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,

 

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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