Industry-University-Research collaboration networks: the identification and driving factors of key technologies

With the intensification of global technological competition and the continuous upgrading of industrial structures, the role of IUR cooperation in promoting technological innovation and industrial upgrading has become increasingly important. In recent years, the evolution of key technologies in IUR cooperation has become a focus of attention in both academic and industrial circles. The evolution of key technologies not only affects the efficiency and effectiveness of cooperation, but also determines the competitiveness and development direction of industries (Wang et al., 2025). However, existing research on the evolution trends and influencing factors of key technologies in IUR cooperation is still not deep enough, especially in terms of the dynamism and complexity of technologies.

In the research on technological evolution, scholars have gradually realized that the development of technology is not a linear process but is influenced by a combination of various factors. The speed and magnitude of technological iteration show significant differences across different industries and fields. For example, in high-tech fields such as artificial intelligence and new energy vehicles, the speed of technological iteration is extremely fast. Companies must work closely with universities and research institutions to layout the research and development of next-generation technologies in advance (Chui et al., 2023). This rapid technological iteration demands that IUR cooperation models become more flexible and efficient to adapt to the swift changes in technology (Cohen et al., 2025). However, existing research lacks a systematic analysis of this rapid technological iteration model, especially in terms of cooperation mechanisms and innovation models.

However, existing research on the mechanisms and influencing factors of interdisciplinary integration in IUR cooperation is still not deep enough. For example, how to effectively integrate the resources and advantages of different disciplines in IUR cooperation, and how to promote the cooperation and innovation of interdisciplinary teams, these questions all need further research.

At the same time, research on technological evolution has also revealed the trend of interdisciplinary integration. In many key technology fields, such as radiative cooling materials and intelligent transportation systems (Xu et al., 2025), the development of technology relies on the knowledge and methods of multiple disciplines. This kind of interdisciplinary cooperation not only accelerates technological innovation but also promotes the wide application of technology (Chandra, 2013). However, existing research on the mechanisms and influencing factors of interdisciplinary integration in IUR cooperation is still not deep enough. For example, how to effectively integrate the resources and advantages of different disciplines in IUR cooperation, and how to promote the cooperation and innovation of interdisciplinary teams, these questions all need further research.

In addition, research on technological evolution has also emphasized the strengthening of the status of cooperation entities. In IUR cooperation, the leading role of enterprises is increasingly prominent. Enterprises are not only the demanders and users of technological innovation but also the key entities for technology transfer and market promotion (Zhao et al., 2024). For example, in China’s marine industry, the enterprise–enterprise (I–I) cooperation model has the highest number of patents, accounting for 41.59%, followed by the enterprise–research institute (I–R) and university–enterprise (U–I) cooperation models. This trend indicates that the leading role of enterprises in technological innovation is continuously strengthening, and IUR cooperation is gradually forming a cooperation network centered on enterprises (Guo et al., 2023). However, existing research lacks in-depth exploration of the mechanisms between the status of cooperation entities and technological evolution. The main shortcoming is the failure to fully reveal the dynamic process and interaction mechanisms of the co-evolution of cooperation entities and technology. Specifically, existing research pays little attention to how changes in the status of cooperation entities affect the direction and speed of technological evolution, and how technological evolution, in turn, shapes the status and behavior of cooperation entities. Moreover, regarding the mechanisms of key factors in this co-evolution process, such as knowledge distribution and policy environment, existing research also lacks systematic analysis and empirical support.

Policy support plays an important role in IUR cooperation. By formulating industrial policies, providing financial support, and building cooperation platforms, the government has promoted the in-depth development of IUR cooperation (Behfar et al., 2024). For example, the government provides policy guarantees and financial support to encourage enterprises to jointly carry out innovation projects with universities (Kabombo et al., 2025). By formulating industrial policies, the government guides resources towards key technology research and development (R&D) areas, ensuring that the direction of technology R&D is consistent with national and local economic development strategies (Li et al., 2022). In addition, the government also builds cooperation platforms and organizes IUR docking activities to promote information exchange and cooperation among all parties. These measures jointly promote the deepening of IUR cooperation, accelerating technological innovation and the transformation of results (Zhou & Wang, 2023). However, when analyzing the evolution of IUR cooperation, existing research often fails to fully combine the specific policies of the year and lacks a systematic analytical framework. Especially in evaluating the precision and effectiveness of key technologies, there is little in-depth discussion in existing research that combines policy tools with the actual evolution process of IUR cooperation to verify whether the technological achievements formed by the existing cooperation evolution truly meet the key technology needs of the year.

To sum up, existing research has not clearly elaborated the recent technological evolution trends of IUR cooperation in a country or region, which makes long-term planning and strategic adjustment of IUR cooperation lack strong basis. The unclear technological evolution trends lead to certain blindness in resource allocation, choice of cooperation models, and policy formulation. At the same time, the unclear technological evolution trends also cause differences in the expectations and strategic planning of technological development among the parties in IUR cooperation. Universities and research institutions may focus more on basic research and technological breakthroughs, while enterprises have more urgent needs in application development and market promotion. Such differences may lead to inconsistent goals and resource misallocation in the cooperation process, affecting the efficiency and effectiveness of cooperation. Moreover, the unclear technological evolution trends also make it difficult for the government to precisely guide and support IUR cooperation when formulating relevant policies, thus greatly reducing the effectiveness of policy support.

In recent years, the endogenous driving forces of IUR cooperation networks have gradually become a focus of attention in both academic and practical circles. Endogenous driving forces refer to the structural and mechanistic factors within the network that drive the formation, evolution, and stability of the cooperation network (Ankrah & Al-Tabbaa, 2015). These factors not only affect the formation and evolution of IUR cooperation networks but also have a significant impact on cooperation efficiency and innovation outcomes. Existing research mainly involves three aspects of the network: First is the proximity mechanism factors. The proximity mechanism is an important part of the endogenous driving forces of IUR cooperation networks, which mainly includes geographical proximity, technological proximity, and institutional proximity. Geographical proximity emphasizes the impact of the physical distance between cooperating entities on cooperation. Research shows that the closer the geographical distance, the higher the likelihood of cooperation (Errico et al., 2024). Technological proximity focuses on the similarity and complementarity of cooperating entities in the technological field. The higher the degree of technological proximity, the easier it is for cooperating entities to transfer knowledge and engage in collaborative innovation (Zhang & Liu, 2024). Institutional proximity emphasizes the similarity of cooperating entities in terms of laws, regulations, and management standards. This similarity provides a stable institutional environment for cooperation, reducing uncertainty and conflicts in the cooperation process (Hong & Su, 2013). Second, there is research on the endogeneity of network mechanisms. The endogeneity of network mechanisms mainly involves the impact of network structure on the formation and evolution of IUR cooperation networks. Research shows that IUR cooperation networks tend to form star-shaped structures and closed triangular structures. The star-shaped structure reflects the Matthew effect, that is, the central nodes in the network (such as well-known universities or large enterprises) are more likely to attract new cooperation opportunities (Wu & Liu, 2024). The closed triangular structure reflects the transitivity effect, that is, the open triangular structures in the network tend to close, forming closer cooperative relationships (Estrada & Arrigo, 2015). In addition, the connectivity of the network also plays an important role in the evolution of IUR cooperation networks. The higher the connectivity, the more stable the cooperation network (Zhang et al., 2017). Finally, there is research on the dynamics and heterogeneity of driving mechanisms. The driving mechanisms of IUR cooperation networks are not static but are influenced by exogenous factors such as policies. For example, the implementation of the “Double First-Class” construction policy has changed the driving mechanisms of IUR cooperation networks from an open structure relying on binary connections to a strong community structure with ternary closure (Yuan et al., 2025). In addition, different types of cooperating entities show different driving mechanisms in the evolution of IUR cooperation networks. For example, there are significant differences in the driving mechanisms of comprehensive universities and specialized universities in IUR cooperation networks. Comprehensive universities are more likely to break through the limitations of geographical proximity and form cross-regional and cross-disciplinary cooperation networks (Garcia et al., 2018).

In addition, with the continuous development of disciplines, the key role of knowledge characteristics in IUR cooperation has become increasingly prominent. In terms of the structural characteristics of knowledge networks, research has found that many networks exhibit scale-free network characteristics (Wang et al., 2023), where a minority of nodes have a large number of partners. These nodes play an important role in the rapid dissemination and diffusion of technology and knowledge, while the majority of nodes only collaborate with a few partners (Fioravanti et al., 2023). At the same time, some networks also have small-world network characteristics, with close local cooperative groups that help accelerate the flow and transformation of knowledge (Fritsch & Kudic, 2022; Wang et al., 2024). In addition, community structure characteristics are also quite common. There are communities of different sizes in the network, with nodes within the community being more closely connected and having a stronger willingness to cooperate. However, there are also a large number of nodes that have not established effective connections with super nodes (Li & Zheng 2024; De Paulo & Porto 2023). The evolution of knowledge networks is another focus of research. By analyzing network data over different periods of time, it has been found that indicators such as network scale and connection density change over time, and the distribution of key innovative entities and technological fields also shows stage characteristics (Chang, 2022; Pu et al., 2022). For example, the cooperation networks in some fields have gradually expanded from local areas to the national and even global level (Yang et al., 2021), the importance of key nodes and edges in the network has become more and more significant, and the cooperation model has also gradually shifted from being centered on a single entity to a pattern of multi-entity collaborative cooperation (Cheng et al., 2020).

However, existing research is relatively lacking in terms of the factors influencing knowledge networks in IUR cooperation, especially regarding the depth and width of knowledge. The depth and width of knowledge play a crucial role in IUR cooperation. They not only affect the construction and operation of IUR cooperation networks but also determine the innovation capability and adaptability of IUR cooperation. From the perspective of knowledge depth, the diversity of knowledge sources and types in knowledge networks is a key factor affecting the effectiveness of IUR cooperation (Wang, 2020). A network with a wide range of knowledge sources can integrate knowledge resources from different disciplines, industries, and regions, thereby providing a more comprehensive perspective and richer solutions for cooperative projects (Chen & Yan, 2023). For example, interdisciplinary cooperation projects often require the integration of knowledge from multiple disciplines such as physics, chemistry, and biology, while cross-industry cooperation may involve multiple fields such as manufacturing, services, and information technology. This diversity of knowledge can promote the collision and integration of innovative thinking, thereby increasing the success rate of cooperative projects (Von Wehrden et al., 2019). From the perspective of knowledge width, the scope of knowledge integration and application in knowledge networks is also an important factor affecting the effectiveness of IUR cooperation. A network with a wide range of knowledge application can successfully transfer knowledge from one field to another, achieving cross-field application and innovation of knowledge (Serrano et al., 2024). For example, artificial intelligence technology is not only widely used in the field of information technology but also shows great application potential in multiple fields such as medical care, finance, and manufacturing. This cross-field integration and application of knowledge can expand the innovation boundaries of knowledge networks, enhance the innovation capability of cooperative projects, and improve market competitiveness (Pathan, 2025).

In current research on IUR cooperation, despite numerous achievements, most studies have focused solely on individual network characteristics, such as the structure and evolution of IUR cooperation networks or knowledge networks, often neglecting the interactions between these two types of networks. This one-sided research perspective not only restricts a comprehensive understanding of the role of knowledge networks in IUR cooperation but also affects the innovation efficiency and effectiveness of IUR cooperation to a certain extent. Therefore, there is an urgent need to conduct systematic research to deeply analyze the interactive impacts between IUR cooperation networks and knowledge networks, in order to provide more scientific theoretical support for the innovative development of IUR cooperation.

The Louvain algorithm is a community detection method based on modularity optimization, used to identify applicant structures within a network. Its core idea is to optimize the network’s modularity to recognize groups of nodes with dense internal connections and sparse external connections (Sahu et al., 2024). The algorithm is characterized by its efficiency and scalability, making it suitable for large-scale networks.

In this study, we utilized the Louvain algorithm to divide the collaborative innovation network of the Double First-Class universities, industries, and research institutions into multiple IUR cooperation applicants. Specifically, the algorithm assesses the strength of connections between nodes by calculating the modularity of each node within the IUR collaborative innovation network. Modularity quantifies the connection patterns among nodes in the network, reflecting the density of internal connections within applicants and the sparsity of external connections across applicants. The number of collaborative patents between IUR entities directly affects their modularity values within the network. The greater the number of collaborative patents, the higher the modularity value between the IUR entities, indicating a stronger connection between them and a higher likelihood of them being assigned to the same applicant.

By doing so, this study was able to clearly reveal the pathways of collaborative knowledge flow between different applicants. This approach provides an important perspective for understanding the underlying mechanisms of IUR collaborative innovation and offers a scientific basis for optimizing the cooperative network and enhancing innovation efficiency.

This study calculates and ranks the information entropy of technical nodes, and combines the encouraged industry categories in the “Catalogue for Guiding Industry Restructuring (2024 Version)” to identify key technologies for industry-university-research collaboration. This catalogue clearly defines the encouraged industry categories, specifying the strategic focus of national economic development, which helps to concentrate resources and efforts on breakthroughs in key areas, promoting the optimization and upgrading of the industrial structure.

In the ERGM model formula, Y represents the network, y is a specific realization of the network, g(y) is a set of graph statistics, θ is the corresponding vector of parameters, and c(θ) is the normalizing constant, which ensures that the sum of the probabilities is 1, as defined in Eq: P(Y=y)=exp(θ′g(y))c(θ)P(Y = y) = {{\exp \left( {{\rm{\theta '}}g(y)} \right)} \over {c({\rm{\theta }})}}

In the analysis of collaboration networks, the ERGM holds significant importance. Collaboration networks typically involve cooperative relationships among multiple entities such as enterprises, research institutions, and individuals. These relationships are often influenced by a variety of factors, including the similarity and complementarity between entities, as well as their past collaborative experiences. The ERGM is capable of effectively capturing these complex relational patterns. Moreover, by estimating the model parameters, the ERGM can quantify the impact of different factors on the structure of the collaboration network, thereby providing a theoretical basis for optimizing the collaboration network.

Figure 2 illustrate that patent applications in IUR Cooperation Applicants. By using the Louvain algorithm, the IUR cooperation network was divided into applicants, resulting in a total of 122 applicants. Among them, two applicants have more than or equal to 1,000 patent applications, seven applicants have patent applications ranging from more than or equal to 5,000 to less than 10,000, 24 applicants have patent applications ranging from more than or equal to 1,000 to less than 5,000, four applicants have patent applications ranging from more than or equal to 100 to less than 1,000, and 85 applicants have patent applications ranging from more than or equal to 1 to less than 100.

Figure 2.

Distribution of Patent Applications in I-U-R Cooperation Applicants.

The Table 1 shows the top ten technological nodes and their corresponding entropy values in the IUR cooperation technology network from 2015 to 2024, ranked by information entropy. Information entropy is an indicator that measures the uncertainty and complexity of a system. The higher the entropy value, the greater the uncertainty of the technology in the IUR cooperation network. This may indicate that the cooperation patterns are more diverse and complex. It may also suggest that the development of the technology field is more active, involving more cooperation entities and innovative activities.

From 2015 to 2024, the ranking and entropy values of the technological nodes have changed. In 2015–2016, G06F (computers, computer systems based on specific computing models) and G01N (measuring, testing) had entropy values of 7.5617 and 7.5268, respectively, ranking first and second. This shows their importance and complexity in IUR cooperation. G06Q (specific configurations for particular computing models) ranked third with an entropy value of 6.9745. These technologies held core positions in the IUR cooperation network at that time, with high diversity and complexity in cooperation. Over time, the entropy values of these technologies continued to rise. For example, the entropy value of G06F reached 9.4506 in 2021–2022 and further increased to 10.5335 in 2023–2024. This indicates a continuous increase in the complexity and uncertainty of G06F in IUR cooperation, with the cooperation network likely involving more entities and broader cooperation patterns. G01N also showed a similar trend, with its entropy value rising from 7.5268 in 2015–2016 to 9.8302 in 2023–2024. This reflects the increasing activity and complexity of this technology field in IUR cooperation.

Other technological nodes, such as G06T (computers specially adapted for specific computational models), G06V (computers specially adapted for specific computational models), G01R (measuring, testing), H02J (circuit arrangements or layouts), and H04L (electric communication technology), also showed relatively high entropy values and fluctuating rankings during different periods. For example, G06T had an entropy value of 6.8715 in 2015–2016, ranking sixth. By 2023–2024, its entropy value had increased to 9.1705, and it ranked fifth. This indicates that the importance of this technology in IUR cooperation has gradually increased, and the complexity of cooperation has also risen. G06V gradually entered the top rankings after 2019–2020, with its entropy value reaching 9.3989 in 2023–2024, ranking fourth. This shows that its position in IUR cooperation has become increasingly important and that cooperation patterns have become more diverse.

Table 2 presents the changes in the number of key technology nodes, applicants, and patents over different periods from 2015 to 2024. From 2015–2016 to 2023–2024, the number of key technology nodes first increased slowly and then gradually stabilized at around 100. The number of applicants involved in the development of key technologies showed a fluctuating upward trend, increasing from 44 to 58. The number of patents applied for also exhibited a clear and continuous growth trend, rising from 9,308 to 46,718, with a more significant increase after 2019–2020, reflecting that the pace of innovation output in this field is accelerating.

In the evolution of IUR cooperation from 2015 to 2024, the R&D of each applicant mainly focused on key technology areas such as smart grids, data processing, energy management, and image processing. Among them, Applicant 40 and Applicant 26 played the main role in R&D, driving technological innovation and industrial upgrading in the relevant fields.

(1) Key technologies in the 2015-2016 period

Figure 3 shows 2015-2016 IUR cooperation stage. The IUR cooperation in this stage focused on areas such as smart grids, the energy internet, energy-information fusion systems, and energy management for wastewater treatment. Through interdisciplinary collaboration, applicants utilized advanced data processing, power supply, communication protocols, and water treatment technologies to promote improvements in energy efficiency, system stability, and environmental sustainability. A total of 87 key technical nodes were identified in this Period, covering 44 applicants.

Figure 3.

The Period of 2015-2016.

The IUR cooperation in the 2015-2016 period was closely related to and mutually promoted by the industrial development policies of the time. During this period, IUR cooperation focused on cutting-edge fields such as smart grids, energy internet, energy information fusion systems, and energy management in wastewater treatment, which were highly consistent with the development directions of the top ten key areas of the new generation of information technology industry, aerospace equipment, and others in the key areas technology roadmap of the “Made in China 2025” released in 2015. It also echoed the eight major fields of the new generation of information technology, new energy vehicles, energy conservation, and environmental protection in the “The 13th Five-Year Plan for the Development of Strategic Emerging Industries”.

During this period, IUR cooperation, through cross-disciplinary cooperation, utilized advanced data processing, power supply, communication protocols, and wastewater treatment technologies to promote the improvement of energy efficiency, system stability, and environmental sustainability. The key technology nodes identified and the number of applicants covered reflected the width and depth of the cooperation. The cooperation applicants 40, 26, and 10 carried out projects in the fields of smart grids (G01R, H02J, G06F, G06Q), energy information fusion systems (H04L, H02J, H04L), and energy management in wastewater treatment (C02F), which not only conformed to the guidance of national industrial development policies but also provided specific practical paths and technical support for achieving policy goals.

While promoting technological innovation and industrial development, these cooperation projects also received strong support from national policies, including financial investment and policy guidance, which accelerated the transformation and application of technologies. This close combination of policy and practice not only promoted the rapid development of related industries but also provided a solid foundation for the cultivation and development of national strategic emerging industries, and promoted the improvement of the entire society’s innovation capability and industrial competitiveness.

(2) Key technologies in the 2017-2018 period

Figure 4 shows 2017-2018 IUR cooperation stage. As can be seen from the figure, the structure of the IUR cooperation network in this stage is more complex, the technological fields are more extensive, and the cooperation is more closely knit. The connections between applicants are more frequent, and the sharing of resources and complementarity of strengths are more evident. A total of 94 key technical nodes were identified in this Period, covering 54 applicants.

Figure 4.

The Period of 2017-2018.

During the period of 2017-2018, the characteristics of IUR cooperation were closely related to the industrial policy orientation at that time. In terms of technological fields, the technological focus of cooperation applicant 40 was concentrated on G01R, G06Q, H02J, and the newly added H02H. These technological fields were closely related to the key areas mentioned in the “Three-Year Action Plan on Enhancing Core Competitiveness in the Manufacturing Sector (2018-2020)”, such as high-end equipment manufacturing, intelligent manufacturing of the manufacturing industry, and new energy. The key role of G06Q technology in the integration of information technology and business processes coincided with the requirements for promoting the deep integration of the Internet, big data, artificial intelligence, and the real economy in intelligent manufacturing. The application of H02J and H02H technologies in the optimization of power systems and power supply echoed the development needs of the new energy automotive industry and the new energy industry, providing energy security and technological innovation support for these industries.

Cooperation applicant 26 focused on G01R, G06K, G06F, and G01N. G06K and G06F technologies played important roles in data processing and computing, respectively. This was closely related to the development of the new generation of information technology industry as classified in the “Classification of Strategic Emerging Industries (2018)”. The new generation of information technology industry emphasizes the innovation and application of information technology. G06K and G06F technologies are the basic support for the development of this industry and can promote the improvement of data processing, information transmission, and computing capabilities, providing technological support for the digital and intelligent development of the new generation of information technology industry.

Cooperation applicant 20 mainly focused on the technological fields of G06F and C21N. G06F technology provided strong computing and data processing capabilities for the entire technological system and was a common technological support for the development of multiple industries. C21N technology, which focused on chemical processes, was closely related to the development of the new materials industry. The “Strategic Emerging Industries Classification (2018)” listed the new materials industry as one of the nine major fields. The technological support of C21N in the development and application of new materials helped to promote the innovative development of the new materials industry and met the demand for high-performance materials in high-end equipment manufacturing and new energy industries.

(3) Key technologies in the 2019-2020 period

Figure 5 shows 2019–2020 IUR cooperation stage. During this cooperation stage, the network structure and cooperation patterns of technology cooperation applicants exhibited more closely-knit and complex characteristics. The IUR cooperation in this stage showed new features and trends in terms of technological distribution and cooperation patterns. Applicants had closer cooperation in specific technological fields, especially in data processing (G06F) and business methods (G06Q). This cross-applicant cooperation facilitated the sharing of technological knowledge and the collision of innovative ideas. As cooperation deepened, the technological cooperation network became more complex, which might imply more frequent interactions among applicant members and more diversified cooperation projects. A total of 94 key technical nodes were identified in this Period, covering 50 applicants.

Figure 5.

The Period of 2019-2020.

Between 2019 and 2020, the technological research and development (R&D) phase of IUR cooperation was well-matched with industrial policies, showing a positive trend of coordinated development. In 2019, “Notice on Accelerating the Construction of Strategic Emerging Industry Clusters”, focuses on the construction of strategic emerging industry clusters and initially laying out 66 national-level clusters. IUR cooperation applicants actively responded. For example, cooperation applicant 40 carried out close cooperation in technical fields such as power supply/distribution devices (H02J) and data processing (G06F), which were highly consistent with the development needs of smart grid and other industrial clusters. This consistency between the direction of technological R&D and policy orientation enabled cooperation applicants to accurately connect with the national industrial layout, provide key technical support for key industries such as smart grids, promote industrial upgrading, and accelerate the formation and development of industrial clusters.

Entering 2020, the focus shifted to the integrated circuit, software, and new energy vehicle industries. The “Notice on Several Policies for Promoting the High-Quality Development of the Integrated Circuit and Software Industries in the New Era,” released in August, although not directly mentioning cooperation applicants, was closely related to data processing (G06F) technology as a key support. Cooperation applicant 26’s in-depth research in this field and cuttingedge technologies such as big data analysis and artificial intelligence provided strong technical momentum for the integrated circuit and software industries, helping them break through technological bottlenecks and enhance industrial competitiveness, which was highly consistent with the policy goal of promoting high-quality industrial development. In October, “Development Plan for the New Energy Vehicle Industry (2021-2035)”, which listed solid-state batteries as a priority. The technical accumulation of cooperation applicant 40 in the fields of power supply/distribution devices and data processing could provide strong support for optimizing the energy management system of new energy vehicles, developing intelligent charging technology, and industrializing solid-state batteries. This would promote the development of the new energy vehicle industry towards intelligence and efficiency, accurately aligning with policy orientation.

(4) Key technologies in the 2021-2022 period

Figure 6 shows 2021-2022 IUR cooperation stage. In this stage, technology cooperation applicants demonstrated more specialized and segmented characteristics. Each Cooperation applicants, based on its research focus and technological strengths, formed a unique technological distribution and cooperation pattern, especially in data processing (G06F) and image data processing (G06T). A total of 100 key technical nodes were identified in this Period, covering 55 applicants.

Figure 6.

The Period of 2021-2022.

Between 2021 and 2022, the technological R&D phase of IUR cooperation was closely matched with industrial policies, showing a high degree of synergy and complementarity. In 2021, the “Outline of the 14th Five-Year Plan (2021-2025) for National Economic and Social Development and Vision 2035 of the People’s Republic of China” clearly deployed a series of future industry incubation and acceleration plans in the fields of frontier science and technology and industrial transformation. The R&D direction of IUR cooperation applicants was highly consistent with these plans. For example, cooperation applicant 26 focused on data processing (G06F) and image data processing (G06T), which are key supports for frontier science and technology fields such as brain-like intelligence, quantum information, and future networks. Data processing technology plays an important role in the neural network simulation of brain-like intelligence and the data processing of quantum information, while image data processing technology provides technical support for intelligent monitoring and image recognition in future networks. In addition, the research of cooperation applicant 1 in the fields of catalysts, chemical or biochemical methods, etc., is closely related to the development of frontier science and technology fields such as gene technology, and provides basic support for future industries such as the bioeconomy.

Entering 2022, the “The 14th Five-Year Plan for the Development of Digital Economy” issued the development of new forms of business such as ultra-high-definition television, interactive video, immersive video, and cloud gaming, as well as the integration of technologies such as artificial intelligence, virtual reality, and 8K ultra-high-definition video. The in-depth research of cooperation applicant 26 in the fields of data processing and image data processing provided a strong technical basis for the application and development of these digital technologies. For example, image data processing technology is one of the core technologies of 8K ultra-high-definition video and immersive video, while data processing technology ensures the efficient operation of artificial intelligence and virtual reality. At the same time, the research of cooperation applicant 9 in the field of angle measurement and measuring devices (G01S) also provided support for the precise application of these technologies, such as spatial positioning and interaction in virtual reality and augmented reality.

(5) Key technologies in the 2023-2024 period

Figure 7 shows 2023-2024 IUR cooperation stage. In this period, technology cooperation applicants continued to deepen their expertise in specialized fields, characterized by crossapplicant and multi-technology integration, to adapt to the ever-changing technological development trends and market demands. A total of 100 key technical nodes were identified in this Period, covering 58 applicants. In the IUR cooperation phase from 2023 to 2024, compared with that from 2021 to 2022, the technological R&D has further deepened the characteristics of crossapplicant and multi-technology integration, and the compatibility with industrial policies has also become closer.

Figure 7.

The Period of 2023-2024.

During the period from 2021 to 2022, the IUR cooperation applicants had already shown a trend of specialization and segmentation in key technical fields such as data processing and image data processing, and the direction of technological R&D was in line with the key areas emphasized in the policies at that time, such as the digital economy and bioeconomy. For example, the research of cooperation applicant 26 in the fields of data processing and image data processing provided support for the application and development of digital technologies, which was consistent with the technological integration emphasized in the “The 14th Five-Year Plan for the Development of Digital Economy”.

Entering the period from 2023 to 2024, with the introduction of the concept of “new-quality productive forces” and the implementation of the industrial structure adjustment guidance catalogue, the technological R&D of IUR cooperation applicants has become more focused on the transformation and upgrading of strategic emerging industries and traditional industries. The research of cooperation applicant 40 in the fields of smart grids and energy management matches the strategic emerging industries emphasized in the new-quality productive forces, and its multi-technology integration model helps to promote the intelligent and green transformation of the energy industry. Cooperation applicants 9 and 10, in the combination of data processing and business methods, not only support the development of emerging industries but also provide impetus for the digital transformation of traditional industries, which is in line with the policy guidance for the transformation of manufacturing industry towards high-end, intelligent and green directions.

Knowledge Depth is a core indicator for measuring the professional level of IUR applicants in key technology research and development. A higher level of knowledge depth implies stronger professional capabilities and deeper technological accumulation, which can provide a solid technical foundation for IUR cooperation, thereby promoting the breakthrough and development of key technologies. Under the current trend of multidisciplinary integration, the impact of the knowledge depth of a cooperative group on innovation performance is becoming increasingly significant. A broad and balanced knowledge depth helps to facilitate the collision and integration of knowledge from different disciplines, thus generating more cross-disciplinary innovative achievements (Zhang et al., 2021). Research has shown that the deeper the knowledge depth of the main body, the more thorough its members’ understanding of knowledge, and consequently, the higher the innovativeness of the external knowledge received during the cooperative R&D process (Singh et al., 2021). In addition, knowledge value, social capital, and the capabilities of team leaders significantly affect the knowledge-sharing performance of scientific research and innovation teams (Sankaran et al., 2021). Among researchers within and outside the organization, sharing and empathy regarding technical issues and values, as well as mutual trust, are the foundations of knowledge sharing. The development and expansion of such preferences and interactions can promote the deepening and innovation of technical knowledge. Meanwhile, the diversity of knowledge depth and the stability of cooperation among R&D personnel play a moderating role in the relationship between the roles of key inventors (such as gatekeeper roles and bridge roles) and the exploratory innovation changes of the main body (Zhang et al., 2019). The knowledge depth network, as a carrier of knowledge flow, is a prerequisite for the efficient utilization of knowledge. The smoothness of knowledge flow can moderate the impact of the knowledge depth network on innovation performance (Yu et al., 2018).

However, an excessive concentration of knowledge depth in a specific field may also bring negative effects. When knowledge is overly concentrated in one field, it may lead to other potentially innovative directions not receiving sufficient resource support, thereby limiting the overall improvement of innovation performance (Yang et al., 2024). Moreover, there is a threshold effect between knowledge depth and diversification in the impact of knowledge acquisition on collaborative innovation. When knowledge acquisition exceeds a certain limit, it may produce a negative effect. This is because the wider the range of integrated knowledge, the more complex the problems in the creation and management of the integration process, which may ultimately lead to integration costs exceeding the profits brought by new knowledge (Yang et al., 2024).

Knowledge Width is used to describe the coverage and diversity of knowledge that an organization or system has in different fields or topics. By measuring knowledge depth, this study can understand whether IUR cooperation applicants have the ability to integrate knowledge across fields in key technology research and development, which is of great significance for addressing complex technological challenges and promoting interdisciplinary innovation. In IUR cooperation, the positive impact of Knowledge Width on IUR applicants is mainly reflected in its ability to expand the knowledge base of the applicant and increase the diversity of knowledge sources, thereby promoting the improvement of innovation performance (Gkypali et al., 2017). Within the applicant, enterprises, universities, and research institutions each possess different knowledge systems and areas of expertise. When Knowledge Width increases, all parties can access more cutting-edge technological trends, changes in market demand, and innovative achievements in interdisciplinary fields (Zhang & Ba, 2024). For example, enterprises can learn about the latest academic research results and technological development trends through cooperation with universities and research institutions, thus providing strong support for their product innovation and strategic planning. Universities and research institutions can also better adjust their research directions with the market experience and industry insights of enterprises, making scientific research results more practical and market-oriented, and promoting the sharing of resources and complementary advantages among IUR applicants at a higher level (Philbin, 2008). In addition, Knowledge Width also has an important impact on the resource integration capabilities of IUR applicants. In the knowledge economy era, knowledge itself is an important resource. By expanding Knowledge Width, applicant members can better identify and utilize various potential resources, including technology, talent, and funding (Moorthy & Polley, 2010). For example, universities and research institutions can provide high-quality talent support and cutting-edge technological consulting for enterprises based on their profound accumulation in academic research and talent cultivation. Enterprises, on the other hand, can use their financial advantages and market channels to provide funding guarantees and platforms for the transformation of scientific research projects from universities and research institutions (Wright et al., 2014). By enhancing Knowledge Width, all parties can more effectively integrate resources, create a favorable situation for collaborative innovation, and improve the overall operational efficiency of IUR applicants.

Despite the many positive effects of Knowledge Width in IUR cooperation, an excessive pursuit of Knowledge Width can also have some negative impacts. When Knowledge Width is overextended, the innovation resources of IUR applicants may be dispersed across multiple fields, making it difficult to form sufficient technological depth and core competitiveness in key areas, thereby weakening their advantages in specific fields (Xu, 2015). At the same time, as Knowledge Width increases, the amount of information that applicant members need to process and integrate rises significantly, which may lead to information overload, increase the cost of information processing, and potentially reduce the effective utilization efficiency of knowledge, thus diminishing the potential innovative benefits brought by Knowledge Width (Wang et al., 2024). In addition, the expansion of Knowledge Width may also lead to knowledge redundancy. The knowledge obtained by applicant members from different channels may overlap significantly, not only wasting knowledge resources but also reducing innovation efficiency and hindering the rapid flow and effective integration of knowledge (Hongwei & Ludan, 2016). Moreover, the expansion of Knowledge Width is usually accompanied by the complexification of the cooperation network structure. IUR applicants need to coordinate with different types of partners in multiple fields, which may lead to a significant increase in coordination costs, reduce the efficiency of knowledge sharing, and even trigger cooperation conflicts (Gui et al., 2019). Finally, the increase in Knowledge Width may weaken the advantages of structural holes in knowledge acquisition and innovation. The resources obtained by applicant members from both ends of the structural holes may overlap with their existing knowledge, making it difficult to obtain the expected heterogeneous information and thus reducing the ability of applicant members to obtain innovative opportunities through structural holes (Khan, 2019).

Knowledge Combination ability is a key behavioral capability in the innovation process. It reflects the ability of individuals or organizations to establish and alter the connections between knowledge units to form new combinations or change existing ones. This ability not only reflects the dynamism of knowledge but also reveals the efficiency of knowledge flow and integration in IUR cooperation. IUR cooperation networks are characterized by their diversity, encompassing knowledge sources from different disciplines, research teams at various levels, and a wide range of collaborative research projects (Zhang et al., 2025). Knowledge combination can re-integrate the knowledge dispersed among these network elements (Han et al., 2020). As the scale of the cooperation network expands, the volume of knowledge grows, making it increasingly difficult to filter out knowledge relevant to specific innovation goals (Jin et al., 2024). The cost of precisely locating such knowledge continues to rise, thereby affecting innovation performance (Craighead et al., 2009). Secondly, the increase in network scale brings about the diversification and fragmentation of knowledge sources (Yoon et al., 2015). Knowledge from different sources varies greatly in structure, expression, and quality (Wang et al., 2023). Lastly, the expansion of the network scale accelerates the pace of knowledge renewal (Wang & Zhou, 2023). New knowledge continuously emerges while old knowledge is rapidly phased out. Universities need to conduct more meticulous screening of knowledge resources, prioritizing the combination of knowledge with the greatest potential to generate high-value innovation outcomes, which in turn reduces the innovation efficiency of IUR cooperation (Tijssen et al., 2009). This requires universities to continuously re-evaluate and adjust existing knowledge combination plans, adding extra time and resource costs (Vyas, 2024).

This part proposes the following three key hypotheses, which aim to explore the core elements driving the formation and development dynamics of IUR cooperation applicants (Figure 8), with network characteristics serving as control variables (Table 3). These hypotheses reveal the key factors influencing the applicants’ dynamic mechanisms and their interplay from different perspectives, thereby laying the foundation for subsequent empirical research.

Figure 8.

Research Hypotheses.

Table 3.

Variable	Feature	Equation	Concept
	Degree	Degreei=∑jAijDegre{e_i} = \sum\nolimits_j {{A_{ij}}}	The number of other nodes directly connected to a node, where Aij is an indicator variable representing whether there is an edge between node i and node j (if there is an edge, Aij=1; otherwise, Aij=0).
Control variables	Betweenness Centrality	BCi=∑s≠i≠tσst(i)σstB{C_i} = \sum\nolimits_{s \ne i \ne t} {{{{{\rm{\sigma }}_{st}}(i)} \over {{{\rm{\sigma }}_{st}}}}}	The measure of a node’s ability to control information flow in the network, where σst represents the number of shortest paths from node s to node t, and σst(i)是 represents the number of those shortest paths that pass through node i.
	Closeness Centrality	CCc(v)=1∑u∈V{ V }d(v,u)C{C_c}(v) = {1 \over {\sum\nolimits_{u \in V\left\{ V \right\}} {d(v,u)} }}	A measure used to assess the importance of a node, reflecting the degree of closeness between a particular node and all other nodes in the network.
	Knowledge Depth	knowledgedepthi=Σ(countNP)2knowledg{e_{dept{h_i}}} = \Sigma {\left( {{{count} \over {NP}}} \right)^2}	The extent, complexity, and systematic nature of an individual’s or organization’s knowledge in a specific field. It reflects the professional level and mastery of a person or organization in a particular domain.
Independent variables	Knowledge Width	knowledgewidh =1−Σ(countNPi)2knowledg{e_{widh }} = 1 - \Sigma {\left( {{{{\rm{ }}count{\rm{ }}} \over {N{P_i}}}} \right)^2}	A measure used to describe the scope and diversity of the knowledge possessed by an organization or system across different fields or subjects.
	Knowledge Combination	Clustering Coefficient−1=12*tripletsk*(k−1)Clustering Coefficien{t^{ - 1}} = {1 \over {{{{2^*}triplets} \over {{k^*}(k - 1)}}}}	The behavior of individuals or organizations in the innovation process of establishing and altering the connections between knowledge units to form new combinations or change existing ones.

• H1: The Knowledge Depth has a differentiated impact on the key technology cooperation in IUR applicants at different stages.

• H2: The Knowledge Width has a differentiated impact on the key technology cooperation in IUR applicants at different stages.

• H3: The Knowledge Combination has a positive impact on the key technology cooperation in IUR applicants at different stages.

Over the decade from 2015 to 2024, the impact of Applicant knowledge depth, Knowledge Width, and knowledge combination capability on the network structure of IUR cooperation applicants has shown distinct stage-specific characteristics. In the early periods (2015-2016 and 2017-2018), the negative impact of knowledge depth and width on network formation was relatively significant, indicating that an excessive focus on knowledge depth in a specific area or an overexpansion of Knowledge Width may weaken the stability and efficiency of the cooperation network. However, the knowledge combination capability showed a significant positive effect in all periods, demonstrating its crucial role in integrating knowledge resources from different fields and promoting network stability and development. As time progressed (from 2019-2020 onwards), the positive impact of Knowledge Width gradually emerged, suggesting that moderately expanding the scope of knowledge can bring more opportunities for cooperation and resource integration to the network. Meanwhile, knowledge depth also showed a significant positive impact in the later periods (2021-2022 and 2023-2024), indicating that in-depth exploration of knowledge in a specific field can provide important support for network stability and cooperative efficiency. This suggests that in the development of IUR cooperation Applicant networks, it is necessary to balance knowledge depth and width in the early stages to avoid negative impacts, while in the later stages, deepening knowledge depth and expanding Knowledge Width can further promote network development. The knowledge combination capability has played a positive role throughout the process and is the core ability to promote the stability and development of IUR cooperation networks. Therefore, when building and optimizing network structures, IUR cooperation applicants should focus on cultivating and enhancing knowledge combination capabilities, and reasonably adjust knowledge depth and width according to the stage of development to achieve efficient operation and sustainable development of the network.

Over the past decade, the evolution of key technologies and their impact on the network structure of IUR cooperation in China have both shown significant dynamic changes.

The high consistency between the evolution of key technologies and industrial development needs. By constructing a “Technology–Applicant” network, this study accurately identified the key technological fields in Chinese IUR cooperation over the past decade, such as G06 (data processing, business methods, image data processing, etc.), G01 (measurement of electrical variables and optical fields), H02 (power supply/distribution devices), and H04 (telecommunication technology). These key technological fields are highly aligned with China’s current industrial development needs, demonstrating the scientific nature and effectiveness of the research method. The IUR cooperation of Chinese “Double-First-Class” universities has shifted from a single technological field to the deep integration of multiple technological fields and expanded from traditional application areas to emerging ones, reflecting China’s competitiveness and influence in global technological competition.

The impact of knowledge characteristics on the formation of IUR cooperation networks. On the one hand, the ability to combine knowledge has always played an important role in promoting the formation of IUR cooperation networks. Applicants can effectively integrate knowledge resources from different key fields, thereby promoting the stability and development of cooperation networks. On the other hand, the dynamic impact of knowledge depth and width is evident. During the periods of 2015–2016, 2021–2022, and 2023–2024, an excessive focus on the depth of a specific key technological field or an over-expansion of knowledge width may weaken the stability and cooperation efficiency of the network. In contrast, during the periods of 2017–2018 and 2019–2020, moderately expanding knowledge width and efficiently integrating knowledge resources from different fields were of great significance to the stability and cooperation efficiency of the network.

The regulatory role of network structural characteristics on cooperation efficiency. Applicant degree and applicant closeness centrality have significant positive impacts on network formation, indicating that these characteristics help enhance the stability and cooperation efficiency of the network. In contrast, applicant betweenness centrality shows a significant negative impact, further demonstrating that the internal structural characteristics of the network play an important regulatory role in network stability and cooperation efficiency.

This study constructs a “Technology–Applicant” bipartite network model to analyze the key technologies in IUR cooperation in higher education institutions. It not only enriches the theoretical framework for analyzing IUR cooperation networks and provides a new research perspective and methodology but also has significant practical guiding significance. On the theoretical level, this study deepens the understanding of the mechanisms of IUR cooperation and the process of technological innovation, providing new research materials and theoretical support for fields such as technology management, innovation management, and network theory. By identifying and analyzing key technologies and their driving forces for cooperation, this study reveals the dynamic evolution patterns and underlying driving mechanisms of key technologies in IUR cooperation Applicant networks at different stages. On the practical level, this study provides clear directions and strategic recommendations for cooperation among universities, enterprises, and research institutions. By identifying key technologies, relevant actors can more effectively allocate resources, optimize cooperation models, and improve R&D efficiency. Meanwhile, the analysis of evolutionary paths provides a scientific basis for corporate technology strategy planning and government science and technology policy-making. It helps promote industrial upgrading and technological innovation. In addition, this study also offers references for the transformation of university scientific research results and the docking of enterprise technology needs. It promotes the deep integration of IUR and accelerates the industrialization process of scientific and technological achievements. In summary, this study not only expands the research boundaries of IUR cooperation network analysis in theory but also provides valuable decision-making support for relevant actors in practice. It has important theoretical and practical significance for promoting IUR cooperation and technological innovation.