Technological-level information analysis

Temporal variation

Between 2001 and 2021, a total of 65,055 patents were transferred from universities to enterprises. Figure 1 illustrates the evolution of patent assignments in chronological order. Overall, the number of patent assignments shows a significant upward trend, with an average annual growth rate of nearly 40%, indicating that an increasing number of academic patents are being transferred to enterprises. Based on the annual number of patent assignments, the period can be divided into three phases with intervals of seven years: 2001–2007, 2008–2014, and 2015–2021.

Fig. 1
figure 1

University patent transfer frequency and growth rate between 2001 and 2021.

The first phase (2001–2007) exhibits a low number of patent assignments, with high volatility in the growth rate, owing to the lack of appropriate incentive policies. In 2000, the Ministry of Science and Technology issued the Opinions on Strengthening the Protection and Management of Science and Technology-Related Intellectual Property Rights, which allowed universities to retain their ownership of government-funded inventions. However, as intangible assets, patents resulting from government-funded research programs are subject to regulations regarding the management of state-owned assets, and their disposal requires the approval of administrative units at all levels (Yi and Long, 2021). Therefore, in principle, universities have no right to dispose of patents.

In 2007, the National People’s Congress passed an amended Science and Technology Progress Law, known as the Chinese version of the Bayh-Dole Act, which delegated the right to dispose of academic patents to universities. Nonetheless, the revenues generated from patents were largely retained by the central government, resulting in the number of patent assignments during this period stabilizing at a relatively low level of less than 3000 per year.

To further promote university patent transfers, the Law on Promoting the Transformation of Scientific and Technological Achievements of the People’s Republic of China was revised by the State Council in 2015. This revision mandated that universities and researchers retain all income generated from academic patent transfers. This significantly stimulated universities’ enthusiasm to engage in patent transactions, and the number of patent assignments skyrocketed to 9,092 in 2019. The surge in 2020 and 2021 may be attributed to the COVID-19 pandemic, which led enterprises to seek domestic university knowledge as an alternative to foreign sources.

Popular technologies

In patent information analyses, the IPC is often used to analyze the technology domains of patents, as each technological classification in a patent is assigned according to its intrinsic nature, function, application, or purpose (Balland and Boschma, 2022). A complete IPC consists of hierarchical symbols representing sections, classes, subclasses, and main groups or subgroupsFootnote 4. This study uses the section and subclass levels as the basis for classifying technology fields to examine the changing trends in patents transferred from universities.

Figure 2 illustrates the annual proportions of patents at the section level and the corresponding changes over time. The proportions of each section changed dramatically before 2008 but remained relatively stable after 2008. Specifically, the share of patents in Categories C (chemistry, metallurgy) and G (physics) was considerably higher than that in other technology fields, accounting for nearly 50% of all patents. However, Category C has gradually decreased, and Category G has gradually increased in recent years. Patents in Categories A (human necessities), B (performing operations, transporting), and H (electricity) accounted for another 40%. Category A and H have declined, whereas Category B has gradually grown in more recent years. Throughout this period, few patents were related to Categories D (textiles, paper), E (fixed constructions), and F (mechanical engineering, lighting, heating, weapons, blasting), which together accounted for the remaining 10% of all patents. Overall, the structure of UTFs at the section level was similar to that of national knowledge flows (Yang et al., 2021b).

Fig. 2: Changing trends in technology fields at the IPC section level.
figure 2

A, human necessities; B, performing operations, transporting; C, chemistry, metallurgy; D, textiles, paper; E, fixed constructions; F, mechanical engineering, lighting, heating, weapons, blasting; G, physics; H, electricity.

To further identify the most popular technology fields of the transferred academic patents, a Sankey diagram was drawn, as shown in Fig. 3, to explore the variations in the top 10 subclasses in three-year intervals from 2001 to 2021. The following trends are observed:

  • In Category A, only Subclass A61K (preparations for medical, dental, or toiletry purposes) had active patent transfers throughout the period, peaking in 2004–2006 and 2010–2012, followed by a downward trend in recent years.

  • Category G had two long-lived subclasses; G01N (investigating or analyzing materials by determining their chemical or physical properties) ranked first after 2012, and G06F (electric digital data processing) ranked second after 2015. This indicates that enterprises recently paid increasing attention to material and computer science. Other short-lived subclasses in Category G included G01R (measuring electric and magnetic variables) in 2013–2015 and G06T (image data processing or generation) in 2019–2021.

  • Category H contained several subclasses of vibrant patent transfer. For instance, Subclass H04L (transmission of digital information) moved from sixth in 2007–2009 to fourth in 2016–2018 and subsequently dropped to ninth in 2019–2021. Other short-lived subclasses in Category H included H04Q (selecting) and H04J (multiplex communication) in 2004–2006 and H04N (pictorial communication) in 2007–2009.

  • Category C contained several subclasses with vibrant and long-lived transfers. For instance, C22C peaked in 2007–2009, and eventually fell out of the top 10 list, whereas C02F rose to second in 2013–2015, followed by a rapid decline in recent years. Other long-lived subclasses in Category C included C07C (acyclic or carbocyclic compounds), C07D (heterocyclic compounds), C08L (compositions of macromolecular compounds), etc.

  • In Category B, only Subclass B01J (chemical or physical processes, their relevant apparatus) was of particular interest to enterprises, rising from tenth in 2013–2015 to third in 2019–2021. The other popular subclasses in Category B appeared only before 2010.

  • Over the entire period, no popular technology fields were observed in Categories D, E, and F.

Fig. 3: Changing trends in the top 10 technology fields at the IPC subclass level.
figure 3

In each period, rectangles represent IPC subclasses, ranked from top to bottom according to the proportion of patents in each subclass to all patents. Colors indicate different IPC sections, and the width of the curves connecting the rectangles represents the proportion of patents transferred.

Regional distributions

Based on the addresses of universities and enterprises, geographical distribution maps of university patents provided and acquired by cities in China between 2001 and 2021 were drawn (Fig. 4). As shown in Fig. 4a, university patents with transferred characteristics are mainly concentrated in the eastern coastal regions and provincial capitals in Northeast, Central, and Western China, which is consistent with the regional inequality of university distribution. These regions host the most prestigious universities in China. University patents in eastern coastal areas are primarily distributed in provincial capitals (or municipalities), such as Beijing, Tianjin, Shanghai, Nanjing, Hangzhou, Guangzhou, and other economically developed regions. University patents in Central China are typically distributed in provincial capitals, such as Taiyuan, Zhengzhou, Wuhan, Hefei, Nanchang, and Changsha. Patents in Northeast China are mostly distributed in the three provincial capitals: Harbin, Changchun, and Shenyang. Patents in the western region are primarily distributed in the three provincial capitals (or municipalities): Xi’an, Chengdu, and Chongqing. Other regions had less than 150 patents.

Fig. 4: Geographical distribution of university patent supply and demand.
figure 4

Map a is the spatial distribution of university patent supply at the city level. Map b is the spatial distribution of university patent demand at the city level. The nodes represent the cities. Node size indicates the number of university patents.

As shown in Fig. 4b, university patents are mainly transferred to Eastern China and the provincial capitals in Central, Northeastern, and Western China, which is similar to the spatial pattern of university knowledge supply. However, compared to patent supply, there is a certain degree of spatial mismatch between university knowledge supply and regional knowledge demand. Universities transfer patents elsewhere owing to a lack of absorption capacity in the host region, and regions with a weak supply of knowledge from local universities search elsewhere. For instance, the supply of knowledge from universities in Harbin, Changchun, and Shenyang in Northeast China, where economic development has been declining since the 1990s, has exceeded regional absorption capacity, resulting in the partial use of university knowledge by other regions. The Pearl River Delta—an economic core but knowledge periphery—has a demand for university knowledge that exceeds the supply within the region, creating the need to access university knowledge outside the region. Nonetheless, a mismatch exists between university technology supply and regional technology demand for technological specialization. For instance, enterprises in Tianjin have absorbed many patented technologies in G01N, G06F, C02F, and A61K, whereas the technologies provided by universities in Tianjin are mostly concentrated in G06F, B01D, G01N, and C07D.

Organization-level UTFN

Topological structure

Table 1 presents the topological structure of UTFN during three periods. The number of nodes and links increased rapidly, while the network density continued to decrease between 2001 and 2021, indicating that the connections between nodes gradually loosened with the expansion of network size. Centralization is generally employed to measure the extent to which a network is organized around or dominated by specific nodes. In-centralization increased from 0.009 in 2001–2007 to 0.017 in 2008–2014 and subsequently decreased to 0.002 in 2015–2021. Out-centralization decreased from 0.056 in 2001–2007 to 0.050 in 2008–2014 and to 0.026 in 2015–2021, suggesting that the network is decentralizing in terms of technology outflows.

Table 1 Topological structure of the UTFN.

The average outdegree and indegree, as well as the average weighted outdegree and indegree, show an upward trend, indicating an increasing number of links between universities and enterprises within the network. Notably, the average weighted outdegree significantly surpasses the average outdegree, indicating that universities tend to establish links with many enterprises with multiple patent transfers. Conversely, the differences between the average indegree and average weighted indegree are minimal, indicating that enterprises tend to establish connections with a single university. Furthermore, the average outdegree considerably surpasses the average indegree, and the average weighted outdegree exceeds the average weighted indegree, suggesting that universities dominate the network.

The coefficient of variation of the average weighted outdegree (indegree) shows an upward trend throughout the study period, indicating an increasing heterogeneity among universities (enterprises) in terms of selling (buying) patents. The coefficient of variation of the average outdegree increased from 1.134 in 2001–2007 to 1.952 in 2008–2014 and decreased to 1.868 in 2015–2021. This indicates a narrowing of differences between universities occupying central positions within the network during the periods of 2008–2014 and 2015–2021. The changing trend in the average indegree mirrors that of the average outdegree.

Key organizations

In this section, we explore the differences between universities and enterprises to identify the organizations that play a central role in the UTFN. In total, 882 universities sold at least one patent to enterprises, and 24,869 enterprises bought at least one patent from universities. Between 2001 and 2021, the number of nodes steadily increased, indicating that universities and enterprises were increasingly involved in the UTFN. Between 2001 and 2005, few nodes were identified in the network. After the implementation of the independent innovation strategy in 2006 and the innovation-driven development strategy in 2013, the number of universities and enterprises experienced a period of rapid growth. Between 2016 and 2021, the number of nodes in the network was considerably higher than that in the other periods.

However, universities and enterprises exhibited significant differences in patent transfer behaviors. For instance, a few universities transferred a large majority of patents (e.g., approximately 67% of patent assignments were from 10% of the universities). This is similar to the situation in the United States (Hu and Zhang, 2021). Similarly, a few enterprises purchased numerous academic patents (e.g., 10% of enterprises bought approximately 51% of all patents).

Overall, 985/211 project universities occupy a more central position within the network, as these universities possess substantial average outdegree and average weighted outdegree. Compared with non-985/211 project universities, 985/211 project universities have advantages in research funding and technological innovation, and their official reputation helps expand their research strength and patent quality over a larger geographical scope and mitigates the problems of information asymmetry (Hong and Su, 2013; Nie et al., 2023). Moreover, these universities must maintain and enhance their prestige through continuous patent transfers to obtain more research funding and policy support. Hence, 985/211 project universities have sufficient motivation and ability to occupy central positions within the network. However, the average weighted outdegree of 985/211 project universities as a proportion of all universities decreased from 83.135% in 2001–2007 to 68.122% in 2008–2014 and 41.716% in 2015–2021, suggesting that the trend is shifting with the rapid expansion of the UTFN.

Specifically, science, engineering, and comprehensive universities have recently started to occupy more central positions in the UTFN. The average weighted outdegree for science, engineering, and comprehensive universities over the three periods was 5.816, 27.252, and 85.065, respectively, whereas the average weighted outdegree for other universities over the three periods was 5.921, 22.895, and 51.880, respectively, suggesting that the widening gap occurred only in the last few years. University type determines its development priority, disciplinary structure, and innovation orientation. Thus, universities that focus on science and engineering have technical advantages in patenting and commercialization activities. As shown in Table 2, the universities with the highest patent assignments focused on science, engineering, and comprehensive disciplines. In addition, universities located in economically developed regions experienced faster growth in patent transfers. For instance, Changzhou University, which sold few patents before 2014, exhibited an annual weighted outdegree of 148.125 in 2014–2021; Nantong University’s annual weighted outdegree in 2001–2018 was less than 4, whereas the figure was close to 130 in 2019–2021; Zhejiang Sci-Tech University sold few patents before 2017, whereas the annual weighted outdegree in 2018–2021 was more than 120. These rising stars are located in the Yangtze River Delta megalopolis, which is China’s most innovative and dynamic region, suggesting that regional technological needs stimulate universities’ participation in technology transfer activities to some extent.

Table 2 Top 10 weighted outdegrees in the UTFN.

Regarding enterprises, the position difference across all industries within a network is relatively small. Across all industries, the most common types of enterprises with high average indegree and average weighted indegree within the network are in the leasing and business services sectors, as well as electricity, heat, gas, and water production and supply. As shown in Table 3, eight of the top ten enterprises based on weighted indegree are intellectual property service companies, such as Guangdong Gaohang Intellectual Property Operations Co., Ltd. and Zhejiang Pinchuang Intellectual Property Service Co., Ltd. Two of the eight firms are operated by universities: Liyang Changda Technology Zhuanyi Center Ltd., operated by Changzhou University, and Jiangyin Zhichanghui Intellectual Property Operation Co., Ltd., operated by Jiangsu University. Another university-run technology enterprise, HIT Robot Group Co., Ltd., is operated by the Harbin Institute of Technology. These university-run enterprises either act as intermediaries to assist universities in transferring their potential technologies to other companies or directly commercialize their patents.

Table 3 Top 10 weighted indegrees in the UTFN.

Spatial-level UTFN

Spatial distance

According to previous literature on the geography of university knowledge spillovers, UTFs decrease with increasing distance. Figure 5 depicts the distribution of UTFs by distance intervals between 2001 and 2021. The distance reaches up to 4100 km; however, nearly 45% of academic patents are assigned to enterprises within 100 km, indicating that UTFs are highly geographically localized. A sharp decrease occurs in the proportion when the distance exceeds 100 km but is less than 400 km, and no apparent decline for the 500–1100 km range. This indicates that geographical distance has no substantial restriction on distant UTFs (Mukherji and Silberman, 2021). UTFs for distances of 900–1100 km show a moderate increase, likely because of the flows between major cities in China. The proportion of UTFs decreases once the distance exceeds 1100 km. The proportion of each distance interval is less than 1% when the distance exceeds 2000 km.

Fig. 5
figure 5

Decay of UTFs with increasing distance.

To observe variations in geographical distance, we calculate the average annual distance between 2001 and 2021 (Fig. 6). Additionally, we categorize UTFs into three based on the location of universities and enterprises: intra-city (academic patents assigned to enterprises from the same city); inter-city within provinces (academic patents assigned to enterprises from different cities but in the same province); inter-city across provinces (academic patents assigned to enterprises from different cities in different provinces). Overall, the geographical distance of UTFs showed an increasing trend with fluctuations. In 2002, the minimum distance reached was 210 km, after which an increasing trend was observed. The average distance peaked at 561 km in 2020 due to the decreasing share of intra-city patent transfers. As shown in Fig. 6, intra-city patent transfers dominated the process of UTF in the early period. Nevertheless, the proportion of intra-city patent transfers peaked at 74% in 2003, followed by a slow decline. The proportion of inter-city patent assignments across provinces increased significantly between 2001 and 2004, after which it fluctuated around 43%. The proportion of inter-city patent assignments within the provinces increased between 2001 and 2013, after which it fluctuated between 14% and 18%. Overall, UTFs underwent a delocalization process.

Fig. 6
figure 6

Average distance and spatial scale trends.

Intra-regional evolution

The number of intra-regional patent transfers in China during the three periods is presented in Fig. 7. In 2001–2007, intra-regional university-enterprise patent transfers occurred in only 32 cities, most of which were provincial capitals and municipalities. Shanghai had the largest number of intra-regional UTFs at 70, followed by Beijing with 51. The numbers in the other regions were below 20.

Fig. 7: Evolution of intra-regional UTFN.
figure 7

Map a shows the spatial pattern of intra-regional UTFN in 2001–2007. Map b shows the same content in 2008–2014. Map c shows the same content in 2015–2021. The nodes represent the cities. Node size indicates the number of university patents.

The pattern for 2008–2014 was similar to that for 2001–2007. Intra-regional UTFs were distributed across 92 cities. Beijing and Shanghai had the highest number of intra-regional patent transfers, at 763 and 380, respectively. Provincial capitals, such as Nanjing and Wuhan, also became active. The numbers in other regions were mostly below 60.

In 2015–2021, intra-regional UTFs occurred in 188 cities, and the differences in the number of flows varied widely. Beijing, Shanghai, and Hangzhou ranked among the top three with 2287, 1472, and 1366, respectively. Moreover, local UTFs significantly increased in Wuhan, Guangzhou, Xi’an, Harbin, and other provincial capital cities with higher-education resources, as well as in Changzhou, Suzhou, Zhenjiang, Wuxi, and other cities in the Yangtze River Delta. The numbers were mostly below 200 in cities other than provincial capitals, as these cities lacked prestigious universities. Nonetheless, intra-regional technology flows were more active in coastal areas than in inland areas.

Inter-regional evolution

Figure 8 illustrates the spatial patterns of inter-regional UTFs. In 2001–2007, the inter-regional network was sparse. A total of 73 cities joined the UTFN, of which 61 received university technology from outside the region. Shanghai and Beijing received the most university patents, with 41 and 34, respectively, whereas the other cities received less than ten. At this stage, inter-regional technology flows were mainly between the provincial capital and municipalities, indicating that the network was dominated by hierarchical diffusion.

Fig. 8: Evolution of inter-regional UTFN.
figure 8

Map a shows the spatial pattern of inter-regional UTFN at the city level in 2001–2007. Map b shows the same content in 2008–2014. Map c shows the same content in 2015–2021. The nodes represent the cities. Node size indicates the number of incoming edges incident on it. The depth of the node color indicates the number of edges stemming from the node. A directed connection between two cities indicates UTFs and the thickness of the directed connection indicates the frequency of flows between the two cities.

In 2008–2014, 276 cities joined the network, of which 272 received university technology from 101 cities. Nantong, Beijing, Suzhou, and Shenzhen had the largest number of technology inflows, at 912, 547, 415, and 217, respectively. Although inflows to other cities have improved to some extent, most did not exceed 100. At this stage, inter-regional technology transfer remained dominated by hierarchical diffusion, and contagion diffusion was not evident. University technology was mainly transferred to the Yangtze and Pearl River Deltas from Beijing and Xi’an. Beijing is a higher education and national administrative center that diffuses a large amount of academic knowledge to other regions and absorbs academic knowledge from the entire country.

In 2015–2021, the inter-regional network became dense. A total of 340 cities joined the network and obtained university technology from 207 cities. Inter-regional UTFs were highly geographically concentrated and presented a trapezoid structure anchored by five megalopolises: the Beijing-Tianjin region in North China, Yangtze River Delta megalopolis in East China, Pearl River Delta megalopolis in South China, Chengdu-Chongqing region in West China, and Harbin-Changchun-Shenyang region in Northeast China. Academic knowledge was mainly transferred from west to east and from north to south. Beijing and other major cities in the Pearl and Yangtze River Deltas became the main destinations for inter-regional UTFs. As the knowledge and economy center in China, the Yangtze River Delta played an important role in the national UTFN, same as Beijing, and exchanged knowledge within the region, indicating that contagion diffusion began to become noticeable. While the Pearl River Delta is an economic core, it is a knowledge-peripheral region. Therefore, it absorbed numerous academic technologies from external areas but rarely spread academic knowledge to external areas. The other cities with trapezoidal structures mainly served as knowledge exporters.

To further clarify the positions of cities within the UTFN, the roles of cities were identified based on the normalized indegree (the ratio of the weighted indegree of each city to the maximum weighted indegree in all cities) and normalized outdegree (the ratio of the weighted outdegree of each city to the maximum weighted outdegree in all cities). This indegree-outdegree dichotomy effectively reflects the impact of cities within a network (Wang et al., 2015). If a city has high normalized indegree and outdegree within the network, it acts as a national hub owing to its strong influence on other cities. If a city has a high normalized indegree but a low normalized outdegree within the network, it acts as a technology importer because it depends mainly on academic knowledge outside the region. If a city has a low normalized indegree but a high normalized outdegree within the network, it may act as a knowledge exporter because it usually has academic strength that exceeds its needs. If a city has low normalized indegree and outdegree within the network, it is at the periphery of the network because of its insignificant impact on other cities. Between 2001 and 2007, the number of inter-regional UTFs was small; therefore, this study focuses on two periods: 2008–2014 and 2015–2021.

As shown in Fig. 9, Beijing was the only national hub during both periods. Nantong was a technology importer in both periods, whereas Shenzhen, Suzhou, and Guangzhou shifted from the periphery to being technology importers. Shanghai, which was located in the exporter quadrant in 2008–2014, acted as a technology importer in 2015–2021. Xi’an and Nanjing were technology exporters during both periods, whereas Hangzhou shifted from the network periphery to the technology importer quadrant. Other cities with low normalized indegree and outdegree had a limited influence on cities within the network, indicating that they were on the periphery of the network. In the future, Huzhou and Jiaxing, two economy-core but knowledge-peripheral cities in the Yangtze River Delta, may move toward the importer quadrant. Shanghai and Nanjing, developed cities with rich higher-education resources, may become national hubs. Wuhan, Chengdu, and Chongqing may become technology exporters.

Fig. 9: Position of cities in UTFN.
figure 9

Figure a shows the position of cities in UTFN in 2008–2014. Figure b shows the same content in 2015–2021.

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