Evolution of organizational network structure and multidimensional proximity explanation of leading manufacturing enterprises in China

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Based on headquarters–branch data of China’s Top 500 Manufacturing Enterprises for the years 2005, 2010, 2015, and 2020, this study maps and analyzes the organizational network structure of China’s leading manufacturing enterprises (CLMEs) in China and further explores the driving factors behind this structure from the perspective of multidimensional proximity. The major findings are: (1) The degree centrality of nodes within the organizational network of CLMEs has steadily increased, showing a clear evolutionary trend from a single-core structure dominated by Beijing toward a polycentric configuration characterized by “one core, two sub-centers, and multiple support points.” (2) The network structure of CLMEs has formed a diamond-shaped manufacturing configuration, with the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions as the four vertices, and the middle reaches of the Yangtze River as the central support. (3) Institutional proximity and social proximity emerge as pivotal factors influencing the formation of the organizational network among CLMEs. With improvements in transportation, information technologies, and adjustments to national policy, the influence of geographical and cognitive proximity has declined. (4) The effects of multidimensional proximity factors on various technology-oriented manufacturing sectors vary: cognitive proximity is a significant driver for the formation of networks among medium- and high-technology enterprises, but its impact on low-technology firms is limited. In contrast, the positive influence of social proximity on low-technology manufacturing networks has shown a clear upward trend. Social science/Development studies Social science/Economics Social science/Geography Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Since the Industrial Revolution, manufacturing has emerged as a critical engine driving regional economic development, playing a pivotal role in advancing national economic modernization and restructuring spatial configurations (Liu et al., 2020). Following China's reform and opening-up policies, the nation's manufacturing sector has sustained decades of rapid expansion, substantially contributing to China's ascension as the world's second-largest economy (Yang, 2022). During this transformative process, the spatial distribution of China's manufacturing has undergone profound realignments, with multi-scale spatial restructuring becoming particularly pronounced after China's accession to the World Trade Organization (WTO) (Lu and Tao, 2009; He and Wang, 2012). Currently, despite ongoing industrial upgrading and transformation, manufacturing retains its fundamental role in regional economic development. Under the dual pressures of domestic demands for high-quality economic growth and uncertainties in international economic cooperation, understanding the evolutionary patterns of China's manufacturing spatial configuration has become imperative for formulating effective industrial development strategies. The spatial restructuring of manufacturing, shaped by the interplay between industrial characteristics and regional attributes, primarily manifests through dynamic shifts in agglomeration levels across geographical scales (Li and He, 2013). The spatial characteristics of the global manufacturing present a U-shaped evolution process of agglomeration-dispersion-agglomeration in the three stages of import substitution, trade liberalization, trade opening and economic liberalization (Jordaan and Garduño, 2024). Within global economic systems, manufacturing agglomeration has been characterized by sequential relocation from developed to developing nations and diffusion from technology-leading countries to other countries (Mcdaniel, 2018). At the local level, suburbanization development has become the mainstream trend of spatial evolution in the manufacturing since the mid-20th century (Lee, 1981), driven by labor costs, transportation accessibility, and government interventions (Sridhar and Wan, 2010; Nakamura, 1985). The industrial transfer and agglomeration caused by this redistribution of enterprise space have reshaped the spatial division of labor in various types of manufacturing. Generally speaking, labor-intensive manufacturing are located in the peripheral areas of cities, while capital and technology-intensive manufacturing are often concentrated in the central areas—a typical exemplified by the U.S. experience of manufacturing relocation from urban cores to suburbs and exurbs (Han et al., 2022; Park, 2023). However, over time, central cities have once again gained favor, and some industrial enterprises are returning to urban areas, giving rise to the phenomenon of "manufacturing re-urbanization" (Park, 2023, Bonello et al., 2022). China's manufacturing landscape demonstrated continuous spatial concentration in the Yangtze River Delta, Pearl River Delta, and Shandong Peninsula regions until 2004, marked by intensifying agglomeration levels (Li and He, 2017). The advent of the Lewis turning point triggered rising the comprehensive operating costs of enterprises and the wage level of the labor, prompting post-2004 manufacturing relocation to the Bohai Rim region and central-western regions (Zhang et al., 2016; Qu et al., 2013). Among them, export-oriented manufacturing enterprises are the main body moving to the Bohai Rim region, while enterprises that meet domestic market demands mainly shift to the central and western regions (Li and He, 2017). At the urban scale, the pronounced suburbanization agglomeration effects of manufacturing space has fostered the gradual formation of polycentric manufacturing clusters (Zheng and Luo, 2020; Ma et al., 2023). Under the dual forces of informatization and economic globalization, the increasing mobility of human capital, products, and services has progressively dissolved traditional boundaries between nations and regions, establishing intricate networks of factor flows (Zhu et al., 2019). As critical carriers of cross-regional element circulation, enterprises maintain mutually complementary and collaborative economic relationships (Sheng et al., 2019). Numerous large corporations strategically allocate distinct segments and zones of their product value chains across different cities, integrating these distributed operations through advanced transportation and communication technologies (Sheng et al., 2019). This spatial reorganization enables inter-firm connections to transcend geographical constraints, facilitating the fundamental formation of enterprise networks. Against the backdrop of enhanced mobility and intensified interregional economic cooperation, network structure analysis has emerged as a vital research direction in manufacturing spatial configuration studies. At the micro scale, scholars systematically investigate localized manufacturing collaboration networks through lenses of green development, industry-university innovation, and scientific-technological advancement. These studies scientifically identify structural characteristics and driving mechanisms of cooperative networks among local organizations, institutions, and individuals within specific geographical contexts, providing strategic insights for promoting regional spatial restructuring and coordinated economic development (Liu et al., 2024; Han et al., 2018; Li and Wei, 2019). On meso-macro levels, research primarily examines China's manufacturing network structures through multi-scalar analyses of corporate production patterns, investment flows, innovation systems, and headquarters-branch configurations (Han et al., 2018; Qiao et al., 2023; Sun and Liu, 2023). Influenced by globalization, marketization, and economic liberalization, the network from the perspective of China's manufacturing enterprises presents increasingly complex core-periphery characteristics (Ye et al., 2021). At the global level, scholars generally construct global trade network connections to map the form of the global production network (Fang et al., 2019). China's deepening integration into global production networks through globalization has partially stimulated technological advancement and innovation capacity in domestic manufacturing sectors. However, this integration simultaneously generates adverse effects on late-developing regions (Xiao et al., 2017). Amidst heightened uncertainties in global economic development, the manufacturing sector remains a pivotal driver for advancing high-quality growth in China's real economy. Current scholarly investigations predominantly focus on how attribute factors such as socioeconomic shape the spatial restructuring of manufacturing, while relatively limited attention has been given to the evolutionary impacts of multidimensional proximity factors on relational networks among manufacturing enterprises. Furthermore, existing studies predominantly emphasize aggregate regression analyses, overlooking critical heterogeneity across different technology-intensive manufacturing sectors. In light of this, this study systematically maps organizational networks of CLMEs in 2005, 2010, 2015, and 2020 and employs stochastic exponential random graph models to decode the formation mechanisms of network relationships. The research mainly answers the following questions: ① What characteristics does the organizational network structure of CLMEs exhibit during the transformation from local space to flow space? ② How do multidimensional proximity factors influence the evolution of CLMEs organizational networks? The research aims to provide references for establishing and improving the organizational network system of manufacturing enterprises, while offering practical guidance for the healthy development of manufacturing enterprises in network-oriented development environments. Research methods and data Research methods Network construction method. From the perspective of intra-firm relationships, this study employs an affiliation linkage model to identify headquarters-branch connections within manufacturing enterprises, constructs a geographical association data matrix, and establishes relational networks between cities where corporate headquarters and branches are located (Li and Xiao, 2021 ). The formulas are as follows: $$\:{R}_{ij}={T}_{ij}+{T}_{ji}\:\left(i\ne\:j\right),(i=\text{1,2},3\dots\:,n)$$ 1 where R ij represents the interrelationship synthesis between City i and City j , reflecting the connection strength of the manufacturing enterprise organizational network between the two cities. T ij ( T ji ) refers to the number of branches established in City j ( i ) where the headquarters is located. Social network analysis. Social network analysis aims to explore the spatial structure and attribute characteristics exhibited by relationship networks between different regions from a structural perspective. In this paper, we use this method to explore the spatial structural characteristics of the contact networks of CLMEs, which mainly include network node degree centrality, network density, network association strength, and average path length. Exponential random graph models. Exponential Random Graph Model (ERGM) is a statistical modeling method specifically for relational data, which can effectively reveal the causes and mechanisms of network relationship formation (Chong and Pan, 2020 ). The dependent variable P θ ( Y = y ) of ERGM is the probability of the actual observed network y appearing in the set of networks Y that may be formed. The explanatory variables in this paper include three categories: endogenous structural variables, network exogenous covariates and attribute variables. The formulas are as follows: where Y is the data set of all possible networks formed by N nodes, and y is the observed network; K is a normalization constant used to ensure that the probability is kept between 0 and 1; θ α , θ β , and θ γ denote the parameters corresponding to their respective statistical variables, respectively. As shown in Table 1 , E is the network structure endogenous variable, because this paper focuses on the influence of multidimensional proximity on the evolution of the manufacturing network structure, so it only incorporates the edge effect as a structural endogenous variable; X is the network node attribute variable, which is represented by the city scale, openness level, and the governmental policy indexes that this paper chooses to represent; C is the exogenous network covariate, which this paper characterizes by using the multidimensional proximity. The specific calculations are as follows: Scholars in evolutionary economic geography, represented by Boschma ( 2005 ), have elaborated a multidimensional proximity framework from both individual and regional perspectives, which primarily encompasses five dimensions: geographical proximity, institutional proximity, organizational proximity, social proximity, and cognitive proximity (Zhou et al., 2021 ). Geographical Proximity ( Geo ). Geographical proximity reflects the spatial distance between urban entities (Boschma, 2005 ). In this study, a spatial weight matrix is constructed based on the geographical distances between city nodes to represent geographical proximity (Chen et al., 2018). Institutional Proximity ( Inst ). Institutional proximity indicates the degree of similarity among members within the network in terms of institutional context (He et al., 2017 ). A similar institutional environment can reduce uncertainties in collaboration and facilitate the establishment of inter-firm connections. In this study, institutional proximity between regions is measured based on whether cities are located within the same province: if city i and city j belong to the same province, it implies that the two cities are subject to similar administrative forces and thus have a high level of institutional proximity, which is assigned a value of "1"; otherwise, it is assigned a value of "0". Organizational Proximity ( Orz ). Organizational proximity reflects the extent to which actors within or across organizations share common organizational arrangements (Boschma, 2005 ). Drawing on the studies of Guan et al. ( 2023 ) and Zhang and Qian ( 2021 ), this paper measures organizational proximity based on the affiliation relationships between firms located in different cities. The formulas are as follows: $$\:{\text{}\text{O}}_{\text{ij}}\text{=}\sum\:_{\text{a}\text{=1}}^{\text{m}}\sum\:_{\text{b}\text{=1}}^{\text{n}}{\text{R}}_{\text{ab}}\text{}\text{(}\text{3}\text{)}$$ Where O ij denotes the organizational proximity between cities i and j ; a and b represent manufacturing enterprises located in cities i and j , respectively; m and n are the numbers of manufacturing enterprises in cities i and j ; and R ab is a dummy variable indicating the organizational relationship between firms a and b . Following prior research (Zhang and Qian, 2021 ), if there is an ownership relationship between a and b , R ab =1; if a and b belong to the same parent company but without direct ownership ties, R ab =0.5; otherwise, if no affiliation exists, R ab =0. Social Proximity ( Soc ). Social proximity refers to the cultural, social relational, and social background similarities among innovation actors. High levels of social proximity promote the formation of trust-based relationships, which in turn facilitate the exchange and diffusion of tacit knowledge and increase the likelihood of industrial interactions (He and Yu, 2022). Following the method proposed by Liu et al. ( 2018 ), this paper adopts a Jaccard index based on the relative intensity of collaboration between actors to measure social proximity. The formula is as follows: $$\:{\text{}\text{S}}_{\text{ij}}\text{=}\frac{{\text{I}}_{\text{ij}}}{{\text{Cs}}_{\text{i}}\text{+}{\text{Cs}}_{\text{j}}\text{+}{\text{I}}_{\text{ij}}}\text{}\text{(}\text{4}\text{)}$$ where S ij denotes the social proximity between cities i and j ; C si and C sj represent the out-degree and in-degree of city nodes i and j , respectively; and I ij indicates the degree value of urban nodes. Cognitive Proximity ( Cog ). Cognitive proximity refers to the degree to which actors share similar knowledge or technological backgrounds, which enhances the potential for knowledge spillovers and innovation connections. Drawing on the work of Hu et al. (2024), this paper uses the number of intercity patent collaborations as a proxy for cognitive proximity. Specifically, the procedure begins with filtering patent applications involving more than one applicant during the study period. The city of each applicant is identified through databases such as Tianyancha, Qichacha, Baidu Baike, and Patentstar. Data entries that cannot be matched to specific cities are excluded. Finally, if the number of patent collaborations between two cities exceeds the average value across all city pairs, the pair is assigned a value of 1; otherwise, it is assigned a value of 0. Table 1 Influence indicator system for the evolution of the spatial structure of leading manufacturing enterprises in China Variable types Variable names Variable Interpretation calculation basis endogenous structural variable side effect( Edges ) Baseline effect of network formation, represented by the intercept term in the model edge exogenous network covariate Geographical Proximity( Geo ) Whether cities in closer geographic proximity are more likely to establish inter-enterprises connections See above for details Institutional Proximity( Inst ) Whether cities with similar institutional environments are more likely to establish inter-enterprises connections See above for details Organizational Proximity( Orz ) Whether cities with higher organizational proximity are more likely to establish inter-enterprises connections See above for details Social Proximity( Soc ) Whether cities sharing similar cultural and social backgrounds are more likely to establish inter-enterprises connections See above for details Cognitive Proximity( Cog ) Whether cities with similar knowledge or technological backgrounds are more likely to establish inter-enterprises connections See above for details attribute variable city scale( Urb ) Whether larger cities are more likely to establish inter-enterprises connections Number of resident urban population openness level( Open ) Whether cities with higher levels of openness are more likely to establish inter-enterprises connections Total foreign trade imports and exports state policy( Pol ) Whether national policy support facilitates the formation of inter-enterprises connections between cities Government expenditure Data sources and processing This study collects data on the headquarters of the “Top 500 China's Manufacturing Enterprises” for the years 2005, 2010, 2015, and 2020, as published by the China Enterprise Confederation and the China Enterprise Directors Association. Cross-validation and supplementation of enterprise information were conducted using multiple business data platforms, including Qichacha ( https://www.qcc.com/ ), Tianyancha ( https://www.tianyancha.com/ ), official company websites, and the national enterprise credit information publicity system ( http://www.gsxt.gov.cn/index.html ). Supplementary information retrieved includes the date of establishment, operational status, registered location, industrial classification, and the presence of branch offices. Only enterprises classified under the manufacturing sector were retained for further analysis. Socioeconomic indicators, such as GDP per capita, gross domestic product, and the permanent urban population, were primarily obtained from the China City Statistical Yearbook, the China Statistical Yearbook, relevant municipal statistical yearbooks, and bulletins on national economic and social development corresponding to the selected years. Patent data were sourced from the INCOPAT patent database. Since the “Top 500 China's Manufacturing Enterprises” list has excluded foreign-invested firms—including those from Hong Kong, Macao, and Taiwan—since 2010, enterprises based in these regions, as well as those that had been deregistered, revoked, or ceased operations, were excluded from the dataset. Finally, the geographical coordinates of headquarters and branch offices were obtained by converting the enterprise address information using the Map Location platform ( https://maplocation.sjfkai.com/ ). Organizational network structure of CLMEs Centrality Features of Organizational Network Nodes During the study period, the total degree of network nodes increased from 4,634 to 9,540, indicating a significant enhancement in degree centrality. This reflects the expansion and strengthened connectivity of the organizational network of CLMEs (Fig. 1 ). In 2005, Beijing, due to its political and economic functions, attracted a large number of headquarters of state-owned and ‌Chinese central state-owned enterprises to gather here, thereby securing its absolute core position in the network. As an international financial center and a key coastal gateway, Shanghai benefited from its port and transport infrastructure, providing robust logistical support for manufacturing. Wuhan, leveraging its geographical advantage of being " Thoroughfare to Nine Provinces" and its key role in the Yangtze River Economic Belt, emerged as a manufacturing hub in central China. Together, Shanghai and Wuhan formed dual sub-core cities in the network. From 2005 to 2020, Beijing consistently maintained its dominant centrality, while Shanghai reached parity in degree centrality in 2015 before subsequently declining. The degree centrality of cities within the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and middle reaches of the Yangtze River city clusters increased markedly. High-centrality nodes were mainly distributed along the eastern coast and the Yangtze River corridor, exhibiting a “T-shaped” spatial pattern that reveals an increasingly polycentric structure in organizational network of CLMEs. The out-degree and in-degree centrality respectively reflect a city's radiation and aggregation capabilities within the manufacturing network. During the study period, the standard deviation of both out-degree and in-degree of the manufacturing network nodes exhibited an inverted U-shaped trend. From 2005 to 2015, the rising standard deviation of the out-degree and in-degree reflected a growing concentration of manufacturing enterprises. Between 2015 and 2020, this metric decreased, indicating a more dispersed spatial layout of manufacturing enterprises at both regional and national scales. Visualizing the in-degree and out-degree centrality of cities in 2005 and 2020 (Fig. 2 ), the number of cities reached by manufacturing nodes increased by 20.62%, with a significant rise in the number of high-radiation cities. Beijing, as the political center of the nation, attracted a large concentration of manufacturing headquarters, expanding its radiation scope by 201 cities over the study period. The manufacturing radiation capacity of Shanghai is second only to that of Beijing, but by 2020 its reach amounted to only 23.22% of Beijing’s. Other cities such as Shenzhen, Qingdao, Hangzhou, and Hohhot maintained strong radiation power throughout. Cities such as Shijiazhuang, Wuxi, Ningbo, Huzhou, Jinhua, Rizhao, and Yantai showed rapid growth in out-degree rankings, all entering the top 20 by 2020 after ranking much lower or being unlisted in 2005. In contrast, Huizhou, Panzhihua, Zhuzhou, Changchun, Tianjin, Xiamen, and Jinan experienced marked declines in radiation rankings, falling from the top 20 in 2005 to 35th, 65th, 23rd, 31st, 28th, 36th and 32nd respectively in 2020. Overall, notable disparities exist in manufacturing network nodes radiation capacity, with central and western cities exhibiting weaker performance. Compared to out-degree centrality, in-degree centrality of the manufacturing network nodes showed less regional disparity. The number of cities with aggregation capability increased by 26.88% over the study period. In-degree nodes of China's manufacturing network were mainly located in the Bohai Rim, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu–Chongqing regions. Among them, Shanghai, under the interwoven influence of multiple forces such as its own geographical advantages, policy and institutional empowerment, and global development, simultaneously possesses both high out-degree and high in-degree characteristics, presenting a two-way hub feature. Some central and western cities, such as Wuhan and Chongqing, fell into a predicament of “in-degree unipolarity” but lagging in out-degree—indicating local manufacturing sectors locked in lower segments of the value chain. Wuhan, the largest central city with a solid heavy industry base, and is an important industrial base in the country. It has a strong appeal to the manufacturing, and consistently ranked first in in-degree centrality. However, its industry chain is primarily concentrated on production rather than high-tech R&D, which tends to concentrate in Beijing and Shanghai. Its limited spillover effect to neighboring cities creates a funnel-like “input > output” pattern. In addition, cities such as Tianjin, Nanjing, Chengdu, Shenyang, Harbin, Suzhou, Xi’an, Qingdao, and Shenzhen have consistently ranked among the top 20 in terms of in-degree and are important industrial cities in China. Tangshan and Hefei advanced significantly from 32nd and 21st in 2005 to 8th and 11th in 2020, respectively. The former, benefiting from local steel resources and port advantages, has become a key area for the concentration of heavy industries in Hebei Province, including chemical raw materials, chemical manufacturing, and specialized equipment production. The latter, under the promotion of an industry-oriented city development strategy, has experienced rapid growth in sectors such as new energy vehicles, information technology, biopharmaceuticals, and smart home appliances, leading to a sustained enhancement in its manufacturing agglomeration capacity. Meanwhile, cities such as Dalian, Kunming, Taiyuan, and Zhengzhou saw declining aggregation capabilities, and Beijing, Baoding, and Wuxi showed significant declines in in-degree centrality. Characteristics of organizational network connections Evolution of the organizational network structure of CLMEs. From 2005 (0.017) to 2020 (0.036), the overall network density of CLMEs increased, while the average path length decreased from 3.058 to 2.730. This trend indicates more frequent collaboration among nodes and improved network accessibility and operational efficiency. As shown in Fig. 3 , following the implementation of the Reform and opening-up policy, China adopted an unbalanced regional economic development strategy, which enabled the eastern region to take the lead in both the level and pace of development, particularly in terms of economic output and manufacturing development (Chen and Xu, 2004 ). Due to path dependence shaped by early-stage policy dividends, large market size, and advantages in transport hub functions, the organizational network structure of CLMEs exhibits a dense eastern and sparse western layout dominated by major cities. More specifically, notable regional disparities exist in the density and strength of inter-regional network connections. In the eastern region, the Yangtze River Delta, Pearl River Delta, and Shandong Peninsula displayed dense internal and external network ties—exhibiting a “balanced internal-external” structure. In the central region, the middle Yangtze River city cluster showed similar, albeit weaker, patterns. In contrast, network nodes in the western region are predominantly oriented toward external connections, forming a “strong external–weak internal” configuration. In addition, Beijing, with its political-economic status and global resource integration capacity, always the core node, and under the Beijing–Tianjin–Hebei coordinated development strategy, its ties with Shijiazhuang and Tangshan strengthened. Meanwhile, with enhanced connections in the Pearl River Delta and Chengdu–Chongqing regions, by 2020 a diamond-shaped network pattern had emerged with Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing at the vertices, and the middle Yangtze River region as the central support. This polycentric network structure meets the demand for coordinated interaction among urban agglomerations and enhances the capacity for cross-regional resource integration. Moreover, the resilience embedded in the diamond-shaped network configuration helps to mitigate the risks associated with reliance on a single urban pole and reduces the development risks faced by enterprises (Wall, 2012 ). However, under the constraints of geographical proximity and institutional barriers, the Chengdu-Chongqing region—due to its inland location and historical policy lag—has relatively weak connections with the Pearl River Delta, the middle reaches of the Yangtze River, and the Yangtze River Delta. As a result, the intensity of regional connections on the right side of the diamond-shaped structure is significantly stronger than that on the left. With the deepening of the domestic circulation strategy and the implementation of regional coordination policies, this "strong east, weak west" pattern has been gradually alleviated. Based on connection strength, organizational networks were classified into four tiers: top 0.8%, 0.8–2.5%, 2.5–15%, and 15–40% (Ye et al., 2017 ). The results reveal that organizational network connections among manufacturing enterprises exhibit distinct spatial structures and connection patterns across different hierarchical levels. The top 0.8% of network ties are primarily concentrated among cities such as Beijing, Shanghai, Shenzhen, and Wuhan, where the organizational layout of CLMEs displays a pronounced hierarchical diffusion pattern based on city size, which facilitates the expansion of regional markets. In contrast, lower-level connections are predominantly formed among ordinary prefecture-level cities, functioning as peripheral zones that establish passive attachment through market-driven diffusion. Over time, connections at all levels increased in number, though growth rates first accelerated and then declined. During this process, cities such as Hangzhou, Xian, and Shijiazhuang have experienced upward mobility in the network hierarchy, driven by a combination of technological advancement, policy adoption, and market integration, leading to an increase in their high-level network connections. Network connection characteristics for enterprises of different technology types. Following the Industrial Classification for National Economic Activities (GB/T 4754—2017) and incorporating existing research and OECD technology classification standards, manufacturing enterprises were categorized into low-, medium-, and high-technology industries (Li, 2010 ; Fu et al., 2014 ). Across the study period, the network connection strength for all categories showed a significant upward trend (Fig. 4 ). Low-tech manufacturing networks were dominated by weak ties, with high-intensity connections mainly limited to regions such as Beijing and Shandong. These enterprises are largely constrained by labor and resource endowments, and the expansion of their network connections follows a cost-driven path (He and Wang, 2012 ). At the early stage of the study period, network connections were mainly concentrated in the eastern region, but as labor costs increased, they gradually diffused toward inland areas. Medium-tech manufacturing networks exhibited a hub-and-spoke structure centered on Beijing, Shanghai, Shenzhen, and Wuhan, radiating outward and eventually forming a spatial structure dominated by the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and middle Yangtze River regions. Among these connections, the Shanghai–Wuhan connection represents a core intercity pair. On the one hand, both cities serve as major industrial bases, and their close network relationship facilitates enterprise collaboration through functional specialization and complementary advantages. As key nodes along the Yangtze River Basin, their convenient transportation infrastructure further enhances mutual exchange and cooperation. Shanghai contributes capital and technology, while Wuhan, leveraging its robust heavy industrial foundation, undertakes production responsibilities, thereby forming a “design–manufacturing” vertical division of labor that promotes coordinated development in manufacturing between the two cities. On the other hand, policy initiatives such as the Guidelines on Promoting the Development of the Yangtze River Economic Belt by Leveraging the Golden Waterway have played a significant role in breaking down administrative barriers and strengthening the connection between Shanghai and Wuhan. High-tech manufacturing enterprises, with greater demands for market access, technological infrastructure, talent, and support facilities (Wang, 2014 ), expanded their networks from 338 to 630 connection pairs during the study period. Their location choices became increasingly flexible, with Beijing playing a pivotal role in high-intensity ties. In addition, the diamond-shaped network cluster of high-tech enterprises—anchored by Beijing, the Yangtze River Delta, the Pearl River Delta, and the Chengdu–Chongqing region as its vertices—has become increasingly pronounced. This structure has further extended along the Harbin–Dalian corridor in Northeast China and toward Urumqi in Xinjiang, forming two emerging triangular network connection zones. Empirical results Regression analysis of CLMEs in all categories The analysis of influencing factors in the structural evolution of China’s manufacturing network was conducted using the Statnet and ERGM packages in R. As shown in Table 2 , from Model (1) to Model (4), both the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values progressively decrease with the addition of more explanatory variables. Model (4), which incorporates both endogenous structural variables and exogenous covariates, exhibits the best goodness-of-fit. Therefore, the discussion primarily focuses on the estimation results of Model (4). Among the endogenous structural variables, the coefficients for edge effects are statistically significant in both 2005 and 2020, indicating that network connections in China’s manufacturing sector are not randomly generated. Instead, they reflect a certain degree of interdependence among enterprises, and this pattern remains relatively stable over time. Regarding the attribute variables, the indicators for openness and fiscal support from local governments show significant positive effects, suggesting that vibrant urban development and strong policy support attract CLMEs and their subsidiaries. In contrast, the urban scale indicator has a predominantly negative effect, implying that the expansion of urban areas does not serve as a strong pull factor for CLMEs. Existing studies have also shown that the relationship between urban size and the spatial distribution of manufacturing exhibits a nonlinear pattern, in which the crowding effect associated with urbanization tends to outweigh its positive externalities (Yin and Liu, 2013 ). Particular attention is given to the mechanisms through which multidimensional proximities influence the organizational network of CLMEs. The results indicate that in 2005, geographical proximity had a significantly negative but relatively weak impact on network formation. This aligns with the principle of distance decay in diffusion processes, where greater geographical distance translates into higher costs for transportation and information exchange (Xu and Li, 2020 ). A higher level of geographical proximity can help reduce firms’ physical transportation costs, while face-to-face communication lowers informational barriers and transaction costs between enterprises (He et al., 2017 ), thereby facilitating connections between headquarters and their branch offices. Institutional, organizational, and social proximities are all found to exert significant positive effects on network formation. The spatial distribution of enterprises is not only shaped by market forces but also influenced by national and regional strategic guidance. Institutional proximity refers to the shared norms, rules, and legal frameworks that regulate individual and collective relations and interactions (Edquist and Johnson, 1997 ). A higher degree of institutional proximity facilitates knowledge exchange among firms, thereby promoting the formation of industrial connection pathways (Liu et al., 2018 ). It is therefore evident that a similar institutional environment is conducive to the construction of inter-firm organizational networks. Organizational proximity and social proximity both exhibit strong positive effects. CLMEs often achieve vertical integration by establishing branch offices; firms affiliated with the same parent company tend to distribute their production bases and R&D centers across different cities, while branches under different parent companies within the same city can enhance efficiency through resource sharing. Boschma and Frenken ( 2010 ) has emphasized the "triadic closure" mechanism of social proximity, which helps suppress opportunism and foster trust, thereby improving collaboration efficiency. Moreover, previous studies have shown that social proximity significantly facilitates intercity technology transfer and the cross-regional flow of knowledge (Liu et al., 2018 ; Ter Wal, 2010), which exert a substantial influence on the locational choices of manufacturing activities. Therefore, higher degrees of organizational and social proximity between cities are conducive to the establishment of inter-firm organizational networks in the manufacturing sector. In addition, cognitive proximity shows a positive but statistically insignificant effect. By 2020, geographical proximity continues to exert a significantly negative effect, though its magnitude remains low. Institutional and social proximities maintain significant positive effects. However, the influence of the former has declined over time, while the latter has become increasingly pronounced. This suggests that advancements in transportation and information technologies, along with adjustments in national policy, have reduced institutional and administrative barriers across regions, thereby weakening the spatial influence of institutional proximity. Meanwhile, the rapid development of the digital economy and internet technologies has accelerated the transformation of manufacturing toward more advanced and intelligent forms. While these developments reduce geographical and institutional constraints, they also intensify information overload. As a result, manufacturing competition is gradually shifting from being cost-driven to being shaped by a comprehensive competition in “information-knowledge-response speed.” Social proximity, rooted in cultural affinity and shared social backgrounds, helps lower communication costs and facilitates the flow of tacit knowledge and information, thereby strengthening inter-firm connections. The influence of organizational proximity significantly declines and becomes statistically insignificant. This can be attributed to multiple factors, including market-oriented reforms, transportation improvements, and globalization, all of which have led firms to increasingly rely on external supply chains and strategic alliances. Particularly under the space of flows replacing space of places, the unrestricted movement of production factors across regions has become the mainstream, thus diminishing the impact of organizational proximity on network formation. Cognitive proximity remains positively associated with network structure, but its influence continues to be non-significant and to be weakening. As China’s manufacturing sector enters a stage of complex innovation, the application of digital technologies has enabled firms to establish inter-organizational connections without requiring deep technological overlap or similar knowledge backgrounds. Meanwhile, the growing influence of social proximity—such as shared cultural identity and similar social backgrounds—has diminished the relative importance of cognitive proximity. In summary, the direction and magnitude of the influence of different types of proximity on the organizational structure of manufacturing networks are not static. As previously discussed, under the backdrop of deepening domestic circulation and intensified regional coordination policies, manufacturing network connections have gradually expanded toward inland regions, with traditional barriers—geographic, administrative, and cultural—being progressively dismantled. Table 2 ERGM estimation results for the factors of the organizational network structure of CLMEs in 2005 and 2020 Variable names model(1) model(2) model(3) model(4) 2005 2020 2005 2020 2005 2020 2005 2020 Edges -4.658 *** (0.036) -3.906 *** (0.025) -32.417 *** (0.844) -35.989 *** (1.078) -7.651 *** (0.612) -4.097 *** (0.067) -33.811 *** (1.060) -40.845 *** (2.058) Geo -0.001 *** (0.000) -0.000 *** (0.000) -0.000 *** (0.000) -0.000 *** (0.000) Inst 0.365 * (0.143) -0.382 ** (0.118) 0.717 *** (0.154) 0.450 *** (0.120) Orz 117.716 *** (8.344) 15.449 *** (1.844) 30.250 *** (7.615) -0.064 (1.849) Soc 73.272 *** (3.037) 121.850 *** (3.166) 67.998 *** (2.909) 123.734 *** (3.192) Cog 3.771 *** (0.240) 2.567 *** (0.091) 0.246 (0.262) 0.176 (0.103) Urb -0.127 (0.069) -0.234 *** (0.064) -0.311 *** (0.080) -0.382 *** (0.077) Open 0.200 *** (0.021) 0.290 *** (0.015) 0.150 *** (0.024) 0.225 *** (0.017) Pol 0.885 *** (0.062) 0.840 *** (0.059) 1.070 (0.076) 1.101 *** (0.075) AIC 8801.195 16036.564 6427.985 12386.860 6563.106 11249.198 5026.719 9397.832 BIC 8810.517 16045.887 6465.275 12424.150 6628.363 11305.133 5110.621 9491.056 Log Likelihood -4399.597 -8017.282 -3209.993 -6189.430 -3274.553 -5618.599 -2504.359 -4688.916 Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Regression analysis of CLMEs in different categories To further explore the organizational networks of CLMEs across different technological classifications (Table 3 ), the results reveal a degree of heterogeneity in the effects of multidimensional proximity. These differences are closely tied to the technological characteristics and locational demands of each enterprise type. The effects of geographical, institutional, and social proximity are largely consistent with those observed in the full-sample regression. Specifically, geographical proximity has a stronger effect on enterprises in medium- and low-technology sectors, suggesting that these firms are more spatially constrained and thus more inclined to establish branch offices in close geographic proximity. Institutional proximity has a more pronounced effect on medium-technology manufacturing enterprises, as the formation of connections among such enterprises relies heavily on similar institutional environments. Social proximity shows a strong positive effect across all three technology levels, with its influence on low-technology enterprises increasing most significantly over time. This trend reflects the growing importance of intercity technology transfer and cross-regional knowledge flows for such enterprises. In contrast, the effects of organizational and cognitive proximity diverge more substantially from the full-sample results. Organizational proximity has a negative effect on low-technology enterprises, is statistically insignificant for medium-technology enterprises, and shifts from negative to positive for high-technology enterprises over time. This pattern may stem from the fact that low- and medium-technology enterprises tend to compete based on economies of scale and cost control rather than cross-regional organizational collaboration. As such, a high level of organizational proximity does not necessarily promote network formation among these firms. However, the innovation and development of high-technology manufacturing enterprises are characterized by high risk, long cycles, and a strong need for coordination. In the early stages, these firms must overcome regional constraints to enable cross-regional knowledge recombination, and excessive organizational constraints may hinder exploratory activities that fall outside established consensus. As firms enter a more mature stage, however, inter-organizational collaboration and mutual support facilitate modular division of labor, during which the effect of organizational proximity shifts from negative to positive. Cognitive proximity emerges as a key factor in the formation of organizational networks among medium- and high-technology manufacturing enterprises. Its influence on medium-technology manufacturing enterprises shifts from statistically insignificant to significantly positive over time. Cognitive proximity provides technological and knowledge-based support for inter-firm connections (He and Yu, 2022). Medium- and high-technology enterprises place greater demands on innovation capacity and knowledge accumulation. Regions with high cognitive proximity often share similarities in policy support, talent availability, and industrial chain integration, which enables firms to more effectively absorb external knowledge spillovers, enhance their own innovation capabilities, and ultimately promote inter-firm technological collaboration. In the early stages, medium-technology enterprises generally lagged behind in development, and technological influences were more pronounced among high-technology enterprises. However, by 2020, medium-technology enterprises had experienced rapid growth, and the similarity in technological conditions and knowledge endowments became a key factor in forming organizational connections. Overall, the direction and significance of most variables remain consistent with the full-sample regression results, lending further support to the robustness of the empirical findings. Table 3 ERGM estimation results for the factors of network structures across different technological types of CLMEs Variable names Low-Technology Type Medium-Technology Type High-Technology Type 2005 2020 2005 2020 2005 2020 Edges -27.570 *** (1.193) -22.227 *** (2.252) -37.206 *** (1.863) -43.042 ** (2.137) -38.052 *** (1.503) -49.847 *** (2,067) Geo -0.001 *** (0.000) -0.000 *** (0.000) -0.001 *** (0.000) -0.000 * (0.000) -0.000 *** (0.000) -0.000 (0.000) Inst 1.046 *** (0.247) 0.641 *** (0.171) 1.332 *** (0.245) 0.926 *** (0.192) 1.016 *** (0.216) 0.750 *** (0.176) Orz -0.956 (1.499) -3.955 ** (1.536) -3.253 (1.703) 0.446 (1.861) -4.272 *** (1.590) 3.411 ** (1.726) Soc 12.110 *** (1.580) 41.050 *** (2.341) 22.407 *** (1.924) 42.379 *** (2.413) 21.130 *** (1.858) 41.160 *** (2.337) Cog -0.062 (0.391) -0.307 (0.176) -0.338 (0.340) 0.692 *** (0.166) 0.717 *** (0.259) 0.310 * (0.131) Urb 0.095 *** (0.155) 0.577 *** (0.123) -0.519 *** (0.137) -0.841 *** (0.135) 0.170 (0.129) -0.706 *** (0.113) Open 0.138 *** (0.044) 0.191 *** (0.027) 0.002 (0.040) 0.161 *** (0.032) 0.162 *** (0.037) 0.259 *** (0.028) Pol 0.636 *** (0.141) 0.145 (0.120) 1.403 *** (0.127) 1.377 *** (0.132) 0.963 *** (0.000) 1.450 *** (0.108) AIC 1983.211 4773.302 1900.589 3475.377 2706.569 4561.043 BIC 2067.113 4857.204 1984.491 3559.279 2790.471 4644.945 Log Likelihood -982.606 -2377.651 -941.294 -1728.689 -1344.284 -2271.521 Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Discussion and conclusions Discussion As the backbone of the national economy, manufacturing serves as the foundation of state development, the instrument of national prosperity, and the cornerstone of national strength (Chen and Tang, 2018 ). This study focuses on CLMEs. Unlike studies covering the entire manufacturing sector, which often highlight the dominance of eastern coastal cities such as Zhejiang and Jiangsu in manufacturing networks (Qiao et al., 2023 ), the organizational network structure of CLMEs reveals that Beijing, due to its unique political status and economic strength, consistently holds a position of absolute dominance within medium- and high-level manufacturing networks. The findings suggest that for CLMEs, differences in administrative hierarchy reinforce the nodal status of core cities through preferential resource allocation and policy support, indicating that state power plays a substantial role in shaping network structures. Meanwhile, ongoing market-oriented reforms and the replacement of “space of places” by “space of flows” have accelerated the interregional circulation of production factors, facilitating the emergence of multi-centric organizational networks and reflecting the market mechanism’s capacity to overcome spatial lock-in. Although advancements in transportation and information technology, along with the implementation of national strategies promoting regional coordination, have to some extent reduced spatial and administrative constraints, the organizational network of CLMEs has exhibited a trend toward diversification. Nevertheless, core city nodes remain predominantly concentrated in large-scale cities such as Beijing and Shanghai, and geographic distance as well as administrative barriers continue to impose significant limitations on the development of medium- and low-technology industries. Furthermore, while cognitive proximity significantly facilitates the organizational networking of high-technology enterprises, the convenience of short-term knowledge sharing may inhibit their capacity for long-term differentiated innovation. Hence, a moderate level of proximity should be maintained. This study has several limitations. First, regarding data, the headquarters–branches relationships of CLMEs primarily reflect spatial organizational networks under multi-location strategies. However, this data structure does not adequately capture more complex input–output relationships among non-affiliated firms. Second, reliance on commercial databases such as Tianyancha and Qichacha introduces issues of incomplete or invalid data, which may lead to a degree of deviation between the empirical results and actual conditions. Finally, each city has a unique development trajectory and set of locational endowments. The impact of any single type of proximity on manufacturing development is not static; as the manufacturing sector evolves, the effects of proximity may also change accordingly. In the future, research should further improve data quality and incorporate the developmental stages of China’s manufacturing sector to analyze the mechanisms through which different forms of proximity influence network formation across distinct periods. Such efforts would allow for a more nuanced understanding of how multidimensional proximity factors exert varying effects at different stages, and help uncover the underlying dynamics driving the evolution of organizational network structures. Conclusions This study, drawing on data from China’s top 500 manufacturing enterprises, analyzes the organizational network structure and spatial evolution characteristics of CLMEs. Employing an ERGM, it further investigates the driving forces underlying this structure from a multidimensional proximity perspective. The main findings are as follows: (1) The degree centrality of nodes within the organizational network of CLMEs has steadily increased, exhibiting a trend toward multi-centric development. Spatially, the dominance of Beijing as a single-core hub has been gradually replaced by a more balanced pattern described as “one core, two sub-centers, and multiple support points.” Cities with higher degree centrality are primarily concentrated in the eastern coastal region and the Yangtze River Economic Belt, forming a spatial pattern akin to a “T” shape. (2) Network connectivity among CLMEs has been progressively strengthened, with operational efficiency continuously improving, though significant regional disparities remain. The eastern region demonstrates a dual orientation in its network ties, balancing internal and external connections. The central region follows this trend to a lesser degree, while the western region exhibits a pattern of “weak internal, strong external” connections. A “diamond-shaped” structure has emerged, anchored by the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, Chengdu-Chongqing, and middle reaches of the Yangtze River regions. However, due to constraints such as geographic location, institutional barriers, and path dependency, the left (western) side of the diamond relatively weaker than the right (eastern) side. Moreover, network structures differ by tier: higher-tier networks demonstrate a hierarchy-based diffusion pattern aligned with city scale, whereas lower-tier networks tend to be marginal and exhibit passive attachment driven by market diffusion. (3) The organizational network of CLMEs shows clear differentiation across technological tiers. The network at the low-technology level follows a cost-driven, decentralized spatial layout. At the medium-technology level, the network exhibits a hub-and-spoke pattern centered on core node cities. In contrast, the high-technology level has developed a cross-regional diamond-shaped organizational network structure under the framework of regional coordinated development, which has further extended along the Harbin–Dalian corridor in Northeast China and toward Urumqi in Xinjiang, forming two triangular network connection zones. (4) From a multidimensional proximity perspective, institutional and social proximity are found to significantly promote the construction of organizational networks. With the advancement of transportation and information technologies, as well as adjustments and improvements in national policies, the negative effect of geographic proximity and the positive effect of organizational proximity have both diminished. For enterprises across different technological tiers, cognitive proximity emerges as a key factor in the formation of organizational networks among medium- and high-technology manufacturing enterprises, while the increasing positive impact of social proximity is most pronounced among low-technology enterprises. Additionally, factors such as urban openness and the intensity of policy support also exert significant influence on the formation and evolution of manufacturing networks. Declarations Ethical approval Ethical assessment is not required prior to conducting the research reported in this article, as the present study does not have experiments on human subjects and animals, and does not contain any sensitive and private information. Informed consent This article does not contain any studies with human participants performed by any of the authors. Author Contribution W.W.:Writing–review & editing, Funding acquisition, Conceptualization.C.M.:Writing–original draft, Methodology, Data curation, Visualization, Software.R.L.:Writing–review & editing, Funding acquisition.C.C.:Writing–original draft, Validation, Software, Methodology, Data curation, Visualization, Investigation, Formal analysis. Acknowledgements The authors would like to acknowledge support from the National Natural Science Foundation of China [NO.42471199] and the Natural Science Foundation of Hebei Province of China (NO. D2024205030). Data availability The datasets generated during the current study are available from the corresponding author on reasonable request. References Bonello, V., Faraone, C., Leoncini, R., Nicoletto, L., Pedrini, G. (2022). (Un)making space for manufacturing in the city: The double edge of pro-makers urban policies in Brussels. Cities , 129: 103816. https://doi.org/10. 1016/j.cities.2022.103816 Boschma, R. (2005). 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Acta Geographica Sinica , 74(8): 1525-1533. https://doi.org/10.11821/dlxb201908003 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6884997","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509734886,"identity":"4e219251-87d2-441d-b36e-5ab7e5e8ceb8","order_by":0,"name":"Wengang Wang","email":"","orcid":"","institution":"Hebei Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wengang","middleName":"","lastName":"Wang","suffix":""},{"id":509734887,"identity":"c522af98-2387-456c-95c2-371910c11e0d","order_by":1,"name":"Chuning Miao","email":"","orcid":"","institution":"Wuan Third Middle 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Normal University","correspondingAuthor":true,"prefix":"","firstName":"chengrun","middleName":"","lastName":"cui","suffix":""}],"badges":[],"createdAt":"2025-06-13 05:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6884997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6884997/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90990140,"identity":"6c3d9540-7067-47be-a4a1-7cd056800ecf","added_by":"auto","created_at":"2025-09-10 11:03:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244507,"visible":true,"origin":"","legend":"\u003cp\u003eThe degree centrality of CLMEs\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6884997/v1/87fed95e0db56dd43cd9d6cb.jpg"},{"id":90990883,"identity":"5b9c60d0-f80f-4c02-a871-5eea9084fb2b","added_by":"auto","created_at":"2025-09-10 11:11:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334006,"visible":true,"origin":"","legend":"\u003cp\u003eThe organizational network node out-degree and in-degree for CLMEs\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6884997/v1/6993d6c32b07b494a29b0090.jpg"},{"id":90990145,"identity":"0b86d531-0958-4be5-b46e-9d96ef1fbb65","added_by":"auto","created_at":"2025-09-10 11:03:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":300625,"visible":true,"origin":"","legend":"\u003cp\u003eThe organizational network connection of CLMEs\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6884997/v1/e64c252575521812a952fe83.jpg"},{"id":90990144,"identity":"563869bd-6156-4d12-951c-7fcf56e7ff74","added_by":"auto","created_at":"2025-09-10 11:03:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":481968,"visible":true,"origin":"","legend":"\u003cp\u003eThe organizational network connection of low, medium and high technology CLMEs\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6884997/v1/364966d2c1a10c1714eef59c.jpg"},{"id":99315985,"identity":"9fee41d6-002c-4505-b456-ed22613552e1","added_by":"auto","created_at":"2025-12-31 16:27:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2441119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6884997/v1/806ac529-6d32-423a-8e0f-74d8cd70d175.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evolution of organizational network structure and multidimensional proximity explanation of leading manufacturing enterprises in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the Industrial Revolution, manufacturing has emerged as a critical engine driving regional economic development, playing a pivotal role in advancing national economic modernization and restructuring spatial configurations (Liu et al., 2020). Following China\u0026apos;s reform and opening-up policies, the nation\u0026apos;s manufacturing sector has sustained decades of rapid expansion, substantially contributing to China\u0026apos;s ascension as the world\u0026apos;s second-largest economy (Yang, 2022). During this transformative process, the spatial distribution of China\u0026apos;s manufacturing has undergone profound realignments, with multi-scale spatial restructuring becoming particularly pronounced after China\u0026apos;s accession to the World Trade Organization (WTO) (Lu and Tao, 2009; He and Wang, 2012). Currently, despite ongoing industrial upgrading and transformation, manufacturing retains its fundamental role in regional economic development. Under the dual pressures of domestic demands for high-quality economic growth and uncertainties in international economic cooperation, understanding the evolutionary patterns of China\u0026apos;s manufacturing spatial configuration has become imperative for formulating effective industrial development strategies.\u003c/p\u003e\n\u003cp\u003eThe spatial restructuring of manufacturing, shaped by the interplay between industrial characteristics and regional attributes, primarily manifests through dynamic shifts in agglomeration levels across geographical scales (Li and He, 2013). The spatial characteristics of the global manufacturing present a U-shaped evolution process of agglomeration-dispersion-agglomeration in the three stages of import substitution, trade liberalization, trade opening and economic liberalization (Jordaan and Gardu\u0026ntilde;o, 2024). Within global economic systems, manufacturing agglomeration has been characterized by sequential relocation from developed to developing nations and diffusion from technology-leading countries to other countries (Mcdaniel, 2018). At the local level, suburbanization development has become the mainstream trend of spatial evolution in the manufacturing since the mid-20th century (Lee, 1981), driven by labor costs, transportation accessibility, and government interventions (Sridhar and Wan, 2010; Nakamura, 1985). The industrial transfer and agglomeration caused by this redistribution of enterprise space have reshaped the spatial division of labor in various types of manufacturing. Generally speaking, labor-intensive manufacturing are located in the peripheral areas of cities, while capital and technology-intensive manufacturing are often concentrated in the central areas\u0026mdash;a typical exemplified by the U.S. experience of manufacturing relocation from urban cores to suburbs and exurbs (Han et al., 2022; Park, 2023). However, over time, central cities have once again gained favor, and some industrial enterprises are returning to urban areas, giving rise to the phenomenon of \u0026quot;manufacturing re-urbanization\u0026quot; (Park, 2023, Bonello et al., 2022). China\u0026apos;s manufacturing landscape demonstrated continuous spatial concentration in the Yangtze River Delta, Pearl River Delta, and Shandong Peninsula regions until 2004, marked by intensifying agglomeration levels (Li and He, 2017). The advent of the Lewis turning point triggered rising the comprehensive operating costs of enterprises and the wage level of the labor, prompting post-2004 manufacturing relocation to the Bohai Rim region and central-western regions (Zhang et al., 2016; Qu et al., 2013). Among them, export-oriented manufacturing enterprises are the main body moving to the Bohai Rim region, while enterprises that meet domestic market demands mainly shift to the central and western regions (Li and He, 2017). At the urban scale, the pronounced suburbanization agglomeration effects of manufacturing space has fostered the gradual formation of polycentric manufacturing clusters (Zheng and Luo, 2020; Ma et al., 2023).\u003c/p\u003e\n\u003cp\u003eUnder the dual forces of informatization and economic globalization, the increasing mobility of human capital, products, and services has progressively dissolved traditional boundaries between nations and regions, establishing intricate networks of factor flows (Zhu et al., 2019). As critical carriers of cross-regional element circulation, enterprises maintain mutually complementary and collaborative economic relationships (Sheng et al., 2019). Numerous large corporations strategically allocate distinct segments and zones of their product value chains across different cities, integrating these distributed operations through advanced transportation and communication technologies (Sheng et al., 2019). This spatial reorganization enables inter-firm connections to transcend geographical constraints, facilitating the fundamental formation of enterprise networks. Against the backdrop of enhanced mobility and intensified interregional economic cooperation, network structure analysis has emerged as a vital research direction in manufacturing spatial configuration studies. At the micro scale, scholars systematically investigate localized manufacturing collaboration networks through lenses of green development, industry-university innovation, and scientific-technological advancement. These studies scientifically identify structural characteristics and driving mechanisms of cooperative networks among local organizations, institutions, and individuals within specific geographical contexts, providing strategic insights for promoting regional spatial restructuring and coordinated economic development (Liu et al., 2024; Han et al., 2018; Li and Wei, 2019). On meso-macro levels, research primarily examines China\u0026apos;s manufacturing network structures through multi-scalar analyses of corporate production patterns, investment flows, innovation systems, and headquarters-branch configurations (Han et al., 2018; Qiao et al., 2023; Sun and Liu, 2023). Influenced by globalization, marketization, and economic liberalization, the network from the perspective of China\u0026apos;s manufacturing enterprises presents increasingly complex core-periphery characteristics (Ye et al., 2021). At the global level, scholars generally construct global trade network connections to map the form of the global production network (Fang et al., 2019). China\u0026apos;s deepening integration into global production networks through globalization has partially stimulated technological advancement and innovation capacity in domestic manufacturing sectors. However, this integration simultaneously generates adverse effects on late-developing regions (Xiao et al., 2017).\u003c/p\u003e\n\u003cp\u003eAmidst heightened uncertainties in global economic development, the manufacturing sector remains a pivotal driver for advancing high-quality growth in China\u0026apos;s real economy. Current scholarly investigations predominantly focus on how attribute factors such as socioeconomic shape the spatial restructuring of manufacturing, while relatively limited attention has been given to the evolutionary impacts of multidimensional proximity factors on relational networks among manufacturing enterprises. Furthermore, existing studies predominantly emphasize aggregate regression analyses, overlooking critical heterogeneity across different technology-intensive manufacturing sectors. In light of this, this study systematically maps organizational networks of CLMEs in 2005, 2010, 2015, and 2020 and employs stochastic exponential random graph models to decode the formation mechanisms of network relationships. The research mainly answers the following questions: ① What characteristics does the organizational network structure of CLMEs exhibit during the transformation from local space to flow space? ② How do multidimensional proximity factors influence the evolution of CLMEs organizational networks? The research aims to provide references for establishing and improving the organizational network system of manufacturing enterprises, while offering practical guidance for the healthy development of manufacturing enterprises in network-oriented development environments.\u003c/p\u003e"},{"header":"Research methods and data","content":"\u003ch2\u003eResearch methods\u003c/h2\u003e\u003cp\u003e\u003cb\u003eNetwork construction method.\u003c/b\u003e From the perspective of intra-firm relationships, this study employs an affiliation linkage model to identify headquarters-branch connections within manufacturing enterprises, constructs a geographical association data matrix, and establishes relational networks between cities where corporate headquarters and branches are located (Li and Xiao, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The formulas are as follows:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{R}_{ij}={T}_{ij}+{T}_{ji}\\:\\left(i\\ne\\:j\\right),(i=\\text{1,2},3\\dots\\:,n)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the interrelationship synthesis between City \u003cem\u003ei\u003c/em\u003e and City \u003cem\u003ej\u003c/em\u003e, reflecting the connection strength of the manufacturing enterprise organizational network between the two cities. \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eji\u003c/em\u003e\u003c/sub\u003e) refers to the number of branches established in City \u003cem\u003ej\u003c/em\u003e (\u003cem\u003ei\u003c/em\u003e) where the headquarters is located.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocial network analysis.\u003c/b\u003e Social network analysis aims to explore the spatial structure and attribute characteristics exhibited by relationship networks between different regions from a structural perspective. In this paper, we use this method to explore the spatial structural characteristics of the contact networks of CLMEs, which mainly include network node degree centrality, network density, network association strength, and average path length.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExponential random graph models.\u003c/b\u003e Exponential Random Graph Model (ERGM) is a statistical modeling method specifically for relational data, which can effectively reveal the causes and mechanisms of network relationship formation (Chong and Pan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The dependent variable \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eθ\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003eY\u003c/em\u003e = \u003cem\u003ey\u003c/em\u003e) of ERGM is the probability of the actual observed network y appearing in the set of networks Y that may be formed. The explanatory variables in this paper include three categories: endogenous structural variables, network exogenous covariates and attribute variables. The formulas are as follows:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"505\" height=\"56.5474\" style=\"width: 505px; height: 56.5474px;\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eY\u003c/em\u003e is the data set of all possible networks formed by \u003cem\u003eN\u003c/em\u003e nodes, and y is the observed network; \u003cem\u003eK\u003c/em\u003e is a normalization constant used to ensure that the probability is kept between 0 and 1; \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eα\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eγ\u003c/em\u003e\u003c/sub\u003e denote the parameters corresponding to their respective statistical variables, respectively. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, E is the network structure endogenous variable, because this paper focuses on the influence of multidimensional proximity on the evolution of the manufacturing network structure, so it only incorporates the edge effect as a structural endogenous variable; \u003cem\u003eX\u003c/em\u003e is the network node attribute variable, which is represented by the city scale, openness level, and the governmental policy indexes that this paper chooses to represent; \u003cem\u003eC\u003c/em\u003e is the exogenous network covariate, which this paper characterizes by using the multidimensional proximity. The specific calculations are as follows:\u003c/p\u003e\u003cp\u003eScholars in evolutionary economic geography, represented by Boschma (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), have elaborated a multidimensional proximity framework from both individual and regional perspectives, which primarily encompasses five dimensions: geographical proximity, institutional proximity, organizational proximity, social proximity, and cognitive proximity (Zhou et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGeographical Proximity (\u003cem\u003eGeo\u003c/em\u003e). Geographical proximity reflects the spatial distance between urban entities (Boschma, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In this study, a spatial weight matrix is constructed based on the geographical distances between city nodes to represent geographical proximity (Chen et al., 2018).\u003c/p\u003e\u003cp\u003eInstitutional Proximity (\u003cem\u003eInst\u003c/em\u003e). Institutional proximity indicates the degree of similarity among members within the network in terms of institutional context (He et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A similar institutional environment can reduce uncertainties in collaboration and facilitate the establishment of inter-firm connections. In this study, institutional proximity between regions is measured based on whether cities are located within the same province: if city \u003cem\u003ei\u003c/em\u003e and city \u003cem\u003ej\u003c/em\u003e belong to the same province, it implies that the two cities are subject to similar administrative forces and thus have a high level of institutional proximity, which is assigned a value of \"1\"; otherwise, it is assigned a value of \"0\".\u003c/p\u003e\u003cp\u003eOrganizational Proximity (\u003cem\u003eOrz\u003c/em\u003e). Organizational proximity reflects the extent to which actors within or across organizations share common organizational arrangements (Boschma, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Drawing on the studies of Guan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Zhang and Qian (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this paper measures organizational proximity based on the affiliation relationships between firms located in different cities. The formulas are as follows:\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\text{}\\text{O}}_{\\text{ij}}\\text{=}\\sum\\:_{\\text{a}\\text{=1}}^{\\text{m}}\\sum\\:_{\\text{b}\\text{=1}}^{\\text{n}}{\\text{R}}_{\\text{ab}}\\text{}\\text{(}\\text{3}\\text{)}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e denotes the organizational proximity between cities \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e; \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e represent manufacturing enterprises located in cities \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, respectively; \u003cem\u003em\u003c/em\u003e and \u003cem\u003en\u003c/em\u003e are the numbers of manufacturing enterprises in cities \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e; and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sub\u003e is a dummy variable indicating the organizational relationship between firms \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e. Following prior research (Zhang and Qian, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), if there is an ownership relationship between \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sub\u003e=1; if a and b belong to the same parent company but without direct ownership ties, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sub\u003e=0.5; otherwise, if no affiliation exists, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sub\u003e=0.\u003c/p\u003e\u003cp\u003eSocial Proximity (\u003cem\u003eSoc\u003c/em\u003e). Social proximity refers to the cultural, social relational, and social background similarities among innovation actors. High levels of social proximity promote the formation of trust-based relationships, which in turn facilitate the exchange and diffusion of tacit knowledge and increase the likelihood of industrial interactions (He and Yu, 2022). Following the method proposed by Liu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this paper adopts a Jaccard index based on the relative intensity of collaboration between actors to measure social proximity. The formula is as follows:\u003c/p\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{\\text{}\\text{S}}_{\\text{ij}}\\text{=}\\frac{{\\text{I}}_{\\text{ij}}}{{\\text{Cs}}_{\\text{i}}\\text{+}{\\text{Cs}}_{\\text{j}}\\text{+}{\\text{I}}_{\\text{ij}}}\\text{}\\text{(}\\text{4}\\text{)}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e denotes the social proximity between cities \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e; \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003esi\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003esj\u003c/em\u003e\u003c/sub\u003e represent the out-degree and in-degree of city nodes \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, respectively; and \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e indicates the degree value of urban nodes.\u003c/p\u003e\u003cp\u003eCognitive Proximity (\u003cem\u003eCog\u003c/em\u003e). Cognitive proximity refers to the degree to which actors share similar knowledge or technological backgrounds, which enhances the potential for knowledge spillovers and innovation connections. Drawing on the work of Hu et al. (2024), this paper uses the number of intercity patent collaborations as a proxy for cognitive proximity. Specifically, the procedure begins with filtering patent applications involving more than one applicant during the study period. The city of each applicant is identified through databases such as Tianyancha, Qichacha, Baidu Baike, and Patentstar. Data entries that cannot be matched to specific cities are excluded. Finally, if the number of patent collaborations between two cities exceeds the average value across all city pairs, the pair is assigned a value of 1; otherwise, it is assigned a value of 0.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInfluence indicator system for the evolution of the spatial structure of leading manufacturing enterprises in China\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable names\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariable Interpretation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecalculation basis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eendogenous structural variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eside effect(\u003cem\u003eEdges\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBaseline effect of network formation, represented by the intercept term in the model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eedge\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eexogenous network covariate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeographical Proximity(\u003cem\u003eGeo\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether cities in closer geographic proximity are more likely to establish inter-enterprises connections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSee above for details\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInstitutional Proximity(\u003cem\u003eInst\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether cities with similar institutional environments are more likely to establish inter-enterprises connections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSee above for details\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrganizational Proximity(\u003cem\u003eOrz\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether cities with higher organizational proximity are more likely to establish inter-enterprises connections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSee above for details\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Proximity(\u003cem\u003eSoc\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether cities sharing similar cultural and social backgrounds are more likely to establish inter-enterprises connections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSee above for details\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCognitive Proximity(\u003cem\u003eCog\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether cities with similar knowledge or technological backgrounds are more likely to establish inter-enterprises connections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSee above for details\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eattribute variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecity scale(\u003cem\u003eUrb\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether larger cities are more likely to establish inter-enterprises connections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of resident urban population\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eopenness level(\u003cem\u003eOpen\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether cities with higher levels of openness are more likely to establish inter-enterprises connections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal foreign trade imports and exports\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003estate policy(\u003cem\u003ePol\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether national policy support facilitates the formation of inter-enterprises connections between cities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGovernment expenditure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eData sources and processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study collects data on the headquarters of the “Top 500 China's Manufacturing Enterprises” for the years 2005, 2010, 2015, and 2020, as published by the China Enterprise Confederation and the China Enterprise Directors Association. Cross-validation and supplementation of enterprise information were conducted using multiple business data platforms, including Qichacha (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.qcc.com/\u003c/span\u003e\u003cspan address=\"https://www.qcc.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Tianyancha (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tianyancha.com/\u003c/span\u003e\u003cspan address=\"https://www.tianyancha.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), official company websites, and the national enterprise credit information publicity system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsxt.gov.cn/index.html\u003c/span\u003e\u003cspan address=\"http://www.gsxt.gov.cn/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Supplementary information retrieved includes the date of establishment, operational status, registered location, industrial classification, and the presence of branch offices. Only enterprises classified under the manufacturing sector were retained for further analysis. Socioeconomic indicators, such as GDP per capita, gross domestic product, and the permanent urban population, were primarily obtained from the China City Statistical Yearbook, the China Statistical Yearbook, relevant municipal statistical yearbooks, and bulletins on national economic and social development corresponding to the selected years. Patent data were sourced from the INCOPAT patent database. Since the “Top 500 China's Manufacturing Enterprises” list has excluded foreign-invested firms—including those from Hong Kong, Macao, and Taiwan—since 2010, enterprises based in these regions, as well as those that had been deregistered, revoked, or ceased operations, were excluded from the dataset. Finally, the geographical coordinates of headquarters and branch offices were obtained by converting the enterprise address information using the Map Location platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maplocation.sjfkai.com/\u003c/span\u003e\u003cspan address=\"https://maplocation.sjfkai.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOrganizational network structure of CLMEs\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCentrality Features of Organizational Network Nodes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring the study period, the total degree of network nodes increased from 4,634 to 9,540, indicating a significant enhancement in degree centrality. This reflects the expansion and strengthened connectivity of the organizational network of CLMEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2005, Beijing, due to its political and economic functions, attracted a large number of headquarters of state-owned and ‌Chinese central state-owned enterprises to gather here, thereby securing its absolute core position in the network. As an international financial center and a key coastal gateway, Shanghai benefited from its port and transport infrastructure, providing robust logistical support for manufacturing. Wuhan, leveraging its geographical advantage of being \" Thoroughfare to Nine Provinces\" and its key role in the Yangtze River Economic Belt, emerged as a manufacturing hub in central China. Together, Shanghai and Wuhan formed dual sub-core cities in the network. From 2005 to 2020, Beijing consistently maintained its dominant centrality, while Shanghai reached parity in degree centrality in 2015 before subsequently declining. The degree centrality of cities within the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and middle reaches of the Yangtze River city clusters increased markedly. High-centrality nodes were mainly distributed along the eastern coast and the Yangtze River corridor, exhibiting a “T-shaped” spatial pattern that reveals an increasingly polycentric structure in organizational network of CLMEs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe out-degree and in-degree centrality respectively reflect a city's radiation and aggregation capabilities within the manufacturing network. During the study period, the standard deviation of both out-degree and in-degree of the manufacturing network nodes exhibited an inverted U-shaped trend. From 2005 to 2015, the rising standard deviation of the out-degree and in-degree reflected a growing concentration of manufacturing enterprises. Between 2015 and 2020, this metric decreased, indicating a more dispersed spatial layout of manufacturing enterprises at both regional and national scales. Visualizing the in-degree and out-degree centrality of cities in 2005 and 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the number of cities reached by manufacturing nodes increased by 20.62%, with a significant rise in the number of high-radiation cities. Beijing, as the political center of the nation, attracted a large concentration of manufacturing headquarters, expanding its radiation scope by 201 cities over the study period. The manufacturing radiation capacity of Shanghai is second only to that of Beijing, but by 2020 its reach amounted to only 23.22% of Beijing’s. Other cities such as Shenzhen, Qingdao, Hangzhou, and Hohhot maintained strong radiation power throughout. Cities such as Shijiazhuang, Wuxi, Ningbo, Huzhou, Jinhua, Rizhao, and Yantai showed rapid growth in out-degree rankings, all entering the top 20 by 2020 after ranking much lower or being unlisted in 2005. In contrast, Huizhou, Panzhihua, Zhuzhou, Changchun, Tianjin, Xiamen, and Jinan experienced marked declines in radiation rankings, falling from the top 20 in 2005 to 35th, 65th, 23rd, 31st, 28th, 36th and 32nd respectively in 2020. Overall, notable disparities exist in manufacturing network nodes radiation capacity, with central and western cities exhibiting weaker performance.\u003c/p\u003e\u003cp\u003eCompared to out-degree centrality, in-degree centrality of the manufacturing network nodes showed less regional disparity. The number of cities with aggregation capability increased by 26.88% over the study period. In-degree nodes of China's manufacturing network were mainly located in the Bohai Rim, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu–Chongqing regions. Among them, Shanghai, under the interwoven influence of multiple forces such as its own geographical advantages, policy and institutional empowerment, and global development, simultaneously possesses both high out-degree and high in-degree characteristics, presenting a two-way hub feature. Some central and western cities, such as Wuhan and Chongqing, fell into a predicament of “in-degree unipolarity” but lagging in out-degree—indicating local manufacturing sectors locked in lower segments of the value chain. Wuhan, the largest central city with a solid heavy industry base, and is an important industrial base in the country. It has a strong appeal to the manufacturing, and consistently ranked first in in-degree centrality. However, its industry chain is primarily concentrated on production rather than high-tech R\u0026amp;D, which tends to concentrate in Beijing and Shanghai. Its limited spillover effect to neighboring cities creates a funnel-like “input \u0026gt; output” pattern. In addition, cities such as Tianjin, Nanjing, Chengdu, Shenyang, Harbin, Suzhou, Xi’an, Qingdao, and Shenzhen have consistently ranked among the top 20 in terms of in-degree and are important industrial cities in China. Tangshan and Hefei advanced significantly from 32nd and 21st in 2005 to 8th and 11th in 2020, respectively. The former, benefiting from local steel resources and port advantages, has become a key area for the concentration of heavy industries in Hebei Province, including chemical raw materials, chemical manufacturing, and specialized equipment production. The latter, under the promotion of an industry-oriented city development strategy, has experienced rapid growth in sectors such as new energy vehicles, information technology, biopharmaceuticals, and smart home appliances, leading to a sustained enhancement in its manufacturing agglomeration capacity. Meanwhile, cities such as Dalian, Kunming, Taiyuan, and Zhengzhou saw declining aggregation capabilities, and Beijing, Baoding, and Wuxi showed significant declines in in-degree centrality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacteristics of organizational network connections\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvolution of the organizational network structure of CLMEs.\u003c/b\u003e From 2005 (0.017) to 2020 (0.036), the overall network density of CLMEs increased, while the average path length decreased from 3.058 to 2.730. This trend indicates more frequent collaboration among nodes and improved network accessibility and operational efficiency.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, following the implementation of the Reform and opening-up policy, China adopted an unbalanced regional economic development strategy, which enabled the eastern region to take the lead in both the level and pace of development, particularly in terms of economic output and manufacturing development (Chen and Xu, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Due to path dependence shaped by early-stage policy dividends, large market size, and advantages in transport hub functions, the organizational network structure of CLMEs exhibits a dense eastern and sparse western layout dominated by major cities. More specifically, notable regional disparities exist in the density and strength of inter-regional network connections. In the eastern region, the Yangtze River Delta, Pearl River Delta, and Shandong Peninsula displayed dense internal and external network ties—exhibiting a “balanced internal-external” structure. In the central region, the middle Yangtze River city cluster showed similar, albeit weaker, patterns. In contrast, network nodes in the western region are predominantly oriented toward external connections, forming a “strong external–weak internal” configuration. In addition, Beijing, with its political-economic status and global resource integration capacity, always the core node, and under the Beijing–Tianjin–Hebei coordinated development strategy, its ties with Shijiazhuang and Tangshan strengthened. Meanwhile, with enhanced connections in the Pearl River Delta and Chengdu–Chongqing regions, by 2020 a diamond-shaped network pattern had emerged with Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing at the vertices, and the middle Yangtze River region as the central support. This polycentric network structure meets the demand for coordinated interaction among urban agglomerations and enhances the capacity for cross-regional resource integration. Moreover, the resilience embedded in the diamond-shaped network configuration helps to mitigate the risks associated with reliance on a single urban pole and reduces the development risks faced by enterprises (Wall, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, under the constraints of geographical proximity and institutional barriers, the Chengdu-Chongqing region—due to its inland location and historical policy lag—has relatively weak connections with the Pearl River Delta, the middle reaches of the Yangtze River, and the Yangtze River Delta. As a result, the intensity of regional connections on the right side of the diamond-shaped structure is significantly stronger than that on the left. With the deepening of the domestic circulation strategy and the implementation of regional coordination policies, this \"strong east, weak west\" pattern has been gradually alleviated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on connection strength, organizational networks were classified into four tiers: top 0.8%, 0.8–2.5%, 2.5–15%, and 15–40% (Ye et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results reveal that organizational network connections among manufacturing enterprises exhibit distinct spatial structures and connection patterns across different hierarchical levels. The top 0.8% of network ties are primarily concentrated among cities such as Beijing, Shanghai, Shenzhen, and Wuhan, where the organizational layout of CLMEs displays a pronounced hierarchical diffusion pattern based on city size, which facilitates the expansion of regional markets. In contrast, lower-level connections are predominantly formed among ordinary prefecture-level cities, functioning as peripheral zones that establish passive attachment through market-driven diffusion. Over time, connections at all levels increased in number, though growth rates first accelerated and then declined. During this process, cities such as Hangzhou, Xian, and Shijiazhuang have experienced upward mobility in the network hierarchy, driven by a combination of technological advancement, policy adoption, and market integration, leading to an increase in their high-level network connections.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNetwork connection characteristics for enterprises of different technology types.\u003c/b\u003e Following the Industrial Classification for National Economic Activities (GB/T 4754—2017) and incorporating existing research and OECD technology classification standards, manufacturing enterprises were categorized into low-, medium-, and high-technology industries (Li, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Across the study period, the network connection strength for all categories showed a significant upward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Low-tech manufacturing networks were dominated by weak ties, with high-intensity connections mainly limited to regions such as Beijing and Shandong. These enterprises are largely constrained by labor and resource endowments, and the expansion of their network connections follows a cost-driven path (He and Wang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). At the early stage of the study period, network connections were mainly concentrated in the eastern region, but as labor costs increased, they gradually diffused toward inland areas.\u003c/p\u003e\u003cp\u003eMedium-tech manufacturing networks exhibited a hub-and-spoke structure centered on Beijing, Shanghai, Shenzhen, and Wuhan, radiating outward and eventually forming a spatial structure dominated by the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and middle Yangtze River regions. Among these connections, the Shanghai–Wuhan connection represents a core intercity pair. On the one hand, both cities serve as major industrial bases, and their close network relationship facilitates enterprise collaboration through functional specialization and complementary advantages. As key nodes along the Yangtze River Basin, their convenient transportation infrastructure further enhances mutual exchange and cooperation. Shanghai contributes capital and technology, while Wuhan, leveraging its robust heavy industrial foundation, undertakes production responsibilities, thereby forming a “design–manufacturing” vertical division of labor that promotes coordinated development in manufacturing between the two cities. On the other hand, policy initiatives such as the Guidelines on Promoting the Development of the Yangtze River Economic Belt by Leveraging the Golden Waterway have played a significant role in breaking down administrative barriers and strengthening the connection between Shanghai and Wuhan.\u003c/p\u003e\u003cp\u003eHigh-tech manufacturing enterprises, with greater demands for market access, technological infrastructure, talent, and support facilities (Wang, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), expanded their networks from 338 to 630 connection pairs during the study period. Their location choices became increasingly flexible, with Beijing playing a pivotal role in high-intensity ties. In addition, the diamond-shaped network cluster of high-tech enterprises—anchored by Beijing, the Yangtze River Delta, the Pearl River Delta, and the Chengdu–Chongqing region as its vertices—has become increasingly pronounced. This structure has further extended along the Harbin–Dalian corridor in Northeast China and toward Urumqi in Xinjiang, forming two emerging triangular network connection zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Empirical results","content":"\u003cp\u003e\u003cb\u003eRegression analysis of CLMEs in all categories\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe analysis of influencing factors in the structural evolution of China’s manufacturing network was conducted using the Statnet and ERGM packages in R. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, from Model (1) to Model (4), both the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values progressively decrease with the addition of more explanatory variables. Model (4), which incorporates both endogenous structural variables and exogenous covariates, exhibits the best goodness-of-fit. Therefore, the discussion primarily focuses on the estimation results of Model (4).\u003c/p\u003e\u003cp\u003eAmong the endogenous structural variables, the coefficients for edge effects are statistically significant in both 2005 and 2020, indicating that network connections in China’s manufacturing sector are not randomly generated. Instead, they reflect a certain degree of interdependence among enterprises, and this pattern remains relatively stable over time. Regarding the attribute variables, the indicators for openness and fiscal support from local governments show significant positive effects, suggesting that vibrant urban development and strong policy support attract CLMEs and their subsidiaries. In contrast, the urban scale indicator has a predominantly negative effect, implying that the expansion of urban areas does not serve as a strong pull factor for CLMEs. Existing studies have also shown that the relationship between urban size and the spatial distribution of manufacturing exhibits a nonlinear pattern, in which the crowding effect associated with urbanization tends to outweigh its positive externalities (Yin and Liu, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eParticular attention is given to the mechanisms through which multidimensional proximities influence the organizational network of CLMEs. The results indicate that in 2005, geographical proximity had a significantly negative but relatively weak impact on network formation. This aligns with the principle of distance decay in diffusion processes, where greater geographical distance translates into higher costs for transportation and information exchange (Xu and Li, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A higher level of geographical proximity can help reduce firms’ physical transportation costs, while face-to-face communication lowers informational barriers and transaction costs between enterprises (He et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), thereby facilitating connections between headquarters and their branch offices. Institutional, organizational, and social proximities are all found to exert significant positive effects on network formation. The spatial distribution of enterprises is not only shaped by market forces but also influenced by national and regional strategic guidance. Institutional proximity refers to the shared norms, rules, and legal frameworks that regulate individual and collective relations and interactions (Edquist and Johnson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). A higher degree of institutional proximity facilitates knowledge exchange among firms, thereby promoting the formation of industrial connection pathways (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is therefore evident that a similar institutional environment is conducive to the construction of inter-firm organizational networks. Organizational proximity and social proximity both exhibit strong positive effects. CLMEs often achieve vertical integration by establishing branch offices; firms affiliated with the same parent company tend to distribute their production bases and R\u0026amp;D centers across different cities, while branches under different parent companies within the same city can enhance efficiency through resource sharing. Boschma and Frenken (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) has emphasized the \"triadic closure\" mechanism of social proximity, which helps suppress opportunism and foster trust, thereby improving collaboration efficiency. Moreover, previous studies have shown that social proximity significantly facilitates intercity technology transfer and the cross-regional flow of knowledge (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ter Wal, 2010), which exert a substantial influence on the locational choices of manufacturing activities. Therefore, higher degrees of organizational and social proximity between cities are conducive to the establishment of inter-firm organizational networks in the manufacturing sector. In addition, cognitive proximity shows a positive but statistically insignificant effect.\u003c/p\u003e\u003cp\u003eBy 2020, geographical proximity continues to exert a significantly negative effect, though its magnitude remains low. Institutional and social proximities maintain significant positive effects. However, the influence of the former has declined over time, while the latter has become increasingly pronounced. This suggests that advancements in transportation and information technologies, along with adjustments in national policy, have reduced institutional and administrative barriers across regions, thereby weakening the spatial influence of institutional proximity. Meanwhile, the rapid development of the digital economy and internet technologies has accelerated the transformation of manufacturing toward more advanced and intelligent forms. While these developments reduce geographical and institutional constraints, they also intensify information overload. As a result, manufacturing competition is gradually shifting from being cost-driven to being shaped by a comprehensive competition in “information-knowledge-response speed.” Social proximity, rooted in cultural affinity and shared social backgrounds, helps lower communication costs and facilitates the flow of tacit knowledge and information, thereby strengthening inter-firm connections. The influence of organizational proximity significantly declines and becomes statistically insignificant. This can be attributed to multiple factors, including market-oriented reforms, transportation improvements, and globalization, all of which have led firms to increasingly rely on external supply chains and strategic alliances. Particularly under the space of flows replacing space of places, the unrestricted movement of production factors across regions has become the mainstream, thus diminishing the impact of organizational proximity on network formation. Cognitive proximity remains positively associated with network structure, but its influence continues to be non-significant and to be weakening. As China’s manufacturing sector enters a stage of complex innovation, the application of digital technologies has enabled firms to establish inter-organizational connections without requiring deep technological overlap or similar knowledge backgrounds. Meanwhile, the growing influence of social proximity—such as shared cultural identity and similar social backgrounds—has diminished the relative importance of cognitive proximity. In summary, the direction and magnitude of the influence of different types of proximity on the organizational structure of manufacturing networks are not static. As previously discussed, under the backdrop of deepening domestic circulation and intensified regional coordination policies, manufacturing network connections have gradually expanded toward inland regions, with traditional barriers—geographic, administrative, and cultural—being progressively dismantled.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eERGM estimation results for the factors of the organizational network structure of CLMEs in 2005 and 2020\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable names\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003emodel(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003emodel(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003emodel(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003emodel(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" 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colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.272\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e121.850\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67.998\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.909)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e123.734\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.192)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCog\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.771\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.240)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.567\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003cp\u003e(0.262)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUrb\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.127\u003c/p\u003e\u003cp\u003e(0.069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.234\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.311\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.382\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOpen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.200\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.290\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.225\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePol\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.885\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.840\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.070\u003c/p\u003e\u003cp\u003e(0.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.101\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.075)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8801.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16036.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6427.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12386.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6563.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11249.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5026.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9397.832\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8810.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16045.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6465.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12424.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6628.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11305.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5110.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9491.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog Likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4399.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-8017.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3209.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6189.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3274.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-5618.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2504.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-4688.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003eRegression analysis of CLMEs in different categories\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further explore the organizational networks of CLMEs across different technological classifications (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the results reveal a degree of heterogeneity in the effects of multidimensional proximity. These differences are closely tied to the technological characteristics and locational demands of each enterprise type. The effects of geographical, institutional, and social proximity are largely consistent with those observed in the full-sample regression. Specifically, geographical proximity has a stronger effect on enterprises in medium- and low-technology sectors, suggesting that these firms are more spatially constrained and thus more inclined to establish branch offices in close geographic proximity. Institutional proximity has a more pronounced effect on medium-technology manufacturing enterprises, as the formation of connections among such enterprises relies heavily on similar institutional environments. Social proximity shows a strong positive effect across all three technology levels, with its influence on low-technology enterprises increasing most significantly over time. This trend reflects the growing importance of intercity technology transfer and cross-regional knowledge flows for such enterprises.\u003c/p\u003e\u003cp\u003eIn contrast, the effects of organizational and cognitive proximity diverge more substantially from the full-sample results. Organizational proximity has a negative effect on low-technology enterprises, is statistically insignificant for medium-technology enterprises, and shifts from negative to positive for high-technology enterprises over time. This pattern may stem from the fact that low- and medium-technology enterprises tend to compete based on economies of scale and cost control rather than cross-regional organizational collaboration. As such, a high level of organizational proximity does not necessarily promote network formation among these firms. However, the innovation and development of high-technology manufacturing enterprises are characterized by high risk, long cycles, and a strong need for coordination. In the early stages, these firms must overcome regional constraints to enable cross-regional knowledge recombination, and excessive organizational constraints may hinder exploratory activities that fall outside established consensus. As firms enter a more mature stage, however, inter-organizational collaboration and mutual support facilitate modular division of labor, during which the effect of organizational proximity shifts from negative to positive. Cognitive proximity emerges as a key factor in the formation of organizational networks among medium- and high-technology manufacturing enterprises. Its influence on medium-technology manufacturing enterprises shifts from statistically insignificant to significantly positive over time. Cognitive proximity provides technological and knowledge-based support for inter-firm connections (He and Yu, 2022). Medium- and high-technology enterprises place greater demands on innovation capacity and knowledge accumulation. Regions with high cognitive proximity often share similarities in policy support, talent availability, and industrial chain integration, which enables firms to more effectively absorb external knowledge spillovers, enhance their own innovation capabilities, and ultimately promote inter-firm technological collaboration. In the early stages, medium-technology enterprises generally lagged behind in development, and technological influences were more pronounced among high-technology enterprises. However, by 2020, medium-technology enterprises had experienced rapid growth, and the similarity in technological conditions and knowledge endowments became a key factor in forming organizational connections. Overall, the direction and significance of most variables remain consistent with the full-sample regression results, lending further support to the robustness of the empirical findings.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eERGM estimation results for the factors of network structures across different technological types of CLMEs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable names\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLow-Technology Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMedium-Technology Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eHigh-Technology Type\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEdges\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-27.570\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.193)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-22.227\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.252)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-37.206\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.863)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-43.042\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-38.052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-49.847\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2,067)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGeo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInst\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.046\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.641\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.332\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.245)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.926\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.016\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.216)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.750\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.176)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOrz\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.956\u003c/p\u003e\u003cp\u003e(1.499)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.955\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.253\u003c/p\u003e\u003cp\u003e(1.703)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003cp\u003e(1.861)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.272\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.590)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.411\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.726)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSoc\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.110\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.580)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.341)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.407\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.924)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.379\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.413)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.130\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.858)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41.160\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.337)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCog\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.062\u003c/p\u003e\u003cp\u003e(0.391)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.307\u003c/p\u003e\u003cp\u003e(0.176)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.338\u003c/p\u003e\u003cp\u003e(0.340)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.692\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.717\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.259)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.310\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.131)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUrb\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.095\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.577\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.123)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.519\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.841\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.135)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003cp\u003e(0.129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.706\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.113)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOpen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.191\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.161\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.162\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.259\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePol\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.636\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003cp\u003e(0.120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.403\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.377\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.963\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.450\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.108)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1983.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4773.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1900.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3475.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2706.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4561.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2067.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4857.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1984.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3559.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2790.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4644.945\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog Likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-982.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2377.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-941.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1728.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1344.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2271.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion and conclusions","content":"\u003ch2\u003eDiscussion\u003c/h2\u003e\u003cp\u003eAs the backbone of the national economy, manufacturing serves as the foundation of state development, the instrument of national prosperity, and the cornerstone of national strength (Chen and Tang, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study focuses on CLMEs. Unlike studies covering the entire manufacturing sector, which often highlight the dominance of eastern coastal cities such as Zhejiang and Jiangsu in manufacturing networks (Qiao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the organizational network structure of CLMEs reveals that Beijing, due to its unique political status and economic strength, consistently holds a position of absolute dominance within medium- and high-level manufacturing networks. The findings suggest that for CLMEs, differences in administrative hierarchy reinforce the nodal status of core cities through preferential resource allocation and policy support, indicating that state power plays a substantial role in shaping network structures. Meanwhile, ongoing market-oriented reforms and the replacement of \u0026ldquo;space of places\u0026rdquo; by \u0026ldquo;space of flows\u0026rdquo; have accelerated the interregional circulation of production factors, facilitating the emergence of multi-centric organizational networks and reflecting the market mechanism\u0026rsquo;s capacity to overcome spatial lock-in.\u003c/p\u003e\u003cp\u003eAlthough advancements in transportation and information technology, along with the implementation of national strategies promoting regional coordination, have to some extent reduced spatial and administrative constraints, the organizational network of CLMEs has exhibited a trend toward diversification. Nevertheless, core city nodes remain predominantly concentrated in large-scale cities such as Beijing and Shanghai, and geographic distance as well as administrative barriers continue to impose significant limitations on the development of medium- and low-technology industries. Furthermore, while cognitive proximity significantly facilitates the organizational networking of high-technology enterprises, the convenience of short-term knowledge sharing may inhibit their capacity for long-term differentiated innovation. Hence, a moderate level of proximity should be maintained.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, regarding data, the headquarters\u0026ndash;branches relationships of CLMEs primarily reflect spatial organizational networks under multi-location strategies. However, this data structure does not adequately capture more complex input\u0026ndash;output relationships among non-affiliated firms. Second, reliance on commercial databases such as Tianyancha and Qichacha introduces issues of incomplete or invalid data, which may lead to a degree of deviation between the empirical results and actual conditions. Finally, each city has a unique development trajectory and set of locational endowments. The impact of any single type of proximity on manufacturing development is not static; as the manufacturing sector evolves, the effects of proximity may also change accordingly. In the future, research should further improve data quality and incorporate the developmental stages of China\u0026rsquo;s manufacturing sector to analyze the mechanisms through which different forms of proximity influence network formation across distinct periods. Such efforts would allow for a more nuanced understanding of how multidimensional proximity factors exert varying effects at different stages, and help uncover the underlying dynamics driving the evolution of organizational network structures.\u003c/p\u003e\n\u003ch3\u003eConclusions\u003c/h3\u003e\n\u003cp\u003eThis study, drawing on data from China\u0026rsquo;s top 500 manufacturing enterprises, analyzes the organizational network structure and spatial evolution characteristics of CLMEs. Employing an ERGM, it further investigates the driving forces underlying this structure from a multidimensional proximity perspective. The main findings are as follows:\u003c/p\u003e\u003cp\u003e(1) The degree centrality of nodes within the organizational network of CLMEs has steadily increased, exhibiting a trend toward multi-centric development. Spatially, the dominance of Beijing as a single-core hub has been gradually replaced by a more balanced pattern described as \u0026ldquo;one core, two sub-centers, and multiple support points.\u0026rdquo; Cities with higher degree centrality are primarily concentrated in the eastern coastal region and the Yangtze River Economic Belt, forming a spatial pattern akin to a \u0026ldquo;T\u0026rdquo; shape.\u003c/p\u003e\u003cp\u003e(2) Network connectivity among CLMEs has been progressively strengthened, with operational efficiency continuously improving, though significant regional disparities remain. The eastern region demonstrates a dual orientation in its network ties, balancing internal and external connections. The central region follows this trend to a lesser degree, while the western region exhibits a pattern of \u0026ldquo;weak internal, strong external\u0026rdquo; connections. A \u0026ldquo;diamond-shaped\u0026rdquo; structure has emerged, anchored by the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, Chengdu-Chongqing, and middle reaches of the Yangtze River regions. However, due to constraints such as geographic location, institutional barriers, and path dependency, the left (western) side of the diamond relatively weaker than the right (eastern) side. Moreover, network structures differ by tier: higher-tier networks demonstrate a hierarchy-based diffusion pattern aligned with city scale, whereas lower-tier networks tend to be marginal and exhibit passive attachment driven by market diffusion.\u003c/p\u003e\u003cp\u003e(3) The organizational network of CLMEs shows clear differentiation across technological tiers. The network at the low-technology level follows a cost-driven, decentralized spatial layout. At the medium-technology level, the network exhibits a hub-and-spoke pattern centered on core node cities. In contrast, the high-technology level has developed a cross-regional diamond-shaped organizational network structure under the framework of regional coordinated development, which has further extended along the Harbin\u0026ndash;Dalian corridor in Northeast China and toward Urumqi in Xinjiang, forming two triangular network connection zones.\u003c/p\u003e\u003cp\u003e(4) From a multidimensional proximity perspective, institutional and social proximity are found to significantly promote the construction of organizational networks. With the advancement of transportation and information technologies, as well as adjustments and improvements in national policies, the negative effect of geographic proximity and the positive effect of organizational proximity have both diminished. For enterprises across different technological tiers, cognitive proximity emerges as a key factor in the formation of organizational networks among medium- and high-technology manufacturing enterprises, while the increasing positive impact of social proximity is most pronounced among low-technology enterprises. Additionally, factors such as urban openness and the intensity of policy support also exert significant influence on the formation and evolution of manufacturing networks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical approval\u003c/h2\u003e\u003cp\u003eEthical assessment is not required prior to conducting the research reported in this article, as the present study does not have experiments on human subjects and animals, and does not contain any sensitive and private information.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.W.:Writing\u0026ndash;review \u0026amp; editing, Funding acquisition, Conceptualization.C.M.:Writing\u0026ndash;original draft, Methodology, Data curation, Visualization, Software.R.L.:Writing\u0026ndash;review \u0026amp; editing, Funding acquisition.C.C.:Writing\u0026ndash;original draft, Validation, Software, Methodology, Data curation, Visualization, Investigation, Formal analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge support from the National Natural Science Foundation of China [NO.42471199] and the Natural Science Foundation of Hebei Province of China (NO. D2024205030).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBonello, V., Faraone, C., Leoncini, R., Nicoletto, L., Pedrini, G. (2022). (Un)making space for manufacturing in the city: The double edge of pro-makers urban policies in Brussels. \u003cem\u003eCities\u003c/em\u003e, 129: 103816. https://doi.org/10. 1016/j.cities.2022.103816 \u003c/li\u003e\n\u003cli\u003eBoschma, R. (2005). Proximity and Innovation: A Critical Assessment. \u003cem\u003eRegional Studies\u003c/em\u003e, 39(1): 61-74. https://doi.org/10.1080/0034340052000320887 \u003c/li\u003e\n\u003cli\u003eBoschma, R., Frenken, K. (2010). 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The hub-network structure of China\u0026apos;s equipment manufacturing industry. \u003cem\u003eActa Geographica Sinica\u003c/em\u003e, 74(8): 1525-1533. https://doi.org/10.11821/dlxb201908003 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6884997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6884997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cdiv language=\"En\" class=\"ArticleSubTitle\"\u003eThe increasing economic globalization and regional integration have brought attention to the spatial network structure and evolutionary patterns of the manufacturing, a significant sector of the national economy. Based on headquarters\u0026ndash;branch data of China\u0026rsquo;s Top 500 Manufacturing Enterprises for the years 2005, 2010, 2015, and 2020, this study maps and analyzes the organizational network structure of China\u0026rsquo;s leading manufacturing enterprises (CLMEs) in China and further explores the driving factors behind this structure from the perspective of multidimensional proximity. The major findings are: (1) The degree centrality of nodes within the organizational network of CLMEs has steadily increased, showing a clear evolutionary trend from a single-core structure dominated by Beijing toward a polycentric configuration characterized by \u0026ldquo;one core, two sub-centers, and multiple support points.\u0026rdquo; (2) The network structure of CLMEs has formed a diamond-shaped manufacturing configuration, with the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu\u0026ndash;Chongqing regions as the four vertices, and the middle reaches of the Yangtze River as the central support. (3) Institutional proximity and social proximity emerge as pivotal factors influencing the formation of the organizational network among CLMEs. With improvements in transportation, information technologies, and adjustments to national policy, the influence of geographical and cognitive proximity has declined. (4) The effects of multidimensional proximity factors on various technology-oriented manufacturing sectors vary: cognitive proximity is a significant driver for the formation of networks among medium- and high-technology enterprises, but its impact on low-technology firms is limited. In contrast, the positive influence of social proximity on low-technology manufacturing networks has shown a clear upward trend.\u003c/div\u003e","manuscriptTitle":"Evolution of organizational network structure and multidimensional proximity explanation of leading manufacturing enterprises in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 11:03:10","doi":"10.21203/rs.3.rs-6884997/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5a54fe9-a9f3-4f4b-841d-fe9c08fb4b6a","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54141429,"name":"Social science/Development studies"},{"id":54141430,"name":"Social science/Economics"},{"id":54141431,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2025-12-29T16:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 11:03:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6884997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6884997","identity":"rs-6884997","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}