.46%	about 22.2%
Undergraduate degree	552	46.23%	about 19.5%
Master degree or above	74	6.20%
Income (per month)
<RMB 3500	550	46.06%	51%
RMB 3500–5500	309	25.88%	35.80%
RMB 5500–10,000	240	20.10%
RMB 10,000–30,000	85	7.12%	13.30%
>RMB 30,000	10	0.84%

^a Data came from the 45th Statistical Report of China Internet Network Development released in April 2020. Due to incomplete statistical classification, part of the data was estimated and integrated.

. Among the surveyed people, the number of males and females was similar; the surveyed people were mainly under 65 years old, and 18–28 years old was the main group, accounting for 39.03%, while those over 40 accounted for 22.45%. Among the respondents, 86.99% were from high school, junior college, and undergraduate institutions. The proportion of junior high schoolers and below was 5.11%, and the proportion of master’s degree or above was 6.20%. Among the respondents, nearly half of the respondents’ monthly income was below RMB 3500, i.e., 46.06%. Compared with the characteristics of Chinese netizens in the 45th Statistical Report on Internet Development in China (National Internet Information Office of the People’s Republic of China (i.e., https://www.cac.gov.cn, accessed on 1 April 2020) [62], the public sample data in this study was in terms of gender and income structure. It was similar in respect, but there were some inconsistencies in age and educational structure. However, the characteristics of the public samples in this study were basically the same as those of other existing online surveys [63,64], and both showed a large proportion of young people and highly educated people.

2.6. Model Construction

The objective of the study was to explore Chinese netizens’ behavioral intention to resist consuming wild animals and its influencing factors. SEM is an analysis method used to explore macroscopic laws through microscopic individual behavior intentions, which can observe the complex relationships among variables and explain the structural relationships among them [65]. In this study, the Chinese public was the micro subject, and their behavioral intention of resisting wildlife consumption was observable. Therefore, the study investigated the public behavioral intention of resisting wildlife consumption and applied SEM to determine its influencing factors [36]. The relationship path of the model reflects the relationship between the factors in the expanded TPB framework. In our model, past experience, attitude, subjective norms, perceived behavioral control, and behavioral intention were latent variables, and their corresponding items in the scale were observed variables [66,67]. There may be multicollinearity between the four cognitive factors and behavioral intention. Therefore, SEM was used to analyze the sample data to determine the key factors affecting behavioral intention. In SEM, the measurement model reflects the relationship between each latent variable and its observed variables. Its general Equations (1) and (2) formulate the model’s variables, as follows:

$$X={\Lambda }_X\xi +\epsilon$$

(1)

$$Y={\Lambda }_Y\eta +\delta$$

(2)

The general equation of the structural model, i.e., Equation (3), is:

$$\eta =B\eta +\Gamma \xi +\zeta$$

(3)

where X and Y are the exogenous and endogenous observed variables respectively; ${\Lambda }_X, {\Lambda }_Y$ are factor loading matrixes; $\epsilon$ and $\delta$ are measurement error matrixes; $\xi$ is the exogenous latent variables, representing past experience, attitude, subjective norm, and perceived behavioral control of the public against consuming wild animals; $\eta$ is the endogenous latent variable, representing the public’s behavioral intention to resist the consumption of wild animals; $B$ is the influencing coefficients between endogenous latent variables; $\Gamma$ is the influencing coefficient of exogenous latent variables on the endogenous latent variable; and $\zeta$ is the residual vector of $\eta$.

Using exploratory SEM, this study integrated three phases [68]; first, the reliability and validity of the scale were evaluated, specifically, the Cronbach’s α and KMO value tests were performed first, and then combined with exploratory factor analysis (EFA) analyze the questionnaire structure. Second, we confirmed the rationality of the measurement model by confirmatory factor analysis. Third, a full SEM was constructed for model fit testing and hypothesis testing. Since the sample data volume reached ten times the title or more than five times the estimated parameter [69], the variables all satisfy skewness less than three and kurtosis less than eight, which were approximately normally distributed [70]. Therefore, the maximum likelihood method was used for parameter estimation. In the initial confirmatory factor analysis, there was a high degree of correlation between variables, so we considered building a higher-order model [71]. SPSS 25 and AMOS 23 were used for the statistical analysis and model construction.

3. Results

3.1. Descriptive Statistics

The descriptive statistics of the observed variables are shown in

Table 2. Descriptive statistics of the observed variables.

Latent Variable	Observed Variable	Mean Value	Standard Deviation
Past experience (EX)	You have participated in activities against consuming wild animals (EX1) (Such as advocating against eating wild animals, sharing information about eating wild animals on Wechat and Weibo, etc.)	3.36	1.77
	You have spoken to your family about resisting consuming wild animals (EX2)	4.25	1.83
	You have spoken to your friends about resisting consuming wild animals (EX3)	4.19	1.83
	You have eaten wild animals (EX4)	1.64	1.09
	You have reported eating wild animals (EX5)	1.89	1.44
Attitude (ATT)	Resisting consuming wild animals is ethical (ATT1)	5.79	1.71
	Resisting consuming wild animals has public health and safety value (ATT2)	6.21	1.37
	You have the responsibility to participate in the resistance of consuming wild animals (ATT3)	6.22	1.19
	It is important to resist eating wild animals (ATT4)	6.46	1.14
	Resisting the consumption of wild animals can cause economic losses to the government (ATT5)	2.41	1.88
	Resisting wildlife can cause economic losses to wildlife farmers (ATT6)	3.34	1.98
	Resisting wildlife causes economic damage to wildlife food processors (ATT7)	3.21	1.94
Subjective norm (NORM)	Social media (Weibo, WeChat, etc.) are actively promoting the resistance of consuming wild animals (NORM1)	5.96	1.30
	Traditional media (TV, newspaper and broadcast) are actively promoting the resistance of consuming wild animals (NORM2)	5.96	1.30
	Government is actively participating in the resistance of consuming wild animals (NORM3)	6.02	1.29
	Your family or relatives think you should get involved in the fight against eating wild animals (NORM4)	6.19	1.17
	Your friends, colleagues and classmates all think you should get involved in the fight against eating wild animals (NORM5)	6.31	1.07
	Present laws against consuming wild animals are much stricter than before (NORM6)	5.57	1.50
	Policies now expect us to resist consuming wild animals more than before (NORM7)	5.56	1.48
	The laws now stricter against eating wild animals than they were before (NORM8)	5.97	1.31
Perceived behavioral control (PBC)	You have enough time to participate in the resistance of consuming wild animals (PBC1)	4.48	1.60
	You have enough money to participate in the resistance of consuming wild animals (PBC2)	3.96	1.74
	You have enough knowledge of laws to participate in the resistance of consuming wild animals (PBC3)	4.33	1.63
	You have enough approaches to participate in the resistance of consuming wild animals (PBC4)	4.18	1.66
Behavioral intention (INT)	You are willing to participate in the resistance of consuming wild animals (INT1)	6.08	1.29
	You will try to participate in the resistance of consuming wild animals (INT2)	6.10	1.26
	You are willing to publicize the resistance of consuming wild animals (INT3)	6.11	1.25
	You are willing to report the consumption of wild animals (INT4)	6.05	1.31

and broken down into five points. (1) In terms of past experience, speaking to family had the highest mean value, followed by speaking to friends, participating in activities, reporting the consumption of wild animals, and eating wild animals, in which the mean value of speaking to family and speaking to friends was greater than 4, while that of participating in activities, reporting the consumption of wild animals, and eating wild animals did not reach the median of 4. (2) As can be seen from the values of the seven observed variables in attitude, the values of public attitude, i.e., from high to low, were the importance of resisting eating wild animals, public health and safety value, sense of responsibility, moral sense, economic loss to farmers, economic loss to processors, and economic loss to the government, in which the mean value of the importance of resisting eating wild animals, public health and safety value, sense of responsibility and moral sense was greater than 4, while that of economic loss to farmers, economic loss to processors and economic loss to the government did not reach the median of 4. (3) According to the mean values of the nine observed variables in subjective norms, the mean value of norms for friends was the largest, followed by family, government, strictness of the laws, social media, traditional media, number of laws, and policy, and the mean values of these variables were all greater than 4. (4) In terms of perceived behavioral control, the mean values of the four observed variables were time, knowledge, approaches, and money in descending order, among which the mean values of time, knowledge, and approaches were greater than 4, while the mean value of money was less than 4. (5) The mean values of the four observed variables in behavioral intention, i.e., from high to low, were participation in publicity activities, efforts to participate in activities, willingness to participate in activities, and report behavior, which were all greater than 6, much higher than the median of 4.

3.2. Reliability and Validity Tests

Sample data obtained from the questionnaire were tested for reliability and validity to measure the quality of the data [72,73]. First, Cronbach’s α was used to verify its reliability. Reliability is a test of the reliability of measurement, which is verified by the Cronbach’s α coefficient, and indicates a high reliability when Cronbach’s α > 0.7 [74]. As shown in

Table 3. Reliability and validity test.

Latent Variable	Cronbach’s α	CR	AVE
Past experience	0.864	0.878	0.711
Attitude	0.739	0.734	0.483
Subjective norm	0.892	0.892	0.626
Perceived behavioral control	0.886	0.887	0.663
Behavioral intention	0.923	0.925	0.756

, Cronbach’s α coefficient of the scale was 0.904, and the coefficients of all latent variables met the standard, indicating that the scale had a high reliability. To test the validity of the scale, the KMO and Bartlett test for sphericity was needed [75]. The calculated KMO value was 0.901, and the p-value of Bartlett’s sphericity test was less than 0.001, indicating that the variables were independent of each other and were suitable for factor analysis. Exploratory factor analysis was applied, and five public factors were extracted, which corresponded to attitude, subjective norm, perceived behavioral control, past experience, and behavioral intention. The observed variable whose loading on its corresponding factor was less than 0.5, or the loadings in the two principal components was greater than 0.4 at the same time, was eliminated, and 19 observed variables were kept after screening, deleting observed variables EX4, EX5, ATT4, ATT5, ATT6, ATT7, NORM6, NORM7, and NORM8.

The results of the confirmatory factor analysis showed that the standardized factor loadings of the observed variables were all greater than 0.55, indicating that the measurement model was reasonable and passed the quality test. The combined reliability (CR) and average variance extraction (AVE) of the latent variables were further calculated, and the results are shown in Table 3. The values of CR were greater than 0.7 and the values of AVE were greater than 0.5, indicating that the observed variables’ setting had high reliability and the measurement model had high aggregation validity [74]. Some of the observed variables of attitude had low factor loadings, leading to the AVE value of attitude being slightly less than 0.5 and poor aggregation validity of these observed variables. However, according to the discriminant validity test results shown in

Table 4. Discriminant validity test.

	Past Experience	Attitude	Subjective Norm	Perceived Behavioral Control	Behavioral Intention
Past experience	0.843
Attitude	0.367 ***	0.695
Subjective norm	0.329 ***	0.608 ***	0.791
Perceived behavioral control	0.418 ***	0.228 ***	0.329 ***	0.814
Behavioral intention	0.429 ***	0.674 ***	0.632 ***	0.448 ***	0.870

Note: The values on the diagonal are the square roots of the latent variables’ AVE, and the values below the diagonal are the correlation coefficients between every two latent variables; *** means significant at the 1% level.

, the absolute values of the correlation coefficients were all less than the square roots of the AVE values, indicating that the latent variables still had good discriminant validity [59].

3.3. Goodness-of-Fit Test

Absolute fitness indexes, value-added fitness indexes, and simplified fitness indexes were calculated to evaluate the goodness-of-fit of the model [70,71,76]. The results are shown in

Table 5. Goodness-of-fit test.

Index	χ2/df	RMSEA	GFI	IFI	NFI	CFI	PGFI	PNFI	PCFI
Standard	1 < χ2/df < 5	<0.08	>0.90	0.90	>0.90	>0.90	>0.50	>0.50	>0.50
Result	5.594	0.057	0.935	0.954	0.945	0.954	0.680	0.752	0.760

, and most of the values met the standard. The value of χ²/df was slightly greater than 5, which was caused by the large sample size used in the model [77]. In general, the goodness-of-fit of the model was acceptable.

3.4. Estimation Results and Hypothesis Test

AMOS 23 is powerful SEM-based software that supports research and theory by extending standard multivariate analysis methods, including regression, factor analysis, correlation analysis, and analysis of variance. In this article, AMOS 23 was applied to estimate the model of public’s behavioral intention to resist consuming wild animals, as illustrated in Figure 3. According to the hypothesis test results shown in

Table 6. Estimation results and hypothesis test.

	Standardized Parameter	S.E.	C.R.	p	Hypothesis	Result
Past experience (EX)	0.069	0.013	2.861	**	H1	Accept
Attitude (ATT)	0.306	0.034	9.004	***	H2	Accept
Subjective norm (NORM)	0.173	0.040	5.743	***	H3	Accept
Perceived behavioral control (PBC)	0.159	0.016	6.572	***	H4	Accept
Income (INCOME)	−0.046	0.018	−2.37	*	H5	Accept
Living area (REGION)	0.346	0.015	15.016	***
Knowledge (KNOWLEDGE)	0.377	0.015	16.228	***

Note: *** represents p < 0.001; ** represents p < 0.01; * represents p < 0.05.

, attitude, subjective norm, perceived behavioral control, and past experience had significant positive effects on public behavioral intention to resist consuming wild animals. The results indicated that the effect of attitude was the highest (i.e., 0.306), followed by subjective norm (i.e., 0.173), perceived behavioral control (i.e., 0.159), and past experience (i.e., 0.069). From these findings, H1, H2, H3, and H4 were accepted. As a result, it can be assumed that H5 should be also accepted since living area (i.e., 0.346), knowledge level (i.e., 0.346), and income level (i.e., −0.046) have a significant impact on behavior willingness, i.e., netizens’ behavior willingness in severely affected areas is stronger, and the netizens who are more informed about wildlife knowledge are more willing to resist. Moreover, lower income levels (i.e., coefficient of −0.046) have higher behavioral intentions of netizens.

4. Discussion and Conclusions

According to the findings, it can be deduced that the behavioral intention of the Chinese public to resist consuming wild animals was moderate, at 55.19% willingness to participate in the resistance of activities (i.e., they take the strongly agree option in the question of ATT4 in which “it is important to resist eating wild animals” as the standard). Moreover, it can be concluded through the model analysis that the attitude of the public toward resisting the consumption of wild animals positively affected their behavioral intention to resist consuming wild animals, which had the greatest impact among the influencing factors. The positive influence of individuals’ attitudes toward wild animals on behavior has been confirmed many times before [61,78]. As a result, around half of the public had a correct judgment on the value of resisting the consumption of wild animals. They believed that resisting the consumption of wild animals was in line with the moral standard and had public health and safety value. They also agreed that they had the responsibility to participate in relevant activities against consuming wild animals.

Subjective norms perceived by the public toward resisting the consumption of wild animals positively affected their behavioral intention. Subjective norms can explain the public’s behavioral intention to protect wild animals [79,80], and the results can also be applied to this research. The study found that pressure from family, friends, media, government, and other external environmental factors has a positive effect on the public’s willingness to resist the consumption of wild animals. This shows that increasing government promotion and media publicity will increase the public’s sense of responsibility for resisting the consumption of wild animals. Perceived behavioral control positively affected behavioral intention. Concerning resisting wildlife consumption, the public’s perception of the difficulty of input has a significant impact on the willingness to resist, i.e., the lower the public’s perception of difficulty in time investment, economic investment, ways of participation, and legal knowledge, the stronger the willingness to resist [81]. Past experience significantly positively affected behavioral intention. Existing studies have found that past experience has a positive impact on behavioral willingness [82,83]. In the past, the public participated in activities to resist the consumption of wild animals, and at the same time, to promote the resistance of wild animals to relatives and friends, to a certain extent, would have a certain positive impact on the willingness of behavior and the generation of behavior. Therefore, to enhance the public’s willingness to resist the consumption of wild animals, work can be started by enriching participation experience and broadening participation channels.

From the perspective of the netizen current living areas, the public is more willing to resist the consumption of wild animals in severely affected areas. In the face of sudden public health safety incidents, positive public and social positive attitudes play an important role in the pre-prevention and interim response to public health safety risks [84,85]. Therefore, the public in severely affected areas can better appreciate the importance of resisting the consumption of wild animals to prevent emergencies from happening again. The level of knowledge significantly and positively affects the willingness to resist. The positive impact of knowledge level on willingness is similar to existing research results [86]. The higher the public’s knowledge about wild animals, the stronger their willingness to resist the consumption of wild animals, and the easier it is to induce behavior. Since the outbreak of COVID-19, the prevention and control of zoonoses have entered a new stage featuring a global cooperative and coordinative campaign advocated and funded by the World Bank [87]. Pandemics have the evolutionary, global threat of hindering the development of human civilization. The joint participation of the government, media, and individuals in the prevention and control system is conducive to the prevention of the occurrence of major public health incidents, the safeguard of human health and safety, and social harmony and stability. In view of the conclusions of this research, it is found that the public’s perceived behavior control is low, the participation experience is limited, and the relevant policies and laws are not sound.

The policy implications of the research are discussed as follows, focusing on how to enhance the public intention in resisting wildlife consumption. First, the government should strengthen public perception of subjective norms by effectively implementing the decision to completely ban the consumption of wild animals [88], improve relevant supporting regulations and systems [89], increase vigilance against illegal wildlife trade, and resolutely combat overeating, poaching, and illegal trading of wildlife and their products [29]. Second, the government should strengthen publicity and education, enrich the public’s knowledge about wildlife and cultivate positive attitudes, through publicity and education of scientific knowledge about the survival status of international wild animals and the importance of protection [90], the huge hidden dangers of overeating wild animals to public safety and personal health, and the legal responsibility of illegal purchases of wild animals, etc. [24]. Third, the government should broaden participation channels and improve the public’s ability to control perceptual behavior by creating opportunities for public participation, promoting the implementation of public wishes into actual actions, accepting social supervision, promptly, and earnestly rectifying the problems reported by the public, and improving social credibility. On the one hand, with the help of modern information platforms, online propaganda, establishment of supervision channels, and opening of fundraising channels are used to reduce the public’s expectations of the difficulty of participation [91], and to ensure that the public has the right to know and participate in issues related to wildlife supervision rights; on the other hand, it gives full play to the subjective initiative of qualified social organizations and law-compliant agencies to encourage active civil and administrative public interest litigation against violations of wildlife protection. We fully understand that the sample of netizens used only represents a specific group of Chinese residents, i.e., active on the Internet, and this is an obvious limitation in this study. This makes the research findings and suggests that the findings should be cautiously treated when used for the whole population of China.