Xingdong Wang
Pan Lu
Jin Tang2- 1Jiangxi Rural Revitalization Strategy Research Institute, Jiangxi Agricultural University, Nanchang, China
- 2School of Economics and Management, Jiangxi Agricultural University, Nanchang, China
Ecological breeding (eco-breeding) is a systematic ecological farming method for the benefit of the environment and human health, but farmers have adopted this method only to a lesser extent. The main objective of this study was to examine the factors that influence farmers' intentions to use eco-breeding methods. The study model was a combined application of the theory of planned behavior (TPB) and the normative activation model (NAM) to investigate the intentions of 527 Chinese farmers to engage in eco-breeding practices. Structural equation modeling analysis revealed that although the farmers' intention to adopt eco-breeding practices was influenced by both self-interested and altruistic motives, self-interested motives had a greater impact on the farmers' choices. Furthermore, favorable attitudes had the greatest effect on the farmers' intentions to implement eco-breeding, while past habits had no statistically significant effect on the intentions. Nonetheless, past habits significantly influenced attitudes and perceived behavior control in the combined model. In addition, the findings indicated that awareness of consequences had a significant effect on personal norms and the attribution of responsibility. Overall, the findings demonstrate the good efficiency as well as comprehensiveness of the integrated TPB-NAM in explaining the farmers' intentions to engage in eco-breeding practices. This study increases our understanding of the factors influencing the farmers' adoption of eco-breeding practices and helps to promote the adoption of eco-breeding in rural areas while providing a basis for the development of eco-breeding policy interventions.
Introduction
Driven by the growing demand for waterfowl products, industrial waterfowl production with higher production efficiency and profitability has become increasingly popular (Hu et al., 2017). Because of intensive rearing, higher stocking density, misuse of feed additives and veterinary drugs for therapy, prophylaxis and growth promotion, and poor waste management, industrial waterfowl production has seriously threatened ecological balance and human health, triggering both official anxiety and public concern (Hu and Cheng, 2016; Bandyopadhyay and Samanta, 2020). Although China has introduced laws and regulations on the use of feed additives and veterinary drugs, such as the Administrative Regulations on Feed Additives and the Regulation on Veterinary Drug Administration (Hu and Cheng, 2015), the unreasonable use of veterinary drugs and abuse of feed additives in waterfowl production are still widespread, posing a serious threat to the environment and public health (Shao et al., 2021). Farmers can further improve the vitality of waterfowl production and reduce environmental pollution by adopting eco-breeding models (Tilman and Clark, 2015). Hence, increasing the willingness of farmers to use eco-breeding models could reduce the harm to the environment (Röös et al., 2018). In recent years, governments have been actively promoting eco-breeding methods to protect the environment and improve agricultural efficiency (Gomiero et al., 2011). For example, Iran actively promotes integrated pest management, and the United States restricts the use of chemicals in agriculture, etc. (Floress et al., 2017). The Chinese government has also vigorously promoted eco-breeding policies (Yang, 2021), but the expected results have not been achieved, and the farmers' willingness to adopt ecological breeding practices is relatively low (Zeng et al., 2019). Eco-breeding practices are closely dependent upon farmers' willingness (Vasquez et al., 2019), and it is necessary to explore in depth the mechanisms that affect the adoption of eco-breeding practices by farmers. This exploration will help the government develop reasonable policies and adopt appropriate incentives to increase the enthusiasm of farmers.
Because the adoption of eco-breeding models can help improve the safety of waterfowl products and maintain public health—in addition to providing benefits to farmers—clarification of the determining factors that drive farmers' willingness in using eco-breeding model is imperative. Recent studies have been focused on either self-interested motives or altruistic motives (Park and Ha, 2014; Floress et al., 2017). In terms of self-interested motivation, the theory of planned behavior (TPB) proposed by Ajzen (1991) is considered as a rational choice model that mainly considers the comparison of cost effectiveness and benefits (Botetzagias et al., 2015; Chen, 2016), ignoring the roles of irrational and altruistic motives in shaping behavior (Roy et al., 2016). Therefore, TPB is not sufficient to effectively explain the generation of pro-environmental behavior (Ahmad et al., 2020). Conversely, the normative activation model (NAM) was proposed by Schwartz (1977) as a classical model to explain pro-environmental behavior, placing more emphasis on the importance of altruism and morality and ignoring the important role of rational and self-interested motives in predicting intention-driven behavior (Onwezen et al., 2013; Kim and Seock, 2019). On the one hand, farmers may be driven by narrow self-interest, regardless of whether their use of antibiotics is rational (Albernaz-Gonçalves et al., 2021). On the other hand, farmers may also be driven by the welfare of others and will adopt conservation measures that benefit the ecosystem and public health (Shi et al., 2017). Therefore, considering that farmers' eco-breeding behavior is a pro-environmental behavior, it can be seen as a mixture of self-interest and altruism. The practice of eco-breeding by farmers is a pro-environmental behavior that can be seen as a mixture of self-interest and altruism (Liu et al., 2017; Tang et al., 2021). This study integrates the structure of TPB and NAM and adds interrelationships between past habits and intentions to enhance the predictive power of the proposed framework for explaining farmers' intentions to use eco-breeding practices.
In this study, we investigated farmers' intentions to adopt waterfowl eco-breeding practices by using the TPB-NAM integrated model in both the economic and social value dimensions. Specifically, we investigated (1) applying the integrated TPB-NAM to explain farmers' intention to adopt eco-breeding practices, (2) exploring the causal relationship between the TPB and NAM structures, and (3) examining realistic ways for waterfowl farmers to adopt eco-breeding practices from the perspectives of self-interest and altruism. To achieve these objectives, in the following section, we review the literature and hypothesize the relationships between the variables. Section Methodology presents the methodology. Section Results presents the estimation results. Section Discussion provides a discussion of the results. Finally, implications for policy are presented.
Theory and hypotheses
The theory of planned behavior
TPB, as proposed by Ajzen (1985, 1991), is an extension of the theory of reasoned action, which explains a person's reasons for choosing indicating a certain behavior mainly in terms of costs and benefits (Montano and Kasprzyk, 2015; Conner, 2020). This theory suggests that behavioral intentions are determined by three main factors: attitudes (ATT), perceived behavioral control (PBC), and subjective norms (SN). TPB is used as the most popular social psychological theory to explain the behavior of individuals in numerous domains (Gao Y. et al., 2017); hundreds of researchers have investigated or applied TPB to predict behavior in areas such as health and environmental sustainability (Ibrahim et al., 2019).
According to TPB, ATT is the degree to which an individual has a favorable or unfavorable evaluation of a particular behavior (Ajzen, 1991). Usually, positive ATT encourage individuals to take action (Chen, 2017). Compared to other TPB variables, ATT toward behavior most significantly influences a person's behavioral intention (De Groot and Steg, 2007; Chen, 2016). Thus, ATT can be considered as a potential determinant of an individual's intentions to implement pro-environmental behaviors (Chuang et al., 2018; Rezaei et al., 2019). Farmers intend to use eco-breeding practices only if they believe that these practices are useful and beneficial and give them positive results. Thus, we present the following hypothesis:
H1: A favorable ATT toward eco-breeding practices positively predicts the intention to engage in eco-breeding.
PBC is defined as the perceived ease or difficulty of performing a particular behavior (Ajzen, 1991). It depends heavily on weighing the costs and benefits, including financial costs, effort, and time in the process of performing the act (Shi et al., 2017). Thus, the higher the individual's PBC, the stronger the willingness to perform a behavior, in this case “higher” means a greater perceived ease (Gao Y. et al., 2017). Similar conclusions were reached by Gao L. et al. (2017) in research on individual's energy-saving intention. In addition, PBC turned out to be important direct predictor of one's behavior (Botetzagias et al., 2015). However, A general version of the TPB revealed PBC's direct influence on ecological behavior to be non-significant and that finding do not challenge PBC's influence on intention (Kaiser and Gutscher, 2003). This fact can be extended to farmer's eco-breeding practices. When they believe they have the relevant knowledge, skills, and resources to use eco-breeding practices under their control, they are more likely to form the intention to participate in these practices. Therefore, the following hypothesis is proposed:
H2: A high PBC of using eco-breeding practices positively influences the intention to use those practices.
SN refers to perceived pressure from significant others to perform certain kinds of behaviors (Ajzen, 1991). In other words, individuals usually prefer to align themselves with the expectations of significant organizations or people (Shi et al., 2017). Thus, the perception of highly relevant SN from significant others can increase an individual's intention to perform a specific behavior (Passafaro et al., 2019). Arli et al. (2018) confirmed this view in his study of the intentions to purchase green products. Also, Rezaei et al. (2019) applied TPB to explore the intention of Iran farmers to engage in IPM practices and found that subjective norm has a significant effect on intention in the original TPB whereas the effect was not statistically significant in the integrative model of TPB-NAM. The main reason for this can be attributed to the effectiveness of subjective norm in predicting intention varies in various fields. Farmers are more likely to use eco-breeding practices if they perceive that they are under social pressure to do so. Based on this, the hypothesis is formulated:
H3: The SN of using eco-breeding practices positively affects the intention to use those practices.
In addition to the aforementioned hypotheses, which are focused primarily on the relationship between intention and the focal structure of TPB (i.e., ATT, PBC, and SN), the results of other studies suggest that past habits reinforce the predictive role in behavioral intention (Høie et al., 2010; Leung and Chen, 2017; Abadi, 2018). Past habits are psychological construct, rather than simply past behavioral frequency (Verplanken and Orbell, 2003), which are defined as automatic or unconscious reactions to future behaviors (Honkanen et al., 2005). That is, farmer's past habits are generated by frequency and satisfactorily pairing behaviors with the execution of relevant eco-breeding practices. Past habit has been shown to influence intention regardless of TPB variables (Tuu, 2015; Semenescu and Gavreliuc, 2021). However, Ajzen (1991) suggested past habit is not sufficient, and some scholars have argued that past habit plays an important role in the influence of PBC on decision-making (Sommer, 2011). Furthermore, the predictive power of attitudes should be diminished if people have past habits (Trafimow, 2000), and conversely, ATT should be good predictors of intention when people do not have a habit of exhibiting a behavior (Tuu, 2015). Evidence from other scholars supports the relationship between past habit and intention, ATT, and PBC (Dean et al., 2012; Leung and Chen, 2017; Chen et al., 2019). Because farmer's intentions regarding the adoption of eco-breeding are influenced by past habits, we added past habit to TPB as a theoretical structure for the study of farmers' intentions to adopt eco-breeding practices. In this regard, the following hypotheses are proposed:
H4: Past habit positively affects ATT toward participation in eco-breeding.
H5: Past habit is positively related to PBC of participation in eco-breeding.
H6: Past habit is positively related to the intention to participate in eco-breeding.
The norm activation model
NAM is a model developed by Schwartz (1977) to explain altruistic and environmental behaviors or intentions and is widely used in a variety of pro-social and pro-environmental domains (Bamberg and Möser, 2007; Onwezen et al., 2013; Wang et al., 2019). Examples pertaining to farmers include the choice of travel methods (Park and Ha, 2014), recycling behavior (Zhang et al., 2014), and ecological farming by farmers (Rezaei et al., 2019). The model suggests that the activation of a personal norm (PN) depends on two elements: awareness of consequences (AC) and attribution of responsibility (AR) (Schwartz, 1977). PNs are the self-expectations of individuals to perform specific behaviors in specific situations and include internalized social norms and a sense of moral obligation. AC involves the positive or negative effects that individuals are aware that their behavior can have, and AR indicates individual's sense of responsibility for the consequences of their behavior (Schwartz, 1977).
NAM assumes that when people are aware of negative impacts on the environment (i.e., AC), they tend to take responsibility for the negative consequences (i.e., AR), and thus AC and AR activate PNs to motivate individuals to exhibit certain behaviors or intentions to mitigate negative effects (Kormos et al., 2015; Møller et al., 2018). In addition, it is important to emphasize that people who are not aware of the impact of their actions on the environment are not likely to be held responsible for the consequences (Liu et al., 2017). In other words, an individual's AC is an important antecedent to AR (Rezaei et al., 2019). By applying the concept of NAM to the act of using eco-breeding, we can see that if farmers feel that they are responsible for the results of practicing eco-breeding and are aware of the positive consequences, they will have a moral obligation to use these practices to protect others and the environment. This sense of obligation in turn leads to a strong intention to engage in eco-breeding practices. In summary, based on the assumptions of NAM and the preceding discussion, the following hypotheses are proposed:
H7: PN will positively affect farmer's intention to engage in eco-breeding practices.
H8: AC will positively affect PN in eco-breeding practices.
H9: AR will positively affect PN in eco-breeding practices.
H10: AC will positively affect AR in eco-breeding practices.
Proposed research model
Based on the preceding literature review and hypotheses development, a conceptual model that combines the constructs of NAM and TPB is proposed (Figure 1) to explain the intentions of Chinese farmers to adopt eco-breeding practices. The model is designed to test the association between the endorsement of TPB and the intention to adopt eco-breeding practices. In addition, PN, predicted by AC and AR, was postulated as predictor of farmers' intentions to adopt eco-breeding practices because AC predicts AR.
Figure 1. Hypothesis model.
Methodology
Measures
The questionnaire consisted of two parts. The first part obtained basic information about the respondents, including age, gender, education, and annual household income. The second part measured potential variables related to TPB-NAM to explore the farmer's intentions to implement eco-breeding. We mainly obtained the original measurements for this study from Verplanken and Orbell (2003), Ajzen (2006), Han (2014), and Rezaei et al. (2019). On this basis, we invited foreign experts from academia and researchers familiar with relevant research topics to review the questionnaire and assess the clarity and rationality of the items. In the prediction review, experts identified problems such as repetitive items, vague sentences, and unreasonable design in the questionnaire, and the final questionnaire was formed through revision. All items were anchored by 1 “do not agree at all” and 5 “strongly agree”. The exact wording of the statements used in all scales is reproduced in Table 1.
Table 1. Mean, standard deviation of items and subsections (n = 527).
Participants and procedure
Because China is the largest waterfowl producer, accounting for more than 75% of the world's waterfowl rearing (Ibrahim et al., 2019), we randomly selected three provinces, Jiangxi, Hubei, and Yunnan, from 21 major waterfowl-producing provinces in China according to the National Waterfowl Industry Technology System (NWITS). As shown in Figure 2, Jiangxi Province, located in southeastern China, has many lakes, well-developed water systems, and a long history of waterfowl breeding and is one of the dominant production areas of the national waterfowl industry. Hubei Province is a well-known waterfowl production province in central China, which is also famous for its unique waterfowl-processing products. Yunnan Province, located in southwestern China, has a warm climate; lush aquatic plants; and abundant fish, shrimp, and aquatic insects and other invertebrates, providing good habitat and food conditions for waterfowl, and is one of the important waterfowl conservation areas in East Asia.
Figure 2. The survey area.
To investigate the farmers' willingness to adopt eco-breeding practices, we carried out in-person interviews during the period of May–October 2020, randomly selecting 550 waterfowl farmers with a two-stage sampling procedure. We selected a total of 11 counties—six in Jiangxi, three in Hubei, and two in Yunnan—based on the ratio of waterfowl production values in the three sample provinces.1 Within a sampled county, we selected 50 waterfowl farmers randomly in the second stage. We recruited trained native interviewers to visit each participant to conduct the survey, offering a gift (a telephone card worth RMB 20) to encourage cooperation.2 Moreover, we kept responses anonymous to encourage respondents to express their true opinions. This survey resulted in 527 usable questionnaires after the removal of 23 invalid entries.
Data analysis
We used the Statistical Package for the Social Sciences 21.0 (SPSS) as statistical software to perform structural equation modeling (SEM) for evaluating the proposed model and hypotheses.3 Before proceeding to the main analysis, we examined the normality of each variable in the model to determine whether the data met the normality assumption of the maximum likelihood estimation. We performed the main statistical analysis in two stages. First, we performed confirmatory factors analysis (CFA) to verify the reliability and validity of all measured variables and the fit of the measurement model to the data. After evaluating the measurement models, we performed SEM to access the proposed model and research hypotheses.
Results
Participants' profile
As shown in Table 2, the sample is dominated by male participants (n = 352, 67.0%). The average age of the respondents was about 50 years old (SD = 14.69), and most of the respondents (n = 473, 90.1%) had completed junior high school or below. The annual household income of most respondents (n = 380, 72.3%) was around RMB 20,000–100,000, and the average number of poultry stock was 5,083.98 (SD = 26,041.04) in 2019. According to the China Statistical Yearbook 2019, China's population has a relatively low level of education, with a per capita disposable income of 32,189 yuan, and about 71.2% of the total population is aged 15–64. All these above indicators suggest that these samples are to some extent representative of the Chinese population.
Table 2. Demographic characteristics information of the respondents (n = 527).
As shown in Table 1, in the constructs of PBC, the mean values of ATT (3.84) and SN (3.58) were relatively high, whereas the mean value of PBC was low (2.89). These findings show that most respondents had a favorable attitude and high personal norm related to the willingness to engage in eco-breeding practices. However, they did not perceive enough control to use eco-breeding properly. This may be because they did not have the required funds and skills to change the current situation. In the constructs of NAM, the mean scores of PN (3.53), AR (3.72), and AC (3.68) were relatively high, and these findings suggest that respondents felt morally committed to using eco-breeding practices. Also, they showed a relatively high intention (3.71) to engage in eco-breeding practices.
We conducted least significant difference analysis to examine whether there was any significant difference in responses across the three different provinces. No significant difference in the participants' profiles or measured constructs was found at p < 0.05. Thus, we combined data from the three provinces in all analyses that followed.
Common method deviation
Common method bias (CMB), as a covariate property, can be potentially biased and can even lead to misleading research conclusions. To ensure that common method bias did not distort the results of this study, we conducted a Harman one-way test to analyze the severity of homoscedastic errors during the analysis of the empirical data (Podsakoff et al., 2003). According to the results of the exploratory factor analysis in SPSS, the variance explained by the unrotated first principal component was < 50%, indicating that the CMB of the study was not serious (Sun et al., 2015; Bai et al., 2019).
Reliability and validity
We performed CFA using analysis of moment structures to evaluate internal consistency reliability, convergent validity, and discriminant validity. To assess the fit of the model, various indices were used in the present research, including Chi-square (χ2), degree of freedom (df), tucker-lewis index (TLI > 0.9), Goodness of-fit Index (GFI > 0.9), comparative fit index (CFI > 0.9), and root mean square error of approximation (RMSEA < 0.08). Initially, the measurement model fit well with the data overall (χ2 = 361.08, df = 131, χ2/df = 2.76, CFI = 0.97, GFI = 0.95, TLI = 0.95, RMSEA = 0.06). In addition, the combined reliability of the seven latent variables in the model ranged from 0.78 to 0.89, exceeding the m] recommended 0.70 (Fornell and Larcker, 1981). As described in Table 3, all standardized factor loadings of the measurement model were >0.7, indicating the high internal consistency of the scale, and the average variance extracted (AVE) values of the latent variables were >0.5, indicating good convergent validity of the scale (Hair et al., 2014).
Table 3. Measurement model results.
In terms of discriminant validity, the scale is considered to have good discriminant validity if the arithmetic square root of each latent variable's AVE is greater than the correlation coefficient between latent variables (Fornell and Larcker, 1981). As shown in Table 4, the absolute values of the correlation coefficients of most latent variables are < 0.5, and the correlation coefficients are smaller than the arithmetic square root of the mean square deviation of the corresponding AVE, so the scale is considered to have good discriminant validity.
Table 4. Results of validity analyses.
Modeling comparisons
To test the robustness of the model, we conducted a modeling comparison. The results of the modeling comparisons are presented in Table 5. First, we tested the original TPB model, and the results showed that the TPB-based predictors explained 59% of the variance in the farmers' behavioral intentions. Second, we tested the expanded TPB model, and ~61% of the variance in farmers' behavioral intentions was explained by the expanded TPB combination. Third, we tested the standard NAM, and 41% of the variance in the farmers' behavioral intentions was contributed by the NAM-based predictors. Finally, we tested the combined model, and the results showed that the combined model performed well in predicting the farmers' behavioral intentions. Approximately 66% of the variance in farmers' behavioral intentions was explained by the predictors of the expanded TPB and NAM. All four models had acceptable fits, but the integrated model outperformed the other models in predicting the farmers' behavioral intentions.
Table 5. Estimated parameters and goodness-of-fit indices of the test models (n = 527).
Structural equation modeling
We demonstrated the criteria of reliability and validity by the measuring model, which laid the foundation for analyzing the structural model. We used structural equations to assess the goodness of fit of the theoretical framework, and the results showed that the theoretical model proposed in this paper matched the measured data (χ2 = 503.78, df = 136, χ2/df = 3.70, CFI = 0.95, GFI = 0.95, TLI = 0.93, RMSEA = 0.07). RMSEA met the criterion of < 0.08 (Browne and Cudeck, 1992), while the other fitness indices (e.g., CFI, GFI, and TLI) were higher than the recommended criterion of close to 0.9 and higher (Bagozzi and Yi, 1988).
The findings from SEM are displayed in Figure 3, Hypotheses 1, 2, and 3 proposed relationships among the original constructs established in TPB. Results showed that ATT (β = 0.50, p<0.001), PBC (β = 0.13, p<0.001), and SN (β = 0.21, p<0.001), were all significant predictors of the intention to adopt eco-breeding practices. Next, findings indicated that past habit positively influenced farmers' attitudes (β = 0.53, p<0.001) toward adopting eco-breeding practices as well as PBC (β = 0.61, p<0.001). Hence, Hypotheses 4 and 5 were supported. Hypothesis 6 was also evaluated. The results of the study indicated that the effect of past habit on farmers' intention (β = 0.04, p > 0.1) to adopt eco-breeding practices was not significant. Therefore, Hypothesis 6 did not pass the test. However, the path from individual norms to the farmers' intention to adopt eco-breeding practices was statistically significant, supporting Hypothesis 7 (β = 0.15, p<0.01). Finally, the original variables of NAM, AC (β = 0.41, p<0.001), and AR (β = 0.43, p<0.001) had a positive effect on PN; and AC (β = 0.75, p<0.001) had a significant effect on individual AR. These results supported Hypotheses 8, 9, and 10.
Figure 3. Structural equations modeling and standardized path coefficients (comprehensive model). *p < 0.1; **p < 0.05; ***p < 0.01.
Discussion
Owing to the lack of research on farmers' ecological approaches to waterfowl breeding, in this study we aimed to gain a comprehensive understanding of farmers' intentions to adopt eco-breeding practices by constructing the TPB-NAM model. We found that self-interest and altruistic motives play important roles in the formation of farmers' intentions to choose eco-breeding practices, which was consistent with the previous studies (Floress et al., 2017; Shin et al., 2018). However, the degree of salience of the two motivations varied across research contexts. In the current study, we found that although both self-interest and altruistic motives influenced farmers' intention, self-interested motives were the more dominant influencing factor. This finding suggests that the farmers' intentions to adopt eco-breeding practices were driven by both economic and social effects and that the farmers' intentions were more likely to be influenced by economic effects.
As expected in H1, H2, and H3, ATT, SN, and PBC are determinants of the intention to choose eco-breeding practices. The results suggest that farmers with good attitudes and more resources and abilities, and who perceive more social pressure, are more likely to choose the eco-breeding practices. This finding is consistent with the research of Rezaei et al. (2019) on the intentions of farmers to adopt integrated pest management. However, the relative importance of the influencing factors varied. In the current study, ATT had the greatest influence on farmers' intentions to choose eco-breeding practices.
The current findings suggest that although H6 was supported for the initial expansion of TPB, the effect was not statistically significant in the integrated TPB-NAM model. The main reason for this situation may be attributed to the effect of past habit in the TPB-NAM integrated model. More precisely, the results suggest that past habits do not directly influence intention (H6) but instead influence intention indirectly through ATT (H4) and PBC (H5). This suggests that the effect of past habit on farmers' intentions to use eco-breeding may be absorbed by ATT and PBC. In this regard, similar findings were reported by Leung and Chen (2017).
PN is influenced by AC and AR, as hypothesized in H8 and H9. The effect of AR on PNs is greater than the effect of AC. In addition, as expected in H10, personal AC has a significant positive effect on AR. This can be interpreted as AR partially mediating the relationship between AC and PN, consistent with the findings of NAM based on empirical evidence (Zhang et al., 2017; Wang et al., 2019). Thus, farmers' PNs can be activated as they recognize the negative consequences of ecological degradation when eco-breeding is not adopted or by feeling responsible for the negative consequences.
Conclusions and policy implications
The current investigation is one of the first attempts to predict Chinese farmers' intentions to use eco-breeding practices based on the TPB-NAM integrated model. Our empirical study showed that although the original TPB or NAM showed a good efficiency in predicting farmers' intention to use eco-breeding practices, the efficiency and comprehensiveness of the integrative model of TPB-NAM were superior to the original TPB or NAM model. In other words, the use of eco-breeding, which involves rational and moral considerations, can be combined with TPB based on self-interest motives and NAM based on altruistic motives, which contributes to improving the utility of the model and the effectiveness of explaining farmers' intentions to use eco-breeding practices. In general, the conclusions drawn from the study have important implications. On the one hand, they help improve the design reference framework for understanding the psychosocial factors influencing farmers' intentions to use eco-breeding; on the other hand, they help relevant planners and policy makers implement various practical interventions to encourage farmers to use eco-breeding practices to reduce environmental pollution.
From a practical point of view, identifying the influential factors on farmers' intention to use eco-breeding is beneficial for government promoting eco-breeding practices, especially with regard to developing overall communication value orientation. Government who aiming to attract farmers with their eco-breeding practices might want to carefully examine their potential farmers' characteristic such as self-interest and altruistic motivations. Interestingly, in our study, we found that farmers' intentions to adopt eco-breeding practices are driven by both self-interested and altruistic motives and that farmers' choices are more likely to be influenced by self-interest. Therefore, government policies to encourage farmers to voluntarily adopt eco-breeding practices should be based not only on farmers' social responsibility to protect the environment by means of publicity and incentives but also on the premise that farmers want to maximize their own benefits. In addition, attitude is salient factor in choosing eco-breeding practices. Therefore, if government prioritizes the creation of farmers' favorable attitudes toward t choosing eco-breeding practices, it may increase the intentions to use eco-breeding and thus directly contribute to the adoption of eco-breeding promotion. Hence, to achieve this goal, the government should make more effort to raise farmers' awareness of eco-breeding methods and improve the overall evaluation of production use. For example, it can encourage more social funds to participate by strengthening relevant policy support; it can also implement a two-way matching model between universities and farmers to scientifically guide farmers' production. Again, our findings suggest that practitioners with previous experience in eco-breeding have a higher ability to overcome various difficulties in using eco-breeding practices (including the lack of knowledge skills, time, and money); they also have more positive attitudes toward eco-breeding practices and have stronger intentions to use eco-breeding practices. In this regard, the government can conduct eco-breeding technology training as well as green-specific subsidy policies to increase the farmers' level of self-efficacy and the consequent impact of their intentions. Finally, our findings suggest that policy implementers can also design programs aimed at increasing farmers' general awareness of environmental problems caused by waterfowl production. This would help strengthen the farmers' sense of moral obligation to protect the environment, thus facilitating the use of the eco-breeding model.
In general, in this study, we presented an important insight about the Chinese farmers' intentions to adopt eco-breeding practices. In addition, the study is also suggestive of several areas for future research. First, because our respondents came from three provinces in China, future scholars could test the proposed research model in different contexts and compare the results with the current study. Second, the combination of various theories such as motivation theory or value-belief-norm theory is recommended so that future researchers may identify other sociological and psychological factors that might influence farmers' intentions toward eco-breeding practices. Third, we used intentions rather than actual behavior as the explained variable, so future scholars need to investigate how the different components of TPB-NAM affect actual eco-breeding practices. Lastly, while diverse processes were successfully integrated into the TPB-NAM in this study, individuals' decision-making regarding engagement in eco-breeding practices may be more complex than is evident in our framework. Future research will incorporate other influential and essential variables (e.g., government policy, socio-culture, or infrastructure) to gain a more complete understanding of individuals' decisions to implement eco-breeding practices.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical review and approval was not required for the study involving human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants in accordance with the national legislation and the institutional requirements.
Author contributions
XW and PL were the principal investigators of the grants and wrote the first draft. JT revised the draft. WL analyzed the model and further revise and improve the paper. XG contributed to research design and data collection. ZW completed the empirical analysis. All authors contributed to the article and approved the submitted version.
Funding
This research was supported by the National Natural Science Foundation of China (Grant Nos. 71963021 and 72264012), Jiangxi Science Planning Fund Program (Grant No. 19GL10). However, the opinions expressed here do not reflect those of the funding agency.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Footnotes
1. ^According to the survey data of NWITS in 2019, the ratio of Jiangxi, Hubei, and Yunnan waterfowl integrated production values is about 6:3:2.
2. ^Interviewers explained every item in the questionnaire for respondents so that they could understand the meaning of each item. The whole process took about 45 min for each respondent.
3. ^A desirable goal of a sample size is to have a 20:1 ration for the number of participants to the number of model parameters (Suhr, 2006); the ration in this study is 27.7:1, which meets the requirement of a sufficient sample size.
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Keywords: waterfowl production, eco-breeding practice, farmers' behavior, past habit, the theory of planned behavior, the normative activation model
Citation: Wang X, Lu P, Tang J, Gao X, Liao W and Weng Z (2022) Drivers of farmers' intentions to use eco-breeding: Integrating the theory of planned behavior and the norm activation model. Front. Environ. Econ. 1:1035176. doi: 10.3389/frevc.2022.1035176
Received: 02 September 2022; Accepted: 31 October 2022;
Published: 23 November 2022.
Edited by:
Francesco Nicolli, University of Ferrara, ItalyReviewed by:
Pouria Ataei, Tarbiat Modares University, IranMohamed Esham, Sabaragamuwa University, Sri Lanka
Copyright © 2022 Wang, Lu, Tang, Gao, Liao and Weng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Pan Lu, MTQ4Njc3NjQyOSYjeDAwMDQwO3FxLmNvbQ==