Mediation Effect
The principle of the mediation effect model is that in addition to the direct impact of the core explanatory variable X on the dependent variable Y, there are other influencing factors M, which indirectly affect Y through the effect on M. If this path exists, M is called a mediator variable. When there is only one mediator variable, the following three regression equations need to be verified:
$''Y=ax+d''$ (1)
$''M=bx+d''$ (2)
$''Y=cx+kM+d''$ (3)
In formula (1), the total effect of the independent variable X on the dependent variable Y is coefficient a; in formula (2), the indirect effect of the independent variable X on the mediator variable M is coefficient b; in formula (3), the direct effect of the independent variable X on the dependent variable Y is coefficient c, and coefficient k represents the effect of the mediator variable M on the dependent variable Y.
The regression analysis steps for conducting a mediation effect are as follows:
Step 1: Test the significance of the regression coefficient a, examine whether the independent variable X has a significant impact on the dependent variable Y. If a is significant, proceed to the next test; otherwise, stop the test.
Second, test the significance of the regression coefficient b. If it is not significant, the mediating effect does not exist, stop the test; if b is significant, it indicates that the explanatory variable X has an impact on the mediating variable M, and there is a mediating effect, proceed to the next test.
Third, simultaneously test the independent variable X and the mediating variable M: put the independent variable X and the mediating variable M into the regression equation at the same time, and compare it with the regression coefficient in step 1. If c is not significant after adding the mediating variable, it means that the mediating variable M plays a complete mediating effect, that is, X affects Y through M; if c is still significant, it means that M is only a partial mediating variable.
In order to further test the effectiveness of the mediating effect, whether the mediating type is serial mediation or parallel mediation, Bootstrap method can be used for testing. Bootstrap method generates a large number of samples by repeatedly resampling from the original dataset, which are used to calculate the estimated value of the mediating effect, thus constructing confidence intervals for the effect size. When there are multiple mediating paths, a corresponding confidence interval can be constructed for each mediating path. If the confidence interval does not include 0, it can be concluded that the significance of that mediating path.
Testing the mediating effect of entrepreneurial learning between psychological capital and entrepreneurial behavior.
According to the aforementioned steps, regression models were constructed for the independent variable, mediator variable, and dependent variable respectively, and the regression analysis results are shown in Table 1.
Table 1 Regression model analysis with entrepreneurial learning as a mediator
变量 
 


 
 0.522**  0.321  
 
 0.839***  0.783***  0.618*** 
 
 0.282***  
F  273.674***  378.514***  156.816*** 
R^2  0.355  0.432  0.387 
 0.353  0.431  0.384 
As shown in the table above, multiple regression analysis was used to test the mediating effect of entrepreneurship learning between psychological capital and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable psychological capital and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.839, reaching significance at a 95% confidence level, and the adjusted Rsquare of the model was 0.353. In the second step, regression analysis was conducted on the independent variable and the mediating variable, with the results showing that the regression coefficient of the independent variable was 0.783, reaching significance at a 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable, entrepreneurship learning, on the dependent variable was 0.282, reaching significance at a 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.618, still significant at a 95% confidence level, and the adjusted Rsquare increased to 0.384, indicating a stronger explanatory power of the model. Therefore, it can be concluded that entrepreneurship learning plays a mediating role between psychological capital and entrepreneurial behavior.
Bootstrap test results as an intermediary for entrepreneurial learning

 Effect  S.E.  t  p 
 
LLCI  ULCI  

 0.618  0.066  9.421  0.000  0.489  0.747 
 0.839  0.051  16.543  0.000  0.739  0.939  
 0.221  0.050  4.364  0.000  0.118  0.311 
Further using the Bootstrap method to examine the 95% confidence interval of the mediation effect, from the table above, it can be seen that the total effect value of psychological capital on entrepreneurial behavior is 0.839, with a 95% confidence interval of [0.739, 0.939], not including 0, indicating the existence of a total effect. In terms of direct effects, the effect value of psychological capital on entrepreneurial behavior is 0.839, with a 95% confidence interval of [0.489, 0.747], not including 0, thus the direct effect is significant. The mediation effect value is 0.221, with a 95% confidence interval of [0.118, 0.311], not including 0, indicating a significant mediation effect, accounting for 26.289%. In conclusion, entrepreneurial learning plays a significant mediating role between psychological capital and entrepreneurial behavior.
Testing the mediating effect of selfefficacy on entrepreneurial behavior in singleloop learning
Following the aforementioned steps, regression models were constructed for the independent variable, mediating variable, and dependent variable, and the regression analysis results are shown in Table 3.
Regression model analysis with singleloop learning as mediator
变量 
 


 
 1.671***  1.080***  
 
 0.495***  0.433***  0.361*** 
 
 0.309***  
F  135.164***  99.951***  102.133*** 
 0.213  0.167  0.291 
 0.212  0.165  0.288 
As shown in the table above, multiple regression analysis was used to test the mediating effect of singleloop learning on the relationship between selfefficacy and entrepreneurial behavior. In the first step, a regression analysis was conducted between the independent variable selfefficacy and the dependent variable entrepreneurial behavior, with the regression coefficient of the independent variable being 0.495, reaching significance at a 95% confidence level, and the adjusted Rsquare of the model being 0.212. In the second step, a regression analysis was conducted between the independent variable and the mediating variable, with the regression coefficient of the independent variable being 0.433, reaching significance at a 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the mediating variable singleloop learning had a regression coefficient of 0.309 on the dependent variable, reaching significance at a 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.361, still significant at a 95% confidence level, and the adjusted Rsquare increased to 0.288, indicating a stronger explanatory power of the model. Therefore, it can be concluded that singleloop learning plays a mediating role between selfefficacy and entrepreneurial behavior.
Table 4 Bootstrap Test Results for SingleLoop Learning as a Mediator

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.361  0.044  8.146  0.000  0.274  0.448 
 0.495  0.043  11.626  0.000  0.411  0.579  
 0.134  0.021  6.427  0.000  0.098  0.174 
Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the total effect of selfefficacy on entrepreneurial behavior is 0.495, with a 95% confidence interval of [0.411, 0.579], which does not include 0, indicating the presence of a total effect. In terms of direct effects, the effect of selfefficacy on entrepreneurial behavior is 0.495, with a 95% confidence interval of [0.274, 0.448], which does not include 0, thus the direct effect is significant. The mediating effect value is 0.134, with a 95% confidence interval of [0.098, 0.174], which does not include 0, indicating a significant mediating effect, accounting for 27.047%. In conclusion, singleloop learning plays a significant mediating role between selfefficacy and entrepreneurial behavior.
Testing the mediating effect of doubleloop learning between selfefficacy and entrepreneurial behavior.
According to the aforementioned steps, regression models were constructed for the independent variable, mediator variable, and dependent variable respectively, and the regression analysis results are shown in Table 5.
Table 5 Regression model analysis with dualloop learning as the mediator.
变量 
 


 
 1.671***  1.152***  
 
 0.495***  0.472***  0.353*** 
 
 0.301***  
F  135.164***  116.535***  100.793*** 
R^2  0.213  0.190  0.289 
 0.212  0.188  0.286 
As shown in the table above, multiple regression analysis was used to test the mediating effect of dualloop learning between selfefficacy and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable selfefficacy and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.495, reaching significance at the 95% confidence level, and the adjusted Rsquare of the model was 0.212. In the second step, regression analysis was conducted on the independent variable and the mediating variable, showing that the regression coefficient of the independent variable was 0.472, reaching significance at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model. The results showed that the mediating variable dualloop learning had a regression coefficient of 0.301 on the dependent variable, reaching significance at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.353, still significant at the 95% confidence level, and the adjusted Rsquare increased to 0.286, indicating a stronger explanatory power of the model. Therefore, it can be concluded that dualloop learning plays a mediating role between selfefficacy and entrepreneurial behavior.
Bootstrap test results with doubleloop learning as mediator

 Effect  S.E.  t  p 
 
LLCI  ULCI  

 0.353  0.045  7.839  0.000  0.265  0.441 
 0.495  0.043  11.626  0.000  0.411  0.579  
 0.142  0.022  6.450  0.000  0.099  0.185 
Further using the Bootstrap method to examine the 95% confidence interval of the mediation effect, from the table above, it can be seen that the total effect value of selfefficacy on entrepreneurial behavior is 0.495, with a 95% confidence interval of [0.411, 0.579], not including 0, indicating the existence of a total effect. In terms of direct effects, the effect value of selfefficacy on entrepreneurial behavior is 0.495, with a 95% confidence interval of [0.265, 0.441], not including 0, thus the direct effect is significant. The mediation effect value is 0.142, with a 95% confidence interval of [0.099, 0.185], not including 0, indicating a significant mediation effect, accounting for 28.690%. In conclusion, doubleloop learning plays a significant mediating role between selfefficacy and entrepreneurial behavior.
Mediating effect test of singleloop learning between resilience and entrepreneurial behavior
Following the aforementioned steps, regression models were constructed for the independent variable, mediating variable, and dependent variable respectively, and the regression analysis results are shown in Table 7.
Regression model analysis mediated by singleloop learning
变量 
 


 
 1.865***  1.296***  
 
 0.440***  0.473***  0.289*** 
 
 0.318***  
F  109.326***  135.318***  86.322*** 
 0.180  0.214  0.258 
 0.178  0.212  0.255 
As shown in the table above, multiple regression analysis was used to test the mediating effect of singleloop learning between resilience and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable resilience and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.440, reaching significance at the 95% confidence level, and the adjusted Rsquare of the model was 0.178. In the second step, regression analysis was performed on the independent variable and the mediating variable, with the results showing that the regression coefficient of the independent variable was 0.473, reaching significance at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable singleloop learning on the dependent variable was 0.318, reaching significance at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.289, which remained significant at the 95% confidence level, and the adjusted Rsquare increased to 0.255, indicating a stronger explanatory power of the model. Therefore, it can be concluded that singleloop learning plays a mediating role between resilience and entrepreneurial behavior.
Table 8 Bootstrap Test Results for SingleLoop Learning as a Mediator

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.289  0.045  6.399  0.000  0.200  0.378 
 0.440  0.042  10.456  0.000  0.357  0.522  
 0.151  0.024  6.244  0.000  0.107  0.200 
Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the robustness has a total effect value of 0.440 on entrepreneurial behavior, with a 95% confidence interval of [0.357, 0.522], excluding 0, indicating the presence of a total effect. In terms of direct effects, the effect value of robustness on entrepreneurial behavior is 0.440, with a 95% confidence interval of [0.200, 0.378], excluding 0, thus the direct effect is significant. The mediating effect value is 0.151, with a 95% confidence interval of [0.107, 0.200], excluding 0, indicating a significant mediating effect, accounting for 34.270%. In conclusion, singleloop learning plays a significant mediating role between robustness and entrepreneurial behavior.
Testing the mediating effect of doubleloop learning between robustness and entrepreneurial behavior.
According to the aforementioned steps, regression models were constructed for the independent variable, mediator variable, and dependent variable respectively, and the regression analysis results are shown in Table 9.
Table 9 Regression model analysis with dualloop learning as the mediator.
变量 
 


 
 1.865***  1.341***  
 
 0.440***  0.493***  0.284*** 
 
 0.315***  
F  109.326***  142.022***  86.887*** 
R^2  0.180  0.222  0.259 
 0.178  0.220  0.256 
As shown in the table above, multiple regression analysis was used to test the mediating effect of dualloop learning between resilience and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable resilience and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.440, reaching significance at the 95% confidence level, and the adjusted Rsquare of the model was 0.178. In the second step, regression analysis was conducted on the independent variable and the mediating variable, showing that the regression coefficient of the independent variable was 0.493, reaching significance at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model. The results showed that the mediating variable dualloop learning had a regression coefficient of 0.315 on the dependent variable, reaching significance at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.284, still significant at the 95% confidence level, and the adjusted Rsquare increased to 0.256, indicating a stronger explanatory power of the model. Therefore, it can be concluded that dualloop learning plays a mediating role between resilience and entrepreneurial behavior.
Bootstrap test results mediated by dualloop learning

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.284  0.045  6.263  0.000  0.195  0.373 
 0.440  0.042  10.456  0.000  0.357  0.522  
 0.156  0.025  6.275  0.000  0.106  0.205 
Further using the Bootstrap method to examine the 95% confidence interval of the mediation effect, from the table above, it can be seen that the total effect value of resilience on entrepreneurial behavior is 0.440, with a 95% confidence interval of [0.357, 0.522], not including 0, indicating the existence of a total effect. In terms of direct effects, the effect value of resilience on entrepreneurial behavior is 0.440, with a 95% confidence interval of [0.195, 0.373], not including 0, thus the direct effect is significant. The mediation effect value is 0.156, with a 95% confidence interval of [0.106, 0.205], not including 0, indicating a significant mediation effect, accounting for 35.389%. In conclusion, doubleloop learning plays a significant mediating role between resilience and entrepreneurial behavior.
Testing the mediating effect of singleloop learning between optimism and entrepreneurial behavior
According to the aforementioned steps, regression models were constructed for the independent variable, mediator variable, and dependent variable respectively, and the regression analysis results are shown in Table 11.
Table 11 Regression model analysis with singleloop learning as the mediator.
变量 
 


 
 1.710***  1.048***  
 
 0.476***  0.386***  0.352*** 
 
 0.323***  
F  128.503***  79.815***  102.959*** 
R^2  0.205  0.138  0.293 
 0.204  0.136  0.290 
As shown in the table above, multiple regression analysis was used to test the mediating effect of singleloop learning between optimism and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable optimism and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.476, which was significant at the 95% confidence level, and the adjusted Rsquared of the model was 0.204. In the second step, regression analysis was conducted on the independent variable and the mediating variable, and the results showed that the regression coefficient of the independent variable was 0.386, which was significant at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable singleloop learning on the dependent variable was 0.323, which was significant at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.352, which remained significant at the 95% confidence level, and the adjusted Rsquared increased to 0.290, indicating that the model's explanatory power was stronger. Therefore, it can be concluded that singleloop learning plays a mediating role between optimism and entrepreneurial behavior.
Bootstrap test results mediated by singleloop learning

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.352  0.043  8.227  0.000  0.268  0.436 
 0.476  0.042  11.336  0.000  0.394  0.559  
 0.125  0.019  6.480  0.000  0.091  0.164 
Further using the Bootstrap method to examine the 95% confidence interval of the mediation effect, from the table above, it can be seen that the total effect value of optimism on entrepreneurial behavior is 0.476, with a 95% confidence interval of [0.394, 0.559], not including 0, indicating the existence of a total effect. In terms of direct effect, the effect value of optimism on entrepreneurial behavior is 0.476, with a 95% confidence interval of [0.268, 0.436], not including 0, thus the direct effect is significant. The mediation effect value is 0.125, with a 95% confidence interval of the mediation effect being [0.091, 0.164], not including 0, indicating a significant mediation effect, with a mediation proportion of 26.198%. In conclusion, singleloop learning plays a significant mediating role between optimism and entrepreneurial behavior.
Testing the mediating effect of doubleloop learning between optimism and entrepreneurial behavior
Following the aforementioned steps, regression models were constructed for the independent variable, mediating variable, and dependent variable, and the regression analysis results are shown in Table 13.
Regression model analysis mediated by dualloop learning
变量 
 


 
 1.710***  1.096***  
 
 0.476***  0.405***  0.347*** 
 
 0.319***  
F  128.503***  84.453***  103.400*** 
R^2  0.205  0.145  0.294 
 0.204  0.143  0.291 
As shown in the table above, multiple regression analysis was used to test the mediating effect of dualloop learning between optimism and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable optimism and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.476, which was significant at the 95% confidence level, and the adjusted Rsquare of the model was 0.204. In the second step, regression analysis was performed on the independent variable and the mediating variable, with the results showing that the regression coefficient of the independent variable was 0.405, significant at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model. The results showed that the regression coefficient of the mediating variable dualloop learning on the dependent variable was 0.319, significant at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.347, still significant at the 95% confidence level, and the adjusted Rsquare increased to 0.291, indicating a stronger explanatory power of the model. It can be concluded that dualloop learning plays a mediating role between optimism and entrepreneurial behavior.
Table 14 Bootstrap Test Results for DualLoop Learning as a Mediator

 Effect  S.E.  t  p 
 
LLCI  ULCI  

 0.347  0.043  8.101  0.000  0.263  0.432 
 0.476  0.042  11.336  0.000  0.394  0.559  
 0.129  0.022  5.861  0.000  0.090  0.171 
Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the optimistic total effect on entrepreneurial behavior is 0.476, with a 95% confidence interval of [0.394, 0.559], which does not include 0, indicating the presence of a total effect. In terms of direct effects, the effect of optimism on entrepreneurial behavior is 0.476, with a 95% confidence interval of [0.263, 0.432], which does not include 0, thus the direct effect is significant. The mediating effect value is 0.129, with a 95% confidence interval of [0.090, 0.171], which does not include 0, indicating a significant mediating effect, accounting for 27.084%. In conclusion, doubleloop learning plays a significant mediating role between optimism and entrepreneurial behavior.
Testing the mediating effect of singleloop learning between hope and entrepreneurial behavior.
According to the aforementioned steps, regression models are constructed for the independent variable, mediator variable, and dependent variable respectively, and the regression analysis results are shown in Table 15.
Table 15 Regression model analysis with singleloop learning as the mediator.
变量 
 


 
 1.787***  1.127***  
 
 0.460***  0.398***  0.330*** 
 
 0.326***  
F  119.180***  86.626***  97.330*** 
 0.193  0.148  0.281 
 0.191  0.146  0.279 
As shown in the table above, multiple regression analysis was used to test the mediating effect of singleloop learning between hope and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable hope and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.460, which was significant at the 95% confidence level, and the adjusted Rsquared of the model was 0.191. In the second step, regression analysis was conducted on the independent variable and the mediating variable, and the results showed that the regression coefficient of the independent variable was 0.398, which was significant at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable singleloop learning on the dependent variable was 0.326, which was significant at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.330, which remained significant at the 95% confidence level, and the adjusted Rsquared increased to 0.279, indicating a stronger explanatory power of the model. Therefore, it can be concluded that singleloop learning plays a mediating role between hope and entrepreneurial behavior.
Bootstrap test results mediated by singleloop learning

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.330  0.043  7.658  0.000  0.245  0.415 
 0.460  0.042  10.917  0.000  0.377  0.542  
 0.130  0.022  5.936  0.000  0.089  0.176 
Further using the Bootstrap method to examine the 95% confidence interval of the mediation effect, from the table above, it can be seen that the total effect value on entrepreneurial behavior is 0.460, with a 95% confidence interval of [0.377, 0.542], not including 0, indicating the existence of a total effect. In terms of direct effect, the effect value on entrepreneurial behavior is 0.460, with a 95% confidence interval of [0.245, 0.415], not including 0, thus the direct effect is significant. The mediation effect value is 0.130, with a 95% confidence interval of [0.089, 0.176], not including 0, indicating a significant mediation effect, accounting for 28.208%. In conclusion, singleloop learning plays a significant mediating role between hope and entrepreneurial behavior.
Testing the mediating effect of dualloop learning between hope and entrepreneurial behavior
Following the aforementioned steps, regression models were constructed for the independent variable, mediator variable, and dependent variable, and the regression analysis results are shown in Table 17.
Regression model analysis with dualloop learning as mediator
变量 
 


 
 1.787***  1.157***  
 
 0.460***  0.405***  0.328*** 
 
 0.324***  
F  119.180***  85.628***  98.903*** 
R^2  0.193  0.147  0.285 
 0.191  0.145  0.282 
As shown in the table above, multiple regression analysis was used to test the mediating effect of doubleloop learning between hope and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable hope and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.460, reaching significance at the 95% confidence level, and the adjusted Rsquared of the model was 0.191. In the second step, regression analysis was performed on the independent variable and the mediating variable, with the results showing that the regression coefficient of the independent variable was 0.405, reaching significance at the 95% confidence level. In the third step, both the independent variable, mediating variable, and dependent variable were included in the regression model. The results showed that the regression coefficient of the mediating variable doubleloop learning on the dependent variable was 0.324, reaching significance at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.328, which remained significant at the 95% confidence level, and the adjusted Rsquared increased to 0.282, indicating a stronger explanatory power of the model. Therefore, it can be concluded that doubleloop learning plays a mediating role between hope and entrepreneurial behavior.
Table 18 Bootstrap Test Results for DoubleLoop Learning as a Mediator

 Effect  S.E.  t  p 
 
LLCI  ULCI  

 0.328  0.043  7.644  0.000  0.244  0.413 
 0.460  0.042  10.917  0.000  0.377  0.542  
 0.131  0.019  6.807  0.000  0.093  0.167 
Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the total effect value on entrepreneurial behavior is 0.460. The 95% confidence interval is [0.377, 0.542], which does not include 0, indicating the presence of a total effect. In terms of direct effect, the effect value on entrepreneurial behavior is 0.460, with a 95% confidence interval of [0.244, 0.413], which does not include 0, thus the direct effect is significant. The mediating effect value is 0.131, with a 95% confidence interval of [0.093, 0.167], not including 0, indicating a significant mediating effect, accounting for 28.558%. In conclusion, doubleloop learning plays a significant mediating role between hope and entrepreneurial behavior.
Testing the mediating effect of entrepreneurial intention between psychological capital and entrepreneurial behavior.
According to the aforementioned steps, regression models are constructed for the independent variable, mediator variable, and dependent variable respectively, and the regression analysis results are shown in Table 19.
Table 19 Regression model analysis with entrepreneurial intention as mediator
变量 
 


 
 0.522**  0.365*  
 
 0.839***  0.714***  0.720*** 
 
 0.167***  
F  273.674***  194.625***  147.822*** 
 0.355  0.281  0.373 
 0.353  0.280  0.370 
As shown in the table above, multiple regression analysis was used to test the mediating effect of entrepreneurial intention between psychological capital and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable psychological capital and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.839, reaching significance at a 95% confidence level, and the adjusted Rsquare of the model was 0.353. In the second step, regression analysis was conducted on the independent variable and the mediating variable, with the results showing that the regression coefficient of the independent variable was 0.714, reaching significance at a 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable, entrepreneurial intention, on the dependent variable was 0.167, reaching significance at a 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.720, still significant at a 95% confidence level, and the adjusted Rsquare increased to 0.370, indicating a stronger explanatory power of the model. Therefore, it can be concluded that entrepreneurial intention plays a mediating role between psychological capital and entrepreneurial behavior.
Bootstrap test results for entrepreneurial intentions as intermediaries

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.720  0.059  12.196  0.000  0.604  0.836 
 0.839  0.051  16.543  0.000  0.739  0.939  
 0.119  0.032  3.745  0.000  0.059  0.180 
Further using the Bootstrap method to examine the 95% confidence interval of the mediation effect, it can be seen from the table above that the total effect value of psychological capital on entrepreneurial behavior is 0.839, with a 95% confidence interval of [0.739, 0.939], not including 0, indicating the existence of a total effect. In terms of direct effect, the effect value of psychological capital on entrepreneurial behavior is 0.839, with a 95% confidence interval of [0.604, 0.836], not including 0, thus the direct effect is significant. The mediation effect value is 0.119, with a 95% confidence interval of [0.059, 0.180], not including 0, indicating a significant mediation effect, accounting for 14.214%. In conclusion, entrepreneurial intention plays a significant mediating role between psychological capital and entrepreneurial behavior.
Examination of the mediating effect of entrepreneurial intention on the relationship between selfefficacy and entrepreneurial behavior
Following the aforementioned steps, regression models were constructed for the independent variable, mediating variable, and dependent variable, and the regression analysis results are shown in Table 21.
Analysis of the return model with entrepreneurial intention as an intermediary
变量 
 


 
 1.671***  1.071***  
 
 0.495***  0.409***  0.370*** 
 
 0.306***  
F  135.164***  94.695***  99.243*** 
R^2  0.213  0.160  0.285 
 0.212  0.158  0.283 
As shown in the table above, multiple regression analysis was used to test the mediating effect of entrepreneurial intention between selfefficacy and entrepreneurial behavior. In the first step, a regression analysis was conducted on the independent variable selfefficacy and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.495, which was significant at the 95% confidence level, and the adjusted Rsquared of the model was 0.212. In the second step, a regression analysis was conducted on the independent variable and the mediating variable, and the results showed that the regression coefficient of the independent variable was 0.409, which was significant at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable entrepreneurial intention on the dependent variable was 0.306, which was significant at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.370, and it remained significant at the 95% confidence level. The adjusted Rsquared increased to 0.283, indicating a stronger explanatory power of the model. Therefore, it can be concluded that entrepreneurial intention plays a mediating role between selfefficacy and entrepreneurial behavior.
Table 22 Bootstrap Test Results for Entrepreneurial Intention as a Mediator

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.370  0.044  8.342  0.000  0.283  0.457 
 0.495  0.043  11.626  0.000  0.411  0.579  
 0.125  0.022  5.714  0.000  0.083  0.172 
Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the total effect of selfefficacy on entrepreneurial behavior is 0.495. The 95% confidence interval is [0.411, 0.579], which does not include 0, indicating the presence of a total effect. In terms of direct effects, the effect of selfefficacy on entrepreneurial behavior is 0.495, with a 95% confidence interval of [0.283, 0.457], which does not include 0, thus the direct effect is significant. The mediating effect value is 0.125, with a 95% confidence interval of [0.083, 0.172], which does not include 0, indicating a significant mediating effect, accounting for 25.310%. In conclusion, entrepreneurial intention plays a significant mediating role between selfefficacy and entrepreneurial behavior.
Testing the mediating effect of entrepreneurial intention between resilience and entrepreneurial behavior.
According to the aforementioned steps, regression models were constructed for the independent variable, mediator variable, and dependent variable respectively, and the regression analysis results are shown in Table 23.
Table 23 Regression model analysis with entrepreneurial intention as mediator.
变量 
 


 
 1.865***  1.227***  
 
 0.440***  0.406***  0.309*** 
 
 0.323***  
F  109.326***  100.604***  86.918*** 
R^2  0.180  0.168  0.259 
 0.178  0.166  0.256 
As shown in the table above, multiple regression analysis was used to test the mediating effect of entrepreneurial intention between resilience and entrepreneurial behavior. In the first step, a regression analysis was conducted on the independent variable resilience and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.440, which was significant at the 95% confidence level, and the adjusted Rsquared of the model was 0.178. In the second step, a regression analysis was conducted on the independent variable and the mediating variable, and the results showed that the regression coefficient of the independent variable was 0.406, which was significant at the 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable, entrepreneurial intention, on the dependent variable was 0.323, which was significant at the 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.309, which remained significant at the 95% confidence level, and the adjusted Rsquared increased to 0.256, indicating a stronger explanatory power of the model. Therefore, it can be concluded that entrepreneurial intention plays a mediating role between resilience and entrepreneurial behavior.
Bootstrap test results for entrepreneurial intentions as intermediaries

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.309  0.044  7.036  0.000  0.222  0.395 
 0.440  0.042  10.456  0.000  0.357  0.522  
 0.131  0.021  6.171  0.000  0.090  0.170 
Further using the Bootstrap method to examine the 95% confidence interval of the mediation effect, from the table above, it can be seen that the total effect value of resilience on entrepreneurial behavior is 0.440, with a 95% confidence interval of [0.357, 0.522], not including 0, indicating the existence of a total effect. In terms of direct effects, the effect value of resilience on entrepreneurial behavior is 0.440, with a 95% confidence interval of [0.222, 0.395], not including 0, therefore the direct effect is significant. The mediation effect value is 0.131, with a 95% confidence interval of [0.090, 0.170], not including 0, indicating a significant mediation effect, accounting for 29.798%. In conclusion, entrepreneurial intention plays a significant mediating role between resilience and entrepreneurial behavior.
Testing the mediating effect of entrepreneurial intention between optimism and entrepreneurial behavior
Following the aforementioned steps, regression models were constructed for the independent variable, mediating variable, and dependent variable respectively, and the regression analysis results are shown in Table 25.
Analysis of the return model with entrepreneurial intention as an intermediary in Table 25
变量 
 


 
 1.710***  1.039***  
 
 0.476***  0.364***  0.360*** 
 
 0.321***  
F  128.503***  75.212***  99.943*** 
R^2  0.205  0.131  0.287 
 0.204  0.129  0.284 
As shown in the table above, multiple regression analysis was used to test the mediating effect of entrepreneurial intention between optimism and entrepreneurial behavior. In the first step, a regression analysis was conducted on the independent variable optimism and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.476, reaching significance at a 95% confidence level, and the adjusted Rsquared of the model was 0.204. In the second step, a regression analysis was conducted on the independent variable and the mediating variable, with the results showing that the regression coefficient of the independent variable was 0.364, reaching significance at a 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model. The results showed that the regression coefficient of the mediating variable entrepreneurial intention on the dependent variable was 0.321, reaching significance at a 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.360, still significant at a 95% confidence level, and the adjusted Rsquared increased to 0.284, indicating a stronger explanatory power of the model. It can be inferred that entrepreneurial intention plays a mediating role between optimism and entrepreneurial behavior.
Table 26 Bootstrap Test Results for Entrepreneurial Intention as a Mediator

 Effect  S.E.  t  p 
 
LLCI  ULCI  

 0.360  0.043  8.410  0.000  0.276  0.444 
 0.476  0.042  11.336  0.000  0.394  0.559  
 0.117  0.020  5.775  0.000  0.081  0.160 
Further using the Bootstrap method to examine the 95% confidence interval of the mediating effect, from the table above, it can be seen that the total effect value of optimism on entrepreneurial behavior is 0.476, with a 95% confidence interval of [0.394, 0.559], not including 0, indicating the existence of a total effect. In terms of direct effect, the effect value of optimism on entrepreneurial behavior is 0.476, with a 95% confidence interval of [0.276, 0.444], not including 0, thus the direct effect is significant. The mediating effect value is 0.117, with a 95% confidence interval of [0.081, 0.160], not including 0, indicating a significant mediating effect, accounting for 24.529%. In conclusion, entrepreneurial intention plays a significant mediating role between optimism and entrepreneurial behavior.
Testing the mediating effect between entrepreneurial intention and entrepreneurial behavior
Following the aforementioned steps, regression models were constructed for the independent variable, mediator variable, and dependent variable, and the regression analysis results are shown in Table 27.
Analysis of the return model with entrepreneurial intention as an intermediary in Table 27
变量 
 


 
 1.787***  1.167***  
 
 0.460***  0.407***  0.331*** 
 
 0.316***  
F  119.180***  98.827***  91.454*** 
 0.193  0.166  0.269 
 0.191  0.164  0.266 
As shown in the table above, multiple regression analysis was used to test the mediating effect of entrepreneurial intention between hope and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable hope and the dependent variable entrepreneurial behavior. The results showed that the regression coefficient of the independent variable was 0.460, reaching significance at a 95% confidence level, and the adjusted Rsquare of the model was 0.191. In the second step, regression analysis was conducted on the independent variable and the mediating variable, with the results showing that the regression coefficient of the independent variable was 0.407, reaching significance at a 95% confidence level. In the third step, the independent variable, mediating variable, and dependent variable were included in the regression model simultaneously. The results showed that the regression coefficient of the mediating variable entrepreneurial intention on the dependent variable was 0.316, reaching significance at a 95% confidence level. After introducing the mediating variable, the regression coefficient of the independent variable on the dependent variable was 0.331, still significant at a 95% confidence level, and the adjusted Rsquare increased to 0.266, indicating a stronger explanatory power of the model. It can be inferred that entrepreneurial intention plays a mediating role between hope and entrepreneurial behavior.
Table 28 Bootstrap Test Results for Entrepreneurial Intention as a Mediator

 Effect  S.E.  t  p  95% CI  
LLCI  ULCI  

 0.331  0.044  7.543  0.000  0.245  0.417 
 0.460  0.042  10.917  0.000  0.377  0.542  
 0.128  0.022  5.781  0.000  0.088  0.170 
Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the total effect value on entrepreneurial behavior is 0.460, with a 95% confidence interval of [0.377, 0.542], which does not include 0, indicating the presence of a total effect. In terms of direct effect, the effect value on entrepreneurial behavior is 0.460, with a 95% confidence interval of [0.245, 0.417], which does not include 0, thus the direct effect is significant. The mediating effect value is 0.128, with a 95% confidence interval of [0.088, 0.170], which does not include 0, indicating a significant mediating effect, accounting for 27.931%. In conclusion, entrepreneurial intention plays a significant mediating role between hope and entrepreneurial behavior.
Validity Analysis
Validity refers to effectiveness, which measures whether the comprehensive evaluation system can accurately reflect the evaluation purposes and requirements. The use of KMO value expresses the magnitude of validity. The closer KMO is to 1, the stronger the correlation between variables, indicating the presence of interactions and influences among variables, making the original variables more suitable for factor analysis. Conversely, when it approaches 0, the correlation between variables gradually weakens, reducing the possibility of factor analysis, and further studying the relationship between variables becomes less meaningful. Bartlett's sphericity test is used to test the independence of variables, with a large value. When the pvalue of Bartlett's sphericity test is less than 0.05, it indicates that the variables are correlated with each other, rejecting the null hypothesis, and the data is spherically distributed, allowing the research to continue with factor analysis.
Next, the validity of selfefficacy, resilience, optimism, hope, entrepreneurial behavior, singleloop learning, doubleloop learning, entrepreneurial intention, entrepreneurial theory education, and entrepreneurial practice education were tested. The results of singlefactor validity test are shown in Table 29:
Table 29 KMO and Bartlett Test Table
变量 
 
KMO 
 df 
 
 0.936  1899.453  21  0.000 
 0.939  2030.773  21  0.000 
 0.914  1618.629  15  0.000 
 0.914  1644.938  15  0.000 
 0.883  1290.234  10  0.000 
 0.886  1263.527  10  0.000 
 0.891  1339.406  10  0.000 
 0.884  1202.164  10  0.000 
 0.824  832.899  6  0.000 
 0.893  1455.387  10  0.000 
As shown in Table 29, the KMO of all variables is greater than 0.7, and Bartlett's sphericity test is less than 0.05, suitable for factor analysis. The overall validity test results are shown in Table 30:
Table 30 Questionnaire Overall KMO and Bartlett Test
 0.955  

 16963.595 
自由度  1485  
显著性  0.000 
From Table 30, it can be seen that the KMO of the questionnaire is greater than 0.8, and it has passed the Bartlett sphericity test at a significance level of 0.05. Therefore, the data of the questionnaire has passed the validity test, indicating that the questionnaire data used in this study can represent the variables used in the subsequent research.