Comprehensive evaluation of college students’ engineering entrepreneurship ability based on improved TOPSIS model

Abstract

Marked by artificial intelligence, big data, cloud computing, revolutionary biotechnology, etc., the fourth scientific and technological revolution and industrial revolution are accelerated. In this new situation, China’s higher engineering education is increasingly closely related to the Industrial Revolution, and it is urgent to train new engineering talents with innovation and entrepreneurship abilities, cross-border integration abilities and comprehensive quality to meet the needs of economic and social development. Entrepreneurial engineering talents have become an important force in promoting industrial progress and social development. Training entrepreneurial engineering talents takes engineering practice training as the driving force of teaching reform. To conduct more scientific and effective evaluation research on college students’ engineering entrepreneurship ability, this study proposes the TOPSIS method based on combining CRITIC and entropy weight methods. It constructs a comprehensive evaluation index system of college students’ engineering entrepreneurship ability composed of 21 indexes from four dimensions, including self-motivation ability, team management ability, technical management ability and market management ability. A questionnaire based survey was conducted among 360 college students in 6 Zhejiang Province, China universities. The results show that the improved TOPSIS model proposed in this study can make weight determination more scientific and reasonable. The improved TOPSIS model can effectively distinguish the level of engineering entrepreneurship ability of different college students. The engineering entrepreneurship ability of the students in the six universities is generally at the middle level. The years of engineering education significantly affect technical management ability ( $F=$ 4.455, $p=$ 0.004) and market management ability ( $F=$ 19.174, $p=$ 0.000) at a 1% level. The research conclusion has important reference value for developing engineering entrepreneurship’s curriculum and practical activity systems based on the ability structure, constructing the entrepreneurship teacher system, and strengthening the cross-departmental cooperation, coordination and integration of engineering entrepreneurship education in schools.

Keywords

Improved TOPSIS model college students engineering entrepreneurship ability comprehensive evaluation analysis of variance

1. Introduction

Nowadays, the high-tech industry based on technology-based entrepreneurship has entered a stage of rapid development, and the training of entrepreneurial engineering talents has become the main driving force for industrial development and social progress. From the perspective of the development trend of international engineering education, with the further development of economic globalization, its connotation has expanded from the original engineering science, technology and management to more and more combined with natural science and social science. Several major UNESCO conferences on higher education for the 21st century have emphasized the need for universities to equip students with “entrepreneurial capacity”. In Engineers 2020: Engineering Vision for the New Century, the United States proposed that with the change in the global environment and the continuous development of high and new technologies, engineering practice professionals needed to become innovators, gain more opportunities in interdisciplinary knowledge fields and engage in entrepreneurial economic practices. From a worldwide perspective, cultivating college students’ more comprehensive engineering entrepreneurship ability requires designing excellent products and outstanding companies. In addition to rigorous engineering education, entrepreneurial engineers need to have an understanding of entrepreneurship and the various factors of entrepreneurship.

The problem of engineering education in Chinese universities is that it pays too much attention to the systematization of theoretical knowledge and neglects the significance of practical training. Engineering practice training is the driving force for cultivating entrepreneurial engineering talents, which can play a leading role. Practical training does not make the status of theoretical knowledge decline, but its comprehensive requirements are getting higher and higher. To this end, this study evaluates college students’ engineering entrepreneurship ability and researches the construction of a scientific entrepreneurial ability evaluation system, formulation of evaluation indexes, development of evaluation tools, and construction of an evaluation database. Evaluation results can provide feedback for students’ employment and the improvement of school teaching. It can also consider jointly developing an evaluation system with the employer to make the evaluation more targeted. Through a more scientific comprehensive assessment of college students’ engineering entrepreneurship ability, it is helpful to determine the desire and ability of individuals or teams with engineering or technology background knowledge to carry out entrepreneurial activities in related fields, which is of great strategic significance for promoting the reform of modern engineering education in China, building an innovative country, and enhancing the competitiveness of science and technology industry.

College students’ engineering entrepreneurship ability means transforming engineering technology and innovative ideas into commercial value and actual products. This ability helps drive industrial innovation and development and elevates the country’s position in global economic competition. With the ability of engineering entrepreneurship, college students can improve their employment competitiveness, create more employment opportunities for society, and relieve employment pressure through entrepreneurship. The relevant research on college students’ engineering entrepreneurship ability has been widely studied in developed countries in Europe and the United States. Creed et al. [1] introduced a technology-based entrepreneurship course to introduce students to entrepreneurship through the unique combination of classroom learning and industry participation to cultivate “team building” skills. Huang-Saad et al. [2] argued that entrepreneurship had seen significant growth as a topic in engineering courses. Engineering entrepreneurship originated as more traditional innovation-focused entrepreneurship and represented a new discipline influenced by engineering and educational practices. Dabbagh and Menascé [3] examined students’ general perceptions of the engineering profession in first-year engineering courses and the effects of teaching methods designed to expose students to engineering entrepreneurship and their perceptions of engineering entrepreneurship. The results showed that students who participated in the market game had perceptions of engineering entrepreneurship, especially professional skills. Duval-Couetil et al. [4] described the survey of engineering entrepreneurship, aiming to check the participation of engineering students in entrepreneurship education and related achievements, and analyzed the attitude of engineering students towards entrepreneurship and their participation in entrepreneurial activities and behaviors. The scale developed had high reliability.

Schuelke-Leech [5] found that the way engineers discussed and presented entrepreneurship differed, whether in engineering projects or professional communication. Engineers focused on engineering design, problem-solving, product development, and idea generation in entrepreneurship. Taking Egypt as an example, Refaat [6] analyzed the necessity for future engineers to receive training to understand and develop new technologies and discussed the potential impact of entrepreneurship education and the urgent need for engineering entrepreneurship education for developing economies. Miranda et al. [7] analyzed how entrepreneurial mindsets and skills could be redefined and evaluated in the context of undergraduate engineering education to provide a more holistic approach to conceiving and evaluating entrepreneurship in engineering education. Wang and Verzat [8] conducted a qualitative analysis of 12 in-depth interviews with French engineering students, and the results showed that the project cultivated students’ careers in entrepreneurship and project management, while the central project promoted the traditional technical model of engineers. Maier and Rowan [9] believed that engineering students needed to cultivate a series of common graduate qualities in entrepreneurship. Bilén et al. [10] introduced the new engineering entrepreneurship program at Penn State, arguing that it would enable them to succeed in innovative, product-focused interdisciplinary teams.

It can be seen from the existing research literature that under the current innovation-driven economic development, cultivating college students’ engineering entrepreneurship ability has become an important goal of higher education. Scholars mainly discuss the cultivation of college students’ engineering entrepreneurship ability from the following aspects. It includes the connotation and composition dimension of engineering entrepreneurship ability, the factors that affect college students’ engineering entrepreneurship ability, the cultivation way of engineering entrepreneurship ability, the evaluation method of engineering entrepreneurship ability, and the countermeasures and suggestions to improve engineering entrepreneurship education. Therefore, this study puts forward the TOPSIS method based on the combination of CRITIC and entropy weight method, constructs the evaluation index system of college students’ engineering entrepreneurship ability, and conducts empirical analysis based on the survey data of college students in six engineering universities in Zhejiang Province, China, to provide reference suggestions for improving the way to cultivate college students’ engineering entrepreneurship ability and long-term mechanism construction.

2. Model introduction and data source

2.1 Introduction to the model

(1) CRITIC method

Diakoulaki et al. [11] proposed the CRITIC method (Criteria Importance Though Intercrieria Correlation). This method is a more comprehensive and objective weighting method, which considers the degree of comparison and conflict between indexes, not the larger the number, the better, but the objective attributes of the data to judge. However, the CRITIC method does not consider the discreteness between indexes, while the entropy weight method indirectly reflects the importance of indexes by using the degree of discreteness. The CRITICC method is superior to the entropy weight, standard deviation, and other objective weighting methods. The objective weight is measured comprehensively according to the contrast intensity and interdigital conflict of evaluation indexes, and the variability of indexes is fully considered under the premise of considering the correlation between indexes. Contrast intensity is the large or small value difference of evaluation objects in the same index, expressed as standard deviation. In contrast, the conflict between indexes is expressed as the correlation coefficient. Its calculation formula is shown in Eq. (1).

$\displaystyle W1_{j}=\frac{\sigma_{j}\mathop{\sum}\nolimits_{i=1}^{n}({1-r_{ij% }})}{\mathop{\sum}\nolimits_{j=1}^{n}\sigma_{j}\mathop{\sum}\nolimits_{i=1}^{n% }({1-r_{ij}})}$ (1)

Equation (1) represents the weight of the $j^{\text{th}}$ index, the correlation coefficient between the index, and $\sigma$ represents the standard deviation of the index $j$ .

(2) Entropy weight

Zeleny [12] believed the Entropy weight method was based on information entropy. The basic idea of this method is as follows: if the information entropy of the index is smaller, the information provided by the index is greater, the role it plays in the evaluation is greater, and the weight is higher. According to the definition of information entropy, the information entropy $E_{j}$ of the $j^{\text{th}}$ index is calculated, as shown in Eq. (2).

$\displaystyle E_{j}=-K\sum\limits_{i=1}^{n}P_{ij}ln(P_{ij})$ (2)

Among them, $K=\frac{1}{\ln n},0\leqslant E_{j}\leqslant 1$ . Then, it calculates the difference coefficient $d_{j}$ , as shown in Eq. (3).

$\displaystyle d_{j}=1-E_{j}$ (3)

Then, the weights of each measurement index are determined, as shown in Eq. (4).

$\displaystyle W2_{j}=\frac{d_{j}}{\sum\limits_{j=1}^{m}{d_{j}}}$ (4)

The weights under different methods are calculated by the CRITIC method and entropy weight. Therefore, this study combines the two methods to assign weights, and assumes that the two methods have the same importance to obtain the weight $W_{j}$ required in this stduy, as shown in Eq. (5).

$\displaystyle W_{j}=(W1_{j}+W2_{j})/2$ (5)

(3) Improved TOPSIS

Hwang and Yoon [13] proposed the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution). The idea of this approach is that the improved TOPSIS model proposed in this study. First, after obtaining the weight matrix, the data should be normalized, and the formula is as follows.

For positive indexes, Eq. (6) is adopted.

$\displaystyle R_{ij}=\frac{r_{ij}-\min({r_{ij}})}{\max({r_{ij}})-\min({r_{ij}})}$ (6)

For negative indexes, Eq. (7) is adopted.

$\displaystyle R_{ij}=\frac{\max({r_{ij}})-r_{ij}}{\max({r_{ij}})-\min({r_{ij}})}$ (7)

In Eqs (6) and (7), $i$ represents different objects and $j$ represents different measure indexes. $r_{ij}$ and $R_{ij}$ respectively represent the original and standardized measurement index values of college students’ engineering entrepreneurship ability. $\max({r_{ij}})$ and $\min({r_{ij}})$ respectively represent the maximum and minimum values of each measurement index $r_{ij}$ . The weighted decision matrix $A$ of each measure index is constructed, as shown in Eq. (8).

$\displaystyle A=({a_{ij}})_{n\times m}$ (8)

Among them, $a_{ij}=W_{j}\times R_{ij}$ .

According to the weighted decision moment $A$ matrix, the optimal scheme $Z_{j}^{+}$ and the worst scheme $Z_{j}^{-}$ are determined.

$\displaystyle Z_{j}^{+}=({Z_{1}^{+},Z_{2}^{+},Z_{3}^{+},\ldots Z_{n}^{+}})=% \mathop{\max}\limits_{1\leqslant i\leqslant n}\{{Z_{ij}}\}$ (9) $\displaystyle Z_{j}^{-}=({Z_{1}^{-},Z_{2}^{-},Z_{3}^{-},\ldots Z_{n}^{-}})=% \mathop{\max}\limits_{1\leqslant i\leqslant n}\{{Z_{ij}}\}$

Then, it uses Euclidean distance formula to calculate $D_{i}^{+}$ and $D_{i}^{-}$ .

$\displaystyle D_{i}^{+}=\sqrt{\sum\limits_{j=1}^{m}{({Z_{j}^{+}-a_{ij}})^{2}}}$ (10) $\displaystyle D_{i}^{-}=\sqrt{\sum\limits_{j=1}^{m}{({Z_{j}^{-}-a_{ij}})^{2}}}$

Then, the relative closeness $S_{i}$ between the decision plan and the ideal plan of different college students’ engineering entrepreneurship ability is calculated.

$\displaystyle S_{i}=\frac{D_{i}^{-}}{D_{i}^{-}+D_{i}^{+}}$ (11)

2.2 Data source

This study makes an empirical study on the engineering entrepreneurship ability of students in 6 engineering universities in Zhejiang Province, China. In the spring semester of the 2022–2023 academic year, the research group went to 6 universities to distribute questionnaires, and randomly surveyed 60 college students of different grades in each school, using the Likert 5-point scale method.

Sixteen student administrators with senior professor titles and entrepreneurship course teachers from different universities were invited to investigate the composition of engineering entrepreneurship ability of college students. After two rounds of collective research, the evaluation index system of engineering entrepreneurship ability of college students was finally formed (Table 1).

Table 1
Evaluation index system of college students’ engineering entrepreneurship ability

Primary index	Secondary index	ID
Self-motivation	Consciousness of self-employment	X-1-1
	Strong motivation for engineering entrepreneurship	X-1-2
	Expectations for future entrepreneurial success	X-1-3
	The individual’s intrinsic need to be recognized for success	X-1-4
Team management ability	Ability to build a startup team	X-2-1
	The ability to work together in a team	X-2-2
	Ability to motivate team members	X-2-3
	Ability to deal with interpersonal relationships in a team	X-2-4
Technical management capability	The ability to look ahead in the technical field	X-3-1
	Ability to judge technical anomalies	X-3-2
	Ability to come up with technical ideas	X-3-3
	Product development capability	X-3-4
	Product design capability	X-3-5
	The ability to manufacture products or provide services	X-3-6
	Product improvement capability	X-3-7
Market management ability	Opportunity recognition capability	X-4-1
	Customer needs analysis ability.	X-4-2
	Marketing capability	X-4-3
	Customer management capability	X-4-4
	Financing capacity	X-4-5
	Ability to handle public relations	X-4-6

3. Weight calculation results

Firstly, according to Eq. (1), the CRITIC method was adopted, and the weight of 21 indexes was calculated by MATLAB R2017b programming, as shown in Table 2.

Table 2
CRITIC method weight of college students’ engineering entrepreneurship ability

Index	Index variability	Index conflict	Index conflict	Index conflict
X-1-1	1.143	20.026	22.884	4.91%
X-1-2	1.102	20.240	22.295	4.79%
X-1-3	1.132	19.663	22.257	4.78%
X-1-4	1.053	20.060	21.121	4.54%
X-2-1	1.095	20.084	21.996	4.72%
X-2-2	1.099	19.833	21.798	4.68%
X-2-3	1.103	20.011	22.072	4.74%
X-2-4	1.093	19.658	21.489	4.61%
X-3-1	1.140	19.850	22.624	4.86%
X-3-2	1.096	19.986	21.898	4.70%
X-3-3	1.100	20.224	22.256	4.78%
X-3-4	1.097	20.130	22.074	4.74%
X-3-5	1.131	20.384	23.046	4.95%
X-3-6	1.129	19.980	22.567	4.85%
X-3-7	1.091	20.112	21.950	4.71%
X-4-1	1.104	19.580	21.623	4.64%
X-4-2	1.092	19.775	21.602	4.64%
X-4-3	1.105	20.187	22.297	4.79%
X-4-4	1.127	19.971	22.507	4.83%
X-4-5	1.101	19.837	21.836	4.69%
X-4-6	1.164	20.185	23.504	5.05%

The weight of X-4-6 index is 5.05%, which is the highest among all indexes (Table 2). It shows that the ability to handle public relations accounts for a relatively high proportion, because the ability of public relations can help college entrepreneurs establish a good reputation, gain public recognition and trust, and attract more customers, investors and partners. It can help college entrepreneurs promote products or services effectively, establish connections with potential customers, and improve brand awareness and influence. The weight of the X-3-5 index is 4.95%, mainly because the product design ability can help college students’ entrepreneurs gain insight into the market demand and design products or services that meet the needs of consumers so as to improve the market competitiveness of enterprises. Excellent product design ability can improve the user experience, make the product closer to the user, and improve user satisfaction and loyalty. Through continuous optimization of product design, graduate entrepreneurs can realize product innovation, thus improving the core competitiveness of enterprises. College entrepreneurs can quickly respond to market changes, adjust product strategies, and adapt to market demand to improve the survival and development ability of enterprises.

Secondly, according to Eqs (2)–(4), using the entropy weight method and MATLAB R2017b programming, the weights of 21 indexes are calculated (Table 3).

Table 3

Entropy and weight of college students’ engineering entrepreneurship ability

Index	Information entropy value $e$	Information utility value $d$	Weight coefficient $w$
X-1-1	0.9903	0.0097	5.25%
X-1-2	0.9913	0.0087	4.72%
X-1-3	0.9909	0.0091	4.92%
X-1-4	0.9924	0.0076	4.13%
X-2-1	0.9918	0.0082	4.43%
X-2-2	0.9917	0.0083	4.49%
X-2-3	0.9911	0.0089	4.81%
X-2-4	0.9917	0.0083	4.47%
X-3-1	0.9902	0.0098	5.29%
X-3-2	0.9916	0.0084	4.52%
X-3-3	0.9912	0.0088	4.79%
X-3-4	0.9913	0.0087	4.71%
X-3-5	0.9911	0.0089	4.80%
X-3-6	0.9909	0.0091	4.90%
X-3-7	0.9910	0.0090	4.87%
X-4-1	0.9910	0.0090	4.87%
X-4-2	0.9912	0.0088	4.76%
X-4-3	0.9913	0.0087	4.72%
X-4-4	0.9909	0.0091	4.92%
X-4-5	0.9916	0.0084	4.55%
X-4-6	0.9906	0.0094	5.08%

It can be seen that the weight of X-3-1 is the largest, at 5.29% (Table 3). The main reason is that college students’ entrepreneurs with forward-looking ability in the technical field can keenly capture the development trend of industry technology, so as to layout in advance and seize market opportunities. The forward-looking ability in the field of technology is one of the important manifestations of innovation ability. By paying attention to the development trend of technology, college student entrepreneurs can promote the innovation of products, services and business models and improve the core competitiveness of enterprises. By mastering the forward-looking ability in the technical field, college students’ entrepreneurs can find advanced production and management models to optimize the production process, improve production efficiency and reduce costs. Secondly, the weight of the X-1-1 index is 5.25%, mainly because the awareness of independent entrepreneurship can stimulate college students’ entrepreneurial willingness, make them more actively participate in entrepreneurial activities, improve the success rate of entrepreneurship, make college students more confident in dealing with entrepreneurship, believe that they can succeed in entrepreneurship, and thus enhance the confidence and determination of entrepreneurship. Stimulate the entrepreneurial ability of college students, including market analysis, management, teamwork and other aspects of improving the ability and level of entrepreneurship.

4. TOPSIS results

According to Eqs (6)–(11), the engineering entrepreneurship ability of 360 college students in China is comprehensively evaluated, and their ranking results are obtained. For statistical convenience, only the top 10 ranking results are shown (Table 4).

Table 4
Comprehensive evaluation results of 360 college students’ engineering entrepreneurship ability in China

Student number	Positive ideal solution distance $D$	Negative ideal solution distance $D-$	Relative proximity $C$	Sort result
Evaluation object 236	0.097	0.166	0.631	1
Evaluation object 26	0.097	0.158	0.620	2
Evaluation object 44	0.103	0.165	0.615	3
Evaluation object 181	0.106	0.169	0.615	3
Evaluation object 252	0.110	0.174	0.612	5
Evaluation object 294	0.105	0.166	0.612	5
Evaluation object 48	0.109	0.167	0.606	7
Evaluation object 51	0.105	0.160	0.604	8
Evaluation object 173	0.104	0.158	0.604	8
Evaluation object 101	0.100	0.150	0.601	10

It can be seen that the relative proximity $C$ values of the top 10 college students’ engineering entrepreneurship ability are higher than 0.6 points, indicating that the 10 universities have strong engineering entrepreneurship ability (Table 4). The possible reason is that college students have received systematic subject education and professional training in colleges and universities and have a solid academic foundation and professional knowledge, which is crucial for engineering entrepreneurship. In learning, college students are exposed to various cutting-edge academic research and innovative ideas and cultivate innovative consciousness and ability, which is very important for entrepreneurship. College students must often work with classmates and teams in academic research and community activities. They have developed teamwork and leadership, which is also very important for entrepreneurship. College students are in the stage of life, usually without the shackles of family and career, dare to take risks and explore, which is also very important for entrepreneurship. College students often need to face new environments and challenges in their studies and life and have good learning and adaptability, which is also very important for entrepreneurship.

5. Analysis of variance

Table 5
Difference between the years of receiving engineering education and college students’ engineering entrepreneurship ability

The primary index of college students’ engineering entrepreneurship ability	Length of years of engineering education (mean $\pm$ SD)				$F$	$P$
	2.0 ( $n=$ 88)	3.0 ( $n=$ 93)	4.0 ( $n=$ 93)	5.0 ( $n=$ 86)
Self-motivation	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	1.465	0.224
Team management ability	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.217	0.885
Technical management capability	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.03 $\pm$ 0.01	0.02 $\pm$ 0.01	4.455	0.004^**
Market management ability	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.02 $\pm$ 0.01	0.03 $\pm$ 0.01	19.174	0.000^**

(1) The length of years receiving engineering education presents 0.01 level significance (Table 5) for technical management ability ( $F=$ 4.455, $p=$ 0.004). The possible reason is that longer engineering education can help college students master professional knowledge and skills more comprehensively and deeply. This is a reasonable reserve for professional knowledge and skills required for technical management positions in the future. In academic research and project practice, college students need to independently analyze problems, design plans, and implement them, which helps cultivate their independent working ability and problem-solving abilities. In addition, a longer engineering education is also conducive to improving college students’ teamwork and communication skills. In team projects and academic exchanges, college students need to cooperate and coordinate closely with other members, which helps to improve their teamwork and communication skills.

(2) The years receiving engineering education showed a 0.01 level significance for market management ability ( $F=$ 19.174, $p=$ 0.000). The main reason is that the length of the years that college students receive engineering education also has a certain promoting effect on the market management ability of college students. Although marketing management ability is mainly cultivated in related disciplines such as marketing, business and management, engineering education can also lay the foundation for college students’ marketing management ability to a certain extent. Engineering education enables college students to master certain technical knowledge and skills. This will greatly help future work related to market management, especially the market management of technology-oriented enterprises or projects. College students with a science and engineering background can have a deeper understanding of the technical characteristics of products when doing market analysis, product positioning, technology promotion and other aspects of work to meet customer needs better. Experiments, projects and studies in engineering education can help college students exercise these abilities.

(3) The length of years receiving engineering education has no significant difference in self-motivation ability. This conclusion also inspires college entrepreneurship educators in China to pay attention to cultivating college students’ self-motivation ability. This is because engineering education usually focuses on professional knowledge and skills and pays less attention to the cultivation of self-motivation ability. While some courses and activities in engineering education deal with teamwork, communication and innovation, these tend to focus more on developing students’ skills than on developing mental qualities. Some engineering courses may pay too much attention to theoretical knowledge and have few practical links, which makes it difficult for students to have a practical sense of achievement in the learning process. In addition, the single teaching method and the lack of interactive and heuristic teaching may also lead to a lack of motivation for students to learn.

(4) The number of years received in engineering education did not significantly affect team management ability. This conclusion suggests that while some courses and activities in engineering education deal with aspects of teamwork, communication, and innovation, these tend to focus more on student skill development than team management. Some engineering courses may pay too much attention to theoretical knowledge and have few practical links, which makes it difficult for students to produce practical team management experience in the learning process. In Chinese universities, engineering majors usually do not require team management courses. Therefore, students may lack the opportunity to contact relevant content in the engineering education process, and it is difficult to develop team management ability.

6. Conclusions

In the era of Industry 4.0, global scientific and technological innovation has entered an unprecedented period of intensive activity. Innovation and entrepreneurship have become an important driving force for national and regional economic growth. To cultivate entrepreneurial engineers and promote innovation and entrepreneurship with technological development as the core, more and more international colleges and universities integrate entrepreneurship education into the engineering education system, making engineering entrepreneurship education courses and projects develop rapidly. Therefore, the organic combination of entrepreneurship and engineering education is a new direction for reforming and developing higher engineering education in China.

This study proposes a TOPSIS method based on CRITIC and entropy weight methods. It constructs a comprehensive evaluation index system of college students’ engineering entrepreneurship ability composed of 21 indexes in four dimensions: self-motivation ability, team management ability, technical management ability and market management ability. A questionnaire survey is conducted among 360 college students from six Zhejiang Province, China universities. Three conclusions are obtained from this study. (1) The improved TOPSIS model can make the weight determination more scientific and reasonable. (2) The improved TOPSIS model can effectively distinguish the level of engineering entrepreneurship ability of different college students; ranking the level of engineering entrepreneurship ability is more scientific and reasonable and can provide a reference for the performance evaluation of engineering entrepreneurship education. (3) The length of years receiving engineering education has a significant effect on technical management ability ( $F=$ 4.455, $p=$ 0.004) and market management ability ( $F=$ 19.174, $p=$ 0.000) at the 1% level. Suggestions are given that in the future, it should continue to establish a dynamic evaluation index system adapted to the characteristics of different majors, to establish an intelligent evaluation and analysis system of college students’ engineering entrepreneurship ability combined with big data technology, and to conduct in-depth research on the continuous tracking evaluation of college students’ engineering entrepreneurship ability to reflect the growth track of college students’ ability.

Footnotes

Acknowledgments

This study was supported by the National Education Science “Thirteenth Five-Year Plan” Project (No. BIA200204).

Declarations of interest

None.

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