Mitigating Credit Risk through Corporate Governance: An Investigation into the Causal Pathways from Board Accountability to Reduced Non-Performing Loans in Uganda’s  Commercial Banks.

Qualitative findings

The quantitative investigation uncovered two primary concerns: (1) the degree of board meddling in loan transactions and (2) their involvement on the board’s part in managing issues within the NPL. Board members were noted to frequently use their influence on loan terms to benefit themselves or their colleagues, resulting in higher NPLs. The board’s efforts to improve loan recovery and bank performance were complemented by strategic steps such as supporting the credit department and implementing policies to reduce NPLs.

From Table 1, the findings also indicate that respondents agreed that banks disclose their corporate governance policies and guidelines as indicated by a high mean of 3.95 and confirmed by a low standard deviation of 0.901 in the same regard, the findings indicate that banks have separated chairman and CEO as indicated by high mean of 3.90 and confirmed by the low standard deviation of 0.87, also the findings show that all executive board members own shares after excluding options held as indicated by high mean of 3.90 and confirmed by the low standard deviation of 0.923. Findings also indicate that banks disclose a code of ethics for senior executives as shown by a high mean of 3.81 and confirmed by a low mean of 0.96, also the findings show that a board or a committee is responsible for CEO succession planning as shown by a high mean of 3.76 and confirmed by the standard deviation of 1.068. in the same regard, the findings indicate that members attended at least 75% of the board meetings as shown by a high mean of 3.71 and confirmed by a low standard deviation of 0.883.

Furthermore, the findings show that banks have failed to adopt the recommendations of a shareholder proposal as indicated by a high mean of 3.71 and confirmed by a standard deviation of 1.078, also the results indicate that non-executive board members have a formal session without executives once a year as shown by high mean of 3.67 and confirmed by the standard deviation of 1.042. The findings also indicate that board members are not subjected to annual election by all shareholders as shown by a moderate mean of 3.33 and supported by a standard deviation of 1.325 lastly, the respondents were neutral about that all non-executive board members own shares after excluding options held as shown by moderate mean of 3.14 and supported standard deviation of 0.943. the overall mean of 3.69 and standard deviation of 0.999 show that most respondents agreed with the statements that were used to measure board accountability.

Correlation results

To examine the relationship between board accountability and non-performing loans of commercial banks, Pearson Linear Correlation Coefficient was used with the help of SPSS and the results were presented in Table 2. From Table 2, the results show that there is strong positive relationship between board accountability and non-performing loans of commercial banks (r=0.779, P=0.00<0.05). the relationship is statistically significant at 0.05, meaning that when members of the board are accountable by performing their duties following the guiding principles and polices, the non-performing loans of commercial banks reduces and the reverse is true.

Regression results

From Table 3, the results show a strong positive overall relationship between board accountability and non-performing loans of commercial banks as indicated by R= 0.7749, and board accountability contributes 60.7% to non-performing loans of commercial banks as indicated by R²= 0.607, meaning that when board members perform their roles very well, non-performing loans reduce by 60.7% and vice versa. The adjusted R square shows that a unit change in board accountability, causes 60.5% change in non-performing loans of commercial banks.

From Table 4, degrees of Freedom (df ) indicate how many separate pieces of information are used to compute the sum of squares. In the case of the Regression, the df is 1, which is equal to the number of independent variables (predictors). The entire number of observations less the total number of predictors is the residual’s df, which is 187. Calculated by dividing the total of squares by the number of degrees of freedom, or mean square (MS). The residual MS is 0.110 and the regression MS is 31.722. F-value = 288.827 is the ratio of the Regression MS to the Residual MS. A greater F-value suggests that the model explains a considerable percentage of the variation. Sig.: This stands for the p-value, which is used to determine the statistical significance of the F-statistic. A score of.000 indicates that the predictors significantly explain the variation in the result, suggesting that the model is highly significant (p < 0.000). The dependent variable may be well explained by the independent variables (predictors) taken together, since the model has statistical significance (p-value =.000). With an F-value of 288.827, the model is able to explain a significant portion of the variation in comparison to the residual, or unexplained variance. Out of the total variation of 52.260, the model explains 31.722 of it, leaving residual variance of 20.538 unexplained.

From Table 5, without normalizing the units, they show the true effects of each independent variable (predictor) on the dependent variable. -The value of the dependent variable when all predictors are zero is called the intercept, and it is 2.917. Keeping all other factors equal, non-performing loans rise by 0.254 units for every unit increase in Board responsibility. The variable representing non-performing loans rises by 0.748 units for every unit increase in financial disclosure. These coefficients are obtained by standardizing the variables by placing them on a same scale, so that comparing the relative importance of each predictor is made simpler. Board accountability appears to have a moderately beneficial impact on non-performing loans (standardized beta = 0.689, t = 9.384, p = .000<0.05), this implies that null hypothesis (H0₁) which stated that there was no statistically significant relationship between board accountability and non-performing loans of commercial banks was rejected.

Latent variable correlations

Latent variable correlations refer to the relationships between unobserved variables (latent variables) that are inferred from observed data. These correlations were typically estimated using statistical models such as structural equation modeling (SEM). The correlations represent the strength and direction of the linear relationship between pairs of latent variables as presented in Table 6.

From Table 6, the relationship between important latent variables, particularly Board Accountability, Oversight Risk Management (BA), Strategic Decision Making (BAb), and Non-Performing Loans (NP), is outlined in this table. To gauge the significance and strength of these correlations, one must refer to its first-ever sample correlation data, which includes standard deviations from previous measurements, t-statistics values for each variable pair, and p-values. The NP relationships expose even stronger connections.’ (P/L) The correlation between NP and BA is the original pair of the sample, which equals to 0.904, but it increases to an average in the mean. This indicates a very good relationship between the samples, with 0.022 as the standard deviation. A p-value of 0.000 and a t-statistic of 40.658 indicate observable and robust statistical relationships between NP and BA. However, the correlation is not very strong. The correlation between NP and BAb is similar, however it is significant. With a standard deviation of 0.030 and 0.897 sample mean, the original correlation of the samples is 0.900. A statistically significant relationship is evident with a t-statistic of 29.601 and p-values of 0.000, which indicate strong associations between BAb, the Model (BA) and the NP. In particular the relationships between NP and both BAand BAb also show strong links with these variables. These results imply that the management of non-performing loans requires effective oversight and strategic decision-making, which are closely linked to the financial system’s health in this situation.

The perceived understanding of Board Accountability and Non-performing Loans of Commercial Banks

This section presents the interviewees’ perceived understanding of board accountability and non-performing loans of commercial banks. To obtain a clearer picture, the interviewees were asked to answer each of following questions (. In your opinion, to what extent does the board interfere or help the bank to solve the problem of non-performing 2. How would banks manage non-performing loans? 3. How often do the board sit to discuss issues related to non-performing loans?). and the themes and sub themes generated were presented in the Figure 1 as explained below.

Figure 1. Reality radial diagram on board accountability and non-performing loans.

Board influence on NPL management

The results in Figure 1 reveal that, generally, interviewees perceived board accountability and non-performing loans of commercial banks as board influence on non-performing loans management. On analyzing the transcripts from qualitative interviews, it was established that two major sub themes emerged to mean board influence which are; Extent of Board Interference in Loan Processes and Board’s Role in Solving NPL Issues.

Board intervention in loan procedures

One of the main themes that emerged in Figure 1 was the extent of board interference in loan processes, with interviewees stating that in most cases, members of the board want to intervene in the loan-giving process by influencing who should get the loans, particularly to favor themselves, their friends, and relatives, which has had an impact on the level of non-performing loans. Interviewees also stated that there is a need to evaluate the nature and frequency of board engagement in loan decision-making, situations of positive vs. negative board influence on loan management, and ways to reduce the impact of board micromanagement on NPL ratios.

Board’s role in solving NPL issues

After analyzing the transcripts as presented in Figure 1, it was determined that the board’s involvement in solving NPL concerns as a sub-theme, may be brought about by strategic interventions by the board to minimize NPLs. Support for the credit department in enhancing loan recovery operations, as well as the board’s implementation of policies and frameworks to reduce NPL growth, which improves commercial banks’ performance in battling non-performing loans. Interviewees said that it is vital to establish the responsibilities and tasks of the board in commercial banks’ operations, especially when dealing with loan issues, in order to minimize conflict between the board members and management.