Innovation and Entrepreneurship Education Innovation in Applied Universities under System Dynamics Modeling

The evolution of college students’ entrepreneurial system is affected by several variables, in this section, mainly combining the conclusions drawn from the above research on the influencing factors of entrepreneurial ability, the two state variables of entrepreneurial drive and entrepreneurial endowment are selected, and the entrepreneurial drive and entrepreneurial endowment are stimulated by adjusting each variable, so as to improve the effectiveness of the system and promote the system’s evolutionary speed. For the entrepreneurial driving force, internal and external driving forces are mainly selected, of which internal and external driving forces are influenced by internship experience, entrepreneurship policy, interest driving force and family support driving force, respectively. Entrepreneurial endowment is mainly moderated by personal traits, knowledge and skills, where these two variables are in turn moderated by the level of entrepreneurship education and the proportion of entrepreneurship teachers. In addition, variables such as entrepreneurial demand index, entrepreneurial projects, incubation rate, entrepreneurial capital, entrepreneurial culture index, and entrepreneurial opportunity index are introduced for the model to regulate the model.

Modeling entrepreneurial system dynamics is a dynamic and complex process that needs to be carried out in a systematic environment with dynamic feedback mechanisms.

1)

System dynamics principle and applicability analysis

System dynamics first appeared in 1956, in the 1990s system dynamics has been developed faster, in control theory, system science, mutation theory and other theoretical links, as well as dissipation coefficients, sensitivity analysis, parameter estimation and other aspects of the study have been strengthened. SD is mainly used to study and analyze the information feedback system, it is a quantitative research method, with openness, constraints, long-term and complexity, etc. The method is based on system theory, information theory and cybernetics, and takes the dialectical relationship between the internal structure, information feedback, system function and behavioral dynamics of social, economic and ecological systems as the object of research, and takes all kinds of binding force and dynamics as well as defining system boundaries to react to the multi-level, multi-loop and non-linear causal feedback structure, so as to make the economic and policy variables simplified and at the same time to make the systematic problems clearer, and it is the cross-discipline of natural and social sciences.

Since in reality, the variables are complex and variable, the SD model should be run to find its alternative indicators as much as possible. However, at the same time, affected by the limitations of reality, if the alternative indicators can not be found or are not suitable, it should be considered to set up virtual assumptions or estimation through alternative variables. SD model can deal with the problems of complexity, long term, nonlinearity and data residuals through some variables with clear descriptions, and has the ability to reach the intuition and effectiveness of the system, and at the same time, it can avoid the contradiction brought about by the long term to the short term and the global to the local. At the same time, it can effectively avoid the contradiction between long-term and short-term, global and local. The entrepreneurship problem of college students studied in this paper involves the complex role between multiple elements such as knowledge and skills, personal traits, interests, internship experience, family support, etc., which is suitable for simulation and analysis by using the SD method [35].

That is, the overall system T contains S subsystems, and R_AA describes the relationship matrix between the subsystems. According to the structure, the subsystems can be categorized into benign and non-benign structures, where the benign structure can be directly represented by level variables, rate variables, auxiliary variables and correlation functions. (2)L′=KR$$L\prime = KR$$ (3)( R A)=W( L A)$$\left( \begin{array}{c} R \\ A \\ \end{array} \right) = W\left( \begin{array}{c} L \\ A \\ \end{array} \right)$$

Where: L′ is the pure rate variable; K is the transfer matrix; R is the rate variable; A is the auxiliary variable; W is the relationship matrix; L is the level variable, except for Eq. (2) which describes the various nonlinear relationships between the variables at a given moment in time, the structure of the system that can not be accurately described by the mathematical function is usually described by qualitative, semiquantitative or semi-quantitative [37]. 4)

Steps in modeling SD models

In the use of system dynamics modeling to analyze the real problem, mostly open systems, so the first step needs to define the object of study and analyze the system; the second step is to analyze the structure of the system, clarify the system hierarchy and sub-module feedback relationships and determine the connection between the system; the third step is to establish the structural equations and the system dynamics model based on the causal relationship between the elements, and to clarify the various types of parameter types and set initial values; the fourth step is to analyze the structure of the system and the system dynamics model. At the same time, the initial values are set; in the fourth step, the model is tested and then analyzed in scenario simulation and policy simulation.

In summary, the main steps of system dynamics modeling are shown in Figure 1.

Figure 1.

Main steps of system dynamics modeling

The national policy guarantee level subsystem is shown in Figure 2. The circuit path of the subsystem at the level of national policy guarantee is as follows: national policy guarantee → + government financial expenditure → + (government’s emphasis on entrepreneurship education) → + government’s investment in entrepreneurship infrastructure → + (national financial investment in entrepreneurial talent cultivation) → + level of entrepreneurial talent → + achievement of entrepreneurial talent cultivation → + number of enterprises founded → + contribution of tax revenue → + GDP → + national policy guarantee.

Figure 2.

National policy security level subsystem diagram

The subsystem at the level of entrepreneurship education in colleges and universities is shown in Figure 3. The circuit path of the subsystem at the level of entrepreneurship education in colleges and universities is as follows: entrepreneurship education in colleges and universities → + (government investment in entrepreneurial talent cultivation in colleges and universities) → + investment in teaching resources in schools → + (investment in entrepreneurial talent cultivation in colleges and universities) → + teaching ability of teachers → + level of entrepreneurial talent → + achievement of entrepreneurial talent cultivation → + number of enterprises founded → + contribution of tax revenues → + GDP → + entrepreneurship education in colleges and universities.

Figure 3.

University entrepreneurship education level subsystem diagram

The industry cooperative cultivation level subsystem is shown in Figure 4. The circuit path of the subsystem at the level of industry collaborative cultivation is as follows: industry collaborative cultivation → + the strength of industry participation in talent cultivation (the intensity of cooperation between the industry and the university) → + the expenditure of the industry on cultivating entrepreneurial talents → + the improvement of the level of entrepreneurial talents → + the achievement of entrepreneurial talents cultivation (the number of enterprises founded) → + the contribution of tax revenues → + the GDP → + the enthusiasm of the industry’s participation → + the collaborative cultivation of the industry.

Figure 4.

The industrial collaborative culture subsystem diagram

Analyzing from the perspective of system theory, the cultivation of entrepreneurial talents is not a simple superposition of the functions of the government, colleges and universities and enterprises, but a systematic and holistic function that they interact with each other and thus show, and at the same time, as the subsystems of different systems, their external demands and endogenous dynamics have their own characteristics. Therefore, according to the overall structure of the entrepreneurial talent cultivation system under the perspective of collaborative innovation, the relationship between the influencing factors of entrepreneurial talent cultivation is shown in Figure 5, and the main causal feedback loops are analyzed as follows:

Figure 5.

Development of entrepreneurial talents from the collaborative innovation

In the system dynamics of entrepreneurial talent cultivation under the perspective of collaborative innovation, the model’s circuit paths are mainly as follows, respectively: 1)

Increase in GDP→+Strengthening of national policy guarantee→+Government financial expenditure→+National financial investment in entrepreneurial talent cultivation→+Level of entrepreneurial talents→+Achievement of entrepreneurial talent cultivation→+Number of enterprises founded→+Tax contribution→+GDP

The above three circuits show that the increase of GDP leads to the enhancement of the national policy guarantee ability, the increase of government financial expenditure, the increase of national financial investment in entrepreneurial talent training, the enhancement of entrepreneurial talent level, the achievement of entrepreneurial talent training, the increase of the number of enterprises founded, the increase of tax contribution, which leads to the increase of GDP; the increase of GDP leads to the increase of emphasis on entrepreneurial education in universities, the increase of investment of educational resources in the schools GDP increases, teachers’ teaching ability improves, entrepreneurial teachers’ teaching quality improves, entrepreneurial talent level improves, entrepreneurial talent cultivation achieves greater success, the number of enterprises founded increases, tax contribution increases, resulting in increased GDP; GDP increases, industry collaborative cultivation is strengthened, industry cultivation expenditures on entrepreneurial talent increase, entrepreneurial talent level improves, entrepreneurial talent cultivation achieves greater success, and the number of enterprises founded increases. The number of enterprises founded by entrepreneurial talents increases, and the contribution of tax revenue increases, which makes GDP increase.

The entrepreneurial talent cultivation system under the perspective of collaborative innovation is a complex system formed by the organic coupling of subsystems at the level of national policy guarantee, subsystems at the level of entrepreneurship education in universities and subsystems at the level of industry collaborative cultivation. In order to realize the effective operation of the entrepreneurial talent cultivation system, the various subsystems within the entrepreneurial talent cultivation system should be organically coordinated to form the final stable system of entrepreneurial talent cultivation under the perspective of collaborative innovation to realize the effective cultivation of entrepreneurial talents.

The testing of models is a must for the quantitative management of system dynamics. This is not only to ensure that the system model is more similar to the simulation and the real system, but more importantly to ensure that the model is constructed in a way that is consistent with the policy analysis and some objective view of the system as valid, reliable and credible.

4.1.1

Model correctness test

The correctness test of the model is to verify that the model is applicable to the system under study and the expectations of the system results. This requires the model to be split and tested individually to see if the simulation results are as expected. In this paper, two key state variables are selected for testing, namely “teachers’ innovative teaching ability” and “industry training expenditure”. 1)

Teachers’ innovative teaching ability

To simulate the trend of teachers’ innovative teaching ability, the number of innovative talents and innovation management expenditure are set as time-varying table functions; the innovation elimination rate is set as a fixed value of 0.02. The simulation results are shown in Figure 6.

Teachers’ innovative lecturing ability increases steadily. This is due to the fact that the pre-innovation and entrepreneurship education has not yet been tested by the market as well as the number of innovators and innovation management expenditures are relatively low, and the amount of innovation increase is lower than the amount of innovation decrease. This results in a low base and thus a rapid rise. As the number of innovators and brand innovation management spending grows, the number of innovations and entrepreneurship accelerates. This matches the expected results and, therefore, passes the correctness test.

2)

Industrial cultivation expenditure test

In the same way, only the partial model diagram of the variables related to industrial cultivation expenditures is considered, including the rate of increase of expenditures, the systematic gain of innovation capacity, the number of industrial cultivation expenditure items, the increase of single item expenditures and the decrease of single item expenditures.

The increase of single item expenditure and the level of innovation and entrepreneurial talent is set as a table function over time, and the rate of increase of industrial cultivation expenditure is set as 0.1, and simulation is carried out, and the results are shown in Figure 7.

As can be seen from the figure, the growth of industrial cultivation expenditures in the early period is slow, and then accelerated. In the early stage, Guangxin entrepreneurship teaching gaps are relatively large, and the industry cultivation expenditure starts from a low base thus causing the rapid growth of users. Later sustained innovation, industry cultivation expenditure will still grow. The test of industry cultivation expenditure is also consistent with the reality and passes the correctness test.

Figure 6.

Teachers’ ability to innovate

Figure 7.

Industry culture expenditure trend

4.1.2

Model validity tests

1)

Sensitivity test

The sensitivity test is divided into parametric sensitivity test and structural sensitivity test. In this paper, only the parameter sensitivity test is done. Parameter sensitivity test refers to the sensitivity of the model behavior when the parameters change within a reasonable range.

In this paper, the change of “innovative talent level” in the model is taken as the variable of sensitivity test, and the adjusted parameter is to increase the entrepreneurship education in colleges and universities from 0.05 to 0.25, and the corresponding quality of teachers’ lectures from 0.2 to 0.5. The change of systematic gain of innovation ability before and after the parameter adjustment is shown in Fig. 8.

After the parameter change, the change over time is relatively small although it starts to differ, and the overall trend is similar. It shows that the fine-tuning of the parameter has a small impact on the model, so the parameter is insensitive and passes the sensitivity test.

2)

Extreme value test

Extreme value test is to test whether the variable is still meaningful and consistent with expectations under extreme conditions within its range of variation.

This model takes the effect of school resource input on teachers’ teaching ability as a variable for extreme value test. Changing the school resource input from 0.2 to the extreme value of 0, and the corresponding entrepreneurship education in higher education from 0.9 to 0.5, the increase in innovation outcomes before and after the parameter adjustment is shown in Figure 9.

As can be seen from the figure, with the development of time, the change of the intensity of the school resource input caused a decrease in the teaching ability of teachers, resulting in a wider gap, so the school resource input becomes more urgent. Therefore, the simulation results of the model meet the expectation and pass the extreme value test.

Figure 8.

Parameter adjustment cross-contrast

Figure 9.

Extreme check variation

It is rare for a system dynamics model to be successful in a single simulation, and repeated simulations and corrections are required for success. The software provides methods for error checking and model validation respectively and, after all the equations have been created, the model can be corrected through these tools until the test is passed before proceeding with the basic simulation.

When the simulation was conducted for innovation and entrepreneurship education, the model was adjusted and modified according to the preliminary model simulation results. The simulation results of the four main state variables, namely, national policy guarantee, university entrepreneurship education, industry collaborative cultivation, and collaborative innovation perspective, are shown in Figures 10 to 13, respectively.

Figure 10.

National policy guarantee simulation

Figure 11.

College entrepreneurship education simulation

Figure 12.

The industrial collaborative culture simulation diagram

Figure 13.

Collaborative innovation perspective simulation diagram

The numerical simulation results of other variables of the entrepreneurial talent cultivation system are shown in Fig. 14 to Fig. 17, respectively. Fig. 14 to Fig. 17 show the government financial expenditure, the quality of teachers’ lectures, the willingness of industry participation, and the degree of tripartite cooperation, respectively.

Figure 14.

Government spending model

Figure 15.

Teacher’s teaching quality simulation

Figure 16.

Industrial participation will simulate

Figure 17.

Trilateral cooperation degree simulation

Combined with the theory of college students’ entrepreneurship, the level of entrepreneurial talent as a measure, summarizing the results of the above dynamic simulation analysis, it can be concluded that the role of factors affecting the results of innovation and entrepreneurship education in applied universities are respectively: 1)

Improving the index of the impact of entrepreneurship teaching resources input on teachers’ entrepreneurship teaching ability can quickly improve the values of state variables such as the quality of lectures and the level of entrepreneurial talents. The higher the input of entrepreneurship teaching resources, the higher the level of entrepreneurial talents will be, therefore, improving the input of entrepreneurship teaching resources is an important method to promote the successful entrepreneurship of college students.

To sum up, the level of entrepreneurial talents is closely related to the factors affecting entrepreneurial activities, and “one hair affects the whole body”, so when considering to improve the level of entrepreneurial talents, we should take into account the influence of multiple factors in order to realize the desired effect.

On the top-level design of the pyramid innovation and entrepreneurship system of applied universities, applied universities should determine the objectives and positioning of carrying out innovation and entrepreneurship education reform according to the current situation of regional economic development and the characteristics of the university, formulate a clear development path with systematic thinking, formulate and issue documents of dual-creation system to guide the practice of innovation and entrepreneurship. In the middle-level design, each school and each major integrates the dual-creation elements into specific plans and programs, breaks down the tasks and implements the responsibilities to ensure that the innovation and entrepreneurship education reform advances in an orderly manner, which include the talent cultivation plan, the dual-creation project guidance service program, and the school-enterprise cooperation program. The basic design, on the other hand, implements the dual-creation plans and programs into specific resource elements.

Calling on students to participate in research projects is hardly enough to immediately improve their innovative ideas and get research results. Similarly, offering some courses and organizing students to participate in competitions is just a formality, and it is difficult for students to start a company instantly. Innovation and entrepreneurship education should not be for the sake of immediate results, and it should not emphasize the number of entries rather than the quality. Most schools have set up credits for dual innovation, carried out credit accumulation and conversion system for credit innovation, and promoted students to participate in disciplinary competitions or dual innovation projects, but it is more important to create a good atmosphere for innovation and entrepreneurship, cultivate students’ innovative and entrepreneurial mindset and value orientation, and give full play to the functions of indoctrination, orientation and motivation of school education.

The team building of innovative and entrepreneurial teachers is the key to cultivating innovative and entrepreneurial talents. On the one hand, to address the problem of teachers’ low passion in participating in innovation and entrepreneurship programs, applied universities should change the existing evaluation mechanism of teaching to include the participation in innovation and entrepreneurship education, so as to directly strengthen the teachers’ attention to innovation and entrepreneurship education from the mechanism. In addition, the university should also explicitly require teachers to involve students in scientific research projects and topics, or teachers to participate as mentors in college students’ innovation and entrepreneurship projects, competitions, and practical training, etc., to help students improve their professional knowledge, cultivate the ability to apply knowledge, and improve the possibility of students’ incubation of innovation and entrepreneurship projects to provide.

As students’ awareness and needs for innovation and entrepreneurship are relatively weak in themselves, it is difficult for faculties to provide all the guarantees for students on their own, so it is necessary to start from the university perspective. This can be done by carrying out personalized innovation and entrepreneurship guidance and services for students. Encourage enterprise mentors to cooperate with full-time teachers to prepare innovative and entrepreneurial teaching materials that meet the characteristics of each specialty. Provide opportunities for students who wish to take courses related to science and technology, etc.