Digital village construction and national innovation systems: coordinated development dynamics

Abstract

This study develops a unified theoretical framework to evaluate the development of China’s digital village construction and its integration with the national innovation system. By constructing a comprehensive evaluation system and utilizing dynamic and static weighting methods along with a physical coupling model, the study assesses the coordinated development levels of digital villages and the innovation system from 2010 –2022. The results show that the coupling and coordination between the digital villages and the national innovation system have significantly improved across most provinces, with economically developed eastern regions demonstrating higher coupling levels than central and western regions. Key drivers of this coordination include urbanization, industrialization, digitalization, marketization, and globalization, which contribute to regional development by improving access to technology, optimizing resource allocation, and strengthening policy support and international cooperation.

Introduction

Despite its numerous economic achievements, China remains the world’s largest developing country, and addressing village development is a crucial issue for both central and local governments. According to a report by China’s National Bureau of Statistics (Fig. 1), approximately 34.78% of the population, or about 490 million people, reside in village areas, where the per capita disposable income is only 40.85% of that in urban areas, amounting to 20,133 yuan. Nevertheless, China’s robust engagement with digital development offers new opportunities to bridge the urban-village divide and enhance village economic diversification and social governance modernization (Wang et al., 2023a). For instance, the internet penetration rate in village areas has reached 58.80%, facilitating access to information and e-commerce platforms for farmers. In 2020, village online retail sales surpassed 1.79 trillion yuan, significantly boosting employment. Furthermore, digital technologies such as the Internet of Things and artificial intelligence have revolutionized agricultural efficiency (Dibbern et al., 2024), helping farmers navigate market volatility and environmental challenges. Digital advancements have also improved village public services (Liu et al., 2022), including telemedicine and online education, enhancing access to crucial services for remote populations. The digital transformation of village areas accelerates local economic and social development and strengthens the foundations for scientific and technological innovation and knowledge dissemination (Tiwasing et al., 2023). This integration facilitates connections between village innovation systems and urban counterparts, injecting vitality into the overall effectiveness of the national innovation system (NISOE). The importance of NISOE for a country’s scientific and technological progress is widely recognized as a driver of economic growth and competitiveness. Despite this, the interaction between the construction of digital villages (DV) and the development of NISOE remains an under-explored area of research. Analyzing the coordinated development and underlying mechanisms between DV-NISOE enriches academic perspectives on their integration. It equips policymakers with evidence to refine and develop strategies that maximize the benefits of digital transformation.

Fig. 1

Comparison of urban and village development in China (2015–2022).

Existing studies on the broader impacts of digital development in village areas are comprehensive, covering various dimensions, including technology, agriculture, social effects, and policy strategies. This paper synthesizes these studies into four main aspects: First, the application and impact of digital technology in village areas are profound. Adopting digital technologies enhances the quality of life for residents and regional economic efficiency, with wide-reaching social and welfare implications. For instance, Dunham (2022) illustrates how broadband networks in Georgia, U.S., improve connectivity, facilitating educational and commercial development in village settings. Afful-Dadzie et al. (2022) discuss how agricultural information systems boost agricultural productivity and sustainability by promoting their acceptance and continued use in village communities. Cowie et al. (2020) examine the mixed effects of Fourth Industrial Revolution technologies as they expand from urban to village areas. Gerli and Whalley (2021) analyze the UK’s village digital divide and propose solutions through ultra-fast broadband delivered via community networks, comparing public and community initiatives. In health and well-being, Drobež et al. (2021) highlight digital transformation’s role in supporting independent living for older people through the Internet of Things and big data, envisioning a smart village future. Räisänen and Tuovinen (2020) also note how digital innovations aid village microenterprises in Central Finland, with workshops facilitating technological adoption. Second, the digital transformation of agriculture is a pivotal global trend. Ingram et al. (2022) note that digitization enhances data management and agricultural performance in the U.K. Leng (2022) finds that digital advancements have notably increased village household incomes in China by promoting employment and entrepreneurship. These studies underscore digital technologies’ role in fostering economic growth and enhancing agricultural productivity. Rijswijk et al. (2021) introduce the socio-cyber-physical system’s framework, offering new insights into the integrated effects of digitization in agriculture, emphasizing the importance of designing and accessing digital technologies, particularly in digital dairy farms. Third, the social effects of village digitization are vast, affecting education, health, and governance. Larsson (2021) outlines the challenges in Norway’s automated public services, especially in child welfare, demonstrating that despite efforts for universal coverage, automated systems face numerous implementation challenges. Foronda-Robles and Galindo-Pérez-de-Azpillaga (2021) explore how village nonprofit organizations in Andalusia use social media to enhance governance and community ties. Taipale et al. (2021) studied the varied media usage among older individuals across six countries, revealing significant gender and national differences essential for understanding digitization’s diverse social impacts. Fourth, policy and strategy recommendations are critical. Digital village initiatives support the development of sustainable village policies. Rupasingha et al. (2023) analyze the labor market effects of the U.S. Broadband Initiative, recommending enhanced support for village broadband to stimulate job growth and economic development. Bokun and Nazarko (2023) suggest strategies for promoting digitization and intelligence in village areas. Ge et al. (2023) advocate for better village healthcare support through IoT-based systems, which enhance services and overall well-being. Frishammar et al. (2023) identify barriers to digital health platform adoption among the elderly and propose strategies to overcome them. Metta et al. (2022) assess responsible digitization in agriculture, suggesting a scientific approach to policy development, ensuring technology application aligns with expectations. These studies emphasize the need for supportive policies, particularly in infrastructure enhancement and digital transformation facilitation.

However, despite the broad scope of these studies, several crucial aspects remain underexplored, leaving a significant gap in the literature. First, existing research often focuses on isolated dimensions—such as technology adoption, agricultural digitization, or social impacts—and does not fully address how these elements integrate within broader national innovation strategies. While studies highlight the positive effects of digital technologies and policies on village development (Wang et al., 2023b), they rarely explore how these initiatives can be integrated with national-level R&D, technology transfer, or the development of NISOE. This gap in understanding prevents the full realization of digital village strategies’ potential to contribute to regional and national growth. Second, DV research is not integrated with national innovation systems. Although numerous case studies explore specific dimensions of digital village development, few systematically analyze how digitization in villages can enhance the structure and effectiveness of NISOE, such as improving information flows or supporting R&D initiatives in rural areas (Květoň & Horák, 2018). This lack of integrative research limits understanding of how digital villages can contribute to the national innovation agenda. Third, existing policy and strategy recommendations remain overly broad. Many studies emphasize the need for supportive policies for digital village initiatives (Xia, 2022). However, these recommendations often lack specificity regarding implementation strategies and how they could be coordinated with other NISOE components, such as technology transfer or entrepreneurship support (Philip et al., 2017). Fourth, there is insufficient regional analysis in the literature. While many studies consider multiple countries, few examine how different regions could tailor digital village strategies to their specific contexts. Ultimately, a unified research framework encompassing DVs and NISOE offers a more comprehensive understanding of their roles in national development strategies and provides a detailed theoretical foundation for crafting policies and strategies that maximize the shared benefits of digital transformation at both regional and national levels.

This study extends and innovates the existing literature in several ways, particularly in terms of theoretical framework, methodology, and practical contributions that distinguish this study from the existing literature. First, the innovation of the theoretical framework is reflected in the in-depth exploration of the relationship between the coordinated development of DV and NISOE. Although studies have focused on the construction of DV or the development of NISOE, most of the literature is limited to macro-level analyses. It fails to explore the two’s dynamic coupling and coordination mechanisms. By introducing the physical capacity coupling system method, this study proposes a more refined framework that refines the key factors affecting the coordinated development of the two and, for the first time, systematically analyses the five dimensions of urbanization, marketization, industrialization, digitalization, and globalization as the core factors. This framework provides a new perspective for studying the synergistic relationship between DV and NISOE and fills the gap in this field in academia. Second, unlike most previous studies that used single indicators or qualitative analyses, this study provides an empirical basis for the interaction between DV construction and the NISOE through the combination empowerment model and the physical capacity coupling system method, which is innovative in that it can reveal the synergistic relationship between different regions and national innovation systems. The innovation lies in its ability to reveal the law of dynamic evolution between different regions and provide a more operational reference for policy formulation. Third, regarding practical contribution, this study enriches theoretical research and provides substantive guidance for policy practice. Against the backdrop of increasing attention to the construction of DV, this study provides policymakers with insights into regional differentiated development, especially in the central and western regions, and highlights how to narrow the digital divide between the East and west and promote balanced regional development by promoting the construction of DV infrastructure and the optimization of the allocation of innovation resources.

This study integrates these elements into a unified analytical framework to comprehensively understand and enhance the coordinated development of DV-NISOE. Based on established concepts, it elucidates the interactive relationship between DV development and the NISOE. The paper demonstrates a comprehensive evaluation system to assess the development level of DVs and NISOE from 2010 to 2022. Employing a combination of dynamic and static weighting methods along with a physical coupling model quantifies the level of their coupled and coordinated development across temporal and spatial dimensions. The study extensively analyzes trends in this coupled development, both temporally and spatially. Furthermore, it identifies key factors influencing the coordinated development of DV-NISOE. Using the Quadratic Assignment Procedure (QAP) methodology, these factors were correlated in terms of urbanization, industrialization, digitalization, marketization, and globalization.

The remainder of this paper is structured as follows: “Conceptualization and theoretical analysis” provides a theoretical analysis of the relationship between DV, NISOE, and their coordinated development. “Comprehensive evaluation system and measurement methods” details the construction of a comprehensive evaluation system for the effectiveness of both DV and NISOE, including the description of measurement methods and data sources. “Results analysis” presents an analysis of the measurement results, covering the general trend and temporal and spatial evolutions. “The formation mechanism of the coordinated development of DV-NISOE” delves into the formation mechanisms behind the coordinated development of DV-NISOE. Finally, “Discussion and conclusion” discusses and summarizes the key findings of this study.

Conceptualization and theoretical analysis

Digital Village (DV)

The term “DV” encapsulates the use of digital and information technologies to foster innovation and development in village economies, societies, and governance structures, aligning the village with the digital era and propelling its modernization (Lundgren & Johansson, 2017). The construction of DVs encompasses four primary dimensions. Firstly, the enhancement of village digital infrastructure involves the development of modern digital facilities such as high-speed internet networks (Niu et al., 2022), 5G communications, the Internet of Things, and cloud computing. These advancements ensure stable and convenient digital environments for agricultural production, village businesses, and public services, equalizing information access and data transmission conditions between village and urban areas. This infrastructure also extends to digital payment systems, data management platforms, and logistics facilities, which are essential for the growth of village e-commerce and online services (Ashmore et al., 2017). Secondly, the digital transformation of agriculture utilizes technologies like the Internet of Things, remote sensing, big data, and artificial intelligence. These tools enable farmers to monitor crop growth parameters accurately, facilitating precise agriculture practices such as irrigation, fertilization, and pest control, thereby enhancing crop yield and quality (Abbasi et al., 2022). Deploying intelligent agricultural equipment, drones, and automated machinery further aids in efficient, cost-effective farming, while digital traceability systems for agrarian products enhance food safety and consumer trust (Forney & Epiney, 2022). Thirdly, the digital transformation of village life allows residents to access diverse services such as e-commerce, telemedicine, online education, and digital entertainment through internet platforms, significantly improving their quality of life (Ullah & Hussain, 2023). Digital technologies also empower village dwellers by enhancing their market participation, employability, and knowledge through digital skills training, enriching their lifestyles. Fourthly, digitalization in village public services through e-government platforms, intelligent community management systems, and digitized financial services enable more effective government management and service delivery (Qian et al., 2024). This includes quicker access to e-government, online legal advice, and digital logistics, improving the efficiency of policy communication and public resource allocation.

The overall effectiveness of the National Innovation System (NISOE)

According to Chen and Song (2024), from the perspective of systems science theory, the NIS can be conceptualized as a complex system comprising multiple actors and functions characterized by multilevel interconnections between subjects and levels. The primary aim of the NIS is to harness the power of system integration to foster national economic growth and enhance international competitiveness. The NISOE is defined as a country’s comprehensive capacity to achieve innovative development following the establishment of its innovation system. Key features of this effectiveness include: first, it systematically reflects the synergistic cooperation among innovation actors, the effective operation of innovation functions, the rational utilization of innovation resources, and the creation of a conducive innovation environment. Second, it emphasizes the harmonious operation and development of its internal subsystems. Third, it prioritizes the functionality and coordination of the innovation process.

The coordinated development relation of DV-NISOE

Promoting digital technology and integrating innovation systems facilitate close coordination and interaction between DV and the NISOE, as depicted in Fig. 2. This relationship manifests in several key aspects:

• (1) Two-way Support: DV is a new growth point for the NISOE. Modernized productivity, logistics, financial services, and information dissemination in village areas, driven by digital technology, enhance the agricultural and village economy, thereby bolstering national innovation capacity (Gómez-Carmona et al., 2023). Conversely, the NISOE provides DV technical, policy, financial, and human resource support. National science and technology policies and innovation mechanisms enable village areas to rapidly adopt new technologies and ideas, fostering the balanced development of DV across various regions (Cho & Park, 2022).

• (2) Resource Sharing and Distribution: DV enables the seamless flow of knowledge, information, and resources between urban and village areas through technologies such as the Internet, the Internet of Things, and big data (Feng et al., 2024). This integration allows village areas to access the NISOE’s technological, market, and financial resources, challenging the traditional urban-village divide. The NISOE, in turn, draws inspiration from DV to invigorate village markets, innovate products, and develop new business models, thereby promoting a more balanced and sustainable innovation development.

• (3) Innovation Ecosystem Construction: The interaction between DV and the NISOE fosters an inclusive innovation ecosystem. Village and urban innovators, scientific research institutions, governments, enterprises, and intermediary organizations are interconnected through digital networks, facilitating resource and information sharing (Y. Wang et al., 2024b). This interdependent and supportive network, as part of the NISOE, enriches the diversity and resilience of the system through the unique industrial, cultural, and market structures of DV.

• (4) Promotion of Inclusive Innovation: The coordinated development of DV-NISOE enhances inclusive national innovation. Historically marginalized village areas can now fully participate in innovation (Snow et al., 2024; Villalba Morales et al., 2023), accessing technology and market opportunities through DV. This inclusive approach addresses regional and industrial innovation disparities, driving village economic development and contributing to a more balanced NISOE.

Fig. 2

Conceptual model of the coordinated development of DV-NISOE.

Comprehensive evaluation system and measurement methods

Construction of a comprehensive evaluation system for the DV and NISOE

Based on the concept of the DV discussed previously, this paper develops a comprehensive evaluation system aligned with its four core dimensions. These dimensions are outlined as primary indicators: digitalization of village infrastructure, village agriculture, village life, and village services. Each primary indicator consists of several secondary indicators, collectively assessing the digital development within these domains. The specific configurations are depicted in Table 1.

• (1) Digitalization of village infrastructure: This dimension encompasses the extent of village internet, cellphone, and computer penetration, along with the coverage of agrometeorological stations and village logistics networks. Such infrastructure supports digital services like e-commerce, online education, and telemedicine. Cellphone and computer penetration enhance digital communication and agricultural management efficiency, respectively. Agrometeorological stations and village logistics networks are crucial for adapting to climate change, optimizing production decisions, and facilitating efficient goods and information flow (Lyubing et al., 2024).

• (2) Digitalization of village agriculture: This indicator is evaluated through metrics such as pesticide and fertilizer usage per unit of output, total power of agricultural machinery, size of irrigated areas, and per capita electricity consumption of village inhabitants. Reduced pesticide and fertilizer usage per unit output indicates efficient digital agricultural practices, minimizing environmental impacts and enhancing sustainability. Mechanization levels are reflected by the power and prevalence of agricultural machinery equipped with digital systems to increase productivity and automate operations. Irrigated area and electricity consumption metrics indicate efficient water management and increasing mechanization of farming processes.

• (3) Digitalization of village life: This includes indicators such as the number of agricultural digital bases, the level of village online payment adoption, village online cultural engagement, per capita disposable income, and the Engel coefficient. Agricultural digital bases, utilizing modern digital technologies, enhance the economic efficiency of agricultural production through shortened supply chains and expanded market access. The adoption level of village online payment reflects the integration of digital financial services into village economic activities. Cultural indicators show the penetration of digital entertainment, enhancing village lifestyles. The Engel coefficient, illustrating the proportion of income spent on food, indicates improved living standards facilitated by digitalization.

• (4) Digitalization of village services: Indicators for this dimension include the consumption level of digital services, the presence of village construction and management agencies, the application of information technology in village areas, and the accessibility of township cultural stations and village health rooms. These indicators gauge the penetration of digital services in transportation, communication, medical care, and education, demonstrating how digital transformation enhances service delivery efficiency. Digital governance tools in village management reflect an advanced digital management system. In contrast, using digital platforms in cultural and health services indicates the overall digitization level of village public services.

Table 1 The comprehensive evaluation system for DV.

The comprehensive evaluation system of the NISOE constructed in this paper refers to the previous research (Chen & Song, 2024). See the Appendix for details.

Description of measurement and test methods

This paper develops a comprehensive evaluation system for assessing the DV and NISOE, incorporating multilevel indicators from 2010 to 2022. This system is characterized by its multidimensionality and cross-annual assessments. It is important to note that the determination of weight values is critical in the quantitative analysis of this system, as these values significantly influence the final evaluation results. Most existing studies utilize assignment methods such as the Analytical Hierarchy Process (AHP), Principal Component Analysis (PCA), Factor Analysis, and Entropy Weighting (EW) method for evaluating indicator systems. However, for multilevel and multi-year time-series data, it is crucial to consider the period to ensure dynamic comparability of the final results. The Vertical and Horizontal Scatter Degree Method (VHSD), a dynamic evaluation approach, incorporates the time factor in weight calculation, emphasizing the variation among indicators across different years. Yet, this method solely relies on the evaluation matrix for determining indicator weights, failing to reflect the informational content of each indicator. To address this limitation, this paper integrates the entropy weighting method, which assigns weights based on the information content behind each indicator, combining the strengths of both methods. However, it is essential to recognize that using different assignment methods can lead to substantial outcome variations. Therefore, conducting a consistency test for the results obtained through the combined evaluation method is crucial to verify its effectiveness.

Finally, to demonstrate the coordinated development between the DV and NISOE, this study utilizes the Physical Capacity Coupling System (PCCS). It employs a coupling degree model to compute the interaction between the DV and NISOE. The resulting coupled coordination value represents the level of their integrated development.

Description of VHSD method

The comprehensive evaluation system assessing the overall effectiveness of the DV and NISOE spans multiple years, involves various evaluation objects, and incorporates specific indicators. The years are denoted as k, the evaluation objects as m, and the particular indicators as n. Consequently, the data is organized into a time-ordered three-dimensional matrix for both entities.

$$X={x}_{i,j}({t}_{k}),(i=1,\,2,\,3\ldots m;\,j=1,\,2,\,3\ldots n;\,k=1,\,2,\,3\ldots k)$$

(1)

where ${x}_{{ij}}({t}_{k})$ denotes the value of the j indicator for the i sample in year k. ${u}_{m}$ denotes the specific name of the i sample, whose time-series stereo data table is shown in Table 2.

Table 2 Timing Stereo Datasheet.

To ensure comparability of the indicators, the data were Z-Score standardized and processed as follows:

$$\begin{array}{l}{Y}_{ijk}=\frac{{x}_{ijk}-{\bar{x}}_{ijk}}{{\sigma }_{ijk}},\,i=1,\,2,\,3\ldots\\m;\,j=1,\,2,\,3\ldots n;\,k=1,\,2,\,3\ldots k\end{array}$$

(2)

In Eq. (2), ${Y}_{{ijk}}$ represents the indicator value of the j indicator of the i sample in the k year after standardization, ${\bar{x}}_{{ijk}}$ is the mean value of the j indicator in the k year, and ${\sigma }_{{ijk}}$ represents the standard deviation of the j indicator in the k year. Subsequently, the indicator weights are determined, and the comprehensive evaluation function is set as follows:

$${z}_{i}({t}_{k})=\mathop{\sum }\limits_{j=1}^{n}{\delta }_{i}{y}_{{ij}}({t}_{k})$$

(3)

Where ${\delta }_{i}$ is the weight of the indicator and ${z}_{i}({t}_{k})$ is the comprehensive evaluation value of sample i in the thorough evaluation system of the DV or NISOE in the k year. For the determination of indicator weights, the method of sum of squares of total deviations can be used to maximize the representation of the differences among the samples:

$${\sigma }^{2}=\mathop{\sum}\limits_{k=1}^{K}{\mathop{\sum}\limits_{i=1}^{m}({z}_{i}({t}_{k})-\bar{z})}^{2}={\delta }^{T}\mathop{\sum}\limits_{k=1}^{K}{H}_{k}\delta ={\delta }^{T}H\delta$$

(4)

Where, $\delta ={({\delta }_{1},{\beta }_{2},\ldots {\delta }_{n})}^{T}$, $H=\mathop{\sum }\nolimits_{k=1}^{K}{H}_{k}$ is a symmetric matrix, ${H}_{k}={A}_{k}^{T}{A}_{k}$, $k=1,2,3\ldots K$.

With the restriction that ${\delta }^{T}\delta =1$, ${\sigma }^{2}$ gets its maximum value when the eigenvector corresponding to the largest eigenvalue of the matrix H is taken. At this point, this eigenvector is normalized to obtain the determined weight ${\delta }_{j}$.

Description of the EW method

Based on the Z-Score normalized indicator data in the previous section, the degree of variability was first calculated:

$${v}_{{ijk}}=\frac{{y}_{{ijk}}}{\mathop{\sum }\nolimits_{i=1}^{m}{y}_{{ijk}}}$$

(5)

Where ${v}_{{ijk}}$ denotes the characteristic weight of the i-evaluation object under the j indicator in the k year. Calculate the EW value of the jth indicator, denoted as ${E}_{{jk}}$:

$${E}_{jk}=-\frac{1}{\mathrm{ln}(m)}\mathop{\sum}\limits_{i=1}^{m}{v}_{ijk}\,\mathrm{ln}({v}_{ijk})$$

(6)

When ${v}_{{ijk}}=0$ or 1, let ${v}_{{ijk}}\mathrm{ln}$ (v_ijk) = 0. The coefficient of variation of the indicator is ${D}_{{jk}}$. At this point:

$${D}_{{jk}}=1-{E}_{{jk}}$$

(7)

When ${D}_{{jk}}$ is larger, it means that the amount of information of the evaluated object contained in the indicator j is larger, the more it should be given a larger weight, from which the EW weight of the indicator can be determined as:

$${\omega }_{{jk}}=\frac{{D}_{{jk}}}{\mathop{\sum }\nolimits_{j=1}^{n}{D}_{{jk}}}$$

(8)

Description of the VHSD-EW method

Based on the weights ${\delta }_{j}$ determined by the vertical and horizontal slotting method in the previous section and the weights ${\omega }_{{jk}}$ determined by the entropy method for each indicator in each year, they are organized into a matrix ${C}_{{jk}}$ by year:

$${C}_{{jk}}={\left[\begin{array}{cc}{\delta }_{1} & {\omega }_{1k}\\ \ldots & \ldots \\ {\delta }_{n} & {\omega }_{{nk}}\end{array}\right]}_{n\times 2}$$

(9)

The final weight ${W}_{{jk}}$ of each indicator can be obtained by summing up the elements of each row in ${C}_{{jk}}$ and taking the arithmetic average of them, while the comprehensive evaluation value of each evaluation object in each year can be obtained after weighting and summarizing layer by layer by the linear weighting method, and the comprehensive evaluation value ${P}_{{mk}}$ can be obtained.

Description of PCCS method

This paper utilizes the concept of the physical capacity coupling system (PCCS) to quantitatively assess the degree of coupling and coordination between DV and NISOE. It is worth mentioning that the PCCS model has been widely used in the field of economics, and many studies have used the PCCS model to successfully investigate the coordinated development relationship between the ecological environment and the socio-economy (Hua et al., 2025), the coordinated development relationship between carbon neutrality and the goal of sustainable development (Guo et al., 2024), as well as the coordinated development relationship between the environmental carrying capacity and the regional economy (Wang et al., 2024a). Therefore, this study uses the PCCS model to explore the coordinated development level of DV and NISOE with certain scientific validity. The formula for the coupling degree, represented as $D$, is shown in Eq. (10):

$$D=\sqrt{{\left[\frac{{U}_{1}\times {U}_{2}}{{({U}_{1}+{U}_{2})}^{2}}\right]}^{\frac{1}{2}}\times ({\beta }_{1}{U}_{1}+{\beta }_{2}{U}_{2})}$$

(10)

In this equation, U₁ and U₂ represent the comprehensive evaluation scores for the DV and the NISOE, respectively. These scores are derived from the set of indicators measured in the previous section, which measure the level of development of each of DV and NISOE.

The coefficients β₁ and β₂ represent the relative importance or weight of the two components in the coupling system. To reflect the equal importance of both systems in this study, we assign a value of 1/2 to both β₁ and β₂, indicating that the coordinated development of both systems is considered equally critical for successful integration.

The degree $D$ calculated by this formula reflects the overall level of coupling and coordination between DV and NISOE. A higher $D$ value indicates a more synchronized and effective relationship between the digital transformation of rural areas and the broader national innovation framework. This coordination is vital for ensuring that digital villages are not isolated but are integrated into and supported by the national innovation system, thereby contributing to sustainable and inclusive regional development.

Data source

This paper selects 30 Chinese provinces, autonomous regions, and municipalities directly under the central government as samples (excluding Hong Kong, Macao, Taiwan, and Tibet). The sample examination period is set at 2010–2022. Most of the data involved come from the China Statistical Yearbook, China Agricultural Statistical Yearbook, China Industrial Statistical Yearbook, China Scientific and Technological Statistical Yearbook, the statistical yearbook of each province (for local governments), and the State Intellectual Property Office database. It is worth noting that the digital finance data in the comprehensive evaluation system of DV comes from the China Digital Finance Index jointly compiled by the Digital Finance Research Center of Peking University and Ant Gold Service Group. The data on digital technology applications and digital governance in the following section on digitalization factors come from word frequency data in local government work reports. Finally, the very few missing values in the data are filled in by interpolation in this paper.

Results analysis

Consistency test results

To verify the stability and validity of the VHSD-EW combined empowerment model, this paper chooses three consistency tests (Puntiroli et al., 2022), namely, Kendall’s tau-b coefficient test, the ICC intra-group correlation coefficient test, and the Spearman test. Suppose a high positive consistency exists between the evaluation results determined by the VHSD-EW combination empowerment model. In that case, the model is more stable, and the subsequent results based on this measure are more reliable. The results of the consistency test are shown in Table 3. The correlation coefficients of the three tests are all positive at the 1% significance level, indicating that all three consistency tests yield significant positive consistency results. This implies that the VHSD-EW combined empowerment model is more stable and effective in evaluating the level of coordinated development of DV-NISOE.

Table 3 Consistency test results of the VHSD-EW model.

Results analysis of the coordinated development level of DV-NISOE

After measuring the coupling coordination level of DV and NISOE, this paper categorizes these into four levels using the four-point level method from Korteweg and Sorensen (2017). The four levels are low coupling, medium-low coupling, medium-high coupling, and high-efficiency coupling, with specific results and grading shown in Fig. 3. From 2010 to 2022, the coupling coordination levels across different provinces show significant changes. Most provinces move from “low” to “medium-high” or “high” coupling, indicating improved, coordinated development of DV-NISOE at the national level. However, there are notable provincial differences. Economically developed regions such as Jiangsu, Zhejiang, Shanghai, and Guangdong maintain high coupling levels throughout the research period.

Fig. 3

Results of the coupled and coordinated development level of the DV-NISOE.

In contrast, less developed provinces like Qinghai, Ningxia, Hainan, and Tibet exhibit lower coupling levels, highlighting regional disparities. Some provinces experience fluctuations in coupling levels over the years, reflecting the impact of factors like policy changes, economic conditions, and technological innovation (Zhou et al., 2024b). For instance, Shanghai’s coupling level fluctuates significantly between 2010 and 2022, especially after 2019, likely due to industrial restructuring and policy changes (Gao et al., 2024). Meanwhile, a few provinces maintain relatively stable coupling coordination levels, indicating consistent DV development and NISOE coordination. Jiangsu Province, for example, has maintained a high coupling level with minimal fluctuations since 2010. As an economically advanced region, Jiangsu’s focus on integrating agricultural digitization and technological innovation has led to stable synergies between DV and NISOE, reflecting the continuity and consistency of its policy formulation and implementation (Huang et al., 2023).

Figure 4 shows the total value of the coupled and coordinated development level of DV-NISOE and the change in its growth rate from 2010 to 2022. Overall, the development level of this coupling shows a fluctuating upward trend. From 2010 –2013, the total value gradually increased from 12 –~14, followed by a slight decline in 2014 and 2015. Between 2016 and 2019, the value steadily increased, reaching a peak of nearly 14.5, but then experienced a slight decrease between 2020 and 2022. Regarding the rate of change, notable peaks occurred in 2012 and 2017, indicating years of faster growth in the development level of the coupling between DV and the NISOE. Conversely, 2014 and 2020 saw negative growth, with 2020 showing a rate of change close to −0.02, indicating a substantial decline in coupling coordination. From 2020 onwards, the rate of change picked up slightly but remained generally low, becoming negative again in 2022.

Fig. 4

Change in the overall level of coupled and coordinated development of DV-NISOE.

Figure 5 presents the level of coupled and coordinated development of DV-NISOE using the quadratic point classification method of the annual mean. This figure shows the average level of coupling and coordinated development in each provincial-level administrative region in China, with different colors representing the four levels: low coupling, medium-low coupling, medium-high coupling, and high coupling. High coupling levels are mainly concentrated in the eastern coastal regions, including Shanghai, Zhejiang, Guangdong, Fujian, and Jiangsu. These provinces are the most economically developed in China (Liang et al., 2021), and the integration effect of DV construction and the NISOE is significant, demonstrating a high level of synergistic development. Provinces with medium-high coupling levels are more widely distributed, including central and southwestern provinces such as Shandong, Anhui, Hunan, Hubei, Sichuan, and Chongqing. These provinces have made progress in developing DV and have a high level of integration with the NISOE, showing specific synergistic effects. Provinces with low to medium coupling levels are mainly located in the Northwest and Northeast regions, including Gansu, Ningxia, Shaanxi, Jilin, and Liaoning. These provinces may face challenges such as inadequate digital infrastructure and uneven distribution of innovation resources (Xue et al., 2023), leading to low integration with the NISOE. Provinces with low coupling levels are mainly in remote or economically underdeveloped regions, including Xinjiang, Inner Mongolia, Qinghai, and Guangxi. The digital infrastructure in these regions is weak (Rodríguez-Pose et al., 2021), and the synergistic effect of the NISOE has not yet been fully realized.

Fig. 5

Status of the mean level of coupled and coordinated development of DV-NISOE.

In summary, the overall coupling level is high in the economically developed eastern coastal regions, while some central and southwestern provinces have reached medium-high coupling levels. Northwestern, northeastern, and remote areas have relatively low coupling levels, indicating more room for improvement in the synergistic development of DV and the NISOE. These findings highlight regional differences in the coupling and coordinated development of DV-NISOE across different regions.

Characterization of temporal and spatial evolution

Due to space limitations, this paper only shows the spatial pattern of the coordinated development level of DV-NISOE for 2010, 2014, 2018, and 2022 in Fig. 6. Figure 6 reveals the following evolutionary characteristics over time:

• (1) Increase in coupling level. Overall, the coupling coordination level of each province has increased between 2010 and 2022. In 2010, most provinces were at the medium-low or low coupling level, whereas by 2022, more provinces had moved to the medium-high or high coupling level. This indicates that as the DV develops and the NISOE improves, the overall coupling level across the country rises.

• (2) The gradual narrowing of regional differences. In 2010 and 2014, the eastern coastal regions (e.g., Zhejiang, Jiangsu, Shanghai, and Guangdong) dominated the high coupling level, while the central and western areas were mainly at medium-low or low coupling levels. However, by 2018 and 2022, the high coupling level in the eastern coastal region remained stable, while the coupling levels in the central and western areas generally improved. Provinces such as Sichuan, Chongqing, and Hunan moved from medium-low to medium-high coupling levels, narrowing the gap with the eastern coastal region.

• (3) Fluctuations in the Northeast. The Northeast (e.g., Liaoning, Jilin, and Heilongjiang) showed a medium-high coupling level in 2010. However, this level gradually declined, with these provinces predominantly at medium-low or low coupling levels in 2018 and 2022. This decline may be related to delays in local economic and industrial restructuring and the transformation of the innovation system (Niu et al., 2022), leading to limited integration of DV development with the NISOE.

• (4) Rise of the western region. In 2010, the coupling level in the Western region was primarily low. By 2022, however, the coupling levels in provinces such as Sichuan, Chongqing, and Yunnan had significantly increased, with some areas rising from medium-low to medium-high or high levels. This indicates that the Western region has progressed in digital infrastructure, industrial structure, and innovation system (Tang et al., 2024).

Fig. 6

Temporal evolution of the coordinated development of DV-NISOE.

In summary, the time evolution from 2010 to 2022 shows a gradual increase in the overall coupling level and a narrowing of regional differences among provinces. Different regions exhibit distinct fluctuations and trends in the coordinated development of DV-NISOE.

Based on measuring the coordinated development level of DV-NISOE from 2010 to 2022, this paper adopts kernel density estimation to analyze its dynamic spatial evolution characteristics in depth (Du et al., 2024). Figure 7 shows the kernel density change curves of the coordinated development level of DV-NISOE in 30 provinces and regions of China from 2010 to 2022. This method reveals the changes behind the coordinated development by analyzing the distribution of wave peaks, the prominent peaks’ width, the distribution’s extensibility, and the polarization phenomenon.

• (1) Wave peak distribution. The most apparent wave peaks in the kernel density plot indicate the dominant level of coupling coordination between years. In 2010 and early 2012, wave peaks are mainly concentrated between 0.4 and 0.6, indicating a low level of coupling coordination in most regions. However, over time, the wave peaks gradually shift to the right, with the dominant wave peaks in 2022 concentrated above 0.7, indicating an overall improvement in coupling coordination.

• (2) Width of the prominent peak. In 2010 and 2012, the wave peaks are broader, showing a wide distribution of coupling coordination levels and significant variation among regions. By 2022, the peak will narrow, indicating that the coupling coordination levels are concentrated towards the middle and high ranges, with decreased regional differences and a consistent trend.

• (3) Distribution extensibility. The distribution gradually extends from the lower range in the early years to the middle-high range, with the right end becoming longer. Initially, the extension focuses on the lower coupling levels, but over time, the center of gravity shifts to the high-level interval around 0.7, reflecting an overall improvement in coupling coordination.

• (4) Polarization phenomenon. In 2010 and 2012, the distribution curve showed scattered sub-peaks, indicating polarization at lower coupling levels, where some areas do not achieve the same level of coordination as the overall trend. By 2022, these sub-peaks disappear, indicating a significant weakening of polarization and more consistent coupling coordination levels across regions.

Fig. 7

Kernel density curve for the coordinated development of DV-NISOE.

The analysis of these characteristics shows that the overall level of coupling coordination between DV and the NISOE has gradually increased, the spatial distribution has become more consistent, and the polarization phenomenon has significantly weakened. This indicates positive progress in the convergence between DV and the NISOE across different regions, leading to more balanced, coordinated development.

The formation mechanism of the coordinated development of DV-NISOE

Identification of factors influencing the coordinated development of DV-NISOE

The coordinated development of DV-NISOE is a complex process involving the interaction of multiple dynamically changing factors. To deeply understand this formation process, it is crucial to identify the key dynamic factors that play a leading role. Therefore, it is necessary to synthesize the dynamics and their interrelationships related to DV and NISOE. These factors constitute the overall framework of innovation activities and regional development. Changes in any of them can affect the coordinated development of the whole system (Bhatt et al., 2023). A comprehensive examination of these factors will help to deeply understand the intrinsic connection between DV and NISOE and their dynamic change process.

Based on the theory of system dynamics and innovation ecology (Huber, 2008; Zhang & Wang, 2023), the coupled coordination of the DV and NISOE can be seen as a complex system with multiple levels and factors. The factors in the system are interdependent and interact with each other, and together, they affect the allocation of innovation resources and the flow of knowledge. The dynamic equilibrium of this complex system requires the coordinated development of multiple levels, with urbanization, marketization, industrialization, digitization, and globalization as the core driving forces in this system. By regulating the flow of innovation resources (Xu & Hu, 2024), the optimization of the production structure, and the openness of the market (Fukuyama et al., 2024), the integration and synergy between the DV and NISOE can be effectively enhanced. Numerous studies have shown that urbanization (Asghar et al., 2024), marketization (van der Loos et al., (2020)), industrialization (Intarakumnerd & Goto, 2018), digitalization and globalization (Cheng et al., 2023; Fischer et al., 2024), as essential power sources in the modern economic system, profoundly affect the interrelationship between the innovation system and regional development.

In summary, five major external dynamic change factors—urbanization, marketization, industrialization, digitization, and globalization—constitute the core framework affecting the coordinated development of DV-NISOE, as summarized in Table 4. Specifically:

• (1) Urbanization promotes the concentration of resources and population, expanding the supply of innovation resources and providing stable technological, financial, and human resources for developing DV (Zhu, 2023). This links village and urban areas (Su et al., 2024). Improving the urban innovation system accelerates the dissemination of technology and information, allowing DV to access more innovation resources and enhancing the NISOE.

• (2) Marketization plays a crucial role in economic activities by releasing the vitality of market players, enhancing competitiveness, and improving the efficiency of resource allocation (Allard et al., 2012). It helps build a market channel between DV and NISOE, facilitating the efficient circulation of resources and information and providing the necessary market mechanisms and environmental support for their synergistic development (Devine & McCollum, 2019).

• (3) Industrialization enhances the synergistic effect of the industrial chain, strengthens the industrial sector’s ability to absorb and apply innovative resources (Barnikol & Liefner, 2024), and ensures the smooth flow of resources between DV and NISOE. The adjustment and upgrading of industrial structures during industrialization promote the diffusion and application of technology (Raihan et al., 2022), providing a solid production chain and supply chain foundation for developing DV.

• (4) Digitization drives the transformation into an information society, making communication and collaboration between DV and NISOE more flexible and efficient. The widespread application of the digital economy, technology, and governance breaks down barriers between urban and village areas, realizing the seamless connection of resources and information and promoting synergistic efficacy (Han et al., 2024).

• (5) Globalization links village and urban economic activities through the internationalization of trade and investment (Wei & Liefner, 2012; Zhang et al., 2025), enabling DV to access more international markets and external resources. This injects new impetus into the synergistic innovation between DV and NISOE, ensuring their competitiveness on a global scale.

Table 4 Dynamic factors affecting the coordinated development of DV-NISOE.

In conclusion, the interaction of the five dynamic factors—urbanization, marketization, industrialization, digitization, and globalization—constitutes the core mechanism of the coupling and coordinated development of DV-NISOE, providing systematic support for their integration and synergistic development. Table 5 shows the descriptive statistics of the variables involved in this study.

Table 5 Descriptive statistics.

An examination of the factors influencing the coordinated development of DV-NISOE

After theoretically establishing the relationship between dynamic factors such as urbanization, marketization, industrialization, digitalization, and globalization, and the level of coordinated development of DV-NISOE, this study employs the double Dekker-Semi-Partialling MRQAP correlation test. This non-parametric test, based on random permutations, is particularly effective for addressing autocorrelation and multicollinearity issues in relational data. The method comprises two steps: correlation analysis and regression analysis. In the correlation analysis, we assess the correlation between two matrices; in the regression analysis, we examine the relationship between multiple matrices and a single matrix. Both analyses follow the same QAP principle: transforming the relationship matrix into a “long” vector, calculating correlation or regression coefficients, and then determining the significance of the parameter estimates through random permutations (Serrat, 2017; Snijders, 2001). The correlation test results are presented in Fig. 8. Specifically:

• (1) Among globalization-related variables, Gloo has a high correlation with other variables in the early stages, but this correlation gradually decreases from 2019 onwards, becoming insignificant in 2021 and 2022. The correlations of Glof and Glot show fluctuating trends, with significant decreases in 2020 and 2022, possibly influenced by the global trade situation and policy environment (Yepez & Leimgruber, 2024).

• (2) For digitization-related variables, the word frequency of Digg and Digt of provincial governments show instability, with no significant correlation with other variables in some years, reflecting changes in digital governance and technology policies. The Dige shows an overall upward trend, especially since 2017, with increased correlations with variables such as industrialization and marketization, indicating the growing importance of the digital economy in innovation and markets.

• (3) The industrialization-related variables Indv, Indn, and Indf maintain significant correlations with other variables, highlighting the dominant role of industrialization in the overall innovation system. Indv, in particular, sustains a high level of correlation throughout the period, reflecting a solid link between industrial development and other driving factors.

• (4) Marketization-related variables, including Mkc, Mke, and Mkd, show high significance levels throughout the period. The correlations of Mkc and Mke with other variables increase after 2020, especially with industrialization and the digital economy, demonstrating the crucial role of market mechanisms in resource allocation and economic growth.

• (5) Urbanization-related variables, such as Urbp, Urba, and Urbs, exhibit relatively weak correlations with other variables, particularly Urbp. However, since 2018, the correlations between Urba and Urbs and industrialization and marketization have strengthened, reflecting the increasingly significant impact of urbanization on innovation and markets.

Fig. 8

Correlation test results.

After identifying the correlations between dynamic factors such as urbanization, marketization, industrialization, digitalization, and globalization, as well as the level of coordinated development of DV-NISOE, the paper proceeds with regression analysis. To analyze these relationships, this study employs the double Dekker-Semi-Partialling MRQAP method, which is a non-parametric statistical approach designed for relational data models.

The QAP method is particularly suited for this study due to its ability to handle the interdependencies inherent in the relational structure of the data. Unlike traditional regression methods, which rely on assumptions of independence among observations, QAP uses permutation testing to generate reference distributions for assessing the significance of correlations. This makes it an ideal tool for analyzing the dynamic interactions between factors such as urbanization, marketization, industrialization, digitalization, and globalization in the context of DV-NISOE coordinated development.

The strengths of the QAP method are: First, handling autocorrelation and multicollinearity. The QAP method naturally addresses autocorrelation and multicollinearity issues, which are common in relational data models. By focusing on the matrix-level relationships and employing permutation testing, QAP avoids the biases that may arise from these statistical challenges in traditional regression methods. Second, flexibility in model assumptions. QAP does not impose strict assumptions about the distribution of data (e.g., normality or linearity) or the independence of errors. This flexibility allows the method to robustly analyze complex systems where traditional assumptions are difficult to satisfy. Third, maintaining relational structure. As QAP operates directly on matrices, it preserves the structural integrity of the data, ensuring that the relationships between variables are appropriately captured. This is particularly important in this study, as the dynamic interactions between the factors influencing DV-NISOE require an analytical approach that respects these interdependencies. Fourth, non-parametric robustness. The permutation-based nature of QAP makes it a highly robust method, capable of producing reliable significance assessments without relying on parametric distributions. This is especially valuable when dealing with small samples or non-standard data distributions.

The double Dekker-Semi-Partialling MRQAP method is applied to analyze the correlations between the five dynamic factors and the coordinated development of DV-NISOE. This method is particularly effective in isolating the effects of individual variables while controlling for interdependence, among other factors. The regression results reveal the following key insights (presented in Fig. 9):

• (1) Low coupling group: In the low coupling group, globalization variables demonstrate a significant role in promoting DV-NISOE integration. Gloo (0.084) and Glof (0.275) are significant at the 1% level, while Glot (0.011) is significant at the 5% level. These results underscore the importance of FDI and trade in mitigating resource constraints and fostering technological spillovers in less integrated regions. In contrast, digitalization variables such as Digg and Dige are insignificant, indicating underdeveloped digital infrastructure and readiness. However, Digt (0.011), which is significant at 10%, suggests that policy focus on digital technologies can marginally improve coordination. Industrialization variables show mixed but significant effects, with Indf (0.290) and Indv (0.380) significant at the 10% level and Indn (0.123) at the 5% level. These findings highlight the importance of industrial production capacity and enterprise networks in bolstering innovation coordination. Similarly, marketization variables Mke (0.232) and Mkc (0.262) are significant at the 5% level, pointing to the vital role of private sector activity in activating local innovation dynamics. Among urbanization variables, only Urbs (0.692) is significant, suggesting that rising income levels among urban residents contribute significantly to innovation integration, while Urba and Urbp remain insignificant.

• (2) Medium-low coupling group: In medium-low coupling regions, globalization variables are not significant, reflecting limited integration into international trade and investment networks. However, Dige (0.013) emerges as a significant factor, indicating the early-stage impact of digital infrastructure on coordination. Indf (0.025) was significant at 5%, and Indn (0.009) was significant at 10%. This also positively influenced coordination, emphasizing the role of industrial capacity in these regions. Mkd is marginally significant (0.053), highlighting the importance of external market connections. Urbanization variables, specifically Urba (0.031), which is significant at 1%, and Urbs (0.063), which is significant at 10%, positively contribute to coordination, reflecting gradual improvements in urban infrastructure and economic conditions.

• (3) Medium-high coupling group: In medium-high coupling regions, globalization plays a critical role, with Gloo (0.274), Glof (0.071), and Glot (0.016) all significant at the 1% level. These findings suggest that robust integration into global trade and investment networks enhances innovation system coordination. Digitalization variables remain insignificant, indicating that digital infrastructure may not yet be fully leveraged in these regions. Industrialization retains its significance, with Indv (0.190) continuing to support innovation capacity. Marketization variables Mkc (0.186), Mke (0.239), and Mkd (0.161) are all significant at the 1% level, emphasizing the critical roles of private enterprise vitality, employment, and market openness. Among urbanization variables, only Urbs (0.174) contributes significantly, indicating the importance of income growth in fostering innovation integration.

• (4) High coupling group: High coupling regions exhibit advanced integration driven by both globalization and digitalization. Glof (0.123) and Glot (0.058) remain significant contributors, while Gloo is not significant. Digitalization variables Digg (0.041) and Digt (0.028) are both significant at the 5% level, highlighting the growing importance of digital governance and technology in promoting seamless integration between DV and NISOE. Industrialization variables Indf (0.066) and Indn (0.040) are significant at the 1% level, emphasizing the foundational role of industrial activity. Marketization is less prominent in high coupling regions, with only Mkc (0.062) demonstrating marginal significance. Among urbanization variables, only Urbp (0.123) is significant, indicating the relevance of the urbanization rate in these regions.

• (5) Full-sample group: Across the full sample, globalization, and digitalization exhibit significant and consistent effects. Gloo (0.395), Glof (0.452), and Glot (0.976) confirm the importance of global economic integration. Digitalization variables Digg, Digt, and Dige are all significant at the 10% level, with coefficients of 0.476, 0.611, and 0.003, respectively, emphasizing the multifaceted impact of digital infrastructure, governance, and economy. Industrialization remains a cornerstone, with Indv (0.661), Indn (0.199), and Indf (0.006) consistently contributing to coordination. Marketization variables Mkc (0.797 5%), Mke (0.434), and Mkd (0.289) further reinforce the importance of private sector vitality. Urbanization variables Urbs (0.330), Urbp (0.003), and Urba (0.136) complete the picture by showcasing the role of urban economic structures in fostering integration.

Fig. 9

The results of double Dekker-Semi-Partialling MRQAP regression.

Discussion and conclusion

Research finding discussion

In this paper, we thoroughly analyze the coordinated development of DV-NISOE. We utilize dynamic and static empowerment methods and physical coupling models to quantify the level of coordinated development. Empirical analysis of data from 2010 to 2022 across various provinces in China reveals a significant improvement in the coordinated development level of DV-NISOE in most provinces. This finding aligns with Zhan et al. (2023) research on the role of digitalization in promoting balanced regional development and Li et al. (2024) research on digital transformation fostering development in village areas. Notably, these results contribute to the broader global discourse on village digitalization and innovation systems. For instance, similar trends have been observed in European contexts, where digital transformation initiatives in village areas, such as smart village programs, have successfully addressed regional disparities in innovation capacity (Cowie et al., 2020; Gerli & Whalley, 2021). However, unlike these cases where public-private partnerships and community-driven approaches dominate, the Chinese context highlights the role of centralized policies and government-led initiatives. This difference underscores the diversity in pathways for integrating village development and innovation systems, offering valuable comparative insights for policymakers worldwide.

Additionally, this paper reveals the unevenness of the coordinated development of DV-NISOE in both temporal and spatial dimensions. While the high level of coupled coordination observed in China’s eastern and central regions is comparable to studies from Europe, where economic hubs often drive digitalization (Philip et al., 2017), the persistent challenges in less-developed areas reflect a more pronounced regional disparity in China (Zhang et al., 2024). The urban-village digital divide remains a significant issue, with village areas often facing greater challenges in accessing digital infrastructure and technology (Fu et al., 2024; Pelucha & Shemetev, 2025). These disparities not only affect the pace of digital village construction but also hinder the integration of village areas into the broader national innovation system. Even if central government policymakers aim to balance regional development disparities, the time lag effect of policies means that top-down measures are unlikely to bring about radical changes in the short term (Fan et al., 2024; Liu & Zhu, 2023; Rahman et al., 2024).

Moreover, rapid digital transformation in village areas, if not managed carefully, could lead to unintended consequences. For example, a focus on digital technologies may inadvertently widen the socioeconomic gap between regions that can easily adopt digital tools and those that face infrastructure limitations (Quan et al., 2024). Additionally, excessive reliance on technology could undermine traditional industries and local cultures, particularly in village communities with limited digital literacy. The quick adoption of digital platforms may also lead to job displacement in sectors that have not yet adapted to digital solutions (Liu et al., 2025). To mitigate these risks, it is essential to balance technological progress with policies that promote social equity and sustainable development (Cheng et al., 2024; Sánchez-García et al., 2024).

In the fifth part, this paper discusses in detail the formation mechanism of the coupled and coordinated development of DV-NISOE. To identify the factors influencing this development, the paper, based on a literature review and theoretical analysis, considers urbanization, industrialization, digitalization, marketization, and globalization as the main influencing factors. These factors encompass various influences on regional development in different dimensions. Specifically, urbanization is directly related to regional infrastructure; industrialization affects economic structure and employment opportunities; digitalization reflects the penetration of information and communication technologies, and marketization and globalization indicate the maturity of the regional market economy and the degree of international openness, respectively. To test the impact of these factors on the coordinated development of DV-NISOE, this paper uses a QAP to test for association. The statistical analysis reveals the following: First, urbanization. Positively correlated with the coordinated development of DV-NISOE. More urbanized areas typically have better infrastructure and services (Su et al., 2024), promoting the development of DV and the integration of the NISOE. Second, industrialization. Positively affects coordinated development, particularly in transformation, upgrading, and improving regional innovation capacity (Asghar et al., 2024). Third, digitalization. Significantly contributes to coupled and coordinated development, highlighting the central role of digital technology in promoting economic and social modernization in village areas (Zhao et al., 2024). Fourth, the marketization and globalization processes also positively impact coupled and coordinated development, indicating that open markets and international exchanges help introduce new technologies and innovative ideas and promote the sharing of knowledge and resources between regions (Gangwani & Bhatia, 2024; Huang, 2024).

Further, the role of local governance and community participation is crucial in ensuring the success of digital village programs. Local governments, by collaborating with community stakeholders, can help identify and address specific needs and challenges unique to village areas (He et al., 2025). Empowering local communities to actively participate in the planning and implementation of digital transformation initiatives can lead to more tailored, effective solutions (Salemink et al., 2025). This involvement is particularly important in fostering trust and ensuring that digital tools are accessible and beneficial to all segments of village populations, including marginalized groups (Zhou et al., 2024a).

This study further enriches the global understanding of village digitalization and its integration with national innovation systems by providing insights from a “Chinese story.” Unlike village development models predominantly observed in developed economies, where market-driven or community-led initiatives prevail, China’s government-led and policy-driven approach offers a unique perspective on how state interventions can facilitate digital transformation in underdeveloped regions. These findings not only contribute to advancing theoretical discussions on digitalization in village development but also offer practical implications for countries with similar socio-economic conditions, particularly those in Asia, Africa, and Latin America.

Conclusion

This study integrates the development of DV construction and NISOE in China into a unified theoretical framework. It demonstrates the coordinated and interactive development relationship between DV and NISOE based on the concept of DV and NISOE. Further, this paper constructs a comprehensive evaluation system for the development level of the overall effectiveness of DV and NISOE, adopts a combination of dynamic and static weighting methods and a physical coupling model to measure the coordinated development level of DV-NISOE from 2010 to 2022, and categorizes the coordinated development level into four grades for classification and discussion. In addition, this paper also discusses the trend of differentiated changes in the coordinated development of DV-NISOE from both time and space dimensions. Finally, this paper identifies and tests the factors affecting the coupled coordinated development of DV-NISOE from five perspectives: urbanization, industrialization, digitization, marketization, and globalization, and conducts a correlation test using the QAP method. The regression results show that these influencing factors have heterogeneous effects on the coordinated development of DV-NISOE at different levels.

Policy recommendations

The findings of this study provide multiple insights for policymakers and practitioners, particularly in promoting the coupled and coordinated development of DV-NISOE. First, infrastructure development should be strengthened, and the digital divide should be narrowed. Governments should increase investment in digital infrastructure in rural areas, especially in the construction of broadband Internet and mobile communication networks (Lu et al., 2023). Ensure that rural areas can fully access and utilize digital technologies, narrow the digital divide between urban and rural areas, and promote balanced regional development. Second, promote the integration of industrialization and agricultural modernization. Policies should encourage and support the digital integration of agriculture and industry and promote projects such as smart agriculture, agricultural big data, and online agricultural product markets. This will not only improve the efficiency of agricultural production but also increase the added value of agricultural products, enrich the form of the rural economy, and promote the diversification of the rural economy (Carvalho do Prado et al., (2024)). Third, accelerating the digitalization process and upgrading the quality of social services. The government should increase its support for the construction of digital education, e-government, and health and wellness systems in villages to promote knowledge sharing, increase the efficiency of public services, and improve the quality of life of rural residents (Qiao & Ao, 2024). Fourth, promote the process of marketization and globalization to enhance rural competitiveness. Policies should focus on breaking down inter-regional trade barriers and optimizing the business environment in rural areas. At the same time, with the help of international cooperation projects, advanced technology, and management experience should be introduced to enhance the competitiveness of rural areas, promote their integration into the global market, and strengthen their development potential in the context of globalization (Melendres et al., 2022). Fifth, cooperation between local governments and communities should be promoted to enhance the sustainability of digital transformation. Policies should support cooperation between local governments and community stakeholders to identify and address the special needs and challenges of rural areas. By empowering local communities to be more participatory, it ensures that digital transformation programs are tailored to local conditions and result in more efficient and sustainable solutions.

Marginal contribution

The marginal contribution of this study is mainly in three aspects: First, in terms of theoretical contribution, this study further refines the mechanism of coordinated development of DV and NISOE based on the existing literature, especially the unique performance in the Chinese context. By focusing on the coordinated development of DV and NISOE, this study fills the gap in this area and emphasizes the central role of DV construction in NISOE. Second, in terms of methodological innovation, this study provides a quantitative tool to measure the level of coordinated development between DV and NISOE by combining dynamic and static empowerment methods and physical coupling models. Unlike most of the traditional literature that uses qualitative or simple quantitative analyses, this study adopts a more refined model that not only reveals inter-regional differences but also accurately quantifies the role of multiple influencing factors on coordinated development. Third, in terms of practical contributions, this study provides an important reference for policymakers, especially for developing countries like China, on how to achieve the coordinated development of DV construction and NISOE through policy guidance. Unlike Western countries that rely mainly on market-driven or community-led models, China’s government-led policies provide a unique perspective that demonstrates how state intervention can facilitate digital transformation in less developed regions. This finding not only enriches the theoretical discussion of DV development but also provides practical policy recommendations for countries with similar socio-economic backgrounds, such as those in Asia, Africa, and Latin America.

Limitation and future work

While this study offers valuable insights into the coordinated development of DV and NISOE, several limitations must be acknowledged. In discussing these limitations, we also highlight potential avenues for future research. First, the process of agricultural digitization has been slow in many rural areas, particularly in remote and economically underdeveloped regions. In these areas, relevant digital agriculture data, such as the use of precision agriculture technologies, have not been systematically recorded, and existing data often lack standardized government statistics. As a result, some of the indirect indicators employed in this study can only serve as proxy measures for the progress of agricultural modernization and digitization. Second, while the coupled coordination model used in this study is well-established in fields like economics and environmental science, it may oversimplify the interactions between variables compared to the original coupled physical capacity model from physics. Third, the period covered by the study’s sample may not be long enough to capture the long-term dynamics between DV development and NISOE. It may take several decades for certain trends, particularly those related to technological development, policy adjustments, and socio-cultural changes, to fully materialize.

In terms of future research directions, there are several key areas for expansion. First, future studies can address data gaps by fostering greater collaboration between local governments and utilizing technologies such as remote sensing or agricultural management systems. Efforts by official statistics departments to collect digital agriculture data are ongoing, and more accurate data and indicators are expected to become available for future academic research. Second, future research can explore more sophisticated statistical and econometric models to better capture the non-linear relationships and dynamics between variables. Third, studies could use longer periods for subsequent tracking analyses, enabling researchers to observe the long-term effects of policy implementation and technological advancement. Comparative studies across different periods (e.g., various stages of policy implementation or economic cycles) could provide further insights into the dynamics of these stages. Fourth, cross-country comparative studies can investigate the synergies between different countries in areas such as digital village construction and innovation systems, along with their socio-economic impacts. This would contribute to promoting digital agriculture and rural revitalization on a global scale.