Study on Employment Decision-making and Market Fluctuation of Graduates of Private Colleges and Universities in Fujian Province from the Perspective of Behavioral Finance

The recruitment of 500 graduates from private colleges and universities in Fujian Province was done through both online and offline channels. Among them, 258 were male and 242 were female, and the validity rate of questionnaire recovery was 98.43%. All subjects participated in the Cognitive Processing Bias Questionnaire for College Graduates’ Employment (NCPBQ), which was distributed in paper form offline and online through Questionnaire Star and TC Lab Psychology’s experimental platform.

Negative Cognitive Processing Bias Questionnaire (NCPBQ): the questionnaire consists of four dimensions: negative attention bias, negative interpretation bias, negative memory bias, and negative rumination bias, and the total questionnaire has a Cronbach’s alpha coefficient of .9371, which is a good reliability and validity and is applicable to the college student population. The results of the validated factor analysis of this study showed a reasonable model fit for the scale: χ²/df = 5.3685, GFI = 0.9318, CFI = 0.9423, IFI = 0.9211, and RMSEA = 0.0749.The Cronbach alpha coefficient for this study was 0.9527.

Similar to the one-dimensional linear regression model, the general linear regression model with multiple independent variables x_i on dependent variable y is: (1) y^i=b0+b1x1i+b2x2i+⋯+bnxni+ui \[{{\hat{y}}_{i}}={{b}_{0}}+{{b}_{1}}{{x}_{1i}}+{{b}_{2}}{{x}_{2i}}+\cdots +{{b}_{n}}{{x}_{ni}}+{{u}_{i}}\] Where: b₀,b₁,b₂,…,b_n is the regression parameter, and u_i is the error term, which represents the sum of the effects of various random factors on y where any change in y that cannot be described by this linear relationship is taken up by this error term.

The parameter prediction of a multiple linear regression model [21] is the coefficients at the time when the sum of the squared errors of the predicted and actual values is minimized, when it is solved using the method of least squares. The method of least squares [22-23] refers to the minimization of the square of the predicted value minus the square of the sum of the squares of the residuals with the true value, i.e., the sum of the squares of the residuals, and the coefficients are solved by using the bias derivatives, or the coefficients can be solved by using the inverse matrix, and the method of least squares is specified as follows:

Residual sum of squares =∑i=1n(yi−yi′)2$=\sum\limits_{i=1}^{n}{(yi-y{i}')}2$, where yi is the true value, yi' is the predicted value.

Where the predicted value is the use of the hypothesis function to solve the value of the real value is the actual value of a group of samples, the difference between the results of the residual sum of squares, the result of the minimum of the residual sum of squares, that is, for the prediction of the model, that is, but also to solve the multiple linear regression equations of the coefficients of the variables, and the value of the constants.

The sample is generally parameterized using h₀(x) = y = b₀ + b₁x₁ + b₂x₂ + b₃x₃ +⋯+ b_nx_n(b₀,b₁,b₂,…….b_n).

The least squares method is ultimately to find a set of b(b₀,b₁,b₂,…….b_n) such that ∑in(h0(xi)−yi)2$\sum\limits_{i}^{n}{({{h}_{0}}(}{{x}_{i}})-{{y}_{i}}{{)}^{2}}$ is minimized.

The algebraic solution to the method of least squares is to take the partial derivative of b_i, the other partial derivative is 0, and then solve the system of equations to get b_i. Of course, the matrix method can also be solved to solve for the coefficients.

Tests of multiple linear regression models:

Multiple linear regression equations used in the least squares method to solve out the equation, that is, for the regression model just trained, you need to carry out the necessary tests on the equation to detect whether the model is applicable, the specific method is as follows:

The null hypothesis of the significance test of multiple linear regression equation is that the individual regression coefficients are not significantly different from zero at the same time. It implies that when the partial regression coefficients are simultaneously zero, no change in the value of each x_i , regardless of the value taken does not cause a linear change in y , all x cannot explain the linear change in y, and there is no linear relationship between y and the whole of x . The test was performed using the F statistic, which is mathematically defined as: (2) F=(y^i⋅y¯)/p(yi⋅y^i)/(n⋅p⋅1) \[F=\frac{({{{\hat{y}}}_{i}}\cdot \bar{y})/p}{({{y}_{i}}\cdot {{{\hat{y}}}_{i}})/(n\cdot p\cdot 1)}\]

In Eq. (2): p the number of explanatory variables in the multiple linear regression equation. The F-statistic obeys an F-distribution with (p,n–p–1) degrees of freedom. SPSS will automatically calculate the observed value of the test statistic and the corresponding probability p value. If the value of probability p is less than the given level of significance, the null hypothesis should be rejected and it is considered that the partial regression coefficients are not zero at the same time, and that the linear relationship between the explanatory variable y and the whole of the explanatory variable x is significant, and that their relationship can be described and reflected by a linear model; conversely, if the value of probability p is greater than the given level of significance, the null hypothesis should not be rejected, and it is considered that the partial regression coefficients are zero at the same time, and that the linear relationship between the explanatory variable y and the whole of the Explanatory variable x of the whole of the linear relationship is not significant, and it is inappropriate to describe and reflect the relationship between them with a linear model.

Through the above discussion it is easy to realize that the test of significance of the regression equation and the test of goodness of fit of the regression equation [24-25] are different and similar. The F statistic corresponds to the adjusted R² in the following way: (3) F=R2/p(1⋅R2)/(n⋅p⋅1) \[F=\frac{{{R}^{2}}/p}{(1\cdot {{R}^{2}})/(n\cdot p\cdot \mathbf{1})}\]

Eq. (3) shows that the higher the goodness of fit of the regression equation, the more significant the test of significance of the regression equation will be; the more significant the test of significance of the regression equation is, the higher the goodness of fit of the regression equation will be.

Significance test of regression coefficients:

The basic starting point for the test of significance of the regression equation is very similar to the test of goodness of fit. It is already known from the above discussion. The test of significance in the regression equation uses ANOVA to examine whether a relatively large proportion of the in. If a larger proportion, it means that the linear relationship with the whole is obvious, the use of linear models to reflect the relationship with all is appropriate; on the contrary, if the proportion of the larger than small, it means that the linear relationship with the whole is not obvious, the use of linear models to reflect the relationship with all is not appropriate. The test statistic used for the significance test of the regression equation is constructed based on this idea.

If an explanatory variable is introduced into the regression equation and passes the test of significance of the regression coefficients, it will reduce the mean squared error R² improve and improve the observed value of the F-statistic, that is: (4) Fch=Rch2(n⋅p⋅1)1⋅R2 \[{{F}_{ch}}=\frac{R_{ch}^{2}(n\cdot p\cdot 1)}{1\cdot {{R}^{2}}}\]

In Eq. (4), F_ch is called the F-statistic, Rch2\[R_{ch}^{2}\] is the improvement of R², and Rch2=R2⋅Ri2$R_{\text{ch}}^{2}={{R}^{2}}\cdot R_{i}^{2}$ (Ri2$R_{i}^{2}$ is the coefficient of determination of the explanatory variable x_i before it enters the equation). It is not difficult to find that: when an explanatory variable x_i is introduced into the equation the observed value of the corresponding partial F-statistic and the t_i of that explanatory variable are quantitatively related to each other as Fch=ti2${{F}_{ch}}=t_{i}^{2}$. Also, in terms of the partial F-statistic, if the introduction of an explanatory variable x_i results in a significant change in the observed value of the partial F-statistic (the partial F-test is significant), then it can be assumed that the x_i contribution to the linearity of the y is significant, and it should be retained in the regression equation. This suggests that the test for the partial F statistic is essentially equivalent to the test for the significance of the regression coefficients. Therefore, the partial F test of significance can be used to make a judgment on the perceived bias of graduates’ employment.

This paper investigates the employment ratio of “state-owned enterprises (SOE), non-public enterprises (NPE), institutions of higher learning (IHL), research and design units (SRDU), organizations and other institutions (OIBU)”. The results of the survey on the employment ratio of each unit are shown in Figure 2. Analyzing the employment destinations of graduates of private colleges and universities in Fujian Province during the period of 2016-2023, the employment proportion of graduates of private colleges and universities in Fujian Province to colleges and universities, scientific research institutes, as well as organs and other institutions continues to decline during the period of 2016-2023. Their employment ratios in 2023 decreased by 1.74%, 6.77% and 23.04% respectively compared with those in 2016. The employment ratio of state-owned enterprises, on the other hand, shows a trend of increasing and then decreasing, with its employment ratio ranging from 13.75% to 27.15% between 2016 and 2023. The employment ratio of non-public enterprises as a whole shows a rapid growth trend, especially in 2018 and 2023, which is 22.78% and 27.13% higher than that of 2016. It shows that in the current situation of increased competition for employment, graduates are more inclined to obtain a stable job. From the data situation, with the deepening of the reform of state-owned enterprises and the increase in the number of large projects, the ability to absorb college graduates has increased. The employment rate of college graduates to state-owned enterprises has increased, but with the progress of education, the impact of students from various colleges and universities in Fujian Province, the difficulty for private college students to work in state-owned enterprises has increased, resulting in a decline in the proportion of private college students employed in state-owned enterprises after 2020. Non-public enterprises continue to play an important role in absorbing graduates for employment and are the main employers of college graduates for employment. The proportion of private college graduates in Fujian Province who are employed in institutions of higher learning and research and design units has continued to decrease, mainly because these units have little demand for private college graduates. Their focus is to acquire talents with graduate degrees and above from prestigious universities. With the reform of China’s personnel system, organs and institutions need to replenish some new forces every year, and at the same time, Fujian Province has implemented positive and encouraging policies and measures in guiding graduates of private colleges and universities to work in grass-roots units such as townships. Therefore, the demand for private college graduates from institutions has increased greatly in recent years.

Figure 2.

The results of the employment ratio survey of each unit

Through the questionnaire survey on the employment quality of graduates of private colleges and universities in Fujian Province during 2016-2023.The results of the employment index of graduates of private colleges and universities in Fujian Province during 2016-2023 are shown in Figure 3. The statistical results show that the employment satisfaction of graduates of private colleges and universities in Fujian Province in 2023 is 84.56%, and the employment quality index is 39.04.The employment quality index of graduates has continued to decrease in recent years, from 46.07 in 2016 to 39.04 in 2023; however, the employment satisfaction has increased from 57.85% in 2016 to 84.56% in 2023. This may be related to the development of education. In recent years, there has been an increase in the number of college students attending famous public colleges and universities, which poses a significant threat to the employment prospects of students from private colleges and universities.It results in a decrease in the quality of their employment. As for the college students who have found a job, they cherish the hard-won job opportunities more, which is the reason why the results of the survey in this paper show that the employment index of the students in private colleges and universities is low but the employment satisfaction is high in 2023. This also makes most of the graduates cherish their job opportunities in the current situation of increasing employment pressure on graduates of private colleges and universities in Fujian Province, and at the same time, their satisfaction with the training programs and salary levels of the employers has slightly increased.

Figure 3.

The results of the employment index of the college graduates in 2016-2023

There is definitely a difference between the subjective perception of university graduates regarding society’s needs and the hiring criteria of enterprises. Therefore, this paper uses the mean value to compare the difference in employment outcomes between graduates with accurate perception and those with biased perception. The dependent variables employment opportunity, current position, job satisfaction, development prospects for current job and salary level (where the lower the value of employment status the higher the employment opportunity, and the current position, job satisfaction and evaluation of current job are arranged in descending order. Current position, job satisfaction, and evaluation of current job are in descending order.That is to say, the lower the value of these three, the higher the career prestige, job satisfaction, and evaluation of job prospects. (Wage level is in ascending order, i.e., the higher the average value of wage water, the higher the wage.) This set of variables indicating the quality of employment is added and subtracted in the sorting direction to generate a new variable expressed in numerical value, which is the quality of employment, and at this point the relationship between the independent variables and the dependent variable can be more clearly seen.

First of all, the correlation analysis model is established with the quality of talents with monthly expectation (ME), most important knowledge structure (TMIKS), ability structure (TMICS), quality structure (TMIQS) and unit valued (UV) as the independent variables and employment status as the dependent variable. When the P-value is less than 0.05, the correlation analysis is significant according to the F-test. The regression results of subjective perception and employment opportunities are shown in Figure 4. Through the statistical results, it is found that the correlation coefficient between monthly salary expectation and employment status is 0.5413, the significance degree is equal to 0.0129, P<0.05, which indicates that the monthly salary expectation is highly correlated with the employment status, and it is correlated in a positive direction. This means that the employment opportunities of graduates with low monthly salary expectations are higher than those of graduates with high monthly salary expectations. The correlation coefficient between the most important knowledge structure and employment status is -0.2102, the significance degree is equal to 0.0451, and the p-value is significantly less than 0.05, which means that the monthly salary expectation value is significantly related to the employment status and is related in the opposite direction. The correlation coefficient between the most important ability structure and employment status is 0.1142, the significance degree is equal to 0.0263, which is significantly less than 0.05, indicating that the most important ability structure is significantly related to employment status and in a positive direction. The correlation coefficient between the quality of the talent that the unit looks for and the employment status is -0.3496, the significance degree is equal to 0.008, which is significantly less than 0.05, indicating that the quality of the talent that the unit looks for is significantly related to the employment status and is related in the opposite direction. It is worth noting that the Pearson correlation coefficient between the quality structure and the employer is 0.0428, with a p-value = 0.7437 > 0.05, indicating that there is no significant difference between the quality structure of graduates and employment opportunities. Therefore, in the process of employment decision-making for graduates, attention should be paid to the comprehensive quality of the individual from the perspective of the enterprise and the market.

Figure 4.

The return of subjective cognition and employment opportunities

500 graduates from private colleges and universities in Fujian Province were divided into 1-5 levels in their monthly salary expectations. 1 (<4500), 2 (4500-5500), 3 (5500-6500), 4 (6500-7500), and 5 (>7500) respectively. The results of the survey on monthly salary expectation and employment status are shown in Table 1. First of all, using monthly salary expectation as independent variable and employment status as dependent variable, the mean comparison is as follows: where the p-mean is 0.0375, which is less than 0.05, and the mean comparison is significant through the F-test (F=0.7864). From the table below, it can be concluded that the same salary expectation (<4500) that has the largest percentage of corporate expectations has the lowest mean value of employment status (1.201), which means that such graduates have the highest employment opportunities. The results of the survey found that more than 85% of the graduates’ employment status is more in line with expectations. As the monthly salary expectation increases, the difficulty of employment also increases. For example, 38.77% of the graduates with monthly salary expectation of $6,500-$7,500 had a mean value of 1.469 for their employment status, which is a lesser increase than the mean value of the employment status of the graduates with monthly salary expectation of <$4,500 (0.268). In contrast, the percentage of students with monthly salary >$7,500 increased by 0.696 over the employment difficulty of students with monthly salary expectations <$4,500, which indicates that 13.15% of the students were extremely difficult to employ.

In this paper, the mean value of employment status is investigated in terms of talent quality for four aspects: social relations of the unit (SRU), ability structure (AS), quality structure (QS), and knowledge structure (KS). The results of the survey on talent quality and employment status are shown in Figure 5. The results indicate that the correlation between talent quality and employment status is not significant beyond 0.05 (P=0.0053), passed the test (F=0.458), and there is a significant difference between the means. It can be found that the mean values of employment status from high to low are knowledge structure, quality structure, ability structure and human social relationship with the employer, which corresponds to the coefficient of employment status of 1.5019, 1.4285, 1.3843, and 1.2567, respectively.Then the employment opportunities from high to low are human social relationship with the unit, ability structure, quality structure and knowledge structure.

Figure 5.

The results of the survey of talent quality and employment status

This paper investigates seven aspects of graduates’ knowledge structure, namely, “A professional knowledge, B foreign language proficiency, C scientific research achievements, D computer proficiency, E academic qualifications, F cultural literacy, G qualifications”. The results of the survey on knowledge structure and employment status are shown in Figure 6. Taking the knowledge structure that graduates consider most important as the independent variable and the employment status as the dependent variable, the comparison of means is as follows: where the p-mean is less than 0.05 (0.0473), and the comparison of means is significant through the F-test (1.2467). It can be seen that the table is arranged in ascending order of the means of the employment status, then it can be found that the employment opportunities are higher in professional knowledge, foreign language proficiency, scientific research achievements and computer proficiency, with a mean value of 1.0, then the employment rate is 100%. This is followed by education (1.2762). The least employable cultural training and qualifications have employment status coefficients of 1.5641 and 1.5687, respectively.The standard ranking of knowledge structure of employers in the enterprises is professional knowledge, level of foreign languages, scientific achievements and education. Thus, it seems that graduates who pay attention to the content of knowledge structure options in line with the needs of society have better employment results.

Figure 6.

Knowledge structure and employment status survey results

The next survey was conducted using the competency structure that graduates consider most important as the independent variable and employment status as the dependent variable. The results of the survey on competency structure and employment status are shown in Table 2. Comparison of means is as follows: where P-value = 0.0197, which is less than 0.05, and F = 0.6428, the comparison of means is significant through the test. It can be found that the mean value of employment status is arranged in ascending order from low to high, then it is concluded that the top five employment opportunities are “social and work experience, independent problem solving ability, business level, organization and management ability and expression ability” in ascending order, and its corresponding mean value of employment status is 1.1307, 1.3253, 1.4018, 1.4018, 1.4018, 1.3253, 1.4018, 1.4379 and 1.4654.The standard ranking of the employing competence structure of enterprises is social work and experience, independent problem solving ability, organizational and management ability and creative ability (1.5096). Thus, it seems that graduates who pay attention to the content of the competence structure options in line with the needs of society have better employment results. It is worth pointing out that although there are not many companies that require business skills, the employment opportunities of graduates who prioritize them are also relatively high.

Finally, this paper takes the quality structure that graduates consider most important as the independent variable and the employment status as the dependent variable, and investigates its relationship with the quality structure of “motivation (UH), image and temperament (IT), honesty (H), teamwork (TS), work ethic (PE), political outlook (PA), dedication and responsibility (PR), and specialties (S)” 8 aspects related to employment status were investigated. The results of the survey on quality structure and employment status are shown in Table 3. The results of the comparison of the mean of linear regression showed that the p-value of employment opportunities was 0.0287 less than 0.05 and F=1.1461, which passed the test that the means were significant. According to the ascending order of the mean value of employment status from low to high, it can be found that the first five rankings of employment opportunities from high to low are motivation, image quality, integrity, teamwork and work ethic, which corresponds to the mean value of the results of the employment status of 1, 1, 1.3146, 1.3305, and 1.3484, respectively.The ranking of the enterprise’s ability to employ the quality standard is teamwork, integrity, motivation and work ethic. It appears that graduates who emphasize quality structure options with content that meets the needs of society have greater employment opportunities.It is worth pointing out that although not many enterprises require image and temperament, the employment opportunities of graduates who emphasize image and temperament are also quite high.It may be the result of employers’ unconscious choices during their recruitment, although they do not specifically mention it.