Journal of Data and Information Science, 2018, 3(2): 38-61
doi: 10.2478/jdis-2018-0008
A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory
Haiyun Xu1, Chao Wang1,3, Kun Dong1,3, Rui Luo1,3, Zenghui Yue4, Hongshen Pang5
1Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
2Institute of Scientific and Technical Information of China, Beijing 100038, China
3University of Chinese Academy of Sciences, Beijing 101407, China
4School of Medical Information Engineering, Jining Medical University, Rizhao 276826, China
5Library, Shenzhen University, Shenzhen 518060, China
 Cite this article:
Haiyun Xu, Chao Wang, Kun Dong, Rui Luo, Zenghui Yue, Hongshen Pang. A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory. Journal of Data and Information Science[J], 2018, 3(2): 38-61 doi:10.2478/jdis-2018-0008


Purpose: This study aims at identifying potential industry-university-research collaboration (IURC) partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory.Design/methodology/approach: The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors.Findings: Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations.Research limitations: In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process.Practical implications: Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements.Originality/value: Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.

Key words: Institutions collaboration) ; Collaboration network) ; Innovation chain) ; Industrial chain) ; Industry-university-research institutions)
1 Introduction

At present, the research and development (R&D) capabilities of Chinese enterprises are generally weak and the conversion rate for scientific achievements at research institutes and universities is relatively low (Shen, 2016). The reason technology transfer does not go smoothly is that there are obstacles to the diffusion of innovation elements in the innovation chain. It has been proven that cooperation across industry, universities, and research institutions is one way to effectively promote the diffusion of such innovation elements in the innovation chain (Huang et al., 2017). Thus, the requirement for enhancing such industry-university-research institution collaboration (IURC) is particularly urgent. Accordingly, identifying the potential IURC partnership is critical for promoting the conversion and implementation of scientific achievements in China. Most existing methods for identifying such cooperative partners are based on technology similarities in institutional R&D. However, the IURC cluster, represented by the industrial cluster in China, has not achieved collaboration innovation. One major reason is the fierce competition that stems from extreme homogenization, making it difficult to generate a cluster effect and achieve collaboration effectively (Huang, 2014). Therefore, methods that are solely based on technical similarities are not necessarily effective in identifying IURC partners.

The method proposed in this paper is meant to improve the matching process for collaborative partners, and thereby, overcome barriers in the innovation process that prevent collaborations from forming and to encourage innovations to move along the chain. In this study, we apply innovation chain theory, considering knowledge spillover effects in the innovation chain and important variables that affect IURC. We begin with the law of innovation dissemination, utilizing multisource data and combining bibliometric and econometrics analyses to identify IURC partners.

2 The status quo of IURC partner identification
2.1 The status quo of the quantitative analysis method in IURC partner identification

Yoon and Song (2014) summarized the methods for partner selection and classified them into three categories: mathematical programming approaches (Soles-vik & Encheva 2010), rating/linear weighting approaches (Wang & Chen 2007), and artificial intelligence techniques (Fischer, Jähn, & Teich, 2004). They utilized patent information to investigate innovation activities by applying morphological analyses (MA) and generative topology maps (GTM) to the process used in identifying technology configurations and visualizing the collected patent information. Park et al. (2015) explored potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analyses. The potential R&D collaboration partners were visualized in the form of a patent-assignee-level map based on the technological similarity between patents using network analyses. Wang et al. (2017) identified R&D partners through subject-action-object (SAO) semantic analyses in a problem and solution pattern and combination term clumping. Xu et al. (2015, 2016) used multiple indicators to synthetically assess the new IURC situation and identify cooperation partners for collaboration. By extending a multimodal data analysis and by considering the technical similarity of IURC institutions, the competitive position, types, and core-periphery structures of these institutions in the cooperation network, potential IURC partners were identified.

Most existing studies on IURC employ a single variable to identify cooperation partners. Such studies still lack comprehensiveness in cooperative partner identification and consider the technology similarity only from a technical point of view. Even if one begins with technology similarity to identify some potential IURC possibilities, it is inappropriate to identify IURC partners using only technical similarity. For example, the IURC cluster—represented by the industrial cluster in China—has not achieved collaboration innovation. The reasons for this are that the degree of industry specialization within the cluster chain is still rather low and fierce competition caused by homogenization make it difficult to achieve collaboration effectively (Huang, 2014). Thus, the similarity of the technology, but not its convergence, is an important factor in cluster innovation and proper IURC. Therefore, it is necessary for institutions to identify proper IURC partners with more complementary capabilities.

2.2 The forming condition of knowledge spillover effect in IURC

There are significant differences between IURC and pure scientific research cooperation. When a sustainable IURC partnership is formed, it needs to consider several market factors beyond research ability from industrial and economic perspectives. When the rules and characteristics of cooperation are known, the IURC can be established. Therefore, only when the factors of collaboration have been recognized, can potential IURC partners be identified effectively. Usually, both cooperation and competition exist at the same time where similar technologies exist. However, for IURC, it is only when cooperation is heavily emphasized that knowledge spillover effects occur.

In an IURC that works well, the expectation from universities and research institutions is to bring their scientific innovations and inventions to market, thereby promoting economic development, and gaining further financial support for R&D (Xu et al., 2017). On the other hand, industry enterprises expect to gain more advanced technology to upgrade their products. Furthermore, some disruptive innovation can possibly generate more competitive advantages and higher profits (Zhang, 2012). The promotion of and demand for scientific innovation in universities, research institutes, and industries are referred to as “knowledge potential.” The essence of knowledge potential is the “push” and “pull” effect in knowledge dissemination. This knowledge potential is then the innovation impetus in the formation of the IURC (Yue et al., 2015).

3 Methodology of IURC partners based on innovation chain theory
3.1 The connotation of innovation chain theory and its applicability in IURC

Cai (2002) defined the innovation chain theory as the commercialized process of science and technology knowledge through the transformation of technical innovation (Cai et al., 2001). The thinking around the innovation chain optimizes the innovation process through systematic analysis. The driving force behind the innovation chain largely comes from demand; it is a functionally linked network structure model that is demand-driven. The functional links are node links that can meet some specific functions (Bamfield, 2006; Larson & Brahmakulam, 2002; Liang, 2007; Timmers, 1999). In this study, the innovation chain is defined as a chain process with five orderly innovation goals: the transformation of scientific problems to scientific theory, the transformation of scientific theory to practical application, the transformation of actual application to product manufacturing, the transformation of product manufacturing to commodities, and the transformation of goods to industrialized production.

Given that the essence of the innovation chain is the dissemination of innovation factors consistent with the IURC process that leads to the knowledge spillover effect, innovation chain theory can effectively describe the characteristics of knowledge spillover and exchange phenomena in IURC. Accordingly, this paper presents a new method to identify IURC partners based on innovation chain theory, attempting to survey the distribution of innovation technology in the innovation chain of different institutions in search of additional complementary capabilities among them.

3.2 Model of the innovation chain

According to the different goals of innovative activities in the innovation chain, we divide the chain into five phases: basic research, application research, transfer and transformation, commercialization, and industrialization (Figure 1). The different innovation phases have specific innovation activities but are connected to each other and work synergistically. Each innovation phase is divided into several functional nodes including enterprises, scientific research institutions (research institutes and universities), government-involved innovative activities, and other innovation elements. The innovation is driven by the interaction between these functional nodes.

The reason technology transfer does not go smoothly is that there are obstacles to the diffusion of innovation elements in the innovation chain. IURC is one way to effectively promote the diffusion of innovation elements in the innovation chain.

Figure 1.

Model of the innovation chain

In this study, IURC identification research is established based on the entire innovation process, including a series of orderly innovation targets. The detailed analysis is as follows:

(1) In the basic research phase, scientific problems are formulated into scientific theories. The evaluation index in this phase is a comparative analysis of quantity and quality based on scientific research papers.

(2) In the application research phase, scientific theory is further transformed into a practical application. The evaluation index in this phase is a comparative analysis of quantity and quality based on patent applications and authorizations.

Both the basic and application phases are part of technology development activity and universities and research institutes are the most important participants, although a few research enterprises with strong R&D divisions also conduct some basic and applied research in China.

(3) In the transfer and transformation phases, enterprises begin to participate as scientific research institutions. The evaluation index in this phase is the number of technologies transformed into technological achievements.

(4) In the commercialization phase, products are developed for sale in the market. The evaluation index is the quantity of the commodities.

(5) The target of the industrialization phase is to achieve industrialization, and the evaluation index is the industrial scale.

The commercialization and industrialization phases are part of the economic activity category, and enterprises are the major participants, while universities and research institutes are also participants.

3.3 Identification method of IURC partners

The process of IURC partner identification based on innovation chain theory is divided into two modules: (1) core network analysis of the existing cooperative institutions, and (2) institutional competitiveness analysis based on the innovation chain.

(1) The core network analysis of the existing institutions

The cooperative network is a complex one. In the cooperative network, although the number of nodes is very large, its core nodes are very small. From the structural viewpoint of the complex network, these core nodes are connected closely, while other noncore nodes are connected to these core nodes only by a few edges.

The core nodes of a complex network can be found through the k-core network. For a network, if any node has at least k neighbors that are also in the same network, then the network is a k-core network (Baxter et al., 2012; Newman, 2003). K-core nodes are the core nodes in a complex network and k-core analysis is often used to mine core members. In this study, the k-core network is used to analyze the core cooperative institutions in the basic and applied research phases.

(2) The institutional competitiveness analysis based on the innovation chain

The institutional competitiveness analysis contains two-levels of evaluation indicators. The first-level indicators are the potential variables: basic research, applied research, transfer and transformation, commercialization, and industrialization. To ensure the integrity and data availability of the evaluation indices, the participants in each phase are considered as the analyzed objects and research papers, patents, industrial, and economic data, as the second-level indicators. The evaluation content is subdivided by the secondary indicators and the comprehensive and accuracy of the institutional competitiveness analysis can be further improved.

According to Table 1, feasible measures are selected to analyze the layout of the innovation chain and institutional competitiveness in the field.

Table 1

Evaluation indexes of institutional competitiveness based on innovation chain.

To access the relationship between the amount of basic research, the influences on the basic research results, and the basic research areas of the core institutions, the research papers in the database of academic resources are identified, and the quantity, influences on these papers, and the relationships between institutions analyzed.

Both the citation analysis and the citation impact analysis index (CNCI) (Incites, 2015) are used to analyze research influence. CNCI is a relative evaluation indicator in the Thomson Reuters InCitesTM database. CNCI is an unbiased indicator that excludes the subject area, publication year, and document type. The influence of collected papers of different sizes and different subjects can be compared using the CNCI. When the value of the CNCI is equal to one, the cited performance of this group of papers is equivalent to the global average. Thus, if the index value is greater than one, the cited performance of this group of papers is higher than the global average. Correspondingly, if the index value is less than one, the cited performance is lower than the global average. If CNCI is equal to N (N≥2), the cited papers of this group are N times higher than the global average.

b. Applied research analysis

The patents are retrieved from a patent database and used to analyze their quantitative characteristics to determine the quantity of applied research, the level of the applied research institution in the core research institution network in basic research, and the relationship between core institutions in applied research.

The quantitative patent analysis also includes the analysis of the quantitative characteristics of patents from various patent institutions and different regions. This study uses comparative analyses to assess and compare the basic research papers and quantitative characteristics of the patents in the basic field and to measure the basic and applied research levels at the same institution.

c. The analysis of transfer and transformation, commercialization, and industrialization

The data for the transformation, commercialization, and industrialization of related research institutions are retrieved from government and trade association websites and economic databases. The transfer and transformation analyses focus on the quantitative characteristics of the products that can be produced. The degree of transfer of the applied research is evaluated by comparing the relationship between the applied research results and the quantity of products that can be produced. The main research of the commercialization analysis is in the context of products converted into goods for sale. The extent of commercialization is evaluated by comparing the number of products and the products that can be marketed as well as the quantity associations. The industry scales can be further divided into the objects of production and sales. The degree of industrialization can be evaluated by analyzing the quantity characteristics and geographical distribution of the objects of production and sales.

3.4 The hypothesis of IURC partner identification

Listed below are the four hypotheses that identify the IURC partners based on the innovation chain theory.

Hypothesis 1: There is a greater potential for collaboration among agencies with different attributes of spatial distribution in the innovation chain. Thus, the competition within universities-enterprises or research institutes-enterprises is less than enterprises- enterprises.

This hypothesis stems from the belief that the heterogeneity capacity of different institutions in the innovation chain can contribute to cooperation potential. The primary choice in the selection of the cooperation partner is whether the partner to be selected can provide some advantage or expertise, and whether it can bring in complementary resources to achieve a goal that cannot be achieved by the primary establishment relying on its own resources. Kogut and Chang (1991) pointed out that complementary resources are a key driver in cross-organizational cooperation based on resources. Laursen and Salter (2006) argued that the rapid development of technology makes the business model of the enterprise more open, and technology is more dependent on external sources, especially in the high-tech industry.

Hypothesis 2: Institutions facing increased competitiveness have more opportunities in establishing cooperative partnerships. If an institution is in a phase with increased competitiveness for basic research, applied research, transfer and transformation, commercialization, and industrialization, then it is more likely that this institution will be selected as a cooperative partner.

This hypothesis arises from the mechanism of preferential attachment in the collaborators’ selection. It has also been shown that competitive factors, such as knowledge capacity and market coverage, are important, and it is more likely that institutions who hold more patents, patent licenses, and trademarks, will be selected as collaborators (Barber, 2007).

Hypothesis 3: Institutions that are geographically closer have more opportunities for cooperation. However, a spatial location advantage is not a major influential factor in the development of current prosperous networks.

This hypothesis derives from that the fact that the spatial advantage can reduce the cost of cooperation. Wen (2012) studied a cooperative model of patents systematically with quantitative methods and conducted a quantitative analysis on the cooperative relationship and cooperation mode between inventors and patent holders. She also analyzed the knowledge exchange accompanied by patent cooperation and summed up the laws and patterns of knowledge exchange; she found that the closer the spatial location between institutions, the greater the possibility for patent cooperation between them.

In modern China, local government is most interested in its own regional economic development through the promotion of cooperation between industry and academia in the local region. Therefore, there are still some obstacles in cross-regional cooperation (Qing & Liu, 2016).

Hypothesis 4: If an institution is very prominent in the cooperative network, and there are prior cooperation activities, the institution represents an open-type innovation institution with greater potential to cooperate.

This hypothesis derives from the belief that if there are cooperation experiences among institutions, there is increased trust among them, establishing a smoother cooperation outcome (Barber, 2007).

4 Empirical analysis
4.1 Data sources and analysis tools

The gene engineered vaccine (GEV), also known as the “genetically engineered vaccine,” is the vaccine produced by recombinant DNA cloning; it is the expression of the protective antigen gene, or produced by the expressed antigen products, or the recombinant itself (Tang et al., 2006). As the vaccine industry is related to strategic security, it has received extensive attention in many countries. Further, the GEV is a typical high technology with an intact knowledge innovation chain. Therefore, this study chose the major research institutions in the field of GEVs to identify potential cooperation partners.

This empirical analysis is based on the SCI papers indexed by the Web of Knowledge and the Derwent innovations index database and retrieved June 2015. We built a detailed search strategy (not shown here owing to its complexity), and obtained 3962 papers and 999 Chinese patent applications. The Thomson Data Analyzer (TDA) (Thomson Reuters, 2015) was used for text data analyses. We then identified major patent holders, technical topics, and types and names of institutions as the basis for the data analysis, ultimately, obtaining a result with 180 institutions.

4.2 The k-core analysis of the existing cooperation institutions network

Figure 2 is the k-core network based on co-authored papers where the number of co-occurrences is no less than three. Figure 2 shows three cooperative networks involving three domestic research institutions including. The first one is Shanghai Jiaotong University, Fudan University, the Chinese Academy of Sciences. The second one is Chinese Academy of Military Medical Sciences and Chinese Academy of Agricultural Sciences. The third one is Academy of Medical Sciences (Peking Union Medical College) and the Russian Academy of Medical Sciences. The elicited results show that these seven institutions are major cooperative institutions in basic research on GEVs. There are close cooperative relationships within the three cooperative networks. At the same time, cooperative relations between research institutions are often frequently compared with these universities.

Figure 2.

K core network of GEV cooperation-based publications in China domestic.

4.3 The key impact factors for the GEV in patent-to-product transformation

A lot of research analyzes the factors blocking the path between R&D results and commercial production and the impact of those factors on patent-to-product transformation. In the area of GEVs, there are many impact factors affecting the decision-making at commercial enterprises in terms of how to acquire and produce the vaccine. Nine patented technology-effect factors considered for the genetic vaccine in its patent-to-product transformation are included in this study: good immunogenicity, production readiness, high safety, good profitability, low cost of production, high-efficiency expression, high purity, active immunization, and good repeatability.

We construct a two mode network of the institutions that have more than 10 GEV patent applications and nine technology-effect, as shown in Figure 3. The larger the institution nodes in the graph, the more technology-effect the institution’s patents have; the larger the technology-effect nodes, the more institutions there are involved in this technology-effect; the thicker connection line between the nodes indicates that the organization has advantages in this technology-effect.

Figure 3.

Two mode network of GEV patent applications and technology-effect.

Here, we use community discovery to analyze different technology-effect communities. The community in the network reflects the local characteristics of the network and the interrelation among its internal nodes. Different communities have different structural characteristics. The nodes in the same community are closely connected with each other and the connections among the communities are relatively sparse. The structure or attributes of the same community have some similarities (Newman, 2004). The patents applied by the patent agencies in the community have the same technology-effect and these institutions show a closer connected status. The fast unfolding community discovery algorithm proposed by Blondel et al. (2008), whch is a non-overlapping community discovery algorithm based on a block modulus. The two mode network is divided into five communities: #1, #2, #3, #4, and #5. The patents applied by patent agencies in community #1 have two technology-effect factors: production-readiness and high-efficiency expression, in #2, active immunization, in #3, high purity, good repeatability, and good profitability, in #4, low cost of production, and in #5, both good immunogenicity and high safety.

To achieve the commercialization of the GEV, all the nine technology-effect factors need to be well developed. There are interdependencies among the agencies in five communities, that is, similarities in technology-effect. Therefore, these patent application agencies have the basis for cooperation with each other.

In addition, the figure shows that the patents applied for by the patent agencies in communities #2 and #4 have fewer patents outside their communities, indicating that the patents of these communities are different from those of other community organizations. In addition, the UNIV PLA THIRD MILITARY MEDICAL organization, except for the unconnected patented effect in #2, has links to the technology-effect factor of patents in other communities, indicating that the patents applied by UNIV PLA THIRD MILITARY MEDICAL are functionally equivalent to those in other communities. The overlap is so strong that this agency may have a greater potential for cooperation with patent agencies in other communities.

4.4 The identification result of IURC partners based on the innovation chain

(1) Competitiveness analysis in basic research

The top 19 institutions with a high number of SCI papers in GEVs in China (Table 2) include five scientific research institutes (accounting for 26.3%). The total number of papers was 72 (44.4%), and the average citation number was 10.36. The results show that the number of scientific research institutes in the field of domestic GEVs is small but the number of fundamental research studies is large and has a high impact, which constitutes the backbone of basic research in the field of GEVs. In addition to the five listed institutions, the other four institutions, Huazhong Agricultural University, China Agricultural University, Jilin Agricultural University, and China Agricultural University, account for a total of 16 papers (9.9%) with an average citation number of 12.65. Although the number of papers from these agricultural universities is very small, despite the high research influence, these four agricultural universities play an important role in the field of basic research on gene vaccines in China. Moreover, most of the remaining colleges and universities are national key institutions with strong capacities in scientific research. Therefore, these colleges and universities constitute the main research power in the research area of the GEV.

Table 2

Publication of SCI papers of domestic GEV by the amount of top19 institutions.

Among the top 19 institutions, there are four institutions whose CNCI value exceeded 1, including two research institutes and two agricultural universities. The citations of papers from these four institutions exceeded the global level. Among them, the CNCI value for the The National Center for Nanoscience and Technology of China was 6.23. The citation of the paper was 6.23 times the global average. There were also 15 institutions (78.9%) below 1, indicating that most of these papers were not influential.

(2) Competitiveness analysis in application research

There are 31 institutions that have more than 10 patents related to GEVs with the total patent number equal to 539 (Table 3). These institutions can be divided into three categories: universities, research institutions, and enterprises. Among them, there are 14 universities (45.2%) with 270 patents, accounting for 50% of the total patents of 31 institutions. There are 12 research institutes (38.7%) with 200 patents (37.1%), and five enterprises (16.2%) with 69 patents (12.8%). This indicates that the universities and research institutes are still the major R&D players in Chinese domestic GEV research. Compared to basic research, the number of patents held by enterprises is only a small proportion of the total patents. However, enterprises are beginning to play a role in the execution of research.

Table 3

Major GEV patents owners in domestic China (institutions have more than 10 patents).

(3) Competitiveness analysis in transfer and transformation

The State Food and Drug Administration of China (CFDA) has implemented a tightly controlled policy on vaccine production, sales, and other aspects. Enterprises have the right to produce and sell vaccines only once they obtain approval from the CFDA. The CFDA database provides the record information for all eligible enterprises. The data regarding all vaccine enterprises in China collected from the CFDA database show that, at present, there are 58 enterprises with vaccine production qualifications in the domestic market, with 10 GEV manufacturers but only five GEVs types have been listed. Compared to the number of domestic GEV patents, the number of GEV patents that have been transformed into products is much smaller (Table 4).

Table 4

GEV types in domestic China.

There are four companies with GEV patents in China, and only one of them can produce the GEV, a fact that also confirms that the rate of patents being transformed into products is still low (Table 5).

Table 5

GEV Production of enterprise with relevant patents.

(4) Competitiveness analysis in commercialization and industrialization

Vaccine sales must be under the batch release control system in China, which means that vaccine inspection by the CFDA is compulsory before it goes into the domestic market or is exported. Thus, the amount of the batch release vaccine is equal to its commercialization scale. Such data are difficult to obtain, and thus, we counted and analyzed quantity features only. There are five types of commercial vaccines in the domestic market. Among them, the recombinant hepatitis B vaccine (Saccharomyces cerevisiae) has the largest proportion in reference to the scale of commercialization, accounting for 61.9%. This shows that the recombinant hepatitis B vaccine is the most mature and extensively used GEV in China’s domestic market (Table 6).

Table 6

Total proportion of GEV commercialization scale in domestic China (2007-2015).

There are 10 enterprises that have achieved commercialization of vaccines in China, and two enterprises meet the standard of producing the GEV: the Lanzhou Institute of Biological Products Company and the Wuhan Institute of Biological Products Research Company. However, they have not produced GEVs, which indicates that they have not yet achieved commercialization of a GEV.

Among these 10 enterprises are the Shenzhen Kangtai Biological Products Company, the Beijing Tiantan Biological Products Company, the Dalian Hissen Bio-pharm Company, and the NCPC GeneTech Biotechnology Development Company with a high commercialization concentration degree, accounting for 90.4%. Meanwhile, Glaxo Smith Kline and Berna Biotech AG are foreign enterprises accounting for 4.3% of the total amount of the commercialization scale.

4.5 The recognition results of the potential IURC partners

Analyzing the features of the domestic GEV industry in China, we can see that this industry presents the pattern of a comprehensive innovation chain. Thus, we can infer that there will be a higher possibility of cooperation among enterprises, universities, and research institutes in the IURC partnership.

In the basic research phase in the innovation chain in the domestic GEV industry of China (Table 7), the selection of the category types for the IURC for institutions can be divided into research institutions, agricultural universities, and comprehensive research universities. In the applied research phase, enterprises begin to join the innovation chain, and most of them are the main participants in the transfer and transformation, commercialization, and industrialization phases. The comprehensive innovation chain needs to be populated with all types of industries, universities, and research innovations. At present, there are no enterprises in basic research. This indicates that enterprises have a higher possibility to join basic research networks. There are five elite enterprises in the applied field of GEVs: Aventis Pasteur Company, Beijing Kaiyin Biological Technology Company, Merial Company, Pulaike Biological Engineering Company, and Yuanlun Biotechnology Company. However, the companies that do not have CFDA qualification, and the other 58 companies that are qualified, own fewer patents. Therefore, considering knowledge transformation and the abilities that are complementary to the process of transformation, the five nonqualified companies and 58 qualified companies are more likely to be candidates for cooperation.

Table 7

Category types of IURC in GEV innovation chain in domestic China.

From the spatial distribution of the innovation chain process, the institutions that have greater potential to cooperate are those that have high relevance and complementary technology capacities, are in close geographical position, and are different types of institutions. GEV R&D and production institutions in Guangdong, Beijing, and Shanghai comprise the complete innovation chain. Each institution in the innovation chain in the three locations has high complementary capacities, which shows that IURC institutions in Beijing, Shanghai, and Guangdong have greater cooperative potential. Compared to the other two areas, there are more industry-university-research institutions in Beijing; these types of institutions vary, the possibility of choosing collaborative partners is, thus, higher, and the spatial distances among these institutions are smaller. Therefore, the GEV research institutions in Beijing have increased cooperative potential.

(1) In the Guangdong province, there are more cooperation possibilities among the South China Agricultural University, the Aventis Pasteur Company, the Zhongshan University, and the Shenzhen Kangtai Biological Products Company. Specifically, the quality of both the basic and applied research of the South China Agricultural University is high, while the corresponding research ability of Shenzhen Kangtai Biological Products Company as a commercial and industrial institution is weaker. Thus, there are many more possibilities for cooperation between the South China Agricultural University and the Shenzhen Kangtai Biological Products Company in view of the convenience of the geographical location and the complementary capacities.

(2) In Beijing, the National Center for Nanoscience and Technology, the Chinese Academy of Medical and Biological Science, and the Chinese Academy of Microbiological Research all belong to the Chinese Academy of Sciences. Considering the organizational relations and the convenience of geography, there are more possibilities for cooperation in basic and applied research in GEV among these institutions.

(3) In Shanghai, the Second Military Medical University is stronger in basic and applied research, while, as a commercial and industrial institution, the research ability of the Shanghai United Cell Biotechnology Company is weaker. Therefore, there are many more possibilities for cooperation between the Second Military Medical University and the Shanghai United Cell Biotechnology Company because of the geographical convenience and the complementary capacities.

Table 8

The spatial distribution of GEV institutions in domestic China.

Table 9 lists the types of vaccine production enterprises in domestic China. There is fierce competition among the enterprises since they produce the same types of vaccines, which leads to few cooperation possibilities. The Shenzhen Kangtai Biological Products Company, the Beijing Tiantan Biological Products Company, and Glaxo Smith Kline all produce recombinant hepatitis B vaccines (Saccharomyces cerevisiae), so there are few cooperative possibilities. At the same time, the cooperation possibilities are also slim among Hualan Biological Engineering, the Dalian Hissen Bio-pharm Company, Berna Biotech AG, as well as for the NCPC GeneTech Biotechnology Development Company and the Beijing Waldorf Shield Biotechnology Company.

Table 9

Types of vaccine production enterprises in domestic China.

The Chinese Academy of Sciences and the Agricultural and Military Medical Sciences are the main research institutions in basic and applied research, and they are both open-types. Thus, the possibility of related cooperation among these science academies, or internally within the same organization, is greater. Considering geographical-spatial convenience, these three academies have a much higher possibility of cooperating with Beijing enterprises on transfer and transformation, commercialization, and industrialization. Meanwhile, the Hebei province, which is adjacent to Beijing, does not have institutions with a strong background in genetic engineering in basic or applied research. Therefore, the NCPC GeneTech Biotechnology Development Company in Hebei tends to seek more research-related cooperation with institutions in Beijing, and these three academies of sciences will constitute preferred cooperative choices.

Although the recognition results of the potential partners can be effective references for cooperation, whether the institutions will actually cooperate is still subject to a number of additional factors.

5 Conclusion

According to the rules of scientific collaboration, and combining the economic factors in IURC, this paper uses the theory of knowledge potential and spillover to analyze the motivations and conditions of IURC. We implement a recognition method of the objects of IURC based on knowledge spillover effects in the innovation chain. When the innovation chain focuses on the technology chain, it targets the transformation and application of innovation elements in different links at the same time. The analysis of industrial competitive intelligence based on the innovation chain is dependent on the entire process of innovation activity, including the analysis of industry and technological innovation that considers both qualitative and quantitative analyses, and comprehensively and systematically analyzes all industrial activity. Using an empirical analysis of potential IURC in the field of GEVs, this paper also investigates the feasibility of these methods.

Compared with previous studies, this study focuses more on the cooperative behavior at the micro level, so the conclusion will be of higher reference value. However, the research methods and conclusions of this study also have limitations. The cooperation across production, study, and research is a complex social phenomenon. The factors affecting the cooperation across industry, universities, and research institutions go far beyond the four hypothetical conditions presented here. Organizations that satisfy the above conditions may have competitive relationships. Ultimately, the pursuit of competition or cooperation will involve more economic interests and the decision-makers’ comprehensive consideration. Further, in this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities at each phase of the chain. As such, this may overlook important feedback mechanisms in the innovation process. In the future, we will also consider non-linearity innovation theory, which can offer more feedback information. Moreover, future IURC research requires more attention to the phenomenon of cooperative practices from the micro viewpoint to establish a specific and thorough optimization strategy with more feasibility and operability.

Author Contributions

Haiyun Xu (, corresponding author) proposed the research idea, designed the research, drafted and revised the manuscript. Chao Wang ( wrote the program to process the data. Kun Dong ( performed the research and revised the manuscript. Rui Luo ( did the data analysis. Zenghui Yue ( performed the research and revised the manuscript. Hongshen Pang ( performed the research and revised the manuscript.


This work is funded by National Natural Science Foundation of China (Grant No. 71704170), the China Postdoctoral Science Foundation funded project (Grant No. 2016M590124), and the Youth Innovation Promotion Association, CAS (Grant No. 2016159).

The authors have declared that no competing interests exist.


Arundel A., &Geuna A. (2004). Proximity and the use of public science by innovative European firms. Economics of Innovation & New Technology, 13(6), 559-580.
Bamfield P. (2006). Research and development in the chemical and pharmaceutical industry. John Wiley & Sons.
[Cite within: 1]
Barber M.J. (2007). Modularity and community detection in bipartite networks. Physical Review E Statistical Nonlinear & Soft Matter Physics, 76(2), 066102.
DOI:10.1103/PhysRevE.76.066102      PMID:18233893      URL    
The modularity of a network quantifies the extent, relative to a null model network, to which vertices cluster into community groups. We define a null model appropriate for bipartite networks, and use it to define a bipartite modularity. The bipartite modularity is presented in terms of a modularity matrix B; some key properties of the eigenspectrum of B are identified and used to describe an algorithm for identifying modules in bipartite networks. The algorithm is based on the idea that the modules in the two parts of the network are dependent, with each part mutually being used to induce the vertices for the other part into the modules. We apply the algorithm to real-world network data, showing that the algorithm successfully identifies the modular structure of bipartite networks.
[Cite within: 2]
Baxter G., Dorogovtsev S., Goltsev A., & Mendes J. (2012). Handbook of Optimization in Complex Networks. Optimization, 57, 229-252.
[Cite within: 1]
Blondel V.D., Guillaume J.L., Lambiotte R., & Lefebvre E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics, (10), 155-168.
Bruneel J., d’Este P., & Salter A. (2010). Investigating the factors that diminish the barriers to university-industry collaboration. Research Policy, 39(7), 858-868.
DOI:10.1016/j.respol.2010.03.006      Magsci     URL    
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Although the literature on university&ndash;industry links has begun to uncover the reasons for, and types of, collaboration between universities and businesses, it offers relatively little explanation of ways to reduce the barriers in these collaborations. This paper seeks to unpack the nature of the obstacles to collaborations between universities and industry, exploring influence of different mechanisms in lowering barriers related to the orientation of universities and to the transactions involved in working with university partners. Drawing on a large-scale survey and public records, this paper explores the effects of collaboration experience, breadth of interaction, and inter-organizational trust on lowering different types of barriers. The analysis shows that prior experience of collaborative research lowers orientation-related barriers and that greater levels of trust reduce both types of barriers studied. It also indicates that breadth of interaction diminishes the orientation-related, but increases transaction-related barriers. The paper explores the implications of these findings for policies aimed at facilitating university&ndash;industry collaboration.</p>
Cai X., Xiao Y.F., & Zeng F.R. (2001). Research on knowledge-innovation-chain. Soft Science, 15(1), 2-4.
The concept of knowledge-innovation-chain emphasizes its motive mechanism main-body organization pattern and management object. Compared with other innovation pattern knowledge-innovation-chain has some distinguished features. The main-body can be understood from macro level and micro level. The research on knowledge-innovation-chain helps to widen the angle of view of knowledge innovation research establish national innovation system and make technology innovation policy.
[Cite within: 1]
Cai X. (2002). Innovation, innovation cluster, innovation chain and their enlightenment. R&D Management, 14(6), 35-39.
Cao J., Fan D.C., & Tang X.X. (2010). Research on the evaluation of technology innovation performance based on industry-university-research cooperation. Science& Technology Progress and Policy, 27(7), 114-118.
Clements C.J., &Wesselingh S.L. (2005). Vaccine presentations and delivery technologies-what does the future hold? Expert Review of Vaccines, 4(3), 281.
Cohen W.M., Nelson R.R., & Walsh J.P. (2002). Links and impacts: the influence of public research on industrial R&D. Management science, 48(1), 1-23.
DOI:10.1287/mnsc.      URL    
In this paper, we use data from the Carnegie Mellon Survey on industrial R&D to evaluate for the U. S. manufacturing sector the influence of "public" (i.e., university and government R&D lab) research on industrial R&D, the role that public research plays in industrial R&D, and the pathways through which that effect is exercised. We find that public research is critical to industrial R&D in a small number of industries and importantly affects industrial R&D across much of the manufacturing sector. Contrary to the notion that university research largely generates new ideas for industrial R&D projects, the survey responses demonstrate that public research both suggests new R&D projects and contributes to the completion of existing projects in roughly equal measure overall. The results also indicate that the key channels through which university research impacts industrial R&D include published papers and reports, public conferences and meetings, informal information exchange, and consulting. We also find that, after controlling for industry, the influence of public research on industrial R&D is disproportionately greater for larger firms as well as start-ups.
D’Este P., &Patel P. (2007). University-industry linkages in the UK: What are the factors underlying the variety of interactions with industry? Research policy, 36(9), 1295-1313.
DOI:10.1016/j.respol.2007.05.002      Magsci    
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">This paper examines the different channels through which academic researchers interact with industry and the factors that influence the researchers&rsquo; engagement in a variety of interactions. This study is based on a large scale survey of UK academic researchers. The results show that university researchers interact with industry using a wide variety of channels, and engage more frequently in the majority of the channels examined &ndash; such as <em>consultancy &amp; contract research, joint research, or training</em> &ndash; as compared to patenting or spin-out activities. In explaining the variety and frequency of interactions, we find that individual characteristics of researchers have a stronger impact than the characteristics of their departments or universities. Finally, we argue that by paying greater attention to the broad range of knowledge transfer mechanisms (in addition to patenting and spin-outs), policy initiatives could contribute to building the researchers&rsquo; skills necessary to integrate the worlds of scientific research and application.</p>
Fischer M., Jähn H., Teich T. (2004). Optimizing the selection of partners in production networks. Robotics & Computer-Integrated Manufacturing, 20(6), 593-601.
DOI:10.1016/j.rcim.2004.05.007      URL    
Non-hierarchical regional production networks are the vision of a virtual enterprise model which is investigated in a collaborative research center at Chemnitz University of Technology. In the center of interest is the development of a virtual enterprise model which is based on small performance unitse so-called competence cells. The general intention of this model is to improve the competitiveness of small and medium-sized enterprises, which results from the enterprise-spanning cooperation of the participants. Thus, the concentration on the own core competences is supported and the market power is contemporaneously increased by the help of the network. The possibly automated selection of the partners is one of the major problems in virtual enterprises. Therefore, we introduce a method for choosing the most capable competence cells from a pool of potential competence cells. The selected competence cells fulfil the tasks of a value chain particularly well. Within our approach, this problem will be solved using Ant Colony Optimization. Additionally, social facts will also be considered in order to select the suitable competence cells for the network.
[Cite within: 1]
Huang M.D., Li W.W., & Huang J. (2017). Research on the present situation and countermeasures of the industry-university-research cooperation in China. Science & Technoloyg Progress and Policy, 34(19), 22-27.
[Cite within: 1]
Huang S.J. (2014). China Industrial Cluster Innovation Development Report: Learning Mechanism in Cluster Network 2011-2012. Economic management publishing house.
[Cite within: 2]
Incites. (2015). InCitesTM quick strat guide of InCitesTM database. .
[Cite within: 1]
Kogut B., &Chang S.J. (1991). Technological capabilities and Japanese foreign direct investment in the United States. Review of Economics and Statistics, 73(3), 401-413.
[Cite within: 1]
Mohnen P., &Hoareau C. (2003). What type of enterprise forges close links with universities and government labs? Evidence from CIS 2. Managerial and decision economics, 24(2-3), 133-145.
Larson E.V., &Brahmakulam I.T. (2001). Building a New Foundation for Innovation. Rand Corporation.
Laursen K., &Salter A. (2006). Open for innovation: the role of openness in explaining innovation performance among UK manufacturing firms. Strategic management journal, 27(2), 131-150.
[Cite within: 2]
Leydesdorff L., &Etzkowitz H. (1996). Emergence of a Triple Helix of university-industry-government relations. Science & public policy, 23(5), 279-286.
Liang Y.M. (2007). A new strategy on the bilateral technical cooperation. Guangzhou. (Jinan University M.S. dissertation)
[Cite within: 1]
Li W.H., Wang C.H., & An N. (2008). Analysis of the relationship and mode between Subject and object of industry-unniversity-research cooperation based on innovation system. Science and Technology Management Research, 28(6), 4-5.
Park I., Jeong Y., Yoon B., & Mortara L. (2015). Exploring potential R&D collaboration partners through patentanalysis based on bibliographic coupling and latent semantic analysis. Technology Analysis & Strategic Management, 27(7), 759-781.
Newman M.E. (2003). The structure and function of complex networks. SIAM review, 45(2), 167-256.
DOI:10.1137/S003614450342480      URL    
Abstract Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
[Cite within: 1]
Newman M.E., &Girvan M. (2004).Finding and evaluating community structure in networks. Physical Review E Statistical Nonlinear & Soft Matter Physics, 69(2), 026113.
DOI:10.1103/PhysRevE.69.026113      PMID:14995526      URL    
We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
[Cite within: 1]
Qing T., &Liu S. (2016). Construction of industry-university-research cooperation innovation system in Chengdu. China Journal of Commerce, (29), 135-137.
[Cite within: 1]
Reuters T. (2015). Thomson Data Analyzer.
[Cite within: 1]
Reuters T. (2015). Derwent innovations index.Thomson Reuters.
Santoro M.D., &Gopalakrishnan S. (2000). The institutionalization of knowledge transfer activities within industry-university collaborative ventures. Journal of engineering and technology management, 17(3-4), 299-319.
DOI:10.1016/S0923-4748(00)00027-8      URL    
Due to the changing competitive landscape, organizations must increasingly focus on acquiring external knowledge to advance new technologies. This study examines the institutionalization of knowledge transfer activities between industrial firms and university research centers. Data were collected from 189 firms collaborating with 21 university research centers in the US. Results show that knowledge transfer activities are facilitated when industrial firms have more mechanistic structures, cultures that are more stable and direction-oriented, and when the firm is more trusting of its university research center partner. Implications for both industry and universities, including their effect on firm performance, are discussed.
Shen H.Data show the conversion rate of scientific and technological achievements in China is less than 30%, China Economic Net. Retrieved from .
[Cite within: 1]
Slotte V., &Tynjälä P. (2003). Industry-university collaboration for continuing professional development. Journal of Education & Work, 16(4), 445-464.
Solesvik M.Z., &Encheva S. (2010). Partner selection for interfirm collaboration in ship design. Industrial Management & Data Systems, 110(5), 701-717.
[Cite within: 1]
Tang Y.L., Song G.C., & Liang W.C. (2005). Research progress and applicaton of genetic engineering vaccines. Jilin Journal of Animal husbandry and veterunary medicine, 12, 18-20.
In this article, the research progress in genetic engineering vaccines in the recent ten years is generalized. Cur- rent situation and the future application of animal genetic engineering vaccines is discussed.
[Cite within: 1]
Timmers P. (2002). Building effective public R&D programs. Portland International Conference on Management of Engineering and Technology, 1999. Technology and Innovation Management. Picmet. IEEE, 2, 591-597.
[Cite within: 1]
Wang T.C., &Chen Y.H. (2007). Applying consistent fuzzy preference relations to partnership selection. Omega, 35(4), 384-388.
[Cite within: 1]
Wang X.F., Wang Z.N., Huang Y., Liu Y.Q., Zhang J., Heng X.F., & Zhu D.H. (2017). Identifying R&D partners through subject-action-object semantic analysis in a problem & solution pattern. Technology Analysis and Strategic Management, 1-14.
Wen F.F. (2012). Study on patent collaboration patterns based on co-inventorship bibliometrics. Wuhan University.
[Cite within: 1]
Xie K.F., &Liu H.L. (2006). Game analysis of R&D entities basesd on industry-unniversity-research institute cooperation. Science of Science and management of S. &T., 27(10), 27-30.
[Cite within: 1]
Xu H.Y., Qi Y., Yue Z.H., & Fang S. (2015). Measurment methods and application research of triple helix model in collaborative innovation management. Journal of the China Society for Scientific and Technical Information, 34(3), 236-246.
Xu H.Y., Wang C., Dong K., Wei L., & Pang H.S. (2017). Methods to identify potential industry-university-research institutions cooperation partners based on the knowledge spillovers effects in the Innovation chain. Journal of the China Society for Scientific and Technical Information, 36(7), 682-694.
According to the law of scientific research cooperation, combining the economic factors in Industry-University-Research institutions collaboration(IURC), and applying the theory of knowledge potential and knowledge spillovers effects, this paper analyzes the dynamics and conditions in the process of IURC and constructed methods to identify potential IURC partners based on innovation chain theory: utilizing multi-source data,qualitative analysis,and quantitative analysis to achieve the network core of the existing collaboration network and institutionscompetitiveness in the innovation chain. Empirical analysis in the field of genetic engineering vaccine shows that analysis based on innovation chains can determine the distribution characteristics of creative technologies from different institutions and thus can find more complementarities between institutions; therefore, it is a suitable method to identify potential IURC partners.
[Cite within: 1]
Yoon B., &Song B. (2014). A systematic approach of partner selection for open innovation. Industrial Management & Data Systems, 114(7), 1068-1093.
Yue Z.H., Xu H.Y., & Fang S. (2015). Modeling knowledge diffusion in scientific collaboration network based on structural parameters. Journal of the China Society for Scientific and Technical Information, 34(5), 471-483.
Knowledge diffusion in scientific collaboration network tends to have combined impact from various network structure factors simultaneously.Thirteen kinds of parameter that represent the structure of,scientific collaboration network from the global,local and individual levels are elected to further extract the structural factors of scientific collaboration network.With knowledge load,knowledge diversity and knowledge redundancy functioning as the evaluation indicators to measure the effect of knowledge diffusion,the structural parameter quantitative relationship model of multivariate linear regression of knowledge diffusion in scientific collaboration network is formed and verified,so as to explore the mechanism by which the scientific collaboration network structure affect knowledge diffusion.The research has shown that;Firstly,The hypothesis that knowledge can further be diffused with the aid of scientific collaboration and the scientific collaboration network structure will influence the process of knowledge diffusion are justified,and the structural parameter quantitative relationship model of multivariate linear regression of knowledge diffusion in scientific collaboration network is rational and reasonable.Secondly,from the perspective of the granularity of structural factors in scientific collaboration network,the distance factor and clustered factor of scientific collaboration network has positive effect on knowledge load,knowledge redundancy and knowledge diversity,while modular factor has positive effect on knowledge load and knowledge redundancy,but no conspicuous effect on knowledge diversity.Thirdly,from the perspective of the granularity of parameter in scientific collaboration network,the influence of different kinds of structural parameter on the effect of knowledge diffusion has both relevance and differences.Fourthly,different kinds of structural parameter or factors have joint influence on the knowledge transmission and diffusion simultaneously.
Zhang L. (2012). Choice of Partners for the Cooperative Innovation of Industries, Universities and Research Institutes by Game Analysis. Science and Technology Management Research, (19), 218-223.
Choice of cooperative innovation partner is the key to successful U-I(University-Industry)cooperation,and the partner is determined through the game between enterprises and research institutes.By establishing an asymmetric information game model,the article analyzes the internal mechanism of partner selection.The conclusion indicates that,if research institutes and enterprises have asymmetric information on the actual ability of the former party,then increasing the price of factor inputs for innovation and lowering the share of research institutes in the future income distribution will reduce the incentives for institutes to pretend to be the other type in advance.And this will improve the matching between U-I cooperative partners.
[Cite within: 1]
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