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

Online veröffentlicht: 20 May 2022
Volumen & Heft: AHEAD OF PRINT
Seitenbereich: -
Eingereicht: 16 Jul 2021
Akzeptiert: 06 Dec 2021
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2444-8656
Erstveröffentlichung
01 Jan 2016
Erscheinungsweise
2 Hefte pro Jahr
Sprachen
Englisch
Abstract

The idea that reform drives innovation and entrepreneurship, and innovation and entrepreneurship drive development, has gradually become the main theme of the new era of Chinese society. As the ‘cradle’ of knowledge and technology innovation and the training and output of innovative talents, colleges and universities are the inevitable choices of the times to conform to the social trend and change to the development model of ‘entrepreneurial universities’. This article takes the reform of the development model of universities in Heilongjiang Province as the research object, and it qualitatively explores the status quo of the reform and development of universities in Heilongjiang Province to entrepreneurial universities, with the aid of the Loet Leydesdorff triple helix algorithm, based on the Web of Science database and national intellectual property ‘patent search and analysis’. The platform conducts data mining, conducts quantitative measurement research on the collaboration relationship between government, industry, university and research, and focuses on Heilongjiang Province for horizontal and vertical empirical analyses. The study proposes suggestions for improvement from the three dimensions of condensing the collaborative awareness of innovation subjects, reforming the development model of scientific research institutions, and improving the efficiency of the absorption and transformation of enterprise scientific and technological achievements. It has certain practical guidance for strengthening the mutual information coordination and structural stability of the government, industry, university and research under the collaborative innovation ecosystem.

Introduction

In the era of knowledge economy, information changes rapidly, market competition tends to become deeper and more complicated, and the mutual penetration between knowledge and economy is becoming stronger. In 2018, the R&D investment intensity of Chinese scientific and technological achievements was 2.14%, which exceeds the average level of 1.97% of the 28 EU countries, which is close to the intensity level of R&D expenditures in developing countries. The national financial science and technology allocations reached 951.82 billion yuan, accounting for 4.30% of the national public financial expenditures. The Chinese government has continuously increased its investment in technological innovation, and the total amount of scientific and technological achievements of innovation represented by government, industry, university and research entities has increased significantly. The aggregation of innovative elements, integration of innovative resources and promotion of the formation of a collaborative innovation ecosystem of multiple entities have become the best way of China to adapt to the era of the knowledge economy [1]. From the perspective of collaborative innovation, building a coupled, symbiotic, stable and coexisting relationship between government, industry, university and research can not only accelerate the release of innovation and entrepreneurship, but also positively drive the efficiency of scientific and technological achievements. It is an inevitable development process to effectively improve regional innovation efficiency [2, 3].

Methodology

From the perspective of input-output, it is impossible to measure the stability of the internal organisational structure of the collaborative innovation system and the smoothness of information flow. In order to measure the degree of coupling and synergy of collaborative innovation subjects and find the ‘power point’ that promotes cross-border organisational collaboration, Leydesdorff [4] proposed the triple helix (TH) algorithm. Based on the TH innovation theory and information entropy theory, the mutual information transfer value T is used to characterize the uncertainty in the interactive communication of knowledge, technology and information between government, industry, university and research institutes. The algorithm takes a redundant measure to reflect the degree of coupling symbiosis of the main body of the innovation system [5]. It should be noted that the large output of scientific and technological achievements does not mean that the degree of collaboration among innovation entities is high. If the system and mechanism of open circulation among different innovation entities are not smooth, and scientific and technological achievements are not fully exploited, a collaborative innovation system will appear to have a weak structural stability [6].

With regard to the mutual information between innovation subjects as a relative frequency distribution, with the help of Shannon's information theory [7], the probability distribution of a certain information variable x can be quantitatively calculated as follows: H(x)=xpxlogpx(Take2asthelogarithmicbase) H\left( x \right) = - \sum\limits_x {{p_x}\log {p_x}\left( {{\rm{Take}}\;2\;{\rm{as}}\;{\rm{the}}\;{\rm{logarithmic}}\;{\rm{base}}} \right)} Information entropy H(x) refers to the uncertainty of variable x before the occurrence of an event or the average amount of self-information provided by the information after its occurrence; the unit is in bits [8]. Multidimensional information entropy can be augmented with variants based on one-dimensional (1D) formulas, such as the average amount of information in two dimensions as follows: H(xy)=xypxylogpxy(Take2asthelogarithmbase) H\left( {xy} \right) = - \sum\limits_x {\sum\limits_y {{p_{xy}}\log {p_{xy}}\left( {{\rm{Take}}\;2\;{\rm{as}}\;{\rm{the}}\;{\rm{logarithm}}\;{\rm{base}}} \right)} } pxy refers to the joint probability distribution of variables x and y.

Using the Abramson formula and the Thiel decomposition algorithm, the information transfer amount T(xy) of interaction between the three helix innovation subsystems can be expressed by means of 1D and two-dimensional (2D) information entropy values [9] as follows: T(xy)=H(x)+H(y)H(xy) T\left( {xy} \right) = H\left( x \right) + H\left( y \right) - H\left( {xy} \right) The transfer amount of 2D mutual information is always a non-negative value; when the variables x and y are independent of each other, T(xy) = 0; when the variables x and y interact, T(xy) > 0; and the weaker the information interaction between them, the smaller the T value.

Based on Eq. (3), Abramson further expressed the three-dimensional (3D) mutual information transfer amount as follows [10]: T(xyz)=H(x)+H(y)+H(z)H(xy)H(xz)H(yz)+H(xyz) T\left( {xyz} \right) = H\left( x \right) + H\left( y \right) + H\left( z \right) - H\left( {xy} \right) - H\left( {xz} \right) - H\left( {yz} \right) + H\left( {xyz} \right) When x = U, y = I and Z = G, Eq. (4) is the basic formula for measuring the innovation synergy of the university-industry-government (U-I-G) TH. The interaction between the 3D innovation subjects will increase the uncertainty of the collaborative innovation system. T(UIG) is a negative value: the smaller the value, the closer the information interaction and communication between the three, and the stronger the regional UIG interaction.

Data acquisition

Scientometrics is a series of in-depth quantitative research on scientific and technological achievements, scientific journals, etc., to provide scientific and accurate analysis and evaluation for the operation mechanism, development trend, and application efficiency of knowledge and technology in regions and institutions. In the TH algorithm, the number of scientific papers co-authored and the patent cooperation applications is an important manifestation of the collaborative innovation among various organisations [12]. The data acquisition is more scientific and effective, so it is often used to characterise ‘government-industry-university-research’ innovation with the support of scientometrics and the data source for the quantitative measurement of the degree of coupling and collaboration between subjects [13]. It should be noted that the data acquisition process is based on the principle of fuzzy matching and the co-occurrence of institutional names. Therefore, in order to avoid weakening the authenticity of data in the statistical classification, universities, academies of sciences, research institutes and technology centres committed to knowledge creation and technological innovation will be regarded as research institutions (A). The ‘government-industry-university-research’ synergy relationship with government-industry-university-research organisations at the core can be characterised by the TH algorithm of the ‘research institutions-industry-government’ TH innovation entity measure [14].

Acquisition and processing of scientific papers data

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.

Definition and acquisition of data variables of scientific papers

Intermediate variables Implication Rules for obtaining variable indicators

Ax Number of papers including information on scientific research institutions Papers with ‘UNIV*’, ‘COLL*’, ‘ACAD*’, ‘NIH*’ in the address field
Ix Number of papers including companies information Papers with ‘GMBH*’, ‘CORP*’, ‘LTD*’, ‘AG*’, ‘INC*’ in the address field
Gx Number of papers including government information Papers with ‘NATL*’, ‘NACL*’, ‘NAZL*’, ‘GOVT*’, ‘MINIST*’, ‘ACAD*’, ‘CNRS*’ in the address field
AIx Number of papers including both scientific research institutions and companies information The address field contains the number of papers representing both Ax and Ix contents
GxA Number of papers including information about scientific research institutions in the GX dataset The number of papers containing Ax in the Gx subset
GIx Number of papers including both government and corporate information The address field contains the number of papers representing Gx and Ix contents at the same time
GxAI Number of papers including information about companies in the GX A dataset The number of papers containing Ix in the GxA subset
Acquisition and processing of patent data

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 (Ay), industrial institutions (Iy), scientific research–industrial institutions (AIy) and three fields to retrieve statistical data.

Patents with the ‘university, college, research institute’ field in the applicant information belong to the Ay dataset; patents with the ‘company, group, factory’ field in the applicant information belong to the Iy 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 AIy 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.

Variable definition and calculation formula of collaborative innovation measure

Variables Implication Calculation formula

A The number of papers and patents exported only by scientific research institutions A = Axy-AIxy-GxA + GxAI
I The number of papers and patents output only by the companies I = Ixy-AIxy-GIx + GxAI
G The number of papers and patents output only by government G = Gxy-GxyA-GIx + GxyAI
AI The number of papers and patents jointly published or applied only by scientific research institutions and enterprises AI = AIxy-GxAI
AG The number of papers and patents jointly published or applied only by scientific research institutions and government AG = GxA-GxAI
GI The number of papers and patent jointly published or applied only by government and enterprises GI = GIx-GxAI
AGI The number of papers and patents output by government agencies, scientific research institutions and enterprises AGI = GxAI
Empirical analysis

As a traditional industrial base, Heilongjiang Province, centring on the foundation and advantages of scientific and technological innovation, deeply connects with national plans and developments such as ‘Made in China 2025’ and ‘Science and Technology Innovation 2030-Major Projects’ and actively explores collaborative innovation mechanisms that serve the regional economy. It established an industry-university-research alliance mechanism to serve development strategies such as the ‘Harbin-Daqing-Qijiang Industrial Corridor’ and the ‘Russian Economic and Trade Strategy’. It also established innovation and entrepreneurship pilot zones, technology transformation centres and other organisations. It initiated the university students’ innovation and entrepreneurship in 2018. Based on the TH collaborative innovation ecosystem built in Heilongjiang Province, this paper uses the TH algorithm to conduct research on the degree of mutual information collaboration of the TH innovation subject ‘research institution-industry-government’ under the collaborative innovation system from both horizontal and vertical dimensions. It comprehensively measures and quantitatively analyses the output of scientific and technological achievements of research institutions-industry-government, and the coordination and stability of the collaborative innovation ecosystem, and optimises the overall system.

Static analysis of the effect of government-industry-university-research collaboration

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.

Summary of scientific papers and patent achievements

Province Variable
A I G AG AI GI AGI

Heilongjiang 8362 4494 702 3166 2147 290 1180
Jilin 8980 5203 100 4869 1523 189 1234
Liaoning 10,351 8577 180 3858 2876 306 728

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.

Two-dimensional and three-dimensional T value in the three northeastern provinces

Province T-value
T(AG) T(AI) T(GI) T(AGI)

Heilongjiang 49.82 11.73 0.25 −309.21
Jilin 51.24 9.27 0.55 −345.13
Liaoning 43.72 12.93 0.49 −302.14
Dynamic analysis of the effect of government-industry-university-research collaboration

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.

Number of scientific papers and patents from 2000 to 2019

Year Variable
A I G AG AI GI GAI

2000–2004 108 80 2 18 12 63 3
2005–2009 738 346 18 129 92 329 42
2010–2014 2533 1013 900 716 369 526 375
2015–2019 16,547 6308 1417 7514 2904 3690 2857

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

Trend of the ratio of AG, AI, GI and AIG

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.

Two-dimensional mutual information transfer volume in 2000–2019

Year T-value
T(AI) T(AG) T(GI)

2000–2004 48.95 64.26 100.41
2005–2009 26.01 23.83 80.98
2010–2014 18.06 25.42 40.52
2015–2019 9.54 37.02 32.18

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 2000–2019 3D mutual information transfer volume

Conclusions and recommendations

Through a comprehensive empirical analysis of the TH collaborative innovation system in Heilongjiang Province, we found that universities and other research institutions independently produced knowledge and technology achievements in different provinces. In addition, in different periods they are always far ahead, and knowledge and technology high-output institutions have become the promotion grounds of collaborative innovation ecology. The backbone of system development, the collaborative innovation system of coupling and symbiosis, needs to maintain a good hyper-cycle of information among subsystems such as universities, enterprises and governments [15]. The monopoly of research institutions will only increase the overall instability of the system. However, Heilongjiang Province has shown a weak degree of coordination in the interaction and cooperation between scientific research institutions and enterprises or government organisations. There are weak links in the process of knowledge and technology marketisation, especially the weak coupling of mutual information between scientific research institutions and enterprises. This will inevitably restrict the release of regional innovation competitiveness. For companies that can bring about huge social and economic impacts, the awareness and ability of innovation and creativity are still insufficient, and they are easily eliminated in the rapidly changing market economy. To this end, we propose the following suggestions.

Gather the sense of collaboration of innovative subjects

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].

Changing the development model of scientific research institutions

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.

Improve the absorption and transformation efficiency of enterprises and their scientific and technological achievements

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.

Fig. 1

Trend of the ratio of AG, AI, GI and AIG
Trend of the ratio of AG, AI, GI and AIG

Fig. 2

Heilongjiang Province 2000–2019 3D mutual information transfer volume
Heilongjiang Province 2000–2019 3D mutual information transfer volume

Definition and acquisition of data variables of scientific papers

Intermediate variables Implication Rules for obtaining variable indicators

Ax Number of papers including information on scientific research institutions Papers with ‘UNIV*’, ‘COLL*’, ‘ACAD*’, ‘NIH*’ in the address field
Ix Number of papers including companies information Papers with ‘GMBH*’, ‘CORP*’, ‘LTD*’, ‘AG*’, ‘INC*’ in the address field
Gx Number of papers including government information Papers with ‘NATL*’, ‘NACL*’, ‘NAZL*’, ‘GOVT*’, ‘MINIST*’, ‘ACAD*’, ‘CNRS*’ in the address field
AIx Number of papers including both scientific research institutions and companies information The address field contains the number of papers representing both Ax and Ix contents
GxA Number of papers including information about scientific research institutions in the GX dataset The number of papers containing Ax in the Gx subset
GIx Number of papers including both government and corporate information The address field contains the number of papers representing Gx and Ix contents at the same time
GxAI Number of papers including information about companies in the GX A dataset The number of papers containing Ix in the GxA subset

Summary of scientific papers and patent achievements

Province Variable
A I G AG AI GI AGI

Heilongjiang 8362 4494 702 3166 2147 290 1180
Jilin 8980 5203 100 4869 1523 189 1234
Liaoning 10,351 8577 180 3858 2876 306 728

Variable definition and calculation formula of collaborative innovation measure

Variables Implication Calculation formula

A The number of papers and patents exported only by scientific research institutions A = Axy-AIxy-GxA + GxAI
I The number of papers and patents output only by the companies I = Ixy-AIxy-GIx + GxAI
G The number of papers and patents output only by government G = Gxy-GxyA-GIx + GxyAI
AI The number of papers and patents jointly published or applied only by scientific research institutions and enterprises AI = AIxy-GxAI
AG The number of papers and patents jointly published or applied only by scientific research institutions and government AG = GxA-GxAI
GI The number of papers and patent jointly published or applied only by government and enterprises GI = GIx-GxAI
AGI The number of papers and patents output by government agencies, scientific research institutions and enterprises AGI = GxAI

Two-dimensional and three-dimensional T value in the three northeastern provinces

Province T-value
T(AG) T(AI) T(GI) T(AGI)

Heilongjiang 49.82 11.73 0.25 −309.21
Jilin 51.24 9.27 0.55 −345.13
Liaoning 43.72 12.93 0.49 −302.14

Number of scientific papers and patents from 2000 to 2019

Year Variable
A I G AG AI GI GAI

2000–2004 108 80 2 18 12 63 3
2005–2009 738 346 18 129 92 329 42
2010–2014 2533 1013 900 716 369 526 375
2015–2019 16,547 6308 1417 7514 2904 3690 2857

Two-dimensional mutual information transfer volume in 2000–2019

Year T-value
T(AI) T(AG) T(GI)

2000–2004 48.95 64.26 100.41
2005–2009 26.01 23.83 80.98
2010–2014 18.06 25.42 40.52
2015–2019 9.54 37.02 32.18

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