# The Construction of Innovation and Entrepreneurship Incentive Mechanism for College Students Based on Hierarchical Analysis

Authors: Zhongmin Guan; Xiaoli Xu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Innovation and entrepreneurship (I&E) are of great significance to China’s modern economic development, and college students are the most innovative and entrepreneurial potential group. I&E requires college students through the effective combination of various incentive methods on the basis of the individual differences and diversity of college students, so that the I&E enthusiasm of college students can be continuously and optimally exercised. Based on the theory of hierarchical analysis, this paper establishes the evaluation system of the implementation effect of college students’ I&E incentive mechanism by setting the evaluation indexes of the effect of college students’ I&E incentive mechanism, calculates and obtains the weights of each evaluation index, and provides a reference basis for the design of college students’ I&E incentive mechanism. Our experiment demonstrate that the CR values of the judgment matrices calculated are less than 0.1, so the consistency test of the judgment matrix is passed.


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## Body

## 1. Introduction

The call for “mass entrepreneurship and innovation” (referred to as “dual innovation”) was first proposed by Premier Li Keqiang and has since been mentioned at the First World Internet Conference, the State Council executive meeting, and other events, with a view to cultivate and give birth to a brand new driving force for national economic development, stimulate the innovation potential and entrepreneurial vitality of the whole society, realize the wealth of the people and the strength of the country, and, to a certain extent, provide a useful supplement to the employment issue. As an important way, I&E education has naturally become a new hot spot in China’s higher education reform. It organically combines scientific research, educational teaching, and business training and prepares the majority of entrepreneurs to engage in innovative and entrepreneurial activities by first conducting theoretical research on innovative and entrepreneurial education and then putting the research results into simulation exercises, psychological quality, and innovative and entrepreneurial ability. However, in order to guarantee the effective development of I&E education, it is imperative to establish a set of scientific and effective incentive and restraint mechanism to regulate the behaviors of universities.The enthusiasm and risk prevention cannot be shirked, which requires colleges and universities to have scientific, standardized, complete, and operable incentive policies, means, and methods [1]. At present, the employment concept of most students still stays in the period when the pursuit of stable jobs is the ultimate goal. They think it is only a special educational activity for the benefit of every individual students with strong innovation ability and excellent theoretical study results, while the mass group finds it difficult to get involved in it. Coupled with the inactive overall environment constituted by society, school, and family, it also strangles the entrepreneurial thoughts of the majority of students in the cradle.The survey shows that most domestic colleges and universities’ I&E education work is led by the president in charge of student work who is in charge of the employment guidance department, student management department, league committee, or ideological and political research department, jointly responsible for completing education and teaching work and organizing students to carry out activities, and there is no special management and research department yet. This messy management situation will inevitably lead to the lack of close connection between entrepreneurship education teaching and professional teaching, as well as the difficulty of effective cooperation between departments in the university [2]. In addition, it is not effectively combined with economic development, and the existing theoretical system only stays at the research level, with fragmented content forms, and is not combined with the existing education and teaching system of higher education to form a systematic theoretical and practical system [3].Lack of Scientificity of the Incentive Scheme. At present, when universities manage the I&E activities of college students, they usually carry out the incentive work according to some rough indicators, lacking comprehensive and objective research on the incentive factors and lacking detailed analysis on the effectiveness of each incentive factor, so the incentive scheme and incentive measures lack scientificity [4].From the perspective of reinforcement theory, the lack of scientificity of incentive programs in colleges and universities is as follows: not enough attention is paid to the spiritual motivation factor in positive reinforcement; not enough attention is paid to the role of negative reinforcement in correcting the bad behaviors and preventing the potential risks of I&E; not enough scientific analysis of the role of each subfactor in incentive factors; not enough evaluation of the effectiveness of incentive measures [5, 6].Nowadays, due to the lack of intelligent data analysis methods and mining tools, colleges and universities cannot quickly and comprehensively grasp the information changes of college students’ I&E when carrying out innovation management work affairs; cannot use big data intelligent facilities to grasp the latest intelligence in time; and cannot take the initiative to discover the bad phenomena or even illegal and unlawful behaviors of college students. We cannot accurately grasp the incentive timing and adjust the incentive strategy in time [7]. Therefore, this paper focuses on establishing the evaluation system of college students’ I&E incentive mechanism based on the evaluation indexes with the theoretical basis of hierarchical analysis.


## 2. Current Situation of I&E Incentive Mechanism for College Students

I&E is a key factor, and it is of great significance to social and economic development. General Secretary Xi Jinping proposed “innovation is the first driving force to lead development” at the two sessions in 2015, and Premier Li Keqiang mentioned I&E at various events such as Davos Forum, World Internet Conference, and State Council executive meeting [8]. It has an important guiding significance in fostering and stimulating the innovation potential and entrepreneurial vitality of the whole society. College students have the most active dynamic factor of innovation drive [9–11]. Therefore, colleges should adjust their strategies to effectively guide college students’ practices and cultivate their innovative spirit. Motivation plays an indispensable management and guidance function in modern organizations [12]. Based on the incentive theory, the establishment of higher education I&E incentive mechanism will have a good promotion effect on effectively solving the problems of students’ innovation education, employment, social adaptation, self-cognition, and career planning [13].Psychologists believe that motivation is a state of mind of human beings, and all actions are caused by some motivational motives [14]. American psychologist Douglas McGregor’s X-Y theory points out that X theory believes that human nature is lazy, puts personal interests and security first, does everything possible to avoid work and resist change, and must adopt coercion and implement both hard and soft management methods; Y theory believes that most people have certain imagination and creative talent [15–17]. The reason for college students’ lack of enthusiasm and their weak innovation and entrepreneurial ability is that the school lacks an effective incentive mechanism to bring out their potential [18].Maslow believed that people can have multiple needs at the same time in the same period, and in each period, each person has different needs. Of these five needs, not all can be met, the higher the level of needs, the more difficult to meet, the needs will determine human action, and not yet satisfied needs in the life stage occupy a major position [19], [20]. The difference between Theory X and Theory Y lies in the different understanding of human nature, so the motivation methods are also different.


## 3. I&E Incentive Mechanism

### 3.1. Overview of Hierarchical Analysis

Analytic Hierarchy Process (AHP) is a decision analysis method for quantifying qualitative problems firstly proposed by Thomas L. Saaty, an operations researcher at the University of Pittsburgh in 1977. The weight of each indicator or factor at each level is calculated by the quantification method of qualitative indicators as the decision basis for the solution of multiobjective optimization problems [21–23]. The specific steps are shown in Figure 1.Figure 1
The flow chart of AHP.Where the consistency ratio CR consistency ratio, CR is the actual fluid that can be divided into Newtonian fluid and non-Newtonian fluid. All gases and most liquids are Newtonian fluids, and their shear stress is proportional to the velocity gradient; the scale coefficient is(1)CR=CIRI.


### 3.2. Determination of Evaluation Indexes

This paper adopts in-depth interviews and questionnaires to determine each evaluation index, and the selected experts are mainly enterprise leaders of various industries, university experts, and students. According to the hierarchical structure of hierarchical analysis, the ability is selected and the three-level target hierarchical structure evaluation system is determined. The first-level target level is the evaluation system X, and the second-level guideline level includes 3 parts as shown in Table1 that incubation platform Y1, teacher level Y2, and student level Y3. The secondary evaluation indexes of Y1 are return rate of special support funds Z11, utilization incubation platform Z12, and student coverage rate of projects Z13. The secondary evaluation indexes of Y2 are number of I&E projects supervised by teachers Z21, award rate of I&E competition supervised by teachers Z22, success rate of transformation of teachers’ innovation results Z23, and rate of transformation of teachers’ innovation results supervised by students Z24. The secondary evaluation indexes of Y2 at teacher level are the number of I&E projects supervised by teachers Z21, the rate of winning I&E competition supervised by teachers Z22, the rate of successful transformation of teachers’ innovation achievements Z23, and the rate of successful transformation of teachers’ I&E Z24; the secondary evaluation indexes at student level are the enhancement ability Z31, the enhancement quality Z32, the growth rate quality Z33, and the rate of successful transformation of patent achievements Z34.Table 1
Evaluation model of the effect incentive mechanism of college students.
General objectivesPrimary targetSecondary targetsEvaluation system of I&E incentive mechanism in higher education XIncubation platform Y1Return rate of special support funds for I&E Z11, utilization rate of I&E incubation platform Z12, coverage rate of students in I&E projects Z13Faculty level Y2Award rate of I&E competition guided by teachers Z22, rate of successful transformation of teachers’ innovation achievements Z23, success rate of I&E of students supervised by teachers Z24Student level Y3I&E ability enhancement degree Z31, enhancement of I&E quality Z32, quality growth rate of I&E Z33, success rate of transformation of patent achievements Z34


### 3.3. Calculation of Indicator Weights

Scoring of Indicators by Domain Experts. In this paper, we adopt interviews with business leaders and university experts to achieve comparative ratings of the relative importance of the three categories of criteria in the model in Table 2 and the 11 evaluation indicators. The ratings are based on a 1–9 scale as shown in Table 3.Table 2
Definition of judgment matrix measures.
ScaleMeaning1Indicates that two factors are of equal importance compared to each other3Indicates that the former is slightly more important than the latter when compared with the other5The former is significantly more important than the latter when compared to the other7The former is more important than the latter when compared to the other9The former is more extremely important than the latter when compared to the other2, 4, 6, and 8Indicates the middle value of the above adjacent judgmentsCountdownIf the ratio of the importance of factori to factor j is aij; then the ratio of the importance of factor j to factor i is aij=1/aijTable 3
X-Y comparative judgment matrix and eigenvectors.
Y1Y2Y3WY111/31/50.0871Y2321/40.2649Y37220.6387We construct the comparison judgment matrix and normalized feature vectorW. The results of the calculation of the feature vector W of the judgment matrix at each level are shown in Tables 4–6.Table 4
Y1-Z comparison judgment matrix and eigenvectors.
Z11Z12Z13WZ1118/390.7687Z123/825/40.1821Z131/72/320.1019Table 5
Y2-Z comparison judgment matrix and eigenvectors.
Z21Z22Z23Z24WZ21125/620.3012Z221/211/31/30.1277Z236/53220.4123Z24131/220.2118Table 6
Y3-Z comparison judgment matrix and eigenvectors.
Z31Z32Z33Z34WZ3115/41/39/80.2568Z323/426/510.2528Z3325/619/80.4134Z347/828/910.2339There may be some subjectivity in the scoring of the indicators of the effect of I&E incentive mechanism by experts in each field. The CR values of the judgment matrices calculated according to formula (1) are shown in Table 7. It can be seen that the CRs of the above four judgment matrices are less than 0.1, so the consistency test of the judgment matrix is passed [24, 25].Table 7
Consistency test results of the judgment matrix.
X-YY1-ZY2-ZY3-Zγmax3.04993.02244.15894.1324CI0.02690.01610.05250.0420RI0.59000.59000.90000.9000CR0.04590.02970.05690.0471As can be seen from Table8, the student level has the greatest weight among the 3 guideline level indicators.Table 8
Table of weights of indicators.
Y to X weightB1B2B3Z to X weight0.08690.27370.6393Z to Y weightZ110.7259000.0598Z120.1658000.0152Z130.1079000.0089Z2100.279700.0778Z2200.125800.0342Z2300.391200.1032Z2400.206800.0576Z31000.24310.1518Z32000.25120.1555Z33000.29490.1879Z34000.22880.1496


## 3.1. Overview of Hierarchical Analysis

Analytic Hierarchy Process (AHP) is a decision analysis method for quantifying qualitative problems firstly proposed by Thomas L. Saaty, an operations researcher at the University of Pittsburgh in 1977. The weight of each indicator or factor at each level is calculated by the quantification method of qualitative indicators as the decision basis for the solution of multiobjective optimization problems [21–23]. The specific steps are shown in Figure 1.Figure 1
The flow chart of AHP.Where the consistency ratio CR consistency ratio, CR is the actual fluid that can be divided into Newtonian fluid and non-Newtonian fluid. All gases and most liquids are Newtonian fluids, and their shear stress is proportional to the velocity gradient; the scale coefficient is(1)CR=CIRI.


## 3.2. Determination of Evaluation Indexes

This paper adopts in-depth interviews and questionnaires to determine each evaluation index, and the selected experts are mainly enterprise leaders of various industries, university experts, and students. According to the hierarchical structure of hierarchical analysis, the ability is selected and the three-level target hierarchical structure evaluation system is determined. The first-level target level is the evaluation system X, and the second-level guideline level includes 3 parts as shown in Table1 that incubation platform Y1, teacher level Y2, and student level Y3. The secondary evaluation indexes of Y1 are return rate of special support funds Z11, utilization incubation platform Z12, and student coverage rate of projects Z13. The secondary evaluation indexes of Y2 are number of I&E projects supervised by teachers Z21, award rate of I&E competition supervised by teachers Z22, success rate of transformation of teachers’ innovation results Z23, and rate of transformation of teachers’ innovation results supervised by students Z24. The secondary evaluation indexes of Y2 at teacher level are the number of I&E projects supervised by teachers Z21, the rate of winning I&E competition supervised by teachers Z22, the rate of successful transformation of teachers’ innovation achievements Z23, and the rate of successful transformation of teachers’ I&E Z24; the secondary evaluation indexes at student level are the enhancement ability Z31, the enhancement quality Z32, the growth rate quality Z33, and the rate of successful transformation of patent achievements Z34.Table 1
Evaluation model of the effect incentive mechanism of college students.
General objectivesPrimary targetSecondary targetsEvaluation system of I&E incentive mechanism in higher education XIncubation platform Y1Return rate of special support funds for I&E Z11, utilization rate of I&E incubation platform Z12, coverage rate of students in I&E projects Z13Faculty level Y2Award rate of I&E competition guided by teachers Z22, rate of successful transformation of teachers’ innovation achievements Z23, success rate of I&E of students supervised by teachers Z24Student level Y3I&E ability enhancement degree Z31, enhancement of I&E quality Z32, quality growth rate of I&E Z33, success rate of transformation of patent achievements Z34


## 3.3. Calculation of Indicator Weights

Scoring of Indicators by Domain Experts. In this paper, we adopt interviews with business leaders and university experts to achieve comparative ratings of the relative importance of the three categories of criteria in the model in Table 2 and the 11 evaluation indicators. The ratings are based on a 1–9 scale as shown in Table 3.Table 2
Definition of judgment matrix measures.
ScaleMeaning1Indicates that two factors are of equal importance compared to each other3Indicates that the former is slightly more important than the latter when compared with the other5The former is significantly more important than the latter when compared to the other7The former is more important than the latter when compared to the other9The former is more extremely important than the latter when compared to the other2, 4, 6, and 8Indicates the middle value of the above adjacent judgmentsCountdownIf the ratio of the importance of factori to factor j is aij; then the ratio of the importance of factor j to factor i is aij=1/aijTable 3
X-Y comparative judgment matrix and eigenvectors.
Y1Y2Y3WY111/31/50.0871Y2321/40.2649Y37220.6387We construct the comparison judgment matrix and normalized feature vectorW. The results of the calculation of the feature vector W of the judgment matrix at each level are shown in Tables 4–6.Table 4
Y1-Z comparison judgment matrix and eigenvectors.
Z11Z12Z13WZ1118/390.7687Z123/825/40.1821Z131/72/320.1019Table 5
Y2-Z comparison judgment matrix and eigenvectors.
Z21Z22Z23Z24WZ21125/620.3012Z221/211/31/30.1277Z236/53220.4123Z24131/220.2118Table 6
Y3-Z comparison judgment matrix and eigenvectors.
Z31Z32Z33Z34WZ3115/41/39/80.2568Z323/426/510.2528Z3325/619/80.4134Z347/828/910.2339There may be some subjectivity in the scoring of the indicators of the effect of I&E incentive mechanism by experts in each field. The CR values of the judgment matrices calculated according to formula (1) are shown in Table 7. It can be seen that the CRs of the above four judgment matrices are less than 0.1, so the consistency test of the judgment matrix is passed [24, 25].Table 7
Consistency test results of the judgment matrix.
X-YY1-ZY2-ZY3-Zγmax3.04993.02244.15894.1324CI0.02690.01610.05250.0420RI0.59000.59000.90000.9000CR0.04590.02970.05690.0471As can be seen from Table8, the student level has the greatest weight among the 3 guideline level indicators.Table 8
Table of weights of indicators.
Y to X weightB1B2B3Z to X weight0.08690.27370.6393Z to Y weightZ110.7259000.0598Z120.1658000.0152Z130.1079000.0089Z2100.279700.0778Z2200.125800.0342Z2300.391200.1032Z2400.206800.0576Z31000.24310.1518Z32000.25120.1555Z33000.29490.1879Z34000.22880.1496


## 4. Measures to Improve the Incentive Mechanism

To build a reasonable incentive mechanism, it is necessary to establish a smooth information communication channel so as to ensure that information users can get relevant information in time. If students get certain achievements in I&E, universities can give them certain credit rewards and make sure that there are records of credit increase in the relevant system.Improving the incentive mechanism of college students needs to be done step by step. In the process of improving the incentive mechanism, colleges and universities should continuously strengthen the establishment of internal reward and punishment mechanism and count I&E into the credit mechanism, so as to continuously improve the ability of college students in all aspects. Colleges and universities should first pay attention to cultivating college students’ entrepreneurial consciousness.For contemporary college students, it is necessary to achieve all-round development. The quality requirements of college students in the new era also cover the quality of I&E, requiring that college students should have a certain entrepreneurial spirit and innovation consciousness, which can only be cultivated through I&E education because there is no way to cultivate them in their daily life. For colleges and universities, cultivating students’ professional skill level so that they can better serve the society is an important part of education, but cultivating students’ innovation and entrepreneurial ability is also one of the essential educational tasks of colleges and universities.The university can give the students a certain amount of money for their achievements in I&E during their school years and can also use the money as a project fund for the students’ research products. This can give students certain financial support, reduce their entrepreneurial risk, increase their interest in entrepreneurship, and further improve the construction of incentive mechanism.The main way to provide college students with the opportunity to practice I&E is to open an I&E area and create an entrepreneurial atmosphere where college students can open their own stores, sell products, or develop products. Colleges and universities should not charge students for their stores and encourage them to try their hand at innovative entrepreneurship on campus, cultivating their sense of relevance and constantly making their businesses stronger and bigger with their own efforts.


## 5. Conclusion

In short, through a combination of rewards and punishments, we should build an incentive mechanism for I&E for teachers and students, which is based on the theme of “combining education with practical education” and runs through the whole process of talent cultivation in colleges and universities. This paper uses the hierarchical analysis method, combines qualitative and quantitative analysis, and establishes the evaluation system of I&E incentive mechanism for college students with 11 indicators in 3 aspects: incubation platform, teacher level, and student level. This not only provides a new way to evaluate the effect of I&E incentive mechanism of college students but also provides a reference basis for how to design an effective I&E incentive mechanism of college students.



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