# Exploring the Entrepreneurship Training Mode of Medical Students in Beijing-Tianjin-Hebei Universities under the Strategic Background of “Healthy China” from the Perspective of Mixed Big Data

Authors: Li Gong; Chao Wang; Jiangtao Bai
Journal: Mobile Information Systems
 (2022)

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## Abstract

In order to solve the problem that postgraduates are affected by the traditional concept of career choice of the native family and their entrepreneurial intention is obviously low, a method to explore the entrepreneurial spirit training model of medical students in Beijing-Tianjin-Hebei colleges and universities from the perspective of mixed big data is proposed. This article chooses to conduct research by conducting a survey on the entrepreneurial willingness of postgraduates in universities in the Beijing-Tianjin-Hebei and its influencing factors. From the perspective of master students from five universities in the Beijing-Tianjin-Hebei region, the selection of personal background information, entrepreneurial attitude, subjective norms, perceived behavior control, and entrepreneurship education index factors establish the index system of the influencing factors of graduate students’ entrepreneurial intention and use the TPB theoretical model and SPSS data statistics and other tools to analyze the obtained data results. The results show that gender, major, place of origin, part-time experience, and entrepreneurial experience of relatives and friends correspond to 0.000, 0.000, 0.482, 0.172, 0.003, and 0.004, respectively. It can be seen that the independent variables, major and place of origin, have no statistical significance. In the statistics of college students’ willingness to start a business, 23.33% of the students said “nothing at all,” and 27.8% of the students said “thought, but would not start a business.” It can be seen that entrepreneurial attitude, subjective norm, and perceived behavioral control all positively affect entrepreneurial willingness, and only learning ability, opportunity grasping ability, self-recognition ability, and decisiveness have a significant effect on entrepreneurial willingness.


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## Body

## 1. Introduction

With the continuous development of medicine, medical education has been paid more and more attention. Especially under the influence of the epidemic situation, the promotion and development of medicine is particularly important. It is against this background that China has put forward the “healthy China” strategy. In the “healthy China” strategy, it is very important to do a good job in cultivating students’ entrepreneurial spirit. In particular, the employment form of medical students is becoming more and more severe. Some foreign medical universities attach great importance to the research of entrepreneurial strategy and entrepreneurial environment, and have presented a mature mode in the cultivation of entrepreneurial ability [1]. At present, China’s health industry only accounts for 4%∼5% of GDP, which is far lower than the proportion of 15% in the United States and 10% in Japan and Canada. According to the characteristics of medical disciplines, it is a problem that many medical universities need to explore and face to carry out mass entrepreneurship and innovation education [2]. Since 2015, more than 23 policy documents have been issued for “mass entrepreneurship and innovation.” However, compared with other majors, medical students’ entrepreneurial endogenetic motivation and practical ability are slightly lacking in the practice of mass entrepreneurship and innovation projects [3]. At present, there are many documents on the “healthy China” strategy in the field of higher education, but under its strategic background, there is less training on the entrepreneurial spirit of medical students. Most medical colleges and universities do not have in-depth entrepreneurial education on the integration of the “great health” industry. Therefore, through the innovative reform mode of “government, enterprise, university, and research,” we will accelerate the development of the medical and health industry [4]. Taking the opportunity of vigorously developing medical and health undertakings and the goal of promoting students’ entrepreneurship, major medical colleges and universities emphasize the cultivation of students’ entrepreneurial awareness and knowledge application ability and, at the same time, link with the great health industry. Through improvement and innovation in the training mode, more entrepreneurial talents will be sent to the society [5].


## 2. Literature Review

Healthy China is a development strategy, and people’s health is an important symbol of national prosperity and national prosperity. It is necessary to improve the national health policy and provide the people with all-round and full-cycle health services [6]. Since we want to promote the development of students’ Entrepreneurship in the actual teaching environment and realize the implementation of the “healthy China” strategy, we need to understand the current situation of medical students’ entrepreneurship training in the current actual teaching work. In this way, we can effectively realize the growth and promotion of students in the actual teaching activities. To cultivate talents, we must first determine a “dream” and carry out three stages of dream building, dream chasing and dream realization around the dream, and carry out training in stages and platforms, as shown in Figure 1.Figure 1
Talent cultivation turns to thinking.Entrepreneurship education has also been well developed in foreign medical colleges. Foreign medical colleges and universities believe that interdisciplinary cooperation is crucial to the innovation of the healthcare industry, especially in transforming medical technology into effective clinical solutions. Therefore, almost all the courses in foreign medical colleges will be interdisciplinary. The courses include medical, engineering, science, business, or law majors. The concepts related to innovation and their applications in entrepreneurship, leadership, technology, medical care system, and pharmaceutical business will be taught [7]. A mixed method study of American idiopathic medical schools found that the number of entrepreneurial education programs in medical education was small but growing rapidly. These courses improve students’ readiness for today’s complex healthcare environment by covering novel educational topics with active and interdisciplinary pedagogy [8]. From 2007 to 2016, the entrepreneurship program began to be popular in the American College of allopathic medicine, forming an entrepreneurship education curriculum with seven education themes (innovation, entrepreneurship, technology, leadership, healthcare system, medical business, and enhanced adaptability) and two teaching methods themes (active learning and cross-disciplinary teaching) [9]. The University of Michigan (UM) School of Medicine designs and implements entrepreneurship programs to help students explore the business potential of medical innovation, support the establishment of entrepreneurial teams around the project, provide entrepreneurial mentors for team members, and provide tailor-made entrepreneurial education courses. Learning objectives are best provided in the form of seminars/teaching. Teaching and seminars can provide more interactive teaching experience than lecture-based learning, increase communication between teachers and students, and promote students’ active participation by holding entrepreneurial seminars, developing entrepreneurial ideas, forming team-led business plans and demonstrations, and developing skills including peer assessment, peer development, communication, critical assessment, creative thinking, problem-solving, and computing skills, to effectively promote and improve the innovative spirit and entrepreneurial skills of medical students.Since our country put forward the entrepreneurship strategy, the major universities in the country have started to carry out the entrepreneurship education reform one after another, and more and more medical colleges are devoted to entrepreneurship education. There are certain differences and professional tendencies in the process of undertaking education in major universities. Different from comprehensive universities or other engineering classes, medical colleges lack resources for cross-learning due to their strong academic and professional characteristics. Moreover, most colleges and universities have misunderstandings in the entrepreneurship education of medical students, which is not significantly different from the innovation education of nonmedical students. The aim is to train students into big entrepreneurs, and there is insufficient correlation with the training objectives and employment characteristics of medical students. In addition, few medical colleges offer entrepreneurship courses for all students alone. Some medical colleges only set this course as an optional course of 2-3 class hours, with low credits, which is difficult to attract students’ attention [10]. Although some colleges and universities have set up relevant courses, entrepreneurship education is only limited to theoretical courses and formalized entrepreneurship competitions, and there is no mature curriculum system. In addition, most of the teachers engaged in entrepreneurship education in medical colleges do not come from professional fields, with a single form of education, lack of entrepreneurial practice experience, and few innovation achievements [11].


## 3. The Influencing Factor Model of Entrepreneurial Intention Based on Plan Following

### 3.1. TPB’s Entrepreneurial Education Factor Framework for College Students

Entrepreneurship education, as a medium for disseminating entrepreneurial knowledge and skills, cannot directly affect the entrepreneurial intention of students, but can exert influence by cultivating students’ entrepreneurial attitude and individual characteristics. Entrepreneurship education can effectively cultivate and change the entrepreneurial attitude of college students, and social psychology research also shows that individual behavior and attitude can be effectively changed through practice and cognitive level, communication, and example encouragement. Therefore, based on the TPB theoretical framework, this article adds entrepreneurship education as an influencing factor. In the case of discussing how much entrepreneurship education occupies in the formation of postgraduate entrepreneurship intention, it also analyzes what kind of entrepreneurship education can maximize the driving effect in the formation of postgraduate entrepreneurship intention [12]. It is assumed that entrepreneurship education has an effect on entrepreneurial intention by changing individual entrepreneurial attitude, subjective norms, and perceived behavior control. The following theoretical factor framework is constructed as shown in Figure 2.Figure 2
Framework of influencing factors of graduate students’ entrepreneurial intention.Based on the theory of planned behavior, this article introduces two variables: personality characteristics and entrepreneurial competence. Based on the above research hypotheses, it builds a perfect influencing factor model of Medical College Students’ entrepreneurial willingness [13], which is in line with the current background environment of mass entrepreneurship. See Figure 3.Figure 3
Model of influencing factors on entrepreneurial intention of medical college students.


### 3.2. Questionnaire Survey and Demonstration

#### 3.2.1. Sample Determination

The subjects of this study are junior undergraduates from five schools in Beijing, Tianjin, and Hebei. Their majors include medicine (traditional Chinese medicine and clinical medicine), pharmacy (traditional Chinese medicine and pharmacy), nursing, psychology, management (marketing and public utilities management), and engineering (medical information engineering).The questionnaire survey was conducted by using the convenient sampling method and the combination of online and offline. A total of 1000 questionnaires were distributed, 988 were recovered, 12 invalid questionnaires were excluded, and 976 valid questionnaires were left, with an effective recovery rate of 97.60%. EpiData 3.1 was used for data entry, and SPSS 21.0 was used for data processing. Single choice questions are described and analyzed by frequency and percentage, and multichoice questions are described and analyzed by the percentage of respondents and the percentage of responses. See Table1 for details.Table 1
Basic information of samples.
CategoryFeaturesQuantityProportion (%)MajorMedical science29129.82Pharmacy20621.11Nursing14214.55Psychology11611.89Management16016.39Engineering616.24Educational systemFour-year system68570.18Five-year system29129.82GenderMale28228.89Female sex69471.12Place of originTown46147.38Countryside51252.62Part-time experienceNo25526.18Yes71973.82Personal entrepreneurial experienceNever88691.34Once767.84Starting a business80.83Entrepreneurial experience of relatives and friendsNever26327.11Once48249.69Starting a business22523.20Not at all22723.33Entrepreneurial intentionHave thought about it, can try it if have a chance46748.01Have thought about it, but will not start a business27127.85Starting a business80.83


#### 3.2.2. Statistical Methods

EpiData 3.1 was used for data entry, and SPSS 21.0 was used for data processing. Cronbach’sa was used to analyze the internal consistency of the questionnaire, factor analysis was used to test the structural validity of the questionnaire, and item domain correlation analysis was used to test the collective validity and discrimination validity of the questionnaire. According to the central limit theorem, when the sample size n gradually increases (usually requires n > 30), the probability distribution of the sample mean will approximately follow the normal distribution regardless of the overall distribution. The sample size of this study is about 1000, which has reached the sample requirements of the central limit theorem and can be regarded as approximately following the normal distribution. Therefore, the measurement data are statistically described by (x + s), and the count data are described by component ratio or percentage (%). When the variance is the same, the two independent samples t-test is used for two groups of measurement data, and one-way ANOVA is used for multiple groups of data to analyze the entrepreneurial willingness and entrepreneurial competence of medical college students with different demographic characteristics. When the variance is not uniform, the rank sum test is used for analysis. Finally, we use multiple linear regression to analyze the influencing factors of entrepreneurial intention.


#### 3.2.3. Descriptive Statistics

As shown in Tables2 and 3, the overall score of entrepreneurial intention of the students of the two medical colleges is 2.54 ± 0.63, which is lower than the survey results of Ge Ruolan based on 290 medical undergraduates of Changsha Medical College in 2017 (2.94 ± 0.32). The scores of the four dimensions in the entrepreneurial willingness scale based on the TPB theory are 2.99 ± 0.78, 2.98 ± 0.75, 2.05 ± 0.76, and 2.30 ± 0.80, respectively, with an average score of less than 3. Among them, the lowest score is perceived behavior control, and the highest score is entrepreneurial attitude. The overall score of entrepreneurial competence of the students of the two medical colleges is 3.18 ± 0.52. Except for “learning ability” and “opportunity grasping ability,” the scores of other variables are higher than 3. Among them, the highest score was “self-cognitive ability” (3.73 ± 0.66), and the lowest score was “opportunity grasping ability” (2.61 ± 0.82).Table 2
Scores of entrepreneurial intention of medical college students.
DimensionEntryn (%)Score (x¯±s)Totally disagreeRelatively disagreeCommonlyRelatively agreeTotally agreeEntrepreneurial attitude (n = 974)AB1112 (11.5)149 (15.3)497 (51.2)182 (18.7)33 (3.4)2.87 ± 0.96AB288 (9.1)143 (14.7)456 (46.8)253 (26.0)34 (3.5)3.00 ± 0.95AB358 (6.0)149 (15.3)525 (53.9)211 (21.7)31 (3.2)3.01 ± 0.86AB444 (4.5)96 (9.9)333 (34.3)406 (41.8)92 (9.5)3.42 ± 0.95AB5107 (11.1)285 (29.2)453 (46.4)105 (10.8)23 (2.4)2.64 ± 0.90Totality2.99 ± 0.78Subjective norms (n = 973)SN177 (7.9)217 (22.3)506 (52.1)140 (14.4)32 (3.3)2.83 ± 0.89SN263 (6.5)155 (15.9)515 (52.9)206 (21.2)34 (3.5)2.99 ± 0.88SN362 (6.4)165 (17.0)534 (54.9)177 (18.2)35 (3.6)2.96 ± 0.87SN469 (7.1)128 (13.2)427 (43.9)289 (297)60 (6.2)3.15 ± 0.97Totality2.98 ± 0.75Perceptual behavior control (n = 974)PBC1278 (28.5)415 (42.6)222 (22.8)54 (5.5)5 (0.5)2.07 ± 0.88PBC2297 (306)406 (41.8)218 (22.4)45 (4.6)6 (0.6)2.03 ± 0.88PBC3263 (26.9)423 (43.3)230 (23.6)49 (5.0)8 (0.8)2.09 ± 0.88PBC4411 (42.3)357 (36.8)169 (17.4)31 (3.2)3 (0.3)1.82 ± 0.85PBC5283 (29.1)360 (37.0)285 (29.3)42 (4.3)3 (0.3)2.10 ± 0.88PBC6300 (30.8)292 (30.0)263 (27.0)99 (10.2)20 (2.1)2.23 ± 1.06Totality2.05 ± 0.76Entrepreneurial intention (n-974)E11190 (19.5)292 (30.0)341 (35.0)130 (13.3)21 (2.2)2.49 ± 1.02EI2145 (14.9)256 (26.3)368 (37.9)173 (17.8)30 (3.1)2.68 ± 1.03EI3278 (28.5)409 (42.1)230 (23.6)50 (5.1)7 (0.7)2.07 ± 0.89EI4258 (265)349 (35.8)292 (30.0)62 (6.4)13 (1.3)2.20 ± 0.95EI5314 (32.2)348 (35.7)261 (26.8)44 (4.5)7 (0.7)2.06 ± 0.91Totality2.30 ± 0.80Overall scale2.54 ± 0.63Table 3
Scores of entrepreneurial competence of medical college students.
DimensionEntryn (%)Score (x¯±s)Totally disagreeRelatively disagreeCommonlyRelatively agreeTotally agreeLearning ability (n = 973)A1149 (15.3)291 (29.9)382 (39.3)130 (13.4)21 (2.2)2.57 ± 0.97A2122 (12.5)208 (21.4)420 (43.3)201 (20.7)22 (2.3)2.79 ± 0.98A3153 (15.7)317 (32.6)394 (40.5)92 (9.5)16 (1.6)2.49 ± 0.92A497 (10.1)164 (16.9)375 (386)293 (30.1)43 (4.4)3.02 ± 1.02Totality2.72 ± 0.82Ability to grasp opportunities (n = 972)B1117 (12.1)269 (27.7)443 (45.6)127 (13.2)15 (1.5)2.64 ± 0.91B2126 (13.2)286 (29.5)415 (42.8)130 (13.4)12 (1.2)2.60 ± 0.92B3129 (13.3)289 (29.7)413 (42.5)131 (13.5)10 (1.0)2.59 ± 0.92B4120 (12.4)290 (29.9)412 (42.5)132 (13.6)15 (1.5)2.62 ± 0.92Totality2.61 ± 0.82Interpersonal skills (n = 972)C136 (3.7)108 (11.1)408 (42.0)358 (36.8)62 (6.4)3.31 ± 0.89C248 (4.9)165 (17.0)386 (39.7)319 (32.8)54 (5.6)3.17 ± 0.94C338 (3.9)167 (17.2)416 (42.8)309 (31.8)42 (4.3)3.15 ± 0.89C422 (2.3)107 (11.0)377 (38.8)401 (41.3)64 (6.6)3.39 ± 0.85C525 (2.6)109 (11.2)359 (37.0)411 (42.3)67 (6.9)3.40 ± 0.87Totality3.28 ± 0.74Planning ability (n-973)D116 (1.6)59 (6.0)308 (31.7)519 (53.4)70 (7.2)3.58 ± 0.78D220 (2.1)63 (6.5)378 (38.8)450 (46.2)62 (6.4)3.48 ± 0.79D321 (2.2)61 (6.3)339 (34.8)488 (50.2)64 (6.6)3.53 ± 0.80Totality3.53 ± 0.72Supply integration capability (n-973)E174 (7.6)236 (24.3)443 (45.6)193 (19.9)26 (2.7)2.86 ± 0.91E250 (5.1)139 (14.3)382 (39.3)347 (357)55 (5.7)3.22 ± 0.94E337 (3.8)140 (14.4)408 (42.0)324 (33.3)63 (6.5)324 ± 0.91E446 (4.7)113 (11.6)365 (37.6)369 (38.0)79 (8.1)3.33 ± 0.95Totality3.16 ± 0.77F114 (1.4)34 (3.5)274 (282)538 (55.4)111 (114)3.72 ± 0.77F215 (1.5)40 (4.1)275 (28.3)536 (551)106 (10.9)3.70 ± 0.78F310 (1.0)35 (3.6)272 (28.0)548 (56.4)107 (11.0)3.73 ± 0.74F411 (1.1)34 (3.5)249 (256)557 (57.3)121 (12.4)3.76 ± 0.75Totality3.73 ± 0.66Self-cognitive ability (n-972)G122 (2.3)82 (8.4)388 (40.0)408 (42.0)71 (7.3)3.44 + 0.84G222 (2.3)100 (103)356 (36.7)408 (42.0)85 (88)3.45 ± 0.88G334 (3.5)124 (12.8)386 (39.9)346 (357)78 (81)3.32 ± 0.92Totality3.40 ± 0.73Decisiveness (n-970)H150 (5.2)195 (20.1)486 (50.2)204 (21.0)35 (3.6)2.98 ± 0.87H236 (37)173 (17.8)451 (46.5)269 (27.7)41 (4.2)3.11 ± 0.87H341 (4.2)186 (19.2)447 (46.2)248 (25.6)48 (4.9)3.08 ± 0.90Totality3.06 ± 0.75Totality3.18 ± 0.52


### 3.3. Reliability and Validity Test of the Scale

#### 3.3.1. Reliability Test of the Scale

The Rasch model in the item response theory is applicable to the dichotomous response data. It is a special case of the single parameter model in the item response theory. Its probability is derived from the relationship between the subject’s ability and the difficulty of the topic. Combining the subject’s abilityθi with the difficulty βj of the item, a difference formula based on θi−βj is proposed. The ratio of coding “correct/incorrect” or “yes/no” is(1)lnPryij=1|θi,βj1−Pryij=1|θi,βj=θi−βj,where Pryij=1|θi,βj represents the probability that subject i evaluates item j as 1 or 0. θi represents the intrinsic ability of subject i; βj represents the difficulty of item j, and there is a 50% probability that the answer is correct. Then, the formula of the two classification Rasch model is(2)Pryij=1|θi,βj=logit−1θi−βj=11+exp−θi−βj.When the model contains the discrimination parameteraj, which represents the discrimination of the item J, the formula can be rewritten as(3)Pryij=1|θi,βj,aj=expajθi−βj1+exp−θi−βj.The partial credit model (PCM) constructed by the master is a special model when GPCM assumes that the discrimination parameter of each item is 1 (aj = 1), which is applicable to multiclassification response data:(4)Pryij=y|θi,βjh=exp∑h=0kajθi−βjh∑k=0mjexp∑h=0kθi−βjh.The PCM is extended to the generalized segment scoring model (GPCM). Since the project differentiation parameter is introduced into the model, GPCM brings more information than PCM:(5)Pryij=y|θi,aj,βjh=exp∑h=0kajθi−βjh∑k=0mjexp∑h=0kajθi−βjh,wherein the parameter aj is expressed as the potential health status or quality of life of the subject, the inherent health status or quality of life intensity measured by the response category threshold, and the discrimination of items under different potential health statuses.


#### 3.3.2. Generalized Segment Scoring Model under Bayesian Theory

Definitiony = (yi1, yi2, …, yij) represents the observed item response vector, θ=θ1,θ2,…θN represents the subject’s ability parameter, and the parameter vector of item j is a = aj, β=βjh. The likelihood function of the observed response vector is(6)ςy|θ,a,β=∏i∏j∏hPryij=y|θi,aj,βjh.N and log N represent normal distribution and lognormal distribution, respectively, and a is non-negative. The priori of parameter β is a priori with relatively no information. The distribution of latent trait θ was not set as standard normal. Instead, set the super priori of μ as standard normal and the super priori of μ as no information priori. The super priori of σ2 obeys the inverse gamma distribution with an average value of 1.0 and a variance of 2 (when the shape rate is −0.5), which places most of the parameter values of β in the commonly observed [−3, +3] limits. Then, the joint posterior distribution is(7)pθ,α,β|y∞pα,βpθ|α,βpy|α,β=papβpθ|α,βιy|θ,α,β.The conditional posterior is proportional to the prior number likelihood ratio of the data. Then, the conditional probability distribution ofθ,α,β is(8)pθ|y,α,β∞pθιy|θ,α,β,pa|y,θ,β∞paιy|θ,α,β,pβ|y,α,β∞pβιy|θ,α,β.


### 3.4. Consistency Test of Inspection Scale

#### 3.4.1. Reliability Analysis of the Scale

GPCM was used to test the internal consistency reliability of the scale. The larger the coefficient, the higher the internal consistency of the variables, indicating that the measurement items between the variables have a good correlation. At present, the common criteria in academic circles are Cronbach’sa is greater than 0.90, and then, the reliability of this test or scale is excellent; if Cronbach’s a is between 0.80 and 0.90, the reliability is acceptable; if Cronbach’s a is between 0.70 and 0.80, it needs to be revised, but it does not lose value; and if Cronbach’s a < 0.7, the scale needs to be redesigned. The reliability analysis results of each part of the questionnaire are shown in Table 4. The reliability of each dimension of the TPB scale and the entrepreneurial competence scale is above 0.78. It can be seen that both scales have high reliability and high reliability.Table 4
Reliability analysis of the scale.
GaugeDimensionCronbach’s coefficientNumber of lowest scoring cases (%)Number of highest scoring cases (%)Entrepreneurial intentionTotality0.941Entrepreneurial attitude0.90232 (3.3)10 (1.0)Subjective norms0.84942 (4.3)12 (1.2)Perceptual behavior control0.918160 (16.4)1 (0.1)Entrepreneurial intention0.887106 (10.9)2 (0.2)Entrepreneurial competencePopulation0.934Learning ability0.86270 (7.2)9 (0.9)Ability to grasp opportunities0.92390 (9.3)4 (0.5)Interpersonal skills0.88615 (1.5)23 (2.4)Planning ability0.89513 (1.3)40 (4.1)Resource integration capability0.85027 (2.8)15 (1.5)Self-cognitive ability0.8899 (0.9)47 (4.8)Willpower0.77710 (1.0)29 (3.0)Decisiveness0.80624 (2.5)19 (1.9)Ceiling effect and floor effect refer to that most scores are concentrated at the very high or low end. These two indicators reflect the important characteristics of score distribution. Calculate the proportion of people with the lowest and highest scores on the entrepreneurial willingness scale and entrepreneurial competence scale, respectively. If the scores of 1% of the subjects reach the limit (the highest score or the lowest score), there will be ceiling effect or floor effect, respectively. 15% of the floor or ceiling effect is considered acceptable. As shown in Table4, only 16.4% of the cases with the lowest score in the perceived behavior control dimension in the entrepreneurial willingness scale have the floor effect. The proportion of the lowest score cases and the highest score cases in each dimension of the entrepreneurial competence scale are less than 15%, and there is no ceiling effect and floor effect.


#### 3.4.2. Entrepreneurial Willingness Scale Based on TPB Theory

Table5 and Figure 4 show the entrepreneurial willingness scale and the gravel chart. It can be seen that the KMO value is 0.942 greater than 0.7, Bartlett’s sphericity test is approximately chi square 14372.737, the degree of freedom is 190, and the significance is 0.000, indicating that it is suitable for factor analysis. The principal component analysis is used for the orthogonal rotation of each item of the entrepreneurial willingness scale, and three common factors are extracted according to the feature root greater than 1 [14]. As shown in Table 5, PBC1∼PBC6 have a high load in factor 1 and can be named perceptual behavior control factor; AB1∼AB5 have a high load in factor 2 and can be named entrepreneurial attitude factor; SN1∼SN4 have a higher load in factor 3 and can be named subjective criterion factor; EI1 and EI2 have higher loads in factor 2, and EI3∼EI5 have higher loads in factor 1. The extracted common factors are basically consistent with the dimensions of the scale, and the cumulative contribution rate is 67.527%, indicating that the entrepreneurial intention scale has good structural validity.Table 5
Factor analysis results of the entrepreneurial willingness scale.
EntryFactor 1Factor 2Factor 3PBC40.8810.0790.068PBC30.8490.1400.164PBC20.8480.1180.192PBC10.8140.1360.130PBC50.7840.2020.188PBC60.6400.2880.183EI50.6310.4510.081EI30.6220.4610.050EI40.5790.5520.091AB10.2110.8120.264AB20.1650.7880.289AB40.0140.7650.282AB50.3160.7200.202AB30.1670.6870.278EI20.3690.6810.207EI10.4890.5280.112SN30.2040.2810.847SN20.1790.2990.845SN10.2240.1720.829SN40.0470.2510.571Figure 4
Factor analysis gravel chart of the entrepreneurial willingness scale.


#### 3.4.3. Entrepreneurial Competence Table

Table6 and Figure 5 are the entrepreneurial competence table and gravel chart. The KMO value of the entrepreneurial competence scale is 0.927, which is greater than 0.7. The sphericity test of Bartlett is approximately chi square 18390.291, the degree of freedom is 435, and the significance is 0.000, which indicates that it is suitable for factor analysis. The principal component analysis is used for the orthogonal rotation of each item of the entrepreneurial competence scale, and seven common factors are extracted according to the feature root greater than 1. As shown in Table 6, C1∼C5 have a high load in factor 1 and can be named interpersonal relationship processing ability factor; B1∼B4 have a higher load in factor 2, which can be named opportunity grasping ability factor; F1∼F4 have a high load in factor 3 and can be named self-cognitive ability factor; H1∼H3 has a high load in factor 4 and can be named decisive factor; E1∼E4 have a higher load in factor 5 and can be named resource integration capability factor; A1∼A4 have a higher load in factor 6, which can be named learning ability factor; D1∼D3 have a higher load in factor 7, which can be named planning capacity factor; and G1∼G3 also show a high load in factor 4 [15]. The extracted common factors are basically consistent with the dimensions of the scale, and the cumulative contribution rate is 71.743%, indicating that the entrepreneurial competence scale has good structural validity.Table 6
Factor analysis results of the entrepreneurial competence scale.
EntryFactor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7C20.8520.0880.1110.1670.0550.1650.050C10.8250.0640.1520.1290.0780.1340.130C30.8140.1260.1070.0920.0600.1400.050C50.6740.1020.2460.1060.1800.0490.255C40.6670.1300.2030.0940.1500.0200.262B20.1200.8470.0900.0970.0880.2590.043B30.0920.8350.0620.1330.1040.2420.048B10.1090.8120.0610.1030.1420.3330.013B40.1550.7770.0250.1400.1360.2950.114F30.1680.0590.8240.1710.0820.0400.145F20.1650.0620.7930.1460.1510.0950.208F40.1660.0790.7790.1920.1000.0600.176F10.2260.0650.7160.1180.1960.0420.258H30.0840.2120.0340.7360.1520.0370.212H20.2680.1880.1090.6760.1110.0980.175G30.116−0.0810.3640.6680.1350.188−0.027H10.0920.4140.1030.6540.1670.0020.167G20.0870.155−0.0910.4300.5690.2200.159G10.0040.1210.3810.4920.2510.0700.143E20.0840.0590.1530.1260.8470.0600.097E30.1610.0900.1720.2300.7840.1100.147E10.0920.269−0.0030.1660.7180.1580.127E40.1140.0890.2600.1500.6890.1080.153A20.1430.3190.0540.1060.0880.7920.078A10.1310.2680.0580.1450.0740.7750.007A40.0890.1690.1390.0050.1460.7360.125A30.1310.3800.0130.1170.1040.7340.033D20.2100.0770.2880.1480.1690.0620.794D30.2130.0890.2680.1830.2020.1290.777D10.2530.0480.2760.1950.1860.0770.751Figure 5
Factor analysis gravel diagram of the entrepreneurial competence scale.


## 3.1. TPB’s Entrepreneurial Education Factor Framework for College Students

Entrepreneurship education, as a medium for disseminating entrepreneurial knowledge and skills, cannot directly affect the entrepreneurial intention of students, but can exert influence by cultivating students’ entrepreneurial attitude and individual characteristics. Entrepreneurship education can effectively cultivate and change the entrepreneurial attitude of college students, and social psychology research also shows that individual behavior and attitude can be effectively changed through practice and cognitive level, communication, and example encouragement. Therefore, based on the TPB theoretical framework, this article adds entrepreneurship education as an influencing factor. In the case of discussing how much entrepreneurship education occupies in the formation of postgraduate entrepreneurship intention, it also analyzes what kind of entrepreneurship education can maximize the driving effect in the formation of postgraduate entrepreneurship intention [12]. It is assumed that entrepreneurship education has an effect on entrepreneurial intention by changing individual entrepreneurial attitude, subjective norms, and perceived behavior control. The following theoretical factor framework is constructed as shown in Figure 2.Figure 2
Framework of influencing factors of graduate students’ entrepreneurial intention.Based on the theory of planned behavior, this article introduces two variables: personality characteristics and entrepreneurial competence. Based on the above research hypotheses, it builds a perfect influencing factor model of Medical College Students’ entrepreneurial willingness [13], which is in line with the current background environment of mass entrepreneurship. See Figure 3.Figure 3
Model of influencing factors on entrepreneurial intention of medical college students.


## 3.2. Questionnaire Survey and Demonstration

### 3.2.1. Sample Determination

The subjects of this study are junior undergraduates from five schools in Beijing, Tianjin, and Hebei. Their majors include medicine (traditional Chinese medicine and clinical medicine), pharmacy (traditional Chinese medicine and pharmacy), nursing, psychology, management (marketing and public utilities management), and engineering (medical information engineering).The questionnaire survey was conducted by using the convenient sampling method and the combination of online and offline. A total of 1000 questionnaires were distributed, 988 were recovered, 12 invalid questionnaires were excluded, and 976 valid questionnaires were left, with an effective recovery rate of 97.60%. EpiData 3.1 was used for data entry, and SPSS 21.0 was used for data processing. Single choice questions are described and analyzed by frequency and percentage, and multichoice questions are described and analyzed by the percentage of respondents and the percentage of responses. See Table1 for details.Table 1
Basic information of samples.
CategoryFeaturesQuantityProportion (%)MajorMedical science29129.82Pharmacy20621.11Nursing14214.55Psychology11611.89Management16016.39Engineering616.24Educational systemFour-year system68570.18Five-year system29129.82GenderMale28228.89Female sex69471.12Place of originTown46147.38Countryside51252.62Part-time experienceNo25526.18Yes71973.82Personal entrepreneurial experienceNever88691.34Once767.84Starting a business80.83Entrepreneurial experience of relatives and friendsNever26327.11Once48249.69Starting a business22523.20Not at all22723.33Entrepreneurial intentionHave thought about it, can try it if have a chance46748.01Have thought about it, but will not start a business27127.85Starting a business80.83


### 3.2.2. Statistical Methods

EpiData 3.1 was used for data entry, and SPSS 21.0 was used for data processing. Cronbach’sa was used to analyze the internal consistency of the questionnaire, factor analysis was used to test the structural validity of the questionnaire, and item domain correlation analysis was used to test the collective validity and discrimination validity of the questionnaire. According to the central limit theorem, when the sample size n gradually increases (usually requires n > 30), the probability distribution of the sample mean will approximately follow the normal distribution regardless of the overall distribution. The sample size of this study is about 1000, which has reached the sample requirements of the central limit theorem and can be regarded as approximately following the normal distribution. Therefore, the measurement data are statistically described by (x + s), and the count data are described by component ratio or percentage (%). When the variance is the same, the two independent samples t-test is used for two groups of measurement data, and one-way ANOVA is used for multiple groups of data to analyze the entrepreneurial willingness and entrepreneurial competence of medical college students with different demographic characteristics. When the variance is not uniform, the rank sum test is used for analysis. Finally, we use multiple linear regression to analyze the influencing factors of entrepreneurial intention.


### 3.2.3. Descriptive Statistics

As shown in Tables2 and 3, the overall score of entrepreneurial intention of the students of the two medical colleges is 2.54 ± 0.63, which is lower than the survey results of Ge Ruolan based on 290 medical undergraduates of Changsha Medical College in 2017 (2.94 ± 0.32). The scores of the four dimensions in the entrepreneurial willingness scale based on the TPB theory are 2.99 ± 0.78, 2.98 ± 0.75, 2.05 ± 0.76, and 2.30 ± 0.80, respectively, with an average score of less than 3. Among them, the lowest score is perceived behavior control, and the highest score is entrepreneurial attitude. The overall score of entrepreneurial competence of the students of the two medical colleges is 3.18 ± 0.52. Except for “learning ability” and “opportunity grasping ability,” the scores of other variables are higher than 3. Among them, the highest score was “self-cognitive ability” (3.73 ± 0.66), and the lowest score was “opportunity grasping ability” (2.61 ± 0.82).Table 2
Scores of entrepreneurial intention of medical college students.
DimensionEntryn (%)Score (x¯±s)Totally disagreeRelatively disagreeCommonlyRelatively agreeTotally agreeEntrepreneurial attitude (n = 974)AB1112 (11.5)149 (15.3)497 (51.2)182 (18.7)33 (3.4)2.87 ± 0.96AB288 (9.1)143 (14.7)456 (46.8)253 (26.0)34 (3.5)3.00 ± 0.95AB358 (6.0)149 (15.3)525 (53.9)211 (21.7)31 (3.2)3.01 ± 0.86AB444 (4.5)96 (9.9)333 (34.3)406 (41.8)92 (9.5)3.42 ± 0.95AB5107 (11.1)285 (29.2)453 (46.4)105 (10.8)23 (2.4)2.64 ± 0.90Totality2.99 ± 0.78Subjective norms (n = 973)SN177 (7.9)217 (22.3)506 (52.1)140 (14.4)32 (3.3)2.83 ± 0.89SN263 (6.5)155 (15.9)515 (52.9)206 (21.2)34 (3.5)2.99 ± 0.88SN362 (6.4)165 (17.0)534 (54.9)177 (18.2)35 (3.6)2.96 ± 0.87SN469 (7.1)128 (13.2)427 (43.9)289 (297)60 (6.2)3.15 ± 0.97Totality2.98 ± 0.75Perceptual behavior control (n = 974)PBC1278 (28.5)415 (42.6)222 (22.8)54 (5.5)5 (0.5)2.07 ± 0.88PBC2297 (306)406 (41.8)218 (22.4)45 (4.6)6 (0.6)2.03 ± 0.88PBC3263 (26.9)423 (43.3)230 (23.6)49 (5.0)8 (0.8)2.09 ± 0.88PBC4411 (42.3)357 (36.8)169 (17.4)31 (3.2)3 (0.3)1.82 ± 0.85PBC5283 (29.1)360 (37.0)285 (29.3)42 (4.3)3 (0.3)2.10 ± 0.88PBC6300 (30.8)292 (30.0)263 (27.0)99 (10.2)20 (2.1)2.23 ± 1.06Totality2.05 ± 0.76Entrepreneurial intention (n-974)E11190 (19.5)292 (30.0)341 (35.0)130 (13.3)21 (2.2)2.49 ± 1.02EI2145 (14.9)256 (26.3)368 (37.9)173 (17.8)30 (3.1)2.68 ± 1.03EI3278 (28.5)409 (42.1)230 (23.6)50 (5.1)7 (0.7)2.07 ± 0.89EI4258 (265)349 (35.8)292 (30.0)62 (6.4)13 (1.3)2.20 ± 0.95EI5314 (32.2)348 (35.7)261 (26.8)44 (4.5)7 (0.7)2.06 ± 0.91Totality2.30 ± 0.80Overall scale2.54 ± 0.63Table 3
Scores of entrepreneurial competence of medical college students.
DimensionEntryn (%)Score (x¯±s)Totally disagreeRelatively disagreeCommonlyRelatively agreeTotally agreeLearning ability (n = 973)A1149 (15.3)291 (29.9)382 (39.3)130 (13.4)21 (2.2)2.57 ± 0.97A2122 (12.5)208 (21.4)420 (43.3)201 (20.7)22 (2.3)2.79 ± 0.98A3153 (15.7)317 (32.6)394 (40.5)92 (9.5)16 (1.6)2.49 ± 0.92A497 (10.1)164 (16.9)375 (386)293 (30.1)43 (4.4)3.02 ± 1.02Totality2.72 ± 0.82Ability to grasp opportunities (n = 972)B1117 (12.1)269 (27.7)443 (45.6)127 (13.2)15 (1.5)2.64 ± 0.91B2126 (13.2)286 (29.5)415 (42.8)130 (13.4)12 (1.2)2.60 ± 0.92B3129 (13.3)289 (29.7)413 (42.5)131 (13.5)10 (1.0)2.59 ± 0.92B4120 (12.4)290 (29.9)412 (42.5)132 (13.6)15 (1.5)2.62 ± 0.92Totality2.61 ± 0.82Interpersonal skills (n = 972)C136 (3.7)108 (11.1)408 (42.0)358 (36.8)62 (6.4)3.31 ± 0.89C248 (4.9)165 (17.0)386 (39.7)319 (32.8)54 (5.6)3.17 ± 0.94C338 (3.9)167 (17.2)416 (42.8)309 (31.8)42 (4.3)3.15 ± 0.89C422 (2.3)107 (11.0)377 (38.8)401 (41.3)64 (6.6)3.39 ± 0.85C525 (2.6)109 (11.2)359 (37.0)411 (42.3)67 (6.9)3.40 ± 0.87Totality3.28 ± 0.74Planning ability (n-973)D116 (1.6)59 (6.0)308 (31.7)519 (53.4)70 (7.2)3.58 ± 0.78D220 (2.1)63 (6.5)378 (38.8)450 (46.2)62 (6.4)3.48 ± 0.79D321 (2.2)61 (6.3)339 (34.8)488 (50.2)64 (6.6)3.53 ± 0.80Totality3.53 ± 0.72Supply integration capability (n-973)E174 (7.6)236 (24.3)443 (45.6)193 (19.9)26 (2.7)2.86 ± 0.91E250 (5.1)139 (14.3)382 (39.3)347 (357)55 (5.7)3.22 ± 0.94E337 (3.8)140 (14.4)408 (42.0)324 (33.3)63 (6.5)324 ± 0.91E446 (4.7)113 (11.6)365 (37.6)369 (38.0)79 (8.1)3.33 ± 0.95Totality3.16 ± 0.77F114 (1.4)34 (3.5)274 (282)538 (55.4)111 (114)3.72 ± 0.77F215 (1.5)40 (4.1)275 (28.3)536 (551)106 (10.9)3.70 ± 0.78F310 (1.0)35 (3.6)272 (28.0)548 (56.4)107 (11.0)3.73 ± 0.74F411 (1.1)34 (3.5)249 (256)557 (57.3)121 (12.4)3.76 ± 0.75Totality3.73 ± 0.66Self-cognitive ability (n-972)G122 (2.3)82 (8.4)388 (40.0)408 (42.0)71 (7.3)3.44 + 0.84G222 (2.3)100 (103)356 (36.7)408 (42.0)85 (88)3.45 ± 0.88G334 (3.5)124 (12.8)386 (39.9)346 (357)78 (81)3.32 ± 0.92Totality3.40 ± 0.73Decisiveness (n-970)H150 (5.2)195 (20.1)486 (50.2)204 (21.0)35 (3.6)2.98 ± 0.87H236 (37)173 (17.8)451 (46.5)269 (27.7)41 (4.2)3.11 ± 0.87H341 (4.2)186 (19.2)447 (46.2)248 (25.6)48 (4.9)3.08 ± 0.90Totality3.06 ± 0.75Totality3.18 ± 0.52


## 3.2.1. Sample Determination

The subjects of this study are junior undergraduates from five schools in Beijing, Tianjin, and Hebei. Their majors include medicine (traditional Chinese medicine and clinical medicine), pharmacy (traditional Chinese medicine and pharmacy), nursing, psychology, management (marketing and public utilities management), and engineering (medical information engineering).The questionnaire survey was conducted by using the convenient sampling method and the combination of online and offline. A total of 1000 questionnaires were distributed, 988 were recovered, 12 invalid questionnaires were excluded, and 976 valid questionnaires were left, with an effective recovery rate of 97.60%. EpiData 3.1 was used for data entry, and SPSS 21.0 was used for data processing. Single choice questions are described and analyzed by frequency and percentage, and multichoice questions are described and analyzed by the percentage of respondents and the percentage of responses. See Table1 for details.Table 1
Basic information of samples.
CategoryFeaturesQuantityProportion (%)MajorMedical science29129.82Pharmacy20621.11Nursing14214.55Psychology11611.89Management16016.39Engineering616.24Educational systemFour-year system68570.18Five-year system29129.82GenderMale28228.89Female sex69471.12Place of originTown46147.38Countryside51252.62Part-time experienceNo25526.18Yes71973.82Personal entrepreneurial experienceNever88691.34Once767.84Starting a business80.83Entrepreneurial experience of relatives and friendsNever26327.11Once48249.69Starting a business22523.20Not at all22723.33Entrepreneurial intentionHave thought about it, can try it if have a chance46748.01Have thought about it, but will not start a business27127.85Starting a business80.83


## 3.2.2. Statistical Methods

EpiData 3.1 was used for data entry, and SPSS 21.0 was used for data processing. Cronbach’sa was used to analyze the internal consistency of the questionnaire, factor analysis was used to test the structural validity of the questionnaire, and item domain correlation analysis was used to test the collective validity and discrimination validity of the questionnaire. According to the central limit theorem, when the sample size n gradually increases (usually requires n > 30), the probability distribution of the sample mean will approximately follow the normal distribution regardless of the overall distribution. The sample size of this study is about 1000, which has reached the sample requirements of the central limit theorem and can be regarded as approximately following the normal distribution. Therefore, the measurement data are statistically described by (x + s), and the count data are described by component ratio or percentage (%). When the variance is the same, the two independent samples t-test is used for two groups of measurement data, and one-way ANOVA is used for multiple groups of data to analyze the entrepreneurial willingness and entrepreneurial competence of medical college students with different demographic characteristics. When the variance is not uniform, the rank sum test is used for analysis. Finally, we use multiple linear regression to analyze the influencing factors of entrepreneurial intention.


## 3.2.3. Descriptive Statistics

As shown in Tables2 and 3, the overall score of entrepreneurial intention of the students of the two medical colleges is 2.54 ± 0.63, which is lower than the survey results of Ge Ruolan based on 290 medical undergraduates of Changsha Medical College in 2017 (2.94 ± 0.32). The scores of the four dimensions in the entrepreneurial willingness scale based on the TPB theory are 2.99 ± 0.78, 2.98 ± 0.75, 2.05 ± 0.76, and 2.30 ± 0.80, respectively, with an average score of less than 3. Among them, the lowest score is perceived behavior control, and the highest score is entrepreneurial attitude. The overall score of entrepreneurial competence of the students of the two medical colleges is 3.18 ± 0.52. Except for “learning ability” and “opportunity grasping ability,” the scores of other variables are higher than 3. Among them, the highest score was “self-cognitive ability” (3.73 ± 0.66), and the lowest score was “opportunity grasping ability” (2.61 ± 0.82).Table 2
Scores of entrepreneurial intention of medical college students.
DimensionEntryn (%)Score (x¯±s)Totally disagreeRelatively disagreeCommonlyRelatively agreeTotally agreeEntrepreneurial attitude (n = 974)AB1112 (11.5)149 (15.3)497 (51.2)182 (18.7)33 (3.4)2.87 ± 0.96AB288 (9.1)143 (14.7)456 (46.8)253 (26.0)34 (3.5)3.00 ± 0.95AB358 (6.0)149 (15.3)525 (53.9)211 (21.7)31 (3.2)3.01 ± 0.86AB444 (4.5)96 (9.9)333 (34.3)406 (41.8)92 (9.5)3.42 ± 0.95AB5107 (11.1)285 (29.2)453 (46.4)105 (10.8)23 (2.4)2.64 ± 0.90Totality2.99 ± 0.78Subjective norms (n = 973)SN177 (7.9)217 (22.3)506 (52.1)140 (14.4)32 (3.3)2.83 ± 0.89SN263 (6.5)155 (15.9)515 (52.9)206 (21.2)34 (3.5)2.99 ± 0.88SN362 (6.4)165 (17.0)534 (54.9)177 (18.2)35 (3.6)2.96 ± 0.87SN469 (7.1)128 (13.2)427 (43.9)289 (297)60 (6.2)3.15 ± 0.97Totality2.98 ± 0.75Perceptual behavior control (n = 974)PBC1278 (28.5)415 (42.6)222 (22.8)54 (5.5)5 (0.5)2.07 ± 0.88PBC2297 (306)406 (41.8)218 (22.4)45 (4.6)6 (0.6)2.03 ± 0.88PBC3263 (26.9)423 (43.3)230 (23.6)49 (5.0)8 (0.8)2.09 ± 0.88PBC4411 (42.3)357 (36.8)169 (17.4)31 (3.2)3 (0.3)1.82 ± 0.85PBC5283 (29.1)360 (37.0)285 (29.3)42 (4.3)3 (0.3)2.10 ± 0.88PBC6300 (30.8)292 (30.0)263 (27.0)99 (10.2)20 (2.1)2.23 ± 1.06Totality2.05 ± 0.76Entrepreneurial intention (n-974)E11190 (19.5)292 (30.0)341 (35.0)130 (13.3)21 (2.2)2.49 ± 1.02EI2145 (14.9)256 (26.3)368 (37.9)173 (17.8)30 (3.1)2.68 ± 1.03EI3278 (28.5)409 (42.1)230 (23.6)50 (5.1)7 (0.7)2.07 ± 0.89EI4258 (265)349 (35.8)292 (30.0)62 (6.4)13 (1.3)2.20 ± 0.95EI5314 (32.2)348 (35.7)261 (26.8)44 (4.5)7 (0.7)2.06 ± 0.91Totality2.30 ± 0.80Overall scale2.54 ± 0.63Table 3
Scores of entrepreneurial competence of medical college students.
DimensionEntryn (%)Score (x¯±s)Totally disagreeRelatively disagreeCommonlyRelatively agreeTotally agreeLearning ability (n = 973)A1149 (15.3)291 (29.9)382 (39.3)130 (13.4)21 (2.2)2.57 ± 0.97A2122 (12.5)208 (21.4)420 (43.3)201 (20.7)22 (2.3)2.79 ± 0.98A3153 (15.7)317 (32.6)394 (40.5)92 (9.5)16 (1.6)2.49 ± 0.92A497 (10.1)164 (16.9)375 (386)293 (30.1)43 (4.4)3.02 ± 1.02Totality2.72 ± 0.82Ability to grasp opportunities (n = 972)B1117 (12.1)269 (27.7)443 (45.6)127 (13.2)15 (1.5)2.64 ± 0.91B2126 (13.2)286 (29.5)415 (42.8)130 (13.4)12 (1.2)2.60 ± 0.92B3129 (13.3)289 (29.7)413 (42.5)131 (13.5)10 (1.0)2.59 ± 0.92B4120 (12.4)290 (29.9)412 (42.5)132 (13.6)15 (1.5)2.62 ± 0.92Totality2.61 ± 0.82Interpersonal skills (n = 972)C136 (3.7)108 (11.1)408 (42.0)358 (36.8)62 (6.4)3.31 ± 0.89C248 (4.9)165 (17.0)386 (39.7)319 (32.8)54 (5.6)3.17 ± 0.94C338 (3.9)167 (17.2)416 (42.8)309 (31.8)42 (4.3)3.15 ± 0.89C422 (2.3)107 (11.0)377 (38.8)401 (41.3)64 (6.6)3.39 ± 0.85C525 (2.6)109 (11.2)359 (37.0)411 (42.3)67 (6.9)3.40 ± 0.87Totality3.28 ± 0.74Planning ability (n-973)D116 (1.6)59 (6.0)308 (31.7)519 (53.4)70 (7.2)3.58 ± 0.78D220 (2.1)63 (6.5)378 (38.8)450 (46.2)62 (6.4)3.48 ± 0.79D321 (2.2)61 (6.3)339 (34.8)488 (50.2)64 (6.6)3.53 ± 0.80Totality3.53 ± 0.72Supply integration capability (n-973)E174 (7.6)236 (24.3)443 (45.6)193 (19.9)26 (2.7)2.86 ± 0.91E250 (5.1)139 (14.3)382 (39.3)347 (357)55 (5.7)3.22 ± 0.94E337 (3.8)140 (14.4)408 (42.0)324 (33.3)63 (6.5)324 ± 0.91E446 (4.7)113 (11.6)365 (37.6)369 (38.0)79 (8.1)3.33 ± 0.95Totality3.16 ± 0.77F114 (1.4)34 (3.5)274 (282)538 (55.4)111 (114)3.72 ± 0.77F215 (1.5)40 (4.1)275 (28.3)536 (551)106 (10.9)3.70 ± 0.78F310 (1.0)35 (3.6)272 (28.0)548 (56.4)107 (11.0)3.73 ± 0.74F411 (1.1)34 (3.5)249 (256)557 (57.3)121 (12.4)3.76 ± 0.75Totality3.73 ± 0.66Self-cognitive ability (n-972)G122 (2.3)82 (8.4)388 (40.0)408 (42.0)71 (7.3)3.44 + 0.84G222 (2.3)100 (103)356 (36.7)408 (42.0)85 (88)3.45 ± 0.88G334 (3.5)124 (12.8)386 (39.9)346 (357)78 (81)3.32 ± 0.92Totality3.40 ± 0.73Decisiveness (n-970)H150 (5.2)195 (20.1)486 (50.2)204 (21.0)35 (3.6)2.98 ± 0.87H236 (37)173 (17.8)451 (46.5)269 (27.7)41 (4.2)3.11 ± 0.87H341 (4.2)186 (19.2)447 (46.2)248 (25.6)48 (4.9)3.08 ± 0.90Totality3.06 ± 0.75Totality3.18 ± 0.52


## 3.3. Reliability and Validity Test of the Scale

### 3.3.1. Reliability Test of the Scale

The Rasch model in the item response theory is applicable to the dichotomous response data. It is a special case of the single parameter model in the item response theory. Its probability is derived from the relationship between the subject’s ability and the difficulty of the topic. Combining the subject’s abilityθi with the difficulty βj of the item, a difference formula based on θi−βj is proposed. The ratio of coding “correct/incorrect” or “yes/no” is(1)lnPryij=1|θi,βj1−Pryij=1|θi,βj=θi−βj,where Pryij=1|θi,βj represents the probability that subject i evaluates item j as 1 or 0. θi represents the intrinsic ability of subject i; βj represents the difficulty of item j, and there is a 50% probability that the answer is correct. Then, the formula of the two classification Rasch model is(2)Pryij=1|θi,βj=logit−1θi−βj=11+exp−θi−βj.When the model contains the discrimination parameteraj, which represents the discrimination of the item J, the formula can be rewritten as(3)Pryij=1|θi,βj,aj=expajθi−βj1+exp−θi−βj.The partial credit model (PCM) constructed by the master is a special model when GPCM assumes that the discrimination parameter of each item is 1 (aj = 1), which is applicable to multiclassification response data:(4)Pryij=y|θi,βjh=exp∑h=0kajθi−βjh∑k=0mjexp∑h=0kθi−βjh.The PCM is extended to the generalized segment scoring model (GPCM). Since the project differentiation parameter is introduced into the model, GPCM brings more information than PCM:(5)Pryij=y|θi,aj,βjh=exp∑h=0kajθi−βjh∑k=0mjexp∑h=0kajθi−βjh,wherein the parameter aj is expressed as the potential health status or quality of life of the subject, the inherent health status or quality of life intensity measured by the response category threshold, and the discrimination of items under different potential health statuses.


### 3.3.2. Generalized Segment Scoring Model under Bayesian Theory

Definitiony = (yi1, yi2, …, yij) represents the observed item response vector, θ=θ1,θ2,…θN represents the subject’s ability parameter, and the parameter vector of item j is a = aj, β=βjh. The likelihood function of the observed response vector is(6)ςy|θ,a,β=∏i∏j∏hPryij=y|θi,aj,βjh.N and log N represent normal distribution and lognormal distribution, respectively, and a is non-negative. The priori of parameter β is a priori with relatively no information. The distribution of latent trait θ was not set as standard normal. Instead, set the super priori of μ as standard normal and the super priori of μ as no information priori. The super priori of σ2 obeys the inverse gamma distribution with an average value of 1.0 and a variance of 2 (when the shape rate is −0.5), which places most of the parameter values of β in the commonly observed [−3, +3] limits. Then, the joint posterior distribution is(7)pθ,α,β|y∞pα,βpθ|α,βpy|α,β=papβpθ|α,βιy|θ,α,β.The conditional posterior is proportional to the prior number likelihood ratio of the data. Then, the conditional probability distribution ofθ,α,β is(8)pθ|y,α,β∞pθιy|θ,α,β,pa|y,θ,β∞paιy|θ,α,β,pβ|y,α,β∞pβιy|θ,α,β.


## 3.3.1. Reliability Test of the Scale

The Rasch model in the item response theory is applicable to the dichotomous response data. It is a special case of the single parameter model in the item response theory. Its probability is derived from the relationship between the subject’s ability and the difficulty of the topic. Combining the subject’s abilityθi with the difficulty βj of the item, a difference formula based on θi−βj is proposed. The ratio of coding “correct/incorrect” or “yes/no” is(1)lnPryij=1|θi,βj1−Pryij=1|θi,βj=θi−βj,where Pryij=1|θi,βj represents the probability that subject i evaluates item j as 1 or 0. θi represents the intrinsic ability of subject i; βj represents the difficulty of item j, and there is a 50% probability that the answer is correct. Then, the formula of the two classification Rasch model is(2)Pryij=1|θi,βj=logit−1θi−βj=11+exp−θi−βj.When the model contains the discrimination parameteraj, which represents the discrimination of the item J, the formula can be rewritten as(3)Pryij=1|θi,βj,aj=expajθi−βj1+exp−θi−βj.The partial credit model (PCM) constructed by the master is a special model when GPCM assumes that the discrimination parameter of each item is 1 (aj = 1), which is applicable to multiclassification response data:(4)Pryij=y|θi,βjh=exp∑h=0kajθi−βjh∑k=0mjexp∑h=0kθi−βjh.The PCM is extended to the generalized segment scoring model (GPCM). Since the project differentiation parameter is introduced into the model, GPCM brings more information than PCM:(5)Pryij=y|θi,aj,βjh=exp∑h=0kajθi−βjh∑k=0mjexp∑h=0kajθi−βjh,wherein the parameter aj is expressed as the potential health status or quality of life of the subject, the inherent health status or quality of life intensity measured by the response category threshold, and the discrimination of items under different potential health statuses.


## 3.3.2. Generalized Segment Scoring Model under Bayesian Theory

Definitiony = (yi1, yi2, …, yij) represents the observed item response vector, θ=θ1,θ2,…θN represents the subject’s ability parameter, and the parameter vector of item j is a = aj, β=βjh. The likelihood function of the observed response vector is(6)ςy|θ,a,β=∏i∏j∏hPryij=y|θi,aj,βjh.N and log N represent normal distribution and lognormal distribution, respectively, and a is non-negative. The priori of parameter β is a priori with relatively no information. The distribution of latent trait θ was not set as standard normal. Instead, set the super priori of μ as standard normal and the super priori of μ as no information priori. The super priori of σ2 obeys the inverse gamma distribution with an average value of 1.0 and a variance of 2 (when the shape rate is −0.5), which places most of the parameter values of β in the commonly observed [−3, +3] limits. Then, the joint posterior distribution is(7)pθ,α,β|y∞pα,βpθ|α,βpy|α,β=papβpθ|α,βιy|θ,α,β.The conditional posterior is proportional to the prior number likelihood ratio of the data. Then, the conditional probability distribution ofθ,α,β is(8)pθ|y,α,β∞pθιy|θ,α,β,pa|y,θ,β∞paιy|θ,α,β,pβ|y,α,β∞pβιy|θ,α,β.


## 3.4. Consistency Test of Inspection Scale

### 3.4.1. Reliability Analysis of the Scale

GPCM was used to test the internal consistency reliability of the scale. The larger the coefficient, the higher the internal consistency of the variables, indicating that the measurement items between the variables have a good correlation. At present, the common criteria in academic circles are Cronbach’sa is greater than 0.90, and then, the reliability of this test or scale is excellent; if Cronbach’s a is between 0.80 and 0.90, the reliability is acceptable; if Cronbach’s a is between 0.70 and 0.80, it needs to be revised, but it does not lose value; and if Cronbach’s a < 0.7, the scale needs to be redesigned. The reliability analysis results of each part of the questionnaire are shown in Table 4. The reliability of each dimension of the TPB scale and the entrepreneurial competence scale is above 0.78. It can be seen that both scales have high reliability and high reliability.Table 4
Reliability analysis of the scale.
GaugeDimensionCronbach’s coefficientNumber of lowest scoring cases (%)Number of highest scoring cases (%)Entrepreneurial intentionTotality0.941Entrepreneurial attitude0.90232 (3.3)10 (1.0)Subjective norms0.84942 (4.3)12 (1.2)Perceptual behavior control0.918160 (16.4)1 (0.1)Entrepreneurial intention0.887106 (10.9)2 (0.2)Entrepreneurial competencePopulation0.934Learning ability0.86270 (7.2)9 (0.9)Ability to grasp opportunities0.92390 (9.3)4 (0.5)Interpersonal skills0.88615 (1.5)23 (2.4)Planning ability0.89513 (1.3)40 (4.1)Resource integration capability0.85027 (2.8)15 (1.5)Self-cognitive ability0.8899 (0.9)47 (4.8)Willpower0.77710 (1.0)29 (3.0)Decisiveness0.80624 (2.5)19 (1.9)Ceiling effect and floor effect refer to that most scores are concentrated at the very high or low end. These two indicators reflect the important characteristics of score distribution. Calculate the proportion of people with the lowest and highest scores on the entrepreneurial willingness scale and entrepreneurial competence scale, respectively. If the scores of 1% of the subjects reach the limit (the highest score or the lowest score), there will be ceiling effect or floor effect, respectively. 15% of the floor or ceiling effect is considered acceptable. As shown in Table4, only 16.4% of the cases with the lowest score in the perceived behavior control dimension in the entrepreneurial willingness scale have the floor effect. The proportion of the lowest score cases and the highest score cases in each dimension of the entrepreneurial competence scale are less than 15%, and there is no ceiling effect and floor effect.


### 3.4.2. Entrepreneurial Willingness Scale Based on TPB Theory

Table5 and Figure 4 show the entrepreneurial willingness scale and the gravel chart. It can be seen that the KMO value is 0.942 greater than 0.7, Bartlett’s sphericity test is approximately chi square 14372.737, the degree of freedom is 190, and the significance is 0.000, indicating that it is suitable for factor analysis. The principal component analysis is used for the orthogonal rotation of each item of the entrepreneurial willingness scale, and three common factors are extracted according to the feature root greater than 1 [14]. As shown in Table 5, PBC1∼PBC6 have a high load in factor 1 and can be named perceptual behavior control factor; AB1∼AB5 have a high load in factor 2 and can be named entrepreneurial attitude factor; SN1∼SN4 have a higher load in factor 3 and can be named subjective criterion factor; EI1 and EI2 have higher loads in factor 2, and EI3∼EI5 have higher loads in factor 1. The extracted common factors are basically consistent with the dimensions of the scale, and the cumulative contribution rate is 67.527%, indicating that the entrepreneurial intention scale has good structural validity.Table 5
Factor analysis results of the entrepreneurial willingness scale.
EntryFactor 1Factor 2Factor 3PBC40.8810.0790.068PBC30.8490.1400.164PBC20.8480.1180.192PBC10.8140.1360.130PBC50.7840.2020.188PBC60.6400.2880.183EI50.6310.4510.081EI30.6220.4610.050EI40.5790.5520.091AB10.2110.8120.264AB20.1650.7880.289AB40.0140.7650.282AB50.3160.7200.202AB30.1670.6870.278EI20.3690.6810.207EI10.4890.5280.112SN30.2040.2810.847SN20.1790.2990.845SN10.2240.1720.829SN40.0470.2510.571Figure 4
Factor analysis gravel chart of the entrepreneurial willingness scale.


### 3.4.3. Entrepreneurial Competence Table

Table6 and Figure 5 are the entrepreneurial competence table and gravel chart. The KMO value of the entrepreneurial competence scale is 0.927, which is greater than 0.7. The sphericity test of Bartlett is approximately chi square 18390.291, the degree of freedom is 435, and the significance is 0.000, which indicates that it is suitable for factor analysis. The principal component analysis is used for the orthogonal rotation of each item of the entrepreneurial competence scale, and seven common factors are extracted according to the feature root greater than 1. As shown in Table 6, C1∼C5 have a high load in factor 1 and can be named interpersonal relationship processing ability factor; B1∼B4 have a higher load in factor 2, which can be named opportunity grasping ability factor; F1∼F4 have a high load in factor 3 and can be named self-cognitive ability factor; H1∼H3 has a high load in factor 4 and can be named decisive factor; E1∼E4 have a higher load in factor 5 and can be named resource integration capability factor; A1∼A4 have a higher load in factor 6, which can be named learning ability factor; D1∼D3 have a higher load in factor 7, which can be named planning capacity factor; and G1∼G3 also show a high load in factor 4 [15]. The extracted common factors are basically consistent with the dimensions of the scale, and the cumulative contribution rate is 71.743%, indicating that the entrepreneurial competence scale has good structural validity.Table 6
Factor analysis results of the entrepreneurial competence scale.
EntryFactor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7C20.8520.0880.1110.1670.0550.1650.050C10.8250.0640.1520.1290.0780.1340.130C30.8140.1260.1070.0920.0600.1400.050C50.6740.1020.2460.1060.1800.0490.255C40.6670.1300.2030.0940.1500.0200.262B20.1200.8470.0900.0970.0880.2590.043B30.0920.8350.0620.1330.1040.2420.048B10.1090.8120.0610.1030.1420.3330.013B40.1550.7770.0250.1400.1360.2950.114F30.1680.0590.8240.1710.0820.0400.145F20.1650.0620.7930.1460.1510.0950.208F40.1660.0790.7790.1920.1000.0600.176F10.2260.0650.7160.1180.1960.0420.258H30.0840.2120.0340.7360.1520.0370.212H20.2680.1880.1090.6760.1110.0980.175G30.116−0.0810.3640.6680.1350.188−0.027H10.0920.4140.1030.6540.1670.0020.167G20.0870.155−0.0910.4300.5690.2200.159G10.0040.1210.3810.4920.2510.0700.143E20.0840.0590.1530.1260.8470.0600.097E30.1610.0900.1720.2300.7840.1100.147E10.0920.269−0.0030.1660.7180.1580.127E40.1140.0890.2600.1500.6890.1080.153A20.1430.3190.0540.1060.0880.7920.078A10.1310.2680.0580.1450.0740.7750.007A40.0890.1690.1390.0050.1460.7360.125A30.1310.3800.0130.1170.1040.7340.033D20.2100.0770.2880.1480.1690.0620.794D30.2130.0890.2680.1830.2020.1290.777D10.2530.0480.2760.1950.1860.0770.751Figure 5
Factor analysis gravel diagram of the entrepreneurial competence scale.


## 3.4.1. Reliability Analysis of the Scale

GPCM was used to test the internal consistency reliability of the scale. The larger the coefficient, the higher the internal consistency of the variables, indicating that the measurement items between the variables have a good correlation. At present, the common criteria in academic circles are Cronbach’sa is greater than 0.90, and then, the reliability of this test or scale is excellent; if Cronbach’s a is between 0.80 and 0.90, the reliability is acceptable; if Cronbach’s a is between 0.70 and 0.80, it needs to be revised, but it does not lose value; and if Cronbach’s a < 0.7, the scale needs to be redesigned. The reliability analysis results of each part of the questionnaire are shown in Table 4. The reliability of each dimension of the TPB scale and the entrepreneurial competence scale is above 0.78. It can be seen that both scales have high reliability and high reliability.Table 4
Reliability analysis of the scale.
GaugeDimensionCronbach’s coefficientNumber of lowest scoring cases (%)Number of highest scoring cases (%)Entrepreneurial intentionTotality0.941Entrepreneurial attitude0.90232 (3.3)10 (1.0)Subjective norms0.84942 (4.3)12 (1.2)Perceptual behavior control0.918160 (16.4)1 (0.1)Entrepreneurial intention0.887106 (10.9)2 (0.2)Entrepreneurial competencePopulation0.934Learning ability0.86270 (7.2)9 (0.9)Ability to grasp opportunities0.92390 (9.3)4 (0.5)Interpersonal skills0.88615 (1.5)23 (2.4)Planning ability0.89513 (1.3)40 (4.1)Resource integration capability0.85027 (2.8)15 (1.5)Self-cognitive ability0.8899 (0.9)47 (4.8)Willpower0.77710 (1.0)29 (3.0)Decisiveness0.80624 (2.5)19 (1.9)Ceiling effect and floor effect refer to that most scores are concentrated at the very high or low end. These two indicators reflect the important characteristics of score distribution. Calculate the proportion of people with the lowest and highest scores on the entrepreneurial willingness scale and entrepreneurial competence scale, respectively. If the scores of 1% of the subjects reach the limit (the highest score or the lowest score), there will be ceiling effect or floor effect, respectively. 15% of the floor or ceiling effect is considered acceptable. As shown in Table4, only 16.4% of the cases with the lowest score in the perceived behavior control dimension in the entrepreneurial willingness scale have the floor effect. The proportion of the lowest score cases and the highest score cases in each dimension of the entrepreneurial competence scale are less than 15%, and there is no ceiling effect and floor effect.


## 3.4.2. Entrepreneurial Willingness Scale Based on TPB Theory

Table5 and Figure 4 show the entrepreneurial willingness scale and the gravel chart. It can be seen that the KMO value is 0.942 greater than 0.7, Bartlett’s sphericity test is approximately chi square 14372.737, the degree of freedom is 190, and the significance is 0.000, indicating that it is suitable for factor analysis. The principal component analysis is used for the orthogonal rotation of each item of the entrepreneurial willingness scale, and three common factors are extracted according to the feature root greater than 1 [14]. As shown in Table 5, PBC1∼PBC6 have a high load in factor 1 and can be named perceptual behavior control factor; AB1∼AB5 have a high load in factor 2 and can be named entrepreneurial attitude factor; SN1∼SN4 have a higher load in factor 3 and can be named subjective criterion factor; EI1 and EI2 have higher loads in factor 2, and EI3∼EI5 have higher loads in factor 1. The extracted common factors are basically consistent with the dimensions of the scale, and the cumulative contribution rate is 67.527%, indicating that the entrepreneurial intention scale has good structural validity.Table 5
Factor analysis results of the entrepreneurial willingness scale.
EntryFactor 1Factor 2Factor 3PBC40.8810.0790.068PBC30.8490.1400.164PBC20.8480.1180.192PBC10.8140.1360.130PBC50.7840.2020.188PBC60.6400.2880.183EI50.6310.4510.081EI30.6220.4610.050EI40.5790.5520.091AB10.2110.8120.264AB20.1650.7880.289AB40.0140.7650.282AB50.3160.7200.202AB30.1670.6870.278EI20.3690.6810.207EI10.4890.5280.112SN30.2040.2810.847SN20.1790.2990.845SN10.2240.1720.829SN40.0470.2510.571Figure 4
Factor analysis gravel chart of the entrepreneurial willingness scale.


## 3.4.3. Entrepreneurial Competence Table

Table6 and Figure 5 are the entrepreneurial competence table and gravel chart. The KMO value of the entrepreneurial competence scale is 0.927, which is greater than 0.7. The sphericity test of Bartlett is approximately chi square 18390.291, the degree of freedom is 435, and the significance is 0.000, which indicates that it is suitable for factor analysis. The principal component analysis is used for the orthogonal rotation of each item of the entrepreneurial competence scale, and seven common factors are extracted according to the feature root greater than 1. As shown in Table 6, C1∼C5 have a high load in factor 1 and can be named interpersonal relationship processing ability factor; B1∼B4 have a higher load in factor 2, which can be named opportunity grasping ability factor; F1∼F4 have a high load in factor 3 and can be named self-cognitive ability factor; H1∼H3 has a high load in factor 4 and can be named decisive factor; E1∼E4 have a higher load in factor 5 and can be named resource integration capability factor; A1∼A4 have a higher load in factor 6, which can be named learning ability factor; D1∼D3 have a higher load in factor 7, which can be named planning capacity factor; and G1∼G3 also show a high load in factor 4 [15]. The extracted common factors are basically consistent with the dimensions of the scale, and the cumulative contribution rate is 71.743%, indicating that the entrepreneurial competence scale has good structural validity.Table 6
Factor analysis results of the entrepreneurial competence scale.
EntryFactor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7C20.8520.0880.1110.1670.0550.1650.050C10.8250.0640.1520.1290.0780.1340.130C30.8140.1260.1070.0920.0600.1400.050C50.6740.1020.2460.1060.1800.0490.255C40.6670.1300.2030.0940.1500.0200.262B20.1200.8470.0900.0970.0880.2590.043B30.0920.8350.0620.1330.1040.2420.048B10.1090.8120.0610.1030.1420.3330.013B40.1550.7770.0250.1400.1360.2950.114F30.1680.0590.8240.1710.0820.0400.145F20.1650.0620.7930.1460.1510.0950.208F40.1660.0790.7790.1920.1000.0600.176F10.2260.0650.7160.1180.1960.0420.258H30.0840.2120.0340.7360.1520.0370.212H20.2680.1880.1090.6760.1110.0980.175G30.116−0.0810.3640.6680.1350.188−0.027H10.0920.4140.1030.6540.1670.0020.167G20.0870.155−0.0910.4300.5690.2200.159G10.0040.1210.3810.4920.2510.0700.143E20.0840.0590.1530.1260.8470.0600.097E30.1610.0900.1720.2300.7840.1100.147E10.0920.269−0.0030.1660.7180.1580.127E40.1140.0890.2600.1500.6890.1080.153A20.1430.3190.0540.1060.0880.7920.078A10.1310.2680.0580.1450.0740.7750.007A40.0890.1690.1390.0050.1460.7360.125A30.1310.3800.0130.1170.1040.7340.033D20.2100.0770.2880.1480.1690.0620.794D30.2130.0890.2680.1830.2020.1290.777D10.2530.0480.2760.1950.1860.0770.751Figure 5
Factor analysis gravel diagram of the entrepreneurial competence scale.


## 4. Analysis of Influencing Factors of Entrepreneurial Intention of Medical College Students

Multiple linear regression was used to analyze the influencing factors of Medical College Students’ entrepreneurial intention. First, the ordered or disordered multiclassification variables are transformed into two classification variables, that is, majors: 1 = medical related (medicine, pharmacy, nursing) and 2 = nonmedical related (psychology, management, engineering); and entrepreneurial experience of relatives and friends: 1 = no entrepreneurial experience (never before) and 2 = entrepreneurial experience (ever and now) [16].Secondly, the applicable conditions of multiple linear regression are analyzed, including the following:(1)
There is a linear relationship between the independent variable and the dependent variable. It is judged by drawing a scatter diagram.(2)
Normality of residuals. It is judged by drawing standardized residual histogram and normal probability diagram (P−P diagram).(3)
Equivariance of residuals. By plotting the scatter plot of the predicted value of the standardized residual, if the standardized residual fluctuates below the zero level without obvious regularity, it can be judged thaty satisfies the assumption of equal variance.(4)
Eliminate influential cases. It is generally believed that if the absolute values of standardized residuals and studentized residuals are less than 3, there is no strong influence point in the sample. However, the removal of strong influence points needs to be carefully selected in combination with professional knowledge.(5)
There should be no collinearity between independent variables. It is generally believed that if tolerance (TOL) < 0.1, variance inflation factor (VIF) > 5, and condition index (CI) > 30, there is severe collinearity.


### 4.1. Regression Analysis of Demographic Characteristics on Entrepreneurial Intention

Taking entrepreneurial willingness as the dependent variable and demographic characteristics as the independent variable, multiple linear regression analysis was conducted by using the forced entry method, and the regression equation is established as follows:(9)Y1=b0+b11X1+B12X2+b13X3+b14X4+b15X5,where Y1 represents the entrepreneurial intention, X1 represents the gender, X2 represents the major, X3 represents the place of origin, X4 represents the part-time experience, X5 represents the entrepreneurial experience of relatives and friends, and b,i represents the regression coefficients of various items.There is a linear relationship between the independent variable and the dependent variable by plotting the scatter plot. The residual histogram and the normal probability diagram (P-P diagram) show that the residual is normal. The residual determined by the scatter plot of the normalized residual prediction value satisfies the isovariance [17]. As shown in Table 7, the minimum and maximum values of the standardized residuals for the correlation between demographic variables and entrepreneurial willingness are −2.011 and 3.779, respectively, and the minimum and maximum values of the studentized residuals are −2.016 and 3.797, respectively.Table 7
Correlation coefficient matrix between demographic variables and entrepreneurial willingness.
X1 genderX2 majorX3 place of originX4 part-time experienceX5 entrepreneurial experience of relatives and friendsY1 entrepreneurial intentionX11X20.0121X30.058∗−0.121∗1X4−0.136∗∗0.0090.203∗∗1X5−0.015−0.075∗∗−0.0030.0321Y10.101∗∗∗−0.0330.074∗0.094∗∗0.095∗∗1The test results of the goodness of fit of the model show that the complex correlation coefficientR is 0.183, the determination coefficient R2 is 0.033, and the adjusted R2 value is 0.028, indicating that personality characteristics can explain about 3.3% of entrepreneurial intention. The regression equation was statistically tested by ANOVA: regression regression, degree of freedom = 5, SS regression = 20.507, and Ms regression = 4.101; residual degree of freedom = 963, SS residual = 592.377, and Ms regression = 0.615. The F value is 6.667, and the P value is < 0.000, which indicates that the regression equation is meaningful. As shown in Table 8, gender, major, place of origin, part-time experience, and entrepreneurial experience of relatives and friends are 0.000, 0.000, 0.482, 0.172, 0.003, and 0.004, respectively. It can be seen that the independent variables, major and place of origin, have no statistical significance. Then, remove the two variables of major and place of origin, and do multiple regression of gender, part-time experience, and entrepreneurial experience of relatives and friends. The results are shown in Table 8.Table 8
Regression analysis results of demographic variables on entrepreneurial willingness.
VariableNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorConstant1.3910.1927.250<0.000X1 gender0.1990.0560.1143.535<0.000X2 major−0.0380.054−0.023−0.7030.482X3 place of origin0.0710.0530.0451.3680.173X4 part-time experience0.1760.0590.0982.9780.003X5 entrepreneurial experience of relatives and friends0.1650.0570.0922.9060.004The test results of the goodness of fit of the model show that the complex correlation coefficientr = 0.175, the determination coefficient R2 = 0.031, and the adjusted R2 value is 0.028, indicating that the personality characteristics can explain about 3.1% of the entrepreneurial intention. The regression equation was statistically tested by ANOVA: regression regression, degree of freedom = 3, SS regression = 18.863, and Ms regression = 6.288; and residua residual freedom is 968, SS residual = 596.450, and Ms residual = 0.616. The F value is 10.205, and the P value is < 0.000, which indicates that the regression equation is meaningful. As shown in Table 9, the coefficients of the three variables are all positive, which indicates that gender, part-time experience, and entrepreneurial experience of relatives and friends have a positive impact on entrepreneurial willingness and have statistical significance (P<0.05). Taking another look at the standardized regression coefficient, gender (0.117) > part-time experience (0.107) > entrepreneurial experience of relatives and friends (0.093), which indicates that the degree of influence of the three variables on entrepreneurial intention is in the order of gender > part-time experience > entrepreneurial experience of relatives and friends. To sum up, the regression equation is(10)Y1=1.408+0.206X1+0.19389X4+0.167X5.Table 9
Regression analysis results of demographic variables on entrepreneurial willingness.
VariableNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorConstant1.4080.1658.545<0.000X1 gender0.2060.0560.1173.665<0.000X4 part-time experience0.1920.0580.1073.3360.001X5 entrepreneurial experience of relatives and friends0.1670.0570.0932.9520.002


### 4.2. Regression Analysis of Entrepreneurial Attitude, Subjective Norms, and Perceived Behavior Control on Entrepreneurial Willingness

First, the control variable (demographic variable) is used as an independent variable to conduct a regression analysis on entrepreneurial intention to obtain model 1, and then, the control variable and entrepreneurial attitude, subjective norms, and perceived behavior control are used as independent variables to conduct a regression analysis on entrepreneurial intention to obtain model 2.First, the applicable conditions of multiple linear regression are tested. There is a linear relationship between the independent variable and the dependent variable by plotting the scatter plot. The residual histogram and the normal probability diagram (P−P diagram) show that the residual is normal. The residual determined by the scatter plot of the normalized residual prediction value satisfies the isovariance [18]. As shown in Table 10, the correlation analysis of entrepreneurial attitude, subjective norms, perceived behavior control, and entrepreneurial intention is carried out. There is a strong correlation between Sn and PBC, and there is a moderate correlation between AB, Sn, and PBC. There may not be a multiple linear relationship. Further analysis showed that the TOL of the five variables in model 1 was between 0.935 and 0.993, all of which were >0.1, and the VIF was between 1.007 and 1.070, all of which were <5. The TOL of the eight variables in model 2 is between 0.584 and 0.977, all of which are >0.1, and the VIF is between 1.024 and 1.711, all of which are <5. And the Ci of the two models is less than 30, so it is judged that there is no collinearity between the independent variables. The minimum and maximum values of standardized residuals are −3.887 and 3.396, respectively, and the minimum and maximum values of student residuals are −3.914 and 3.421, respectively.Table 10
Correlation coefficient matrix of entrepreneurial attitude, subjective norms, perceived behavior control, and entrepreneurial willingness.
Entrepreneurial attitudeSubjective normsPerceptual behavior controlEntrepreneurial intentionAB1SN0.572∗∗∗1PBC0.454∗∗∗0.392∗∗∗1EI0.646∗∗∗0.473∗∗∗0.685∗∗∗1The test results of the model goodness of fit show that the complex correlation coefficientR of model 1 is 0.184, the determination coefficient R2 is 0.034, and the adjusted R2 value is 0.029. The complex correlation coefficient R of model 2 is 0.786, the determination coefficient R2 is 0.617, and the adjusted R2 value is 0.614, which indicates that the TPB variable can explain 61.7% of entrepreneurial intention.The regression equation was statistically tested by ANOVA: model 1 was regression, with a degree of freedom = 5, SS regression = 20.660, and Ms regression = 4.132; and residual residual freedom = 962, SS residual = 591.418, and Ms residual = 0.615; the F value is 6.721, and theP value is < 0.000, which indicates that the regression equation is meaningful. Model 2 was regression with degree of freedom = 8, SS regression = 377.772, and Ms regression = 47.221; residual residual, degree of freedom = 959, SS residual = 234.306, and Ms residual = 0.244; the F value is 193.275, and the P valueis < 0.000, which indicates that the regression equation is meaningful.As shown in Table11, the P values corresponding to entrepreneurial attitude, subjective norms, and perceived behavior control in model 2 are <0.000, 0.010, and <0.000, respectively, and the coefficients are all positive, which indicates that entrepreneurial attitude, subjective norms, and perceived behavior control have a significant positive impact on entrepreneurial willingness. According to the standardized regression coefficient, the effects of the three variables on entrepreneurial willingness are perceived behavior control, entrepreneurial attitude, and subjective norms from high to low.Table 11
Regression analysis of entrepreneurial attitude, subjective norms, and perceived behavior control on entrepreneurial willingness.
VariableModel 1Model 2Nonstandard coefficientNormalized regression coefficienttPNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorCoefficient (B)Standard errorConstant1.3890.1927.2430.000−0.3710.135−2.743<0.000Gender0.1980.0560.1133.5100.0000.0200.0360.0110.5490.58Major−0.0390.054−0.023−0.7260.468−0.0150.034−0.009−0.4560.64Place of origin0.0730.0520.0461.4000.1620.0810.0330.0512.4500.01Part-time experience0.1770.0590.0982.9910.0030.0500.0380.0281.3220.18Entrepreneurial experience of relatives and friends0.1660.0570.0932.9280.0030.0360.0360.0200.9980.31Entrepreneurial attitude0.3810.0270.37414.319Subjective norms0.0700.0270.0652.5950.01Perceptual behavior control0.5100.0240.48821.014<0.000R20.0340.617AdjustR20.0290.614F value6.721∗∗∗193.275∗∗∗


### 4.3. Regression Analysis of Entrepreneurial Competence Variables on Entrepreneurial Willingness

First, the control variable (demographic variable) is used as an independent variable to conduct regression analysis on entrepreneurial willingness to obtain model 1, and then, the control variable and entrepreneurial competence-related variables such as learning ability, opportunity grasping ability, and interpersonal relationship processing ability are used as independent variables to conduct regression analysis on entrepreneurial willingness to obtain model 2 [19]. First, the applicable conditions of multiple linear regression are tested. There is a linear relationship between the independent variable and the dependent variable by plotting the scatter plot. The residual histogram and the normal probability diagram (P−P diagram) show that the residual is normal. The residual determined by the scatter plot of the normalized residual prediction value satisfies the isovariance [20]. As shown in Table 12, the correlation analysis of entrepreneurial competence and entrepreneurial willingness shows that there is a moderate correlation between the variables, and there may not be a multiple linear relationship. Further analysis showed that the TOL of the five variables in model 1 was between 0.936 and 0.993, all of which were >0.1, and the VIF was between 1.007 and 1.068, all of which were <5. The TOL of thirteen variables in model 2 is between 0.5140.975, all of which are >0.1, and the VIF is between 1.025 and 1.944, all of which are <5. Therefore, it is judged that there is no collinearity between independent variables. The minimum and maximum values of standardized residuals are −3.956 and 3.589, respectively, and the minimum and maximum values of student residuals are −3.992 and 3.640, respectively [21].Table 12
Correlation coefficient matrix between entrepreneurial competence variables and entrepreneurial willingness.
X1 learning abilityX2 grasp the opportunityX3 interpersonal relationshipX4 planX5 resource integrationX6 self-cognitionX7 willX8 decisivenessY1 entrepreneurial intentionX11X20.633∗∗∗1X30.357∗∗∗0.3301X40.269∗∗∗0.268∗∗∗0.518∗∗∗1X50.352∗∗∗0.357∗∗∗0.369∗∗∗0.479∗∗∗1X60.257∗∗∗0.208∗∗∗0.476∗∗∗0.587∗∗∗0.432∗∗∗1X70.287∗∗∗0.253∗∗∗0.393∗∗∗0.467∗∗∗0.467∗∗∗0.573∗∗∗1X80.354∗∗∗0.437∗∗∗0.410∗∗0.449∗∗∗0.467∗∗∗0.431∗∗∗0.557∗∗∗1Y10.615∗∗∗0.537∗∗∗0.230∗∗∗0.149∗∗∗0.240∗∗∗0.109∗∗∗0.191∗∗∗0.307∗∗∗1The test results of model goodness of fit show that the complex correlation coefficientr of model 1 is 0.184, the determination coefficient R2 is 0.034, and the adjusted R2 value is 0.029. The complex correlation coefficient r of model 2 is 0.663, the determination coefficient R2 is 0.440, and the adjusted R2 value is 0.432, which indicates that the TPB variable can explain 44.0% of entrepreneurial intention [22].The regression equation was statistically tested by ANOVA: model 1 was regression, with a degree of freedom = 5, SS regression = 20.546, and Ms regression = 4.109; and residual residual, degree of freedom = 953, SS residual = 586.034, and Ms residual = 0.615; the F value is 6.682, and theP value is  < 0.000, which indicates that the regression equation is meaningful [23]. Model 2 was regression with a degree of freedom = 13, SS regression = 266.838, and Ms regression = 20.526; and residual residual, degree of freedom 945, SS residual = 339.742, and Ms residual = 0.360; the F value is 57.093, and the P value is < 0.000, which indicates that the regression equation is meaningful.As shown in Table13, the corresponding P values of learning ability, opportunity grasping ability, interpersonal relationship ability, planning ability, resource integration ability, self-cognition ability, perseverance, and decisiveness are P values 0.000, 0.000, 0.775, 0.494, 0.809, 0.010, 0.610, and 0.039, respectively [24]. Among them, the coefficient of learning ability, opportunity grasping, and decisiveness is positive, and the coefficient of self-cognitive ability is negative, which indicates that learning ability, opportunity grasping, and decisiveness have a significant positive impact on entrepreneurial intention, while self-cognitive ability has a negative impact (PG 0.05). Let’s look at the standardized regression coefficient. The degree of effect of the four variables on entrepreneurial willingness from high to low is learning ability (0.457), opportunity grasping ability (0.238), self-awareness ability (0.088), and decisiveness (0.068).Table 13
Regression analysis of entrepreneurial competence variables on entrepreneurial willingness.
VariableModel 1Model 2Nonstandard coefficientNormalized regression coefficienttPNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorCoefficient (B)Standard errorConstant1.3840.1930.1117.1730.0000.2860.1871.5260.127Gender0.1940.0573.4260.0010.1300.0440.0742.9320.003Major−0.0410.054−0.025−0.7690.442−0.1130.042−0.067−2.7210.007Main source0.0760.0520.0481.4480.1480.1060.0410.0672.6050.009Part-time experience0.1780.0590.0993.0080.0030.0680.0460.0381.4790.140Entrepreneurial experience of relatives and friends0.1680.0570.0942.9340.0030.0590.0440.0331.3490.178Learning ability0.4440.0320.45714.007<0.000Grasp the opportunity0.2290.0330.2387.026<0.000Interpersonal relationship0.0090.0330.0090.2860.775Plan−0.0250.037−0.023−0.6840.494Resource integration−0.0080.032−0.007−0.2420.809Self-cognition−0.1060.041−0.088−2.5800.010Will0.0190.0360.0170.5110.610Decisiveness0.0730.0350.0682.0720.039R20.0340.440AdjustR20.0290.432F value6.682∗∗∗57.093∗∗∗The hypothesis test results can be obtained according to the above multiple linear regression analysis results, as shown in Table14. In the dimension of personality characteristics, only gender, part-time experience, and entrepreneurial experience of relatives and friends affect entrepreneurial intention. Among the TPB variables, entrepreneurial attitude, subjective norms, and perceived behavior control have significant positive effects on entrepreneurial willingness, and there are also significant interactions among the three variables. Among the entrepreneurial competence variables, only learning ability, opportunity grasping ability, self-awareness ability, and decisiveness have a significant positive impact on entrepreneurial intention [25].Table 14
Hypothesis test results.
HypothesisContentSupport or notH1aThe gender of college students positively affects their entrepreneurial intention;SupportH1bThe majors of college students positively affect their entrepreneurial intention;Do not support itH1cThe place of origin of college students has a positive impact on their entrepreneurial intention;Do not support itH1dThe part-time experience of college students positively affects their entrepreneurial intention;SupportH1eThe entrepreneurial experience of relatives and friends positively affects the entrepreneurial intention of college students;SupportH2aEntrepreneurial attitude positively affects entrepreneurial intention;SupportH2bSubjective norms positively affect entrepreneurial intention;SupportH2cPerceived behavior control positively affects entrepreneurial intention;SupportH3aLearning ability positively affects entrepreneurial intention;SupportH3bThe ability to grasp opportunities has a positive impact on entrepreneurial intention;SupportH3cInterpersonal relationship processing ability positively affects entrepreneurial intention;Do not support itH3dPlanning ability positively affects entrepreneurial intention;Do not support itH3eThe ability of resource integration positively affects entrepreneurial intention;Do not support itH3fSelf-cognitive ability positively affects entrepreneurial intention;SupportH3gPerseverance has a positive impact on entrepreneurial intention;Do not support itH3hDecisiveness positively affects entrepreneurial willingness.Support


## 4.1. Regression Analysis of Demographic Characteristics on Entrepreneurial Intention

Taking entrepreneurial willingness as the dependent variable and demographic characteristics as the independent variable, multiple linear regression analysis was conducted by using the forced entry method, and the regression equation is established as follows:(9)Y1=b0+b11X1+B12X2+b13X3+b14X4+b15X5,where Y1 represents the entrepreneurial intention, X1 represents the gender, X2 represents the major, X3 represents the place of origin, X4 represents the part-time experience, X5 represents the entrepreneurial experience of relatives and friends, and b,i represents the regression coefficients of various items.There is a linear relationship between the independent variable and the dependent variable by plotting the scatter plot. The residual histogram and the normal probability diagram (P-P diagram) show that the residual is normal. The residual determined by the scatter plot of the normalized residual prediction value satisfies the isovariance [17]. As shown in Table 7, the minimum and maximum values of the standardized residuals for the correlation between demographic variables and entrepreneurial willingness are −2.011 and 3.779, respectively, and the minimum and maximum values of the studentized residuals are −2.016 and 3.797, respectively.Table 7
Correlation coefficient matrix between demographic variables and entrepreneurial willingness.
X1 genderX2 majorX3 place of originX4 part-time experienceX5 entrepreneurial experience of relatives and friendsY1 entrepreneurial intentionX11X20.0121X30.058∗−0.121∗1X4−0.136∗∗0.0090.203∗∗1X5−0.015−0.075∗∗−0.0030.0321Y10.101∗∗∗−0.0330.074∗0.094∗∗0.095∗∗1The test results of the goodness of fit of the model show that the complex correlation coefficientR is 0.183, the determination coefficient R2 is 0.033, and the adjusted R2 value is 0.028, indicating that personality characteristics can explain about 3.3% of entrepreneurial intention. The regression equation was statistically tested by ANOVA: regression regression, degree of freedom = 5, SS regression = 20.507, and Ms regression = 4.101; residual degree of freedom = 963, SS residual = 592.377, and Ms regression = 0.615. The F value is 6.667, and the P value is < 0.000, which indicates that the regression equation is meaningful. As shown in Table 8, gender, major, place of origin, part-time experience, and entrepreneurial experience of relatives and friends are 0.000, 0.000, 0.482, 0.172, 0.003, and 0.004, respectively. It can be seen that the independent variables, major and place of origin, have no statistical significance. Then, remove the two variables of major and place of origin, and do multiple regression of gender, part-time experience, and entrepreneurial experience of relatives and friends. The results are shown in Table 8.Table 8
Regression analysis results of demographic variables on entrepreneurial willingness.
VariableNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorConstant1.3910.1927.250<0.000X1 gender0.1990.0560.1143.535<0.000X2 major−0.0380.054−0.023−0.7030.482X3 place of origin0.0710.0530.0451.3680.173X4 part-time experience0.1760.0590.0982.9780.003X5 entrepreneurial experience of relatives and friends0.1650.0570.0922.9060.004The test results of the goodness of fit of the model show that the complex correlation coefficientr = 0.175, the determination coefficient R2 = 0.031, and the adjusted R2 value is 0.028, indicating that the personality characteristics can explain about 3.1% of the entrepreneurial intention. The regression equation was statistically tested by ANOVA: regression regression, degree of freedom = 3, SS regression = 18.863, and Ms regression = 6.288; and residua residual freedom is 968, SS residual = 596.450, and Ms residual = 0.616. The F value is 10.205, and the P value is < 0.000, which indicates that the regression equation is meaningful. As shown in Table 9, the coefficients of the three variables are all positive, which indicates that gender, part-time experience, and entrepreneurial experience of relatives and friends have a positive impact on entrepreneurial willingness and have statistical significance (P<0.05). Taking another look at the standardized regression coefficient, gender (0.117) > part-time experience (0.107) > entrepreneurial experience of relatives and friends (0.093), which indicates that the degree of influence of the three variables on entrepreneurial intention is in the order of gender > part-time experience > entrepreneurial experience of relatives and friends. To sum up, the regression equation is(10)Y1=1.408+0.206X1+0.19389X4+0.167X5.Table 9
Regression analysis results of demographic variables on entrepreneurial willingness.
VariableNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorConstant1.4080.1658.545<0.000X1 gender0.2060.0560.1173.665<0.000X4 part-time experience0.1920.0580.1073.3360.001X5 entrepreneurial experience of relatives and friends0.1670.0570.0932.9520.002


## 4.2. Regression Analysis of Entrepreneurial Attitude, Subjective Norms, and Perceived Behavior Control on Entrepreneurial Willingness

First, the control variable (demographic variable) is used as an independent variable to conduct a regression analysis on entrepreneurial intention to obtain model 1, and then, the control variable and entrepreneurial attitude, subjective norms, and perceived behavior control are used as independent variables to conduct a regression analysis on entrepreneurial intention to obtain model 2.First, the applicable conditions of multiple linear regression are tested. There is a linear relationship between the independent variable and the dependent variable by plotting the scatter plot. The residual histogram and the normal probability diagram (P−P diagram) show that the residual is normal. The residual determined by the scatter plot of the normalized residual prediction value satisfies the isovariance [18]. As shown in Table 10, the correlation analysis of entrepreneurial attitude, subjective norms, perceived behavior control, and entrepreneurial intention is carried out. There is a strong correlation between Sn and PBC, and there is a moderate correlation between AB, Sn, and PBC. There may not be a multiple linear relationship. Further analysis showed that the TOL of the five variables in model 1 was between 0.935 and 0.993, all of which were >0.1, and the VIF was between 1.007 and 1.070, all of which were <5. The TOL of the eight variables in model 2 is between 0.584 and 0.977, all of which are >0.1, and the VIF is between 1.024 and 1.711, all of which are <5. And the Ci of the two models is less than 30, so it is judged that there is no collinearity between the independent variables. The minimum and maximum values of standardized residuals are −3.887 and 3.396, respectively, and the minimum and maximum values of student residuals are −3.914 and 3.421, respectively.Table 10
Correlation coefficient matrix of entrepreneurial attitude, subjective norms, perceived behavior control, and entrepreneurial willingness.
Entrepreneurial attitudeSubjective normsPerceptual behavior controlEntrepreneurial intentionAB1SN0.572∗∗∗1PBC0.454∗∗∗0.392∗∗∗1EI0.646∗∗∗0.473∗∗∗0.685∗∗∗1The test results of the model goodness of fit show that the complex correlation coefficientR of model 1 is 0.184, the determination coefficient R2 is 0.034, and the adjusted R2 value is 0.029. The complex correlation coefficient R of model 2 is 0.786, the determination coefficient R2 is 0.617, and the adjusted R2 value is 0.614, which indicates that the TPB variable can explain 61.7% of entrepreneurial intention.The regression equation was statistically tested by ANOVA: model 1 was regression, with a degree of freedom = 5, SS regression = 20.660, and Ms regression = 4.132; and residual residual freedom = 962, SS residual = 591.418, and Ms residual = 0.615; the F value is 6.721, and theP value is < 0.000, which indicates that the regression equation is meaningful. Model 2 was regression with degree of freedom = 8, SS regression = 377.772, and Ms regression = 47.221; residual residual, degree of freedom = 959, SS residual = 234.306, and Ms residual = 0.244; the F value is 193.275, and the P valueis < 0.000, which indicates that the regression equation is meaningful.As shown in Table11, the P values corresponding to entrepreneurial attitude, subjective norms, and perceived behavior control in model 2 are <0.000, 0.010, and <0.000, respectively, and the coefficients are all positive, which indicates that entrepreneurial attitude, subjective norms, and perceived behavior control have a significant positive impact on entrepreneurial willingness. According to the standardized regression coefficient, the effects of the three variables on entrepreneurial willingness are perceived behavior control, entrepreneurial attitude, and subjective norms from high to low.Table 11
Regression analysis of entrepreneurial attitude, subjective norms, and perceived behavior control on entrepreneurial willingness.
VariableModel 1Model 2Nonstandard coefficientNormalized regression coefficienttPNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorCoefficient (B)Standard errorConstant1.3890.1927.2430.000−0.3710.135−2.743<0.000Gender0.1980.0560.1133.5100.0000.0200.0360.0110.5490.58Major−0.0390.054−0.023−0.7260.468−0.0150.034−0.009−0.4560.64Place of origin0.0730.0520.0461.4000.1620.0810.0330.0512.4500.01Part-time experience0.1770.0590.0982.9910.0030.0500.0380.0281.3220.18Entrepreneurial experience of relatives and friends0.1660.0570.0932.9280.0030.0360.0360.0200.9980.31Entrepreneurial attitude0.3810.0270.37414.319Subjective norms0.0700.0270.0652.5950.01Perceptual behavior control0.5100.0240.48821.014<0.000R20.0340.617AdjustR20.0290.614F value6.721∗∗∗193.275∗∗∗


## 4.3. Regression Analysis of Entrepreneurial Competence Variables on Entrepreneurial Willingness

First, the control variable (demographic variable) is used as an independent variable to conduct regression analysis on entrepreneurial willingness to obtain model 1, and then, the control variable and entrepreneurial competence-related variables such as learning ability, opportunity grasping ability, and interpersonal relationship processing ability are used as independent variables to conduct regression analysis on entrepreneurial willingness to obtain model 2 [19]. First, the applicable conditions of multiple linear regression are tested. There is a linear relationship between the independent variable and the dependent variable by plotting the scatter plot. The residual histogram and the normal probability diagram (P−P diagram) show that the residual is normal. The residual determined by the scatter plot of the normalized residual prediction value satisfies the isovariance [20]. As shown in Table 12, the correlation analysis of entrepreneurial competence and entrepreneurial willingness shows that there is a moderate correlation between the variables, and there may not be a multiple linear relationship. Further analysis showed that the TOL of the five variables in model 1 was between 0.936 and 0.993, all of which were >0.1, and the VIF was between 1.007 and 1.068, all of which were <5. The TOL of thirteen variables in model 2 is between 0.5140.975, all of which are >0.1, and the VIF is between 1.025 and 1.944, all of which are <5. Therefore, it is judged that there is no collinearity between independent variables. The minimum and maximum values of standardized residuals are −3.956 and 3.589, respectively, and the minimum and maximum values of student residuals are −3.992 and 3.640, respectively [21].Table 12
Correlation coefficient matrix between entrepreneurial competence variables and entrepreneurial willingness.
X1 learning abilityX2 grasp the opportunityX3 interpersonal relationshipX4 planX5 resource integrationX6 self-cognitionX7 willX8 decisivenessY1 entrepreneurial intentionX11X20.633∗∗∗1X30.357∗∗∗0.3301X40.269∗∗∗0.268∗∗∗0.518∗∗∗1X50.352∗∗∗0.357∗∗∗0.369∗∗∗0.479∗∗∗1X60.257∗∗∗0.208∗∗∗0.476∗∗∗0.587∗∗∗0.432∗∗∗1X70.287∗∗∗0.253∗∗∗0.393∗∗∗0.467∗∗∗0.467∗∗∗0.573∗∗∗1X80.354∗∗∗0.437∗∗∗0.410∗∗0.449∗∗∗0.467∗∗∗0.431∗∗∗0.557∗∗∗1Y10.615∗∗∗0.537∗∗∗0.230∗∗∗0.149∗∗∗0.240∗∗∗0.109∗∗∗0.191∗∗∗0.307∗∗∗1The test results of model goodness of fit show that the complex correlation coefficientr of model 1 is 0.184, the determination coefficient R2 is 0.034, and the adjusted R2 value is 0.029. The complex correlation coefficient r of model 2 is 0.663, the determination coefficient R2 is 0.440, and the adjusted R2 value is 0.432, which indicates that the TPB variable can explain 44.0% of entrepreneurial intention [22].The regression equation was statistically tested by ANOVA: model 1 was regression, with a degree of freedom = 5, SS regression = 20.546, and Ms regression = 4.109; and residual residual, degree of freedom = 953, SS residual = 586.034, and Ms residual = 0.615; the F value is 6.682, and theP value is  < 0.000, which indicates that the regression equation is meaningful [23]. Model 2 was regression with a degree of freedom = 13, SS regression = 266.838, and Ms regression = 20.526; and residual residual, degree of freedom 945, SS residual = 339.742, and Ms residual = 0.360; the F value is 57.093, and the P value is < 0.000, which indicates that the regression equation is meaningful.As shown in Table13, the corresponding P values of learning ability, opportunity grasping ability, interpersonal relationship ability, planning ability, resource integration ability, self-cognition ability, perseverance, and decisiveness are P values 0.000, 0.000, 0.775, 0.494, 0.809, 0.010, 0.610, and 0.039, respectively [24]. Among them, the coefficient of learning ability, opportunity grasping, and decisiveness is positive, and the coefficient of self-cognitive ability is negative, which indicates that learning ability, opportunity grasping, and decisiveness have a significant positive impact on entrepreneurial intention, while self-cognitive ability has a negative impact (PG 0.05). Let’s look at the standardized regression coefficient. The degree of effect of the four variables on entrepreneurial willingness from high to low is learning ability (0.457), opportunity grasping ability (0.238), self-awareness ability (0.088), and decisiveness (0.068).Table 13
Regression analysis of entrepreneurial competence variables on entrepreneurial willingness.
VariableModel 1Model 2Nonstandard coefficientNormalized regression coefficienttPNonstandard coefficientNormalized regression coefficienttPCoefficient (B)Standard errorCoefficient (B)Standard errorConstant1.3840.1930.1117.1730.0000.2860.1871.5260.127Gender0.1940.0573.4260.0010.1300.0440.0742.9320.003Major−0.0410.054−0.025−0.7690.442−0.1130.042−0.067−2.7210.007Main source0.0760.0520.0481.4480.1480.1060.0410.0672.6050.009Part-time experience0.1780.0590.0993.0080.0030.0680.0460.0381.4790.140Entrepreneurial experience of relatives and friends0.1680.0570.0942.9340.0030.0590.0440.0331.3490.178Learning ability0.4440.0320.45714.007<0.000Grasp the opportunity0.2290.0330.2387.026<0.000Interpersonal relationship0.0090.0330.0090.2860.775Plan−0.0250.037−0.023−0.6840.494Resource integration−0.0080.032−0.007−0.2420.809Self-cognition−0.1060.041−0.088−2.5800.010Will0.0190.0360.0170.5110.610Decisiveness0.0730.0350.0682.0720.039R20.0340.440AdjustR20.0290.432F value6.682∗∗∗57.093∗∗∗The hypothesis test results can be obtained according to the above multiple linear regression analysis results, as shown in Table14. In the dimension of personality characteristics, only gender, part-time experience, and entrepreneurial experience of relatives and friends affect entrepreneurial intention. Among the TPB variables, entrepreneurial attitude, subjective norms, and perceived behavior control have significant positive effects on entrepreneurial willingness, and there are also significant interactions among the three variables. Among the entrepreneurial competence variables, only learning ability, opportunity grasping ability, self-awareness ability, and decisiveness have a significant positive impact on entrepreneurial intention [25].Table 14
Hypothesis test results.
HypothesisContentSupport or notH1aThe gender of college students positively affects their entrepreneurial intention;SupportH1bThe majors of college students positively affect their entrepreneurial intention;Do not support itH1cThe place of origin of college students has a positive impact on their entrepreneurial intention;Do not support itH1dThe part-time experience of college students positively affects their entrepreneurial intention;SupportH1eThe entrepreneurial experience of relatives and friends positively affects the entrepreneurial intention of college students;SupportH2aEntrepreneurial attitude positively affects entrepreneurial intention;SupportH2bSubjective norms positively affect entrepreneurial intention;SupportH2cPerceived behavior control positively affects entrepreneurial intention;SupportH3aLearning ability positively affects entrepreneurial intention;SupportH3bThe ability to grasp opportunities has a positive impact on entrepreneurial intention;SupportH3cInterpersonal relationship processing ability positively affects entrepreneurial intention;Do not support itH3dPlanning ability positively affects entrepreneurial intention;Do not support itH3eThe ability of resource integration positively affects entrepreneurial intention;Do not support itH3fSelf-cognitive ability positively affects entrepreneurial intention;SupportH3gPerseverance has a positive impact on entrepreneurial intention;Do not support itH3hDecisiveness positively affects entrepreneurial willingness.Support


## 5. Conclusion

The results show that the overall score of entrepreneurial willingness of medical college students is 2.54 ± 0.63, 23.33% of the students said “no at all,” and 27.8% of the students said “considered but would not start a business”; that is, half of the students clearly said that they would not start a business, and their entrepreneurial willingness is low, which is consistent with the research results of many scholars at home and abroad. However, with the rapid development of economy and the upsurge of national innovation and entrepreneurship, it is the general trend that medical college students’ entrepreneurial willingness is rising. Improving the entrepreneurial willingness of medical college students is the root of the problem. Under the concept of frequency, the generalized partial scoring model uses the LTM package inR software to estimate the parameters of GPCM. However, it has a sample size limit when screening and evaluating the scale items, and generally requires a relatively large sample size. Then, in the face of relatively small samples, it is suggested to use the method of combining the Bayesian theory with the generalized segment scoring model, R software, and WinBUGS software for analysis. Based on the appearance of the Bayesian method and the applicability of item response theory, the Bayesian generalized segment scoring model enables us to still obtain more reliable parameter estimates when facing small samples and then make relevant statistical inference.In short, the long-term students of traditional Chinese medicine have insufficient self-awareness and career awareness. Their awareness and ability of career planning need to be improved. The degree of innovation and entrepreneurship education and the innovation and entrepreneurship ability of students still need to be strengthened. Therefore, the school should attach importance to the career planning education of college students, improve the curriculum, establish a long-term learning mechanism for teaching teachers, improve the degree of specialization of guidance personnel, and pay attention to the psychological guidance of students. At the same time, we should follow the trend of the times, integrate innovation, and entrepreneurship education into the whole process of undergraduate and postgraduate training of long-term students of traditional Chinese medicine, provide all-round support for medical students’ entrepreneurship projects, make the future career development of medical students more diversified, and promote the industrialization transformation of medical-related scientific research achievements in multiple dimensions, so as to form a good cycle of employment driven by entrepreneurship.



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*Source: 8177760-2022-10-06.xml*