Introduction

Common prosperity is a fundamental requirement of socialism, an important characteristic of Chinese-style modernization (Kakwani et al. 2022), and a social state that relies on economic factors (Li et al. 2023). Based on China’s national conditions and development stage, achieving common prosperity primarily involves expanding and improving the “cake” through the collective efforts of the entire population, enhancing the level of economic and social development, and laying a more solid material foundation for ensuring social fairness and justice. Additionally, it is essential to address relative poverty, fundamentally increase the income of low-income groups, and expand the proportion of the middle-income group to achieve shared prosperity. After China achieved a comprehensive victory in poverty alleviation in 2020, its main goal shifted to continuously narrowing the wealth gap and promoting common prosperity for all people. As early as 1955, Kuznets’ research pointed out the inverted “U” relationship between income disparity and economic growth (Kuznets, 2019). Since then, addressing poverty and narrowing the income gap has become a focal point of academic attention. For China, narrowing the poverty gap and achieving common prosperity has long been an “old topic.” A wealth of research has emerged on this subject, and further enriching and perfecting relevant studies based on existing research not only promotes the realization of common prosperity in China but also provides references and insights for other countries, rural areas, and developing economies around the world.

With the rapid advancements in technologies such as cloud computing, big data, blockchain, and mobile internet, internet-based technology support and applications are influencing the work and lives of Chinese residents, particularly those in rural areas. Empowered by digital technologies, internet usage has a significant impact on the realization of common prosperity in China. At the macro level, internet usage has become an important engine for economic growth (Haldar et al. 2023). This is reflected in its role in promoting industrial structural upgrades (Cheng et al. 2021), increasing total factor productivity (Peter et al. 2023), and enhancing regional innovation levels (Ren et al. 2023). At the micro level, opinions on whether internet usage can narrow the income gap are divided. Supporters argue that internet usage can significantly reduce income disparity and alleviate poverty. For instance, Meng (2023) found that internet usage helps individuals enhance their production efficiency, thereby improving personal work capabilities and income levels. Conversely, some scholars argue that the digital divide caused by low internet usage rates among certain groups leads to significant barriers in accessing information, exacerbating differences among individuals and affecting income distribution patterns (David and Dorn, 2013; Yang et al. 2023). Thus, while internet usage can impact economic growth and narrow income gaps, there is no consensus on its effects on income disparity. Consequently, whether internet usage can positively influence common prosperity remains to be explored further. This article aims to investigate the impact of internet usage on common prosperity, providing additional evidence for related studies.

Currently, rural areas still face significant issues such as a large wealth and income gap and an excessive proportion of low-income individuals, which hinder the progress toward common prosperity. Therefore, the rural regions of China provide an attractive environment for studying the issue of common prosperity. First, by the end of 2020, China had reduced its rural poor population by a cumulative total of 98.99 million, with an average annual poverty reduction of 12.37 million and an average annual decline in the poverty rate of 1.3 percentage points (The official website of the National Bureau of Statistics of China, http://www.stats.gov.cn/), leading to a fundamental change in the poverty situation in China and the elimination of absolute poverty. It is evident that in rural areas of China, farmers are the main force driving the realization of common prosperity. Secondly, the dual drive of innovation and entrepreneurship is an important measure to promote the achievement of common prosperity goals. China has always emphasized mass entrepreneurship and innovation, stating that “talents are the primary resource, and innovation is the primary driving force.” The key to achieving common prosperity lies in people, and supporting and encouraging farmers to start businesses and find employment in rural areas is an effective way to reach the goals of common prosperity (Hong et al. 2024). Therefore, farmers’ entrepreneurship has a significant impact on common prosperity. At the same time, as a new technology, the internet has profoundly changed the economic decision-making behavior of farmers by embedding itself within their communities, leveraging advantages such as transcending time and space, information sharing, and lower costs. With the increasing prevalence of the internet in rural areas, the rising internet usage rate has further boosted the level of entrepreneurship in these regions. Relevant studies suggest that the use of the internet helps optimize the entrepreneurial environment and address information barriers between regions (Wang et al. 2023), while also aiding in breaking traditional financial service boundaries to alleviate financing constraints (Li et al. 2023), thereby encouraging farmers to start businesses. Furthermore, farmer entrepreneurship will further narrow the wealth gap and positively promote the realization of common prosperity. Yin Zhichao et al. (2019) identified rural non-agricultural employment as a key channel for digital inclusive finance to reduce income disparity among Chinese families, and their research results indicate that internet use can promote rural family entrepreneurship, reduce income gaps, and improve family income levels. In this context, exploring the role of farmer entrepreneurship as an intermediary variable in examining how the internet helps narrow the wealth gap provides a new perspective for this study.

Chinese society exhibits a typical urban-rural dual structure, with unbalanced and insufficient development being the main reasons for income disparity. The mistrust arising from these differences can alter people’s tendencies to interact and collaborate, leading to significant impacts on social capital in rural areas. Rural China is a quintessential “acquaintance society,” where the extensive social networks based on kinship among farmers serve as a dominant force throughout the entrepreneurial process (Guo Hongdong et al. 2013). Wang and Wang (2022) argue that the integration of the internet replicates real-world social relationships into online networks, providing channels for the flow of funds to support entrepreneurship. Meanwhile, the social network can ensure the transmission and sharing of entrepreneurial information due to the reverse embedding of the internet. In other words, farmers can rely on their social networks to obtain informal loans when they need to start a business, and they can also use the internet to transmit and share relevant resources and information within their social networks during the entrepreneurial process. However, this network relationship, which intertwines social networks and the internet, has not received adequate attention in research related to the internet, farmer entrepreneurship, and common prosperity. Therefore, it is essential to explore the moderating role of this “interwoven” network relationship in how internet usage influences common prosperity through the intermediary path of farmer entrepreneurship.

Based on the above analysis, this paper studies the relationship between the use of the Internet and common prosperity, focusing on the mediating role of farmers’ entrepreneurship, so as to enrich the channel understanding of the role of the Internet in common prosperity. At the same time, digital entrepreneurial networks are introduced to analyze how digital entrepreneurial networks regulate the impact of Internet use on common prosperity. This study will help clarify the internal logical relationship between rural household entrepreneurship and digital entrepreneurship network in the Internet use and narrowing the gap between the rich and the poor, deepen the relevant theoretical system of the economic effect of Internet use, and provide certain theoretical and policy references for exploring the improvement of digital entrepreneurship network, the increase of entrepreneurial willingness, and the realization of common prosperity.

The remaining content of this paper is organized as follows: The second section presents the theoretical analysis and research hypotheses; the third section outlines the research design; the fourth section conducts the empirical analysis; and the fifth section concludes with findings and policy recommendations.

Theoretical analysis and research hypotheses

The impact of Internet use on common prosperity

The use of the internet effectively alleviates issues such as information asymmetry and financing constraints, positively impacting various aspects of farmers’ production and life, thereby reducing income disparities among farmers and promoting the achievement of common prosperity. With the continuous advancement of initiatives like “Broadband China” and “Digital Rural Development,” the internet infrastructure in many rural areas of China has gradually improved, providing the necessary external conditions for increasing farmers’ incomes. Specifically, first, the use of the internet facilitates the expansion of social networks and helps farmers identify employment opportunities. Farmers, who have long been embedded in a typical Chinese rural social structure characterized by strong kinship ties, share similar family backgrounds, thought processes, and knowledge structures with their local peers. As a result, the homogeneity of information transfer among them is quite strong, making imitation-based entrepreneurship easier. However, with the development and application of the internet, strong relationship networks have gradually opened up, and the role of weak relationship networks has become more prominent, allowing farmers to access diverse and non-homogeneous information resources that are beneficial for innovative employment (Yang Xueru and Zou Baoling, 2018; Wu et al. 2024). Second, the application of internet technology can break through spatial and temporal limitations, lowering the barriers to financing for farmers by reducing the transaction and time costs of financial services (Zhang, 2022). Additionally, digital technology effectively alleviates financing constraints by establishing credit risk protection mechanisms (Wang, 2016). The internet encourages and guides farmers to explore new financing channels, specifically through the establishment of internet financing platforms. These platforms rely on electronic systems for lending and utilize big data to assess clients’ repayment abilities, thus reinforcing the constraints of credit risk. They have become an effective means of addressing the financing challenges faced by farmers in entrepreneurship. By relying on internet financing platforms, farmers can alleviate financing constraints for their entrepreneurial ventures, which in turn promotes resource allocation within households, stimulates entrepreneurial activities, and drives economic growth and the enhancement of wealth levels in rural areas of China. Third, information technologies such as the internet can effectively accelerate the processing and flow of data, improving the matching of supply and demand in the labor market and alleviating information constraints (Ding and Liu, 2022), thereby promoting entrepreneurship.

However, while the use of the internet effectively alleviates issues such as information asymmetry and high transaction costs, the development of the internet still creates significant digital divide effects among regions, urban and rural areas, and different groups. The current digital divide mainly manifests in three forms: the access gap due to inadequate digital infrastructure, the usage gap arising from varying levels of exposure to the internet that leads to differences in the use of emerging tools like digital finance, and the capability gap resulting from differences in digital literacy (Shen, 2023). Qiu Zeqi et al. (2016) argue that the existence of these digital divides allows farmers in areas with high internet usage rates to gain richer digital experiences and enjoy digital dividends, thereby widening the income growth gap between them and non-entrepreneurial groups. This further expands income disparities and hinders the realization of common prosperity, which contradicts the goal of achieving common prosperity. Additionally, the digital divide exacerbates social inequities, leading to limited opportunities in education, employment, and healthcare for those unable to access or effectively utilize the internet, thus restricting their ability to improve their living conditions (Wan et al. 2024). Based on this, we propose Hypothesis 1.

H1: The use of the internet by farmers will widen the wealth gap between classes and hinder the achievement of common prosperity (Fig. 1).

Fig. 1
figure 1

Mechanism diagram of the impact of Internet use on common prosperity.

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Theoretical analysis of the relationship between internet use, farmer entrepreneurship, and common prosperity

Numerous studies have indicated that internet use significantly promotes rural household entrepreneurship. In the contemporary era of rapid digital technology development, farmers with higher educational levels increasingly demand internet access to meet their work and life needs. Consequently, farmers with experience in internet skills training or those who have stronger learning and application abilities regarding internet knowledge also see improvements in their entrepreneurial performance. Specifically, participation in knowledge and technology training, as well as enhanced internet learning ability, is associated with an increase in entrepreneurial performance by 0.303 and 0.060 percentage points, respectively (Su Lanlan et al. 2020). This indicates that farmers benefit from internet skills training or learning, enjoying greater informational advantages that help them acquire more resources and accurately identify entrepreneurial opportunities (David and Dorn, 2013; Yang et al. 2023). Moreover, households with greater wealth accumulation, due to sufficient startup capital, show a greater willingness to engage in entrepreneurial activities. Digital technologies, represented by the internet, have given rise to various new forms of employment, altering the operational and organizational structures of traditional market participants in rural areas (Huang et al. 2018). Rural e-commerce, empowered by typical internet technologies, has become the mainstream form of rural household entrepreneurship (Zang et al. 2023). According to Yin et al. (2019), individuals decide to pursue entrepreneurship only when the expected income from entrepreneurship exceeds the associated costs. The emergence of e-commerce breaks geographical constraints, enhances farmers’ access to information resources, reduces information gathering costs (Song and Li 2021), and facilitates cost-effective procurement and sales through online diversified product choices and bargaining assistance (Su Lanlan et al. 2020). This, in turn, reduces the difficulty of independent entrepreneurship, promoting entrepreneurial activities among farmers. Finally, Su Lanlan et al. (2020) suggest that the more individuals in a farmer’s social network use the internet, the more likely the farmer is to encounter and learn from it, thereby stimulating their enthusiasm for entrepreneurial activities.

Internet technology, characterized by high convenience, strong penetration, and extensive coverage in information transmission, can reduce barriers to market information transmission in terms of time and space. Farmers, through the use of the internet for work, learning, entertainment, socialization, and business activities, become embedded in the entire social network, actively participating in entrepreneurship, contributing to achieving common prosperity.

With the rapid development of digital technology, the widespread use of the internet has significantly reduced the digital divide, promoting farmer entrepreneurship and thereby narrowing income gaps among households. This is manifested in three main aspects. First, the widespread use of the internet reduces the access gap. Su and Kong (2020) suggest that e-commerce entrepreneurship, relying on internet platforms, overcomes disadvantages such as information asymmetry, long transportation times, and high transaction costs. This creates favorable conditions for farmer entrepreneurship and further confirms the dividends brought by internet use in reducing access gaps. Second, a reduction in the usage gap in digital tools. Farmers can use the internet to access services such as internet loans, financial management, and insurance. This not only helps alleviate credit constraints and increase information availability, diversifying family income sources and facilitating rational resource allocation, but also enhances family financial literacy through internet-provided social interactions, strengthening social trust, increasing farmers’ willingness to start a business (He and Li, 2019). It leads to increased household income and helps narrow the wealth gap. Third, internet use effectively opens up channels for obtaining information resources. Farmers can not only uncover potential information through the internet but also gain diverse financial knowledge (Lin and Lin, 2021), thereby enhancing their financial literacy and reducing the digital ability gap. Related studies suggest that the internet helps reduce the digital usage gap, enabling farmers to participate in digital finance, which, in turn, helps expand financing and identify entrepreneurial opportunities. It also aids in reducing information asymmetry, thereby enhancing entrepreneurial intentions (Xiong and Dong, 2021). Moreover, it contributes to solving the problem of insufficient funds and a single sales channel in rural e-commerce entrepreneurship (He, 2022). Therefore, internet use effectively reduces the digital divide, promotes entrepreneurship, and entrepreneurship significantly stimulates the equalization of family income (Zhang et al. 2019). Based on this, we propose hypothesis 2.

H2: Internet usage enhances farmers’ willingness to start businesses, thereby positively contributing to common prosperity (Fig. 2).

Fig. 2
figure 2

Influencing mechanism of Internet use, rural entrepreneurship and common prosperity.

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Analysis of the moderating effect of the digital entrepreneurial network

There is a consensus in academia regarding the positive role of the internet and social networks in rural household entrepreneurship. The analysis of the mediating effect of social networks focuses on the directional influence of the internet on social networks. During the entrepreneurial process, the internet becomes embedded in the social networks of rural households, allowing the replication of real-life entrepreneurial networks and forming digital entrepreneurial networks. Due to the convenience of the internet, online interactions among entrepreneurs may even surpass face-to-face exchanges in fostering social relationships (McKenna and Bargh, 2000). However, online interactions may also crowd out offline opportunities. Consequently, the impact of digital entrepreneurial networks on rural household entrepreneurship remains uncertain. The discussion around the role of digital entrepreneurial networks primarily revolves around the internet and social networks.

The internet has a strong information-accessing function. In the unique entrepreneurial environment of rural China, where infrastructure is underdeveloped and information dissemination is limited, the internet offers entrepreneurs access to critical information, enabling efficient information processing and ultimately supporting entrepreneurial decision-making. Due to disparities in development between urban and rural areas, information often concentrates in cities, giving urban residents more information access than rural residents. However, as an information-sharing medium, the internet effectively lowers the information costs for rural entrepreneurs, granting them easy access to government, market, and technological information for entrepreneurial decision-making. This, in part, alleviates the inherent information asymmetries in the entrepreneurial process and helps bridge the urban–rural information gap. Nevertheless, most farmers lack the skills to filter information independently, and the vast, unfiltered information on the internet may make it difficult for them to identify relevant entrepreneurial information accurately.

Social networks also play an information-sharing role. Rural China has the characteristics of a close-knit community. Although information flows less freely, it still benefits from strong social cohesion and localized resources. Rural entrepreneurs often obtain entrepreneurial information through social interactions within their networks, relying particularly on local connections. Studies show that ~75% of the entrepreneurial information farmers acquire comes from relatives, friends, or neighbors (Tang et al. 2020a). Specifically, in the early stages of entrepreneurship, the inherent risks of starting a business make it challenging for rural entrepreneurs to obtain necessary resources through formal channels in an immature economic system. Strong emotional connections within close-knit social networks can tolerate the high uncertainty of the initial entrepreneurial phase, serving as primary supporters. In rural China, strong relationships carry significant trust and reliance, particularly when facing risky ventures or unfamiliar fields, favoring the creation of networks based on strong social ties. In the middle stages of entrepreneurship, close relatives and friends provide initial information, but relying solely on strong ties is insufficient for sustained support. Rural entrepreneurs will thus expand their networks, incorporating weaker ties to form a mixed entrepreneurial network. In the later stages, as rural enterprises grow, more contractual relationships based on weak ties emerge, while emotionally based strong ties, though relatively less critical, remain vital. The diversification of resource channels enables sustained business growth, making entrepreneurs increasingly dependent on adapting existing weak ties and establishing new ones.

Thus, given the similarities in the information-sharing functions of the internet and social networks, there is potential for the internet to become embedded in social networks, thereby creating a digital entrepreneurial network with a moderating effect. In other words, the internet integrates with social networks, where its anonymity helps build new interpersonal connections and fosters the development of strong social ties while also replicating weaker social ties from real-life networks, thus promoting neighborly interactions. This highlights the critical role of social networks in securing financial resources, where social network relationships show a positive mediating effect on the impact of internet-enabled entrepreneurship. Moreover, the internet’s integration into social networks creates a digital entrepreneurial network that aids farmers in accessing entrepreneurial information and knowledge within these networks. It also provides a platform for information exchange, helping them identify entrepreneurial opportunities and enabling information and knowledge sharing (Wang Haolin and Wang Ziming, 2022). The combination of the internet’s new influence with the typical social structure of rural areas greatly optimizes the entrepreneurial environment for rural households. Within a strong digital entrepreneurial network, the internet’s integration further enhances entrepreneurial intentions, paving the way toward common prosperity. Accordingly, Hypothesis 3 is proposed.

H3: Digital entrepreneurship networks positively moderate the impact of internet usage on common prosperity (Fig. 3).

Fig. 3
figure 3

The regulation mechanism of digital entrepreneurship networks.

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Research design

Data source

The research data in this study comes from two parts: the first part is the 2019 China Household Finance Survey data (CHFS2019). This survey covers 34,643 households in 29 provinces and municipalities in mainland China (excluding Tibet and Xinjiang Autonomous Region), with national and provincial representativeness. It includes three datasets: individual, family, and master. The database was established by the China Household Finance Survey and Research Center at Southwestern University of Finance and Economics as a national sampling survey project. It aims to collect micro-level information related to household finance, including housing assets and financial wealth, liabilities and credit constraints, income and consumption, social security and insurance, intergenerational transfer payments, demographic characteristics, and employment, as well as payment habits. This survey is conducted every two years with nationwide household tracking, covering 29 provinces, 355 counties (districts, county-level cities), and 1428 communities, including a total of 40,011 households and 127,000 individuals, ensuring representativeness at the national, provincial, and sub-provincial levels.

The micro-survey database includes samples from both rural (farming) households and non-rural (non-farming) households. Since this study focuses on rural households in China, we selected only rural household residents as the study sample.Footnote 1 In addition, samples aged 75 and above, as well as outliers, are removed, and continuous variables are truncated and logarithmically transformed. The final sample consists of 12,878 rural households, including 1442 entrepreneurial households (11.2%) and 11,436 non-entrepreneurial households (88.8%). The second part is the Digital Economy Index, obtained from the Cai Xin Think Tank. The calculation of this index mainly includes three dimensions: information development, internet development, and digital transaction development. It contains eight secondary indicators, including information infrastructure, information impact, fixed-end internet infrastructure, mobile-end internet impact, fixed-end internet impact, mobile-end internet impact, digital transaction infrastructure, and digital transaction impact. In order to match the digital economy index of each province with the rural households of each province, this paper matches the selected digital economy index in 2019 and the CHFS data in 2019 at macro and micro levels.

Variable selection

Dependent variable

The dependent variable in this study is common prosperity. Common prosperity is one of the important goals of the Chinese path to modernization, the core of which is to achieve the universal improvement of the living standards of all people. The income gap is often used as a key indicator to measure people’s living standards, the gap between the rich and the poor, and other equality issues. An excessive income gap will lead to uneven distribution of social resources, affect social fairness, and then affect social stability and sustainable development of the economy. The goal of achieving common prosperity is to reverse the expanding trend of the income gap, so narrowing the income gap is one of the important signs of common prosperity. Therefore, this paper, following the approach of Li et al. (2020), selects the “total household income” indicator from the database to measure the degree of household wealth based on actual income levels. To clearly assess the relative wealth gap, the ratio of each household’s wealth level to the highest income level in their region is used as an indicator of income disparity. A larger ratio indicates a smaller wealth gap, suggesting a closer alignment with the goal of achieving common prosperity.

Core explanatory variable

The main explanatory variable in this study is internet use. Based on the study by Cui and Cheng (2023), the question “What type of phone do you currently use?” is selected from the database as a proxy variable for “Internet usage.” Respondents are asked to choose from: “1. Smartphone (used for online shopping, social chatting, and other internet applications); 2. Non-smartphone; 3. No phone.” If the respondent uses a smartphone for online shopping, social chatting, or other internet applications, they are considered to meet the “internet user” criteria and are assigned a value of 1. If the respondent selects “2. Non-smartphone” or “3. No phone,” they are considered not to meet the “internet user” criteria and are assigned a value of 0.

Mediating variable

The mediating variable in this study is farmer entrepreneurship. Based on the study by Cui and Cheng (2023), the question “What type of phone do you currently use?” is selected from the database as a proxy variable for “Internet usage.” Respondents are asked to choose from: “1. Smartphone (used for online shopping, social chatting, and other internet applications); 2. Non-smartphone; 3. No phone.” If the respondent uses a smartphone for online shopping, social chatting, or other internet applications, they are considered to meet the “internet user” criteria and are assigned a value of 1. If the respondent selects “2. Non-smartphone” or “3. No phone,” they are considered not to meet the “internet user” criteria and are assigned a value of 0.

Moderating variable

Digital entrepreneurial network

The digital entrepreneurship network is a network relationship where social relationship networks and the internet are “inter-embedded.” The embedding of digital internet replicates real-world social relationships into online relationship networks, providing a channel for the circulation of entrepreneurial funds. At the same time, the reverse embedding of social relationship networks through the digital internet facilitates the transmission and sharing of information for the respondent’s entrepreneurship (Wang Haolin and Wang Ziming (2022). As a relationship network represented by the internet, the digital entrepreneurship network not only promotes the acquisition of social capital for farmers but also has a network operation effect, playing an important role in the entrepreneurial process of farmers (Tang et al. 2020b). In this study, the frequency of internet socializing is used as a proxy variable for the family’s digital entrepreneurship network. The survey question selected is “How much did your household spend on communication services such as phone bills, internet fees, and postage last year?” This question helps determine the respondent’s internet socializing frequency, and the natural logarithm of this indicator is used to represent the digital entrepreneurship network.

Control variables

To avoid the influence of confounding factors and improve the credibility of causal relationships in the study, building on the studies of Huang et al. (2021) and Song et al. (2021), we selected several control variables to ensure the accuracy and reliability of the research and to help obtain more accurate conclusions. Specifically, the following control variables were selected for this study:

Gender

In traditional rural Chinese society, men are often the heads of households, and the household head is the decision-maker for family production and business activities, thus holding decision-making power. Compared to women, men are generally considered to have an advantage in entrepreneurship, such as access to entrepreneurial capital and networks, which could help reduce income inequality and promote common prosperity.

Age

Entrepreneurship is a long-term and challenging process that requires sufficient physical strength to cope with various problems and pressures. Younger farmers generally have better physical health, which is an essential condition for entrepreneurship. Additionally, younger farmers tend to have a stronger willingness to take risks, whereas older farmers may prefer a more stable lifestyle and may be less willing to take on the additional risks of entrepreneurship.

Education

Education level is closely related to an individual’s knowledge, skills, and cognitive abilities. Farmers with higher education levels have more human capital, greater knowledge, and skills, enabling them to better recognize entrepreneurial opportunities. This increases the likelihood of farmers engaging in entrepreneurship, which contributes to the high-quality development of the rural economy and promotes common prosperity.

Marrage

Married farmers often have a more stable financial foundation and the support of family members, making them more likely to pursue entrepreneurship, which can increase household income.

Health

Farmers in good health have better physical and mental conditions, which may make them more likely to engage in entrepreneurial activities, thus enhancing their entrepreneurial capacity and economic income.

Political

Farmers who are members of the Communist Party of China (CPC) may have advantages in accessing policy support and social resources.

Financial literacy

Financial literacy reflects an individual’s ability to understand and apply financial knowledge, as well as their awareness and attitude towards risks. Farmers with higher financial literacy can rationally analyze the benefits and risks of financing, plan their finances more effectively, increase their enthusiasm for entrepreneurial financing, reduce entrepreneurial risks, and promote entrepreneurship and family income growth.

Household size

Household size is relevant to the study in two ways. On one hand, the number of family members determines whether the household has enough labor and resources. Larger households can contribute more to entrepreneurial activities. On the other hand, a larger household size reflects higher economic pressure, as a significant proportion of the population in China (around 40%) consists of non-working elderly individuals (over 60) and children (under 16). A larger household may face greater financial strain, potentially reducing the energy and funds available for entrepreneurial activities, leading to lower household income and greater income inequality.

Total household income

Farmers with higher income face less financial pressure when starting a business, allowing them to better utilize idle funds for entrepreneurship, which enhances their entrepreneurial capacity and economic stability, ultimately promoting common prosperity.

Risk preference

Risk preference reflects an individual’s attitude towards the types and extent of risks they are willing to take during entrepreneurship. Farmers with a higher risk preference are more capable of balancing the risks and rewards of entrepreneurship rationally and are more willing to engage in entrepreneurship to promote family income growth. To accurately measure the level of risk, we classify responses as follows: “High risk, high return” and “Slightly higher risk, slightly higher return” are considered high-risk investment types, while “Average risk, average return,” “Slightly lower risk, slightly lower return,” “Not willing to take any risk,” and “Don’t know” are considered low-risk investment types. The specific variables are defined in Table 1.

Table 1 Variable definitions and descriptive statistics.
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Online banking usage

Through online banking, rural residents can access financial services more conveniently, stimulate farmers’ entrepreneurial willingness and decision-making, which will help increase farmers’ income, narrow the income gap between urban and rural areas, promote rural revitalization, and promote common prosperity.

Commercial loan

Commercial lending provides rural entrepreneurs with the necessary financial support to help them overcome financial bottlenecks, thereby increasing rural household incomes and narrowing income gaps.

Model construction

Mediation effects model

In order to analyze the impact of internet usage on shared prosperity, drawing on the mediation analysis method proposed by Wen and Ye (2014), this study aims to verify whether farmers’ entrepreneurship is an effective means of achieving common prosperity. The model is constructed as shown in Eqs. (1)–(3).

$${{{ {wealth}}}}_{i}={\alpha }_{0}+{\alpha }_{1}{T}_{i}+{\alpha }_{2}{X}_{i}+{\varepsilon }_{i}$$
(1)
$${M}_{i}={c}_{0}+{c}_{1}{T}_{i}+{c}_{2}{X}_{i}+{\varepsilon }_{i}$$
(2)
$${{{ {wealth}}}}_{i}={\gamma }_{0}+{\gamma }_{1}{T}_{i}+{\gamma }_{2}{M}_{i}+{\gamma }_{3}{X}_{i}+{\varepsilon }_{i}$$
(3)

In the mediation effect model, if the independent variable T influences the dependent variable wealth through the variable M, then M is referred to as the mediator. In this pathway, the effect of T on wealth is known as the total effect. The total effect is divided into direct and indirect effects, with the mediation effect belonging to the indirect effect. Specifically, in Model (1), the coefficient α1 represents the total effect of internet usage on common prosperity, while in Model (3), the coefficient γ1 represents the direct effect of internet usage on common prosperity, and \({\gamma }_{2}\) represents the mediating effect of farmer entrepreneurship, with ε indicating the error term. For the test of mediation effects, first, the significance of the coefficient \({\alpha }_{1}\) in Model (1) is tested to determine if there is a total effect. If it is significant, then a total effect exists. Secondly, if the coefficient \({c}_{1}\) in Model (2) is significant, it indicates that internet usage has a significant impact on farmer entrepreneurship. Finally, if the coefficient \({\gamma }_{1}\) in Model (3) is significant, it suggests that the direct effect of internet usage on common prosperity is significant; otherwise, only the mediation effect holds.

Endogenous transformation model

Since it is not possible to observe the entrepreneurial intentions of the same household under both internet use and non-internet use conditions, it is not directly possible to evaluate the impact of internet use on farmers’ entrepreneurship. Therefore, this study constructs an endogenous switching probit model and, based on the regression results, builds a “counterfactual” analysis framework to further estimate the treatment effect of internet use on the probability of farmers’ entrepreneurship.

In this study, internet use is treated as the treatment variable \({T}_{i}\), and the model for farmers’ entrepreneurial intentions is set as follows:

$${T}^{* }={Z}_{i}\gamma +{\mu }_{i},{T}_{i}=\left\{\begin{array}{c}1,{T}^{* } > 0\\ 0,{T}^{* }\le 0\end{array}\right.$$
(4)
$${Y}_{i}={X}_{i}{\beta }_{i}+\delta {T}_{i}+{\varepsilon }_{i}$$
(5)

In Eq. (4), \({T}^{* }\) is the latent variable, \({T}_{i}=1\) indicates that household i uses the internet, and \({T}_{i}=0\) indicates that household i does not use the internet. \({Z}_{i}\) is the control variable, and \({\mu }_{i}\) is the random disturbance term. In Eq. (5), \({Y}_{i}\) represents the entrepreneurial activity of household i, \({X}_{i}\) is the control variable, and \({Z}_{i}\) is the random disturbance term.

The model for the entrepreneurial intentions of farmers under internet use and non-internet use is expressed as

$${Y}_{{ia}}={X}_{{ia}}{\beta }_{a}+{\sigma }_{\mu a}{\lambda }_{{ia}}+{\varepsilon }_{{ia}},{{\rm {if}}}\,{T}_{i}=1$$
(6)
$${Y}_{{in}}={X}_{{in}}{\beta }_{n}+{\sigma }_{\mu n}{\lambda }_{{in}}+{\varepsilon }_{{in}},{{\rm {if}}}\,{T}_{i}=0$$
(7)

In Eqs. (6) and (7), \({Y}_{{ia}}\) and \({Y}_{{{\rm {in}}}}\) represent the entrepreneurial intentions of households using the internet and not using the internet, respectively. \({\sigma }_{\mu a}\) and \({\sigma }_{\mu n}\) are the covariances, where \({\sigma }_{\mu a}=\mathrm{cov}\left({\mu }_{i},{\varepsilon }_{{ia}}\right)\) and \({\sigma }_{\mu n}=\mathrm{cov}\left({\mu }_{i},{\varepsilon }_{{in}}\right)\). \({\lambda }_{{ia}}\) and \({\lambda }_{{{\rm {in}}}}\) are introduced inverse Mills ratios, and \({\beta }_{a}\) and \({\beta }_{n}\) are the coefficients to be estimated. Finally, a simultaneous estimation is performed using the full information maximum likelihood method.

Comparisons of the expected values of entrepreneurial intentions under internet use and non-internet use are made through counterfactual assumptions, thereby estimating the average treatment effect of internet use on farmers’ entrepreneurial intentions.

The expected value of farmers’ entrepreneurial intentions under internet use is given by

$${\rm{E}}\left[\left.{Y}_{{ia}}\right|{T}_{i}=1\right]={X}_{{ia}}{\beta }_{a}+{\sigma }_{\mu a}{\lambda }_{{ia}}$$
(8)

The expected value of farmers’ entrepreneurial intentions under non-internet use is given by

$${\rm{E}}\left[\left.{\rm{Y}}_{\rm{in}}\right|{\rm{T}}_{\rm{i}}=0\right]={\rm{X}}_{\rm{in}}{{\upbeta }}_{\rm{n}}+{{\sigma }}_{{\rm{\mu }}{\rm{n}}}{{\lambda }}_{{\rm{in}}}$$
(9)

Considering the counterfactual assumptions, the expected value of farmers’ entrepreneurial intentions under non-internet use when using the internet is given by

$$E\left[\left.{\rm{Y}}_{\rm{in}}\right|{\rm{T}}_{\rm{i}}=1\right]={\rm{X}}_{\rm{ia}}{{\upbeta }}_{\rm{n}}+{{\sigma }}_{{\rm{\mu }}{\rm{n}}}{{\lambda }}_{\rm{ia}}$$
(10)

And the expected value of farmers’ entrepreneurial intentions under internet use when not using the internet is given by

$${\rm{E}}\left[\left.{\rm{Y}}_{\rm{ia}}\right|{\rm{T}}_{\rm{i}}=0\right]={\rm{X}}_{\rm{in}}{{\upbeta }}_{\rm{a}}+{{\sigma }}_{{\rm{\mu }}{\rm{a}}}{{\lambda }}_{{\rm{in}}}$$
(11)

Therefore, the average treatment effect of internet use on farmers’ entrepreneurial intentions is

$${{{ {ATT}}}}_{i}=E\left[\left.{Y}_{{ia}}\right|{T}_{i}=1\right]-E\left[\left.{Y}_{{{in}}}\right|{T}_{i}=1\right]={X}_{{ia}}\left({\beta }_{a}-{\beta }_{n}\right)+\left({\sigma }_{\mu a}-{\sigma }_{\mu n}\right){\lambda }_{{ia}}$$
(12)

And the average treatment effect of non-internet use on farmers’ entrepreneurial intentions is

$${{{ {ATU}}}}_{i}=E\left[\left.{Y}_{{ia}}\right|{T}_{i}=0\right]-E\left[\left.{Y}_{{in}}\right|{T}_{i}=0\right]={X}_{{in}}\left({\beta }_{a}-{\beta }_{n}\right)+\left({\sigma }_{\mu a}-{\sigma }_{\mu n}\right){\lambda }_{{in}}$$
(13)

Moderation effects model

Referring to Wen and Ye's (2014) test methods for the moderated mediation model, this paper establishes models (14) and (15), respectively, to judge whether non-income factors play an indirect moderating effect in the process of Internet use affecting common prosperity. The specific models are as follows.

$${{{\rm {wealth}}}}_{i}={\pi }_{0}+{\pi }_{1}{T}_{i}+{\pi }_{2}{F}_{i}+{\pi }_{3}{X}_{i}+{\varepsilon }_{i}$$
(14)
$${{{\rm {wealth}}}}_{i}={\omega }_{0}+{\omega }_{1}{T}_{i}+{\omega }_{2}{F}_{i}+{\omega }_{3}{{T}_{i}* F}_{i}+{\omega }_{4}{X}_{i}+{\varepsilon }_{i}$$
(15)

Among them, F represents the moderating variable non-income factors (digital entrepreneurship network, education level, and financial literacy level), and \({{T}_{i}* F}_{i}\) represents the interaction term after decentralization.

Empirical testing and results analysis

Multicollinearity test

Before conducting empirical testing and analysis, this study performs a multicollinearity test on the selected variables to avoid spurious regression results. The results show that the maximum variance inflation factor (VIF) is 1.82, and the average VIF is 1.34, both well below 10. Therefore, it is considered that there is no serious multicollinearity among the variables, ensuring the effectiveness of the estimation results using the methods employed in this study.

Baseline regression results analysis

The impact of internet use on common prosperity

The regression results indicate that internet usage has a significant negative impact on common prosperity at the 1% statistical level. For each 1% increase in internet usage, the wealth gap will expand by 0.12%. This finding suggests that due to the skill-biased nature of the internet, differences in internet usage levels among farmers lead to disparities. To some extent, it allows groups with greater information processing advantages to obtain more economic benefits, resulting in an increase rather than a decrease in household wealth disparities and widening wealth gaps between different social strata. Hypothesis 1 is validated, as shown in Table 2.

Table 2 The mediating effect of rural households’ entrepreneurship is tested.
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The impact of the relationship between Internet Use, Farmer Entrepreneurship, and Common Prosperity

Impact of internet use on farmers’ entrepreneurship

This study takes internet use as the core explanatory variable and farmers’ entrepreneurial intentions as the outcome variable. Stata 17 software is used to perform full information maximum likelihood estimation on the model (Eqs. (4)–(6)). The regression results are presented in Table 3.

Table 3 Results of joint estimation between the Internet use decision model and the farmer households’ entrepreneurial willingness model.
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Table 3 demonstrates that the model fit Wald test is significantly non-zero at the 1% level, indicating the effectiveness of the model setup in statistical terms. The LR test of independence rejects the null hypothesis of rho1 = rho0 at the 5% level, and the negative significance of rho1 at the 1% level suggests the presence of unobservable factors simultaneously influencing internet usage and farmers’ entrepreneurship decisions, indicating a potential “self-selection” bias in the ESP model.

1. Analysis of the factors influencing farmers’ internet adoption. The digital economy index, age, gender, education level, health status, political affiliation, financial literacy, household income, and risk preference significantly promote farmers’ internet adoption at the 1% level, while marital status and household size significantly influence farmers’ internet adoption at the 10% level. Specifically, the digital economy index has a statistically significant positive effect on farmers’ internet adoption, indicating that for every 1% increase in the digital economy index, farmers’ internet adoption increases by 0.5154%. This shows that the digital economy index objectively measures the construction of various internet information infrastructure, and the widespread adoption of information infrastructure like the internet greatly enhances internet coverage and depth of integration, effectively bridging the digital divide for rural communities in remote areas and promoting internet adoption. The coefficient for the age variable of farmers is statistically significant and negative (−0.0766), suggesting that younger individuals are more inclined to adopt the internet compared to older individuals, who may prefer interpersonal interactions. Similarly, the gender variable coefficient for farmers is statistically significant and negative (−0.1819), indicating that women are more willing to engage with the internet than men, which aligns with the characteristics of those who adopt the internet. This could be due to women’s smaller social circles compared to men, where internet adoption can help them expand their information access channels. The marital status variable shows a significant positive effect on farmers’ internet adoption (0.1055), reflecting that married households may require internet access more due to the need to support their children’s education. Farmers with higher educational level, financial literacy, and political status as members of the Communist Party of China (CPC) have more opportunities to contact and use the Internet than other farmers, and are more likely to be embedded in the Internet. Among them, the political status of farmers has the greatest impact on Internet embeddedness (0.5927), while the financial literacy of farmers has the least impact (0.0852). Households of farmers with higher health levels and more income have significantly smaller economic burdens compared to other households, which also indicates that this group has a higher capacity to bear internet costs, thus increasing the likelihood of internet adoption. Conversely, larger households may choose not to adopt the internet due to heavier economic burdens (with an impact coefficient of −0.0170). Additionally, farmers who prefer high risks rely more on obtaining information resources through the internet. Overall, for every 1% increase in farmers’ risk, internet adoption increases by 0.1704%.

2. Factors influencing farmers’ entrepreneurial intentions analysis: Among farm households using the internet, age and education level negatively affect farmers’ entrepreneurial intentions at the 1% level; the influencing coefficients were −0.0125 and −0.1133, respectively. However, the impact on farmers’ entrepreneurship decisions under non-internet usage is not significant. This could be because entrepreneurship itself is a risky activity, and as individuals age, their risk preferences tend to be more conservative. Internet-using farmers may be exposed to more negative entrepreneurial information through the internet, reducing the probability of making entrepreneurial decisions. Farmers with higher education levels tend to use their existing knowledge and skills to become employees and earn more income. Gender (0.1058), financial literacy (0.0491), and marital status (0.1632) positively influence farmers’ entrepreneurial intentions under internet usage, but the impact on farmers’ entrepreneurship intentions without internet usage is not significant. Rural males have more opportunities to encounter new externalities, resulting in stronger internet usage capabilities. Internet usage helps farmers overcome information barriers, increases access to financial and economic information channels, and promotes financial literacy, which is conducive to farmers’ entrepreneurship. Married families using the internet have accumulated a certain amount of wealth, providing financial support for entrepreneurship. The regression results in Table 3 show that political affiliation (−0.2641) negatively affects farmers’ entrepreneurship intentions under non-internet usage at the 5% level, but is not significant for internet-using farmers. This could be because farmers without internet usage have weaker internet social circles, primarily within local circles, and being a CPC member may provide stable job opportunities, thereby weakening their entrepreneurial intentions.

3. Analysis of the processing effects of internet usage on farmers’ entrepreneurial intentions: Table 4 presents the estimated results of the average treatment effects of internet usage on farmers’ entrepreneurial intentions, with a and b representing the factual results of whether farmers use the internet and their impact on entrepreneurship (corresponding to Eqs. (8) and (9)), while c and d represent counterfactual results (corresponding to Eqs. (10) and (11)). The results indicate that the average treatment effect of farmers’ internet usage on entrepreneurship is significantly positive at the 1% level. For internet-using farmers, if they did not use the internet, their probability of entrepreneurship would decrease by 0.1292 percentage points. For non-internet-using farmers, if they used the internet, their probability of entrepreneurship would increase by 0.0646 percentage points. In summary, internet usage significantly increases the probability of farmers’ entrepreneurship.

Table 4 The average effect of Internet use on the impact of rural households’ willingness to start a business.
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Mediation analysis of farmers’ entrepreneurship

As concluded earlier, internet usage significantly positively promotes farmers’ entrepreneurial intentions. Related studies also suggest that farmers’ entrepreneurship can lead to increased income, thereby narrowing income disparities (Yin et al. 2023). The negative correlation between internet usage and wealth disparity has been validated in previous sections. Regression results based on the mediation effect test are presented in Table 5. In column (1), the results indicate that for every 1% increase in farmers’ entrepreneurship, the probability of narrowing wealth disparity increases by 0.45%. Farmers’ entrepreneurship has the potential to reduce wealth disparity. In column (2), the regression results demonstrate that, beyond promoting farmers’ entrepreneurship, the internet contributes to reducing the digital divide. The effect of the internet on widening wealth disparity gradually diminishes; the coefficient increased from −0.0012 to −0.0011, with the mediation effect accounting for 24.23%. Therefore, hypothesis 2 is validated.

Table 5 The mediating effect of rural households’ entrepreneurship is tested.
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To further validate the mediation effect of farmers’ entrepreneurship, columns (3) and (4) present the effects of including online banking usage and commercial borrowing for farmers on common prosperity, based on the model in column (2). The study found that internet-based commercial borrowing for farmers significantly negatively affects the level of common prosperity, while the use of online banking by farmers positively promotes common prosperity. This is likely because many rural households lack financial knowledge and risk awareness, making it easier for them to incur high-interest debt during the borrowing process and fall into debt traps, which further widens the wealth gap. The use of online banking breaks the time and space limitations of financial services and expands financial and financing channels, such as mobile payments and online banking for farmers’ entrepreneurship, further promoting employment and increasing family wealth. Meanwhile, online banking promotes the improvement of farmers’ financial and digital literacy through user education, which helps farmers identify entrepreneurial opportunities and stimulates their business acumen, ultimately leading to a positive impact on narrowing the wealth gap.

Moderation effect test of digital entrepreneurial networks

The interaction and partial overlap of social networks and the Internet play an important role in the process of farmers’ entrepreneurship. The development of the digital entrepreneurship network directly reflects the level of digital infrastructure construction. Digital infrastructure improves people’s living standards by promoting the sharing of educational resources, optimizing the accessibility of public services and other aspects, and is an important part of China’s modern social welfare system. Therefore, we regard the digital entrepreneurship network as a factor representing social welfare. Digital entrepreneurship network can, to a certain extent, reflect the impact of interaction between social welfare factors and Internet use on common prosperity.

This study draws on the mediation-moderation model with an intermediary proposed by Wen and Ye (2014) to verify the moderating mechanism of digital entrepreneurial networks (see Table 6). In column (1), the regression results showed that the regression coefficient of the digital entrepreneurship network was significantly positive (0.0013) at the 1% level, indicating that the digital entrepreneurship network played a moderating effect in the process of narrowing the gap between the rich and the poor in the process of Internet use. In column (2), the multiplication term is still significantly positive (0.0158), and the use of the Internet has a positive effect on farmers’ entrepreneurship (0.0418), suggesting that digital entrepreneurial networks enhance the promoting effect of Internet usage on farmers’ entrepreneurship. That is, with the improvement of digital entrepreneurship network relations, the impact of Internet use on rural households’ entrepreneurship will gradually increase. In column (3), after adding the intermediary variable, the interaction term between digital entrepreneurial networks and internet usage remains significantly positive (0.0031). However, at this point, internet usage is still not statistically significant, but its impact on the gap between rich and poor is smaller than that in (1). (−0.0006 > −0.0007),. This implies that the moderating effect of digital entrepreneurial networks can effectively inhibit the widening of wealth disparity. Hypothesis 4 is validated.

Table 6 Moderating effect test of digital entrepreneurship network.
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Moderaton effect test of other non-income factors

The interaction between non-income factors and the Internet will also affect the realization of common prosperity. This paper further verifies the moderating effect of farmers’ education and financial literacy in the process of Internet use promoting common prosperity (see Table 7). Column (1) research shows that education level has a significant positive effect on narrowing the gap between the rich and the poor. Column (2) The cross between Internet use and education level is significantly positive at the level of 1%, indicating that farmers’ education level can strengthen the role of Internet use in promoting common prosperity. This proves that groups with higher education levels are more willing to participate in digital economic activities and use the Internet, so as to improve personal digital literacy through interaction with the Internet, and enhance the ability to access information and network skills application, thus helping to narrow the digital divide, promote entrepreneurship, increase family wealth, narrow the income gap, achieve fair distribution of resources, and finally help achieve common prosperity. The results of Column (3) show that farmers’ financial literacy is positively promoting the realization of common prosperity at the level of 1%. Column (4) shows that after adding the interaction item of Internet use and financial literacy, the interaction item is significantly positively correlated with common prosperity, which indicates that financial literacy also strengthens the promotion of Internet use on common prosperity. The possible reason within is that the improvement of financial literacy means the enhancement of farmers’ Internet financing, credit, and risk prevention capabilities. This enhancement helps to promote farmers’ participation in digital finance, promote the rational allocation of resources, wealth accumulation and appreciation, and promote common prosperity.

Table 7 Moderating effect of other non-income factors.
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Robustness tests

Endogeneity issues

Considering endogeneity issues arising from measurement errors, omitted variables, and reverse causality, this study employed the digital economy index as an instrumental variable. This choice was based on the consideration that the index’s construction involves weighting calculations based on indicators of information infrastructure, providing an objective reflection of the digital economic development in various regions. The final variable results are not expected to directly impact farmers’ entrepreneurial choices. Initially, using internet usage as the dependent variable and the digital economy index, along with other control variables, as explanatory variables in a Probit regression, the coefficient for the digital economy index was 0.499 with a corresponding p-value of 0.000. This indicates a significant correlation between the digital economy index and internet usage. To validate the reasonability of selecting the instrumental variable, a two-stage instrumental variable estimation was performed using a Probit model with an endogenous variable. The first stage F-value was 514.6, significantly greater than the critical value of 10, eliminating the possibility of weak instrumental variables. In the second stage, the p-value for the Wald test was 0.0021, rejecting the null hypothesis of “endogenous variable is exogenous” at the 1% significance level. Therefore, internet usage can be considered endogenous at the 1% significance level. The positive and significant effect of internet usage on farmers’ entrepreneurship, consistent with the previous regression results, confirms the robustness. Finally, weak instrument identification tests resulted in AR and Wald p-values of 0.000, significant at the 1% level, supporting the hypothesis that the endogenous variable and instrumental variable are correlated. This further validates the chosen instrumental variable. Regression results demonstrate that, after considering robustness, the study’s conclusions remain robust (see Table 8, columns (1)–(3)).

Table 8 Robustness test results.
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Narrowing age range

To test the robustness of the results, the study narrowed the age range of the sample to farmers aged between 35 and 60. Farmers in this age range typically possess social experience and wealth accumulation, making the sample more representative of entrepreneurial characteristics. The primary regression results in column (4) of Table 8 remain consistent. That is, for every unit of Internet use, the gap between the rich and the poor will widen by 0.78%.

Sample replacement

By replacing the sample, utilizing the 2017 CHFS questionnaire data and re-cleaning the data to obtain a new sample of entrepreneurial farmers, the regression results in column (5) of Table 8 still support the established conclusions. That is, for every unit of Internet use, the gap between the rich and the poor will widen by 0.50%.

Model replacement

Substituting the Probit model with a Logit model and re-regressing the sample yields consistent results, as shown in column (6) of Table 8. That is, for every unit of Internet use, the gap between the rich and the poor will widen by 0. 68%.

Conclusion and policy recommendations

Conclusion

This study utilizes data from the 2019 China Household Finance Survey (CHFS) and employs an endogenous switching Probit model to empirically analyze the impact of farmers’ internet usage on the realization of common prosperity. It investigates the effects of internet usage on farmers’ entrepreneurial intentions, the mediating role of farmer entrepreneurship in promoting common prosperity through internet usage, and the moderating role of the digital entrepreneurship network in this process. The research findings indicate that: First, farmers’ internet usage tends to widen the income gap, hindering the realization of common prosperity. Specifically, for every 1% increase in internet usage among farmers, the income gap between them expands by 0.0012 percentage points. This finding is inconsistent with previous studies that support the view that internet usage reduces income disparity and promotes common prosperity (Chengyou Li et al. 2023). Our research also corroborates the conclusions of scholars (David and Dorn, 2013; Yang et al. 2023) who argue that the digital divide exacerbates social inequalities due to the uneven development of the internet, leading to increased informational barriers among farmers, which affects income distribution and ultimately widens the wealth gap, obstructing the process of achieving common prosperity.

Second, the positive impact of internet usage on farmer entrepreneurship is significant. To thoroughly investigate the mediating role of farmer entrepreneurship, we empirically tested the extent to which internet usage influences it. However, since it is impossible to simultaneously observe a farmer’s entrepreneurial intention under both internet-using and non-internet-using states, we cannot directly assess the impact of internet usage on farmer entrepreneurship. Therefore, we employed the endogenous switching model to explore this relationship. The study found that internet usage significantly enhances the level of farmer entrepreneurship, validating the research hypothesis and aligning with Li’s (2023) findings. A notable contribution of this study is its innovative application of the endogenous switching model to deeply explore the relationship between internet usage and farmer entrepreneurship, including the factors influencing both. The further empirical results demonstrate that the average treatment effect of farmers’ internet usage on their entrepreneurship has a significant positive impact at the 1% level. For farmers who use the internet, the probability of entrepreneurship decreases by 0.1292 percentage points if they do not use the internet; conversely, for those who do not use the internet, the probability of entrepreneurship increases by 0.0646 percentage points if they start using it. Visual data further illustrates that the internet facilitates entrepreneurship by providing convenience, high penetration, and extensive coverage, effectively addressing issues like information asymmetry and high transaction costs in the entrepreneurial process, thus reducing the uncertainties faced by household entrepreneurship and promoting farmers’ entrepreneurial intentions.

Next, farmer entrepreneurship has a significant positive effect on common prosperity, indicating that it is a vital pathway through which internet usage influences the achievement of common prosperity. This aligns with the views of scholars (Yin Zhichao et al. 2023; Li et al. 2023). Additionally, the results contribute to research traditionally focused on optimizing the entrepreneurial environment and addressing regional information barriers (Wang et al. 2023), and on how digital inclusive finance alleviates financing constraints (Seven, 2022). This finding implies that the internet, due to its skill-biased characteristics, enables groups with superior information-processing capabilities to reap more economic benefits. Although this may widen the wealth gap among classes, the internet nonetheless provides favorable conditions for farmer entrepreneurship, such as financing channels, information access, and agricultural product sales channels, thereby reducing the digital divide and helping rural households enhance their digital capabilities. This encourages them to engage in entrepreneurial activities, consequently improving their wealth levels and narrowing income inequalities among households, which is beneficial for achieving the goals of “comprehensive” prosperity, “universal” prosperity, and ultimately, common prosperity.

Finally, social network relationships and the digital entrepreneurship network formed by the internet play a significant positive moderating role in promoting common prosperity through farmer entrepreneurship. Compared to previous studies, another contribution of this research is its examination of the moderating role of the digital entrepreneurship network. Rural China is a typical “acquaintance society,” where farmers possess extensive social network relationships within the kinship-based social networks. These social networks serve as a dominant force throughout the entrepreneurial process. Farmers, often in economically disadvantaged positions due to low income and education levels, may face financial exclusion in social networks. Therefore, social capital based on geographic and kinship ties promotes information sharing and resource allocation, forming the foundation of informal organizations (Xu et al. 2023). In this context, the intertwined relationship between social networks and the internet deserves further attention. This study incorporates the key variable of the digital entrepreneurship network as a moderating variable in the mediation effect model. Empirical tests reveal that the digital entrepreneurship network strengthens the promoting effect of internet usage on farmer entrepreneurship, and it plays a moderating role in the process of narrowing the income gap, effectively inhibiting the expansion of wealth disparities.

Policy recommendations

Based on the conclusions drawn, this study proposes the following policy recommendations to better promote internet usage among farmers, encourage active participation in entrepreneurial activities, and facilitate the development of the internet in the digital economy era, maximizing the common prosperity effect of internet usage.

Improving rural internet infrastructure

The government should enhance rural internet infrastructure construction, increasing internet penetration rates and service quality in remote rural areas. This can be achieved through initiatives such as establishing platforms for sharing information resources, enabling farmers to identify and respond to market trends rapidly.

Financial support and cost reduction

Government departments should focus on providing guidance and training for rural internet usage. Efforts should be made to bridge the digital divide caused by regional, human, and educational factors, creating conditions for farmers to engage in entrepreneurship and achieve common prosperity. This may involve subsidizing digital credit interest rates and reducing transaction fees for mobile payments, thus lowering the cost of internet services and inspiring farmers’ enthusiasm for entrepreneurship.

Digital literacy education

Emphasis should be placed on increasing guidance and training for rural internet usage, improving and compensating for the digital divide resulting from geographical and educational disparities. Establishing pilot programs for online education in internet-related fields, such as internet and big data, could help build a comprehensive system for nurturing digital talents, enriching the income sources of rural households, and ultimately raising their income levels, thus reducing income inequality.

Building digital entrepreneurial networks

Encourage farmers to construct personal weak-tie networks through various online channels, fostering a new type of entrepreneurial network represented by industry relationships and friendships. This facilitates the transmission and sharing of entrepreneurial resources, weakens the reverse impact of the digital divide on achieving common prosperity, and ultimately enhances the wealth levels of rural households.

In summary, leveraging the internet to promote common prosperity among farmers is an achievable goal in the digital economy. The implementation of these policy recommendations could contribute to the effective utilization of internet resources, creating an environment conducive to entrepreneurial activities and ultimately fostering common prosperity.

Limitations and future research recommendations

Although this study has drawn important research conclusions and holds certain value, there are still several aspects worthy of further exploration in future research. First, the complex relationship between internet usage, farmers’ entrepreneurial behavior, and the realization of common prosperity deserves deeper investigation. This study uses farmers’ entrepreneurship as an intermediary pathway, and empirical research clearly shows the connections among internet usage, farmers’ entrepreneurial behavior, and common prosperity. However, in reality, there are many intermediary pathways through which internet usage impacts common prosperity, making it difficult to fully consider all influencing factors regarding the relationship between internet usage and common prosperity. Therefore, future research should continue to focus on internet usage, farmers’ entrepreneurial behavior, and the degree of common prosperity in rural areas of China, conducting in-depth tracking and investigation of such studies while also exploring other factors affecting internet usage and common prosperity. Secondly, sample selection bias is a concern. Since the sample in this study is based on the China Household Finance Survey, which has specific characteristics, it may limit the depth of understanding regarding internet usage and rural residents’ entrepreneurship. Future research could employ interdisciplinary methods, such as cross-disciplinary approaches, to develop more comprehensive and targeted questionnaires, expanding the sample scope to include more regions and diverse types of farmers, thereby enhancing the representativeness and universality of the research findings. Thirdly, the study may not have fully considered the heterogeneity among different regions in China, as there are significant differences in internet usage, rural entrepreneurial activity, and the realization of common prosperity between the eastern, central, western, and northeastern regions. This could affect the general applicability of the research findings.