The Key Points for the Development of Digital Villages in 2025, which states that the core tasks of China’s digital village policy encompass seven areas: rural digital infrastructure, smart agriculture, new rural industries, rural digital culture, rural digital governance, digital public services, and bright and beautiful villages (Cyberspace Administration of China, 2025). First, digital village policies have significantly improved communication infrastructure in rural areas by introducing digital technologies such as 5G, big data, and cloud computing (Zeng et al., 2023). This improvement has not only reduced the cost of information acquisition (Choung et al., 2023) but also enabled new agricultural operators to access market information and network resources in real time (Guo et al., 2023), thereby enhancing their market entry efficiency (Hanisch et al., 2023; Mapiye et al., 2023) and further promoting entrepreneurial activities. Additionally, the application of digital technology has broken through constraints on the allocation of innovation resources, promoted the dissemination and diffusion of technology, and ultimately facilitated innovation among new agricultural operators (Zhao et al., 2022; Pang et al., 2023).

The adoption of technologies in agricultural contexts varying in terms of levels of engagement and specific preferences among farming communities. For instance, Seid and Yizengaw (2025) explored farmers’ preferences and applications of information and communication technologies (ICTs). The study revealing as ICTs are utilized throughout different stages of agricultural productions namely, pre-cultivation, during cultivation and post-harvest. This can significantly enhance farmers’ access to information, innovative farming techniques, and training opportunities. Such access could a primary driver of innovation within agriculture, which can lead to new practices, products, or business models.

While various factors influence the choice and application of these digital technologies among rural farmers including gender, age, education level, farm income, network access, electricity access, perceived value, information quality, and perceptions regarding relative advantage, hedonic motivation, and compatibility of technology. This study emphasizing the need for context-specific approaches in digital agricultural initiatives, enabling infrastructural development, and policy (Seid and Yizengaw, 2025). From this, we can understand that digital policy is important for agricultural development.

Second, with the development of information infrastructure, the growth of the rural digital economy and finance is expected to accelerate. Unlike traditional financial institutions, digital inclusive finance simplifies user credit assessment, lowering the barriers to financial services (Qing and Chen, 2024) and reducing obstacles to accessing capital, thereby encouraging IENAO (Cai et al., 2023).

Finally, digital village policies enable new agricultural operators to adapt and capitalize on the digital landscape by training in digital tools, including e-commerce platforms. These operators are then able to expand traditional agricultural production and engage in diversified commercial activities such as online marketing of agricultural products and agritourism (Ante et al., 2023).

Given the above analysis, this paper formulates Hypothesis 1 as follows.

In addition to directly influencing markets and industries, rural digital transformation indirectly promotes IENAO by restructuring the allocation of production factors. Based on the perspective of factor mobility, this study proposes a mechanism analysis framework of “digitalization—factor mobility—innovation and entrepreneurship vitality” (Figure 1) to reveal and assess this indirect mechanism, with labor mobility and land transfer as key links in the factor flow chain.

Digital village policy has increased the attractiveness of rural areas by improving infrastructure and fostering emerging industries such as e-commerce and logistics, thus becoming an important “pull” factor for labor return. Meanwhile, high living costs in cities, inadequate public services, and barriers to social integration have served as “push” factors, collectively encouraging the return of agricultural labor (ALR) (Lee, 1966).

ALR promotes IENAO through two key mechanisms: capital accumulation and knowledge spillovers. Whether they choose employment or entrepreneurship, returning workers usually possess the capital, experience, and skills. These advantages not only increase their chances of entrepreneurial success but also significantly enhance the innovation capabilities of new agricultural operators (Wahba, 2021; Shen and Wang, 2024; Liu et al., 2024).

Urban employment experience enables returning labor to overcome the limitations of traditional rural social activities and establish social networks that span both urban and rural areas, thereby gaining access to more entrepreneurial resources (Liu et al., 2024). In a society like China, where interpersonal relationships are highly valued, returning agricultural laborers can access valuable entrepreneurial resources through these social networks, thereby further promoting the establishment of new agricultural operators.

For new agricultural operators, innovation often relies on knowledge flow and sharing from other enterprises (Audretsch and Lehmann, 2005). Labor mobility and personnel exchanges play a crucial role in this process, facilitating knowledge spillovers (Liu et al., 2010; Yi et al., 2021). ALR have played a key mediating role in this process, driving innovation among new agricultural operators. For example, the technologies brought back by returning agricultural laborers have facilitated the transition of agriculture from traditional to modern agricultural models (Shen and Wang, 2024).

Given the above analysis, this study formulates Hypothesis 2 as follows.

China has implemented a policy of separating the three rights to rural land: ownership, contracting, and operating rights. Against this backdrop, transferring land operating rights is the core transfer of land factors (LTF), granting farmers greater flexibility over land use and facilitating land flow among agricultural producers and enterprises. Digital village development leverages blockchain and other technologies to enhance the transparency of land tenure information and the credibility of property rights confirmation, thereby reducing transaction costs and the risk of property disputes, and providing a solid foundation for LTF (Hanisch et al., 2023).

LTF primarily promotes IENAO through the wealth effect and by alleviating financing constraints. LTF to high-productivity entities, such as cooperatives and agricultural enterprises, creates conditions for the application of innovative agricultural technologies and drives the transformation of agricultural production methods (Petit et al., 2018). The wealth effect refers to the accumulation of wealth by individuals or households through tangible assets, such as land and real estate (Corradin and Popov, 2015; Liu and Zhang, 2021). In rural areas, land ownership is often one of the most important assets for farmers (Hu and Chen, 2024). The funds obtained through LTF make IENAO possible.

Additionally, the transferability of land management rights allows them to be used as collateral for financing, thereby expanding the opportunities for new agricultural operators to obtain financial support (Carlsson et al., 2013). This mechanism effectively alleviates the constraints of start-up capital, providing a continuous source of funding for innovation and entrepreneurship.

Given the above analysis, this study formulates Hypothesis 3 as follows.

This study adopted a quasi-experimental research design, justified by the exogenous policy intervention nature of CNDVPP. The non-random designation of pilot (treated) and non-pilot (control) counties provided a natural setting for causal inference, where we evaluated policy effects by comparing changes in the dependent variable (IENAO) between groups before and after implementation.

In terms of research approach, a quantitative method was employed. Analysis relied on county-level panel data in 2014–2022. Quantitative techniques such as DID regression, event study, and mediating effect analysis were used to test hypotheses, quantify effects, and identify mechanisms, ensuring objective causal inference.

This section introduced the three models used in this study. First, the DID model estimated the effect of CNDVPP on IENAO to test Hypothesis 1. Second, the event study approach tested the parallel trends assumption, ensuring consistency in IENAO trends between the treated and control groups before CNDVPP implementation, thus checking the robustness of Equation 1. Third, the mechanism analysis examined the mediating variables that CNDVPP promotes IENAO, to test Hypotheses 2 and 3.

Innovation and entrepreneurship activities are rooted in ecosystems shaped by the characteristics of actors and external environments (Spigel and Stam, 2018; Stam and Van De Ven, 2021). Key factors include subject characteristics (such as human capital) and environmental conditions (such as institutions and markets), which may lead to heterogeneity in policy implementation outcomes (Stam, 2015). Since the impact of digital rural policies on IENAO is constrained by differences in the characteristics of innovation actors and environmental conditions, this study conducted heterogeneity analysis from two dimensions: (i) the characteristics of innovation and entrepreneurship actors; (ii) environmental conditions (further subdivided into informal institutional and market conditions).

Table 3 presented the baseline regression results based on Equation 1. The estimates for the independent variable were shown in Columns (1) through (3). The estimated coefficients for the CNDVPP in all columns exhibited a significantly positive value. This suggested that the policy had a meaningful impact on promoting IENAO, thus supporting Hypothesis 1.

Notably, the significance of other control variables in the table reflected their independent effects on IENAO, but Hypothesis 1 focused on the causal relationship between CNDVPP and IENAO, with the core focus on the coefficient and significance of the “policy” variable. Specifically, in Column (1) (without fixed effects and control variables), the CNDVPP coefficient was significant at the 1% level; in Column (2) (with county and year fixed effects but no control variables), it remained significant at the 5% level; and in Column (3) (with fixed effects and all control variables included), the coefficient was 0.841, still significant at the 5% level after controlling for control variables, county-year fixed effects, and clustering robust standard errors at the county level. This stability across specifications confirmed the robustness of the CNDVPP on IENAO.

In practical terms, the coefficient of 0.841 indicated that the digital village policy had led to an increase of 0.841 standardized units in the rural innovation and entrepreneurship index, approximately 4.97% of the sample mean (16.93). This effect corresponded to an increase in the county’s IENAO level from the median (50th percentile, 16.524) to the 58th percentile (17.640).

Figure 2 presented the results of the parallel trend test derived from Equation 2. The estimation results showed that, prior to the implementation of the CNDVPP, the estimated coefficients for each period were not statistically significant. Therefore, the null hypothesis— which posits that the estimated coefficient equals zero—could not be rejected, indicating that the trends in IENAO for the treated and control groups were generally consistent before the CNDVPP was implemented. As a result, the parallel trend assumption was upheld.

In terms of dynamic effects, the coefficients for the implementation year and the first post-policy year were significantly positive, suggesting that the designation of pilot counties had an immediate and substantial effect on IENAO. However, in the second post-policy period, the estimated coefficient declined, and the corresponding confidence interval widened, suggesting that the policy’s effect may have weakened or even become statistically insignificant.

The results of the mechanism analysis based on Equation 3 were presented in Table 7. Columns (1) and (2) reported the impact of the policy on ALR. The smaller sample size in Column (1) was attributed to missing values in the original dataset. To address this issue, mean imputation was applied to fill in the missing data, and the model was re-estimated using the completed dataset, with the results shown in Column (2). The findings in Table 7, Columns (1) and (2), indicated that the coefficient of the independent variable remained significantly positive, suggesting that the CNDVPP effectively promoted ALR and thereby enhanced IENAO.

Table 7, Column (3) showed that the CNDVPP significantly increased LTF, with an estimated coefficient of 0.545, which was significant at the 10% level. This result suggested that the policy raised counties’ average land transfer fees by approximately 54.5%. Although the result was marginally significant, considering practical constraints such as frictions in the land transfer system and potential measurement errors, the economic impact remained considerable, and the conclusions were robust and reliable.

Thereby, hypotheses 2 and 3 were verified.

The implementation of CNDVPP significantly enhances the level of IENAO, as evidenced by Table 3. This result remains consistent after a series of robustness tests, a finding that aligns with existing studies (Tang et al., 2022; Del Olmo-García et al., 2023).

This study further found that CNDVPP can rapidly prompt IENAO in the short term. However, its marginal effect gradually diminishes over time, as illustrated in Figure 2, which presents the results of the parallel trend test and dynamic effect analysis. This phenomenon may result from the gradual weakening of policy incentives or the loss of motivation after the subject has adapted to the policy environment; especially in the case of continuous intervention, the effect of the policy may be gradually limited. Existing study (Li et al., 2024a) demonstrated the positive impact of the digital village pilot policy on innovation and entrepreneurship, but because the data stops at 2021, it fails to reveal the dynamics of the policy effect over time, which leads to the assessment of the policy effect not fully reflecting the actual situation. By extending the data to 2022, this paper reveals the temporal heterogeneity of the digital village policy effect through the dynamic analysis results in Figure 2, finding that the policy effect is significant in 2020 and 2021 but decreases to a non-significant level in 2022. This challenges the traditional view of the ‘linear persistence of the policy effect’ and suggests that the policy effect has time fluctuation, emphasizing the importance of dynamic policy evaluation and iterative optimization in policy design.

Second, this study examined the mechanism through which CNDVPP promotes IENAO. As shown in Table 7, the results indicate that CNDVPP enhances the level of IENAO by promoting two paths: ALR and LTF. Most existing studies (Stojanova et al., 2022; Li et al., 2024a) focus on the macroeconomic impacts of policies on the rural economy rather than on how to enhance innovation and entrepreneurship through the reallocation of resource factors. Therefore, this study extends existing research by providing empirical evidence on how digital village policy promotes IENAO by facilitating resource flows.

This study conducted a heterogeneity analysis, with key findings presented in Table 8. First, we examine the varying effects of education level on policy outcomes. The CNDVPP has a significant impact on IENAO in regions with lower education levels, whereas it is less significant in places with higher education levels. One possible reason for this is that entrepreneurs in regions with lower levels of education are often less digitally competent, so they rely more heavily on external policy support to compensate for this deficiency and thereby promote innovation and entrepreneurship. In these regions, policy interventions can effectively compensate for entrepreneurs’ disadvantages in digital transformation and promote innovation and entrepreneurship activities. On the contrary, in regions with higher education levels, entrepreneurs typically possess stronger digital competence and more resources (Kudama et al., 2021; Simovic et al., 2023), and they rely more on their competence and capital to promote innovation and entrepreneurial activities, with a relatively lower need for policy support. Therefore, the promotion effect of CNDVPP on IENAO is not significant and may even be weaker in regions with higher education levels.

Second, we explore the differential impact of clan networks on policy effects. In regions with weaker clan networks, CNDVPP significantly promotes IENAO, while in regions with tighter clan networks, the policy effect is insignificant or even negative. Much of the entrepreneurship literature argues that tighter clan or kinship networks support entrepreneurial activities through social capital, trust, and resource sharing (Jiang et al., 2022). However, this view does not take into account the complexity of China’s rural social structure, especially the “differential order pattern” in the clan structure. Under this structure, policy resources often rely on affinity rather than efficiency, and clan elites may monopolize resources, limiting opportunities for ordinary entrepreneurs and resulting in elite capture (Zhao and Yu, 2024). This phenomenon leads to policy dividends not benefiting everyone and may even inhibit innovation and entrepreneurship. Therefore, in regions where social capital is relatively scarce, external policies can compensate for market failures and provide key resources, which in turn can more effectively incentivize innovation and entrepreneurship. This is because, in these regions, traditional power structures do not hinder policy implementation, and the effects of policies are more pronounced. Specifically, in regions with weaker clan networks, policies play a “substitution role,” while in regions with tighter networks, closed clan structures may lead to a “crowding out” effect, limiting the policy effect. This finding reveals the duality of social capital and provides a theoretical basis for differential policy design, suggesting that different regions should develop distinct policy interventions tailored to their clan network characteristics.

Third, we examine the varying effects of market conditions on policy outcomes. In regions with less favorable market conditions, the incentive effects of CNDVPP to IENAO are more pronounced, and the marginal benefits are higher. This phenomenon may be because, in regions with less favorable market conditions, government-led digital village policy may alleviate systemic transaction costs and enhance entrepreneurial viability by improving infrastructure, providing financial support, thereby promoting innovation and entrepreneurship.

Although the findings of this study have expanded existing research to some extent, there are still some limitations and areas for further exploration.

First, this study lacks comparative research across different countries. Although the CREI provides a large sample size for studying IENAO, the research subjects are limited to China, lacking an international perspective. Future research could integrate global innovation and entrepreneurship monitoring data to explore innovation and entrepreneurship from an international perspective, thereby enriching the understanding of this field.

Second, the data used in this study is updated to 2022, thus failing to reflect the latest developments following policy implementation and limiting the assessment of the long-term effects of the policies. As data continues to be updated, future research can expand the temporal scope of the sample to more accurately grasp the long-term effects and evolving trends of digital village policies, particularly the impacts following the deep implementation of the policies.

Third, this study primarily focuses on two key mechanisms: ALR and LTF. It fails to fully explore other flowable factors that may influence IENAO, such as the roles of capital, technology, and data. Therefore, future research should further expand the examination of these factors and explore their potential mechanisms of action within the framework of digital village policies.

This study utilizes the first batch of CNDVPP as a quasi-natural experiment to investigate the impact of digital village policy, represented by CNDVPP, on IENAO. The findings show that the implementation of CNDVPP significantly enhances IENAO, and this positive effect remains robust even after rigorous robustness tests, confirming the effectiveness of digital village policies in driving rural development. The policy effect exhibits notable temporal heterogeneity, as CNDVPP rapidly promotes IENAO in the short term, but its marginal effect gradually diminishes over time, suggesting a “policy fatigue” phenomenon. Mechanism analysis reveals that CNDVPP enhances IENAO through two critical pathways: accelerating ALR and LTF. Heterogeneous analysis indicates that the policy’s promoting effect is more pronounced in regions with lower education levels, weaker clan networks, and less favorable market conditions. Overall, this study enriches the literature on digital village policy evaluation by revealing its dynamic effects, core mechanisms, and contextual dependencies, offering valuable insights for policy design in rural digital transformation.

Based on the findings of this study, the following policy implications are proposed: First, the Digital Village Initiative should be implemented and scaled, drawing on China’s “pilot-promotion” model to explore digital transformation pathways tailored to the characteristics of each region. A series of pilot areas with diverse resource endowments should be selected in the initial phase, and targeted fiscal and technical support should be provided. During the intermediate phase, the effectiveness of the pilots should be evaluated, focusing on identifying successful models that can be replicated. In the promotion phase, regions should draw on appropriate pilot areas based on local resource endowments and use these as benchmarks.

Second, policies must ensure long-term stability while incorporating flexible adjustment mechanisms. Key measures include integrating digital villages into national medium- and long-term development strategies, with precise specifications regarding funding and technical support cycles. Additionally, a dynamic policy adjustment mechanism should be implemented to assess the effectiveness of policies and make necessary adjustments regularly.

Third, the reallocation of production factors is essential. China’s experience highlights the importance of establishing a unified platform to stimulate the mobility of these factors (e.g., labor, land, and capital), thereby promoting rural industrial evolution. Policies should also include tax incentives to attract returning digital talent, similar to China’s “Agricultural Innovators” initiative, and implement digital literacy training programs to enhance the digital capabilities of the rural workforce, such as the Philippines’ “Digital Farmers Programme.”

Finally, policies must be tailored to regional differences. In areas with lower education levels, mobile digital training should be prioritized. Community-based digital cooperation platforms should be established in areas with weaker social capital to promote information sharing. In areas with poorer market conditions, government service efficiency can be improved through digital government construction and compensated for weaknesses in the market environment. Moreover, countries with governance constraints adopt a hybrid public-private partnership model (e.g., Mexico’s “Red Compartida” model) to attract private sector investment and stimulate market vitality.