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The objective of this chapter is to analyse and interpret data collected for the research. Data analysis and interpretation are important to determine whether data collected is useful and reliable for the study. The results are then discussed to get clearer representation of the aspects related to the audit committee characteristics and firm performance.
This chapter is separated into 3 sections which are summary of sample, descriptive statistics, correlation matrix, test of hypotheses and summary. Section 4.2 discusses on the descriptive statistics which also highlights the distribution of data. To check multicollinearity problem, correlation matrix is explained in section 4.3. The highlight of the study is presented in Section 4.4 in which entire four hyphothesis are tested using Multiple Regression analysis to determine whether to accept or reject null hypothesis and draw a conclusion from that hypothesis. Summary of the chapter is presented in Section 4.6.
4.2 Descriptive Statistics
4.2.1 Descriptive Analysis of Dependent Variables
From the Table 4.1, we can see that the average mean of Return on Asset is 4.78 with standard deviation of 18.32. According to Table 4.1, the minimum return on assets recorded by the companies is -197.95 and the maximum is 44.74.
Table 4.1: Descriptive analysis of the Return on Asset
Return On Assets
4.2.2 Decriptive Analysis of Size Audit Committee
From the Table 4.2, we can see that the average mean of size is 3.24 with standard deviation of 0.453. According to Table 4.1, the minimum size of audit committee in the companies is 2 and the maximum is 5.
Table 4.2: Descriptive analysis of the Size audit commitee
0.4534.2.3 Decriptive analysis of Independence Audit Committee
As shown from the Table 4.3, it is clearly stated that the average mean of independence is 1 with standard deviation of 0.452. According to Table 4.3, the minimum independence by the companies is 1 and the maximum is 2. This evidently indicates that majority of audit committees in company are full independent.
Table 4.3: Descriptive analysis of the Independence Audit Commitee
4.2.4 Decriptive Analysis of Activity Audit Committee
Based on the above Table 4.4, we can plainly observe that the average mean of Activity is 4.44 with standard deviation of 0.762. According to Table 4.4, the minimum Activity recorded by the companies is 4 and the maximum are 9.
Table 4.4: Descriptive analysis of the Activity Audit Commitee
4.2.5 Decriptive Analysis of Financial Literate Audit Committee
As declared from the Table 4.5, it has been shown that the average mean of Financial Literate is 1.08 with standard deviation of 0.276. According to Table 4.1, the minimum Financial Literate by the companies is 1 and the maximum are 2.
Table 4.5: Descriptive analysis of the Financial Literate Audit Committee
4.2.6 Decriptive Analysis of Leverage
The Table 4.6 shows that the average mean of Leverage is 8.07 with standard deviation of 9.17. According to Table 4.1, the minimum leverage recorded by the companies is 1 and the maximum are 56. This visibly signifies that Leverage varies very widely between the companies evident by the high standard deviation.
Table 4.6: Descriptive analysis of the leverage
4.3 Test of Normality
Kolmohorov-Smirnov test is used to determine the normality of the data. It is important to determine the normality of data, as the result of the data revealed, it functions as the researcher's reference to make a decision either to use parametric or non parametric analysis in further study. The data is assumed as normally distributed if its significant level is more than 0.05.
Table 4.7 displays result from the Kolmogorov- Smirnov tests for all the samples. All the variables have significant level of less than 0.05; therefore it is concluded that all variables are not normally distributed. Since the data is not normally distributed, non- parametric tests were used in subsequent analysis
Table 4.7 : Normality Test
Kolmogorov- Smirnov (sig)
Return on Asset
4.4 Correlation Matrix
The correlation matrix is used to check for correlation that may exist between the regressors used in this study. This is part of an effort to ensure robustness of the findings, which probably would weaken due to the existence of multicollinearity. According to Gujarati (1995), multicollinearity may be a problem when the correlation exceeded 0.8. Hence, to check for multicollinearity problem, the result of the correlation matrix is presented in Table 4.8.
Table 4.8 : Spearman's Correlations Coefficient Matrix
From the above table, there is positive correlation between size of audit committee and the company performance. This positive correlation between size and company performance indicates that size of audit committee can help the company to increase the performance. A bigger the size of audit committee can increase the company performance through sharing opinion and views before coming out to a decision. Activity of audit committee is also having the positive significant correlation with company performance. Activity here is proxies by the number of meeting held by the audit committee; it indicates that the more they meet in the financial period; it can indirectly help the company to increase the performance. Same goes to the financial literate that encompass the positive significant correlation with the company performance. It can be a proxy by the member having professional certificate from professional body of accounting. It shows that the knowledge in financial part such as analyze and understand the flow of financial is imperative to help the company to gain more profits.
Nonetheless, from Table 4.8, it can indicate that the correlations are generally low. In general, the value of positive and negative correlation between the independence variables is only around 0.011 until 0.281. The highest correlation is between financial literate and the Return on Asset at the value of 0.281. Since none of the correlation exceeds 0.8 or even nearing it, thus we can indicate that there is no severe multicollinearity problem among the regressors.
4.5 Empirical Result
As discussed in Chapter 3, the hypothesis H1, H2, H3 and H4 which are developed to examine the characteristics of audit committee with the firm performance are tested using the multiple regression.
4.5.1 Multiple Regression Analysis
To determine factors that influence the company performance, a multiple regression analysis is run on the data. While Company performance were proxies as a Return on Assets is dependent variable, the audit committee characteristics (size, independence, activity and financial literate) will be the independent variable. Although the data is not normally distributed, however according to the De Vaus (2002) regression analysis can be used as long as the sample size is larger than 30. In this study the sample size is 169, where the regression analysis can be applied.
Table 4.9: ANOVA
Predictors: (Constant), Leverage
Predictors: (Constant), LEVERAGE, SIZE,INDE,FINLIT
Table 4.10 shows the results for model 1 and model 2; model 1 represents the model that only have the control variable (LEVERAGE) contradictory to the dependent variable (Return on Assets). The significant result indicates 0.007, which means that leverage cannot influence the performance of the company. The second model represent the control variable (LEVERAGE) together with the Independent Variable (Size, Independence, Activity and Financial Literate). The significant value is 0.000, showing that the model used as a whole has a relationship with the dependent variable.
Table 4.10 (a) : REGRESSION ANALYSIS
Dependent Variable : ROA
Table 4.8 (a) displays the result from the regression analysis where Return On Assets is the dependent variable and control variable as the predictor. It can be concluded that leverage is not significant with the Return On Assets as the value of significant is 0.008 (p<0.05). This result is consistent with the Fratini and Tettamanzi (2008) in their research found that leverage is insignificant and negative relationship with the company performance.
Table 4.10 (b) : REGRESSION ANALYSIS
Table 4.8 (b) displays the result from the regression analysis where Return On Assets is the dependent variable and Size, Independence (IND), Activity (ACTIVITY), Financial Literate (FINLIT) and Leverage (LEVERAGE) are the dependent variables.
ACTIVITY of audit committee is measured by the number of meeting that have the significant associated with the Return on Assets (ROA). This result is in line with previous literatures such as Bryan et al. (2004) stated that audit committees who meet regularly do improve the transparency of reported profits as to improve the quality of the profit for the company. Another researcher found that audit committee activity also can be an instrument to reduce the earning management (Biao Xie et al., 2003); as they regularly meet, discussion on further company's issues can be held and problem solving actions can be done as efficient as possible, plus, it can probably avoid any intentions in manipulating the earning as those actions can harm the performance of the company. Fratini and Tettamanzi (2008) also found the positively relationship with the performance of company, and they state that this is relation to the appreciate of them because actively monitoring the company performance.
FINANCIAL LITERATE measured by having the professional certificate is found to have significantly associated with the Return On Assets (ROA). This result is in line with previous literatures such as Canepa and Ruigrok (2005); found that financial expertise increased the quality of financial reporting. Davidson et al. (2004) found the positive significant stock price reaction when new members of audit committees have financial expertise.
The size of audit committee have no influence on the company performance since the significant value of SIZE is 0.842, (p<0.05). This result is in line with the Li.J and C.Chan.K (2008), that are stated in their study proof that the size of audit committee is insignificant in relation to the firm value. They also believe when the size are combined with other characteristics of audit committee the size of audit committee is no longer important.
Independence (IND) also have no influence on the company performance since the significant value of IND is 0.122, (p<0.05). This result is in line with the Sunday O.K. (2008), her study show that the independent of audit committee is insignificant in relation to the firm performance. This suggest it is probably critical to appoint the person who are not having at all the relationship in the company. (Kyreboah Coleman.A, 2007)
The result showed that the regression equation applied on pooled sample has R square of 0.239 for Return On Assets. It connotes that the equation explained 23.9% of variability in Return On Assets. From the F-test showed that the equation is not valid at 1% significant level. Therefore, based on the result of the pooled sample, all the variables ( Size, Independence, Activity and Financial Literate) are found to have the relationship with the Return On Assets.
Overall, this study concludes that H3 and H4 are accepted and H1, H2 and H5 are rejected as there is no evidence that they have any relationship with the Return on Assets. Therefore, it can be concluded that activity and financial literate of audit committee has an effect on the Return On Assets.