Realization path of the stability of university-industry coupling symbiotic network

Abstract

This paper empirically analyzes the influence of partners’ characteristics of university-industry on their network stability using projection pursuit model and multiple intermediary models. This paper also analyzes the mediating role of collaborative innovation and environmental integration. Based on the empirical results, this paper proposes two realization paths for the stability of university-industry coupling symbiotic network. Under the condition that the partners are compatible, the partners with the same goals and interests can realize the stable development of university-industry coupling symbiotic network through collaborative innovation. Under the condition that the partners are conflicting, the symbiosis environment can be integrated to gradually weaken the contradictions and conflicts, so as to realize the stable development of university-industry coupling symbiotic network.

Keywords

University-industry coupling symbiotic network stability realization path

1. Introduction

The university-industry cooperation is a kind of technological innovation cooperation activity among industries, universities and research institutions. The partners of university-industry can achieve sharing the benefits, resources, technology, knowledge and culture. At the same time, they also share the threats and risks in the process of cooperation. In the case of deep cooperation of university-industry, the partners of university-industry can establish complementary and mutually compatible coupling relationships. This coupling relationship can achieve the maximum collection and utilization of various resources [1, 2]. With the positive guidance of the government on university-industry cooperation and the increase in the participation of enterprises in innovative activities, the number of exchanges and interactions between the partners of university-industry cooperation continues to increase, and the use of complementary resources is also more effective. The mode of university-industry cooperation is no longer just a single linear cooperation. It is gradually developing to the network cooperation mode [3, 4]. In addition, the university-industry cooperation also shows strong coupling and symbiotic characteristics. Therefore, the coupling theory, symbiotic theory and network theory can provide guidance for the in-depth research of university-industry cooperation.

The university-industry coupling symbiotic network refers to a network system formed by the research and development, interactive cooperation and other symbiotic activities of network nodes such as industries, universities and research institutions in a symbiotic network environment. The coupling symbiosis network of university-industry uses different types of symbiosis modes to form a coupled cooperative relationship with complementary advantages, mutual compatibility, mutual coordination and mutual promotion on the symbiosis interface [5, 6]. The coupling symbiosis network of university-industry realizes the sustainability of university-industry cooperation through resource coupling, target coupling, knowledge coupling and cultural coupling among network nodes. The symbiosis environment provides environmental support for the four types of coupling among network nodes.

The stability of university-industry coupling symbiotic network is a relative and dynamic balance within the normal fluctuation range. It means that the partners of university-industry can mutual coordinate and develop together toward the same expected goal, thereby maintaining the continuous operation of the network. In addition, it can maintain the stable operation through internal self-regulation when it is affected by various partners inside and outside the symbiotic environment [7]. The stability of university-industry coupling symbiotic network is manifested in that the partners can communicate in a timely and effective manner in accordance with agreed goals and plans. At the same time, it also shows that it can improve the efficiency and quality of collaborative interaction and cooperative innovation by saving operating costs and reducing contradictions among various bodies [8, 9].

The stable university-industry cooperation can promote the integration and dissemination of knowledge and technology. During the operation of the university-industry coupling symbiotic network, it may be disturbed by internal partners or external environment. These disturbances will affect the stable operation of the university-industry coupling symbiotic network. Therefore, what path can be used to realize the stable and effective operation of the university-industry coupling symbiotic network has become an important issue that needs to be studied and solved urgently. Compared with previous research results on the stability of university-industry network, this research mainly starts from the perspective of the four major characteristics of the university-industry body, and clarifies the relationship between the four major characteristics of the body and the stability of the coupled symbiotic network. This research takes the two intermediary effects of collaborative innovation and symbiosis environment integration as the main line, and analyzes the different realization paths of different intermediary effects on the stability of the coupled symbiosis network in the case of subject compatibility and subject conflict.

2. Research hypothesis

2.1 The partners’ characteristics and the stability of university-industry coupling symbiotic network

The partners’ characteristics of university-industry coupling symbiotic network are manifested as the resource coupling, target coupling, knowledge coupling and cultural coupling. These characteristics jointly promote the stable development of university-industry coupling symbiotic network. The resource among the partners of university-industry includes complementary resources such as technology, equipment and capital. The resource coupling greatly enhances the dependence among the partners, which is conducive to the stability of the university-industry coupling symbiotic network. The target coupling among the partners is reflected in the compatibility of the partners’ target. The partners of university-industry with the common vision are more conducive to communicating information, strengthening trust, and making effective decisions, so as to maintain the stability of the university-industry coupling symbiotic network [10].

Having different knowledge systems is the key to effective cooperation of university-industry. Complementarity and mutual learning of the partners of university-industry can reduce conflicts and differences in the process of cooperation, so as to maintain a stable cooperative relationship of university-industry. At the same time, the cultural coupling among the partners is conducive to the diversification of cooperation channels of university-industry. It will increase access to resources for the partners of university-industry. The integration of business culture and technological culture will also enhance mutual trust, which is conducive to maintaining the stability of the university-industry coupling symbiotic network [11]. Based on the above analysis, the hypothesis 1 is proposed:

H ${}_{1}$ : The partners’ characteristics have a positive impact on the stability of the university-industry coupling symbiotic network. That is, the more significant partners’ characteristics, the more conducive to the improvement of the stability of the university-industry coupling symbiotic network.

2.2 The mediating role of collaborative innovation

Collaborative innovation is a new mode of university-industry cooperation. The collaborative innovation system is an organic combination of the various elements of the university-industry coupling symbiotic network [12]. As the core partners, enterprises, universities and scientific research institutions rely on and merge with each other. They should increase the degree of cooperation and actively carry out large-scale and large-span resource integration. At the same time, the government and financial institutions are the third parties in the university-industry coupling symbiotic network, which assist all partners in the mutual exchange, reference and absorption of knowledge, technology, resources and culture [13].

The partners’ resource coupling, target coupling, knowledge coupling and cultural coupling are the basis for the in-depth cooperation, information reciprocity and optimal synchronization in the university-industry coupling symbiotic network. The partners’ characteristics of the university-industry coupling symbiotic network make the behavior and connection become more closely through complementary and integration. It is conducive to the mutual assistance and communication between the partners of university-industry, and promote the improvement of collaborative innovation performance. Based on the above analysis, the hypothesis 2 is proposed:

H ${}_{2}$ : The partners’ characteristics of university-industry have a positive impact on the collaborative innovation. That is, the more significant the partners’ characteristics of the university-industry coupling symbiotic network, the better the effect of collaborative innovation.

Good collaborative innovation is the guarantee for the stable development of the university-industry coupling symbiotic network. The integrity of collaborative innovation can effectively promote the university-industry coupling symbiotic network to form a stable innovation team [14, 15]. When partners are compatible, collaborative innovation helps to fully realize the deep coupling effect between partners in terms of capital, talents, technology, and information resources, and promotes the promotion and application of the latest technology among various entities. This will help ensure the stable operation of the coupling symbiosis network of university-industry. In the case of conflicts between partners, collaborative innovation can help the partners of university-industry to make better use of their own unique advantages resources, reduce the obstacles of innovation resources and technological elements between the main bodies of university-industry, thereby weakening contradictions and conflicts. Based on the above analysis, the hypothesis 3 is proposed:

H ${}_{3}$ : Collaborative innovation has a positive impact on the stability of the university-industry coupling symbiotic network. That is, the better the effect of collaborative innovation, the more conducive to the stability of the university-industry coupling symbiotic network.

Based on the hypothesis H ${}_{2}$ and H ${}_{3}$ , it is deduced that collaborative innovation plays a mediating role in the relationship between the partners’ characteristics and the stability of the university-industry coupling symbiotic network.

2.3 The mediating role of symbiotic environment integration

Symbiotic environment integration can provide a good symbiotic network environment for knowledge, technology, manpower, energy and other resources in the process of university-industry cooperation. It also can strengthen the management and integration of all partners through resource integration and benefit coordination. Due to the partners’ characteristics of university-industry have four characteristics: resource coupling, goal coupling, knowledge coupling and culture coupling, the university-industry coupling symbiotic network is a management mode of mutual benefit and win-win cooperation. Under this management mode, it can better promote the resource sharing and coordinated interaction among the partners, thereby forming a good symbiotic environment [16]. In addition, the more prominent the partners’ characteristics of university-industry, the more conducive to strengthening the sense of trust, mutual assistance and autonomy among the partners. The partners’ characteristics can promote the active participation in the creation and integration of the symbiotic environment. Therefore, the partners’ characteristics of university-industry can help improve the efficiency and effectiveness of environmental integration. Based on the above analysis, the hypothesis 4 is proposed:

H ${}_{4}$ : The partners’ characteristics of university-industry have a positive impact on the integration of symbiotic environment. That is, the more prominent the partners’ characteristics of the university-industry coupling symbiotic network, the better the effect of symbiotic environment integration.

Since the partners of university-industry come from different enterprises, universities and research institutions, they may have certain contradictions and conflicts due to different cultural environments, inconsistent goals, and unequal distribution of benefits. These contradictions and conflicts may affect the cooperative relationship between the partners of university-industry and threaten the stability of the university-industry coupling symbiotic network. The integration of the symbiotic environment is crucial in the contradictions and conflicts among the partners of university-industry. Positive integration of the symbiotic environment is the key to the stable development of the university-industry coupling symbiotic network. The integration of symbiotic environment can realize breakthroughs in various information, equipment, funds, materials and other elements. This can maintain the stable operation of the university-industry coupling symbiotic network. Therefore, the integration of symbiotic environment promotes the coordination and unity of resources, economy and ecology in the university-industry activities. So the integration of symbiotic environment will help improve the stability of the university-industry coupling symbiotic network [17]. Based on the above analysis, the hypothesis 5 is proposed:

H ${}_{5}$ : The integration of symbiotic environment has a positive impact on the stability of the university-industry coupling symbiotic network. That is, the better the effect of the integration of symbiotic environment, the more conducive to the stability of the university-industry coupling symbiotic network.

Based on the hypothesis H ${}_{4}$ and H ${}_{5}$ , it is deduced that environmental integration plays a mediating role in the relationship between partners’ characteristics and the stability of the university-industry coupling symbiotic network.

3. Research model

3.1 Projection pursuit model

In order to analyze high-dimensional data, the projection pursuit model projects high-dimensional data onto low-latitude subspaces, so that the structure and characteristics of high-dimensional data can be obtained. Because the projection pursuit model has the advantages of anti-interference, accuracy and robustness, it is widely used in different fields [18]. The research variables involved in this article have high-latitude and non-linear correlations, which affect the accuracy of empirical tests. The projection pursuit model can eliminate the interference of data in the projection direction which is not related to the structure. It helps to automatically find the internal laws of the data. This better method of processing multivariate data is divided into the following four steps:

(1) Calculate the projection value of variable $z(i)$ :

$\displaystyle z(i)=\sum_{j=1}^{p}a(j)x({i,j})(i=1,2,\ldots,n)$ (1)

Among them, $a(j)$ represents the projection direction of the variable feature, and $x({i,j})$ represents the data of simple standardized treatment for the j-th problem variable of the i-th survey object in the Eq. (1).

(2) Establish the projection exponential function $Q(a)$ :

$\displaystyle S_{z}=\sqrt{\frac{\sum_{t=1}^{n}({z(i)-E_{z}})^{2}}{n-1}}$ (2) $\displaystyle D_{z}=\sum_{i=1}^{n}\sum_{j=1}^{n}({R-r_{ij}})u({R-r_{ij}})$ (3) $\displaystyle u(t)=\left\{\frac{0,t<0}{1,t\geqslant 0}\right.$ (4) $\displaystyle Q(a)=S_{z}D_{z}$ (5)

In the Eqs (2) and (3), the mean value of $z(i)$ is represented by $E_{z}$ , the radius of the local density window is represented by $R$ , and the distance between each sample ( $z(i)-z(j)$ ) is represented by $r_{ij}$ . In Eq. (4), the unit step function is expressed as $u(t)$ . The signal represented by the unit step function is an idealized model that can grasp the main factors when analyzing the problem. When $t<$ 0, the function semaphore is always 0; when $t\geqslant$ 0, the function semaphore is always 1. Equation (5) shows that the projection exponential function $Q(a)$ is the product of the standard deviation $S_{z}$ of $z(i)$ and the local density $D(z)$ .

(3) Optimize the projection index function

Data structure characteristics are often reflected by the projection direction of the data. The best projection direction appears often in the projection direction of high-dimensional data. Therefore, the maximum of projection index function can be used to estimate the best projection direction of the data. As shown in Eq. (6):

$\displaystyle\left\{{\begin{array}[]{l}\max Q(a)=S_{z}D_{z}\\ s.t.\sum_{j=1}^{p}{a^{2}(j)}=1\\ \end{array}}\right.$ (6)

(4) Priority arrangement to determine the best projection value

Observe the data from different angles and find the best projection direction that can mine the data. Randomly select several initial projection directions $a({a_{1},a_{2},\ldots,a_{m}})$ and substitute them into Eq. (1) to obtain the projection values $z(i)$ of each variable. The value of from large to small indicates the pros and cons of the sample, and the largest projection value is determined as the best projection value.

3.2 Multiple intermediary models

The multiple intermediary models are to study the situation where there are two or more intermediary variables between the independent variables and the dependent variables [19]. This paper selects two intermediary variables: collaborative innovation and symbiotic environment integration. Due to the co-existence of cooperation and competition among the various subjects of university-industry, the mediating effect of collaborative innovation in the cooperative relationship is more significant; while in the competitive relationship, the subjects are more likely to have contradictions and conflicts, so the integration of the symbiosis environment plays a larger intermediary effect. In order to better analyze the mediating effect of collaborative innovation and symbiotic environment integration between the partners’ characteristics and the stability of the university-industry coupling symbiotic network, the multiple intermediary model is selected for regression analysis. The equations are as follows:

$\displaystyle Y=\tau X+\varepsilon_{1}$ (7) $\displaystyle M_{1}=\alpha_{1}X+\varepsilon_{2}$ (8) $\displaystyle M_{2}=\alpha_{2}X+\varepsilon_{3}$ (9) $\displaystyle Y=\tau^{\prime}X+\beta_{1}M_{1}+\beta_{2}M_{2}+\varepsilon_{4}$ (10)

In the formulas from Eqs (7) to (10), $\varepsilon_{1}$ , $\varepsilon_{2}$ , $\varepsilon_{3}$ and $\varepsilon_{4}$ represent residuals, $\tau$ , $\tau^{\prime}$ , $\alpha_{1}$ , $\alpha_{2}$ , $\beta_{1}$ and $\beta_{2}$ represent estimated coefficients. From this, the following Eqs (11) and (12) can be derived. The Eq. (11) is the total effect of $X$ on $Y$ . The Eq. (12) is the overall mediation effect test.

$\displaystyle\tau=\tau^{\prime}+\alpha_{1}\beta_{1}+\alpha_{2}\beta_{2}$ (11) $\displaystyle\tau-\tau^{\prime}=\alpha_{1}\beta_{1}+\alpha_{2}\beta_{2}$ (12)

The significance of the overall mediation effect of the multiple mediation model is analyzed by $t$ -testing, as shown in Eq. (13).

$\displaystyle t_{N-2}=\frac{\tau-\tau^{\prime}}{\sqrt{\sigma_{\tau}^{2}+\sigma% _{\tau^{\prime}}^{2}-2\rho_{\tau\tau^{\prime}}\sigma_{\tau}\sigma_{\tau^{% \prime}}}}$ (13)

In the Eq. (13), $\sigma_{\tau}$ and $\sigma_{\tau^{\prime}}$ are the standard errors of $\tau$ and $\tau^{\prime}$ respectively, and $\rho_{\tau\tau^{\prime}}$ is the correlation coefficient of $\tau$ and $\tau^{\prime}$ .

The estimated coefficient calculated by formulas from Eqs (7) to (10) can be used to calculate the mediating effect of collaborative innovation and environmental integration. The basic formulas are as follows:

$\displaystyle Z_{\alpha\beta}=f_{c}/\sqrt{\text{var}({f_{c}})}$ (14)

In Eq. (14), $\text{var}({f_{c}})=\beta_{1}\sigma_{\alpha_{1}}^{2}+\beta_{2}\sigma_{\alpha_{% 2}}^{2}+\alpha_{1}\sigma_{\beta_{1}}^{2}+\alpha_{2}\sigma_{\beta_{2}}^{2}-2% \beta_{1}\beta_{2}\sigma_{\alpha_{1}\alpha_{2}}-2\alpha_{1}\alpha_{2}\sigma_{% \beta_{1}\beta_{2}}$ ; $f(c)=\alpha_{1}\beta_{1}-\alpha_{2}\beta_{2}$ . Among them, $\alpha_{1}\beta_{1}$ and $\alpha_{2}\beta_{2}$ respectively represent the mediating effect of collaborative innovation and symbiotic environment integration.

4. Empirical analysis

4.1 Data collection

This paper uses the questionnaire survey method for data collection. The main objects of investigation are the management and scientific research personnel of enterprises, universities and scientific research institutions participating in the operation of the university-industry coupling symbiotic network. Most of the topic options in this research questionnaire are derived from the existing mature items in the relevant literature. At the same time, the reasonable compilation is made based on the particularity of university-industry coupling symbiotic network, and finally the questionnaire survey are formed. Among them, the partners’ characteristics is designed from four aspects: resource coupling, target coupling, knowledge coupling and culture coupling. Collaborative innovation is designed from the three aspects of optimal synchronization, information reciprocity and deep cooperation. Environmental integration is designed from the three aspects of resource integration, benefit coordination and element breakthrough. The stability of the university-industry coupling symbiotic network is designed from four aspects: organic whole, member satisfaction, self-regulation and continuous cooperation. Except for the basic information of the partners participating in the survey, the scales are all in the form of a 5-point Likert scale. Five levels are set up according to the respondents’ cognition degree of the influence of a certain factor on the stability of the university-industry coupling symbiotic network. These five levels include “completely disagree”, “not quite disagree”, “generally agreement”, “relatively agree” and “completely agree”.

The questionnaire survey adopts two methods: paper questionnaire and online questionnaire. A total of 500 questionnaires were distributed in this survey, and after excluding invalid questionnaires, the final valid questionnaire was 451. Among them, from the perspective of enterprise management positions, senior management personnel account for 24%, and intermediate management personnel account for 41%. From the perspective of the source of experts, enterprise experts account for 29%, university experts account for 32%, and scientific research institution experts account for 31%. From the perspective of the duration of university-industry cooperation, cooperative projects with a duration of more than three years account for 62%. The interviewees have a good cognition and understanding of the content of the questionnaire in terms of academic qualifications and work experience. And they can complete the questionnaire survey well. Therefore, the samples obtained have good representativeness.

4.2 Reliability and validity analysis

In terms of reliability test, this paper uses Cronbach Alpha coefficient to test the reliability of the variables in the questionnaire. In terms of validity testing, confirmatory partners are chosen to test the validity of the scale [20]. The reliability and validity analysis results of the scale are shown in Table 1.

Table 1
Table of reliability and validity test results of the scale

Variable	Item	$\alpha$	Comprehensive $\alpha$	KMO	Sig.
Partners’ characteristics	Resource coupling	0.812	0.814	0.828	0.000
	Target coupling	0.840
	Knowledge coupling	0.752
	Cultural coupling	0.763
Collaborative innovation	Optimal synchronization	0.857	0.822	0.843	0.000
	Information reciprocity	0.811
	Deep cooperation	0.799
Symbiotic environment integration	Resource integration	0.809	0.796	0.755	0.000
	Benefit coordination	0.842
	Element breakthrough	0.871
Stability of symbiotic network	Organic whole	0.768	0.772	0.795	0.000
	Member satisfaction	0.799
	Self-regulation	0.832
	Continuous cooperation	0.824

Generally, when the reliability coefficient $\alpha$ of the scale is greater than 0.5, the reliability of the scale can be accepted. It can be seen from Table 1 that the values of variable $\alpha$ in the questionnaire are all greater than 0.7. Indicating that the variables have good internal consistency. Therefore, this scale has a high reliability. The KMO value of each variable is greater than 0.7, and Sig. is less than 0.001, reaching a significant level. It shows that each factor has a high degree of fitting, and there are common partners existing in the correlation matrix of the mother population. In summary, the above four variables meet the standard in terms of reliability and validity.

Table 2

The best projection direction table for each variable

Variable	Best projection direction
partners’ characteristics (X)	(0.3914 0.4017 0.3845 0.3271)
Collaborative innovation (M ${}_{1}$ )	(0.4240 0.4532 0.3785)
Symbiotic environment integration (M ${}_{2}$ )	(0.3698 0.2657 0.3499)
Stability of the university-industry coupling symbiotic network (Y)	(0.4366 0.4429 0.2685 0.3578)

Table 3

Table of mean, maximum value, standard deviation and correlation coefficient of variables

Variable	Sample size	Mean	Standard deviation	X	M ${}_{1}$	M ${}_{2}$	Y
X	451	1.527	0.363	1.000
M ${}_{1}$	451	1.492	0.402	0.549 ${}^{**}$	1.000
M ${}_{2}$	451	1.476	0.372	0.358 ${}^{**}$	0.405 ${}^{**}$	1.000
Y	451	1.511	0.421	0.487 ${}^{**}$	0.522 ${}^{**}$	0.514 ${}^{**}$	1.000

Note: ${}^{**}$ . is significantly correlated at 0.01 level (bilateral).

Table 4

Multiple intermediary models estimation results table

		Standard error	$t$	$p$
$X\to M_{1}(M_{2})$	$\alpha$
$M_{1}$	0.562 ${}^{***}$	0.052	12.344	$P<$ 0.001
$M_{2}$	0.421 ${}^{***}$	0.049	10.265	$P<$ 0.001
$M_{1}(M_{2})\to Y$	$\beta$
$M_{1}$	0.395 ${}^{***}$	0.071	8.026	$P<$ 0.001
$M_{2}$	0.387 ${}^{***}$	0.046	7.766	$P<$ 0.001
The direct effect $\tau$ of $X\to Y$
$\tau$	0.522 ${}^{***}$	0.049	11.142	$P<$ 0.001
The direct effect $\tau^{\prime}$ of $X\to Y$
$\tau^{\prime}$	0.179 ${}^{*}$	0.057	3.241	$P<$ 0.05

Note: ${}^{*}$ . is significantly correlated at 0.05 level (bilateral), ${}^{**}$ . is significantly correlated at 0.01 level (bilateral), ${}^{***}$ . is significantly correlated at 0.001 level (bilateral).

4.3 Dimensionality reduction results of projection pursuit model

The projection pursuit model is used to reduce the dimension of four variables: actor’ characteristics, collaborative innovation, symbiotic environment integration and stability of the university-industry coupling symbiotic network. And the matlab7.1 software is used to project high-dimensional measurement index data into low-dimensional space. In this way, the best projection direction of the four variables corresponding to the measurement index data is determined. The results of dimension reduction are shown in Table 2.

The optimal projection value is obtained from the optimal projection direction of the dimensionality reduction processing of variables in Table 2. The optimal projection value can be used as the measurement value of the four variables. Then the SPSS17.0 software is used to analyze the mean, standard deviation and correlation of the obtained projection values, as shown in Table 3.

It can be seen from Table 3 that the four variables are all significantly correlated at the level of correlation coefficient, which is less than 0.01. Therefore, the optimal projection value obtained by the dimensionality reduction process is suitable for the estimation test of the multiple mediating effect models.

4.4 Estimation results of multiple intermediary models

Using SPSS17.0 software, the multiple intermediary models are used to empirically analyze the internal relationships among the four variables of partners’ characteristics, collaborative innovation, symbiotic environment integration and the stability of the university-industry coupling symbiotic network. The measured values obtained by the projection pursuit dimensionality reduction process are substituted into the multiple mediation model, and the estimated results are shown in Table 4.

It can be seen from Table 4 that the total effect $\tau$ is equal to 0.522, which is significantly correlated at the level of P, which is less than 0.001. This means that the partners’ characteristics have the positive promotion effect on the stability of the university-industry coupling symbiotic network. So the hypothesis H ${}_{1}$ is verified. Because $\alpha_{1}$ is equal to 0.562 and $\alpha_{2}$ is equal to 0.421, both are significantly correlated at the level of P, which is less than 0.001. It means that the partners’ characteristics have the positive promotion effect on the two intermediary partners of collaborative innovation and the symbiotic environment integration. So the hypotheses H ${}_{2}$ and H ${}_{4}$ are verified.

Figure 1.

The realization path diagram of the stability of university-industry coupling symbiotic network.

Because $\beta_{1}$ is equal to 0.395 and $\beta_{2}$ is equal to 0.387, both are significantly correlated at the level of P, which is less than 0.001. It means that the collaborative innovation and symbiotic environment integration have the positive promotion effect on the stability of the university-industry coupling symbiotic network. So the hypotheses H ${}_{3}$ and H ${}_{5}$ are verified. The realization path of the stability of university-industry coupling symbiotic network is shown in Fig. 1.

5. Research conclusions and policy suggestions

5.1 Research conclusions

Through the above empirical analysis, the following conclusions are drawn: The partners’ characteristics of university-industry have a positive impact on the stability of the university-industry coupling symbiotic networks; Collaborative innovation and environmental integration play the intermediary role between the partners’ characteristics and the stability of the university-industry coupling symbiotic network.

According to the relationship among the above four variables, two paths are obtained to realize the stability of the university-industry coupling symbiotic network. The first path is that under the condition that the partners are compatibility, all partners with the same goals and interests can realize the stable development of the university-industry coupling symbiotic network through collaborative innovation. The second path is that under the condition that the partners are conflict, the symbiotic environment can be integrated to gradually weaken contradictions and conflicts. Thus, the stable operation and development of the university-industry coupling symbiotic network can be realized. The two implementation paths are analyzed as follows:

(1) The first path: the partners are compatibility $\to$ collaborative innovation $\to$ stable development of the university-industry coupling symbiotic network

The four characteristics of the partners of university-industry include resource coupling, target coupling, knowledge coupling and cultural coupling. The four characteristics are not only the embodiment of compatibility among the partners, but also the basis for the stable development of the university-industry coupling symbiotic network. Collaborative innovation means that the partners of university-industry can collaborate to the greatest extent to carry out technological innovation activities. At the same time, it also means that the partners strive to realize the complementary advantages and resource sharing of all parties, and accelerate the promotion of technology applications [21, 22]. When the goals and interests of the partners of university-industry are consistent, they have the characteristics of compatibility among the partners. The partners of university-industry can cooperate with each other, complement each other’s advantages, and form an organic whole of stable development. So as to better carry out collaborative innovation activities, promote the sharing of information and technology resources, and promote the continuous collaborative interaction and optimal synchronization of all partners. Therefore, under the condition that the partners are compatibility, collaborative innovation is the first path to realize the stable operation of the university-industry coupling symbiotic network.

(2) The second path: the partners are conflicting $\to$ environmental integration $\to$ stable development of the university-industry coupling symbiotic network

The stable development of the university-industry coupling symbiotic network is reflected in the unity of durability and anti-interference [23, 24]. The partners’ characteristics of university-industry can not only promote the coordination and unity of the partners, but also adjust the relationship among the partners through the integration of the symbiotic environment. After the interfering partners are eliminated by communication, assistance and running-in, the partners of university-industry can achieve resource integration, interest coordination and element breakthroughs. Then the university-industry coupling symbiotic network can achieve stable operation. The operation process of the university-industry coupling symbiotic network has dynamic fluctuation. The stable development will inevitably be affected by internal and external interference partners such as egoism, opportunism, and information asymmetry, which will cause certain contradictions and conflicts [25, 26]. When there are conflicts among the partners of university-industry, it is necessary to strengthen the integration and management of the symbiotic environment. Strengthening the unity of goals and the consistency of interests of all partners is conducive to the continuous and stable cooperation among the partners of university-industry. Therefore, under the condition that the partners are conflicting, mitigating the conflict of partners through the integration of the symbiotic environment is the second path to realize the stable development of the university-industry coupling symbiotic network.

5.2 Policy suggestions

(1) Constantly strengthen the partners’ characteristics of university-industry

The partners’ characteristics of university-industry coupling symbiotic network mainly include resource coupling, target coupling, knowledge coupling and cultural coupling. The more significant the partners’ characteristics of university-industry coupling symbiotic network, the more conducive to its stable operation [27, 28].

First of all, it is necessary to promote the resource coupling of university-industry coupling symbiotic network. Breaking the institutional barriers among network nodes can realize the efficient flow of resources among the partners of university-industry. At the same time, the internal financial resources, market resources, human resources and project resources of the enterprises can be integrated with the scientific resources, technical resources, knowledge resources and laboratory resources of research institutions and universities.

Secondly, it is necessary to strengthen the goal coupling of university-industry coupling symbiotic network. The actual demands of the partners of university-industry are respected. At the same time, the communication mechanism and expression mechanism among the network entities are constantly improved in order to improve their fairness and equality.

Furthermore, it is important to strengthen the knowledge coupling of university-industry coupling symbiotic network. Cooperating parties should send professionals to other partners for knowledge learning and knowledge exchange and communication. At the same time, they should also jointly participate in the development of research projects, so as to give full play to the knowledge advantages of the partners.

Finally, it is necessary to establish the cultural coupling mechanism of university-industry coupling symbiotic network. The explicit and clearly expressed culture should be actively shared to enhance the sense of cultural identity; the intrinsic and different substantive hidden cultures should be actively integrate and exchange.

(2) Establish a network sharing platform for university-industry coupling symbiosis

The improvement of university-industry collaborative innovation mechanism requires the establishment of network sharing platform for university-industry to achieve cross-regional university-industry cooperation. On the one hand, the sharing platform of university-industry coupling symbiosis network can give full play to the advantages of factor aggregation and further promote the in-depth cooperation between enterprises, universities and scientific research institutions. On the other hand, the sharing platform of university-industry coupling symbiosis network can strengthen the ability of university-industry network partners to integrate information resources, and provide diversified products and services for each network partner. In order to realize the stable development of university-industry coupling symbiosis network, it is necessary to better supervise the operation and development of the coupling symbiosis network, which is conducive to enhancing the resource gathering power of the coupling symbiosis network. The innovation of symbiotic network technology can be promoted by improving the regional coordinated development mechanism and optimizing the strategy of talent development environment. When the symbiosis network system is in a relatively stable state, you can choose to further expand the scale of university-industry cooperation, deepen the breadth and depth of cooperation, and establish a cross-regional cooperation system to make the cooperation between university-industry subjects more dynamic.

(3) Give full play to the leading role of the government in the symbiosis network environment

The government should increase capital investment and support for major cooperation projects of university-industry, so as to reduce the risk of innovative and research. In addition, the government should improve the tax reduction and exemption mechanism for scientific and technological innovation, which can help the university-industry partners to retain sufficient R&D reserves, and enhance the enthusiasm of R&D and innovation of the university-industry partners.

Footnotes

Acknowledgments

We gratefully acknowledge the financial support from the National Social Science Fund Project of China (no. 19BJY068).

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Realization path of the stability of university-industry coupling symbiotic network

Abstract

Keywords

1. Introduction

2. Research hypothesis

2.1 The partners’ characteristics and the stability of university-industry coupling symbiotic network

2.2 The mediating role of collaborative innovation

2.3 The mediating role of symbiotic environment integration

3. Research model

3.1 Projection pursuit model

4.1 Data collection

4.2 Reliability and validity analysis

Table 1 Table of reliability and validity test results of the scale

4.4 Estimation results of multiple intermediary models

5.1 Research conclusions

5.2 Policy suggestions

Footnotes

Acknowledgments

References

Table 1
Table of reliability and validity test results of the scale