*5.1. Descriptive Statistics and Basic Analysis Results*

Table 2 shows the descriptive statistics of all the variables. The maximum value of the dependent variable innovation quality is 0.625, while the minimum is 0, which reveals that the innovation quality of different enterprises varies greatly. The mean and standard deviation of innovation quality are 0.139 and 0.182, which indicates that the overall level of innovation quality of Chinese listed companies is not high. The mean of TMT functional experience heterogeneity and industrial experience heterogeneity are 0.647 and 0.560, which shows that the TMT experience in most enterprises is heterogeneous. In addition, the average, standard deviation, maximum, and minimum values of the other variables in this study are all within reasonable limits.

Table 3 reports the correlation coefficients between the variables. It can be seen that there is a significant correlation among independent variables, regulatory variables, intermediary variables, and dependent variables. Among them, FEH is significantly positively correlated with the Eiq, indicating that with the enhancement of FEH, the innovation quality also gradually improves, which is consistent with Hypothesis 1. Meanwhile, IEH is significantly negatively correlated with the Eiq, indicating that with the enhancement of IEH, the innovation quality gradually decreases, which is consistent with Hypothesis 2.

Table 4 reports the regression results of model (1). The dependent variable is Eiq, the independent variables are FEH and IEH. The control variables, year dummy variables, and industry variables are gradually added. Column (1) to (4) report the regression results with the independent variable as FEH, while column (5) to (8) reports the regression results with the independent variable as IEH. Eiq is significantly positively correlated with FEH (significant at the 1% level), indicating that FEH can improve innovation quality, which is consistent with Hypothesis 1. Eiq is significantly negatively correlated with IEH (significant at the 1% level), indicating that IEH can hinder the improvement of innovation quality, which is consistent with Hypothesis 2. The remaining variables are within the typical range and have no extreme values.



**Table 3.** Correlation analysis of each variable.


Note: t statistics in parentheses; \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

**Table 4.** Basic results analysis.



**Table 4.** *Cont.*

Note: t statistics in parentheses; \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

#### *5.2. Mediating Effect Test*

Table 5 reports the regression results for Model (2). As mentioned in the previous theoretical analysis, enterprise partner diversity (Epd) is an important mechanism by which TMT experience heterogeneity affects innovation quality. This paper examines the mediating effect of Epd based on a three-step method. The first step involves testing the relationship between TMT experience heterogeneity and innovation quality. The results of column (1) of Table 5 show that FEH has a significantly positive correlation with innovation quality, and the results of column (4) show that IEH has a significantly negative correlation with innovation quality. They are consistent with the main effect's test results, indicating that FEH can improve innovation quality, but IEH can hinder the improvement of innovation quality. The second step involves testing the regression of the intermediary variable and the independent variable. It can be seen from columns (2) and (5) in Table 5 that there is a significantly positive correlation between FEH and Epd but a negative correlation between IEH and Epd, indicating that FEH increases enterprise partner diversity, whereas IEH reduces it. The third step involves testing the dependent variable's relationship with the independent and mediating variables. The results of column (3) show a significantly positive correlation between FEH and innovation quality and a significantly positive correlation between Epd and innovation quality. The results of column (6) show a significantly negative correlation between IEH and innovation quality and a significantly positive correlation between Epd and innovation quality [80–82]. The influence of the coefficients of the independent factors on the dependent variable decreases when an intermediary variable is added. Epd partially mediates between TMT experience heterogeneity and innovation quality, according to the test of the mediation effect. The above results support Hypotheses 3 and 4.


**Table 5.** Intermediary effect test.

Note: t statistics in parentheses; \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

#### *5.3. Moderating Effect Test*

Table 6 reports the regression results for model (3). It presents the moderating effect of the technological participation of the top management team (TMTTP) on the relationship between TMT experience heterogeneity and Epd. Column (2) and (3) added FEH, IEH and their interaction terms to test Hypotheses 5 and 6, respectively. In column (2), the regression coefficients of the interaction items between FEH×TMTTP and Epd are positive at the level of 1%, indicating that TMTTP enhances the positive correlation between FEH and Epd, which supports Hypothesis 5. In column (3), the regression coefficients of the interaction items between IEH×TMTTP and Epd are positive at the level of 1%, indicating that TMTTP strengthens the negative correlation between IEH and Epd, which supports Hypothesis 6.


**Table 6.** Moderation effect test.

Note: t statistics in parentheses; \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

#### *5.4. Robustness Test*

Table 7 shows the robustness of the evaluation methods and indicators. That is, when changing certain parameters, the evaluation methods and indicators still maintain a relatively consistent and stable interpretation of the evaluation results. This research uses the method of replacing the dependent variable for the robustness test. According to the existing research, this paper chooses the ratio of the invention patents number to all patents numbers to measure innovation quality from the perspective of innovation output [83,84]. In columns (1) and (2), the regression coefficients between FEH and Eiq are positive at the level of 1%, while the regression coefficients are negative at the level of 1%. Therefore, the results of the multiple regression analysis remain unchanged after altering the measurement method of innovation quality, indicating that FEH promotes innovation quality while IEH inhibits it.


**Table 7.** Robustness Test.

Note: t statistics in parentheses; \*\* *p* < 0.05, \*\*\* *p* < 0.01.
