multicollinearity among explanatory variables of the study. The absence of complete multicollinearity among the variables is indicated by the mean VIF, which is less than 5 as provided in Table 3.

Table 4. Results of Diagnostic Tests

Source: STATA 17.0 Output, 2023.

Legend: Except for SRQ and CSIZE where p-value>0.05

Additionally, post-estimation tests for data normality (Skewness/Kurtosis and Shapiro-Wilk), the Breusch and Pagan Lagrange Multiplier, contemporaneous correlation, panel serial correlation, and group-wise heteroscedasticity were performed. Table IV presents an overview of the findings from these testing.

Table 5. Regression Results

Source: STATA 17.0 Output, 2023.

Legend: Coefficients: ** P-value <.01; * P-value <.05.

The skewness/kurtosis and Shapiro-Wilk tests are run on the FE regression residuals to determine whether or not the study’s data is normally distributed. Based on the findings in Table IV, each joint adjusted Chi2 has a skewness/kurtosis alpha value that is less than 0.05 (with the exception of SRQ and CSIZE), suggesting that the majority of the variables provide a z-statistic that is larger than 1.96 as a whole. This suggests that the study’s data is biased (not symmetrical). Similarly, the Shapiro-Wilk z-scores fall outside the bracket of +/-1.96, all of which are significant at p-values less than 0.05, which indicates that data for the variables is not normally distributed.

The presence of serial correlation in the panels makes the idiosyncratic errors terms of the coefficients to become smaller than their actual state, while the R2 is higher (Wooldridge, 2002). Based on the evidence in Table IV, which shows an F-value of 16.754 and a p-value of 0.0027, which suggests the presence of serial correlation in the panel data’s structure, the null hypothesis of “no serial correlation” cannot be supported.

In addition, the study conducts Pesaran’s cross-sectional test of independence to ascertain the correlation of residuals or otherwise across the sampled companies. This is due to the presence of contemporaneous correlations also known as cross-sectional dependence among residuals of the models amounts to bias the estimates. The study’s null hypothesis is that there is no correlation between residuals at a 5-percent level of significance. For the test with a p-value of 0.1296, Table IV shows the Chi2 to be 1.516. With this supporting the null hypothesis, the study comes to the conclusion that the model residuals are not associated.

Furthermore, the study conducts the group-wise heteroscedasticity using residuals of the generalized least square (GLS) regression and based on the modified Wald statistic. This is because, based on the assumption of homoscedasticity across residuals, the presence of heteroscedasticity leads to bias among standard errors of the estimates. The regression models’ residuals being homoscedastic at a 5-percent level of significance is the null hypothesis for this test. Based on the significant Chi2 of 1875.31, with a p-value of 0.000 as presented in Table IV, the study fails to support the null hypothesis but concludes that residuals of the models are heteroscedastic.

As mentioned above, the Shapiro-Wilk and skewness/kurtosis test findings show that outliers exist within the conventional residuals of the FE regression. Data transformation, however, is not an option because the study’s explanatory variables, which are based on the VIF, do not exhibit multicollinearity. In the same vein, the presence of contemporaneous correlation, panel serial correlation and groupwise heteroskedasticity in the panel adversely affect parameter estimates and bias standard errors (Cameron, 2009). Therefore, to correct these abnormalities and ensure the estimation of parameter coefficients are consistent, efficient and standard error bias-free, the study adopts Panel Corrected Standard Error (PCSE) estimate as suggested in Beck and Katz (1996, 1995).

SRQi,t = β0 + β1SCSIZEi,t + β2SCINDEPi,t + β3SCDIVi,t + β4SCDILi,t + β5CSIZEi,t + εi,t  (1)

Table 6. Test of Research Hypotheses

Source: STATA 17.0 Output, 2023.

Legend: * P-value <.01; * P-value <.05.

Table 5 contains the z-scores, p-values and PCSEs of each predictor variable on the dependent variables. It is based on the model restated thus:

The PCSE result for the GLS model shows an R2 of 10.84 percent compared to the total R2 of 30.12 percent for the FE model, showing a significant fall of 19.28 percent. With a significant p-value of 0.000 and a Wald Chi2 of 43.11, Table V shows that the model is fit for the data and that the combined contribution of businesses from the two nations strongly explains differences in SRQ.

Regarding the attributes of sustainability committees, SCSIZE and SCDIV have an inverse relationship with SRQ, while SCINDEP, SCDIL and CSIZE demonstrate a positive association with SRQ; however, only SCDIL is insignificant. At the p-value larger than 0.05, SCSIZE reveals a specific negative and insignificant impact on SRQ. Kolk and Pinkse (2010) and Lodhia et al. (2012) reported a positive association between the two variables, which is in contrast to this finding. In contrast, and in line with Cho et al., the effect of SCINDEP on SRQ is positive and significant at the p-value less than 0.05. (2020). Similar to this, SCDIV shows a detrimental but important effect (p-value 0.05). This viewpoint is in agreement with Orlitzky et al. (2017), who discovered a substantial relationship between a company’s diversity and reporting quality. However, the impact of SCDIL on SRQ is positive but insignificant in contradiction with the Global Reporting Initiative (2015). Thus, the general results in the statistical model imply that increasing the number of the committee’s independent directors, the frequency of annual meetings, as well as the total assets of the company will favorably improve sustainability reporting quality, while a rise in the number of committee members along with improvement in the ratio of female directors to overall directors would rather affect SRQ adversely.

These findings in respect of SCINDEP and SCDIV support the position of Cho et al. (2020) that sustainability committee attributes are SRQ-relevant. Furthermore, the stakeholder theory is consistent with these findings in that revealing