How Did China's Digital Rural Policy Rapidly Diffuse? An Event History Analysis

How Did China's Digital Rural Policy Rapidly Diffuse? An Event History Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article How Did China's Digital Rural Policy Rapidly Diffuse? An Event History Analysis chunlin xiong, siqi hu, Duo jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5570966/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract How to achieve the rapid dissemination of digital rural policies has become an important issue for bridging the digital divide in rural areas and realizing the modernization of agriculture and rural regions. This study adopts a dual perspective of policy adoption and internalization, constructing an analytical framework that includes internal demand pull, local government fiscal capacity to support rural development, central authority push, and intergovernmental competition as internal and external driving factors. It collects and analyzes data on the diffusion of digital rural policies among provincial governments in China from 2018 to 2022, employing event history analysis to explore the influencing factors and potential mechanisms behind the dissemination of digital rural policies in different regions of China.The findings reveal that, first, the diffusion of digital rural policies among provincial governments in China exhibits different outcomes at various stages. During the adoption stage, the diffusion outcomes show convergent characteristics, while in the internalization stage, they display divergent and diverse features. Second, in the process of digital rural policy diffusion, only a small number of provinces synchronized internalization with adoption, whereas most provinces adopted a strategy of first adopting and then internalizing.Third, the driving logic behind local governments' adoption and internalization of digital rural policies shares both commonalities and differences. The common driving forces include the intrinsic motivation brought about by population scale, the vertical authority push from the central government, and the horizontal learning and implicit competitive pressures from peer governments.This indicates that the diffusion of digital rural policies at the provincial level in China is driven by pull mechanisms, coercive mechanisms, learning mechanisms, and competitive mechanisms, with local governments employing competitive strategies in the adoption and internalization of digital rural policies.The differences manifest in that local government capacity is a significant influencing factor for adoption but does not notably impact the sustained advancement of policies, highlighting the important tendency to mobilize various stakeholders and activate resources during the internalization stage. These findings illuminate how digital rural policies can rapidly diffuse in China and provide empirical insights for developing countries to accelerate the dissemination of rural digital policies. Social science/Science technology and society Social science/Social policy Digital rural policy Policy diffusion Policy adoption Policy internalization Event history analysis Figures Figure 1 Figure 2 Introduction The global trend of digitalization increasingly encompasses rural areas, with the digital transformation of agriculture and rural regions becoming a focal point of policy at the global level(Rijswijk et al., 2021). The digital rural development initiatives in various countries aim to bridge the digital divide, promote economic growth in rural areas, and enhance the quality of life in rural communities(Lythreatis et al., 2022; Meng et al., 2023). In this global context, China is actively advancing the digitalization of rural areas, aiming to fully unleash the diffusion effects of digital technology innovation, knowledge spillover effects, and inclusive sharing effects. These efforts seek to bridge the urban-rural development gap, enhance the level of agricultural and rural modernization, and meet the demands of farmers for a better digital life(Li & Zhou, 2024; Liu et al., 2024). In China, the construction of digital villages has been elevated to the level of national strategy. The "Opinions of the Central Committee of the Communist Party of China and the State Council on Implementing the Rural Revitalization Strategy," issued in 2018, proposed the "implementation of the digital rural strategy," marking the formal introduction of the digital rural strategy. In 2019, the General Office of the Central Committee of the Communist Party of China and the General Office of the State Council released the "Outline of the Digital Rural Development Strategy," which has since received sustained attention from the national level. In 2020, the Central Cybersecurity and Information Commission and the Ministry of Agriculture and Rural Affairs, along with seven other departments, jointly issued the "Notice on Carrying Out National Digital Rural Pilot Work," designating 117 counties (cities, districts) across all 31 provinces (autonomous regions, directly governed municipalities) and the Xinjiang Production and Construction Corps as the first batch of national digital rural pilot areas. The No.1 Central Document in 2021 proposed the "implementation of digital rural construction and development projects." In 2022, the No. 1 Central Document once again emphasized the need to vigorously promote the construction of digital villages. To implement the spirit of the No. 1 Central Document, the Central Cybersecurity and Information Commission, the Ministry of Agriculture and Rural Affairs, and other departments successively released documents such as the "Digital Rural Development Action Plan (2022-2025)" and the "Guidelines for the Construction of Digital Rural Standard Systems," providing a pathway for digital rural construction. With the promotion of a series of central policies, local governments have actively implemented the spirit of these policies and introduced relevant measures. By the end of 2021, all 31 provinces in China had adopted digital rural policies, which have been comprehensively rolled out across various provinces. However, there are disparities in the pace of adoption of digital rural policies among different provinces. What key factors influence the rapid diffusion of digital rural policies in China? What are the driving mechanisms behind the diffusion of these policies? There is limited research addressing this question, despite a wealth of research outcomes regarding digital rural policies. Existing studies have largely overlooked the exploration of the diffusion mechanisms and the underlying logic of digital rural policy diffusion. Meanwhile, there is a relatively rich body of research on the diffusion of policy innovations, which provides important insights for this study. However, traditional research on policy innovation diffusion often analyzes the issue solely from the perspective of whether or not policies are adopted, simplistically defining it as a binary decision while neglecting the distinction between diffusion triggered by initial adoption and that achieved through absorption and internalization. This oversight hinders a comprehensive understanding of the adoption and internalization processes of digital rural policies. Therefore, it is of practical significance to investigate the influencing factors and potential mechanisms behind the rapid diffusion of digital rural policies in China, as well as to identify the similarities and differences in the factors influencing local governments' adoption and internalization of these policies. In summary, the marginal contributions of this paper are as follows : (1) From a research perspective, grounded in the theory of innovation diffusion, this study subdivides the innovation diffusion process into two phases: "Adoption" and "Internalization. " The former primarily refers to the decision-making process through which local governments choose to adopt a new policy that has not been previously introduced in their region. This is typically marked by the initial release of policy documents; for instance, the first issuance of opinions related to digital rural policies by various provinces can be seen as a signal of innovation adoption, representing the first phase of innovation diffusion. The latter phase, Internalization, occurs after local governments initially adopt a policy innovation and begin the process of reproducing the policy content based on their unique characteristics and circumstances. This phase signifies the transition from nominal diffusion of innovation to its substantial operationalization. (2) In terms of research content, within the strategic context of "Digital Rural, " the study explores the impacts of various factors—such as rural economic development needs, rural population size, financial support for agriculture, central authority push, national competition, and neighboring competition—on local governments' policy adoption and internalization processes, thereby enriching the research on digital rural initiatives. (3) Regarding practical applications, this study examines the factors influencing the rapid diffusion of digital rural policies in China and the potential mechanisms behind it, revealing the unique paths and experiences of this diffusion in the Chinese context. This provides valuable insights for developing countries seeking to accelerate the dissemination of rural digital policies. Literature review Policy innovation refers to the adoption of a policy or program that is considered "new" by a government, regardless of whether this policy or program has been adopted by other governments previously(Walker, 1969). The process through which an innovation is disseminated is known as diffusion(Rogers, 2003). When the policy innovation from government A is disseminated to and adopted by government B, this constitutes the diffusion of policy innovation; the process of government A's policy innovation spreading to government B also represents the first adoption of government A's policy innovation by government B. For government B, adopting a new policy signifies a process of creation from nothing, which is a manifestation of policy innovation(Zhu, 2014). By reviewing the literature on policy innovation diffusion, we can broadly distinguish between two aspects: identifying the driving factors of innovation diffusion and exploring the process of innovation diffusion. First, in identifying the driving factors of innovation diffusion, scholars focus on understanding why a policy innovation spreads from one government to others. The question of "why diffusion occurs" examines the influencing factors for the first adoption of policy innovation by the adopting government. In the early 1990s, Berry et al. proposed the Internal Determinants Model, the Regional Diffusion Model, and the National Interaction Model(Berry & Berry, 1990). Their research indicated that the adoption of policy innovations by governments is influenced by both internal and external factors, ultimately forming two main models that explain the internal and external influencing factors of policy innovation—the Internal Determinants Model and the Diffusion Model(Berry & Berry, 1992). The Diffusion Model encompasses sub-models such as the Vertical Influence Model, the Regional Diffusion Model, the "Leader-Follower" Model, and the National Interaction Model(Rui, 2023). The Vertical Influence Model emphasizes top-down influences, while the other three sub-models share horizontal diffusion paths and can be collectively referred to as the Horizontal Influence Model. Internal factors include motivation, resources, and obstacles, specifically addressing public demand, economic level, population structure, income characteristics, and financial resources. External factors include influences of vertical hierarchical diffusion and horizontal peer diffusion. Vertical hierarchical diffusion refers to the promotion of policy vertical diffusion by higher-level governments through regulations, administrative orders, and mandatory policies. Horizontal peer diffusion occurs when local governments, due to geographic proximity or similarities in economic, social issues, institutional environments, or assessment criteria, may experience policy diffusion horizontally.Regarding how the influencing factors of policy innovation diffusion relate to policy adoption, scholars have summarized mechanisms such as learning, competition, imitation, implementation of administrative orders, and social construction from the perspective of policy innovation diffusion mechanisms(Wang & Lai, 2013). Chinese scholars, building on Berry et al.'s framework of internal and external factors, have conducted studies on innovation diffusion within the context of Chinese practice. Empirical analyses have been performed in various policy areas, including the urban minimum living allowance system(Xufeng & Hui, 2018), government procurement of services(Li & Zhang, 2019), residence permit systems(Chen & Li, 2020), "One-time Completion" reforms(Liu & Liu, 2020), environmental information disclosure systems(Han & Wei, 2021), and technology innovation voucher policies(Guo et al., 2022). These studies not only exploratorily test classical theories of policy innovation diffusion but also enrich the theoretical and practical research outcomes of innovation diffusion in the Chinese context, providing important references for our study of the influencing factors and diffusion mechanisms of policy innovation in China. Second, when exploring the process of innovation diffusion, scholars focus on the stages of the innovation diffusion process; discussions in this area are comparatively less extensive than those at the first level. In terms of the stages of the innovation diffusion process, Lucas considers the innovation diffusion process to comprise five stages: policy reinvention, policy development, policy piloting, policy borrowing and modification, and policy integration(Yang & Zhao, 2015). The diffusion of government policy innovations requires a series of initial adoptions followed by subsequent legitimization processes.From the perspective of new institutionalism, organizations seek to maintain consistency between internal and external institutions during the decision-making process of "initial adoption" of institutions, which represents the construction phase of external legitimacy for the policy(Kostova & Zaheer, 1999). Unlike the "formulate then execute" model prevalent in developed countries, China exhibits a widespread "learning by doing" approach(Wang & He, 2022). In addition to the decision-making phases of institutional adoption and implementation, the internal legitimacy under institutional isomorphic change also warrants attention(Kostova & Roth, 2002), referring to the "internalization" stage of policy innovation diffusion. The internalization stage primarily examines whether local governments have re-clarified policy objectives, localized policy content, and engaged in policy reproduction(Berry & Berry, 1992). In the realm of digital rural policies, current research primarily focuses on aspects such as policy evolution characteristics(Li et al., 2023), implementation strategies(Wang et al., 2021), and effectiveness assessments(Duan et al., 2023). There is a paucity of studies that examine digital rural policies from the perspective of policy diffusion. Although a recent study explored the diffusion of digital rural policies across different regions in China(Yang & Yang, 2022), it did not conduct a nationwide empirical study on the diffusion of digital rural policy innovations. At present, there is a limited understanding of the diffusion patterns of digital rural policies in China, and the processes, influencing factors, and mechanisms of such diffusion remain unclear. Therefore, it is evident that current research on the influencing factors and mechanisms of policy innovation diffusion mainly concentrates on the adoption phase, with relatively few studies delving into the internalization phase. This paper, based on the theory of policy innovation diffusion and within the context of Chinese policies, comprehensively analyzes the influencing factors of digital rural policy innovation diffusion during both the adoption and internalization phases, exploring the varying degrees of impact of different factors at different stages of digital rural policy innovation diffusion. This approach aims to gain a holistic understanding of the operational patterns of digital rural policy innovation diffusion in China. Theoretical analysis and research hypothesis This study systematically collects, organizes, and analyzes the literature on policy innovation diffusion. Based on the theory of innovation diffusion and the practical implementation of digital rural policies in China, a dual perspective of policy adoption and internalization is employed. The analysis considers both internal dual driving forces and external horizontal and vertical driving forces. Specifically, the factors driving policy diffusion are clarified from four dimensions: regional demand pull, local government fiscal capacity to support rural development, central authority push, and intergovernmental competition, thereby constructing a dual four-dimensional theoretical analysis framework for the diffusion of digital rural policies in China (as shown in Fig. 1 ).The innovation diffusion process undertaken by local governments may be influenced by both internal dual driving forces and external horizontal and vertical driving forces. The internal dual driving forces include internal demand pull and the fiscal capacity of local governments to support rural development, while the external horizontal and vertical driving forces encompass central authority push and intergovernmental competition. The following sections will discuss these four dimensions of driving factors in greater detail. Regional demand pull Regional demand pull forces include the demand for rural economic development and the driving force elicited through rural population size. The rural economic development level Mohr revealed that organizational innovation is influenced by the interplay of motives, resources, and barriers(Mohr, 1969). Walker pointed out that responding to internal public demands is an important motivation for local government innovation(Walker, 1969). Considering the characteristics of digital rural policies, the demand for rural economic development is a primary concern. In terms of rural economic development needs, Huang et al. found that the level of rural economic development in China is negatively correlated with the government's adoption of innovations(Huang et al., 2020); that is, the lower the level of rural economic development, the greater the rural economic development needs, leading to a higher probability of government adoption of innovations. Fundamentally, digital rural policies are products aimed at promoting common prosperity among farmers in rural areas. Therefore, regions with a lower level of rural economic development are more in need of government adoption and internalization of digital rural policies to address the interests of farmers and issues related to their common development. Consequently, the motivation for local adoption and internalization of digital rural policies is stronger. Based on this, the following hypotheses are proposed: Hypothesis 1-1: The lower the level of rural economic development, the greater the demand for digital rural development, and the more likely it is to adopt digital rural policies. Hypothesis 1-2: The lower the level of rural economic development, the greater the demand for digital rural development, and the more likely it is to internalize digital rural policies. Rural population size Organizational innovation theory posits that the population size of a region influences organizational innovation; specifically, the larger the population, the stronger the government's demand for management reform and policy innovation(Ma, 2015). Digital rural policies emphasize a people-centered approach, aiming to establish a digital rural development model that is compatible with the rural population size, to address the interests and immediate concerns of farmers. Digital rural policies are closely linked to safeguarding the fundamental interests of farmers and promoting their common prosperity in rural areas. Therefore, it can be predicted that in regions with larger rural populations and higher population densities, there will be a greater need to address farmers' interests and protect their fundamental rights, leading to a higher demand for digital reforms and consequently a stronger demand for digital rural policies; thus, these areas are more likely to adopt and internalize digital rural policies. Based on this, the following hypotheses are proposed: Hypothesis 2-1: The larger the rural population size, the more likely it is to adopt digital rural policies. Hypothesis 2-2: The larger the rural population size, the more likely it is to internalize digital rural policies. Fiscal capacity of local governments to support rural development The capacity of local governments is one of the key factors in government innovation. The fiscal capacity of local governments to support rural development is reflected in their ability to adopt and internalize innovations in terms of resource allocation, specifically whether they possess the necessary supporting resources and capabilities to overcome the obstacles faced by innovations. Resource slack theory posits that organizations with slack resources are more likely to achieve innovation(Tolbert et al., 2008). Ma confirmed a positive correlation between local government fiscal resources and the adoption of innovations while studying the diffusion of public bicycle programs(Ma, 2015). In the case of digital rural policies, the initial phases of rural digital transformation require investments in network infrastructure development, digital upgrades of traditional infrastructure, and the establishment of various digital platforms. Adopting and internalizing digital rural policies may require significant resource investments from the government as a safeguard. Based on this, the following hypotheses are proposed: Hypothesis 3-1: The higher the level of local government fiscal support for agriculture, the more likely it is to adopt digital rural policies. Hypothesis 3-2: The higher the level of local government fiscal support for agriculture, the more likely it is to internalize digital rural policies. Central authority push As a unitary state with a hierarchical political structure, local governments are subordinate to the central government(Mertha, 2009). When a policy receives emphasis and authoritative mobilization from the central government, local governments actively respond(Berry & Berry, 1990). The central authority push originates from the central government’s efforts to promote the adoption of policy innovations by local governments through various means such as policies, plans, opinions, notices, and documents. This represents a top-down force characterized by authority and coercion, which not only encourages local governments to adopt policy innovations but also influences the process of internalization. Research by Zhu & Zhao indicated that administrative orders encouraging policy innovation issued by the central government positively influence the likelihood of local government adoption of policy innovations 13 . Since the central government proposed the implementation of the digital rural strategy in Document No. 1 in 2018, it has introduced relevant policy documents to promote the top-down diffusion of digital rural policies, explicitly suggesting the exploration of digital rural development models and expediting digital rural construction. Subsequently, digital rural policies rapidly spread to local governments, with various provinces adopting these policies and most regions developing specialized digital rural policies tailored to their circumstances. Based on this, the following hypotheses are proposed: Hypothesis 4-1: The central government's push for digital rural policies (2018) will enhance the likelihood of local governments adopting digital rural policies. Hypothesis 4-2: The central government's push for digital rural policies (2020 and 2022) will enhance the likelihood of local governments internalizing digital rural policies. Competitiveness among peer governments National competition National competition arises from the mutual learning, imitation, and competitive pressures among peer governments. The policy innovations of local governments are influenced by horizontal learning and implicit competition. Competition among local governments occurs within the same tier; when one local government adopts a policy innovation while others do not, it creates competitive pressure on the latter(Rogers, 2003). Additionally, there exists a competitive drive among local governments characterized by "competing through learning." In the United States, local governments typically choose affluent and reputable states as models for learning 6 . In China, a province may learn from a higher-performing province, viewed as a benchmark for innovation, in hopes of surpassing it to gain recognition from the central government(Rui, 2023). Under the drive of "governance competition," local governments are inclined to adopt policy innovations to enhance their competitiveness, thereby attracting the attention of higher-level authorities(Peng & Zhao, 2019; Zhu & Zhang, 2015). The mechanisms of learning and competition influence the horizontal diffusion of policy innovations among peer governments. Based on this, the following hypotheses are proposed: Hypothesis 5-1: The greater the number of provinces that have adopted digital rural policies, the greater the competitive pressure on provinces that have not adopted them, increasing the likelihood of adoption. Hypothesis 5-2: The greater the number of provinces that have internalized digital rural policies, the greater the competitive pressure on provinces that have not internalized them, increasing the likelihood of internalization. Neighboring competition Neighboring competition arises from the mutual learning, imitation, and competitive pressures among geographically adjacent peer governments. Berry and Berry have suggested that the likelihood of a state adopting policy innovations is influenced by the adoption of such innovations by neighboring states(Rui, 2023). Existing studies indicate a positive correlation between the proportion of neighboring states that adopt policy innovations and the probability that a state government will adopt similar innovations(Mooney, 2001).Based on this, the following hypotheses are proposed: Hypothesis 6-1: The greater the number of provinces among neighboring regions that have adopted digital rural policies, the greater the competitive pressure on provinces that have not adopted them, increasing the likelihood of adoption. Hypothesis 6-2: The greater the number of provinces among neighboring regions that have internalized digital rural policies, the greater the competitive pressure on provinces that have not internalized them, increasing the likelihood of internalization. Research design Sample and data source In terms of the research subjects, this study primarily analyzes the factors influencing the diffusion of digital rural policies among the 31 provincial-level governments (including provinces, municipalities, and autonomous regions) excluding Hong Kong, Macau, and Taiwan. In 2018, the Central Committee of the Communist Party of China and the State Council first proposed the implementation of the digital rural strategy in Document No. 1, thereby marking 2018 as the starting point for the policy diffusion of the digital rural strategy and defining the observation period as 2018–2022.As of December 31, 2021, all 31 provincial-level governments had adopted digital rural policies. By December 31, 2022, 24 of these governments had achieved a certain level of internalization of the digital rural policies they had adopted; however, the internalization largely lagged behind the adoption timeline, as shown in Fig. 2. The primary reasons for this lag include the need to invest substantial human, material, and financial resources to build rural information infrastructure and agricultural big data platforms, which entails certain risks and requires time. Consequently, local governments may be more inclined to quickly adopt policies in response to central directives before further researching and developing the specific implementation details for the internalization of digital rural policies. The author organizes the data into a province-year dataset for event history analysis, retaining the years of adoption (or internalization) of the digital rural policy as well as the preceding years, while removing the years following the adoption (or internalization) of the digital rural policy. In the policy adoption section, a survival dataset was ultimately constructed, consisting of 80 samples from 31 provincial-level governments; in the policy internalization section, a survival dataset containing 124 samples from the same number of provincial-level governments was established. The data utilized in this study includes the timings of both adoption and internalization of policies by the 31 provincial-level governments during the observation period, relevant governmental text data regarding digital rural policies from the central and provincial governments, as well as related indicators such as agricultural GDP per capita, rural population size, and financial support for agriculture. The data on agricultural GDP per capita, rural population size, and financial support for agriculture were sourced from the "China Statistical Yearbook (2018–2022)" and "China Rural Statistical Yearbook (2018–2022)" databases compiled by China National Knowledge Infrastructure.For the policy text portion, searches were conducted using keywords such as "digital rural" and "digital agriculture and rural areas" in the "Peking University Law Database" and the official websites of provincial-level governments and relevant departments. Additionally, searches using "provincial government name + digital rural" and "provincial government name + digital agriculture and rural areas" were executed on Baidu to collect relevant policy documents issued by the central and provincial governments, covering the period from January 2018 to December 2022. Consequently, the timing of the issuance of digital rural policies by each provincial-level government was determined and a database was established. Variable selection Dependent variable This study includes two dependent variables, defining "policy adoption" and "policy internalization" as the probabilities of provincial government ( i ) adopting or internalizing digital rural policies at time ( t ). According to the discrete-time event history model, the dependent variables "policy adoption" and "policy internalization" are binary dummy variables. Specifically, the policy adoption status is determined based on whether provincial government ( i ) has issued policy documents related to digital rural initiatives. The policy internalization status is determined based on whether provincial government ( i ) has implemented specific types of digital rural policies (including action plans, work plans, implementation plans, and implementation opinions). If a given provincial government ( i ) adopts (or internalizes) digital rural policies in a particular analysis year ( t ), the value of the dependent variable for that year is set to 1, while the values for all preceding years are set to 0. Following event history analysis principles, all data after that year must be omitted, resulting in right censoring of the sample data. Explanatory variable Based on the hypotheses regarding the influencing factors of provincial governments' adoption (or internalization) of digital rural policies, the following explanatory variables are established: Internal Demand Pull Force This variable reflects whether there is sufficient internal motivation for local governments to adopt and internalize digital rural policies. It consists of two components: the level of rural economic development and the scale of the rural population, measured by agricultural GDP per capita and rural population size, respectively. Agricultural GDP per capita is calculated as the agricultural GDP of province i in year t-1 divided by the rural population of year t-1. Both agricultural GDP per capita and rural population size are continuous variables, and to avoid heteroscedasticity, an exponential transformation is applied, with the final calculated results incorporated into the model. Financial Capacity of Local Governments to Support Rural Development This variable indicates whether local governments have sufficient financial resources to support agriculture for the adoption and internalization of digital rural policies. It is measured by the level of financial support for agriculture, calculated as the agricultural, forestry, and water expenditures of province i in year t-1 divided by the rural population of year t-1. This variable is also continuous and is similarly subjected to an exponential transformation. Central Authority Push This variable examines the impact of important top-down initiatives from the central government on the diffusion of digital rural policy adoption and internalization. To measure the short-term effects of central policies, a binary variable is used for central authority push. In the year when the central government proposed the "Implementation of the Digital Rural Strategy" (2018), the value for central push (2018) is set to 1, and 0 in other years. In the year of issuing the "Notice on Carrying Out National Digital Rural Pilot Work" (2020), the value for central push (2020) is set to 1, and 0 in other years. In the year when the "Guidelines for the Construction of Digital Rural Standard System" was promulgated (2022), the value for central push (2022) is set to 1, and 0 in other years. In the robustness check section, we adjusted the coding rules for central push: in the policy adoption section, central push (2018) was changed to central push (2019) and central push (2020). In the policy internalization section, central push (2020) and central push (2022) were adjusted to central push (2019) and central push (2022), as well as central push (2020) and central push (2021). Competitiveness among Peer Governments This variable reflects the influence of other provinces' innovative adoption (or internalization) of digital rural policies on a provincial government, highlighting the competitive dynamics among peer governments. It encompasses horizontal diffusion effects and includes two independent variables: national competition and neighboring competition. National competition is measured as the cumulative number of provinces that have adopted digital rural policies divided by the total number of provinces on the mainland. This variable serves as a positive predictor, indicating that the more provinces that adopt and internalize digital rural policies, the greater the impact on province i's adoption and internalization of these policies, reflecting the national competitive pressure faced by province i. Neighboring competition is measured as the ratio of the cumulative number of provinces that have adopted digital rural policies among adjacent provinces to the total number of neighboring provinces. The measurement methods and data sources for the variables used in this study are shown in Table 1 . Table 1 Variable measurement methods and data sources. Variable Variable Measurement Data Sources Dependent Variables Adoption of Digital Rural Policies Assigned a value of 1 if a province adopted digital rural policies in a particular year; otherwise, 0. Official websites of provincial governments and relevant departments. Internalization of Digital Rural Policies Assigned a value of 1 if a province internalized digital rural policies in a particular year; otherwise, 0. Official websites of provincial governments and relevant departments. Explanatory Variables Rural Economic Development Level Natural logarithm of the agricultural GDP per capita of each province in the previous year. “China Statistical Yearbook” “China Rural Statistical Yearbook” Rural Population Size Natural logarithm of the rural population of each province in the previous year. “China Rural Statistical Yearbook” Agricultural Financial Support Level Natural logarithm of the ratio of agricultural, forestry, and water expenditure to rural population of each province in the previous year. “China Statistical Yearbook” “China Rural Statistical Yearbook” Central Push (2018) Assigned a value of 1 for the year 2018, otherwise 0. The authors’ database Central Push (2020) Assigned a value of 1 for the year 2020, otherwise 0. The authors’ database Central Push (2022) Assigned a value of 1 for the year 2022, otherwise 0. The authors’ database National Competition Proportion of the cumulative number of provinces that have adopted digital rural policies to the total number of provinces in China. Calculated by the authors Neighbor Competition Ratio of the cumulative number of provinces adopting digital rural policies in adjacent provinces to the total number of all adjacent provinces. Calculated by the authors Model selection This paper employs Event History Analysis (EHA) to empirically examine the diffusion of digital rural policy innovation. EHA has been widely used in the social sciences and was introduced into the study of policy innovation diffusion by the Berry couple in 1990(Blossfeld & Rohwer, 2019). EHA has since become a classic methodology for researching policy innovation diffusion. The EHA models include both continuous-time and discrete-time models. In this study, the dependent variable is a binary variable indicating “adoption (or internalization) of digital rural policies/non-adoption (non-internalization) of digital rural policies.” Time is measured in years, thus a discrete-time event history analysis model is utilized. The analysis is conducted using Stata 17 statistical software with a binary Logit regression model. The model is specified as follows: $$\:logit\left({p}_{i,t}\right)=\text{log}\left(\frac{{p}_{i,t}}{1-{p}_{i,t}}\right)$$ $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:={\beta\:}_{0}+{\beta\:}_{1}{GD{P}_{PC}}_{i,t-1}+{\beta\:}_{2}{Siz{e}_{RP}}_{i,t-1}+{\beta\:}_{3}{Financial}_{i,t-1}+{\beta\:}_{4}{Central}_{t}+\:\:{\beta\:}_{5}{Competitior\_\:neighbor}_{i,t}+{\beta\:}_{6}{Competitior\_Nation}_{i,t}+{\epsilon\:}_{i,t}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ 1 In the above equation, \(\:{p}_{i,t}\) represents the probability of Province i adopting (or internalizing) the digital rural policy in year t and \(\:{p}_{i,t}/（1-{p}_{i,t}）\) represents the odds. The right side of the equation addressed the impact of the rural economic development level ( \(\:{GDP\_PC}_{i,t-1}\) ), rural population size ༈ \(\:{Size\_RP}_{i,t-1}\) ༉, agricultural financial support level ༈ \(\:{Financial}_{i,t-1}\) ༉, central government push ༈ \(\:{Central}_{t}\) ༉, neighbor competition ༈ \(\:{Competitior\_\:neighbor}_{i,t}\) ༉, and national competition ༈ \(\:{Competitior\_Nation}_{i,t}\) ༉ on the adoption and internalization of the digital rural policy. \(\:{\beta\:}_{0}\) depicts the constant term and \(\:{\epsilon\:}_{i,t}\) represents the error term. Results and analysis Descriptive statistics The complete descriptive statistics of each variable for the two stages of policy adoption and policy internalization are presented in Table 2 . Prior to conducting the binary Logit regression, the author performed multicollinearity diagnostics for the variables. The results showed that the variance inflation factor (VIF) for all variables was below 6.835, which is well below the critical VIF threshold of 10, indicating that there is no multicollinearity problem among the variables. Table 2 Descriptive statistics of variables.Regarding the measurement of variables with *, refer to the “Robustness Test” section. Variable Adoption stage Internalization stage Observations Mean Std. dev Minimum Maximum Observations Mean Std. dev Minimum Maximum Dependent Variable(Adoption / Internalization) 80 0.388 0.490 0 1 124 0.194 0.397 0 1 Rural Economic Development Level (logarithm) 80 9.891 0.399 8.942 10.724 124 9.940 0.462 8.942 11.006 Rural Population Size(logarithm) 80 7.112 0.930 5.357 8.469 124 7.045 0.933 5.338 8.469 Agricultural Financial Support Level(logarithm) 80 8.403 0.536 7.562 9.821 124 8.528 0.603 7.562 9.915 Central Push (2018) 80 0.388 0.490 0 1 124 0.250 0.435 0 1 Central Push (2019) * 80 0.375 0.487 0 1 124 0.250 0.435 0 1 Central Push (2020) 80 0.188 0.393 0 1 124 0.242 0.430 0 1 Central Push (2021) * 80 0.050 0.219 0 1 124 0.161 0.369 0 1 Central Push (2022) - - - - - 124 0.097 0.297 0 1 National Competition 80 0.293 0.317 0.001 1.000 124 0.149 0.198 0.000 0.599 Neighbor Competition 80 0.352 0.390 0.000 1.000 124 0.228 0.328 0.000 1.000 Descriptive statistics The complete descriptive statistics of each variable for the two stages of policy adoption and policy internalization are presented in Table 2 . Prior to conducting the binary Logit regression, the author performed multicollinearity diagnostics for the variables. The results showed that the variance inflation factor (VIF) for all variables was below 6.835, which is well below the critical VIF threshold of 10, indicating that there is no multicollinearity problem among the variables. Table 2 Descriptive statistics of variables.Regarding the measurement of variables with *, refer to the “Robustness Test” section. Variable Adoption stage Internalization stage Observations Mean Std. dev Minimum Maximum Observations Mean Std. dev Minimum Maximum Dependent Variable(Adoption / Internalization) 80 0.388 0.490 0 1 124 0.194 0.397 0 1 Rural Economic Development Level (logarithm) 80 9.891 0.399 8.942 10.724 124 9.940 0.462 8.942 11.006 Rural Population Size(logarithm) 80 7.112 0.930 5.357 8.469 124 7.045 0.933 5.338 8.469 Agricultural Financial Support Level(logarithm) 80 8.403 0.536 7.562 9.821 124 8.528 0.603 7.562 9.915 Central Push (2018) 80 0.388 0.490 0 1 124 0.250 0.435 0 1 Central Push (2019) * 80 0.375 0.487 0 1 124 0.250 0.435 0 1 Central Push (2020) 80 0.188 0.393 0 1 124 0.242 0.430 0 1 Central Push (2021) * 80 0.050 0.219 0 1 124 0.161 0.369 0 1 Central Push (2022) - - - - - 124 0.097 0.297 0 1 National Competition 80 0.293 0.317 0.001 1.000 124 0.149 0.198 0.000 0.599 Neighbor Competition 80 0.352 0.390 0.000 1.000 124 0.228 0.328 0.000 1.000 Results and analysis of policy adoption Table 3 reports the regression results from the Event History Analysis (EHA) model regarding the adoption of digital rural policies. Model 1 regresses the internal influencing factors, which include internal demand pull and the financial capacity of local governments to support rural development. Model 2 regresses the external influencing factors, which include central authority push and the competitiveness among peer governments. Both the internal demand pull from Model 1 and the financial capacity of local governments to support rural development, as well as the central authority push and the competitiveness among peer governments from Model 2, pass significance tests, demonstrating a significant influence on the adoption of digital rural policies.Compared to Models 1 and 2, Model 3 integrates internal demand pull, financial capacity of local governments, central authority push, and competitiveness among peer governments. The log-likelihood, pseudo R², and chi-squared values progressively increase, indicating improved explanatory power of the model. The regression results in Model 3 reveal that the internal demand pull brought about by the level of rural economic development is statistically significant and negative. In contrast, the internal demand pull generated by rural population size, the level of financial support for agriculture from local governments in support of rural development, central authority push, and national competition among peer governments are statistically significant and positive. In contrast, the neighboring competition among peer governments is statistically significant and negative.Hypotheses 1–1, 2 − 1, 3 − 1, 4 − 1, 5 − 1, and 6 − 1 are validated at the 0.1, 0.01, 0.01, 0.1, 0.01, and 0.01 significance levels, respectively. First, the internal demand pull generated by the level of rural economic development shows a significant negative effect in the integrated model, indicating that a lower level of rural economic development negatively influences the adoption of digital rural policies. This means that provinces with lower levels of rural economic development have a greater demand for digital rural policies, thereby strengthening the motivation for their adoption, which validates Hypothesis 1 – 1 . The internal demand pull from rural population size is significant at the 0.05 level in Model 1 and at the 0.01 level in the integrated model, demonstrating a significant positive correlation between the adoption of digital rural policies and rural population size. Provinces with larger rural populations have a greater demand for digital rural policies and are more likely to adopt them, thus confirming Hypothesis 2 − 1. Furthermore, the financial capacity of local governments to support rural development is significantly positive at the 0.01 level in both Models 2 and 3, indicating that the level of financial support for agriculture has a significant positive impact on the adoption of digital rural policies, which supports Hypothesis 3 − 1. Additionally, the hypothesis regarding the correlation between central authority push and the probability of innovation adoption by provinces is confirmed, showing that central government policies play a positive role in motivating local governments to adopt digital rural policies. Lastly, the competitiveness among peer governments in national competition is significant at the 0.01 level in both Models 2 and 3, indicating that the national competition effect is substantial and validating Hypothesis 5 − 1. As more provinces adopt digital rural policies, they create pressure on those that have not adopted, thus promoting the adoption of digital rural policies in these provinces. National competitive pressure has a positive influence on the adoption of digital rural policies across provinces.In terms of neighboring competition, this variable is significant at the 0.01 level in both Model 2 and the integrated Model 3, but the regression coefficient is negative, indicating a significant negative correlation between the adoption of digital rural policies and neighboring competition, contrary to expectations. Hypothesis 6 − 1 is partially validated. This suggests that neighboring provinces, due to their geographical proximity, exhibit a willingness to learn from and communicate with one another in the adoption of digital rural policies. However, barriers stemming from information gaps and resource competition may create obstacles. Overall, the positive competition based on learning and experience exchange provides an important guarantee for the adoption of digital rural policies. Table 3 Event history analysis results of digital rural policy adoption.*P < 0.1, **P < 0.05, ***P < 0.01. Standard errors are shown in parentheses. Model 1 Model 2 Model 3 Regional Internal Driving Force Rural Economic Development Level 0.420 (0.624) -2.205 * (1.222) Rural Population Size 1.363 ** (0.557) 3.053 *** (1.010) The Local Government’s Capacity to Undertake a Policy Agricultural Financial Support Level 2.570 *** (0.943) 4.198 *** (1.573) The Central Authority’s promotion Force Central Push(2018) 2.612 ** (1.246) 2.435 * (1.455) Peer Government Competitiveness National Competition 10.265 *** (3.868) 16.734 *** (5.532) Neighbor Competition -6.275 ** (2.691) -9.460 *** (3.614) cons -35.943 *** (13.392) -3.381 *** (1.017) -39.348 * (21.336) N 80 80 80 LR chi2 8.77 47.03 63.75 df 3 4 7 Pseudo R 2 0.0821 0.440 0.597 Log likelihood -49.026 -29.895 -21.536 Results and analysis of policy internalization Table 4 reports the regression results of the Event History Analysis (EHA) model for the internalization of digital rural policies. The analysis includes Model 1, which incorporates the internal demand pull factor and the financial capacity of local governments to support rural development; Model 2, which includes the central authority push and peer government competitiveness; and the integrated model. The results indicate significant improvements in the log-likelihood value, pseudo R², and chi-square value of the integrated Model 5 across the five sets of models, suggesting an enhanced explanatory power of the model.From the regression results of Model 5, it is evident that the internal demand pull generated by rural population size continues to have a significant positive impact on the internalization of digital rural policies. Compared to peer government competitiveness, the role of central authority push is more pronounced during the internalization phase and serves as a crucial motivating factor for provinces to internalize digital rural policies. Hypotheses 2–2, 4 − 2, 5 − 2, and 6 − 2 are supported at significance levels of 0.1, 0.01, 0.05, and 0.01, respectively. There are notable similarities in the influence of the four types of innovation diffusion drivers on the adoption and internalization of digital rural policies. First, the internal demand pull resulting from rural population size has a positive effect not only during the adoption phase but also plays a significant role in the internalization of digital rural policies, as evidenced by significant positive coefficients in Models 1, 3, 4, and 5, thus validating Hypothesis 2 – 2 .Second, compared to the adoption phase, the feedback mechanism of central authority push is more significant during the internalization stage and has a larger positive effect; both Hypotheses 4 − 2 and 5 − 2 are confirmed. From the regression coefficients, it can be observed that the impact of central authority push in 2022 on the internalization of digital rural policies is greater than that in 2020, indicating that the cumulative effect of the policies in 2022 is stronger than in 2020.Third, the competitiveness among peer governments arising from national competition shows a similarly strong significant positive effect in the internalization models as observed in the adoption phase. Empirical tests in Models 2, 3, 4, and 5 confirm Hypothesis 5 − 2 at the 0.01 level, highlighting the critical influence of national competition on both the adoption and internalization of digital rural policies. Moreover, the mechanisms of generation and influence of innovation diffusion drivers, which include the sources, direction, and scale, exhibit differences between the two distinct stages of adoption and internalization of digital rural policies. First, regarding the internal demand pull, the level of rural economic development does not show significance during the internalization phase, suggesting that it does not have a meaningful impact on the internalization of digital rural policies, and Hypothesis 1 – 2 is not validated. This indicates that the internal demand stemming from rural economic development has its impact on the diffusion of digital rural policies primarily during the adoption phase, rather than during internalization. Second, concerning the financial capacity of local governments to support rural development, the level of financial support for agriculture shows a significant positive effect on the internalization of digital rural policies at the 0.1 level in Model 1; however, it is not significant at the 0.1 level in Models 3, 4, and the integrated Model 5. This suggests that while financial support for agriculture has a strong positive effect on the adoption of digital rural policies, its influence does not extend to the internalization phase, thus failing to support Hypothesis 3 − 2. Additionally, neighboring competition due to geographical proximity is not significant during the internalization phase, leading to the rejection of Hypothesis 6 − 2. This finding may be attributed to the closer performance evaluations and more intense governance competitions among provinces within the same region, rather than merely those that are geographically adjacent. Table 4 Event history analysis results of digital rural policy internalization.*P < 0.1, **P < 0.05, ***P < 0.01. Standard errors are shown in parentheses. Model 1 Model 2 Model 3 Model 4 Model 5 Regional Internal Driving Force Rural Economic Development Level 0.612 (0.542) -0.599 (0.685) -0.635 (0.680) -0.695 (0.690) Rural Population Size 1.279 ** (0.602) 1.438 ** (0.688) 1.341 * (0.686) 1.112 * (0.673) The Local Government’s Capacity to Undertake a Policy Agricultural Financial Support Level 1.468 * (0.843) 0.419 (1.002) -0.031 (1.070) -0.399 (1.059) The Central Authority’s promotion Force Central Push(2020) 2.213 *** (0.792) 1.948 *** (0.664) 2.278 *** (0.826) Central Push(2022) 2.426 ** (1.154) 2.747 ** (1.254) 3.106 ** (1.331) Peer Government Competitiveness National Competition 11.485 *** (3.363) 9.607 *** (2.559) 13.331 *** (3.814) 16.653 *** (4.377) Neighbor Competition -0.994 (1.009) -0.964 (1.196) -2.135 (1.400) -2.149 (1.327) cons -29.191 *** (11.494) -6.541 *** (1.889) -11.714 (13.878) -8.968 (13.896) -5.555 (13.987) N 124 124 124 124 124 LR chi2 8.17 31.21 38.04 33.39 43.81 df 3 4 6 6 7 Pseduo R 2 0.067 0.256 0.312 0.274 0.360 Log likelihood -56.840 -45.319 -41.903 -44.232 -39.018 Robustness tests The above text examines the impacts of rural economic development level, rural population size, agricultural financial support level, central push, neighboring competition, and national competition on the adoption and internalization of digital rural policies. Based on this, robustness tests are conducted by altering the coding methods of the relevant independent variables. Robustness test for policy adoption To examine whether different codings of central push influence the research findings related to the adoption of digital rural policies, a recoding of the central push variable was performed. In Table 5 , two alternative coding schemes for central push are explored: Central Push (2019) and Central Push (2020) are treated as dummy variables. Central Push (2019) assesses the impact of the "Digital Rural Development Strategy Outline" issued by the central government in 2019 on the adoption of digital rural policy innovation, with a value of 1 in that year and 0 in other years. Central Push (2020) measures the vertical diffusion effect of the central government’s "Implementation of Digital Rural Construction and Development Project," likewise coded as 1 in that year and 0 in other years.The results of the tests are presented in Table 5 . As shown in the table, changing the coding method yields model results consistent with previous findings, demonstrating robustness. First, all three coding methods for central push are significant across different models, indicating that the impact of central policy push on the likelihood of local governments adopting digital rural policies is robust; the policies from the central government indeed promote local government adoption of these policies. Secondly, the variable representing rural economic development level consistently shows a significant negative effect, while rural population size, agricultural financial support level, and national competition maintain statistically significant positive impacts on the probability of adopting digital rural policies, aligning with the hypotheses proposed in this paper. Table 5 Digital rural policy adoption Robustness test.*P < 0.1, **P < 0.05, ***P < 0.01. Standard errors are shown in parentheses. Variable Model 1 Model 2 Rural Economic Development Level -2.219 * (1.233) -2.212 * (1.230) Rural Population Size 3.054 *** (1.002) 3.092 *** (1.008) Agricultural Financial Support Level 4.221 *** (1.576) 4.239 *** (1.561) Central Push(2019) 1.755 * (0.998) Central Push(2020) 5.144 * (2.760) National Competition 19.200 *** (5.285) 24.801 *** (6.548) Neighbor Competition -8.951 ** (3.585) -8.295 ** (3.578) cons -39.493 * (21.312) -45.057 ** (21.550) N 80 80 LR chi2 64.15 64.65 df 6 6 Pseduo R 2 0.601 0.605 Log likelihood -21.335 -21.086 Robustness test for policy internalization To examine whether different coding methods for central push affect the research findings related to the internalization of digital rural policies, a recoding of the central push variable was performed. In Table 6 , Models 1 and 2 investigate two alternative combinations of central push codings. The results of the tests are presented in Table 6 , and it is evident that changing the coding method shows no significant differences compared to previous findings, indicating robustness.Firstly, the variables Central Push (2020), Central Push (2021), and Central Push (2022) consistently demonstrate significant positive effects on the probability of internalizing digital rural policies, highlighting their robustness. However, Central Push (2019) did not pass the significance test, primarily due to the time lag effect associated with its role in promoting policy internalization.To account for the time lag effect of Central Push (2019) on the internalization of digital rural policies, Model 3 in Table 6 includes both the contemporaneous value and the lagged value of Central Push (2019). The results indicate that central government policies do indeed facilitate the internalization of digital rural policies by local governments. Notably, Central Push (2019) did not show a direct effect on policy internalization in the same year it was introduced but significantly increased the probability of internalization in the following year. This delay can be attributed to the fact that internalization requires a certain amount of time compared to direct adoption. As mentioned earlier in Table 5 , Central Push (2019) significantly enhanced the likelihood of policy adoption in the year it was issued.Secondly, the variables representing rural population size and national competition consistently exhibit significant positive impacts on the probability of internalizing digital rural policies, supporting the reliability of the conclusions drawn in this study. Table 6 Digital Rural Policy Internalization Robustness Test.*P < 0.1, **P < 0.05, ***P < 0.01. Standard errors are shown in parentheses. Variable Model 1 Model 2 Model 3 Rural Economic Development Level -0.613 (0.677) -0.694 (0.690) -0.598 (0.685) Rural Population Size 1.355 ** (0.688) 1.112 * (0.673) 1.437 ** (0.688) Agricultural Financial Support Level 0.048 (1.067) -0.399 (1.059) 0.417 (1.002) Central Push(2019) 1.085 (1.146) 0.094 (1.349) Central Push(2019)༈ t -1༉ 1.918 ** (0.785) Central Push(2020) 2.928 *** (0.945) Central Push(2021) 1.945 ** (0.834) Central Push(2022) 2.429 * (1.278) National Competition 12.127 *** (3.897) 11.466 *** (2.997) 9.535 *** (2.754) Neighbor Competition -2.048 (1.379) -2.149 (1.327) -0.972 (1.201) _cons -10.413 (13.894) -2.453 (14.240) -11.752 (13.878) N 124 124 124 LR chi2 34.46 43.81 38.05 df 7 7 7 Pseduo R 2 0.282 0.360 0.312 Log likelihood -43.694 -39.020 -41.901 Conclusions and recommendations Conclusions This study is based on the theory of innovation diffusion and establishes an analytical framework that includes internal demand pull, local government financial capacity to support rural development, central authority push, and intergovernmental competition. It collects and analyzes data on the diffusion of digital rural policies among provincial governments in China from 2018 to 2022. Using event history analysis, this research empirically investigates the influencing factors and potential mechanisms behind the dissemination of digital rural policies across different regions in China. The findings are as follows: First, the diffusion of digital rural policies among provincial governments in China presents different outcomes at various stages. In the adoption stage, the diffusion results exhibit convergence characteristics, while in the internalization stage, they display divergence and diversification traits. Adoption serves as the first step in the diffusion of digital rural policies for the adopting government. Influenced by both internal and external driving forces, most provinces tend to initially reference policies from the central government or advanced regions, adopting innovative decision-making and execution strategies without localizing their policy reproduction, which may lead to convergent diffusion outcomes. Internalization, however, requires localization in policy reproduction that aligns with the specific needs and circumstances of the adopting government. When internalization is combined with localization, it is more likely to result in divergent and diversified diffusion outcomes. Second, in the process of digital rural policy diffusion, most provinces adopt a strategy of first adopting and then internalizing the policies, with only a small number of provinces synchronously internalizing at the time of adoption. Compared to adoption, the internalization of digital rural policies in most provinces exhibits a certain degree of lag. This delay primarily arises from the risks and costs that local governments face when attempting to internalize digital rural policies in a context-specific manner, as achieving policy localization and reproduction requires a considerable amount of time. Third, the results of the event history analysis indicate that the driving logic behind local governments' adoption and internalization of digital rural policies shares commonalities as well as differences. The common driving forces include the intrinsic motivation arising from population size, the vertical authority push from the central government, and the horizontal learning and competitive pressure from parallel governments.From the perspective of internal demand pull, the intrinsic motivation stemming from population size exhibits a significant positive effect on both the adoption and internalization of digital rural policies. The external driving force provided by the central authority push is also confirmed, indicating that the central government's policy push plays an important role in both the adoption and internalization of digital rural policies by local governments. This suggests that the central government’s policy push significantly influences the diffusion of digital rural policies across provinces, with a clear top-down vertical diffusion effect.Regarding intergovernmental competition, the nationwide learning and implicit competition positively impact the adoption and internalization of digital rural policies. This shows that the diffusion of digital rural policies at the provincial level in China is driven by a combination of pull mechanisms, coercive mechanisms, learning mechanisms, and competitive mechanisms. Local governments exhibit strategies of competition in the adoption and internalization of digital rural policies.The difference lies in the fact that the financial capacity of local governments to support rural development is a significant influencing factor in policy adoption, but does not have a notable impact on the continuous promotion of the policies. This indicates the crucial tendency to mobilize stakeholders and activate resources during the internalization stage. Recommendations Based on these findings, this study proposes the following recommendations:firstly, explore the establishment of digital rural development policies that align with rural population size and knowledge structure. Strengthen the construction of digital infrastructure according to rural population size, and develop and promote information terminals, technological products, and mobile applications (apps) that cater to the characteristics of agriculture, rural areas, and farmers. Utilize digital inclusivity to enhance rural residents' sense of gain, happiness, and security. Secondly, optimize the policy environment and initiate a competitive excellence model. To achieve the rapid diffusion of digital rural policies, higher-level governments should optimize the policy environment through top-level design and issue relevant supplementary documents for digital rural policies. This will demonstrate the commitment of higher-level governments to advancing digital rural construction and reduce the risks and costs faced by lower-level governments. Additionally, by initiating a competitive excellence incentive model, an evaluation and assessment index system can be established to select "best practices" based on the achievements of lower-level governments. The assessment results of digital rural construction should be incorporated into the annual performance evaluation of government, promoting excellence through competition and thereby enhancing the speed and extent of the diffusion of digital rural policies. Thirdly, enhance learning and communication between governments. Continuously strengthen intergovernmental policy learning and innovation through experience exchange meetings and similar initiatives to summarize and effectively promote successful experiences in the reproduction of digital rural policies. Fourthly, mobilize stakeholder enthusiasm and activate diverse resources. Stimulate market vitality, raise funds from multiple sources, and enhance financial support. Encourage the participation of various stakeholders by offering tax incentives and promoting initiatives such as trade-in programs for household appliances in rural areas. This will guide society, enterprises, and individuals to actively engage in the construction of digital rural policies, facilitating their rapid diffusion. Declarations Author Contribution “Conceptualization, X.C. and H.S.; methodology, H.S.; software, H.S.; validation, H.S., X.C. and J.D; formal analysis, X.C. and H.S.; investigation, H.S.; resources, X.C.; data curation, H.S.; writing—original draft preparation, H.S.; writing—review and editing, X.C.,H.S. ; visualization, H.S.; supervision, X.C.; project administration, H.S.,J.D; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.” Data Availability The datasets used or analysed during the current study available from the corresponding author on reasonable request. References Berry, F. S., & Berry, W. D. (1990). State Lottery Adoptions as Policy Innovations: An Event History Analysis. American Political Science Review , 84 (2), 395-415. https://doi.org/10.2307/1963526 Berry, F. S., & Berry, W. D. (1992). Tax Innovation in the States: Capitalizing on Political Opportunity. American Journal of Political Science , 36 (3), 715-742. https://doi.org/10.2307/2111588 Blossfeld, H.-P., & Rohwer, G. (2019). Event History Analysis With Stata: 2nd Edition (2nd ed.) . Routledge. https://doi.org/https://doi.org/10.4324/9780429260407 Chen, T., & Li, Y. (2020). Influencing Factors of Public Policy Innovation Diffusion: Based on the Analysis of Data of 31 Provincial Residence Permit Systems. Journal of Central South University(Social Sciences) , 26 (5), 107–118. Duan, Y., Yi, Y., & Yao, L. (2023). Effectiveness analysis of digital village construction from the perspective of policy. Library and Information Work , 27 (6), 32–42. https://doi.org/ https://doi.org/10.13266/j.issn.0252-3116.2023.06.004 Guo, J., Huang, J., & Xu, N. (2022). Research on the Diffusion Mechanism of Science and Technology Innovation Voucher Policy: An Analysis of the Event History of 282 Prefecture-level Cities. Forum on Science and Technology in China (2), 23-31. Han, X., & Wei, C. (2021). How does risk affect policy diffusion?—— taking environmental information disclosure as an example. Public Administration & Policy Review , 10 (5), 95–104. Huang, A., Zhang, Z., & Zhu, C. (2020). Diffusion Analysis of China's Science and Technology Commissioner's System: An Empirical Evidence Based on Inter-Provincial Diffusion. Soft Science , 34 , 14-20. https://doi.org/https://doi.org/10.13956/j.ss.1001-8409.2020.11.03 Kostova, T., & Roth, K. (2002). Adoption of an Organizational Practice by Subsidiaries of Multinational Corporations: Institutional and Relational Effects. The Academy of Management Journal , 45 (1), 215-233. https://doi.org/10.2307/3069293 Kostova, T., & Zaheer, S. (1999). Organizational Legitimacy under Conditions of Complexity: The Case of the Multinational Enterprise. The Academy of Management Review , 24 (1), 64-81. https://doi.org/10.2307/259037 Li, J., & Zhang, W. (2019). A Study on the Diffusion of Government Procurement of Services: An Event History Analysis Based on the Data of 31 Provinces in China. China Soft Science (5), 60–67. Li, L., Zeng, Y., & Guo, H. (2023). Digital Village Construction: Underlying Logic, Practical Misunderstandings and Optimization Paths. Chinese Rural Economy (1), 77-92. https://doi.org/https://doi.org/10.20077/j.cnki.11-1262/f.2023.01.005 Li, Y., & Zhou, B. (2024). The Impact of digital finance on rural energy poverty-empirical evidence from rural China. Scientific Reports , 14 (1), 16645. https://doi.org/10.1038/s41598-024-67669-4 Liu, J., & Liu, J. (2020). A Study on the Diffusion Mechanism of the "One Run at Most" Reform: An Analysis of the Incident History of 294 Prefecture-level Cities in China. Journal of Gansu Institute of Administration (4), 26–36,125. Liu, Y., Dai, Z., & Zhao, X. (2024). Unveiling the blueprint for rural digital prosperity: A comparative examination of top 100 digital counties in China. Technological Forecasting and Social Change , 208 , 123625. https://doi.org/https://doi.org/10.1016/j.techfore.2024.123625 Lythreatis, S., Singh, S. K., & El-Kassar, A.-N. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change , 175 , 121359. https://doi.org/https://doi.org/10.1016/j.techfore.2021.121359 Ma, L. (2015). The Diffusion of Public Service Innovation: An Empirical Analysis of Urban Public Bicycle Programs in China. Journal of Public Administration , 8 , 51-75. Meng, X., Wang, X., Nisar, U., Sun, S., & Ding, X. (2023). Mechanisms and heterogeneity in the construction of network infrastructure to help rural households bridge the “digital divide”. Scientific Reports , 13 (1), 19283. https://doi.org/10.1038/s41598-023-46650-7 Mertha, A. (2009). “Fragmented Authoritarianism 2.0”: Political Pluralization in the Chinese Policy Process. The China Quarterly , 200 , 995-1012. https://doi.org/10.1017/S0305741009990592 Mohr, L. B. (1969). Determinants of Innovation in Organizations. American Political Science Review , 63 (1), 111-126. https://doi.org/10.2307/1954288 Mooney, C. Z. (2001). Modeling Regional Effects on State Policy Diffusion. Political Research Quarterly , 54 (1), 103-124. https://doi.org/10.2307/449210 Peng, B., & Zhao, J. (2019). From Growth Championship to Governance Competition: The Transformation of China’s Urban Governance Mode and Its Problems. Inner Mongolia Social Sciences , 40 , 63-70. https://doi.org/https://doi.org/10.14137/j.cnki.issn1003-5281.2019.01.010 Rijswijk, K., Klerkx, L., Bacco, M., Bartolini, F., Bulten, E., Debruyne, L., Dessein, J., Scotti, I., & Brunori, G. (2021). Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation. Journal of Rural Studies , 85 , 79-90. https://doi.org/https://doi.org/10.1016/j.jrurstud.2021.05.003 Rogers, E. M. (2003). Diffusion of Innovations, 5th Edition . Free Press. https://books.google.com.sg/books?id=9U1K5LjUOwEC Rui, P. (2023). Adoption and Internalization: How Multiple Institutional Pressures Affectthe Innovation Diffusion of the River-Chief System —— A Directed Dyadic Event-History Analysis Based on Provincial Governments. Journal of Public Management. , 20 (02), 111–125. https://doi.org/https://doi.org/10.16149/j.cnki.23-1523.20230116.002 Tolbert, C. J., Mossberger, K., & McNeal, R. (2008). Institutions, Policy Innovation, and E-Government in the American States. Public Administration Review , 68 (3), 549-563. https://doi.org/https://doi.org/10.1111/j.1540-6210.2008.00890.x Walker, J. L. (1969). The Diffusion of Innovations among the American States. American Political Science Review , 63 (3), 880-899. https://doi.org/10.2307/1954434 Wang, H., & He, J. (2022). The Policy Change Model of the Chinese Government in the Process of Innovation Diffusion: A Policy Experimental Study of the Shanghai Free Trade Zone from the Perspective of Central-Local Interaction. Journal of Public Management. (3), 1-11. https://doi.org/.https://doi.org/10.16149/j.cnki.23-1523.20210609.001 Wang, P., & Lai, X. (2013). Analysis of the Model and Mechanism of Public Policy Diffusion in China. Journal of Peking University (Philosophy and Social Science) , 50 (6), 14–23. Wang, S., Yu, N., & Fu, R. (2021). Digital Village Construction: Mechanism of Action, Practical Challenges and Implementation Strategies. Reform (4), 45–59. Xufeng, Z., & Hui, Z. (2018). Social Policy Diffusion from the Perspective of Intergovernmental Relations: An Empirical Study of the Urban Subsistence Allowance System in China (1993-1999). Social Sciences in China , 39 (1), 78-97. https://doi.org/10.1080/02529203.2018.1414390 Yang, H., & Zhao, D. (2015). Performance Legitimacy, State Autonomy and China's Economic Miracle. Journal of Contemporary China , 24 (91), 64-82. https://doi.org/10.1080/10670564.2014.918403 Yang, J., & Yang, R. (2022). Policy diffusion, obstruction and mitigation of the implementation of the digital village strategy. Journal of Jiangxi Normal University (Philosophy and Social Sciences Edition) , 55 , 67–73. Zhu, X. (2014). Mandate Versus Championship: Vertical government intervention and diffusion of innovation in public services in authoritarian China. Public Management Review , 16 (1), 117-139. https://doi.org/10.1080/14719037.2013.798028 Zhu, X., & Zhang, Y. (2015). Political Mobility and Dynamic Diffusion of Innovation: The Spread of Municipal Pro-Business Administrative Reform in China. Journal of Public Administration Research and Theory , 26 (3), 535-551. https://doi.org/10.1093/jopart/muv025 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5570966","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":395920818,"identity":"cd1a4ca9-184e-4b61-8ad8-7ac27dcae226","order_by":0,"name":"chunlin xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFAC5obDfyoOQDlsRGlhbDzAc4ZELc0HeNtI0WJw/GDDAcl5dxK3s58xYPhQdpiBf3YDfi2SPYkNBwy3PUvc2ZNjwDjj3GEGiTsH8Gvhl2BsOJC47XDihgM5Bsy8bYcZDCQS8GthA2k5OAeo5fwbA+a/xGgB2XKwsQGo5QbQFkZitID8cpjh2GHjDTeeFRzsOZfOI3GDgBaD44cPf2aoOSy74Xzyxgc/yqzl+GcQ0IICDgAxDwnqR8EoGAWjYBTgAgAGDUyHf6ETZAAAAABJRU5ErkJggg==","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"chunlin","middleName":"","lastName":"xiong","suffix":""},{"id":395920820,"identity":"43be4b3b-effe-4333-96f9-6400e453e255","order_by":1,"name":"siqi hu","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"siqi","middleName":"","lastName":"hu","suffix":""},{"id":395920822,"identity":"855bc9ed-03ae-4b56-9969-2f1f70757afc","order_by":2,"name":"Duo jiang","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Duo","middleName":"","lastName":"jiang","suffix":""}],"badges":[],"createdAt":"2024-12-03 09:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5570966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5570966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72964780,"identity":"f3356090-e4d6-45e7-b2ea-1d6ece9ed345","added_by":"auto","created_at":"2025-01-04 14:27:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47197,"visible":true,"origin":"","legend":"\u003cp\u003eDual Four-Dimensional Theoretical Analysis Framework for the Diffusion of Digital Rural Policies in China.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5570966/v1/cb63057c85cb561ec786ef11.png"},{"id":72964779,"identity":"3d4c7cb5-7b30-4f26-a794-1841452b53a4","added_by":"auto","created_at":"2025-01-04 14:27:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17822,"visible":true,"origin":"","legend":"\u003cp\u003eThe Process of Innovative Diffusion of Digital Rural Policy.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5570966/v1/3106c913740fb22476797967.png"},{"id":74364160,"identity":"6ab0ea8f-4ffe-4d3c-b111-a2f19926f70d","added_by":"auto","created_at":"2025-01-21 13:41:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1503096,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5570966/v1/6a1cc5ec-4a67-4de9-93a1-714223acb597.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Did China's Digital Rural Policy Rapidly Diffuse? An Event History Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global trend of digitalization increasingly encompasses rural areas, with the digital transformation of agriculture and rural regions becoming a focal point of policy at the global level(Rijswijk et al., 2021). The digital rural development initiatives in various countries aim to bridge the digital divide, promote economic growth in rural areas, and enhance the quality of life in rural communities(Lythreatis et al., 2022; Meng et al., 2023). In this global context, China is actively advancing the digitalization of rural areas, aiming to fully unleash the diffusion effects of digital technology innovation, knowledge spillover effects, and inclusive sharing effects. These efforts seek to bridge the urban-rural development gap, enhance the level of agricultural and rural modernization, and meet the demands of farmers for a better digital life(Li \u0026amp; Zhou, 2024; Liu et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn China, the construction of digital villages has been elevated to the level of national strategy. The \u0026quot;Opinions of the Central Committee of the Communist Party of China and the State Council on Implementing the Rural Revitalization Strategy,\u0026quot; issued in 2018, proposed the \u0026quot;implementation of the digital rural strategy,\u0026quot; marking the formal introduction of the digital rural strategy. In 2019, the General Office of the Central Committee of the Communist Party of China and the General Office of the State Council released the \u0026quot;Outline of the Digital Rural Development Strategy,\u0026quot; which has since received sustained attention from the national level. In 2020, the Central Cybersecurity and Information Commission and the Ministry of Agriculture and Rural Affairs, along with seven other departments, jointly issued the \u0026quot;Notice on Carrying Out National Digital Rural Pilot Work,\u0026quot; designating 117 counties (cities, districts) across all 31 provinces (autonomous regions, directly governed municipalities) and the Xinjiang Production and Construction Corps as the first batch of national digital rural pilot areas. The No.1 Central Document in 2021 proposed the \u0026quot;implementation of digital rural construction and development projects.\u0026quot; In 2022, the No. 1 Central Document once again emphasized the need to vigorously promote the construction of digital villages. To implement the spirit of the No. 1 Central Document, the Central Cybersecurity and Information Commission, the Ministry of Agriculture and Rural Affairs, and other departments successively released documents such as the \u0026quot;Digital Rural Development Action Plan (2022-2025)\u0026quot; and the \u0026quot;Guidelines for the Construction of Digital Rural Standard Systems,\u0026quot; providing a pathway for digital rural construction.\u003c/p\u003e\n\u003cp\u003eWith the promotion of a series of central policies, local governments have actively implemented the spirit of these policies and introduced relevant measures. By the end of 2021, all 31 provinces in China had adopted digital rural policies, which have been comprehensively rolled out across various provinces. However, there are disparities in the pace of adoption of digital rural policies among different provinces. What key factors influence the rapid diffusion of digital rural policies in China? What are the driving mechanisms behind the diffusion of these policies? There is limited research addressing this question, despite a wealth of research outcomes regarding digital rural policies. Existing studies have largely overlooked the exploration of the diffusion mechanisms and the underlying logic of digital rural policy diffusion. Meanwhile, there is a relatively rich body of research on the diffusion of policy innovations, which provides important insights for this study. However, traditional research on policy innovation diffusion often analyzes the issue solely from the perspective of whether or not policies are adopted, simplistically defining it as a binary decision while neglecting the distinction between diffusion triggered by initial adoption and that achieved through absorption and internalization. This oversight hinders a comprehensive understanding of the adoption and internalization processes of digital rural policies. Therefore, it is of practical significance to investigate the influencing factors and potential mechanisms behind the rapid diffusion of digital rural policies in China, as well as to identify the similarities and differences in the factors influencing local governments\u0026apos; adoption and internalization of these policies.\u003c/p\u003e\n\u003cp\u003eIn summary, the marginal contributions of this paper are as follows : (1) From a research perspective, grounded in the theory of innovation diffusion, this study subdivides the innovation diffusion process into two phases: \u0026quot;Adoption\u0026quot; and \u0026quot;Internalization. \u0026quot; The former primarily refers to the decision-making process through which local governments choose to adopt a new policy that has not been previously introduced in their region. This is typically marked by the initial release of policy documents; for instance, the first issuance of opinions related to digital rural policies by various provinces can be seen as a signal of innovation adoption, representing the first phase of innovation diffusion. The latter phase, Internalization, occurs after local governments initially adopt a policy innovation and begin the process of reproducing the policy content based on their unique characteristics and circumstances. This phase signifies the transition from nominal diffusion of innovation to its substantial operationalization. (2) In terms of research content, within the strategic context of \u0026quot;Digital Rural, \u0026quot; the study explores the impacts of various factors\u0026mdash;such as rural economic development needs, rural population size, financial support for agriculture, central authority push, national competition, and neighboring competition\u0026mdash;on local governments\u0026apos; policy adoption and internalization processes, thereby enriching the research on digital rural initiatives. (3) Regarding practical applications, this study examines the factors influencing the rapid diffusion of digital rural policies in China and the potential mechanisms behind it, revealing the unique paths and experiences of this diffusion in the Chinese context. This provides valuable insights for developing countries seeking to accelerate the dissemination of rural digital policies.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003ePolicy innovation refers to the adoption of a policy or program that is considered \"new\" by a government, regardless of whether this policy or program has been adopted by other governments previously(Walker, 1969). The process through which an innovation is disseminated is known as diffusion(Rogers, 2003). When the policy innovation from government A is disseminated to and adopted by government B, this constitutes the diffusion of policy innovation; the process of government A's policy innovation spreading to government B also represents the first adoption of government A's policy innovation by government B. For government B, adopting a new policy signifies a process of creation from nothing, which is a manifestation of policy innovation(Zhu, 2014). By reviewing the literature on policy innovation diffusion, we can broadly distinguish between two aspects: identifying the driving factors of innovation diffusion and exploring the process of innovation diffusion.\u003c/p\u003e \u003cp\u003eFirst, in identifying the driving factors of innovation diffusion, scholars focus on understanding why a policy innovation spreads from one government to others. The question of \"why diffusion occurs\" examines the influencing factors for the first adoption of policy innovation by the adopting government. In the early 1990s, Berry et al. proposed the Internal Determinants Model, the Regional Diffusion Model, and the National Interaction Model(Berry \u0026amp; Berry, 1990). Their research indicated that the adoption of policy innovations by governments is influenced by both internal and external factors, ultimately forming two main models that explain the internal and external influencing factors of policy innovation—the Internal Determinants Model and the Diffusion Model(Berry \u0026amp; Berry, 1992). The Diffusion Model encompasses sub-models such as the Vertical Influence Model, the Regional Diffusion Model, the \"Leader-Follower\" Model, and the National Interaction Model(Rui, 2023). The Vertical Influence Model emphasizes top-down influences, while the other three sub-models share horizontal diffusion paths and can be collectively referred to as the Horizontal Influence Model. Internal factors include motivation, resources, and obstacles, specifically addressing public demand, economic level, population structure, income characteristics, and financial resources. External factors include influences of vertical hierarchical diffusion and horizontal peer diffusion. Vertical hierarchical diffusion refers to the promotion of policy vertical diffusion by higher-level governments through regulations, administrative orders, and mandatory policies. Horizontal peer diffusion occurs when local governments, due to geographic proximity or similarities in economic, social issues, institutional environments, or assessment criteria, may experience policy diffusion horizontally.Regarding how the influencing factors of policy innovation diffusion relate to policy adoption, scholars have summarized mechanisms such as learning, competition, imitation, implementation of administrative orders, and social construction from the perspective of policy innovation diffusion mechanisms(Wang \u0026amp; Lai, 2013). Chinese scholars, building on Berry et al.'s framework of internal and external factors, have conducted studies on innovation diffusion within the context of Chinese practice. Empirical analyses have been performed in various policy areas, including the urban minimum living allowance system(Xufeng \u0026amp; Hui, 2018), government procurement of services(Li \u0026amp; Zhang, 2019), residence permit systems(Chen \u0026amp; Li, 2020), \"One-time Completion\" reforms(Liu \u0026amp; Liu, 2020), environmental information disclosure systems(Han \u0026amp; Wei, 2021), and technology innovation voucher policies(Guo et al., 2022). These studies not only exploratorily test classical theories of policy innovation diffusion but also enrich the theoretical and practical research outcomes of innovation diffusion in the Chinese context, providing important references for our study of the influencing factors and diffusion mechanisms of policy innovation in China.\u003c/p\u003e \u003cp\u003eSecond, when exploring the process of innovation diffusion, scholars focus on the stages of the innovation diffusion process; discussions in this area are comparatively less extensive than those at the first level. In terms of the stages of the innovation diffusion process, Lucas considers the innovation diffusion process to comprise five stages: policy reinvention, policy development, policy piloting, policy borrowing and modification, and policy integration(Yang \u0026amp; Zhao, 2015). The diffusion of government policy innovations requires a series of initial adoptions followed by subsequent legitimization processes.From the perspective of new institutionalism, organizations seek to maintain consistency between internal and external institutions during the decision-making process of \"initial adoption\" of institutions, which represents the construction phase of external legitimacy for the policy(Kostova \u0026amp; Zaheer, 1999). Unlike the \"formulate then execute\" model prevalent in developed countries, China exhibits a widespread \"learning by doing\" approach(Wang \u0026amp; He, 2022). In addition to the decision-making phases of institutional adoption and implementation, the internal legitimacy under institutional isomorphic change also warrants attention(Kostova \u0026amp; Roth, 2002), referring to the \"internalization\" stage of policy innovation diffusion. The internalization stage primarily examines whether local governments have re-clarified policy objectives, localized policy content, and engaged in policy reproduction(Berry \u0026amp; Berry, 1992).\u003c/p\u003e \u003cp\u003eIn the realm of digital rural policies, current research primarily focuses on aspects such as policy evolution characteristics(Li et al., 2023), implementation strategies(Wang et al., 2021), and effectiveness assessments(Duan et al., 2023). There is a paucity of studies that examine digital rural policies from the perspective of policy diffusion. Although a recent study explored the diffusion of digital rural policies across different regions in China(Yang \u0026amp; Yang, 2022), it did not conduct a nationwide empirical study on the diffusion of digital rural policy innovations. At present, there is a limited understanding of the diffusion patterns of digital rural policies in China, and the processes, influencing factors, and mechanisms of such diffusion remain unclear.\u003c/p\u003e \u003cp\u003eTherefore, it is evident that current research on the influencing factors and mechanisms of policy innovation diffusion mainly concentrates on the adoption phase, with relatively few studies delving into the internalization phase. This paper, based on the theory of policy innovation diffusion and within the context of Chinese policies, comprehensively analyzes the influencing factors of digital rural policy innovation diffusion during both the adoption and internalization phases, exploring the varying degrees of impact of different factors at different stages of digital rural policy innovation diffusion. This approach aims to gain a holistic understanding of the operational patterns of digital rural policy innovation diffusion in China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Theoretical analysis and research hypothesis","content":"\u003cp\u003eThis study systematically collects, organizes, and analyzes the literature on policy innovation diffusion. Based on the theory of innovation diffusion and the practical implementation of digital rural policies in China, a dual perspective of policy adoption and internalization is employed. The analysis considers both internal dual driving forces and external horizontal and vertical driving forces. Specifically, the factors driving policy diffusion are clarified from four dimensions: regional demand pull, local government fiscal capacity to support rural development, central authority push, and intergovernmental competition, thereby constructing a dual four-dimensional theoretical analysis framework for the diffusion of digital rural policies in China (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).The innovation diffusion process undertaken by local governments may be influenced by both internal dual driving forces and external horizontal and vertical driving forces. The internal dual driving forces include internal demand pull and the fiscal capacity of local governments to support rural development, while the external horizontal and vertical driving forces encompass central authority push and intergovernmental competition. The following sections will discuss these four dimensions of driving factors in greater detail.\u003c/p\u003e\u003ch2\u003eRegional demand pull\u003c/h2\u003e\u003cp\u003eRegional demand pull forces include the demand for rural economic development and the driving force elicited through rural population size.\u003c/p\u003e\u003ch3\u003eThe rural economic development level\u003c/h3\u003e\u003cp\u003eMohr revealed that organizational innovation is influenced by the interplay of motives, resources, and barriers(Mohr, 1969). Walker pointed out that responding to internal public demands is an important motivation for local government innovation(Walker, 1969). Considering the characteristics of digital rural policies, the demand for rural economic development is a primary concern. In terms of rural economic development needs, Huang et al. found that the level of rural economic development in China is negatively correlated with the government's adoption of innovations(Huang et al., 2020); that is, the lower the level of rural economic development, the greater the rural economic development needs, leading to a higher probability of government adoption of innovations. Fundamentally, digital rural policies are products aimed at promoting common prosperity among farmers in rural areas. Therefore, regions with a lower level of rural economic development are more in need of government adoption and internalization of digital rural policies to address the interests of farmers and issues related to their common development. Consequently, the motivation for local adoption and internalization of digital rural policies is stronger. Based on this, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eHypothesis 1-1: The lower the level of rural economic development, the greater the demand for digital rural development, and the more likely it is to adopt digital rural policies.\u003c/p\u003e\u003cp\u003eHypothesis 1-2: The lower the level of rural economic development, the greater the demand for digital rural development, and the more likely it is to internalize digital rural policies.\u003c/p\u003e\u003ch3\u003eRural population size\u003c/h3\u003e\u003cp\u003eOrganizational innovation theory posits that the population size of a region influences organizational innovation; specifically, the larger the population, the stronger the government's demand for management reform and policy innovation(Ma, 2015). Digital rural policies emphasize a people-centered approach, aiming to establish a digital rural development model that is compatible with the rural population size, to address the interests and immediate concerns of farmers. Digital rural policies are closely linked to safeguarding the fundamental interests of farmers and promoting their common prosperity in rural areas. Therefore, it can be predicted that in regions with larger rural populations and higher population densities, there will be a greater need to address farmers' interests and protect their fundamental rights, leading to a higher demand for digital reforms and consequently a stronger demand for digital rural policies; thus, these areas are more likely to adopt and internalize digital rural policies. Based on this, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eHypothesis 2-1: The larger the rural population size, the more likely it is to adopt digital rural policies.\u003c/p\u003e\u003cp\u003eHypothesis 2-2: The larger the rural population size, the more likely it is to internalize digital rural policies.\u003c/p\u003e\u003ch3\u003eFiscal capacity of local governments to support rural development\u003c/h3\u003e\u003cp\u003eThe capacity of local governments is one of the key factors in government innovation. The fiscal capacity of local governments to support rural development is reflected in their ability to adopt and internalize innovations in terms of resource allocation, specifically whether they possess the necessary supporting resources and capabilities to overcome the obstacles faced by innovations. Resource slack theory posits that organizations with slack resources are more likely to achieve innovation(Tolbert et al., 2008). Ma confirmed a positive correlation between local government fiscal resources and the adoption of innovations while studying the diffusion of public bicycle programs(Ma, 2015). In the case of digital rural policies, the initial phases of rural digital transformation require investments in network infrastructure development, digital upgrades of traditional infrastructure, and the establishment of various digital platforms. Adopting and internalizing digital rural policies may require significant resource investments from the government as a safeguard. Based on this, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eHypothesis 3-1: The higher the level of local government fiscal support for agriculture, the more likely it is to adopt digital rural policies.\u003c/p\u003e\u003cp\u003eHypothesis 3-2: The higher the level of local government fiscal support for agriculture, the more likely it is to internalize digital rural policies.\u003c/p\u003e\u003ch3\u003eCentral authority push\u003c/h3\u003e\u003cp\u003eAs a unitary state with a hierarchical political structure, local governments are subordinate to the central government(Mertha, 2009). When a policy receives emphasis and authoritative mobilization from the central government, local governments actively respond(Berry \u0026amp; Berry, 1990). The central authority push originates from the central government’s efforts to promote the adoption of policy innovations by local governments through various means such as policies, plans, opinions, notices, and documents. This represents a top-down force characterized by authority and coercion, which not only encourages local governments to adopt policy innovations but also influences the process of internalization. Research by Zhu \u0026amp; Zhao indicated that administrative orders encouraging policy innovation issued by the central government positively influence the likelihood of local government adoption of policy innovations\u003csup\u003e13\u003c/sup\u003e. Since the central government proposed the implementation of the digital rural strategy in Document No. 1 in 2018, it has introduced relevant policy documents to promote the top-down diffusion of digital rural policies, explicitly suggesting the exploration of digital rural development models and expediting digital rural construction. Subsequently, digital rural policies rapidly spread to local governments, with various provinces adopting these policies and most regions developing specialized digital rural policies tailored to their circumstances. Based on this, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eHypothesis 4-1: The central government's push for digital rural policies (2018) will enhance the likelihood of local governments adopting digital rural policies.\u003c/p\u003e\u003cp\u003eHypothesis 4-2: The central government's push for digital rural policies (2020 and 2022) will enhance the likelihood of local governments internalizing digital rural policies.\u003c/p\u003e\u003ch3\u003eCompetitiveness among peer governments\u003c/h3\u003e\u003cp\u003e\u003cstrong\u003eNational competition\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNational competition arises from the mutual learning, imitation, and competitive pressures among peer governments. The policy innovations of local governments are influenced by horizontal learning and implicit competition. Competition among local governments occurs within the same tier; when one local government adopts a policy innovation while others do not, it creates competitive pressure on the latter(Rogers, 2003). Additionally, there exists a competitive drive among local governments characterized by \"competing through learning.\" In the United States, local governments typically choose affluent and reputable states as models for learning\u003csup\u003e6\u003c/sup\u003e. In China, a province may learn from a higher-performing province, viewed as a benchmark for innovation, in hopes of surpassing it to gain recognition from the central government(Rui, 2023). Under the drive of \"governance competition,\" local governments are inclined to adopt policy innovations to enhance their competitiveness, thereby attracting the attention of higher-level authorities(Peng \u0026amp; Zhao, 2019; Zhu \u0026amp; Zhang, 2015). The mechanisms of learning and competition influence the horizontal diffusion of policy innovations among peer governments. Based on this, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eHypothesis 5-1: The greater the number of provinces that have adopted digital rural policies, the greater the competitive pressure on provinces that have not adopted them, increasing the likelihood of adoption.\u003c/p\u003e\u003cp\u003eHypothesis 5-2: The greater the number of provinces that have internalized digital rural policies, the greater the competitive pressure on provinces that have not internalized them, increasing the likelihood of internalization.\u003c/p\u003e\u003cp\u003eNeighboring\u0026nbsp;competition\u003c/p\u003e\u003cp\u003eNeighboring competition arises from the mutual learning, imitation, and competitive pressures among geographically adjacent peer governments. Berry and Berry have suggested \u0026nbsp;that the likelihood of a state adopting policy innovations is influenced by the adoption of such innovations by neighboring states(Rui, 2023). Existing studies indicate a positive correlation between the proportion of neighboring states that adopt policy innovations and the probability that a state government will adopt similar innovations(Mooney, 2001).Based on this, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eHypothesis 6-1: The greater the number of provinces among neighboring regions that have adopted digital rural policies, the greater the competitive pressure on provinces that have not adopted them, increasing the likelihood of adoption.\u003c/p\u003e\u003cp\u003eHypothesis 6-2: The greater the number of provinces among neighboring regions that have internalized digital rural policies, the greater the competitive pressure on provinces that have not internalized them, increasing the likelihood of internalization.\u003c/p\u003e"},{"header":"Research design","content":"\u003ch2\u003eSample and data source\u003c/h2\u003e\u003cp\u003eIn terms of the research subjects, this study primarily analyzes the factors influencing the diffusion of digital rural policies among the 31 provincial-level governments (including provinces, municipalities, and autonomous regions) excluding Hong Kong, Macau, and Taiwan. In 2018, the Central Committee of the Communist Party of China and the State Council first proposed the implementation of the digital rural strategy in Document No. 1, thereby marking 2018 as the starting point for the policy diffusion of the digital rural strategy and defining the observation period as 2018–2022.As of December 31, 2021, all 31 provincial-level governments had adopted digital rural policies. By December 31, 2022, 24 of these governments had achieved a certain level of internalization of the digital rural policies they had adopted; however, the internalization largely lagged behind the adoption timeline, as shown in Fig.\u0026nbsp;2. The primary reasons for this lag include the need to invest substantial human, material, and financial resources to build rural information infrastructure and agricultural big data platforms, which entails certain risks and requires time. Consequently, local governments may be more inclined to quickly adopt policies in response to central directives before further researching and developing the specific implementation details for the internalization of digital rural policies.\u003c/p\u003e\u003cp\u003eThe author organizes the data into a province-year dataset for event history analysis, retaining the years of adoption (or internalization) of the digital rural policy as well as the preceding years, while removing the years following the adoption (or internalization) of the digital rural policy. In the policy adoption section, a survival dataset was ultimately constructed, consisting of 80 samples from 31 provincial-level governments; in the policy internalization section, a survival dataset containing 124 samples from the same number of provincial-level governments was established.\u003c/p\u003e\u003cp\u003eThe data utilized in this study includes the timings of both adoption and internalization of policies by the 31 provincial-level governments during the observation period, relevant governmental text data regarding digital rural policies from the central and provincial governments, as well as related indicators such as agricultural GDP per capita, rural population size, and financial support for agriculture. The data on agricultural GDP per capita, rural population size, and financial support for agriculture were sourced from the \"China Statistical Yearbook (2018–2022)\" and \"China Rural Statistical Yearbook (2018–2022)\" databases compiled by China National Knowledge Infrastructure.For the policy text portion, searches were conducted using keywords such as \"digital rural\" and \"digital agriculture and rural areas\" in the \"Peking University Law Database\" and the official websites of provincial-level governments and relevant departments. Additionally, searches using \"provincial government name + digital rural\" and \"provincial government name + digital agriculture and rural areas\" were executed on Baidu to collect relevant policy documents issued by the central and provincial governments, covering the period from January 2018 to December 2022. Consequently, the timing of the issuance of digital rural policies by each provincial-level government was determined and a database was established.\u003c/p\u003e\u003ch2\u003eVariable selection\u003c/h2\u003e\u003ch2\u003eDependent variable\u003c/h2\u003e\u003cp\u003eThis study includes two dependent variables, defining \"policy adoption\" and \"policy internalization\" as the probabilities of provincial government (\u003cem\u003ei\u003c/em\u003e) adopting or internalizing digital rural policies at time (\u003cem\u003et\u003c/em\u003e). According to the discrete-time event history model, the dependent variables \"policy adoption\" and \"policy internalization\" are binary dummy variables.\u003c/p\u003e\u003cp\u003eSpecifically, the policy adoption status is determined based on whether provincial government (\u003cem\u003ei\u003c/em\u003e) has issued policy documents related to digital rural initiatives. The policy internalization status is determined based on whether provincial government (\u003cem\u003ei\u003c/em\u003e) has implemented specific types of digital rural policies (including action plans, work plans, implementation plans, and implementation opinions). If a given provincial government (\u003cem\u003ei\u003c/em\u003e) adopts (or internalizes) digital rural policies in a particular analysis year (\u003cem\u003et\u003c/em\u003e), the value of the dependent variable for that year is set to 1, while the values for all preceding years are set to 0. Following event history analysis principles, all data after that year must be omitted, resulting in right censoring of the sample data.\u003c/p\u003e\u003ch2\u003eExplanatory variable\u003c/h2\u003e\u003cp\u003eBased on the hypotheses regarding the influencing factors of provincial governments' adoption (or internalization) of digital rural policies, the following explanatory variables are established:\u003c/p\u003e\u003ch2\u003eInternal Demand Pull Force\u003c/h2\u003e\u003cp\u003eThis variable reflects whether there is sufficient internal motivation for local governments to adopt and internalize digital rural policies. It consists of two components: the level of rural economic development and the scale of the rural population, measured by agricultural GDP per capita and rural population size, respectively. Agricultural GDP per capita is calculated as the agricultural GDP of province i in year t-1 divided by the rural population of year t-1. Both agricultural GDP per capita and rural population size are continuous variables, and to avoid heteroscedasticity, an exponential transformation is applied, with the final calculated results incorporated into the model.\u003c/p\u003e\u003ch2\u003eFinancial Capacity of Local Governments to Support Rural Development\u003c/h2\u003e\u003cp\u003eThis variable indicates whether local governments have sufficient financial resources to support agriculture for the adoption and internalization of digital rural policies. It is measured by the level of financial support for agriculture, calculated as the agricultural, forestry, and water expenditures of province i in year t-1 divided by the rural population of year t-1. This variable is also continuous and is similarly subjected to an exponential transformation.\u003c/p\u003e\u003ch2\u003eCentral Authority Push\u003c/h2\u003e\u003cp\u003eThis variable examines the impact of important top-down initiatives from the central government on the diffusion of digital rural policy adoption and internalization. To measure the short-term effects of central policies, a binary variable is used for central authority push. In the year when the central government proposed the \"Implementation of the Digital Rural Strategy\" (2018), the value for central push (2018) is set to 1, and 0 in other years. In the year of issuing the \"Notice on Carrying Out National Digital Rural Pilot Work\" (2020), the value for central push (2020) is set to 1, and 0 in other years. In the year when the \"Guidelines for the Construction of Digital Rural Standard System\" was promulgated (2022), the value for central push (2022) is set to 1, and 0 in other years. In the robustness check section, we adjusted the coding rules for central push: in the policy adoption section, central push (2018) was changed to central push (2019) and central push (2020). In the policy internalization section, central push (2020) and central push (2022) were adjusted to central push (2019) and central push (2022), as well as central push (2020) and central push (2021).\u003c/p\u003e\u003ch2\u003eCompetitiveness among Peer Governments\u003c/h2\u003e\u003cp\u003eThis variable reflects the influence of other provinces' innovative adoption (or internalization) of digital rural policies on a provincial government, highlighting the competitive dynamics among peer governments. It encompasses horizontal diffusion effects and includes two independent variables: national competition and neighboring competition. National competition is measured as the cumulative number of provinces that have adopted digital rural policies divided by the total number of provinces on the mainland. This variable serves as a positive predictor, indicating that the more provinces that adopt and internalize digital rural policies, the greater the impact on province i's adoption and internalization of these policies, reflecting the national competitive pressure faced by province i. Neighboring competition is measured as the ratio of the cumulative number of provinces that have adopted digital rural policies among adjacent provinces to the total number of neighboring provinces.\u003c/p\u003e\u003cp\u003eThe measurement methods and data sources for the variables used in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable measurement methods and data sources.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Measurement\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Sources\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDependent Variables\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdoption of Digital Rural Policies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssigned a value of 1 if a province adopted digital rural policies in a particular year; otherwise, 0.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOfficial websites of provincial governments and relevant departments.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternalization of Digital Rural Policies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssigned a value of 1 if a province internalized digital rural policies in a particular year; otherwise, 0.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOfficial websites of provincial governments and relevant departments.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExplanatory Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Economic Development Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural logarithm of the agricultural GDP per capita of each province in the previous year.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e“China Statistical Yearbook” “China Rural Statistical Yearbook”\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Population Size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural logarithm of the rural population of each province in the previous year.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e“China Rural Statistical Yearbook”\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Financial Support Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural logarithm of the ratio of agricultural, forestry, and water expenditure to rural population of each province in the previous year.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e“China Statistical Yearbook” “China Rural Statistical Yearbook”\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2018)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssigned a value of 1 for the year 2018, otherwise 0.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe authors’ database\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2020)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssigned a value of 1 for the year 2020, otherwise 0.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe authors’ database\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2022)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssigned a value of 1 for the year 2022, otherwise 0.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe authors’ database\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of the cumulative number of provinces that have adopted digital rural policies to the total number of provinces in China.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalculated by the authors\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio of the cumulative number of provinces adopting digital rural policies in adjacent provinces to the total number of all adjacent provinces.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalculated by the authors\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eModel selection\u003c/h2\u003e\u003cp\u003eThis paper employs Event History Analysis (EHA) to empirically examine the diffusion of digital rural policy innovation. EHA has been widely used in the social sciences and was introduced into the study of policy innovation diffusion by the Berry couple in 1990(Blossfeld \u0026amp; Rohwer, 2019). EHA has since become a classic methodology for researching policy innovation diffusion. The EHA models include both continuous-time and discrete-time models. In this study, the dependent variable is a binary variable indicating “adoption (or internalization) of digital rural policies/non-adoption (non-internalization) of digital rural policies.” Time is measured in years, thus a discrete-time event history analysis model is utilized. The analysis is conducted using Stata 17 statistical software with a binary Logit regression model. The model is specified as follows:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:logit\\left({p}_{i,t}\\right)=\\text{log}\\left(\\frac{{p}_{i,t}}{1-{p}_{i,t}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:={\\beta\\:}_{0}+{\\beta\\:}_{1}{GD{P}_{PC}}_{i,t-1}+{\\beta\\:}_{2}{Siz{e}_{RP}}_{i,t-1}+{\\beta\\:}_{3}{Financial}_{i,t-1}+{\\beta\\:}_{4}{Central}_{t}+\\:\\:{\\beta\\:}_{5}{Competitior\\_\\:neighbor}_{i,t}+{\\beta\\:}_{6}{Competitior\\_Nation}_{i,t}+{\\epsilon\\:}_{i,t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the above equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i,t}\\)\u003c/span\u003e\u003c/span\u003erepresents the probability of Province i adopting (or internalizing) the digital rural policy in year t and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i,t}/（1-{p}_{i,t}）\\)\u003c/span\u003e\u003c/span\u003erepresents the odds. The right side of the equation addressed the impact of the rural economic development level (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GDP\\_PC}_{i,t-1}\\)\u003c/span\u003e\u003c/span\u003e), rural population size ༈\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Size\\_RP}_{i,t-1}\\)\u003c/span\u003e\u003c/span\u003e༉, agricultural financial support level ༈\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Financial}_{i,t-1}\\)\u003c/span\u003e\u003c/span\u003e༉, central government push ༈\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Central}_{t}\\)\u003c/span\u003e\u003c/span\u003e༉, neighbor competition ༈\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Competitior\\_\\:neighbor}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e༉, and national competition ༈\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Competitior\\_Nation}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e༉ on the adoption and internalization of the digital rural policy. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e depicts the constant term and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e represents the error term.\u003c/p\u003e"},{"header":"Results and analysis","content":"\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eThe complete descriptive statistics of each variable for the two stages of policy adoption and policy internalization are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Prior to conducting the binary Logit regression, the author performed multicollinearity diagnostics for the variables. The results showed that the variance inflation factor (VIF) for all variables was below 6.835, which is well below the critical VIF threshold of 10, indicating that there is no multicollinearity problem among the variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of variables.Regarding the measurement of variables with *, refer to the “Robustness Test” section.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eAdoption stage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eInternalization stage\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. dev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStd. dev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent\u003c/p\u003e \u003cp\u003eVariable(Adoption / Internalization)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Economic Development Level\u003c/p\u003e \u003cp\u003e(logarithm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.891\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.942\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.724\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.940\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.942\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.006\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Population Size(logarithm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.112\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.469\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.338\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.469\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003cp\u003eFinancial Support\u003c/p\u003e \u003cp\u003eLevel(logarithm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.403\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.562\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.528\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.562\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.915\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2018)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push\u003c/p\u003e \u003cp\u003e(2019)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2020)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push\u003c/p\u003e \u003cp\u003e(2021)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2022)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003cp\u003eCompetition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\u003cp\u003eThe complete descriptive statistics of each variable for the two stages of policy adoption and policy internalization are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Prior to conducting the binary Logit regression, the author performed multicollinearity diagnostics for the variables. The results showed that the variance inflation factor (VIF) for all variables was below 6.835, which is well below the critical VIF threshold of 10, indicating that there is no multicollinearity problem among the variables.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of variables.Regarding the measurement of variables with *, refer to the “Robustness Test” section.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eAdoption stage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eInternalization stage\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. dev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStd. dev\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent\u003c/p\u003e \u003cp\u003eVariable(Adoption / Internalization)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Economic Development Level\u003c/p\u003e \u003cp\u003e(logarithm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.891\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.942\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.724\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.940\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.942\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.006\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Population Size(logarithm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.112\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.469\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.338\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.469\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003cp\u003eFinancial Support\u003c/p\u003e \u003cp\u003eLevel(logarithm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.403\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.562\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.528\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.562\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.915\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2018)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push\u003c/p\u003e \u003cp\u003e(2019)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2020)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push\u003c/p\u003e \u003cp\u003e(2021)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push (2022)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003cp\u003eCompetition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eResults and analysis of policy adoption\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the regression results from the Event History Analysis (EHA) model regarding the adoption of digital rural policies. Model 1 regresses the internal influencing factors, which include internal demand pull and the financial capacity of local governments to support rural development. Model 2 regresses the external influencing factors, which include central authority push and the competitiveness among peer governments. Both the internal demand pull from Model 1 and the financial capacity of local governments to support rural development, as well as the central authority push and the competitiveness among peer governments from Model 2, pass significance tests, demonstrating a significant influence on the adoption of digital rural policies.Compared to Models 1 and 2, Model 3 integrates internal demand pull, financial capacity of local governments, central authority push, and competitiveness among peer governments. The log-likelihood, pseudo R², and chi-squared values progressively increase, indicating improved explanatory power of the model. The regression results in Model 3 reveal that the internal demand pull brought about by the level of rural economic development is statistically significant and negative. In contrast, the internal demand pull generated by rural population size, the level of financial support for agriculture from local governments in support of rural development, central authority push, and national competition among peer governments are statistically significant and positive. In contrast, the neighboring competition among peer governments is statistically significant and negative.Hypotheses 1–1, 2 − 1, 3 − 1, 4 − 1, 5 − 1, and 6 − 1 are validated at the 0.1, 0.01, 0.01, 0.1, 0.01, and 0.01 significance levels, respectively.\u003c/p\u003e\u003cp\u003eFirst, the internal demand pull generated by the level of rural economic development shows a significant negative effect in the integrated model, indicating that a lower level of rural economic development negatively influences the adoption of digital rural policies. This means that provinces with lower levels of rural economic development have a greater demand for digital rural policies, thereby strengthening the motivation for their adoption, which validates Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e–\u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The internal demand pull from rural population size is significant at the 0.05 level in Model 1 and at the 0.01 level in the integrated model, demonstrating a significant positive correlation between the adoption of digital rural policies and rural population size. Provinces with larger rural populations have a greater demand for digital rural policies and are more likely to adopt them, thus confirming Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e2\u003c/span\u003e − 1.\u003c/p\u003e\u003cp\u003eFurthermore, the financial capacity of local governments to support rural development is significantly positive at the 0.01 level in both Models 2 and 3, indicating that the level of financial support for agriculture has a significant positive impact on the adoption of digital rural policies, which supports Hypothesis 3 − 1. Additionally, the hypothesis regarding the correlation between central authority push and the probability of innovation adoption by provinces is confirmed, showing that central government policies play a positive role in motivating local governments to adopt digital rural policies.\u003c/p\u003e\u003cp\u003eLastly, the competitiveness among peer governments in national competition is significant at the 0.01 level in both Models 2 and 3, indicating that the national competition effect is substantial and validating Hypothesis 5 − 1. As more provinces adopt digital rural policies, they create pressure on those that have not adopted, thus promoting the adoption of digital rural policies in these provinces. National competitive pressure has a positive influence on the adoption of digital rural policies across provinces.In terms of neighboring competition, this variable is significant at the 0.01 level in both Model 2 and the integrated Model 3, but the regression coefficient is negative, indicating a significant negative correlation between the adoption of digital rural policies and neighboring competition, contrary to expectations. Hypothesis 6 − 1 is partially validated. This suggests that neighboring provinces, due to their geographical proximity, exhibit a willingness to learn from and communicate with one another in the adoption of digital rural policies. However, barriers stemming from information gaps and resource competition may create obstacles. Overall, the positive competition based on learning and experience exchange provides an important guarantee for the adoption of digital rural policies.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvent history analysis results of digital rural policy adoption.*P \u0026lt; 0.1, **P \u0026lt; 0.05, ***P \u0026lt; 0.01. Standard errors are shown in parentheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegional Internal Driving Force\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Economic Development Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003cp\u003e(0.624)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.205\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.222)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Population Size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.363\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.557)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.010)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe Local Government’s Capacity to Undertake a Policy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Financial Support Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.570\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.943)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.198\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.573)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe Central Authority’s promotion Force\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2018)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.612\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.246)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.435\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.455)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeer Government Competitiveness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.265\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(3.868)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.734\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(5.532)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.275\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.691)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.460\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(3.614)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-35.943\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(13.392)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.381\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.017)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-39.348\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(21.336)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-49.026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29.895\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21.536\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eResults and analysis of policy internalization\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the regression results of the Event History Analysis (EHA) model for the internalization of digital rural policies. The analysis includes Model 1, which incorporates the internal demand pull factor and the financial capacity of local governments to support rural development; Model 2, which includes the central authority push and peer government competitiveness; and the integrated model. The results indicate significant improvements in the log-likelihood value, pseudo R², and chi-square value of the integrated Model 5 across the five sets of models, suggesting an enhanced explanatory power of the model.From the regression results of Model 5, it is evident that the internal demand pull generated by rural population size continues to have a significant positive impact on the internalization of digital rural policies. Compared to peer government competitiveness, the role of central authority push is more pronounced during the internalization phase and serves as a crucial motivating factor for provinces to internalize digital rural policies. Hypotheses 2–2, 4 − 2, 5 − 2, and 6 − 2 are supported at significance levels of 0.1, 0.01, 0.05, and 0.01, respectively.\u003c/p\u003e\u003cp\u003eThere are notable similarities in the influence of the four types of innovation diffusion drivers on the adoption and internalization of digital rural policies. First, the internal demand pull resulting from rural population size has a positive effect not only during the adoption phase but also plays a significant role in the internalization of digital rural policies, as evidenced by significant positive coefficients in Models 1, 3, 4, and 5, thus validating Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e2\u003c/span\u003e–\u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e2\u003c/span\u003e.Second, compared to the adoption phase, the feedback mechanism of central authority push is more significant during the internalization stage and has a larger positive effect; both Hypotheses 4 − 2 and 5 − 2 are confirmed. From the regression coefficients, it can be observed that the impact of central authority push in 2022 on the internalization of digital rural policies is greater than that in 2020, indicating that the cumulative effect of the policies in 2022 is stronger than in 2020.Third, the competitiveness among peer governments arising from national competition shows a similarly strong significant positive effect in the internalization models as observed in the adoption phase. Empirical tests in Models 2, 3, 4, and 5 confirm Hypothesis 5 − 2 at the 0.01 level, highlighting the critical influence of national competition on both the adoption and internalization of digital rural policies.\u003c/p\u003e\u003cp\u003eMoreover, the mechanisms of generation and influence of innovation diffusion drivers, which include the sources, direction, and scale, exhibit differences between the two distinct stages of adoption and internalization of digital rural policies. First, regarding the internal demand pull, the level of rural economic development does not show significance during the internalization phase, suggesting that it does not have a meaningful impact on the internalization of digital rural policies, and Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e–\u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e2\u003c/span\u003e is not validated. This indicates that the internal demand stemming from rural economic development has its impact on the diffusion of digital rural policies primarily during the adoption phase, rather than during internalization.\u003c/p\u003e\u003cp\u003eSecond, concerning the financial capacity of local governments to support rural development, the level of financial support for agriculture shows a significant positive effect on the internalization of digital rural policies at the 0.1 level in Model 1; however, it is not significant at the 0.1 level in Models 3, 4, and the integrated Model 5. This suggests that while financial support for agriculture has a strong positive effect on the adoption of digital rural policies, its influence does not extend to the internalization phase, thus failing to support Hypothesis 3 − 2. Additionally, neighboring competition due to geographical proximity is not significant during the internalization phase, leading to the rejection of Hypothesis 6 − 2. This finding may be attributed to the closer performance evaluations and more intense governance competitions among provinces within the same region, rather than merely those that are geographically adjacent.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvent history analysis results of digital rural policy internalization.*P \u0026lt; 0.1, **P \u0026lt; 0.05, ***P \u0026lt; 0.01. Standard errors are shown in parentheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegional Internal Driving Force\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Economic Development Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.612 (0.542)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.599 (0.685)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.635 (0.680)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.695 (0.690)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Population Size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.279\u003csup\u003e**\u003c/sup\u003e (0.602)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.438\u003csup\u003e**\u003c/sup\u003e (0.688)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.341\u003csup\u003e*\u003c/sup\u003e (0.686)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.112\u003csup\u003e*\u003c/sup\u003e (0.673)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe Local Government’s Capacity to Undertake a Policy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Financial\u003c/p\u003e \u003cp\u003eSupport Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.468\u003csup\u003e*\u003c/sup\u003e (0.843)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419 (1.002)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.031 (1.070)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.399 (1.059)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe Central Authority’s promotion Force\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2020)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.213\u003csup\u003e***\u003c/sup\u003e (0.792)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.948\u003csup\u003e***\u003c/sup\u003e (0.664)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.278\u003csup\u003e***\u003c/sup\u003e (0.826)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2022)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.426\u003csup\u003e**\u003c/sup\u003e (1.154)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.747\u003csup\u003e**\u003c/sup\u003e (1.254)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.106\u003csup\u003e**\u003c/sup\u003e (1.331)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeer Government Competitiveness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.485\u003csup\u003e***\u003c/sup\u003e (3.363)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.607\u003csup\u003e***\u003c/sup\u003e (2.559)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.331\u003csup\u003e***\u003c/sup\u003e (3.814)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.653\u003csup\u003e***\u003c/sup\u003e (4.377)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.994 (1.009)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.964 (1.196)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.135 (1.400)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.149 (1.327)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-29.191\u003csup\u003e***\u003c/sup\u003e (11.494)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.541\u003csup\u003e***\u003c/sup\u003e (1.889)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.714 (13.878)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.968 (13.896)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.555 (13.987)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.81\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseduo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-56.840\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-45.319\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-41.903\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-44.232\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-39.018\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eRobustness tests\u003c/h2\u003e\u003cp\u003eThe above text examines the impacts of rural economic development level, rural population size, agricultural financial support level, central push, neighboring competition, and national competition on the adoption and internalization of digital rural policies. Based on this, robustness tests are conducted by altering the coding methods of the relevant independent variables.\u003c/p\u003e\u003ch2\u003eRobustness test for policy adoption\u003c/h2\u003e\u003cp\u003eTo examine whether different codings of central push influence the research findings related to the adoption of digital rural policies, a recoding of the central push variable was performed. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, two alternative coding schemes for central push are explored: Central Push (2019) and Central Push (2020) are treated as dummy variables. Central Push (2019) assesses the impact of the \"Digital Rural Development Strategy Outline\" issued by the central government in 2019 on the adoption of digital rural policy innovation, with a value of 1 in that year and 0 in other years. Central Push (2020) measures the vertical diffusion effect of the central government’s \"Implementation of Digital Rural Construction and Development Project,\" likewise coded as 1 in that year and 0 in other years.The results of the tests are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. As shown in the table, changing the coding method yields model results consistent with previous findings, demonstrating robustness. First, all three coding methods for central push are significant across different models, indicating that the impact of central policy push on the likelihood of local governments adopting digital rural policies is robust; the policies from the central government indeed promote local government adoption of these policies. Secondly, the variable representing rural economic development level consistently shows a significant negative effect, while rural population size, agricultural financial support level, and national competition maintain statistically significant positive impacts on the probability of adopting digital rural policies, aligning with the hypotheses proposed in this paper.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital rural policy adoption Robustness test.*P \u0026lt; 0.1, **P \u0026lt; 0.05, ***P \u0026lt; 0.01. Standard errors are shown in parentheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Economic Development Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.219\u003csup\u003e*\u003c/sup\u003e (1.233)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.212\u003csup\u003e*\u003c/sup\u003e (1.230)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Population Size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.054\u003csup\u003e***\u003c/sup\u003e (1.002)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.092\u003csup\u003e***\u003c/sup\u003e (1.008)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Financial Support Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.221\u003csup\u003e***\u003c/sup\u003e (1.576)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.239\u003csup\u003e***\u003c/sup\u003e (1.561)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2019)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.755\u003csup\u003e*\u003c/sup\u003e (0.998)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2020)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.144\u003csup\u003e*\u003c/sup\u003e (2.760)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.200\u003csup\u003e***\u003c/sup\u003e (5.285)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.801\u003csup\u003e***\u003c/sup\u003e (6.548)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.951\u003csup\u003e**\u003c/sup\u003e (3.585)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.295\u003csup\u003e**\u003c/sup\u003e (3.578)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-39.493\u003csup\u003e*\u003c/sup\u003e (21.312)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-45.057\u003csup\u003e**\u003c/sup\u003e (21.550)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.65\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseduo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21.335\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21.086\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eRobustness test for policy internalization\u003c/h2\u003e\u003cp\u003eTo examine whether different coding methods for central push affect the research findings related to the internalization of digital rural policies, a recoding of the central push variable was performed. In Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Models 1 and 2 investigate two alternative combinations of central push codings. The results of the tests are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and it is evident that changing the coding method shows no significant differences compared to previous findings, indicating robustness.Firstly, the variables Central Push (2020), Central Push (2021), and Central Push (2022) consistently demonstrate significant positive effects on the probability of internalizing digital rural policies, highlighting their robustness. However, Central Push (2019) did not pass the significance test, primarily due to the time lag effect associated with its role in promoting policy internalization.To account for the time lag effect of Central Push (2019) on the internalization of digital rural policies, Model 3 in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e includes both the contemporaneous value and the lagged value of Central Push (2019). The results indicate that central government policies do indeed facilitate the internalization of digital rural policies by local governments. Notably, Central Push (2019) did not show a direct effect on policy internalization in the same year it was introduced but significantly increased the probability of internalization in the following year. This delay can be attributed to the fact that internalization requires a certain amount of time compared to direct adoption. As mentioned earlier in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Central Push (2019) significantly enhanced the likelihood of policy adoption in the year it was issued.Secondly, the variables representing rural population size and national competition consistently exhibit significant positive impacts on the probability of internalizing digital rural policies, supporting the reliability of the conclusions drawn in this study.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital Rural Policy Internalization Robustness Test.*P \u0026lt; 0.1, **P \u0026lt; 0.05, ***P \u0026lt; 0.01. Standard errors are shown in parentheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Economic Development Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.613 (0.677)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.694 (0.690)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.598\u003c/p\u003e \u003cp\u003e(0.685)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Population Size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.355\u003csup\u003e**\u003c/sup\u003e (0.688)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.112\u003csup\u003e*\u003c/sup\u003e (0.673)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.437\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.688)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Financial Support Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048 (1.067)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.399 (1.059)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003cp\u003e(1.002)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2019)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.085 (1.146)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003cp\u003e(1.349)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2019)༈\u003cem\u003et\u003c/em\u003e-1༉\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.918\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.785)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2020)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.928\u003csup\u003e***\u003c/sup\u003e (0.945)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2021)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.945\u003csup\u003e**\u003c/sup\u003e (0.834)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Push(2022)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.429\u003csup\u003e*\u003c/sup\u003e (1.278)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.127\u003csup\u003e***\u003c/sup\u003e (3.897)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.466\u003csup\u003e***\u003c/sup\u003e (2.997)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.535\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.754)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighbor Competition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.048 (1.379)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.149 (1.327)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.972\u003c/p\u003e \u003cp\u003e(1.201)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.413 (13.894)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.453 (14.240)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.752\u003c/p\u003e \u003cp\u003e(13.878)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR chi2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseduo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-43.694\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-39.020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-41.901\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Conclusions and recommendations","content":"\n\u003ch3\u003eConclusions\u003c/h3\u003e\n\u003cp\u003eThis study is based on the theory of innovation diffusion and establishes an analytical framework that includes internal demand pull, local government financial capacity to support rural development, central authority push, and intergovernmental competition. It collects and analyzes data on the diffusion of digital rural policies among provincial governments in China from 2018 to 2022. Using event history analysis, this research empirically investigates the influencing factors and potential mechanisms behind the dissemination of digital rural policies across different regions in China. The findings are as follows:\u003c/p\u003e \u003cp\u003eFirst, the diffusion of digital rural policies among provincial governments in China presents different outcomes at various stages. In the adoption stage, the diffusion results exhibit convergence characteristics, while in the internalization stage, they display divergence and diversification traits. Adoption serves as the first step in the diffusion of digital rural policies for the adopting government. Influenced by both internal and external driving forces, most provinces tend to initially reference policies from the central government or advanced regions, adopting innovative decision-making and execution strategies without localizing their policy reproduction, which may lead to convergent diffusion outcomes. Internalization, however, requires localization in policy reproduction that aligns with the specific needs and circumstances of the adopting government. When internalization is combined with localization, it is more likely to result in divergent and diversified diffusion outcomes.\u003c/p\u003e \u003cp\u003eSecond, in the process of digital rural policy diffusion, most provinces adopt a strategy of first adopting and then internalizing the policies, with only a small number of provinces synchronously internalizing at the time of adoption. Compared to adoption, the internalization of digital rural policies in most provinces exhibits a certain degree of lag. This delay primarily arises from the risks and costs that local governments face when attempting to internalize digital rural policies in a context-specific manner, as achieving policy localization and reproduction requires a considerable amount of time.\u003c/p\u003e \u003cp\u003eThird, the results of the event history analysis indicate that the driving logic behind local governments' adoption and internalization of digital rural policies shares commonalities as well as differences. The common driving forces include the intrinsic motivation arising from population size, the vertical authority push from the central government, and the horizontal learning and competitive pressure from parallel governments.From the perspective of internal demand pull, the intrinsic motivation stemming from population size exhibits a significant positive effect on both the adoption and internalization of digital rural policies. The external driving force provided by the central authority push is also confirmed, indicating that the central government's policy push plays an important role in both the adoption and internalization of digital rural policies by local governments. This suggests that the central government\u0026rsquo;s policy push significantly influences the diffusion of digital rural policies across provinces, with a clear top-down vertical diffusion effect.Regarding intergovernmental competition, the nationwide learning and implicit competition positively impact the adoption and internalization of digital rural policies. This shows that the diffusion of digital rural policies at the provincial level in China is driven by a combination of pull mechanisms, coercive mechanisms, learning mechanisms, and competitive mechanisms. Local governments exhibit strategies of competition in the adoption and internalization of digital rural policies.The difference lies in the fact that the financial capacity of local governments to support rural development is a significant influencing factor in policy adoption, but does not have a notable impact on the continuous promotion of the policies. This indicates the crucial tendency to mobilize stakeholders and activate resources during the internalization stage.\u003c/p\u003e\n\u003ch3\u003eRecommendations\u003c/h3\u003e\n\u003cp\u003eBased on these findings, this study proposes the following recommendations:firstly, explore the establishment of digital rural development policies that align with rural population size and knowledge structure. Strengthen the construction of digital infrastructure according to rural population size, and develop and promote information terminals, technological products, and mobile applications (apps) that cater to the characteristics of agriculture, rural areas, and farmers. Utilize digital inclusivity to enhance rural residents' sense of gain, happiness, and security.\u003c/p\u003e \u003cp\u003eSecondly, optimize the policy environment and initiate a competitive excellence model. To achieve the rapid diffusion of digital rural policies, higher-level governments should optimize the policy environment through top-level design and issue relevant supplementary documents for digital rural policies. This will demonstrate the commitment of higher-level governments to advancing digital rural construction and reduce the risks and costs faced by lower-level governments. Additionally, by initiating a competitive excellence incentive model, an evaluation and assessment index system can be established to select \"best practices\" based on the achievements of lower-level governments. The assessment results of digital rural construction should be incorporated into the annual performance evaluation of government, promoting excellence through competition and thereby enhancing the speed and extent of the diffusion of digital rural policies.\u003c/p\u003e \u003cp\u003eThirdly, enhance learning and communication between governments. Continuously strengthen intergovernmental policy learning and innovation through experience exchange meetings and similar initiatives to summarize and effectively promote successful experiences in the reproduction of digital rural policies.\u003c/p\u003e \u003cp\u003eFourthly, mobilize stakeholder enthusiasm and activate diverse resources. Stimulate market vitality, raise funds from multiple sources, and enhance financial support. Encourage the participation of various stakeholders by offering tax incentives and promoting initiatives such as trade-in programs for household appliances in rural areas. This will guide society, enterprises, and individuals to actively engage in the construction of digital rural policies, facilitating their rapid diffusion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026ldquo;Conceptualization, X.C. and H.S.; methodology, H.S.; software, H.S.; validation, H.S., X.C. and J.D; formal analysis, X.C. and H.S.; investigation, H.S.; resources, X.C.; data curation, H.S.; writing\u0026mdash;original draft preparation, H.S.; writing\u0026mdash;review and editing, X.C.,H.S. ; visualization, H.S.; supervision, X.C.; project administration, H.S.,J.D; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.\u0026rdquo;\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerry, F. S., \u0026amp; Berry, W. D. (1990). State Lottery Adoptions as Policy Innovations: An Event History Analysis. \u003cem\u003eAmerican Political Science Review\u003c/em\u003e,\u003cem\u003e 84\u003c/em\u003e(2), 395-415. https://doi.org/10.2307/1963526 \u003c/li\u003e\n\u003cli\u003eBerry, F. S., \u0026amp; Berry, W. D. (1992). Tax Innovation in the States: Capitalizing on Political Opportunity. \u003cem\u003eAmerican Journal of Political Science\u003c/em\u003e,\u003cem\u003e 36\u003c/em\u003e(3), 715-742. https://doi.org/10.2307/2111588 \u003c/li\u003e\n\u003cli\u003eBlossfeld, H.-P., \u0026amp; Rohwer, G. (2019). \u003cem\u003eEvent History Analysis With Stata: 2nd Edition (2nd ed.)\u003c/em\u003e. Routledge. https://doi.org/https://doi.org/10.4324/9780429260407 \u003c/li\u003e\n\u003cli\u003eChen, T., \u0026amp; Li, Y. (2020). Influencing Factors of Public Policy Innovation Diffusion: Based on the Analysis of Data of 31 Provincial Residence Permit Systems. \u003cem\u003eJournal of Central South University(Social Sciences)\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(5), 107\u0026ndash;118. \u003c/li\u003e\n\u003cli\u003eDuan, Y., Yi, Y., \u0026amp; Yao, L. (2023). Effectiveness analysis of digital village construction from the perspective of policy. \u003cem\u003eLibrary and Information Work\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e(6), 32\u0026ndash;42. https://doi.org/ https://doi.org/10.13266/j.issn.0252-3116.2023.06.004 \u003c/li\u003e\n\u003cli\u003eGuo, J., Huang, J., \u0026amp; Xu, N. (2022). Research on the Diffusion Mechanism of Science and Technology Innovation Voucher Policy: An Analysis of the Event History of 282 Prefecture-level Cities. \u003cem\u003eForum on Science and Technology in China\u003c/em\u003e(2), 23-31.\u003c/li\u003e\n\u003cli\u003eHan, X., \u0026amp; Wei, C. (2021). How does risk affect policy diffusion?\u0026mdash;\u0026mdash; taking environmental information disclosure as an example. \u003cem\u003ePublic Administration \u0026amp; Policy Review\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(5), 95\u0026ndash;104. \u003c/li\u003e\n\u003cli\u003eHuang, A., Zhang, Z., \u0026amp; Zhu, C. (2020). Diffusion Analysis of China\u0026apos;s Science and Technology Commissioner\u0026apos;s System: An Empirical Evidence Based on Inter-Provincial Diffusion. \u003cem\u003eSoft Science\u003c/em\u003e,\u003cem\u003e 34\u003c/em\u003e, 14-20. https://doi.org/https://doi.org/10.13956/j.ss.1001-8409.2020.11.03 \u003c/li\u003e\n\u003cli\u003eKostova, T., \u0026amp; Roth, K. (2002). Adoption of an Organizational Practice by Subsidiaries of Multinational Corporations: Institutional and Relational Effects. \u003cem\u003eThe Academy of Management Journal\u003c/em\u003e,\u003cem\u003e 45\u003c/em\u003e(1), 215-233. https://doi.org/10.2307/3069293 \u003c/li\u003e\n\u003cli\u003eKostova, T., \u0026amp; Zaheer, S. (1999). Organizational Legitimacy under Conditions of Complexity: The Case of the Multinational Enterprise. \u003cem\u003eThe Academy of Management Review\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e(1), 64-81. https://doi.org/10.2307/259037 \u003c/li\u003e\n\u003cli\u003eLi, J., \u0026amp; Zhang, W. (2019). A Study on the Diffusion of Government Procurement of Services: An Event History Analysis Based on the Data of 31 Provinces in China. \u003cem\u003eChina Soft Science \u003c/em\u003e(5), 60\u0026ndash;67. \u003c/li\u003e\n\u003cli\u003eLi, L., Zeng, Y., \u0026amp; Guo, H. (2023). Digital Village Construction: Underlying Logic, Practical Misunderstandings and Optimization Paths. \u003cem\u003eChinese Rural Economy\u003c/em\u003e(1), 77-92. https://doi.org/https://doi.org/10.20077/j.cnki.11-1262/f.2023.01.005 \u003c/li\u003e\n\u003cli\u003eLi, Y., \u0026amp; Zhou, B. (2024). The Impact of digital finance on rural energy poverty-empirical evidence from rural China. \u003cem\u003eScientific Reports\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(1), 16645. https://doi.org/10.1038/s41598-024-67669-4 \u003c/li\u003e\n\u003cli\u003eLiu, J., \u0026amp; Liu, J. (2020). A Study on the Diffusion Mechanism of the \u0026quot;One Run at Most\u0026quot; Reform: An Analysis of the Incident History of 294 Prefecture-level Cities in China. \u003cem\u003eJournal of Gansu Institute of Administration\u003c/em\u003e(4), 26\u0026ndash;36,125. \u003c/li\u003e\n\u003cli\u003eLiu, Y., Dai, Z., \u0026amp; Zhao, X. (2024). Unveiling the blueprint for rural digital prosperity: A comparative examination of top 100 digital counties in China. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e,\u003cem\u003e 208\u003c/em\u003e, 123625. https://doi.org/https://doi.org/10.1016/j.techfore.2024.123625 \u003c/li\u003e\n\u003cli\u003eLythreatis, S., Singh, S. K., \u0026amp; El-Kassar, A.-N. (2022). The digital divide: A review and future research agenda. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e,\u003cem\u003e 175\u003c/em\u003e, 121359. https://doi.org/https://doi.org/10.1016/j.techfore.2021.121359 \u003c/li\u003e\n\u003cli\u003eMa, L. (2015). The Diffusion of Public Service Innovation: An Empirical Analysis of Urban Public Bicycle Programs in China. \u003cem\u003eJournal of Public Administration\u003c/em\u003e,\u003cem\u003e 8\u003c/em\u003e, 51-75.\u003c/li\u003e\n\u003cli\u003eMeng, X., Wang, X., Nisar, U., Sun, S., \u0026amp; Ding, X. (2023). Mechanisms and heterogeneity in the construction of network infrastructure to help rural households bridge the \u0026ldquo;digital divide\u0026rdquo;. \u003cem\u003eScientific Reports\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(1), 19283. https://doi.org/10.1038/s41598-023-46650-7 \u003c/li\u003e\n\u003cli\u003eMertha, A. (2009). \u0026ldquo;Fragmented Authoritarianism 2.0\u0026rdquo;: Political Pluralization in the Chinese Policy Process. \u003cem\u003eThe China Quarterly\u003c/em\u003e,\u003cem\u003e 200\u003c/em\u003e, 995-1012. https://doi.org/10.1017/S0305741009990592 \u003c/li\u003e\n\u003cli\u003eMohr, L. B. (1969). Determinants of Innovation in Organizations. \u003cem\u003eAmerican Political Science Review\u003c/em\u003e,\u003cem\u003e 63\u003c/em\u003e(1), 111-126. https://doi.org/10.2307/1954288 \u003c/li\u003e\n\u003cli\u003eMooney, C. Z. (2001). Modeling Regional Effects on State Policy Diffusion. \u003cem\u003ePolitical Research Quarterly\u003c/em\u003e,\u003cem\u003e 54\u003c/em\u003e(1), 103-124. https://doi.org/10.2307/449210 \u003c/li\u003e\n\u003cli\u003ePeng, B., \u0026amp; Zhao, J. (2019). From Growth Championship to Governance Competition: The Transformation of China\u0026rsquo;s Urban Governance Mode and Its Problems. \u003cem\u003eInner Mongolia Social Sciences\u003c/em\u003e,\u003cem\u003e 40\u003c/em\u003e, 63-70. https://doi.org/https://doi.org/10.14137/j.cnki.issn1003-5281.2019.01.010 \u003c/li\u003e\n\u003cli\u003eRijswijk, K., Klerkx, L., Bacco, M., Bartolini, F., Bulten, E., Debruyne, L., Dessein, J., Scotti, I., \u0026amp; Brunori, G. (2021). Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation. \u003cem\u003eJournal of Rural Studies\u003c/em\u003e,\u003cem\u003e 85\u003c/em\u003e, 79-90. https://doi.org/https://doi.org/10.1016/j.jrurstud.2021.05.003 \u003c/li\u003e\n\u003cli\u003eRogers, E. M. (2003). \u003cem\u003eDiffusion of Innovations, 5th Edition\u003c/em\u003e. Free Press. https://books.google.com.sg/books?id=9U1K5LjUOwEC \u003c/li\u003e\n\u003cli\u003eRui, P. (2023). Adoption and Internalization: How Multiple Institutional Pressures Affectthe Innovation Diffusion of the River-Chief System \u0026mdash;\u0026mdash; A Directed Dyadic Event-History Analysis Based on Provincial Governments. \u003cem\u003eJournal of Public Management.\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(02), 111\u0026ndash;125. https://doi.org/https://doi.org/10.16149/j.cnki.23-1523.20230116.002 \u003c/li\u003e\n\u003cli\u003eTolbert, C. J., Mossberger, K., \u0026amp; McNeal, R. (2008). Institutions, Policy Innovation, and E-Government in the American States. \u003cem\u003ePublic Administration Review\u003c/em\u003e,\u003cem\u003e 68\u003c/em\u003e(3), 549-563. https://doi.org/https://doi.org/10.1111/j.1540-6210.2008.00890.x \u003c/li\u003e\n\u003cli\u003eWalker, J. L. (1969). The Diffusion of Innovations among the American States. \u003cem\u003eAmerican Political Science Review\u003c/em\u003e,\u003cem\u003e 63\u003c/em\u003e(3), 880-899. https://doi.org/10.2307/1954434 \u003c/li\u003e\n\u003cli\u003eWang, H., \u0026amp; He, J. (2022). The Policy Change Model of the Chinese Government in the Process of Innovation Diffusion: A Policy Experimental Study of the Shanghai Free Trade Zone from the Perspective of Central-Local Interaction. \u003cem\u003eJournal of Public Management.\u003c/em\u003e(3), 1-11. https://doi.org/.https://doi.org/10.16149/j.cnki.23-1523.20210609.001 \u003c/li\u003e\n\u003cli\u003eWang, P., \u0026amp; Lai, X. (2013). Analysis of the Model and Mechanism of Public Policy Diffusion in China. \u003cem\u003eJournal of Peking University (Philosophy and Social Science)\u003c/em\u003e,\u003cem\u003e 50\u003c/em\u003e(6), 14\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eWang, S., Yu, N., \u0026amp; Fu, R. (2021). Digital Village Construction: Mechanism of Action, Practical Challenges and Implementation Strategies. \u003cem\u003eReform\u003c/em\u003e(4), 45\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eXufeng, Z., \u0026amp; Hui, Z. (2018). Social Policy Diffusion from the Perspective of Intergovernmental Relations: An Empirical Study of the Urban Subsistence Allowance System in China (1993-1999). \u003cem\u003eSocial Sciences in China\u003c/em\u003e,\u003cem\u003e 39\u003c/em\u003e(1), 78-97. https://doi.org/10.1080/02529203.2018.1414390 \u003c/li\u003e\n\u003cli\u003eYang, H., \u0026amp; Zhao, D. (2015). Performance Legitimacy, State Autonomy and China\u0026apos;s Economic Miracle. \u003cem\u003eJournal of Contemporary China\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e(91), 64-82. https://doi.org/10.1080/10670564.2014.918403 \u003c/li\u003e\n\u003cli\u003eYang, J., \u0026amp; Yang, R. (2022). Policy diffusion, obstruction and mitigation of the implementation of the digital village strategy. \u003cem\u003eJournal of Jiangxi Normal University (Philosophy and Social Sciences Edition)\u003c/em\u003e,\u003cem\u003e 55\u003c/em\u003e, 67\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eZhu, X. (2014). Mandate Versus Championship: Vertical government intervention and diffusion of innovation in public services in authoritarian China. \u003cem\u003ePublic Management Review\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(1), 117-139. https://doi.org/10.1080/14719037.2013.798028 \u003c/li\u003e\n\u003cli\u003eZhu, X., \u0026amp; Zhang, Y. (2015). Political Mobility and Dynamic Diffusion of Innovation: The Spread of Municipal Pro-Business Administrative Reform in China. \u003cem\u003eJournal of Public Administration Research and Theory\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(3), 535-551. https://doi.org/10.1093/jopart/muv025 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital rural policy, Policy diffusion, Policy adoption, Policy internalization, Event history analysis","lastPublishedDoi":"10.21203/rs.3.rs-5570966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5570966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHow to achieve the rapid dissemination of digital rural policies has become an important issue for bridging the digital divide in rural areas and realizing the modernization of agriculture and rural regions. This study adopts a dual perspective of policy adoption and internalization, constructing an analytical framework that includes internal demand pull, local government fiscal capacity to support rural development, central authority push, and intergovernmental competition as internal and external driving factors. It collects and analyzes data on the diffusion of digital rural policies among provincial governments in China from 2018 to 2022, employing event history analysis to explore the influencing factors and potential mechanisms behind the dissemination of digital rural policies in different regions of China.The findings reveal that, first, the diffusion of digital rural policies among provincial governments in China exhibits different outcomes at various stages. During the adoption stage, the diffusion outcomes show convergent characteristics, while in the internalization stage, they display divergent and diverse features. Second, in the process of digital rural policy diffusion, only a small number of provinces synchronized internalization with adoption, whereas most provinces adopted a strategy of first adopting and then internalizing.Third, the driving logic behind local governments' adoption and internalization of digital rural policies shares both commonalities and differences. The common driving forces include the intrinsic motivation brought about by population scale, the vertical authority push from the central government, and the horizontal learning and implicit competitive pressures from peer governments.This indicates that the diffusion of digital rural policies at the provincial level in China is driven by pull mechanisms, coercive mechanisms, learning mechanisms, and competitive mechanisms, with local governments employing competitive strategies in the adoption and internalization of digital rural policies.The differences manifest in that local government capacity is a significant influencing factor for adoption but does not notably impact the sustained advancement of policies, highlighting the important tendency to mobilize various stakeholders and activate resources during the internalization stage. These findings illuminate how digital rural policies can rapidly diffuse in China and provide empirical insights for developing countries to accelerate the dissemination of rural digital policies.\u003c/p\u003e","manuscriptTitle":"How Did China's Digital Rural Policy Rapidly Diffuse? An Event History Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-04 14:27:27","doi":"10.21203/rs.3.rs-5570966/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcb6425d-5203-40ed-ae44-404ebf4de97c","owner":[],"postedDate":"January 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42177036,"name":"Social science/Science technology and society"},{"id":42177037,"name":"Social science/Social policy"}],"tags":[],"updatedAt":"2025-01-21T13:40:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-04 14:27:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5570966","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5570966","identity":"rs-5570966","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}