Exploration on the collaborative relationship between government, industry, and university from the perspective of collaborative innovation

The Web of Science database covers the world's most comprehensive high-quality paper information with country addresses, author names, affiliation, etc., and has a comprehensive record of grant funding. It is the preferred data for indicating the source of current research on the degree of collaboration across organisational boundaries. We obtain the data index of the database retrieval by changing the address field within a certain time range, as shown in Table 1.

Based on the national intellectual property ‘patent search and analysis’ platform, we classified search ‘patent applicant’ information to characterise the data source of patent cooperation in the TH algorithm. Patent applicants are generally organisations with technological creativity, such as the company's R&D department, scientific research institutes and universities, so it is mainly based on a certain time frame, scientific research institutions (A_y), industrial institutions (I_y), scientific research–industrial institutions (AI_y) and three fields to retrieve statistical data.

Patents with the ‘university, college, research institute’ field in the applicant information belong to the A_y dataset; patents with the ‘company, group, factory’ field in the applicant information belong to the I_y dataset; and the applicant information includes the scientific research institution field. If it also contains the enterprise organisation field, the patent belongs to the cross-field cooperation AI_y dataset.

Using the bibliometric trend change mining 1.0 software to clean the data of the retrieved scientific papers and patent titles, the valid data variables are retained and the data indicators of the intermediate variables are automatically obtained. Then the TH is performed according to the calculation formula in Table 2 to obtain the algorithm variable conversion summary. The A, I, G, AI, AG, GI, AGI values are input into the specific program th.exe of the TH algorithm, and then the T value is generated that characterises the degree of synergy between the relevant innovation subjects.

According to the ‘China Regional Innovation Capability Evaluation Report 2019’, from the comprehensive ranking of the innovation environment based on factors such as innovation infrastructure, entrepreneurship level, market environment and labour quality factors, Jilin and Liaoning Provinces, which rank close to Heilongjiang Province, were selected after conducting a horizontal comparative analysis of the degree of collaborative innovation coupling. We retrieved and extracted the panel data information of scientific papers and patent achievements in the three northeastern provinces in 2019. After data cleaning and classification transformation, the data of government, industry, university and research in each province are as summarised in Table 3.

From the data of Heilongjiang, Jilin and Liaoning Provinces, the main strength in the output of papers and patents is scientific research institutions (A), accounting for 41.11%, 40.94% and 38.66% of the total output of papers and patents in each province in 2019. The total amount of knowledge and technology output of interaction and integration between scientific research institutions and the government or industrial enterprises is 5313, 6392 and 6734, respectively, which is much higher than the number of results of intercommunication and collaboration between the government and enterprises (GI). The backbone and promoter of collaborative innovation system development is universities and scientific research institutions (A) and industrial enterprises (I). (A) and (I) achieved remarkable results in the knowledge creation and innovation system, but the collaborative output (AI) of the two parties still has a lot of plastic development space compared with the interactive cooperation (AG) of the government and scientific research institutions. A number of studies have shown that the application of knowledge and technological achievements shared by universities and industrial enterprises has great scalability potential, and is a sharp edge in improving the efficiency of transformation of technological achievements and the development efficiency of regional collaborative innovation ecosystems. In the 2019 regional innovation capability rankings, among the three provinces, Liaoning Province, which has the highest total AI and AGI, was ranked 19th, and Jilin and Heilongjiang Provinces ranked 27th and 28th, respectively. Therefore, for Heilongjiang Province, expanding the path of industry-university-research cooperation can further enhance the competitiveness of regional collaborative innovation and inject new vitality into Longjiang's economic transformation and development.

The information transfer amount T value is used to characterise the correlation degree of collaborative innovation in 2D or 3D innovation entities (Table 4). In the 2D mutual information collaboration degree of the three provinces, the mutual information coupling T (AG) between scientific research institutions and governments is significantly better than the cooperation between the other 2D innovation entities. This is in sharp contrast to the structural pattern in which the amount of information transfer between research institutions and enterprises in developed countries T(AI) is significantly better than T (AG), reflecting that substantial breakthroughs have not been made in the organisational boundaries between scientific research institutions and industrial enterprises in the three northeastern provinces. The low conformity of mechanisms and systems has led to low-efficiency knowledge and technology conversion rates, which indirectly reduces the possibility of seizing opportunities in technological competition. This is the key weak factor restricting the penetration of regional innovation vitality. The amount of 3D mutual information transfer further characterises the degree of synergy of the collaborative innovation ecosystem. Although the total AGI achievement of Heilongjiang Province is 1180, it is greater than that of Liaoning Province, at 728. However, its T(AGI) value is −309.21.1 mbit, which is lower than that of Liaoning Province. This reflects to a certain extent that Heilongjiang Province is not operating smoothly in the government-industry-university-research interactive cooperation mechanism, and there is still room for further improvement in the mining and utilisation of knowledge and technological achievements.

Based on 20-year time series data indicators, a 5-year time period is used to longitudinally explore the changing trends of the collaborative innovation system in Heilongjiang Province (Table 5). Since 2015, China has comprehensively deepened the reform of innovation and entrepreneurship education. Heilongjiang Province, which has 81 universities including Harbin Institute of Technology, 21 institutes directly under the central government, and 139 provincial scientific research institutes, has actively studied and responded to the call and implemented the national innovation-driven development strategy deployment. Variable indicators of A, AG, AI and GAI have increased significantly in the past 5 years, especially the independent output of universities and other research institutions and the results of interaction and collaboration with the government. Although the R&D and innovation of industrial enterprises only increased from 1 to 308, their joint output with universities and other institutes (AI) increased from 12 to 2904, a growth rate of 87.30% in the past 5 years, reflecting the new economic condition. The technological competition among industrial enterprises in Heilongjiang Province has consciously focused on cooperation and win–win cooperation with research institutions. Considering the high knowledge and technology output capabilities of universities and other scientific research institutions, there is still a lot of room for exploration in the interactive cooperation between research institutions and industrial enterprises and the joint output of research institutions and governments. The ability of industrial enterprises to absorb knowledge spillovers meets the needs of the industry competitive demand and can consolidate and extend its indisputable service regional economic development status.

From the data in Table 5, we can further visually express the proportions of the four 2D and 3D variable indicators in the total output in each time dimension (Figure 1). Research institution-government cooperation (AG) and research institution-industry-government cooperation (GAI) output results have a significant increase in proportion of the collaborative innovation ecosystem, while the proportion of knowledge and technology achievements of research institutions and enterprise collaboration (AI) has been stable in various periods. The level of joint output and synergy matches with the development of the innovation ecosystem; however, compared with the collaborative development of research institutions and government, the interactive communication between the two is suppressed by barriers of the mechanism and system, and the potential for development capabilities has not been effectively stimulated. The cooperation between government and industrial enterprises (GI) dropped from 2.04% to 0.05%. However, with implementation of the action plans of Heilongjiang Province for cultivation of innovative enterprises and construction of science and technology parks, the results of cooperation between 2015 and 2019 showed a clear upward trend.

Fig. 1

Compared with the period of 2000–2004, in the following 15 years, the interaction and collaboration between 2D innovation subjects has declined to a greater extent (Table 6), and the coupling and collaboration between industrial enterprises and research institutions have shown a serious weakening trend. The joint output of the authors increased significantly from the initial 12 to 2904, but the structural stability of the coupled system formed by the two and the efficiency of the knowledge and information flow is relatively low. Between 2000 and 2009, the amount of mutual information T (AG) between the government and universities and other research institutions dropped significantly, but after 2010, the government's various support policies and service facilities consciously tilted towards the development of university students’ innovation and entrepreneurship, leading to the establishment of an innovative and entrepreneurial pilot zone and technology transformation centre, which have beneficially broken the interactive communication barriers between government organisations and research institutions. With the significant increase in the collaborative products between the two parties, the T value that characterises the closeness of the interaction between the two has also rebounded.

As shown in Figure 2, from 2000 to 2014, the negative value of the amount of 3D mutual information transfer in Heilongjiang Province became smaller and smaller, indicating that the structural stability and overall effectiveness of the collaborative innovation system comprising research institutions-industrial enterprises-government has gradually increased and the overall effectiveness of the system has been effectively optimised. However, after 2015, with continuous advancement of the ‘Thirteenth Five-Year Science and Technology Innovation Plan of Heilongjiang Province’, the implementation of various activity plans such as the strategic alliance of industrial technology innovation and the scientific and technological innovation base, although the total output of scientific papers and patents is significant, the synergy of innovation entities represented by the amount of 3D mutual information transfer has not been consolidated and optimised overall, and the structural stability of the innovation system has weakened.

Fig. 2

Heilongjiang Province has shown a weakening trend in the amount of mutual information transfer that char-acterises the degree of super-circular synergy between the three-helix collaborative innovation subjects, which to a certain extent reflects every innovation. None of the subjects took the initiative to promote the coupling and symbiosis of the system. They were more compatible with some of the unique capabilities of the other two subjects, and the synergy across organisational boundaries was not better utilised [16]. The institutional reform has the characteristics of self-reinforcing, and a weak sense of coordination will solidify the functional positioning of the innovation subject and weaken the synergy of collaboration between innovation subjects.

To cultivate the collaborative cultural ideology of the innovation subject in the TH collaborative innovation ecosystem, the innovation subsystem must be guided by the common goal of ‘using collaborative innovation to guide the rapid development of the regional economy’; this sense of collaboration is based on a sound benefit distribution mechanism. Cross-organisation project cooperation should not only satisfy the academic value pursuit of scientific research institutions, but also meet the pursuit of corporate economic interests and the social interests of the government [17, 18].

Knowledge innovation and technology transformation are important sources of social development in the era of knowledge economy [19]. As the core body of knowledge production and output, universities and other research institutions, on the basis of cognitive ability and intellectual level, are teaching and researching to meet the needs of national and regional economic development. To realise the smooth and orderly interactive sharing of information and resources with the external environment, and to rationally promote transformation of the entrepreneurial model of knowledge and technology capitalisation has become an inevitable choice under the new normal era of the economy. At the level of knowledge creation, to do fine and detailed innovation and entrepreneurship education, we must break down the barriers between departments and disciplines, form an interdisciplinary research team, and fully create an atmosphere for the interactive sharing of resources and information among internal organisations. It is important to deeply establish mutual assistance and cooperation links between research institutions and enterprises and other collaborative innovation subsystems, promote the development of collaborative education and collaborative research, and maximise the output of human resources and technical resource information required by the TH innovation subsystem. At the level of knowledge application, universities and other scientific research organisations must consciously establish an ideological awareness of the dual rationality of economy and culture. On the basis of ensuring the output of high-quality technical results, it is important to pay attention to the commercialisation and capitalisation of knowledge and technology, and change the production of those technical results behind closed doors that result in low social value.

Perkmannm's research on the cooperation between universities and enterprises has shown that the interactive cooperation between industry, university and research would promote the emergence of technological achievements and improve the efficiency of technological achievements transformation in a recursive manner. In China, as the main body of technological innovation, enterprises have not fully realised their functional advantages as ‘transformers’ of technological achievements in the TH collaborative innovation system [20]. In 2018, the basic research funding of enterprises only accounted for 0.20% of the total R&D funding. Enterprises must pay attention to the construction of basic R&D platforms so that they can have a certain absorption capacity to undertake transformation of the overflow of scientific and technological achievements from universities and other research institutions. Regional basic industries should prioritise technological innovation, actively seek synergy and win–win cooperation with other innovation entities, build strategic alliances of industrial technology innovation and implement technical support from research institutions [21]. On one hand, they need to deepen the innovation-oriented transformation of basic industries to ensure that they can maintain their competitive advantage in the market and sustainable technological innovation vitality. On the other hand, they need to seize the new opportunities of technological innovation of prioritised industries to effectively enhance regional innovation competitiveness.