Редакция журнала «Позитивные изменения»
Позитивные изменения. Том 3, № 4 (2023). Positive changes. Volume 3, Issue 4(2023)
Table 1. The general matrix of life
Source: Field Survey, 2023.
Data obtained is analyzed via the multiple regression technique. The model specified for the study is stated as follows:
SRQi,t = β0 + β1SCSIZEi,t + β2SCINDEPi,t + β3SCDIVi,t + β4SCDILi,t + β5FSIZEi,t + εi,t (1)
where:
SRQ = Sustainability reporting quality;
SCSIZE = Sustainability committee size;
SCINDEP = Sustainability committee independence;
SCDIV = Sustainability committee diversity;
SCDIL = Sustainability committee diligence; CSIZE = Size of total assets;
ε = Error term;
β0 = Constant/intercept;
β1 – β5 = Coefficients of explanatory variables; and
i,t = company i for time period t.
Furthermore, the a priori expectation of the study is that sustainability committee size, independence, diversity and diligence positively impact sustainability reporting quality of the sampled banks. This is expressed as SCSIZE > 0; SCINDEP > 0; SCDIV > 0; and SCDIL > 0.
RESULTS AND DISCUSSION Descriptive and correlational statistics are provided in Tables 2 and 3 respectively for the variables being examined in this study: SRQ (N, mil.), SCSIZE (N, mil.), SCINDEP (mil.), SCDIV (mil.), SCDIL (mil.) and CSIZE (mil.)
Table 2 presents the minimum and maximum values of the indicator for sustainability reporting quality (SRQ), which are 0.264 and 0.868, respectively, and shows that the data for SRQ of sampled companies are narrowly dispersed from the mean of 0.601, given the standard deviation of 0.144.
On the other hand, the average sustainability committee size (SCSIZE) for the independent variables is 1.78, with a standard deviation of 2.64, indicating that the data is widely dispersed from the mean, as seen by the minimum and maximum values of 0 and 9 respectively. Given that the lowest and maximum values are 0 and 1, SCINDEP also shows a mean of 0.205 and a standard deviation of 0.325, showing a modest departure from the mean value. This means that even though at least one of the sampled companies succeeded in disclosing sustainability committee director’s independence to the fullest extent possible, the quality of disclosure among the sampled banks in both Nigeria and South Africa does not have significant variations because the majority of the companies only disclose a small portion of the information that is required to be disclosed.
The same is true for SCDIV data, which shows an average disclosure of 7.5 percent and a standard deviation of 0.14, both of which indicate that the data is not significantly out of the range of the mean value as shown by the lowest and maximum values of 0 and 0.5, respectively. Although the minimum number of 0 suggests no disclosure at all, this shows that one business of the companies studied achieved a 50 % disclosure of SCDIV. Similarly, the data for SCDIL has a mean value of 1 (which represents disclosure of 100 % of annual board meeting frequency information disclosable) and a standard deviation of 1.54, which suggests that the data for SCDIL is not significantly scattered from its mean value. It indicates that at least one bank from each of the two countries under review reports having attended no sustainability committee meetings during any of the research years (minimum value of 0), with the highest number of meetings per year being five (maximum value of 5).
Table 2. Results of Descriptive Statistics of Model Variables
Source: STATA 17.0 Output, 2023.
Table 3. Correlation Matrix of SRQ and Explanatory Variables
Source: STATA 17.0 Output, 2023.
Legend: P-value < 0.05
In addition, CSIZE, a control variable, has a mean value of 8.585 and a standard deviation of 0.765 when calculated using the natural logarithm of the sampled banks’ total assets. As seen by the minimum and maximum CSIZE values of 6.64 and 9.75, respectively, this demonstrates that the data for CSIZE is widely scattered from its mean.
High-quality sustainability reporting aids investors in making informed decisions about their investments’ longterm sustainability.
According to Table 3, the coefficients for SRQ’s positive relationships with SCSIZE, SCINDEP, SCDIL, and CSIZE are 0.147, 0.344, 0223, and 0.472, respectively; at the same time, SCDIV has a negative value of -0.007. However, only the associations with SCINDEP, SCDIL and CSIZE are significant. In addition, all the explanatory and control variables have moderate-to-strong positive and significant association with each other, except for the two positive associations between CSIZE/SCSIZE and CSIZE/SCDIV (0.177 and 0.023 respectively) which are not significant. These coefficients indicate that improvements in sustainability committee attributes are positively correlated to each other. Additionally, the control variable CSIZE exhibits a positive correlation with every research variable, with coefficients ranging from a low of 0.0227 (SCDIV) to a high of 0.4715 (SRQ). This suggests that as a result of the increase in asset size for the companies, the quality of their sustainability reporting also rises.
The variance inflation factor (VIF) for multicollinearity is used to further confirm that there is no multicollinearity among the explanatory variables. According to Murray, Nguyen, Lee, Remmenga and Smith (2012), continuous variables calculated over time (timevariant) are bound to have some elements of multicollinearity. Thus, the use of VIF measures the extent to which explanatory variables explain themselves in the models. Based on the position of Cohen, Cohen, West and Aiken (2013) and Akinwande, Dikko and Samson (2015), VIFs greater than 5 with the tolerance levels drawing closer to 0 show evidence of high multicollinearity among explanatory variables. The 8th column in Table 3 presents the individual VIFs of the explanatory variables with the lowest at 1.36 (CSIZE) and the highest at 7.86 (SCDIL). This implies that there is no perfect 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 sustainability engagements communicates to stakeholders that an organization values social justice, the environment, and the economy. This facilitates value maximization for relevant stakeholders in an organization[142].
Table 6 summarizes the results of the study’s assessments of direct relationships. The Table lists the type of regression (positive or negative), its significance (significant or insignificant), and its conclusion (indicating null or not) for the research.
This study investigates five (5) research hypotheses to assess the influence of the sustainability reporting committee on SRQ. The involvement of the board in SRQ is predicted by research hypothesis 1–4. The first study hypothesis examines the connection between SCSIZE and SRQ. According to the findings, there is a weak and negative correlation between board size and SRQ. H0(1) is therefore accepted. The results are at odds with earlier studies in the area of sustainability reporting (Chau & Gray, 2010; Shamil et al., 2014). This demonstrates that SRQ may be raised by using a bigger board. This conclusion is consistent with that of Carels et al. (2013) and De Villiers and Sharma (2017), who found that powerful boards may persuade management to provide appropriate sustainability reporting data.
The relationship between sustainability committee independence and SRQ is examined in the second hypothesis. The findings show a robust and beneficial association between the two, leading to the rejection of H0(2). Our findings are in line with those of the studies conducted by Shamil et al. (2014), Kaur and Lodhia (2014) and Garca-Sanchez et al. (2019). It implies that independent sustainability committees positively influence sustainability reporting quality, indicating the need for further research on their effectiveness in Nigerian and South African corporate reporting contexts.
The relationship between sustainability committee’s gender diversity on the board and SRQ is the subject of the third study hypothesis. The results demonstrate a significant and unfavorable correlation between SRQ and gender diversity. This finding contradicts earlier studies (Adams & McNicholas, 2007; Faisal et al., 2012; Cebrian et al., 2013). According to Bakar et al. (2019), female directors have a detrimental effect on corporate reporting procedures. Therefore, the study fails to support the third hypothesis of the study (H0(3)).
The fourth research hypothesis predicts how the diligence of sustainability committee and SRQ interact. The results show that sustainability committee’s diligence has a beneficial but insignificant effect on SRQ. This suggests that the committee’s meeting frequency is crucial, although holding meetings all year long does not raise the standard of sustainability reports. These results support the research of Arumona et al. (2019), which led to the acceptance of H0(4).
Furthermore, the study analyzed sustainability disclosures in Nigerian and South African banks using an independent t-test/two-sample t test. Results showed that South African banks have higher levels of disclosure than Nigerian banks, so the hypothesis H0(5) cannot be supported.
The Two-sample t-Test with equal variances showed t-statistics and the probability values of SRQ, SCSIZE, SCINDEP, SCDIV, SCDIL and CSIZE respectively (-5.029(0.000); -2.819(0.0058), – 5.995(0.000), – 2.289(0.0243), – 4.367(0.000) and -12.082(0.000); p-value=0.000<0.05; 98 d.o.f.) for respective sustainability committee disclosures as shown in Table 7. This suggests that both at equal and unequal variance levels, the difference in the mean values of sustainable committee attributes, CSIZE and SRQ dimensions of both countries do not vary substantially at 1 % level of significance. Thus, the results of the analyses support the null hypothesis of no significant differences in sustainability reporting between South African and Nigerian banks.
Table 7. Summary of 2-sample t-test
* “Ha” is the abbreviation or short form for “alternative hypothesis”.
Source: STATA 17.0 Output, 2023.
CONCLUSION AND RECOMMENDATIONS
The study has analyzed the impact of sustainability committee attributes on sustainability reporting quality (SRQ) of banks in Nigeria and South Africa. The GRI G4 sustainability reporting guideline’s ESG disclosure index was used to calculate SRQ. The highest score was achieved when sustainability reporting was independently verified by the big four or other audit firms.
The research aims to understand which attributes have the greatest influence on SRQ. The study reveals that the Sustainability Committee had a significant impact on SRQ in Nigerian banks from 2012 to 2022. Each committee attribute has positive correlation/ significant extent, positive correlation/ insignificant extent, negative correlation/ significant extent, or negative correlation/ insignificant extent effect on SRQ, respectively.
The study has also revealed that the importance of the sustainability committee in improving sustainability reporting quality is similar among South African banks.
The study suggests that increased SRQ levels are primarily influenced by the independence, diligence, and size of sustainability committees. It recommends dedicated committee with frequent meetings and expertise in sustainability-related duties, as well as unbiased external assurance to enhance corporate sustainability reporting quality. This suggests exploring the applicability of assurance and sustainability committees in different regulatory settings, comparative analysis of corporate governance and sustainability reporting across continents, and developing minimum requirements for sustainability reporting. Overall, it contributes to accounting and management literature on corporate sustainability practices and also to understanding company-level sustainability reporting in Nigeria and other developing nations.
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