CHAPTER FOUR: DISCUSSION AND RESULTS
Overview
This section provides the results as brought out by the various tools of analysis discussed in this research. Each result is followed by an in-depth analysis to help in understanding the exemplifications of the research topic. Additionally, an overall discussion will follow to comments on the various results postulated in this chapter with keen concern on drawing profound conclusion on the findings from the results analysis. As mentioned earlier, descriptive statistics, correlation and regression analysis is extensively used in this chapter. In this section, we are going to do the analysis of collected data on the impact corporate governance has on firm performance. Selected data has been analyzed by using the statistical package for social sciences SPSS tool and some little application of excel spreadsheet documental analysis. Therefore various analysis carried out on the data will involve time variations and basic statistical studies and hypothetical relations. The set of data presents hypothesis test that needs to be analyzed based on the type of variable in question. As mentioned earlier, there are five sets of independent variables and one dependent variable that this research analyzes. The five set of independent variables are used to explain the performance of these companies. Additionally, these variables form the tenets of corporate governance while the dependent variable forms the company performance parameter. The inception of these types of variables is very critical when exemplifying the real impact of corporate governance on a firm’s performance. The first analysis is correlation analysis of the six variables
Descriptive Statistics
Analyzing On Board Size In Relation To Corporate Governance
The section is going to look at statistical results presented from board size analysis on the set of firms selected. This section thus has to infer on how board size and corporate governance are related in ensuring firm performance is effective. The following diagram presents analytical results on descriptive statistic made on board size in relation to other factors:
| Descriptive Statistics | |||||||||||||
| N | Range | Minimum | Maximum | Sum | Mean | Std. Deviation | Variance | Skewness | Kurtosis | ||||
| Statistic | Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| BSIZE | 77 | 10.34 | 6.33 | 16.67 | 823.66 | 10.6969 | .25191 | 2.21050 | 4.886 | .725 | .274 | .316 | .541 |
| Valid N (listwise) | 77 | ||||||||||||
Table 2: Descriptive statistic on board size
Analytical definition of board size is the total number of members the board is constituted of. There has not been any specific number conventionally agreed on to constitute board size. Research also suggests that an ideal board should constitute of a smaller number of the member with reason that it enhances fast decision making (Crawley, 2002, p.98). For large numbers of board members, statistics show that it becomes difficult for members to reach a decision and it’s usually easily manipulated by the management. The figure above shows that the mean board size stands at 10.6 for the 77 firms analyzed. This figure means that the firms should have at most 10 board members in order to ensure proper management of the company performance. Additionally, the range is around 10.34 with a small standard deviation of 2.2 meaning that the data has less outlier in its construct making it quite authentic. The small skewness count of 0.725 of the data shows that the data assumes a normal distribution curve making it suitable for the analysis (Harford, Mansi, and Maxwell, 2012, p.110).
Therefore it has been argued that small boards are effective as compared large boards which have proven ineffective and it has shown that it becomes difficult to enhance coordination between board members when it grows big. It thus proves that there is a high cost involved in coordinating large boards and significant delays when it comes to passing information. With such large group, there is a tendency for other members to fail in accounting their input and it makes it difficult for members to reach a conclusive decision (Ho, 2006, p.67).
Hypothetical study to be used during analysis and measurement of relationship between performance and number of members or rather board size is as follows:
Ho: there is no relationship between number of members in the board and performance of the firm
Ha: there is negative or positive relationship between board size and company performance
From the statistic presented in the above table of descriptive statistics, the values presented shows that for a total of 77 companies tested, there is the standard deviation of 2.2. The value is too small showing that the total number of board members for most companies is kept at 12 which is rather a small number. The given results help to infer on the earlier hypothesis for board size which shows that most companies would keep their board size small. This has ensured that their activities run efficiently enhancing performance. Therefore on the null hypothesis, the decision is to reject the null hypothesis and infer that there is a relationship between board size and performance of the firm.
Analyzing Independence within the Board In Relation To Firm Performance
It has been proven that for the board to be said it is independent, it needs the number of non-executive directors to be kept at a minimum. It has been difficult in deciding the relationship between board independence and firm’s performance with regard to a number of non-executive members present on the board. For instance executive members seem to have enough knowledge about the company thus their number representation seems to have a significant impact on performance. On the other side, the number of non-executive directors seems to have a positive impact on performance since they provide a professional output. Thus this section provides a rather difficult hypothetical analysis that requires being studied on (Ho, 2006, p.167).
Thus looking at firm strategy, evidence shows that independent directors will be reputation driven thus not allows instances of negligence. Thus here they need to make scrutinized decisions which make them a better choice in dominating the board. Therefore proper study will need to be made on the two variables on how they affect firm’s performance and can follow null hypothesis shown below:
Ho: there is no significant relationship seen between independence of the board and firm performance. Therefore above criteria proves that no matter what independence of board seems to be like, it will never affect the financial performance of any given firm.
Analysis of the Firm Performance Statistics
Here we analysis the descriptive statistics of the firms performance. Here we give analysis of the descriptive statistics and provide a discussion on other metrics that are important in determining the firms’ performances. For the 77 firms, the total assets, the total debts, total liabilities return on equity and other parameters’ descriptive statistics are as shown below.
| Descriptive Statistics on specific characteristics of the firms | ||||||||||||
| N | Range | Minimum | Maximum | Mean | Std. Deviation | Variance | Skewness | Kurtosis | ||||
| Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| MVOVERTA | 77 | 13.08 | .25 | 13.33 | 15.705 | .20520 | 1.80058 | 3.242 | 4.461 | .274 | 25.067 | .541 |
| TOTALASSETS | 77 | 2.69E8 | 504093.83 | 2.69E8 | 21694867.6882 | 4607836.26070 | 40433599.09058 | 1.635E15 | 4.231 | .274 | 21.173 | .541 |
| TOTALDEBT | 77 | 35712435.17 | 4633.33 | 35717068.50 | 5201943.3662 | 881635.96089 | 7736324.15944 | 5.985E13 | 2.337 | .274 | 5.294 | .541 |
| LIABILITIESTOTAL | 77 | 1.33E8 | 160530.17 | 1.33E8 | 12658003.6430 | 2451785.33246 | 21514328.97783 | 4.629E14 | 3.830 | .274 | 17.533 | .541 |
| RETONEQUITY | 77 | 836.14 | .77 | 836.91 | 37.1195 | 11.22756 | 98.52146 | 9706.478 | 7.350 | .274 | 58.949 | .541 |
| CURRENTRATIO | 77 | 7.75 | .26 | 8.01 | 1.4419 | .12930 | 1.13463 | 1.287 | 3.195 | .274 | 14.424 | .541 |
| DEBTEQUITYRATIO | 76 | 467.07 | .31 | 467.38 | 93.4659 | 10.76861 | 93.87856 | 8813.183 | 1.685 | .276 | 2.909 | .545 |
| DIVYIELD | 77 | 5.59 | .41 | 6.00 | 3.0092 | .16394 | 1.43856 | 2.069 | .152 | .274 | -.676 | .541 |
| Valid N (listwise) | 76 | |||||||||||
From the above statistics, the company performance represented by the market value divided by the total assets has a mean of 15. 7 which are above the industry average of 12. Additionally, the companies chosen have mean total assets of around 22 Billion which is way above the industry average. The total liabilities on the other hand stand at around 12 billion which is quite considerate. The current ratio average is also considerable at 1.45 making the firms chosen very proper and appropriate for this analysis (Akaike, 2016, p.42). The dividend yield on similarly is recorded to have a high of 3.0092.
Diversification within the Board Analysis
It is seen that diversification within the board breeds suitable environment for the development and performance of any given firm. For instance looking at population present within the board, one can confirm that a heterogeneous environment ensures that there is no room for manipulation from external factors such as executive directors. Also when the board is highly diversified and with gender consideration it promotes equality within the organization ensures effective decision making for an organization which confirms diversity to be a positive strategy.
Another advantage associated with diversification within boards of organizations is that having diverse representation ensures no domination with decision making. Therefore firms whose boards recognize diversification enjoy the privilege of having decisions made without bias. Therefore taking hypothetical analysis on gender diversification it can be analyzed on whether it helps towards ensuring proper performance of the firm or not. Here it is recorded that gender diversity enjoys support from most firms as it ensures a variety of advancements within the company. Analytical study can follow the following research criteria for both null and alternative hypothesis:
Ho: there is no significant relationship between board diversity with firms’ financial performance
Ha: there is positive or negative impact of board diversity on the performance of films
Therefore looking at statistical analysis carried out on various companies it shows that there are many positive impacts associated with diversification on firm performance. For instance looking at decision-making strategy it is seen that having a variety of opinions from different groups will ensure that final results are efficient (Ho, 2006, p.69).
The section is going to look at all the stated variables that are used in determining firm performance on view of corporate governance. Some sections above have represented their descriptive statistics and thus here clear relationship will have to be made on how they interact. The following table present SPSS results for descriptive analysis on all major variables. The next section that needs to be analyzed is impact played by a total number of assets owned by the company. Here it is presented that the values recorded from the sample set have got greatest value in variation. For instance, from a sample of 77 companies, all values presented show larger variation between them for the total asset.
| Descriptive Statistics | |||||||||||||
| N | Range | Minimum | Maximum | Sum | Mean | Std. Deviation | Variance | Skewness | Kurtosis | ||||
| Statistic | Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| INSTO | 75 | 24.17 | .83 | 25.00 | 376.60 | 5.0213 | .44360 | 3.84165 | 14.758 | 2.342 | .277 | 9.361 | .548 |
| BMFREQ | 77 | 14.50 | 4.33 | 18.83 | 648.02 | 8.4158 | .27429 | 2.40690 | 5.793 | 1.323 | .274 | 3.971 | .541 |
| DIRINDEP | 77 | 12.83 | 1.00 | 13.83 | 517.93 | 6.7264 | .26129 | 2.29279 | 5.257 | .905 | .274 | 1.261 | .541 |
| MGTOWN | 77 | .98 | .03 | 1.01 | 46.24 | .6005 | .02750 | .24134 | .058 | -.482 | .274 | -.594 | .541 |
| MVOVERTA | 77 | 13.08 | .25 | 13.33 | 120.93 | 1.5705 | .20520 | 1.80058 | 3.242 | 4.461 | .274 | 25.067 | .541 |
| BSIZE | 77 | 10.34 | 6.33 | 16.67 | 823.66 | 10.6969 | .25191 | 2.21050 | 4.886 | .725 | .274 | .316 | .541 |
| ACTCMSIZE | 77 | 10.00 | 2.83 | 12.83 | 337.71 | 4.3858 | .15117 | 1.32652 | 1.760 | 3.611 | .274 | 20.992 | .541 |
| Valid N (listwise) | 75 | ||||||||||||
Table 7: Descriptive statistics for Major variables
In above table, it is evident that values presented have got major variation. For instance deviation of values from the mean of 10.34 is seen to be much smaller. Thus for the set of data presented it is evident that board size for companies has got no major impact on firm performance. Considering most companies selected have shown major performance in their daily operation, with such greater variation the inference is that total asset for any given firm has no impact on the firm’s performance. For instance, hypothesis presented will be to deny alternative hypothesis and accept null hypothesis presented such that there is no relationship between total asset and firm performance (Akaike, 2016, p.88).
The following analysis is made on the impact played by market capitalization on firm performance. Data presented in descriptive analysis shows that set of data analyzed for market capitalization had a large level of deviation when related to the overall mean. Therefore from a sample of 77 firms, recorded standard deviation was too large to limit the observations as being very different from each other. Thus with this kind of deviation considering that sample was appreciably too small, it means that most companies have got varied levels in market capitalization. Therefore in studying the role played by market capitalization in relation to corporate governance, it is evident that there is little impact capitalization will play in firm performance. Thus statistical hypothesis shows that in relation to firm performance, market capitalization may not be a major factor in the determination.
Next set of data to be analyzed is a return on equity. Here it is observed that from a sample of 77 firms, the overall deviation recorded is somehow small compared to other factors. For instance, the standard deviation is seen to be 3.84 which mean that most of the values taken for analysis on return on equity deviate from the overall mean by 3.84. The value presented to show that considering a total number of firms put under study, there is the somewhat small amount of relationship between each set of statistic given (Ho, 2006, p.117). For instance, the sum of squares of deviations from the mean for all sets of data taken is small as it would be expected. Therefore inference for this section tries to explain the role played by return on equity in relation to firms’ performance. The set of data shown below represents the above analysis as displayed by descriptive analysis in excel spreadsheet:
| Descriptive Statistics | |||||||||||||
| N | Range | Minimum | Maximum | Sum | Mean | Std. Deviation | Variance | Skewness | Kurtosis | ||||
| Statistic | Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| RETONEQUITY | 77 | 836.14 | .77 | 836.91 | 2858.20 | 37.1195 | 11.22756 | 98.52146 | 9706.478 | 7.350 | .274 | 58.949 | .541 |
| Valid N (listwise) | 77 | ||||||||||||
Table 8: Descriptive statistic on return on equity ROE
Therefore statistical inference required for the return on equity shows that most companies with a properly set return on equity percentage would enjoy average firm performance compared to those that do not put the variable under statistical scrutiny.
Next set of data that requires analysis for its relationship with company performance is dividend yield. Looking at the overall table for descriptive analysis, the statistical variable seems to have the least value for deviation from the mean (Ts’o, Gilbert, and Wiesel, 2016, p.221). For instance, values taken from a sample of 77 firms presented total sample deviation of 98.581 which is rather small considering the size of the sample taken. Therefore looking at a set of data being analyzed, it can be concluded that most companies and firms have set their dividend at a certain level that ensures that their overall performance is guaranteed. Thus for a set of 77 companies, the following data shows that dividend yield plays a major role in firm performance (Cohen, Cohen, West, and Aiken, 2013, p.48).
| Descriptive Statistics | |||||||||||||
| N | Range | Minimum | Maximum | Sum | Mean | Std. Deviation | Variance | Skewness | Kurtosis | ||||
| Statistic | Statistic | Statistic | Statistic | Statistic | Statistic | Std. Error | Statistic | Statistic | Statistic | Std. Error | Statistic | Std. Error | |
| DIVYIELD | 77 | 5.59 | .41 | 6.00 | 231.71 | 3.0092 | .16394 | 1.43856 | 2.069 | .152 | .274 | -.676 | .541 |
| Valid N (listwise) | 77 | ||||||||||||
Table 9: Sample descriptive statistic for dividend yield
From above statistic, it is evident that with a mean of 3.0093 values ranging between 5.59 and zero has got a variation of 2.069. With this kind of value, one can deduce that for 77 firms most of them have got their dividend yield at 3.0092 as most record thus ensuring their success. Therefore in order to infer on the relationship between firm performance and corporate governance it is important to understand the impact of the dividend yield on performance (Akaike, 2016, p.171). Therefore in order to retain suitable valuation in firm performance, most firms have ensured that they keep their yield at an average of 3.0092 which makes them progress effectively.
Correlation Analysis
This analysis tries to test whether there indeed a relationship between various variables of the study. The relation is deemed to permit us to get to the next point of analysis by stating the degree of the relationship between these variables. The statistical correlation results as per the SPSS output are as shown below,
| Correlations | ||||||||
| INSTO | BSIZE | BMFREQ | DIRINDEP | ACTCMSIZE | MGTOWN | MVOVERTA | ||
| INSTO | Pearson Correlation | 1 | -.142 | -.026 | -.259* | -.224 | -.043 | .004 |
| Sig. (2-tailed) | .225 | .822 | .025 | .053 | .717 | .975 | ||
| N | 75 | 75 | 75 | 75 | 75 | 75 | 75 | |
| BSIZE | Pearson Correlation | -.142 | 1 | -.095 | .742** | .429** | -.074 | -.195 |
| Sig. (2-tailed) | .225 | .411 | .000 | .000 | .525 | .089 | ||
| N | 75 | 77 | 77 | 77 | 77 | 77 | 77 | |
| BMFREQ | Pearson Correlation | -.026 | -.095 | 1 | .016 | .220 | -.129 | -.023 |
| Sig. (2-tailed) | .822 | .411 | .893 | .054 | .263 | .841 | ||
| N | 75 | 77 | 77 | 77 | 77 | 77 | 77 | |
| DIRINDEP | Pearson Correlation | -.259* | .742** | .016 | 1 | .484** | -.119 | -.123 |
| Sig. (2-tailed) | .025 | .000 | .893 | .000 | .303 | .285 | ||
| N | 75 | 77 | 77 | 77 | 77 | 77 | 77 | |
| ACTCMSIZE | Pearson Correlation | -.224 | .429** | .220 | .484** | 1 | -.008 | -.106 |
| Sig. (2-tailed) | .053 | .000 | .054 | .000 | .946 | .358 | ||
| N | 75 | 77 | 77 | 77 | 77 | 77 | 77 | |
| MGTOWN | Pearson Correlation | -.043 | -.074 | -.129 | -.119 | -.008 | 1 | .473** |
| Sig. (2-tailed) | .717 | .525 | .263 | .303 | .946 | .000 | ||
| N | 75 | 77 | 77 | 77 | 77 | 77 | 77 | |
| MVOVERTA | Pearson Correlation | .004 | -.195 | -.023 | -.123 | -.106 | .473** | 1 |
| Sig. (2-tailed) | .975 | .089 | .841 | .285 | .358 | .000 | ||
| N | 75 | 77 | 77 | 77 | 77 | 77 | 77 | |
| *. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
|
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The abbreviations for the above results are as shown below
| DIRINDEP | The number of board independent who are on the board. |
| BSIZE | The number of directors who are on the board |
| BMFREQ | The number of meetings of board directors a year |
| ACTCMSIZE | The number of members held in audit committee |
| MGTOWN | The percentage of equity ownership held by management who employ the operation of the firm |
| INSTO | The percentage of Institutional on the total ownership. |
| MVOVERTA | Dependent variable = market value /total assets |
Company performance (market value over total assets=MVOVERTA)
From the above results of correlation analysis, it comes out clearly that the management ownership has the highest correlation with the company performance. This correlation stands at 0.473 which means that the higher the management ownership the higher the company performance since it is a positive correlation (Akaike, 2016, p.142). Conversely, institutional ownership has little effect on the company performance as described by the lowest correlation coefficient of 0.004. It should be noted that the company performance of the company as noted earlier was determined by the market capitalization divided by the total assets. There is a negative correlation between the company performance and the board size of -0.195. This would mean that as the board size increases, it is only expected that the company performance would decline which is also supported theoretically (Blair, 2006, p.21). The theoretical support stems from the fact that as the board size grows; reaching a consensus over various decisions as a board would be quite daunting thus creating bureaucracy problems. There is a higher correlation between the company performance and the audit committee size. This notion is strengthened by the correlation of about 0.123. High number of audit committee ensures that the firm is indeed controlled from the various fraudulent activities that it might be facing (Cadbury, 2012, p.76). The role of audit department in streamlining the company and unveiling its true and fair position cannot be underestimated as connoted by this correlation. The graph below shows that correlation stance as of the company performance as the independent variable.
Management ownership (MGOWN)
The as noted earlier, there is high correlation between the management ownership and the company performance which stands at 0.473. On the other hand, there is less correlation between the audit size and the management ownership of about -008. This shows that these two variables do not affect each other in any way (Cameron, and Trivedi, 2013, p.19). The two tail significance of test of 0.946 accentuates that we can now rely on this data and the results as well. From this table, there is a negative correlation between the management ownership and the board meeting frequency of -0.129. This shows that many board meetings do not add up to good management ownership. This has been strengthened by the fact that as the meetings of the board increases, the management ownership influence also reduces as well (Chatterjee, and Hadi, 2015, p.29). The directors’ independence also has almost the same correlation with management ownership as that of board meeting frequency.
Audit committee size (ACTCMSIZE)
There is high correlation between the audit size and the director’s independence of 0.484. This correlation means that the increase in the committee creates a platform where the directors can independently at and therefore increasing the chances of improved performance of the company. On the other hand, there is little correlation between the management ownership and the audit size of -0.008 which means that there is not relationship between them (Council, 2003, p.176). On the account of board size, the correlation with audit size stands at 0.429. The correlation is deemed to be quite high and strengthened by the fact as the board size increases, there is need to look into their affairs through the use of audit committee who are charged with the responsibility of streamlining the company affairs. The correlation between the company performance and the audit size is as shown on the correlation graph below.
Correlation analysis for return on equity has been previously presented for other variables that have been discussed earlier. But complete analysis shows that in relation to institutional size and dividend yield shows that the two have got negative correlation with return on equity. For common equity, results show the considerably low level of correlation of about 0.0243. The level shows that when either of them is varied the resultant change in another side would display minimum effect (Cameron, and Trivedi, 2013, p.221). This implies that either of the variables has got a minimum impact on how the other variable changes. For negative correlation, variables involved would result in opposite change to the other variable so that when the return on equity increases, those with negative correlation will decrease in value.
The last section to be analyzed is correlation analysis for the current ratio. The table presented from excels spreadsheet shows that there is a negative correlation between current ratio and the two variables of common equity and dividends yield. For instance, the relationship between current ratio with common equity and dividend yield has got a correlation coefficient of -0.067 and -0.356 respectively. The results show that when varied, current ratio would result in a negative change on common equity by about 0.067 while for dividend yield it gives the value of 0.356. This means that there is the considerably higher amount of negative impact that current ratio possess on dividend yield when compared to common equity (Claessens, 2007, p.182).
Next section is going to analyze these factors by looking how each one of them relates to others by looking at the role the play in firm performance. The main determinant used for determination of performance will return on equity. The factors will be studied relative to a number of firms used in the sample thus standard deviation from the mean, mean and sample total will play a major role in making an inference. Since companies involved have proven record of performance any slight deviation from mean tells a lot about factor being analyzed (Cleveland, and Devlin, 2008, p.22).
It has been seen that there are various factors that play a major role in impacting corporate governance to enhance company performance. Based on the results taken the following sets of variables are going to be analyzed and the present hypothetical question that is to be answered from the output presented by the Statistical packages for social sciences tool. In finding the next results, the regression analysis is used to give a further analysis on the relationship between the dependent and the independent variables (Cohen, Cohen, West, and Aiken, 2013, p.98).
Regression Results
The research model for the study as mentioned earlier took the form of a regression line. This equation is as shown alongside. t
The results summary is as shown below
| Model Summaryb | ||||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | Durbin-Watson | ||||
| R Square Change | F Change | df1 | df2 | Sig. F Change | ||||||
| 1 | .511a | .261 | .196 | 1.63258 | .261 | 3.998 | 6 | 68 | .002 | 1.934 |
| a. Predictors: (Constant), MGTOWN, ACTCMSIZE, INSTO, BMFREQ, BSIZE, DIRINDEP
b. Dependent Variable: MVOVERTA
|
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| ANOVAb | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 63.929 | 6 | 10.655 | 3.998 | .002a |
| Residual | 181.243 | 68 | 2.665 | |||
| Total | 245.172 | 74 | ||||
| a. Predictors: (Constant), MGTOWN, ACTCMSIZE, INSTO, BMFREQ, BSIZE, DIRINDEP
b. Dependent Variable: MVOVERTA
|
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| Coefficientsa | ||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | |||
| B | Std. Error | Beta | Lower Bound | Upper Bound | ||||
| 1 | (Constant) | .814 | 1.458 | .558 | .578 | -2.095 | 3.722 | |
| INSTO | .006 | .052 | .013 | .122 | .903 | -.097 | .110 | |
| BSIZE | -.189 | .131 | -.233 | -1.448 | .152 | -.450 | .072 | |
| BMFREQ | .025 | .083 | .033 | .301 | .764 | -.140 | .190 | |
| DIRINDEP | .116 | .130 | .149 | .892 | .375 | -.144 | .377 | |
| ACTCMSIZE | -.102 | .171 | -.075 | -.598 | .552 | -.444 | .239 | |
| MGTOWN | 3.680 | .825 | .476 | 4.461 | .000 | 2.034 | 5.326 | |
| a. Dependent Variable: MVOVERTA
|
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| Residuals Statisticsa | |||||
| Minimum | Maximum | Mean | Std. Deviation | N | |
| Predicted Value | -.8005 | 3.4114 | 1.5848 | .92947 | 75 |
| Std. Predicted Value | -2.566 | 1.965 | .000 | 1.000 | 75 |
| Standard Error of Predicted Value | .279 | 1.276 | .472 | .162 | 75 |
| Adjusted Predicted Value | -1.6649 | 3.2139 | 1.5543 | .97900 | 75 |
| Residual | -2.16508 | 9.91863 | .00000 | 1.56500 | 75 |
| Std. Residual | -1.326 | 6.075 | .000 | .959 | 75 |
| Stud. Residual | -1.371 | 6.396 | .008 | 1.010 | 75 |
| Deleted Residual | -2.31391 | 10.99459 | .03051 | 1.74564 | 75 |
| Stud. Deleted Residual | -1.380 | 10.060 | .061 | 1.362 | 75 |
| Mahal. Distance | 1.179 | 44.225 | 5.920 | 6.267 | 75 |
| Cook’s Distance | .000 | .634 | .017 | .077 | 75 |
| Centered Leverage Value | .016 | .598 | .080 | .085 | 75 |
| a. Dependent Variable: MVOVERTA
|
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From the above results the following regression equation is derived;
Y = 0.814 +0.06X1 -0.189X2 + 0.025X3 + 0.116X4 – 0.102X5 + 3.680et
It only means here that what mostly affects the company performance is the management ownership. The coefficient for this variable is deemed to be 3.68 which are higher than for the rest of the variables. The audit size as depicted by the fifth variable has a negative impact on the general performance of the company. The coefficient for the audit size variable stands at -0.102, meaning that the firm is indeed incurring higher costs when it employs more auditors which reduces the firm’s performance (Council, 2003, p.153). The director independence on the other hand is seen to improve the performance of the companies. The coefficient for the director’s independence variable stands at 0.116 confirming that the more independent the directors are, the greater the performance of the firm. It should be noted again that independence of the directors helps in enhancement of a profound decision making within the firm which would easily improve the performance of the firm. Board meeting frequency has little effect on the dependent variable in this case since its coefficient stands at 0.025. Board size on the other hand has a negative relationship with the company performance. The coefficient of -189 connotes that as the board size increases, the performance of the firm would go down (Ezekiel, and Fox, 2009, p.172). The same results are seen with the correlation analysis which confirms that indeed the company performance has a negative effect with the board size. The firms should therefore reduce their board size in order to ensure high performances. Institutional ownership has little effect on the company performance as depicted by the coefficient of 0.006. This low coefficient confirms that the institutional stakes in a company does not warrant the firm to have a control over the institutional management (Fox, 2007, p.21).
In trying to authenticate the data above, the interpretation of the coefficient of determination is very important. A closer look at the R squared which represents the coefficient of determination, this figure stands at 0.261 which means that we can rely on the data above up to a tune of 26.1%. Therefore, according to the line of regression above, 26.1% of the predictor variables can be used to explain the dependent variable. It is from this analysis that we attest to the fact that the data got for the analysis is at a good position to predict the performance of the companies (Giroud, and Mueller, 2011, p.127). On the analysis of the dependent variable as connoted by the histogram above, it is evident that the data of the follows a normal distribution in accordance with the standardized residuals.
Empirical Results
From the investigations of the 77 firms in the light of the five independent variables analyzed in this research, it has come out clearly that there is indeed an impact that corporate governance creates on the performance of the company. Notably, the results found out that the board independence plays a critical role in ensuring higher performance of the firm. This is shown by the strong positive correlation between the board independence and the company performance. Additionally, the empirical results above showed that the percentage of management ownership has a profound effect on the company performance (Cadbury, 2012, p.97). This notion is strengthened by a strong coefficient for the management ownership in the regression model. The strong positive coefficient revealed that as the management ownership increases, it is only expected that the firm would realize a higher performance standards. On the account of board size, the results showed that the larger the board size the poorer the company performance gets. In this case, we recommend that the largest number of board size should be approximately 10 members since this was the mean board size for the 77 firms investigated. The results also showed a positive correlation between audit committee size and the company performance meaning that the efficiency of the audit team has a positive effect on the firm’s performance (Akaike, 2016, p.98). These results have been explained extensively in the correlation analysis, regression analysis and descriptive statistics part of this chapter.
Conclusion
This chapter has exemplified that there are various factors affect the performance of a company performance. Corporate governance as connoted by various variables such as the board size, board meeting frequency and audit size among other is deemed to affect the company performance in many ways. On the other part of the descriptive analysis, there were many features of variables in corporate governance that were realized (Cohen, Cohen, West, and Aiken, 2013, p.98). For instance, looking at gender diversity, it was realized that it has got major effects when studying firm performance. For instance ensuring that the company’s gender is well checked for equality will ensure proper operation of its activities. The next section confirmed on the role played by the size of the board in ensuring that firm performance is well managed. Here it was statistically proven that firms which have got large board sizes, for instance, firms with large numbers of board members risk going through losses. This is because there are many losses found to result from keeping firms with a large number of board members (Ts’o, Gilbert, and Wiesel, 2016, p.121). One of the disadvantages of these large numbers is that it makes it hard for company or directors to make decisions concerning firm’s operation. The paper also introduced board independence as one the factor that is seen to affect firm’s performance. Despite some reasonable facts presented on the importance of board independence, statistical results showed that the two variables have got least relationship. Here it was presented that each side has got major impacts on firm’s performance thus showing that there is mutual dependence between members of the board with types of directors involved (Cadbury, 2012, p.177).
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