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Article

The Mediating Role of Virtual Agglomeration in How ICT Infrastructure Drives Urban–Rural Integration: Evidence from China

by
Lei Zhang
1,
Jingfeng Yuan
1,*,
Bing Zhu
2,
Bingsheng Liu
3 and
Qiqi Ai
1
1
School of Civil Engineering, Southeast University, Nanjing 211189, China
2
School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany
3
School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(10), 2032; https://doi.org/10.3390/land14102032 (registering DOI)
Submission received: 4 September 2025 / Revised: 8 October 2025 / Accepted: 9 October 2025 / Published: 11 October 2025
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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Abstract

Information and communication technology (ICT) infrastructure can facilitate urban–rural integration. However, few studies have explored the role of virtual agglomeration in the mechanisms underlying this process, which can enable geographically dispersed market participants (both urban and rural) to achieve proximity in network space through digital connectivity provided by ICT. This study uses the PLS-SEM method to empirically analyzes the relationships among ICT infrastructure, virtual agglomeration, and urban–rural integration based on data obtained from 31 provincial-level regions in China from 2012 to 2022. The results indicate that: (1) ICT infrastructure can promote urban–rural integration. (2) Virtual agglomeration plays a significant mediating role in the relationship between ICT infrastructure and urban–rural integration. In relatively developed eastern China, virtual agglomeration fully mediates the impact of ICT infrastructure on urban–rural integration. (3) Other complementary infrastructures—including transport and education—have positive moderating effects on the process of virtual agglomeration facilitated by ICT. This study advances the understanding of ICT’s effects on regional development from the perspective of employing a new form of spatial agglomeration (i.e., virtual agglomeration). Meanwhile, this study indicates that in order to address the global challenge of urban–rural divide, it is necessary to strengthen the development of ICT infrastructure in remote rural areas, while developing complementary infrastructure such as transportation or education in alignment with regional characteristics.

1. Introduction

Spatial agglomeration refers to the proximity of market participants and the concentration of economic activities, where upstream suppliers and downstream customers gather in a shared space [1]. This proximity facilitates information exchange, reduces information asymmetry, and enhances transactions [2]. In the traditional real economy, “proximity” is largely dependent on geographical closeness and face-to-face interactions, which drive the geographical agglomeration of economic activities and the formation of cities [3,4]. To take advantage of agglomeration economies, most rural workers migrate to cities, while firms choose to establish themselves in urban areas [5,6], thereby exacerbating the outflow of the rural population and the urban–rural divide [7].
However, nowadays, information and communication technology (ICT) is increasingly challenging the dominance of cities as economic hubs and reducing regional inequality [8,9]. ICT enables the digital capture, processing, storage, and communication of information [10], eliminating geographical distance as a barrier to contact [8]. With ICT, rural workers and firms can access new customers and find less expensive or more reliable suppliers without migrating to cities [10,11,12]. Transactions once confined to urban areas can now take place in rural areas [13,14,15]. ICT enhances rural development by reshaping information flows and expanding economic opportunities in rural areas [16,17].
Regarding ICT’s potential contribution to urban–rural integration, numerous empirical studies have been conducted, which can be categorized into two main streams. The first stream examines the direct impact of ICT on urban–rural integration, with multiple studies consistently reporting positive effects [18,19,20,21]. The second stream explores the indirect effects, identifying key mediating factors through which ICT promotes urban–rural integration, including industrial structure upgrading [22,23,24], innovation [19,25], entrepreneurship [26], and digital financial inclusion [20,27].
However, existing studies have not applied the concept of virtual agglomeration to examine the ICT-driven urban–rural integration process. As mentioned above, a key cause of the urban–rural divide is that agglomeration economy primarily exists within geographical space and cities [5,6,28]. However, ICT extends economic activities beyond geographical space into virtual space—alternatively conceptualized as network space or digital space—thereby creating new possibilities for spatial organization and economic development [29]. Correspondingly, the spatial agglomeration is also divided into traditional geographical agglomeration and virtual agglomeration [30]. Virtual agglomeration, also known as the virtual industrial cluster [31,32] or e-cluster [11,29], enables market participants (both urban and rural) to achieve proximity in network space through digital connectivity provided by ICT [11,29,30,31,32]. Virtual agglomeration complements geographical agglomeration [33]. It transforms the traditional spatial logic based on geographical proximity, allowing the benefits of economic agglomeration—reducing information asymmetry and promoting transactions—to be realized in network space rather than being strictly tied to geographical space [34]. The benefits of ICT for rural areas, such as enabling rural workers and firms to access markets without migrating to cities [13,14,15], are closely related to the concept of virtual agglomeration [35].
This study focuses on the case of China. China has a dualistic urban–rural structure, where the benefits of economic development have been heavily concentrated in urban areas [35,36]. To achieve more balanced urban–rural development, the Chinese government has implemented a series of Digital Village construction policies aimed at promoting rural development by expanding ICT infrastructure in rural areas [9,17,36]. Therefore, the significant relationships among ICT, virtual agglomeration, and urban–rural integration may be particularly observable in China. Although this study is based on China, its findings hold broader relevance for other regions. Currently, the inequality among regions has become a global issue and has been highlighted in the Sustainable Development Goals (SDGs), which were delivered by the United Nations in 2015 [37]. Many developed or developing countries are trying to leverage ICT to foster balanced urban–rural development, such as America [8], France [38], and Kenya [39]. The insights from this study can help regions globally understand the effects of ICT on regional development.
Against this background, based on the data from China’s 31 provincial-level regions from 2012 to 2022, this study aims to explore the effects of ICT on urban–rural integration from the perspective of virtual agglomeration and to explain the underlying mechanisms. This study answered the following question: does ICT promote urban–rural integration through virtual agglomeration? This study contributes two aspects: (1) From the perspective of ICT, this study supports previous research on the effects of ICT on urban–rural integration [18,19,20,21], and particularly identifies a new mediating factor—virtual agglomeration—that has received limited attention in previous studies [19,20,22,23,24,25,26,27]. (2) From the perspective of spatial agglomeration, this study expands the forms of spatial agglomeration and analyzes the concept of virtual agglomeration and its significance. It complements traditional geographical agglomeration and provides a viable option for the unified conceptualization of a range of phenomena—such as e-commerce, digital platform economy, virtual marketplaces, and virtual geographies—that have become increasingly prevalent [8,25,33,35,40,41,42].
The rest of this study is organized as follows. Section 2 presents the literature review and research hypotheses. Section 3 introduces the methodology. Section 4 presents the empirical results. Section 5 and Section 6 discuss and conclude in sequence.

2. Literature Review and Hypotheses Development

2.1. The Effects of ICT on Urban–Rural Integration

Rural development generally lags behind urban development due to factors such as information isolation, inconvenient transportation, inadequate public services, and a lack of economic opportunities [5,6]. Urban–rural integration aims to gradually narrow the development gap between urban and rural areas by developing infrastructure in rural regions and promoting the inflow of production factors (information, labor, advanced technologies, investment), thereby enabling urban and rural residents to reap the benefits of development [9,43]. Specifically, the development of rural industries (particularly the non-agricultural industries), the increase in employment opportunities, and the rise in income—as outcome-based indicators that directly reflect the urban–rural gap—have often been a critical focus on the topic of urban–rural integration [24,44].
Many studies have been conducted to examine whether ICT promotes rural-urban integration. One stream argues that the gap between urban and rural areas decreases as ICT infrastructure constructed [8]. This stream generally considers that the information is a key resource underlying all economic activities [14,15]. The processes of discovery, matching, and transactions between customers and suppliers all depend on the exchange of information [14,15]. Before the advent of ICT, information exchange primarily required face-to-face communication [45]. As a result, large numbers of rural populations migrated to cities—the information-rich areas—to search for more economic opportunities. ICT, however, enhances access to information through digital means [35,46], effectively breaking geographical barriers to information flow [27]. This allows different regions—particularly rural areas—to participate more equally in trade [47]. Thereby, rural areas have access to opportunities to develop. This phenomenon can be attributed to the concept of smart villages. Smart villages emphasize the importance of investing in ICT infrastructure in rural areas in order to strengthen connectivity between core and peripheral regions, expand employment opportunities beyond agriculture, and improve public services [7]. Smart villages, in essence, leverage ICT to improve living environment and economic opportunities in rural areas, thereby addressing the traditional disadvantages of rural areas in terms of distance and population outflow [7].
Conversely, many studies have also argued that ICT does not necessarily reduce the urban–rural divide [12,48]. These studies indicate that urban areas are benefiting disproportionately from ICT and that the urban–rural development gap may even be widened as ICT infrastructure constructed [12,48]. However, this does not negate the positive effect of ICT in promoting regional development. The negative impact of ICT on urban–rural integration is mainly due to a lack of basic conditions in rural areas, as they cannot reap the benefits of ICT immediately and directly [49]. For example, Liu, Min [50] and Li, Zeng [12] indicated that the inability to use ICT makes it difficult for rural people to share digital dividends. Wu, Wang [51] indicated that when transportation costs are too high, the rural people may still choose to migrate to cities, even if they have access to information in rural areas. This highlights that many other complementary infrastructures—such as education and transport—are also important in promoting urban–rural integration. The effects of other complementary infrastructures will be discussed separately in Section 2.3. This study still maintains that ICT can reduce the urban–rural divide.
Based on the above analysis, this study proposes the first hypothesis:
Hypothesis 1 (H1).
The development of ICT infrastructure can directly promote urban–rural integration.

2.2. The Formation and Function of Virtual Agglomeration

ICT expands the concept of “space” and transforms economic organization, challenging the traditional notion that cities are the sole vehicle for spatial agglomeration. By enabling information flow within network space and across geographical boundaries, ICT significantly reduces the perceived distance between people [38,40].The concept of “proximity” in agglomeration shifts from “physical” proximity to a broader notion of “organizational” proximity, which relies on digital communication technologies rather than face-to-face or place-based interactions [52]. As a result, various market participants can form industrial clusters without relocating to a shared geographical space [53].
Few studies have used virtual agglomeration to conceptualize these phenomena. Furthermore, the connotation of the concept of virtual agglomeration is also less studied. In this context, this study defines virtual agglomeration as people achieving organizational proximity in network space through ICT, rather than physical proximity in geographical space through face-to-face interactions, while the people participating in virtual industrial clusters remain geographically dispersed [11,29,30,31,32]. The relationship between virtual agglomeration and ICT is that virtual agglomeration is generated based on ICT, and people need to use ICT to connect with each other [30]. Meanwhile, the relationship between virtual agglomeration and urban–rural integration is that since market participants can form agglomeration economies while being geographically dispersed, remote rural areas can thus participate in trade, achieve development, and narrow the gap between themselves and cities without relocating to them [29]. In practice, virtual agglomeration is specifically reflected in certain digital trade patterns, particularly e-commerce and digital platform economy [40,41,42]. These patterns create “borderless” virtual marketplaces, where geographically dispersed customers and suppliers can freely interact, bypassing traditional place-based interactions [8,25,35]. Many studies indicated that compared to physical marketplaces, virtual marketplaces can greatly expand the scope of information flow and the scope of transactions [15,35,54,55]. Thereby, more remote resources—particularly rural areas’ resources—can be found and developed [32,51,56].
ICT-based virtual agglomeration benefits rural areas. For example, in China, Li, Zeng [12] noted that rural e-commerce industrial parks, rural e-commerce service centers, Taobao villages, and other new developments have successively emerged in Chinese villages, e-commerce has become an important way to increase farmers’ income and reduce the urban–rural development gap. Through joining virtual industrial clusters, rural market participants can also reach more downstream consumers and upstream suppliers to trade, and finally benefit from agglomeration economy as they would in cities [5,57]. These rural market participants transact with urban customers online and deliver products or services offline. Increased trade, in turn, attracts labor, firms, advanced equipment, and other resources from urban to rural areas [44,58,59]. As a result, the capital and technologies flow from urban to rural areas, while the flow of rural products and services targets urban areas [16,34,60]. This process—virtual agglomeration guiding offline resource allocation through online information exchange [28,34]—helps reverse the long-term outflow of rural production factors, leading to a more balanced distribution of resources between urban and rural areas [16]. Therefore, unlike geographical agglomeration, virtual agglomeration does not facilitate the centralization of industries; rather, it enhances decentralization [45]. The urban–rural spatial structure evolves from monocentric to polycentric, fostering more balanced development across urban and rural areas [51].
In this context, this study argues that virtual agglomeration constructs a new pattern of resource allocation, which dissolve the geographical isolation among different regions. Virtual agglomeration in network space guides and redefines the process of resource clustering in geographical space and enables villages to have the same potential to become the center of geographical agglomeration as cities. This process is shown in Figure 1.
Based on the above analysis, this study proposes the second hypothesis:
Hypothesis 2 (H2).
The development of ICT infrastructure can indirectly promote urban–rural integration by forming virtual agglomeration.

2.3. The Effects of Other Complementary Infrastructures

As mentioned earlier in Section 2.1, one consensus that has been reached in existing studies is that relying solely on ICT infrastructure may not be sufficient to form virtual agglomeration [49,61]. Many complementary infrastructures are necessary for ICT infrastructure to have meaningful effects [61]. For example, in the absence of reliable electricity, clean water, education, transport, and other essential infrastructure, the effectiveness of ICT infrastructure may diminish or even disappear [17,39,56,62]. Among these, transport and education receive more emphasis.
Transport infrastructure is essential for ensuring the swift and efficient movement of labor, goods, and physical capital between regions [63]. While digital networks facilitate the exchange of information, the factors involved in physical production cannot be transported via the “information highway”; their movement still depends on actual transport infrastructure [8]. In network space, there are no tangible physical products—only the information that represents them. The offline flow of materials and the online flow of information remain distinct. Although physical products can be traded via virtual agglomeration or e-commerce, their delivery still requires logistics infrastructure [28,60].
The education infrastructure plays a key role in overcoming the second-level digital divide, also referred to as the skills and usage digital divide [64]. The first-level digital divide concerns access to ICT, while the second-level digital divide highlights the gap in users’ ability to effectively utilize ICT [64]. Even when ICT infrastructure is available (thus reducing the first-level divide), its benefits may be limited if individuals lack digital skills—the ability to operate hardware and software—or information literacy, which enables them to effectively use information resources for problem-solving [14,38,62,64]. Consequently, learning how to use ICT is even more critical than simply having access to it [47]. Generally, education serves as a key pathway for enhancing digital skills and information literacy; the higher one’s level of education, the easier it is for them to learn and master a new tool [64]. Without a well-developed education infrastructure, market participants may struggle to effectively utilize ICT to participate in e-commerce, preventing the formation of virtual agglomeration [50].
Based on the above analysis, this study proposes the third hypothesis:
Hypothesis 3 (H3).
The formation of virtual agglomeration through ICT infrastructure is moderated by the development of transport and education infrastructure.

3. Methodology

To test Hypothesis 1~Hypothesis 3, this study chose the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. In this section, first, the PLS-SEM and its applicability will be introduced. Second, the variables used in this study will be presented in PLS-SEM format with corresponding data. Third, the estimated model developed in this study will be presented in PLS-SEM format for testing Hypothesis 1~Hypothesis 3. The three main research phases are as shown in Figure 2.

3.1. The PLS-SEM Method

PLS-SEM is a dominant method for analyzing complex structural relationships among various constructs and variables [65]. This method allows the measurement model and the structural model (path model) to be evaluated in a systematic and integrative way [58]. This study used this method to test hypotheses for two reasons.
First, PLS-SEM is superior to regression analysis when assessing mediating effects, which involve examining a model where a construct (i.e., the mediator) intervenes between two other constructs [66]. In mediation analysis, regression methods only allow for the sequential testing of individual model components without considering the overall model structure. In contrast, PLS-SEM evaluates the entire structural model in a single estimation process [66]. This study contains a mediating effect of virtual agglomeration between ICT infrastructure and urban–rural integration (i.e., Hypothesis 2). Compared to regression methods, the PLS-SEM is more suitable.
Second, when a model is complex and includes numerous constructs, variables, and relationships, PLS-SEM is more applicable [67]. PLS-SEM allows for the simultaneous assessment of both the measurement model and the structural model within a single estimation process. In particular, the measurement model can use a set of variables rather than a single variable to measure the construct [58]. Notably, PLS-SEM supports both reflective measurement models, where measurement variables reflect the construct, and formative measurement models, where measurement variables define the construct [66,67]. Because PLS-SEM can accommodate formative measurement models, it not only verifies relationships among various constructs but also identifies which measurement variables are most influential in defining these constructs [58]. In this study, ICT infrastructure, virtual agglomeration, and urban–rural integration are complex and comprehensive systems. Various variables have been used to measure these constructs in existing studies and a consensus has not been reached [49]. The PLS-SEM not only allows for a unified consideration for these measurement variables to explore the relationships between these constructs, but also enables a more in-depth exploration of relationships within the model by using formative approaches—specifically, which particular infrastructures contribute the most [58].
Currently, many studies have begun using the PLS-SEM method to explore causal relationships in real-world settings based on secondary data. Such as Wang, Zhou [49] and Fernandez-Portillo, Almodovar-Gonzalez [58], analyzed the impact of ICT on urbanization and the impact of ICT on economic growth using the PLS-SEM, respectively. This provides a methodological reference for this study.

3.2. Variables and Data

Regarding the empirical measurement, for the independent constructs of ICT infrastructure and complementary infrastructure, this study considered them as formatively measured constructs. This approach allows for a comparison of the effects of different types of ICT and complementary infrastructures [58]. In contrast, for the dependent constructs of virtual agglomeration and urban–rural integration, this study treated them as reflectively measured constructs. Furthermore, among the existing studies exploring the impacts of ICT infrastructure on urban–rural integration, some focus solely on rural ICT infrastructure, while others examine “general” ICT infrastructure, a comprehensive construct that includes and measures the overall development of ICT infrastructure in both urban and rural areas [36]. This study adopted the “general” ICT infrastructure construct because the formation of virtual agglomeration requires both urban and rural areas to be connected to network space, with interaction between the two areas being bidirectional; the use of ICT by only one party cannot achieve balanced development [16,34,60].
The measurement variables for the different constructs with their descriptive statistics are shown in Table 1. This study focuses on the 31 provincial-level regions in China (excluding Hong Kong, Macao, and Taiwan). To reduce contemporaneous endogeneity, the measurement variables for the independent constructs of ICT infrastructure and complementary infrastructure have been lagged by one year [46], using data from 2012 to 2021. For the dependent constructs of virtual agglomeration and urban–rural integration, the measurement variables are based on data from 2013 to 2022. The data for the variables INT, MOB, EDU, ING, and COG are sourced from the Chinese Research Data Services Platform (CNRDS) database, while the data for ROA, DEC, EXP, and NAG are obtained from the China Stock Market and Accounting Research (CSMAR) database. The data for REC is sourced from the Ali Research Institute (www.aliresearch.com). All measurement variables are logarithmic.
This study further divides the data sample into two sub-samples (western area and eastern area) to conduct heterogeneity analysis. China has an uneven geographical pattern, particularly between the east and the west, divided by the Hu Huanyong Line [70]. The Hu Huanyong Line—an imaginary line from Heihe city in northern China to Tengchong city in southern China—divides Chinese territory into two parts according to the natural and human geography [70]. The western part has severe physical environment, adverse location, and weak socio-economic foundation, while the eastern part has a suitable physical environment, an optimal location, and a solid socio-economic foundation [6,70]. These regional differences should be considered in the analysis. To capture this heterogeneity, this study divides the sample into two sub-samples according to the Hu Huanyong Line: the western area and the eastern area. The descriptive statistics for these two sub-samples are presented in Table 2. As shown in Table 2, the mean values of the measurement variables in western regions are consistently lower than those in eastern regions. For instance, in the construct of virtual agglomeration, the mean value for the development of rural e-commerce is 2.65 in the eastern regions, compared to only 0.67 in the western regions. This highlights the regional differences and sets the stage for the regional heterogeneity analysis.
Furthermore, this study divided the research period into the pre-COVID-19 period (before 2020) and the post-COVID-19 period (after 2020) to conduct a robustness test. During the COVID-19 pandemic, many economic factors could not flow normally, while remote work, e-commerce, and various digital trade patterns played a crucial role [71]. Many people accepted, used, and even became accustomed to and dependent on ICT. COVID-19 has generally boosted the demand for digital industries [71]. Consequently, the roles of ICT and virtual agglomeration may have been magnified during this period. Therefore, this study split the research samples to isolate the impact of COVID-19 and identify more universal mechanisms.

3.3. Model

Based on the measurement variables, in order to test Hypothesis 1~Hypothesis 3, this study developed the estimated model in PLS-SEM format, as shown in Figure 3.
The model will be estimated using SmartPLS v.4.1.0.0. According to PLS-SEM method, the model estimation results require sequential analysis of the formative measurement model, the reflective measurement model, and the structural model, while the check list and corresponding criteria [58,65] are presented in Table 3.

4. Results

4.1. Correlation Tests

Before using PLS-SEM for analysis, this study first conducted Pearson correlation tests to examine the relationships between the variables in independent constructs and those in dependent constructs. Table 4 reports the results of the Pearson correlation tests. These results indicate significant correlations among these variables, rendering them suitable for further analysis.

4.2. Baseline Results

The baseline results were obtained by using the data of all regions. Table 5 reports the results of formative measurement models. The maximum VIF value of formative variables is 2.843 (the VIF of INT and MOB), less than 3, which indicates that there are no multicollinearity issues. Meanwhile, the weights of INT, MOB, ROA, and EDU are all significant. In the construct of ICT infrastructure, INT (0.844) has a larger weight than MOB (0.186), which indicates that the internet makes the greatest contribution. In the construct of complementary infrastructure, EDU (0.666) has a larger weight than transport (0.507), which indicates that education makes the greatest contribution.
Table 6 reports the results of reflective measurement models. The minimum loading of reflective variables is 0.760 (the loading of COG), greater than 0.708. The minimum value of Cronbach’s alpha, rho_A, and CR is 0.801 (the Cronbach’s alpha of urban–rural integration), greater than 0.7, which ensures the internal consistency reliability. The minimum value of AVE is 0.708 (the AVE of urban–rural integration), greater than 0.5, which ensures the convergent validity.
Table 7 reports the results of the structural model. The maximum VIF value of paths is 2.683 (the VIF of ICT infrastructure→urban–rural integration and virtual agglomeration→urban–rural integration), less than 3, which indicates that there are no multicollinearity issues. Meanwhile, all the path coefficients are significant. The predictive power of the model is large as the minimum R2 is 0.597 (the R2 of urban–rural integration), which is greater than 0.35.
The baseline results are summarized in Figure 4. The direct relationship between ICT infrastructure and urban–rural integration (Hypothesis 1), the mediating role of virtual agglomeration (Hypothesis 2), and the moderating role of other complementary infrastructures (Hypothesis 3) are all tested.

4.3. Heterogeneity Analysis Results

Figure 5 and Figure 6 present the results of the heterogeneity analysis. Figure 5 shows the results for the western area, while Figure 6 shows the results for the eastern area. According to the findings in Figure 5, for the western area sub-sample, Hypotheses 1, 2, and 3 are supported and all align with the results from the whole-sample analysis in Figure 4. However, as shown in Figure 6, for the eastern area sub-sample, only Hypothesis 2 is supported, while Hypotheses 1 and 3 are not. Additionally, the weights of particular infrastructures—internet (INT), mobile phone (MOB), road (ROA), and high school education (EDU)—are inconsistent with the whole-sample results in both the western and eastern areas. In the construct of ICT infrastructure, in the eastern area (Figure 6), the results align with the whole sample, where the internet’s contribution remains greater than that of mobile phones, with weights of 0.714 and 0.320, respectively. In contrast, in the western area (Figure 5), the results diverge from the whole sample. The weight of the internet is 0.521, which is lower than the 0.527 of mobile phones. In the construct of complementary infrastructure, according to Figure 5, in the western area (in the eastern area, the weights of education and transport are insignificant), the weight of education is 0.570, which is less than that of transport (0.713) and diverges from the whole sample.

4.4. Robustness Test Results

Table 8 presents the results of the robustness test, which aims to isolate the impact of COVID-19. These results indicate that in the pre-COVID-19 period, all hypotheses are supported and align with the baseline results. However, in the post-COVID-19 period, only Hypothesis 2 is supported, while Hypotheses 1 and 3 are not, which indicates that the virtual agglomeration fully mediates the impact of ICT infrastructure on urban–rural integration. This finding indicates that without considering the COVID-19’s driving effect on the demand for digital industries, virtual agglomeration still exerts a significant mediating effect. However, when considering the effect of COVID-19, the role of virtual agglomeration becomes even more important.

5. Discussions

5.1. The Relationships Among ICT Infrastructure, Virtual Agglomeration, and Urban–Rural Integration

This study provides empirical evidence on the relationships among ICT infrastructure, virtual agglomeration, and urban–rural integration. The development of ICT infrastructure can directly promote urban–rural integration (Hypothesis 1). This finding aligns with many prior studies [18,19,20,21] and provides more evidence for the direct effect of ICT infrastructure. ICT infrastructure enables people in different regions to access information equally, allowing rural areas to reach customers, engage in trade, and develop industries. Consequently, this helps narrow the urban–rural development gap [16,47].
Meanwhile, ICT promotes urban–rural integration by forming virtual agglomeration (Hypothesis 2). Urban–rural integration challenges the traditional centrality of cities within the dual urban–rural structure [8]. This shift is facilitated by the complementary relationship between virtual agglomeration and geographical agglomeration (cities). ICT-based virtual agglomeration disrupts the traditional spatial logic based on geographical proximity, enabling the benefits of economic agglomeration—such as reducing information asymmetry and promoting transactions—to extend beyond cities [34]. Rural areas, by joining virtual industrial clusters within the network space rather than physically relocating to urban agglomerations, can also harness the benefits of agglomeration to boost their economies [29]. In virtual industrial clusters, rural market participants do not need to move to cities or other geographical agglomeration hubs [53]. Prior studies have identified many mediating factors through which ICT promotes urban–rural integration, such as industrial structure upgrading [22,23,24], innovation [19,25], and entrepreneurship [26], but not include the virtual agglomeration. This study supplements an underlying mechanism by which ICT promotes urban–rural integration.
The path of “ICT infrastructure → virtual agglomeration → urban–rural integration” aligns with many case studies. For instance, in China, the expansion of ICT infrastructure has facilitated interaction between urban and rural areas in network space, which has led to the rapid growth of rural e-commerce [35]. The villages that have joined virtual industrial clusters—like Taobao Villages—can achieve added development based on local comparative advantages, such as natural resources, ecological environments, unique agricultural products, labor costs, or land prices [12,15,44]. Zhang, Long [34] highlighted that in China’s Xiaying Village (a Taobao Village), local specialty products like kallaite are sold to nearly 70% of cities nationwide via e-commerce. This growth has attracted service providers—such as logistics, packaging, and customer service—creating more job opportunities and encouraging migrant workers to return to their hometowns to either start businesses or find employment. Correspondingly, Li, Zeng [12] noted that the Taobao village residents are about 26% less likely to migrate than non-Taobao village residents. Without ICT-based virtual industrial clusters, many rural areas have few livelihood opportunities outside of agriculture, but now, the villages can upgrade their industrial structure from traditional agriculture to manufacturing or service industries [72]. Beyond industrial structure upgrading, virtual agglomeration also exerts a promotional effect on agriculture itself. Virtual agglomeration can optimize the agricultural supply chain by reducing the supply-demand mismatch—particularly in terms of production planning and high inventory turnover for perishable agricultural products—thereby ultimately promoting the effective utilization of land and enhancing land productivity [59].
Beyond China, there are many such cases globally. For example, Braesemann, Lehdonvirta [8] noted that in the U.S., online labor platforms—web applications for remote informational labor like software development, graphic design, and data analysis—create a form of “virtual migration”. These platforms allow rural workers to access broader job markets without having to leave their hometowns. On the other hand, the development of ICT infrastructure also encourages urban firms and workers to relocate to rural areas. Duvivier and Bussiere [38] found that in France, many creative workers are moving to villages with green spaces or tourist attractions. ICT enables these workers to deliver their services remotely, while the more pleasant living environments of rural areas make them an attractive option [38]. Therefore, while the empirical results of this study are derived from China, they still hold reference significance for other regions. The effect of virtual agglomeration is widely prevalent.
While ICT-based virtual agglomeration can generate the benefits of economic agglomeration even in decentralized geographical spaces, some studies argue that distance, geography, and cities are becoming less relevant [73]. However, this study does not claim that virtual agglomeration can fully replace geographical agglomeration or cities. Geographical spaces are not uniform; some regions are too rugged or lack natural resources to be fully utilized, meaning industries are unlikely to be uniformly distributed across these spaces [1,3]. In fact, ICT-based virtual agglomeration does not eliminate geographical agglomeration but rather complements it. Virtual agglomeration reduces the impact of traditional location factors by enabling agglomeration economies to manifest in digital forms, offering greater locational flexibility and promoting a more even distribution of industries [45,74]. In a sense, virtual agglomeration can be regarded as a typical phenomenon in the transition stage from traditional geographies to virtual geographies, which emphasizes the transition of multiple dimensions of industrial production and human life to the digital space [33].
Furthermore, the ICT-based virtual agglomeration will have a greater scale in an external condition with better complementary infrastructure, particularly transport and education (Hypothesis 3). This result indicates that the provision of ICT infrastructure alone is not sufficient for creating virtual agglomeration and promoting urban–rural integration, and many other infrastructures, including transport and education, are also essential. ICT only facilitates the flow of transactional information, but the flow of transactional goods still requires transport infrastructure [28,60]. In China, ICT infrastructure development and transport infrastructure development are often mentioned together in government documents about rural e-commerce [36]. Meanwhile, although the ICT infrastructure has been built, if people lack the ability to learn new technologies due to lack of education, they are unable to effectively utilize ICT for connectivity and trades [47]. This result presents a response to Liu, Min [50], who also found that education significantly impacts farmers’ decisions to adopt e-commerce.

5.2. Relationship Heterogeneity

The relationships among ICT infrastructure, virtual agglomeration, and urban–rural integration change in various areas. The heterogeneity is related to the underlying conditions (reflected in measurement variables) in different regions. Meanwhile, for measurement variables, the contributions of particular infrastructures—internet (INT), mobile phone (MOB), road (ROA), and high school education (EDU)—are different in the whole-sample results and in the western and eastern areas. These results are discussed together in this section.
In the western area, the ICT infrastructure promotes urban–rural integration not only directly (Hypothesis 1) but also indirectly through facilitating virtual agglomeration (Hypothesis 2). Meanwhile, the other necessary complementary infrastructures can expand the scale of ICT-based virtual agglomeration (Hypothesis 3). However, in the eastern area, ICT infrastructure promotes urban–rural integration that is fully mediated by virtual agglomeration (only Hypothesis 2 has been tested; Hypotheses 1 and 3 have not been tested). The virtual agglomeration has a full mediating effect. According to the descriptive statistics of two sub-samples shown in Table 2, in the construct of virtual agglomeration, the e-commerce or rural e-commerce in eastern regions is far more developed than that in western regions. For example, the mean of rural e-commerce development in eastern regions is 296% greater than that in western regions. Therefore, there is a possibility that if ICT has formed a large enough virtual industrial cluster, then virtual agglomeration may become the main path for ICT infrastructure to promote urban–rural integration. This result verifies the key role of virtual agglomeration in balanced regional development.
In the construct of ICT infrastructure, the internet makes the greatest contribution in the whole sample (the weight of internet is 0.844, greater than the 0.186 of mobile phones). In the eastern area, this trend remains. However, in the western area, mobile phones make the greatest contribution (the weight of mobile phones is 0.527, greater than the 0.521 of mobile phones). This discrepancy may be due to the fact that the development stage in the western regions lags behind that of the eastern regions, with internet adoption occurring later and at lower penetration levels [35]. As a result, the impact of the internet has not yet been fully realized in these regions [54,62]. According to the descriptive statistics of two sub-samples shown in Table 2, the mean of internet penetration in eastern regions is 15% greater than that in western regions. However, the mean of mobile phone penetration in eastern regions is only 1.75% greater than that in western regions, which is not a considerable gap. Therefore, only the weight of internet has changed significantly.
Meanwhile, in the construct of complementary infrastructure, the education makes the greatest contribution in the whole sample (the weight of education is 0.666, greater than the 0.507 of transport). Education ensures that people possess the necessary skills to use ICT for connectivity and trade [50]. However, in the western area (in the eastern area, the weights of education and transport are insignificant), transport makes the greatest contribution (the weight of transport is 0.713, greater than the 0.570 of education). This result may be due to the fact that western China has higher elevations, vast grasslands, deserts, and more complex terrain [70]. As western regions are generally poorly accessible, the transport infrastructure is particularly important in these regions [6]. Without a sound transport, even if these regions can join virtual industrial clusters, they are still not able to participate substantively in trades, as goods cannot flow around them [38,55].

5.3. Theoretical and Practical Implications

This study contributes to the research on ICT’s effects and spatial agglomeration. This study not only verifies the positive effects of ICT on urban–rural integration but also provides a new underlying mechanism from the perspective of virtual agglomeration. Particularly, the virtual agglomeration is a mediating factor that has received less attention in existing studies [19,20,22,23,24,25,26,27]. Regarding the virtual agglomeration, few studies have focused on the expansion of the content of “space” in the context of ICT applications. This study explores this typical phenomenon in the context of virtual geographies and, by analyzing its relationship with urban–rural integration, reveals its complementary significance to traditional geographical agglomeration (cities). Meanwhile, the concept of virtual agglomeration also provides a viable option for the unified conceptualization of a range of phenomena—such as e-commerce, digital platform economy, virtual marketplaces—that have become increasingly prevalent [8,25,33,35,40,41,42].
Furthermore, this study also has practical significance in policy-making. Reducing the inequality among regions has been advocated in SDGs delivered by United Nations [37]. This study offers practical implications for addressing this global challenge of the urban–rural divide. In particular, first, it is crucial to accelerate the full coverage of ICT infrastructure—especially in remote rural areas which are information poor [27], to ensure that the people in different regions can connect with each other and form virtual industrial clusters in digital space. This will reduce the centralization of economic activity and promote balanced development across urban and rural areas [51]. In this process, the natural resources, ecological environments, unique agricultural products, and other characteristics of villages deserve attention and exploration to develop more rural e-commerce clusters in the context of virtual agglomeration, which can bring economic opportunities outside of agriculture to rural residents [12,15]. Second, complementary infrastructures, such as transport and education, must be enhanced to fully realize the potential effects of ICT and ICT-based virtual agglomeration [54]. Furthermore, for rural areas, in addition to formal campus education, lectures and internet skills training given by experienced e-commerce leaders are also useful educational measures that can drive more people to engage in e-commerce and benefit from virtual agglomeration [12]. Lastly, when prioritizing this infrastructure development, regional characteristics should be considered, including development stages and natural conditions. For example, in western China, where the terrain is more complex, transport infrastructure is particularly important [70]. Therefore, policy-making should focus on inclusiveness. For regions that are at a significant disadvantage (such as western China, with its complex terrain), investment should be allocated preferentially to address prominent gaps, so as to enable relatively balanced development among different regions.

6. Conclusions

ICT-based virtual agglomeration is an agglomeration phenomenon in the network space. Virtual agglomeration allows even geographically dispersed market participants to form industrial clusters within a network space. It complements traditional geographical agglomeration and cities, offering a possible method for urban–rural integration. This study empirically analyzed the relationships among ICT infrastructure, virtual agglomeration, and urban–rural integration using the data obtained from China’s 31 provincial-level regions from 2012 to 2022. The results indicated that, (1) ICT infrastructure can promote urban–rural integration. (2) Virtual agglomeration plays a significant mediating role in the relationship between ICT infrastructure and urban–rural integration. In relatively developed eastern China, virtual agglomeration fully mediates the impact of ICT infrastructure on urban–rural integration. (3) Other complementary infrastructures—including transport and education—have positive moderating effects on the process of virtual agglomeration facilitated by ICT. These findings advance the understanding of ICT’s effects on regional development from the perspective of employing a new form of spatial agglomeration (i.e., virtual agglomeration). Meanwhile, these findings also have practical significance in policy-making. It is necessary to strengthen the development of ICT infrastructure and complementary infrastructure (such as transportation or education) in remote rural areas, so as can enable the expansion of rural e-commerce or other digital economic patterns to form virtual agglomeration.
It was found through the robustness test that COVID-19 boosted the demand for digital industries, and the roles of ICT and virtual agglomeration may have expanded since 2020. However, at the time of writing, COVID-19 had emerged a relatively short time ago. In the future, this study will continue to track data and explore whether the importance of virtual agglomeration will grow in the post-COVID-19 era. Furthermore, this study primarily focuses on whether rural areas can narrow the development gap in comparison to urban areas. However, a digital divide also exists among people within rural communities. Individuals of different genders and ages exhibit differences in their ability to use ICT, which may prevent them from equally accessing the benefits of technology. In the future, this study will focus on individual differences within rural communities as well as digital inclusion.

Author Contributions

Conceptualization, L.Z., J.Y. and B.L.; methodology, L.Z.; software, L.Z.; validation, J.Y., B.Z. and B.L.; formal analysis, J.Y., B.Z. and B.L.; investigation, L.Z.; resources, L.Z.; data curation, Q.A.; writing—original draft preparation, L.Z.; writing—review and editing, J.Y. and B.Z.; visualization, Q.A.; supervision, J.Y.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 72072031 and 72134002; and the Qinglan Project of Jiangsu Province of China.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The formation and function of virtual agglomeration.
Figure 1. The formation and function of virtual agglomeration.
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Figure 2. The three main research phases.
Figure 2. The three main research phases.
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Figure 3. The estimated model for testing Hypothesis 1~Hypothesis 3.
Figure 3. The estimated model for testing Hypothesis 1~Hypothesis 3.
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Figure 4. Baseline results. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Figure 4. Baseline results. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
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Figure 5. Results of western area. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Figure 5. Results of western area. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
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Figure 6. Results of eastern area. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Figure 6. Results of eastern area. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
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Table 1. Measurement variables.
Table 1. Measurement variables.
ConstructsMeasurement VariablesObs.MeanS.D.References
ICT infrastructureInternet penetrationMeasured by the proportion of internet subscribers in total population.INT3100.440.08Acheampong, Opoku [63]
Mobile phone penetrationMeasured by the number of mobile phones owned per 100 people.MOB3104.610.22Haftu [54]
Complementary infrastructureTransport infrastructure: Road densityMeasured by dividing the length of roads (in kilometers) by the land area (in square kilometers).ROA3100.620.29Wu, Wang [51]
Education infrastructure: High school education penetrationMeasured by the proportion of people with high school education or higher in total population. In China, computer courses have been included in high school education since 1980s. Therefore, regions with higher high school education penetration indicate a greater proportion of people with the skills necessary to use ICT.EDU3100.270.07Yin and Choi [17]
Virtual agglomerationDevelopment of e-commerceMeasured by the proportion of online sales in GDP.DEC3100.120.11Li, Zeng [12]
Development of rural e-commerceMeasured by the number of Taobao villages per 10,000 village administrative units. Taobao (www.taobao.com) is the leading e-commerce platform in China, and Taobao villages are clusters of rural e-retailers, where at least 10% of rural households engage in e-commerce or at least 100 online shops operate in the village. The data of Taobao Villages is compiled and released by the Ali Research Institute (www.aliresearch.com), which is a specialized research institute under the operator of the Taobao platform. This data has been cited not only by many governments, including the Fujian Provincial Department of Commerce (swt.fujian.gov.cn), but also by numerous studies—such as Liu and Zhou [36], Lin and Li [59], Lin [57], and Wei, Lin [28]—that use this data to explore the development status of rural e-commerce. In terms of the dualistic urban–rural structure, if a region shifts from urban-centered geographical agglomeration to virtual agglomeration, then more villages should begin to join the virtual industrial clusters.REC3101.882.06Liu and Zhou [36], Lin and Li [59], Lin [57], Wei, Lin [28]
Scale of expressMeasured by per capita express delivery volume (in pieces). Trades in virtual industrial clusters still rely on offline logistics; thus, the scale of express delivery can also reflect the development level of virtual agglomeration.EXP3102.651.21Yin and Choi [17]
Urban–rural integrationIncome gapMeasured by per capita disposable income, and is expressed as the ratio of rural to urban residents.ING3100.340.04Zeng and Chen [68], Ma, Liu [69]
Consumption gapMeasured by per capita consumption expenditure, and is expressed as the ratio of rural to urban residents.COG3100.400.05Zeng and Chen [68], Ma, Liu [69]
The proportion of non-agricultural employed populationMeasured by the proportion of workers employed in the secondary and tertiary sectors relative to the total employed population.NAG3100.530.08Zhang [25]
Table 2. Descriptive statistics of the two sub-samples.
Table 2. Descriptive statistics of the two sub-samples.
ConstructsMeasurement VariablesMean
Western RegionsEastern Regions
ICT infrastructureINT0.400.46
MOB4.564.64
Complementary infrastructureROA0.420.76
EDU0.230.29
Virtual agglomerationDEC0.090.15
REC0.672.65
EXP1.883.14
Urban–rural integrationING0.300.36
COG0.380.41
NAG0.470.57
Western Regions: Gansu, Guangxi, Guizhou, Nei Mongol, Ningxia, Qinghai, Shaanxi, Sichuan, Xizang, Xinjiang, Yunnan, Chongqing. Eastern Regions: Anhui, Beijing, Fujian, Guangdong, Hainan, Hebei, Henan, Heilongjiang, Hubei, Hunan, Jilin, Jiangsu, Jiangxi, Liaoning, Shandong, Shanxi, Shanghai, Tianjin, Zhejiang.
Table 3. Check list and criteria for model estimation results.
Table 3. Check list and criteria for model estimation results.
Check ListCriteria
formative measurement modelvariable VIF: ensure that there are no multicollinearity issuesless than 3
variable statistical significancethe confidence interval does not include zero
reflective measurement modelvariable loadinggreater than 0.708
Cronbach’s alpha, rho_A, and CR: ensure internal consistency reliabilitygreater than 0.7
AVE: ensure convergent validitygreater than 0.5
structural modelpath VIF: ensure that there are no multicollinearity issuesless than 3
path statistical significancethe confidence interval does not include zero
R2: ensure the predictive power of the modelgreater than 0.35
Table 4. The results of Pearson correlation tests. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Table 4. The results of Pearson correlation tests. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
DECRECEXPINGCOGNAG
INT0.595 ***0.636 ***0.808 ***0.469 ***0.455 ***0.707 ***
MOB0.660 ***0.532 ***0.720 ***0.293 ***0.258 ***0.645 ***
ROA0.508 ***0.492 ***0.616 ***0.424 ***0.339 ***0.586 ***
EDU0.744 ***0.380 ***0.644 ***0.460 ***0.310 ***0.665 ***
Table 5. The results of formative measurement models. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Table 5. The results of formative measurement models. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
ConstructsMeasurement VariablesWeightsVIFSignificanceConfidence Intervals
ICT infrastructureINT0.8442.843***0.696~0.979
MOB0.1862.843**0.026~0.350
Complementary infrastructureROA0.5071.243***0.428~0.593
EDU0.6661.243***0.585~0.734
Table 6. The results of reflective measurement models.
Table 6. The results of reflective measurement models.
ConstructsMeasurement VariablesLoadingsCronbach’s Alpharho_ACRAVE
Virtual agglomerationDEC0.7670.8340.8690.9020.756
REC0.869
EXP0.961
Urban–rural integrationING0.8860.8010.8640.8790.708
COG0.760
NAG0.873
Table 7. The results of the structural model. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Table 7. The results of the structural model. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Hypotheses and PathsPath CoefficientsVIFSignificanceConfidence Intervals
H1ICT infrastructure→Urban–rural integration0.1402.683**0.010~0.264
H2ICT infrastructure→Virtual agglomeration0.5271.549***0.476~0.579
Virtual agglomeration→Urban–rural integration0.6562.683***0.543~0.770
mediating effect
ICT infrastructure→Virtual agglomeration→Urban–rural integration		0.346/***0.284~0.412
H3moderating effect
Complementary infrastructure→(ICT infrastructure→Virtual agglomeration)		0.1141.135***0.062~0.161
R2. Virtual agglomeration: 0.775. Urban–rural integration: 0.597.
Table 8. The results of the robustness test. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Table 8. The results of the robustness test. *** indicates significance (1% level); ** indicates significance (5% level); * indicates significance (10% level).
Hypotheses and PathsBaseline Results (2013–2022)Pre-COVID-19 Period Results (2013–2020) Post-COVID-19 Period Results (2020–2022)
H1ICT infrastructure→Urban–rural integration0.140 **0.179 *−0.069
H2ICT infrastructure→Virtual agglomeration0.527 ***0.546 ***0.345 ***
Virtual agglomeration→Urban–rural integration0.656 ***0.591 ***0.871 ***
mediating effect
ICT infrastructure→Virtual agglomeration→Urban–rural integration		0.346 ***0.323 ***0.300 ***
H3moderating effect
Complementary infrastructure→(ICT infrastructure→Virtual agglomeration)		0.114 ***0.101 ***−0.012
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Zhang, L.; Yuan, J.; Zhu, B.; Liu, B.; Ai, Q. The Mediating Role of Virtual Agglomeration in How ICT Infrastructure Drives Urban–Rural Integration: Evidence from China. Land 2025, 14, 2032. https://doi.org/10.3390/land14102032

AMA Style

Zhang L, Yuan J, Zhu B, Liu B, Ai Q. The Mediating Role of Virtual Agglomeration in How ICT Infrastructure Drives Urban–Rural Integration: Evidence from China. Land. 2025; 14(10):2032. https://doi.org/10.3390/land14102032

Chicago/Turabian Style

Zhang, Lei, Jingfeng Yuan, Bing Zhu, Bingsheng Liu, and Qiqi Ai. 2025. "The Mediating Role of Virtual Agglomeration in How ICT Infrastructure Drives Urban–Rural Integration: Evidence from China" Land 14, no. 10: 2032. https://doi.org/10.3390/land14102032

APA Style

Zhang, L., Yuan, J., Zhu, B., Liu, B., & Ai, Q. (2025). The Mediating Role of Virtual Agglomeration in How ICT Infrastructure Drives Urban–Rural Integration: Evidence from China. Land, 14(10), 2032. https://doi.org/10.3390/land14102032

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