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:

  (1)

  (2)

  (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

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 R-square 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 R-square 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

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 self-efficacy on entrepreneurial behavior in single-loop 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 single-loop learning as mediator

As shown in the table above, multiple regression analysis was used to test the mediating effect of single-loop learning on the relationship between self-efficacy and entrepreneurial behavior. In the first step, a regression analysis was conducted between the independent variable self-efficacy 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 R-square 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 single-loop 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 R-square increased to 0.288, indicating a stronger explanatory power of the model. Therefore, it can be concluded that single-loop learning plays a mediating role between self-efficacy and entrepreneurial behavior.

Table 4 Bootstrap Test Results for Single-Loop Learning as a Mediator

Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the total effect of self-efficacy 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 self-efficacy 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, single-loop learning plays a significant mediating role between self-efficacy and entrepreneurial behavior.

Testing the mediating effect of double-loop learning between self-efficacy 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 dual-loop learning as the mediator.

As shown in the table above, multiple regression analysis was used to test the mediating effect of dual-loop learning between self-efficacy and entrepreneurial behavior. In the first step, regression analysis was conducted on the independent variable self-efficacy 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 R-square 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 dual-loop 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 R-square increased to 0.286, indicating a stronger explanatory power of the model. Therefore, it can be concluded that dual-loop learning plays a mediating role between self-efficacy and entrepreneurial behavior.

Bootstrap test results with double-loop learning as mediator

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 self-efficacy 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 self-efficacy 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, double-loop learning plays a significant mediating role between self-efficacy and entrepreneurial behavior.

Mediating effect test of single-loop 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 single-loop learning

As shown in the table above, multiple regression analysis was used to test the mediating effect of single-loop 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 R-square 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 single-loop 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 R-square increased to 0.255, indicating a stronger explanatory power of the model. Therefore, it can be concluded that single-loop learning plays a mediating role between resilience and entrepreneurial behavior.

Table 8 Bootstrap Test Results for Single-Loop Learning as a Mediator

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, single-loop learning plays a significant mediating role between robustness and entrepreneurial behavior.

Testing the mediating effect of double-loop 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 dual-loop learning as the mediator.

As shown in the table above, multiple regression analysis was used to test the mediating effect of dual-loop 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 R-square 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 dual-loop 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 R-square increased to 0.256, indicating a stronger explanatory power of the model. Therefore, it can be concluded that dual-loop learning plays a mediating role between resilience and entrepreneurial behavior.

Bootstrap test results mediated by dual-loop learning

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, double-loop learning plays a significant mediating role between resilience and entrepreneurial behavior.

Testing the mediating effect of single-loop 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 single-loop learning as the mediator.

As shown in the table above, multiple regression analysis was used to test the mediating effect of single-loop 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 R-squared 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 single-loop 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 R-squared increased to 0.290, indicating that the model's explanatory power was stronger. Therefore, it can be concluded that single-loop learning plays a mediating role between optimism and entrepreneurial behavior.

Bootstrap test results mediated by single-loop learning

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, single-loop learning plays a significant mediating role between optimism and entrepreneurial behavior.

Testing the mediating effect of double-loop 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 dual-loop learning

As shown in the table above, multiple regression analysis was used to test the mediating effect of dual-loop 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 R-square 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 dual-loop 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 R-square increased to 0.291, indicating a stronger explanatory power of the model. It can be concluded that dual-loop learning plays a mediating role between optimism and entrepreneurial behavior.

Table 14 Bootstrap Test Results for Dual-Loop Learning as a Mediator

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, double-loop learning plays a significant mediating role between optimism and entrepreneurial behavior.

Testing the mediating effect of single-loop 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 single-loop learning as the mediator.

As shown in the table above, multiple regression analysis was used to test the mediating effect of single-loop 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 R-squared 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 single-loop 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 R-squared increased to 0.279, indicating a stronger explanatory power of the model. Therefore, it can be concluded that single-loop learning plays a mediating role between hope and entrepreneurial behavior.

Bootstrap test results mediated by single-loop learning

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, single-loop learning plays a significant mediating role between hope and entrepreneurial behavior.

Testing the mediating effect of dual-loop 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 dual-loop learning as mediator

As shown in the table above, multiple regression analysis was used to test the mediating effect of double-loop 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 R-squared 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 double-loop 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 R-squared increased to 0.282, indicating a stronger explanatory power of the model. Therefore, it can be concluded that double-loop learning plays a mediating role between hope and entrepreneurial behavior.

Table 18 Bootstrap Test Results for Double-Loop Learning as a Mediator

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, double-loop 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

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 R-square 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 R-square 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

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 self-efficacy 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

As shown in the table above, multiple regression analysis was used to test the mediating effect of entrepreneurial intention between self-efficacy and entrepreneurial behavior. In the first step, a regression analysis was conducted on the independent variable self-efficacy 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 R-squared 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 R-squared 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 self-efficacy and entrepreneurial behavior.

Table 22 Bootstrap Test Results for Entrepreneurial Intention as a Mediator

Further use of the Bootstrap method to examine the 95% confidence interval of the mediating effect reveals that the total effect of self-efficacy 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 self-efficacy 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 self-efficacy 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.

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 R-squared 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 R-squared 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

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

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 R-squared 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 R-squared 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

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

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 R-square 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 R-square 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

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 p-value 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 self-efficacy, resilience, optimism, hope, entrepreneurial behavior, single-loop learning, double-loop learning, entrepreneurial intention, entrepreneurial theory education, and entrepreneurial practice education were tested. The results of single-factor validity test are shown in Table 29:

Table 29 KMO and Bartlett Test Table

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

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.