Internet Use, Social Capital, and Farmers’ Green Production Behavior: Evidence from Agricultural Cooperatives in China

Abstract

Agricultural cooperatives are the main vehicle for farmers to engage in green agriculture. With the digital transformation in rural areas, it is crucial to explore how cooperative members can effectively access online information and integrate it into green production decision-making processes. Based on the survey data of 530 members of rice planting cooperatives in Heilongjiang Province in China, this paper selected eight green production behaviors commonly used by rice farmers as explained variables, and constructed an ordered probit model. Using the social capital theory, the impact and mechanism of internet use on cooperative members’ green production behavior were examined. The results showed the following: (1) Internet use facilitates the cooperative members’ green production behavior. This conclusion remains valid even after addressing the endogeneity test and robustness test. (2) The heterogeneity analysis revealed that the internet is particularly effective in enhancing the green production behaviors of farmers who are less educated, middle-aged, and those with strong connections to cooperatives. (3) A further mechanism test indicates that internet use not only significantly influences farmers’ trust in cooperatives but also aids them in comprehending the cooperative’s production specifications, thereby further advancing the improvement in green production behaviors. (4) Members’ satisfaction with cooperative sales can serve as a substitute for the internet in influencing their green production behavior.

1. Introduction

Achieving green development in agriculture has emerged as the predominant approach in global agricultural production and is essential for enhancing food security and nutrition [1]. Currently, emissions of greenhouse gasses resulting from the food system account for more than one-third of worldwide human-induced emissions, primarily driven by agricultural production [2]. As the demand for food increases, the total anthropogenic greenhouse gas emissions from agriculture continue to rise. Therefore, promoting green agricultural production as the primary strategy for reducing agricultural carbon emissions has become a focal point for various countries [3]. As a significant grain producer, China accounts for 25% of the world’s grain production, despite having only 9% of the world’s arable land. However, the sustained growth of China’s grain production is occurring at the expense of the ecological environment [4]. The incorrect disposal of agricultural waste represents a significant danger to the environment, and the conflict between agricultural progress and the safeguarding of environmental health is highlighted [5]. Given the growing constraints on environmental resources, adopting greener agricultural production methods and development models is imperative for China. To address this, the Chinese government has introduced a series of policies aimed at adjusting farmers’ production practices, enhancing rural production environments, and facilitating the transition towards green practices. Since 2008, the Central Committee’s “No. 1 Document” has continuously proposed the concept of green production. In 2017, the Central Committee’s “No. 1 Document” pointed out the need to promote green agricultural production methods. In 2023, the Central Committee’s “No. 1 Document” pointed out that promoting green agricultural development and accelerating agricultural investment promote and apply technology for product reduction and efficiency improvement.

As key decision makers in agricultural production, farmers significantly influence the sustainable advancement of agriculture [6]. In practice, due to the low production awareness of farmers and the difficulty in bearing the transaction costs involved in new technologies [7], farmers show limited enthusiasm for adopting green production technologies, leading to a low adoption rate. Cooperatives, as institutional arrangements, help farmers reduce transaction costs and safeguard their professional assets from opportunistic behavior, enabling them to engage more effectively in the market [8]. At the same time, combined with the social capital theory, there is a wealth of social capital within cooperatives that can facilitate communication, help access information [9], set the same value goals, and provide corresponding production services, and it helps to create a sense of identity, reduce opportunism [10], and increase the adoption of green production technologies [11,12]. The cooperative model is increasingly seen as the ideal organizational choice for farmer households, driving the transformation towards more sustainable agricultural practices. However, some scholars have highlighted issues with the current development of cooperatives in China. They point out that there is an excessive administrative intervention, leading to the emergence of “fake” cooperatives [13]. Additionally, concerns have been raised about “elite capture” within cooperatives [14], limiting members’ access to resources and services and hindering the effective utilization of social capital [15]. Consequently, this adversely affects the productivity of the members [16]. To advance the growth of green agricultural production, cooperative members may need to seek external support to effectively utilize cooperative resources.

As an important part of modern information technology, the internet has had a profound impact on farmers and a rural society, and “internet + agriculture” has become an important link between small-scale farmers and modern agriculture [17]. The implementation of projects such as “village to village”, “broadband to the countryside”, and “digital village” has made the internet rapidly popular in rural areas, and it has reshaped rural household consumption diversity [18], increased rural household income [19], and improved agricultural green productivity. A review of the existing research indicates that, on one hand, for the growth of macro-agricultural economy, the internet serves as an information transmission medium that can assist farmers in utilizing fertilizers more effectively, thereby enhancing the overall value of agricultural production [20]. Additionally, there is evidence of a negative relationship between internet use (IU) and environmental pollution [21]. Wang et al. (2024) further elucidate that the internet promotes sustainable agricultural development via two main mechanisms: the transfer of land and agricultural production services [22]. On the other hand, in terms of farmers’ green production decisions, IU can diminish the information barriers that hinder access to production technology. The internet assists farmers in acquiring essential information for both production and sales, lowering the expenses associated with information retrieval, and aiding them in making decisions related to production [23].

There are also some studies that have conducted a mechanism analysis, and Huang et al. point out that information access, risk attitudes, and expected returns play an important mediating role between IU and green production technology adoption [24]. Social capital is an important intermediary variable, and IU can enhance social interaction among farmers. Through the use of the internet, farmers have increased their communication with technical professionals in the network [25], acquiring technical knowledge related to green production at a lower cost and changing traditional production perceptions. Strengthening the social connections among farmers can enhance the beneficial effects of the internet on green production behavior [26]. It is worth noting that some studies have discussed the role of satisfaction in farmers’ decision making, pointing out that IU reduces farmers’ satisfaction with government support, which has a negative impact on farmers’ rice green manure rotation adoption behavior [27]. In conclusion, we can see that the internet can make up for the lack of information of farmers and help them to make production decisions. Then, can IU improve the green production behavior of cooperative members? If so, what is the mechanism?

This paper focused on cooperative members, using data from 530 participating cooperative members in Heilongjiang in China. It constructs an ordered probit model, a mediating effect model, and a moderating effect model, integrating these with the conditional mixed process estimation method (CMP). Through empirical analysis, this research intends to examine how IU affects the green production behavior of farmers participating in cooperatives. Additionally, it seeks to investigate the influence of different types of cooperative social capital within this context. The main contributions of this paper are as follows: firstly, this paper focuses on members of rice planting cooperatives as the subject of research, with the aim of examining the role that agricultural cooperatives should play in the transformation of green production influenced by the internet. Cooperatives in China are found to be advantageous in facilitating the adoption of green production behaviors among their members [28]. Nevertheless, cooperative members may not fully leverage the cooperative resources for green production. The analysis of the impact of IU on green production behavior of cooperative members can enrich the existing research system and offer a fresh viewpoint for investigating the agricultural green transformation. Secondly, this research applies the social capital theory to explore the green production behaviors of cooperative members. IU, cooperative social capital, and green production behavior are included in the same research framework. Most of the existing studies use the theory of planned behavior or the theory of rational behavior as the theoretical basis of green production behavior. Social capital plays an important role in the development of cooperatives and breaking the dilemma of collective action, and cooperatives facilitate the development of a “peer effect” among members by providing standardized production demonstrations, promoting the diffusion of green technology, and encouraging learning and imitation [29]. The cooperatives have provided help to their members in terms of the organization standard, the organization trust, the sales service, and so on. To facilitate the green transformation of agricultural production methods, cooperatives must enhance their primary role and leverage their social capital to foster collaboration among members. This study employs the social capital theory to further examine the influence of cooperative social capital on members’ adoption of green production behavior, offering new theoretical insights into this area of research.

2. Theoretical Analysis and Hypothesis

2.1. Social Capital Theory

The idea of social capital emerged from sociology and was initially introduced and thoroughly developed by Bourdieu (1986). Putnam has evolved from the individual level to the collective level, highlighting the collective attributes of social capital. It emphasizes that social capital can facilitate coordination among members through organizations, leading to cooperative behavior that ultimately enhances efficiency [30]. Ostrom categorized social capital into two forms, i.e., trust and norms, emphasizing the significance of establishing trust and fostering group concepts in institutional settings. Members cultivate social capital by building mutual understanding and trust, as well as by developing shared behavioral norms to guide their actions.

In China, social capital is a key factor in promoting the progress of farmer cooperatives [31] and expanding the technology adoption rate of cooperative members [32]. The “relationship orientation” is a key characteristic of the rural society, where farmers view those around them as trustworthy providers of information and expertise [33]. Their technological learning heavily relies on interactions with others. Recent studies have underscored the impact of social capital on the utilization of organic fertilizers, green prevention and control technologies, and climate change adaptation behaviors [26,34,35]. Based on this, this paper summarizes the social capital of cooperatives into two parts, i.e., organizational norms and organizational trust, in order to consider the impact of cooperative social capital on the adoption of green production behaviors by members.

In this paper, organizational trust is defined as social trust within cooperatives. Fukuyama (1995) introduced the concept of social trust in group organizations, characterizing it as the familiarity, sincerity, and cooperative behavior exhibited by members within an organization [36]. As a non-profit cooperative economic entity, the internal social trust of farmer cooperatives significantly influences the effectiveness of cooperation among members. Organizational norms are based on the perspective of cooperatives and are specific guidelines that emerge from long-term interactions and actions between cooperatives and their members. These norms serve a guiding and supervisory role in production decisions and can be internalized into individual consciousness [37]. By establishing production management regulations at each stage, the cooperative directs the development of production and operational activities, assisting members in aligning their behavioral decisions and thereby promoting green production behavior.

The logical framework is shown in Figure 1.

2.2. Research Hypothesis

(1) The direct impact of internet use on the green production behavior of cooperative members

On the one hand, IU can lower the cost for farmers to access information. By viewing images and short videos of relevant reports on straw burning, pesticide use, and fertilizers, farmers can quickly grasp the market environment, technological advancements, and other information. This helps them acquire updated agricultural production knowledge at a reduced information acquisition cost [38]. On the other hand, IU by cooperative members can help them know the needs of consumers and motivate them to alter traditional production practices. In the conventional agricultural production model, producers and consumers are geographically separated, resulting in information asymmetry and moral hazard issues in green production. Through the internet, farmers can directly learn about consumers’ demand for environmentally friendly agricultural products, leading to emotional connection and changes in traditional production methods [39]. Therefore, this paper proposes the following research hypothesis:

H1: 

IU can improve the green production behavior of cooperative members.

(2) The mediating effect of organizational trust and organizational norms

Cooperative members can leverage the internet to enhance their trust in cooperatives and facilitate compliance with standardized systems. As a form of institutional arrangement, cooperatives can assist farmers in increasing their output and income by fostering trust and implementing standardized systems [40], thereby improving the adoption of green production behavior.

First, IU enhances the flow of information to farmers, and improved information flow fosters greater trust [41]. Putnam noted that trust, as a crucial component of social capital, facilitates spontaneous cooperation [42]. Additionally, the formation of trust has characteristics of self-accumulation and reinforcement. On the one hand, information technologies such as mobile phones and computers can promote members’ offline social interactions, improve interpersonal satisfaction [43], deepen the understanding of professional cooperatives, improve trust in cooperative leaders, and understand their decision-making behavior. On the other hand, trust is a necessary condition for the formation, existence, and sustainable and healthy development of cooperatives [44]. In cooperatives, the exchange of knowledge among members is more frequent, knowledge sharing and training can take place on a regular basis, the level of trust among members is increased in the process [45], opportunism within cooperatives declines, and the adoption of green production technologies increases [46].

Second, organizational norms are the rules and standards felt by all members of a cooperative [47]. Cooperatives have strict requirements for the process and quality of agricultural production. When information about production pollution circulates online, it can evoke emotional fluctuation and a heightened sense of crisis among individuals, and members form a sense of urgency for environmental protection [48]. When information about the benefits of green agriculture is disseminated on the web, members link it to their own interests and develop a positive environmental awareness [49] that leads to a better understanding of cooperative values, abiding by its norms and regulations. According to the value–belief–norm theory, upon joining a cooperative, members are guided by the cooperative’s values, which leads them to gradually recognize the negative consequences of non-green production behaviors. This realization fosters the development of positive environmental beliefs [50,51]. Consequently, individuals within a society often mimic the effective behaviors exhibited by others, incorporating these actions into their personal standards of behavior. This process encourages actions aligned with group norms [52], enhances their green production values, and subsequently improves the adoption of green production technologies [53].

Therefore, this paper proposes the following research hypotheses:

H2-a: 

Organizational trust mediates the green production behavior of cooperative members in IU.

H2-b: 

Organizational norms mediate the green production behavior of cooperative members in IU.

(3) Analysis of the moderating effect of sales satisfaction

Satisfaction is the pleasure a subject gets when his or her needs are met, reflecting the degree to which the subject likes or dislikes the service or product [54]. Satisfaction is the number that measures this state [55]. In this paper, sales satisfaction is defined as the degree of preference for the sales services provided by cooperatives to their members. According to the transaction cost theory, the presence of asymmetric information can lead to increased transaction costs for individuals seeking information and engaging in market negotiations. For small farmers, whose bargaining power is often limited, these transaction costs tend to rise during sales. To mitigate this impact, small farmers can leverage internet technology to broaden their information sources and effectively reduce procurement costs for supplies and equipment. Alternatively, they may choose to join a cooperative, thereby relying on the cooperative’s sales services to stabilize their sales channels and expand their market reach [56]. Research has shown that cooperatives utilize unified branding to market agricultural products, enhancing their market negotiation capabilities and facilitating multi-channel sales [57]. Membership in cooperatives can provide access to sales channels for a greater variety of green agricultural products while reducing transaction costs. Furthermore, cooperatives oversee the production quality of their members by acquiring agricultural products [58] and guiding them in improving their understanding and practices related to green production. Therefore, members’ satisfaction with cooperative sales services is an important factor affecting their green production adoption behavior [59]. Based on the above analysis, this paper believes that the application of internet technology and joining cooperatives can help farmers effectively reduce the dependence on traditional sales channels and reduce transaction costs. The sales channels of cooperatives can replace the information acquisition channels generated by the internet, and the sales satisfaction of members has a negative moderating effect on the impact of IU on members’ green production behavior. This paper proposes the following research hypothesis:

H3: 

Sales satisfaction weakens the impact of IU on the green production behavior of cooperative members.

3. Research Design

3.1. Data Sources

Heilongjiang Province is one of the main rice-producing regions in China. The rice-planting area in Heilongjiang Province for 2022 is 3.6 million hectares, representing 12.2% of the nation’s total rice-planting area. Additionally, Heilongjiang Province leads the country in rice production, contributing 13% to the total rice production [60]. The province benefits from unique geographical advantages, including significant temperature fluctuations between day and night, fertile soil, and high organic matter content, all of which contribute to the high quality of rice produced in Heilongjiang [61]. Currently, the province is actively promoting the development of specialized agricultural cooperatives. According to the Market Supervision Bureau of Heilongjiang Province, there are nearly 70,000 registered planting cooperatives in the region, representing over 70% of all specialized farmers’ cooperatives in the province [62]. These farmers’ specialized cooperatives have played a key role in the sustainable development of agriculture in Heilongjiang Province, leading to a significant reduction in the use of fertilizers for major crops like rice [63].

The research team conducted a micro-survey on Heilongjiang’s rice cooperatives from July to October in 2023, and conducted face-to-face and telephone interviews with members of the cooperatives. A stratified random sampling survey was conducted among rice growers in Heilongjiang’s main rice-producing areas. Based on natural conditions, geographical locations, the economic levels of villages, and differences in rice-sown areas, 10 major rice-producing areas in Heilongjiang were selected as research areas, including Wuchang City, Mulan County, Jiamusi, Huachuan County, Fujin City, Qing’an County, Ning’an City, Dongning City, Fuyu County, and Tailai County. The research area covers the key and non-key rice counties of Heilongjiang as stipulated in the 14th five-year plan for rice production and development in Heilongjiang, and can represent the overall situation of rice development in the province. The contents of the investigation mainly include the present situation of green rice production, the strength of local policy support, the present situation of production services of cooperative organizations, and the economic benefits of farmers. The selected cooperatives mainly include national model cooperatives, provincial model cooperatives, and non-model cooperatives. After excluding incomplete or outlier samples, the study had an effective questionnaire size of 530 households.

3.2. Variable Selection

Explained variable: In 2015, the Ministry of Agriculture in China stated in “implementation opinions on the fight against agricultural non-point source pollution” that it aims to strive to effectively curb the trend of increasing agricultural non-point source pollution by 2020, achieve “one control, two reduction, three basic”, strictly control the total amount of water used in agriculture, reduce the use of fertilizers and pesticides, and increase the basic resource utilization of animal manure, crop straws, and agricultural films. With these goals in mind, this paper constructed indicators of green production behavior of cooperative members from three production stages, i.e., pre-natal, mid-natal, and post-natal, specifically for the use of fertilizer and pesticide inputs in the pre-production, the adoption rate of soil testing, the green control of plant diseases and insect pests, organic fertilizer and UAV application in the on-production, and the adoption rate of straw-returning technology in the post-production stage.

Explanatory variables: The quantified variables in this study mainly come from the existing literature and are fine-tuned according to the research theme. Among them, IU is characterized by “whether to use the internet” [64]. Organizational trust is characterized by “you trust the major decisions of the cooperative”. Organizational norms are the values spontaneously formed by people in long-term contacts, and this study is characterized by “I and the cooperative attach importance to agricultural green production” [65]. Sales satisfaction expresses members’ satisfaction with the cooperative’s sales service, and this paper adopts the representation that “your transaction cost will increase if you quit the cooperative”.

Control variables: The control variables in this paper refer to the existing research [66], covering three aspects: self-characteristics, family endowment, and external environment, and, specifically, seven variables, such as age, education level, whether you are a village cadre, whether you go out to work, rice-planting area, level of joining cooperative, and policy perception.

In addition, this paper selected age, education level, and the degree of dependence on cooperatives for heterogeneity analysis.

The specific meaning and descriptive statistics are shown in

Table 1. Descriptive statistics.

Type	Variable’s Name		Variable’s Meaning	Description	Mean	Sd.	Min.	Max.
Explained variable	Green production behavior		Green production behavior of cooperative members	Number of green production technologies adopted	5.047	1.578	1	8
Explanatory variable	Internet use (IU)		Whether to use the internet	1 = yes; 0 = no	0.715	0.451	0	1
Mediating variable	Organizational trust		You trust the major decisions of the cooperative.	1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = fully agree	4.02	0.921	2	5
	Organizational norms		I and the cooperative attach importance to agricultural green production.	1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = fully agree	3.989	0.862	2	5
Moderating variable	Sales satisfaction		Your transaction cost will increase if you quit the cooperative.	1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = fully agree	3.74	0.98	1	5
Grouping variable	Degree of dependence on cooperatives		Whether a member of your family works in a cooperative	1 = yes; 0 = no	0.179	0.384	0	1
Control variables	Farmers’ characteristics	Age	Actual age	-	49.509	8.496	27	73
		Educational level	Education years	-	9.78	2.913	2	20
		Village cadre status	Whether you are a village cadre	1 = yes; 0 = no	0.126	0.333	0	1
	Family endowments	Work	Whether to work away from hometown	1 = yes; 0 = no	0.251	0.433	0	1
		Planting area	Family rice-planting area	Mu	189.67	571.3	3	12,000
	External environment	Cooperative level	The level of the cooperative you join	1 = ungraded level; 2 = county level; 3 = municipal level; 4 = provincial; and 5 = national level	3.347	1.153	1	5
		Policy perception	The agricultural green production demonstration provided by the government every year has a great impact on you.	1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree; and 5 = fully agree	3.636	0.823	0	5

. It can be seen that the average green production behavior of the respondents is 5.047, and the average number of IUs is 0.7. The majority of the participants are largely middle-aged or older, with an average age of 49.5 years. The average number of years of education is 9.7 years, and the level of education is relatively low. In addition, the average grade of the respondents who joined the cooperative is 3.3, which means the cooperative grade is not high.

3.3. Model Design

3.3.1. Ordered Probit Model

In order to test the impact of IU on green production behavior of cooperative members, we choose an ordered probit model for regression. It is because the explained variable is the number of green production technologies adopted, with values of 0, 1, 2, 3, 4, 5, 6, 7, and 8, showing a clear progressive relationship. The specific expression is as follows:

$$Y_i^{\mathrm{\ast }}={{\alpha }_0+{\alpha }_{1}inter}_i+{\sum }_{i=1}^n{{\alpha }_{i}control}_i+{\epsilon }_i$$

(1)

Among them, $Y_i^{\ast }$ is an unobservable variable; ${inter}_i$ is a variable of IU; ${control}_i$ is a control variable that includes individual characteristics, family endowments, and external environment; ${\alpha }_0,{\alpha }_1,{\alpha }_i$ is the coefficient to be estimated; and ${\epsilon }_i$ is a random perturbation term.

$$Y_i=\left\{\begin{array}{c}0,Y_i^{\mathrm{\ast }}\le r_1\\ 1,r_1<Y_i^{\mathrm{\ast }}\le r_2\\ 2,r_2<Y_i^{\mathrm{\ast }}\le r_3\\ 3,r_3<Y_i^{\mathrm{\ast }}\le r_4\\ 4,r_4<Y_i^{\mathrm{\ast }}\le r_5\\ 5,r_5<Y_i^{\mathrm{\ast }}\le r_6\\ 6,r_6<Y_i^{\mathrm{\ast }}\le r_7\\ 7,r_7<Y_i^{\mathrm{\ast }}\le r_8\\ 8,=Y_i^{\mathrm{\ast }}>r_8\end{array}\right.$$

(2)

Among them, $r_1$ to $r_8$ 8 are the tangent points of $Y_i$, and $r_1<r_2<r_3<r_4<r_5<r_6<r_7<r_8.$

3.3.2. Mechanism Test

To further analyze the transmission mechanism of social capital affecting the green production behavior of cooperative members, this paper constructed the following model:

$$M_i={{\beta }_0+{\beta }_{1}inter}_i+{\sum }_{i=1}^n{{\beta }_{i}control}_i+{\vartheta }_i$$

(3)

$$Y_i^{\mathrm{\ast }}={{\gamma }_0+{\gamma }_{1}inter}_i+{\gamma }_2{M}_i+{\sum }_{i=1}^n{{\gamma }_{i}control}_i+{\sigma }_i$$

(4)

$$Y_i^{\mathrm{\ast }}={{\delta }_0+{\delta }_{1}inter}_i+{\delta }_2{sell}_i+{\delta }_3{inter}_i \mathrm{\ast } {sell}_i+{\sum }_{i=1}^n{{\delta }_{i}control}_i+{\tau }_i$$

(5)

Among them, $M_i$ is a variable, including organizational trust and organizational norms. ${sell}_i$ is sales satisfaction. ${\beta }_0$, ${\beta }_1$, ${\beta }_i$, ${\gamma }_0,{\gamma }_1$, ${\gamma }_2$, and ${\gamma }_i$ are the coefficients to be estimated, and ${\vartheta }_i,{\sigma }_i,$ and ${\tau }_i$ are random perturbation terms. If there is a mediating effect, the proportion of the mediating effect on the green production behavior of cooperatives is ${\displaystyle \frac{{\beta }_1{\gamma }_2}{{\beta }_1{\gamma }_2+{\gamma }_1}}.$

4. Empirical Analysis

4.1. Baseline Regression: Analysis of the Impact of Internet Use on the Green Production Behavior of Cooperative Members

In this paper, Stata15 was used to analyze the influence of IU on the green production behavior of cooperative members. It is found that IU has a significant positive impact on the green production behavior of cooperative members, and the green production behavior of cooperative members increases by 0.318 units for every unit of IU, assuming that H1 is verified (

Table 2. Analysis of the effects and marginal effects of internet use on the green production behavior of cooperative members.

Variables	Model 1	Marginal Effects
		Adopt 1	Adopt 2	Adopt 3	Adopt 4	Adopt 5	Adopt 6	Adopt 7	Adopt 8
Internet use	0.318 *** (0.108)	−0.015 ** (0.0060)	−0.020 ** (0.0077)	−0.036 *** (0.013)	−0.022 *** (0.008)	−0.0073 ** (0.0035)	0.033 *** (0.011)	0.050 *** (0.0178)	0.016 ** (0.0062)
Age	0.0389 *** (0.00973)	−0.002 *** (0.0006)	−0.002 *** (0.0007)	−0.004 *** (0.0011)	−0.003 *** (0.0007)	−0.0009 ** (0.0004)	0.004 *** (0.001)	0.006 *** (0.0015)	0.002 *** (0.0006)
Educational level	0.0824 *** (0.0282)	−0.004 ** (0.0015)	−0.005 ** (0.0019)	−0.009 *** (0.0032)	−0.006 *** (0.002)	−0.002 ** (0.0009)	0.009 *** (0.0029)	0.013 *** (0.0043)	0.004 ** (0.0017)
Village cadre status	−0.0150 (0.113)	0.0007 (0.0052)	0.0009 (0.007)	0.002 (0.0127)	0.001 (0.0079)	0.0003 (0.0026)	−0.0016 (0.0119)	−0.0023 (0.018)	−0.0008 (0.0056)
Work	0.182 (0.113)	−0.008 (0.0055)	−0.011 (0.0072)	−0.0204 (0.0126)	−0.0128 (0.0079)	−0.004 (0.0028)	0.019 (0.0118)	0.029 (0.0176)	0.009 (0.0059)
Planting area	0.000121 * (0.000065)	−0.000006 * (0.000003)	−0.000007 * (0.000004)	−0.000014 * (0.0000074)	−0.0000085 * (0.000005)	−0.000003 (0.000002)	0.000013 * (0.000007)	0.000019 * (0.00001)	0.000006 * (0.000003)
Cooperative level	0.491 *** (0.0614)	−0.023 *** (0.007)	−0.030 *** (0.006)	−0.055 *** (0.007)	−0.034 *** (0.005)	−0.011 *** (0.004)	0.052 *** (0.007)	0.078 *** (0.010)	0.024 *** (0.005)
Policy perception	−0.288 *** (0.0408)	−0.023 *** (0.006)	−0.030 *** (0.0007)	−0.055 *** (0.009)	−0.035 *** (0.005)	−0.011 *** (0.004)	0.052 *** (0.008)	0.078 *** (0.010)	0.024 *** (0.005)
Regional control variable	Yes
Obs.	530
LR chi2	130.45
Pseudo R2	0.1142

Note: (1) *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively, and are the same below. (2) Robust standard error in parentheses, and are the same below. (3) LR chi2 and Pseudo R2 represent chi-square statistics and virtual decision coefficients, and are the same below.

). Research findings indicate that utilizing the internet aids farmers in gaining a deeper comprehension of green production practices, lowering both information search and transaction costs, boosting farmers’ motivation for production, and facilitating the adoption of more sustainable production methods.

In addition, considering that the estimated coefficient of the ordered probit model provides insufficient information, this paper further calculates the marginal effect of IU on farmers’ adoption of different green production technologies, and the results are shown in Table 2. The influence of IU on the adoption of different quantities of green production behaviors is different. Among them, IU has a negative impact on the adoption of 1–5 green production technologies. With the increase in adoption types, the probability of farmers adopting six green production technologies increases by 0.033 for each unit increase in IU. The likelihood of implementing seven green production technologies increased by 0.05, while the chance of adopting eight green production technologies saw an increase of 0.016.

This conclusion indicates that IU can improve the green production behavior of cooperative members, particularly in the uptake of 6-8 green production methods.

4.2. Endogeneity Test

Roodman (2011) suggests that, for ordered probit models with endogenous variables, a combination of instrumental variables and CMP estimation methods can effectively address the issue of endogeneity in the model.

This paper chooses “the average IU of other sample farmers in the same village” as the tool variable, because the overall IU of farmers in the same village must be closely related to the IU of farmers, meeting the requirement that the tool variables are highly related to the original variables. At the same time, the IU of other farmers in the same village is not directly related to the decision-making behavior of farmers, which meets the exogenous requirements. In the estimation of tool variables, the F value is 30, i.e., greater than 10, and there is no problem of weak tool variables, so “average IU of other sample farmers in the same village” is the appropriate tool variable for IU.

The estimated results show that atanhrho is −0.65 and significant, indicating that there is a significant correlation between the two equations in the simultaneous equation model, and the CMP method is more effective (

Table 3. Endogeneity test.

Variables	Model 2 (CMP Method)
	First-Stage Coefficient	Second-Stage Coefficient
Internet use		1.77 *** (0.47)
The average internet use of other sample farmers in the same village	1.41 *** (0.528)
Control variable	Yes	Yes
Regional control variable	Yes	Yes
Obs.	530	530
atanhrho_12		−0.65 ***

Note: (1) *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively; (2) Robust standard error in parentheses.

). The results of instrumental variable method also show that the estimates for IU are positive, and the findings obtained by above model are credible. IU has a positive influence on the adoption of green production behavior of members.

4.3. Heterogeneity Analysis

The continuous development of the rural society has led to an expansion in the heterogeneity of farmers. Consequently, the ways in which this heterogeneity influences the impact of IU on green production behavior warrant further discussion. This paper investigates the effect of IU on the green production behavior of farmers with varying characteristics, focusing on three aspects: education level, generation, and degree of dependence on cooperatives (

Table 4. Heterogeneity analysis.

Variables	Different Education Level		Degree of Dependence on Cooperatives		Different Generation
	High-Education Group	Low-Education Group	High-Dependency Group	Low-Dependency Group	Youth Group	Middle-Aged Group	Elderly Group
Internet use	0.282 * (0.149)	0.806 *** (0.225)	0.681 ** (0.315)	0.283 ** (0.113)	0.462 * (0.26)	0.492 *** (0.149)	0.428 (0.498)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Regional control variable	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	384	146	95	435	195	270	65
LR chi2	153.68	95.46	69.27	158.03	72.72	124.08	59.09
Pseudo R2	0.1074	0.1923	0.2166	0.0990	0.0983	0.1266	0.3333

Note: (1) *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively; (2) Robust standard error in parentheses.

).

First, the paper categorizes farmers based on their education level: those with more than nine years of education are classified as the high-education group, while those with less than nine years are classified as the low-education group. The results presented in the table indicate that groups with lower academic qualifications are more inclined to utilize the internet to acquire essential green production information for their daily activities. This enables them to address their agricultural knowledge gaps, enhance their literacy, and subsequently alter their production behavior.

Second, this paper categorizes interviewed farmers and their families based on their employment status within the cooperative to evaluate the degree of dependence between the farmers and the cooperative. Those who are employed are classified as part of the high-dependence group, characterized by a stronger identification with the cooperative and greater access to its resources. Their information channels are more effective, enabling them to leverage the internet to acquire relevant information about green production and to enhance their understanding of production practices. Conversely, farmers not employed by the cooperative fall into the low-dependence group. Research findings indicate that the internet usage coefficient for farmers in the high-dependence group is significantly higher than that of their low-dependence counterparts, measuring at 0.681.

Third, this paper further categorizes farmers by generation, using 1977 and 1963 as dividing points. Farmers older than 60 years are classified into the elderly group, those born after 1977 into the young group, and the remainder into the middle-aged group. Notably, the IU coefficient for the middle-aged group is higher, suggesting that this demographic relies more on internet assistance in their daily production activities.

4.4. Robustness Test

In order to ensure the reliability of the research results, this study conducted robustness testing using two methods: model replacement and restricted samples. Initially, an ordered logit model was utilized to assess the influence of IU on cooperative members’ green production behaviors. Subsequently, due to the specific requirements of green production technology on farmers’ learning ability and physical fitness, individuals over 65 years old were excluded from the sample group in the regression analysis. The data presented in

Table 5. Analysis of robustness test.

Variables	Change the Model (Ordered Logit Model)	Restricted Sample
	Model 3	Model 4
Internet use	0.481 *** (0.188)	0.363 *** (0.110)
Control variable	Yes	Yes
Regional control variable	Yes	Yes
Obs.	530	506
LR chi2	170.79	128.17
Pseudo R2	0.1231	0.1088

Note: (1) *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively;(2) Robust standard error in parentheses.

illustrate that the regression outcomes align with earlier findings, suggesting a strong degree of reliability in the model estimation.

4.5. Mechanism Analysis: Analysis of the Intermediary Effect of Organizational Trust and Organizational Norms

In order to further verify the impact path of IU on farmers’ green production behavior, this study takes organizational norms and organizational trust as mediating variables to test. The results of the empirical analysis are shown in

Table 6. The impact of organizational trust and organizational norms on internet use.

Variables	Organizational Trust	Organizational Norms
	Model 5	Model 6
Internet use	0.342 *** (0.121)	0.283 ** (0.116)
Age	0.0452 *** (0.00925)	0.0159 * (0.00936)
Educational level	0.0396 (0.0275)	−0.00147 (0.0268)
Village cadre status	0.300 ** (0.151)	0.185 (0.142)
Work	0.0206 (0.118)	−0.0417 (0.112)
Planting area	0.00191 *** (0.000594)	0.00171 *** (0.000585)
Cooperative level	0.177 *** (0.0469)	0.106 ** (0.0473)
Policy perception	0.517 *** (0.0667)	0.503 *** (0.0645)
Regional control variable	Yes	Yes
Obs.	530	530
LR chi2	164.09	113.95
Pseudo R2	0.1249	0.0978

Note: (1) *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively; (2) Robust standard error in parentheses.

and

Table 7. Analysis of mediating effect.

Variables	Model 7	Model 8
Internet use	0.191 * (0.109)	0.222 * (0.113)
Organizational trust	0.700 *** (0.0575)	-
Organizational norms	-	0.567 *** (0.0557)
Age	0.0210 ** (0.0103)	0.0359 *** (0.0101)
Educational level	0.0634 ** (0.0301)	0.0849 *** (0.0286)
Village cadre status	−0.153 (0.123)	−0.0783 (0.120)
Work	0.228 ** (0.111)	0.227 ** (0.114)
Planting area	0.0000634 * (0.0000382)	0.0000663 * (0.0000378)
Cooperative level	0.459 *** (0.0664)	0.491 *** (0.0649)
Policy perception	0.305 *** (0.0689)	0.338 *** (0.0701)
Regional control variable	Yes	Yes
Obs.	530	530
LR chi2	244.56	220.28
Pseudo R2	0.1829	0.1591

Note: (1) *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively; (2) Robust standard error in parentheses.

.

Firstly, the results presented in Table 6 demonstrate a significant and positive impact of IU on the organizational trust and norms of cooperative members. The regression coefficients of 0.342 and 0.283, respectively, indicate that utilizing the internet can assist cooperative members in better understanding the importance of green production and strengthening their trust in the cooperative.

Secondly, the paper examines the mediating effect of organizational trust on the impact of IU on cooperative members’ green production behavior. The regression coefficients of Model 7 and Model 5 were 0.7 and 0.342, respectively, which indicated that organizational trust played a role in mediating the effect of IU on the green production behavior of cooperative members. According to the above formula, the mediating effect of organizational trust on the impact of IU on the green production behavior of cooperative members is 55.62%.

Finally, the paper examines the mediating effect of organizational norms on the impact of IU on cooperative members’ green production behavior. The regression coefficient of organizational norms in Model 8 was 0.567, while that of IU in Model 6 was 0.283, which indicated that organizational norms play a mediating role in how IU influences the green production behaviors of cooperative members, and the proportion of mediating effect was 41.95%.

This paper found that IU positively affected farmers’ green production behavior through promoting the members’ perception of green production and enhancing the cooperative’s trust. The utilization of the internet and the enhancement of organizational trust and norms have significantly improved the green production behavior of cooperative members. The mediating effect of organizational trust is particularly strong. This indicates that the internet enables cooperative members to access current information and share experiences related to green production, leading to increased awareness and motivation to adopt green production practices. At the same time, members’ greater trust in the major decisions on green production made by the president of the cooperative motivated their adoption.

4.6. Further Discussion: Analysis of the Moderating Effect of Sales Satisfaction

This part examines the moderating effect of sales satisfaction on the impact of IU on the green production behavior of cooperative members, and

Table 8. Analysis of moderating effect.

Variables	Model 9
Internet use	3.531 * (1.925)
Sales satisfaction	0.461 *** (0.0898)
Internet use × sales satisfaction	−0.550 * (0.323)
Age	0.0333 *** (0.00979)
Educational level	0.0841 *** (0.0285)
Village cadre status	−0.00579 (0.113)
Work	0.205 * (0.112)
Planting area	0.0000950 * (0.0000569)
Cooperative level	0.468 *** (0.0625)
Policy perception	0.368 *** (0.0705)
Regional control variable	Yes
Obs.	530
LR chi2	151.33
Pseudo R2	0.1370

Note: (1) *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively; (2) Robust standard error in parentheses.

presents the results. The regression analysis indicates that the interaction coefficient between sales channels and IU is −0.55 and significant at the level of 10, which suggests that sales channels negatively moderate the influence of IU on green production behavior of members and verifies the hypothesis H3.

It can be seen from the research results that, although the internet can help cooperative members alleviate the market information asymmetry, reduce the transaction costs of members for participating in the market, and improve their bargaining power, members are still at a disadvantage in agricultural product sales. Cooperatives exhibit stronger market participation capabilities and a more solid economic foundation compared to their individual members. This enables them to lower transaction costs and incentivizes the adoption of green production practices. In a way, this suggests that the sales activities of cooperatives can serve as a substitute for traditional online platforms in promoting green production among their members.

5. Discussion

This paper aims to address the issues outlined in Section 1 regarding the impact of internet technology on the green transformation of agriculture within the context of digital transformation. In this context, it seeks to explore the role that agricultural cooperatives, as a vital organizational form for the advancement of modern agriculture, should play in this process.

First, this paper found that IU can enhance green production behaviors among cooperative members. The rapid advancement of information and communication technology, particularly the internet, has facilitated the widespread dissemination of agricultural knowledge [67]. Research indicates that IU can lead to a reduction in chemical fertilizer application and an increase in the adoption of agricultural technologies [23], as well as an increase in rice yields [68]. This study further confirms that this viewpoint also applies to farmers who join cooperatives. The heterogeneity analysis results show that farmers with a lower education level need the internet more to broaden their access to information, and middle-aged groups and people close to cooperatives can better apply information technology to green production.

On this basis, this study integrates the social capital theory into the impact of IU on farmers’ green production, while considering the mediating role of cooperative social capital. Previous research has affirmed the significant role of cooperatives in promoting agricultural green production technology [69]. Cooperatives provide a range of services that assist their members, including agricultural supply services, technical support, and marketing services [70]. The social capital theory offers a robust theoretical framework for understanding how cooperatives deliver both economic and social psychological benefits to their members [71]. The conclusions of this study further reinforce the notion that cooperative social capital enhances the green production behaviors of members. Notably, trust within the cooperative emerges as a crucial factor. Research indicates that organizational trust serves a significant intermediary role in influencing the green production behaviors of cooperative members utilizing the internet, accounting for more than 50% of the intermediary effect. Therefore, during the establishment and development of cooperatives, it is not only necessary to have strict systems to regulate the values of members, but also to strive to maintain trust among members and between members and managers.

Secondly, this paper finds that members’ satisfaction with cooperative sales services will replace the role of the internet in green production behavior. Previous research has categorized farmers’ agricultural product sales channels into self-sale, middleman sales, cooperatives, and other avenues. The adoption of internet technology has been shown to impact the sales channel selection of vegetable farmers [72]. This study further expands upon this conclusion. A possible explanation lies in the transaction cost theory, which posits that farmers prioritize minimizing transaction costs. The internet has enabled farmers to alleviate the burdens associated with agricultural product sales, reducing bargaining costs, increasing sales income, and enhancing decision making [73]. Similarly, the sales services offered by cooperatives also contribute to lowering transaction costs and facilitating farmers’ production decisions. Therefore, in the process of members’ adoption of green production technology, members’ satisfaction with the sales service provided by cooperatives will replace the role of the internet. This observation underscores that, in the context of digitalization, the internet can indeed fulfill the production material and sales needs of farmers by minimizing information barriers. Low transaction costs significantly influence farmers’ willingness to engage in green production. Nevertheless, cooperative managers can facilitate a transformation in production methods by enhancing sales satisfaction, thereby achieving sustainable production. It is important to note that, in the future, cooperatives should actively explore digital marketing strategies, effectively leverage internet information technology to expand marketing channels, and convert the challenges posed by internet technology into opportunities for mutual benefit.

This study has several limitations. Firstly, the current research is confined to rice farmers in Heilongjiang Province, resulting in a limited sample size. While there has been some research and analysis on the adoption of agricultural green production in other regions of China [63], it remains unclear whether similar results would be observed in other regions of China. Future studies should select representative areas across the country to further explore the relationship between IU, social capital, and cooperative members’ green production behaviors. Secondly, this paper analyzes eight green production behaviors among rice farmers as the explained variables, but does not examine each behavior in isolation. Therefore, future research should consider the impact of green production behaviors at different stages of the production process.

6. Conclusions

This paper utilizes survey data from 530 members of rice planting cooperatives in Heilongjiang Province to develop an analytical framework based on the social capital theory. The paper first evaluates the green production behavior of cooperative members across three stages: pre-production, mid-production, and post-production. Subsequently, it employs an ordered probit model to analyze the influence of IU on the green production behavior of cooperative members, utilizing the CMP estimation method to address potential endogeneity issues. Lastly, by considering organizational trust and organizational norms as mediating factors and sales satisfaction as a moderating factor, the study validates the impact pathway of IU on the green production behavior of cooperative members. The results show the following:

Firstly, IU has a significant promoting effect on the green production behavior of cooperative members, particularly among farmers with lower educational attainment, those that are middle-aged group individuals, and those with strong connections to cooperatives, and increasing the probability of IU will help cooperative members adopt six or more green production technologies. Therefore, it is necessary to strengthen the internet promotion in rural areas to help members better adopt green production technology.

Secondly, organizational trust and norms play a part in mediating the impact of IU on cooperative members’ green production behavior, and among them, organizational trust plays a more important intermediary role in the use of internet for developing the green production behavior of cooperative members. The use of the internet by cooperative members can enhance social capital within the cooperative and promote green production behavior. This is achieved by fostering trust among cooperative members and increasing recognition of the cooperative’s values.

Thirdly, sales satisfaction negatively moderates the impact of IU on the green production behavior of cooperative members, which indicates that transaction costs are particularly important for members’ production decisions, so members’ use of the internet forms a substitution relationship with cooperatives’ provision of sales services, and when the members are not satisfied with the sales of the cooperative, the internet can help the members of the cooperative to broaden the sales channels, thus improving their green production behavior.

This paper suggests that encouraging IU in rural areas could be a significant strategy to advance the green transformation of cooperative members’ production and facilitate green and sustainable agricultural development in the future. Based on the research findings, this paper believes that the following recommendations and suggestions should be proposed for both cooperative members and the cooperative:

Firstly, cooperative members should enhance their information literacy by utilizing online resources to learn about green production technologies and implementing them in their actual production processes. Local governments should establish green production exchange platforms to facilitate the integration of green production technologies with local practices and assist farmers in adopting these technologies.

Secondly, for cooperatives, it is essential to fully leverage their social capital advantages to foster cooperation among members. On the one hand, cooperatives must enhance their internal trust and establish clear rules and regulations. When making decisions regarding significant matters, cooperatives should prioritize the interests of their members and conduct votes in an open and democratic manner. The board of directors should ultimately make decisions by integrating the opinions of the members. Additionally, cooperatives should clearly define regulations concerning production standards and internal management, while also helping to deepen members’ understanding of these aspects and enhancing their perceived value of green production. On the other hand, cooperatives should concentrate on providing sales services. Transaction costs significantly influence farmers’ production decisions, and the use of the internet by cooperative members may jeopardize their own sales services. Therefore, in the context of agricultural digitalization, cooperatives should capitalize on the informational advantages of the internet to actively explore innovative methods for reducing farmers’ transaction costs. For instance, creating a digital marketing platform via WeChat can expand digital marketing channels, enhance brand development, deliver satisfactory sales services to members, and more effectively drive farmers to pursue green transformation.