Impact of Working Capital Management and Corporate Governance on Firm Performance

This study analyzes the impact of working capital management and corporate governance on firm performance using ROA, ROE, and NPM as parameters. Descriptive statistics, correlation, and regression analysis are used to test the hypotheses. Control variables such as firm size, leverage, and liquidity are also considered.

Running head: IMPACT OF WCM AND CG ON FIRM PERFORMANCE
IMPACT OF WCM AND CG ON FIRM PERFORMANCE
Name of Student
Name of University
Author Note

1IMPACT OF WCM AND CG ON FIRM PERFORMANCE
Table of Contents
Chapter 4..........................................................................................................................................2
Analysis.......................................................................................................................................2
Descriptive Statistics...............................................................................................................3
Correlation...............................................................................................................................5
Regression................................................................................................................................8
Discussion..................................................................................................................................11
Chapter 5........................................................................................................................................15
Conclusion.................................................................................................................................15
Limitations.................................................................................................................................16
Further Research........................................................................................................................17

2IMPACT OF WCM AND CG ON FIRM PERFORMANCE
Chapter 4
Analysis
The results of the analysis of the connection between firm performance and working
capital management and again on how the firm performance might be affected by corporate
governance of the firm are hence discussed. To study these two relationships the study first
defined firm performance using the Return on Asset or the ROA, the Return on Equity or the
ROE and the Net Profit Margin or NPM of the firm. The study then defined a number of
parameters to gauge the working capital management, using aspects such as, Cash Conversion
Cycle or CCC, the ACP or the Average Collection Period, the AIP or the Average Inventory
Period and the APP or Average Payment Period. The idea is to see how each of these aspects
may impact each of the parameters which represent firm performance. Again, the study defined a
number of parameters to gauge the corporate governance, using aspects such as, Board Size
denoted as BSIZE, Non-executive directors denoted as NEDs and CEO Duality. The study then
investigates the relationship between these parameters with those of firm performance.
Furthermore the study also considers a number of control variables such as firm size or FSIZE,
LEVERAGE and LIQUIDITY.
This section reports on the results of the tests for validity of the seven hypotheses as
defined for the purpose of the study. The analysis included a scrutiny of the descriptive measures
on all the variables defined. The correlation measure between each of the independent variables
with that of the dependent variable, firm performance was then computed to look for any
tentative linear relationship. The study further explores the veracity of the conjectures presuming
a linear relationship between the dependent and seven of the independent variables as specified

3IMPACT OF WCM AND CG ON FIRM PERFORMANCE
in the previous sections using linear regression analysis. It is in this part of the analysis that the
relationship is considered in light of the control variables as well.
Descriptive Statistics
A total of 400 firms were considered for the scope of the study. The mean ROA was of
all the firms considered was found to be 0.02387 that is 2.38%. The maximum was found to be
0.5489 or 54.89%, the minimum was -0.4569 or -45.69% that is a loss of 45.69%. The median
was 0.2271or 2.27% and the standard deviation 0.0944 which means that the estimate of mean is
quite consistent. Again the mean value of ROE of all the firms included in the study was
computed as 0.01751 that is 1.751% and the standard deviation 0.2005 which means that the
estimate of mean is quite consistent. The median value was found to be 0.0338 or 3.38%.Again,
the maximum ROE was found to be 0.781541 or 78.15% and the minimum ROE among the
observed firms was –1.987 or -198.76% that is a loss of 198.75%. The average NPM of all the
observed firms was found to be 0.02043 that is 2.043%. The maximum NPM was found to be
equal to -0.77325 or 77.325% and the minimum NPM was found to be equal to -1.38081 or -
138.081% that is a loss of 138.081%. The median of the NPM of the firms that were observed
was 0.0335 or 3.35% and the standard deviation of the NPM was 0.1966 implying that the
estimated mean is consistent. The mean ACP of all the firm sobserved in the study was found to
be equal to 100.56. The median was observed to be 73.5 , now this differs from the mean and the
standard deviation being equal to 83.30 suggests that the data is not that consistent and is
possibly negatively skewed. The maximum ACP of the firms was computed to be 608, the
minimum was found to be 0. The data for ACP is suspected to have outliers which is inflating
the mean. The average of AIP of all the firms considered in the sudy was found to be equal to

4IMPACT OF WCM AND CG ON FIRM PERFORMANCE
173.428. The maximum AIP among all the firms was 856 and the minimum was 0. The median
was found to be 149 implying that the estimated mean differs from median to a great degree and
the standard deviation of the AIP values was equal to 129.6942. This suggests that the data is not
that consistent.The data is suspected to have outliers which is inflating the mean.The mean APP
of all the firms considered was found to be 89.132. The maximum was found to be 642 the
minimum was 0. The median value of APP was found to be 65.The standard deviation was
computed as 83.098 which suggests that the data is not that consistent and the estimated mean
differs from median to a great degree. The data is suspected to have outliers which is inflating
the mean. The mean CCC of all the firms in the study that were considered was found to be
187.855. The maximum CCC was found to be 960 and the minimum of the CCC was computed
as -141. The median was found to be 154. The standard deviation of CCC is 154.7745 which
suggests that the estimate of mean CCC is not consistent.. The observations on CCC is suspected
to have outliers which is inflating the mean.The mean BSIZE of all the firms considered was
found to be 8.24 or approximately 8. The maximum was found to be 13 the minimum was 5. The
median was 9 standard deviation 2.178 which suggests that the data is consistent.The mean
number NEDs of all the firms considered was found to be 0.87 or approximately 1. The
maximum was found to be 1 the minimum was 0. The median was 0.88 which is also
approximately 1 standard deviation 0.112 which suggests that the data is consistent. CEO duality
has the mean 0.17 or approximately 0 and median is also 0. The standard deviation is 0.37 which
is low and so the estimate is believed to be consistent. The maximum is 1 and minimum 0. The
mean value of leverage of all the observed firms that were included in the analysis was found to
be 0.315 and the standard deviation was 0.1832 which suggests that the data is consistent. The
median was computed as 0.31558. The maximum was found to be 0.998 and the minimum was

5IMPACT OF WCM AND CG ON FIRM PERFORMANCE
computed to be 0.2961. The mean liquidity of all the firms considered was found to be 2.855.
The maximum was found to be 28.09 the minimum was 0.37946. The median of the observed
liquidity of all the firms was 2.124 and the standard deviation of liquidity was 2.617 which
suggests that the data is consistent.
The following table shows all the statistics that have been computed as described above.
Descriptive Statistics
Variable N Minimum Maximum Median Mean Standard
Deviation
ROA 400 -0.45698 0.548939 0.02271 0.0238
7
0.094475
ROE 400 -1.98767 0.781541 0.033832 0.0175
1
0.200551
NPM 400 -1.38081 0.773258 0.033512 0.0204
3
0.196603
ACP 400 0 608 73.5 100.56 83.30228
AIP 400 0 856 149 173.42
8
129.6942
APP 400 0 642 65.5 86.132
5
83.09849
CCC 400 -141 960 154 187.85
5
154.7745
BSIZE 400 5 13 9 8.245 2.178725
NEDs
400 0.4 1 0.888889 0.8749
8
0.111916
CEO-
Duality
400 0 1 0 0.17 0.376103
FSIZE 400 6.56335 9.087522 7.277677 7.4232 0.528611
LEVERAG
E
400 0.01611 0.998157 0.296171 0.3155
8
0.183215
LIQUIDITY 400 0.37946 28.09015 2.124005 2.8556
3
2.617098
Table 1: Descriptive Measures of Variables

6IMPACT OF WCM AND CG ON FIRM PERFORMANCE
Correlation
The Pearson’s correlation coefficient between the independent variable ACP with the
dependent variables ROA, ROE and NPM are -0.2299, -0.1948 and -0.363 respectively. All were
found to be significant at 5% level of significance. Hence it is suggested that ACP effects firm
performance is a negative and mild way, that is increase in ACP corresponds with decrease in
firm performance.The Pearson’s correlation coefficient between the independent variable P with
the dependent variables ROA, ROE and NPM are -0.1763, -0.09 and -0.209 respectively. All
except the correlation with ROE were found to be significant at 5% level of significance.The
Pearson’s correlation coefficient between the independent variable APP with the dependent
variables ROA, ROE and NPM are -0.2673, -0.2528 and -0.398 respectively. All were found to
be negatively correlated and significant at 5% level of significance. The Pearson’s correlation
coefficient between the independent variable CCC with the dependent variables ROA, ROE and
NPM are -0.1279, -0.047 and -0.156 respectively. All except the correlation with ROE were
found to be significant at 5% level of significance.

7IMPACT OF WCM AND CG ON FIRM PERFORMANCE
Correlation
Probability ROA ROE NPM ACP AIP APP CCC
ROA 1.000000
-----
ROE 0.843302 1.000000
0.0000 -----
NPM 0.753463 0.624078 1.000000
0.0000 0.0000 -----
ACP -0.229921 -0.194896 -0.363264 1.000000
0.0000 0.0001 0.0000 -----
AIP -0.176333 -0.092925 -0.209596 0.356050 1.000000
0.0004 0.0634 0.0000 0.0000 -----
APP -0.267370 -0.252847 -0.398993 0.522257 0.332781 1.000000
0.0000 0.0000 0.0000 0.0000 0.0000 -----
CCC -0.127956 -0.047010 -0.156928 0.556171 0.850917 0.023043 1.000000
0.0104 0.3484 0.0016 0.0000 0.0000 0.6459 -----
Figure 1:Correlation for Working Capital Management (Independent Variable)
Correlation
Probability ROA ROE NPM BSIZE NEDS CEO_DUALI...
ROA 1.000000
-----
ROE 0.843302 1.000000
0.0000 -----
NPM 0.753463 0.624078 1.000000
0.0000 0.0000 -----
BSIZE 0.109421 0.100315 0.312002 1.000000
0.0287 0.0450 0.0000 -----
NEDS 0.067201 0.023958 0.125650 0.260060 1.000000
0.1798 0.6328 0.0119 0.0000 -----
CEO_DUALITY 0.170880 0.149005 0.000806 -0.002019 -0.173523 1.000000
0.0006 0.0028 0.9872 0.9679 0.0005 -----
Figure 2: Correlation for Corporate Governance (Independent Variable)

8IMPACT OF WCM AND CG ON FIRM PERFORMANCE
The Pearson’s correlation coefficient between the independent variable BSIZE with the
dependent variables ROA, ROE and NPM are 0.109, 0.1003 and 0.312 respectively All were
found to be positive and significant at 5% level of significance. The Pearson’s correlation
coefficient between the independent variable NEDS with the dependent variables ROA, ROE
and NPM are 0.067, 0.0239 and 0.125 respectively. Only the correlation with NPM was found
to be positive and significant at 5% level of significance. The Pearson’s correlation coefficient
between the independent variable CEO duality with the dependent variables ROA, ROE and
NPM are 0.170, 0.149 and 0.0008 respectively. All except the correlation with NPM were found
to be significant at 5% level of significance and positive.
Correlation
Probability ROA ROE NPM FSIZE LEVERAGE LIQUIDITY
ROA 1.000000
-----
ROE 0.843302 1.000000
0.0000 -----
NPM 0.753463 0.624078 1.000000
0.0000 0.0000 -----
FSIZE 0.287126 0.197156 0.294787 1.000000
0.0000 0.0001 0.0000 -----
LEVERAGE -0.329597 -0.386018 -0.360076 0.085655 1.000000
0.0000 0.0000 0.0000 0.0871 -----
LIQUIDITY 0.214212 0.166382 0.333771 9.40E-05 -0.606748 1.000000
0.0000 0.0008 0.0000 0.9985 0.0000 -----
Figure 3: Correlation for Control Variables
The Pearson’s correlation between FSIZE and ROA, ROE and NPM are 0.287, 0.197 and
0.294 respectively. All are significant at 5% level of significance and positive. So increase in

9IMPACT OF WCM AND CG ON FIRM PERFORMANCE
FSIZE corresponds with increase in firm performance. The correlation between LEVERAGE
and ROA, ROE and NPM are -0.329, -0.386 and -0.36 respectively. All were found to be
significant at 5% level and have negative relationship with firm performance. So increase in
Leverage corresponds with decrease in firm performance.The Pearson’s correlation between
LIQUIDITY and ROA, ROE and NPM are 0.2142, 0.166 and 0.334 respectively. All are
significant at 5% level of significance and positive. So increase in LIQUIDITY corresponds with
increase in firm performance.
Regression
Two models were then developed using linear regression analysis to examine the
relationship between the dependent and independent variables. Model 1 was developed to
explain the association between firm performance and working capital management. Model 2
was developed to explain the relationship between firm performance and corporate governance.
Model 1
Three linear models were constructed, with variables representing firm performance with
the variables representing working capital management and the control variables. The model on
ROA was found to have adjusted R2 equal 0.238, that is, it explains 23.8% of the variation of
ROA. The variables FSIZE and LEVERAGE were found to be significant at 5% level of
significance and LIQUIDITY had negative effect.The model on ROE was found to have adjusted
R2 equal 0.225, that is, it explains 22.5% of the variation of ROE. The variables APP, FSIZE,
LEVERAGE and LIQUIDITY were found to be significant at 5% level of significance and all
except FSIZE which had positive effect, had negative effect on firm performance. The model on
NPM was found to have adjusted R2 equal 0.364, that is, it explains 36.4% of the variation of
NPM. The variables ACP, APP, FSIZE, LEVERAGE and LIQUIDITY were found to be

10IMPACT OF WCM AND CG ON FIRM PERFORMANCE
significant at 5% level of significance with ACP, APP and LEVERAGE having negative impact
on performance.
Variable
ROA ROE NPM
Coefficien
t t-statistic Coefficien
t t-statistic Coefficien
t t-statistic
ACP -8.60E-05 -1.430584 -0.000149 -1.161535 -0.00043
-
3.709789**
*
AIP -6.52E-05 -1.792878* -4.12E-05 -0.528798 ####### -1.336106
APP -0.000114 -1.798655* -0.000261 -
1.925052** -0.00043
-
3.515686**
*
CCC -6.52E-05 -1.792878* -4.12E-05 -0.528798 ####### -1.336106
FSIZE 0.050856 6.262053**
* 0.083825 4.823157**
* 0.10245 6.585742**
*
LEVERAG
E -0.170914
-
5.800952**
*
-0.493454
-
7.826238**
*
-0.24646
-
4.367088**
*
LIQUIDIT
Y -0.000235 -0.117425 -0.010194 -
2.381511** 0.01119 2.920968**
*
* p < 0.1 ** p <0.05 *** p < 0.01
Table 2: Regression Model 1 Specifications
ROA ROE NPM
Coefficient Prob. Coefficien
t Prob. Coefficient Prob.
R2 0.249599 - 0.237381 - 0.36425 -
Adjusted R2 0.238143 - 0.225738 - 0.35454 -
F-statistics 21.78669 0.0000 20.38824 0.0000 37.5276 0.0000
Table 3: Regression Model 1 Fitness Measures
Model 2
Three linear models were constructed, with variables representing firm performance with the
variables representing corporate governance and the control variables. The model on ROA was
found to have adjusted R2 equal 0.217, that is, it explains 21.7% of the variation of ROA. The

11IMPACT OF WCM AND CG ON FIRM PERFORMANCE
variables CEO-duality, FSIZE and LEVERAGE were found to be significant at 5% level of
significance. LEVERAGE had a negative effect on ROA where as CEO duality and FSIZE wer
positively related wiith ROA. The model on ROE was found to have adjusted R2 equal 0.2095,
that is, it explains 20.95% of the variation of ROE. The variables CEO-duality, FSIZE,
LEVERAGE and LIQUIDITY were found to be significant at 5% level of significance with the
latter two having negative effect on ROE. The model on NPM was found to have adjusted R2
equal 0.268, that is, it explains 26.8% of the variation of NPM. The variables BSIZE, FSIZE,
LEVERAGE and LIQUIDITY were found to be significant at 5% level of significance and
LEVERAGE had a negative effect on firm performance as measured by the NPM.
Variable
ROA ROE NPM
Coefficien
t t-statistic Coefficien
t t-statistic Coefficien
t t-statistic
BSIZE -0.0026 -1.214889 -0.0026 -0.573722 0.0136 3.197047**
*
NEDs 0.0632 1.605706* 0.0504 0.599288 0.0787 0.992474
CEO-
duality 0.0331 2.900794**
* 0.0496 2.033735** -0.0188 -0.817334
FSIZE 0.0579 6.919709**
* 0.0911 5.105909**
* 0.1021 6.063324**
*
LEVERAG
E -0.1776
-
6.062792**
*
-0.5158
-
8.251913**
*
-0.3043
-
5.161385**
*
LIQUIDIT
Y 0.0003 0.157899 -0.009 -
2.078222** 0.0097 2.374963**
* p < 0.1 ** p <0.05 *** p < 0.01
Table 4: Regression Model 2 Specifications
ROA ROE NPM
Coefficien
t Prob. Coefficient Prob. Coefficient Prob.
R2 0.2294 - 0.2214 - 0.279 -
Adjusted R2 0.2177 - 0.2095 - 0.268 -
F-statistics 19.504 0.0000 18.621 0.000 25.344 0.000

12IMPACT OF WCM AND CG ON FIRM PERFORMANCE
0 0
Table 5: Regression Model 2 Fitness Measures
Discussion
Working Capital Management Model
The efficiency of the working capital management can be verified with the help of a
model which uses independent variables like cash conversion cycle , average collection period ,
average payment period, average inventory period with control variables like firm size, liquidity
and leverage.The dependent variables include return on assets, return on equity and net profit
margin The return on assets results indicate that the assets will generate a high amount of profit.
Return on assets signifies increasing profitability(Mathuva 2015).This signifies that the company
make use of its assets in a very efficient manner. Return on equity signifies how well a firm uses
its share capital to generate returns and dividends in proportion of the funds invested by
shareholders(Lins, Servaes. and Tamayo 2017).Net profit margin represents the profit margin in
relation to the total revenue to the firm earns(Mathuva 2015).
Coming to the dependent variables, cash conversion cycle is the length of time taken to
alter cash into inventory and accounts payable and back into cash again(Agha 2014).Cash
conversion cycle ,according to Mathuva boosts firm performance because it frees up more cash
to carry out the daily operations. The average time taken by the firm was found to be 188
days.The basic lenghth of time suited for cash conversion cycle is three months. Here a
significant difference in the length of time was found. This may be due to the fact that the
company has a liberal credit policy or the payments given by the debtors take time(Mathuva
2015). This suggests that the more quickly the company can turn cash into inventory and back

13IMPACT OF WCM AND CG ON FIRM PERFORMANCE
into cash again, the better off the company will be and its financial performance will
improve(Lins, Servaes. and Tamayo, 2017).
Average collection period implies the lenghth of time it takes to collect payments from
debtors(Mathuva 2015). The average collection period is 100 days and is negatively correlated
with ROA, ROE and NPM. This is in confiramnce with one of the articles published by Agha,
where it is found to be negatively correlated with ROA AND ROE. So there is a similarity of
relationship in this case. The industry prescribes a standard of 3 month as adequate, for good
firm performance.The difference exists maybe because of a poor credit policy and cash problems
of debtors.Average inventory period implies the length of time it takes for the goods to convert
from inventory to sales(Mathuva 2015).The average inventory period is 173 days with a standard
deviation of 130 days, with a negative correalation coefficient to that of ROA, ROE and NPM.
This is in confirmance to one of the articles as per Agha, where it is also found to have a
negative correlation.So there is a similarity of relationship in this case..The inventory on an
average remains unsold for a period of 173 days.This is in confimance to the sampled enterprises
needs an average 173 days to sell their inventory.Usually the lesser no of days the better it is for
the firm. However it is essential to keep in mind that the average inventory period varies from
firm to firm.
Existing literature indicates that number of days inventory are significantly correlated
with that of firm performance(Mathuva 2015). The ideal length of time should be between 3
Months to 5 months ,as per Agha.The difference may be due to the fact that the firm has a liberal
credit policy, low liquidity levels or poor credit decisions. Based on previous literature as per
another author the retailer incresed its inventory to increase sales but sales do not increase at that
level in this case.This implies that the firm is not efffecient in managing working capital cycle

14IMPACT OF WCM AND CG ON FIRM PERFORMANCE
and takes too much time in converting inventory to sales.Hence inventory days
increase(Agustina et al. 2015).The inventory period should also be kept at a bare minimum to
facilitate efficient firm performance(Agha 2014).
Average payment period implies the length of time it takes to pay off creditors(Bhalla
2014). The average payment period to creditors is 86 days which suggests that the company
makes due payments quickly. The industry standard average is 90 days which means that the
firm has paid off the creditors quicky, according to the previous literature published by Bhalla.
This suggests that as payments due to creditors are paid quickly and in due time, more effective
will be the firm performance and vice versa(Lins, Servaes. and Tamayo 2017).The correlation
coefficient is negatively related to ROA, and positive with that of ROE and NPM.This is in
confirmance to previous studies, except for the ROA, which is positively correlated to average
payment period . This may be due to the fact that firm has sufficient working capital and a
healthy cash balance. If the firm pays off creditors quicky, it will free up the funds to get assets
and consequently use assets to improve firm performance(Mathuva 2015).
Return on equity can be maximised to the extent of almost 78%, which is a significant
amount of return in relation to the capital invested in the company while the minimum extends to
almost double the negative return.The higher the ROA, the better it is for the management.this is
in confirmance to the existing literature articles, in the works of author Agha. ROE is negatively
correlated with that of all variables mentioned in model 1.One of the control variables used in
this analysis is leverage which is used to check the debt financing and its relationship with the
profitability(Agha 2014).According to Bhalla, the leverage of the firm has a positive correlation
with that of ROA, ROE and NPM.This is in confirmance with the results drawn .more levered
the firm is, more significantly the chances will improve in maximising return on inome, return on

15IMPACT OF WCM AND CG ON FIRM PERFORMANCE
assets and net profit margin.The average total debt to total asset ratio is 32% with a standard
deviation of 18%.Another control variable will be the liquidity which has a mean value of 2.9
with a standard deviation of 2.62.This suggests that the company has almost thrice the liquid
assets available to pay off the current debt obligations(Bhalla 2014).
Corporate Goverance Model
The efficiency of the corporate governance model on firm performance can be analysed
with the help of non executive directors, the size of the board and the CEO duality(Baños-
Caballero, García-Teruel and Martínez-Solano 2014).The size of the board on an average is
computed to be 8 members, which positively affects the performance of the firm as there are
more decision makers involved in running the day to day operations., with a standard deviation
of 2.17. More the board members, better will be the corporate governance model.Existing
literature indicates the size of thr board is a compatable tool while measuring corporate
governance and positively affects corporate govrnance(Hasan, Hossain and Habib 2015).The
above anlaysis also correspond to the same conclusion.The non executive directors has a mean of
almost 1, with a standard deviation of 0.11.More non executive director swill positively affect
the firm performance.CEO duality, which refers to a state when the ceo holds the position of
both ceo as well as the chairman of the board, has an average of 0.17 with a standard deviation
of 0.37.
Existing literature also indicates that there is negative relation between ceo duality and
corporate governance(Hasan, Hossain and Habib 2015.). This should be effectively kept to a
bare minimum so as to avoid a conflict of interest between the ceo and the board, of which the
ceo himself is the chairman(Shahwan 2015). In the corporate governance model, the relation
between board size and that of ROA, ROE and NPM are positively correlated.According to

16IMPACT OF WCM AND CG ON FIRM PERFORMANCE
Shahwan, 2015 this suggests that that as the number of board members increases, firm
performance increases and vice versa. More board members facilitate better decision making and
make the management more competitive(Luo et al. 2015). It is in conformace to the existing
article as per Luo.The relation between number of non executive directors with that of ROA
and ROE was found to be positively correlated while that of NPM was negatively correlated.It
may be due to the fact that the salaries drawn by non executive directors reduced the overall net
profit margin of the company and thus produced a negative impact on firm performance.
The relation between ceo duality and that of ROE, ROA and NPM is positively
correlated. This may be due to the fact that as the ceo takes the position of the chairman of the
board, he will have a better oversight of its functions and hence will improve firm performance.
Research article portays a negative correalation as per Luo. According to Luo,duality of the ceo’s
position will affect his ability to make decisions at both ends of the spectrum.The multitasking
ability of the ceo will impact firm performave in the long run. In the first model there was a
significant negative relationship between the rate of change between the dependent variables of
NPM,ROE and ROA, with a coefficient of -0.00043,-0.000149 and -8.60 respectively.The results
indicate there is no significant relationship between average collection period with that of return
on asset, return on equity and net profit margin. According To Mun and Jang their research
portrays a significant relationship between the variables.This is in stark contrast to the results .
This difference might be due to the fact the size of the firm wsas not considered in the previous
article. This is also found in case of average inventory period, average payment period and cash
conversion cycle as well(Mun and Jang 2015).
Out of the control variables, only F SIZE had a positive coefficient while the leverage
and liquidity had a negative coefficient..As hypothsized in the hypothesis section of this research

17IMPACT OF WCM AND CG ON FIRM PERFORMANCE
number of days accounts receivable significantly affects profitability measures negatively.This
indicates that the numnber of days accounts receivable significantly and negativrly affects the
profitability of enterprises( Sraer and Thesmar 2018).The explanatory powers(adjusted R square)
of the three regressed models are 24.59 , 23.73, 36.42 for ROA, ROE and NPM
respectively.This mean that the weighted combination of the independent variables used
explained approximately 24.59 %,23.73% and 36.42 of the variance of ROA,ROE AND NPM.
However the remaining 75.41 percent changes in return on assets, 76.27% percent variability in
retutr on equity and 63.58 % of changes in the operating profitas are caused by other factors that
are not included in these models.Another three regresson models were run to measure the effect
of cash converson cycle on profitability measures( Sraer and Thesmar 2018).The table below
shows that cash conversion cycle affects profitability.According to Sraer and Thesmar 2018,.firm
size has no significant effect on profitability margin.Leverage and liquidity also has no
significant relationship on profitability margin as well.
In the second model, there was a negative coefficient between board size and that of
ROA,ROE and NPM while a positive coefficient with that of non executive directors and ceo-
duality. The positive coefficient implies that if the rate of change of ROA, ROE and NPM is
inversely proportional with that of board members. Similarly the negative coefficient implies that
if the rate of change of ROA, ROE and NPM is inversely proportional with that of non executive
directors and CEO duality(Sraer and Thesmar 2018). The leverage control variable also had a
negative coefficient with that of firm performance while the liquidity had a positive coefficient
with that of firm performance. According to Afrifa, the more liquid assets available to the firm ,
the better it will perform. More the proportion of debt or outsider’s capital employed by the firm

18IMPACT OF WCM AND CG ON FIRM PERFORMANCE
in their capital structure, more the company will be able to generate potential return on an
investment(Afrifa 2016).
Chapter 5
Conclusion
Role of working capital management is very crucial determinant of a firm’s road to
profitability and efficient performance. Firstly, the study concludes that an adequate level of
working capital is needed to deal with the challenges of liquidity that the firm must meet the
critical components, average collection period, average payment period, average inventory
period and cash conversion cycle. It was seen that ACP, APP, liquidity and leverage negatively
affect ROA and ROE of the firm where as FSIZE postively impacts it. However liquidity was
found to have a negative impact on the NPM. It is therefor crucial for the firm toeither identify
and prioritize aspects of performance they want to improve and find a balance.All in all, the
lesser time the firm takes to convert cash into inventory and accounts payable and back into cash
again,the better off the firm will be. Similarly the firm will be benefitted more if the firm takes
least amount of time to collect payment from debtors and give payment to creditors.This will
impact firm performance in a positive way, since shorter the collection period and the payment
period, the firm needs less working capital to run the business.
Secondly, gauging the impact of corporate governance on performance, the study finds
that when the board’s chairman is also the CEO of a firm , it is expected to perform better. The
empirical evidence illustrated that the size of board members will positively affect firm
performance. It was also found that the relationship between benefits of shareholders and that of
the management of the firm contributes positively to firm performance.Again this study checked
for any tentative linear relationship between corporate governance and board’s ownership. On

19IMPACT OF WCM AND CG ON FIRM PERFORMANCE
this account contradictory phenomena were observed whereby the number of board memebrs
positively impacted ROA and ROE however NPM was found to be negatively impacted by the
same.
Limitations
The limitation of this study are as follows:
 This study has been conducted with the assumption that there is a linear
relationship between the dependent variables and the independent models in both
models of corporate governance and working capital management , while taking
the control variables as well .However it has not been positively verified in this
instance.
 It assumes that there is a straight line relationship between dependent and
independent variables , which can be incorrect(Katrutsa and Strijov, 2017). The
data considered in this case is very sensitive to the anomalies in the data.Thus the
linear relationship model is a great tool for predicting he firm performance and
make predictions regarding the efficiency of working capital and corporate
governance.However it is susceptible to oversimplification of data, which is not
practically relevant.
 While we are measuring the correlation coefficient between dependent and
independent variables, it is being assumed that a measure of linear relationship
between the variables exists.(Katrutsa and Strijov 2017) It is based on a large
number of assumptions viz linearrelationship , cause and effect relationship which
may not always hold good.It is extremely affected by the values of the extreme

20IMPACT OF WCM AND CG ON FIRM PERFORMANCE
items which has a huge probability of error(Gorgees and Ali 2017).It also takes
more time to arrive at the results , which accounts for a longer time duration.
 Based on the analysis above, it is seen that the correlation between the
independent variables is large.This suggests that there is multicollinearity which
means that the results are not that verifiable.Multicollinearity reduces the
accuracy of the estimate coefficients, which weakens the statistical power of the
regression model(Gorgees and Ali 2017). The p values cannot be trusted enough
to identify independent variables that are statistically significant.This problem
makes it difficult to specify the correct model and to justify the model if many of
the pvalues are not statistically significant. In short multicollineraity affects the
coefficients and pvalues (Gorgees and Ali 2017).
Further Research
In the analysis considered above, ROA, ROE and NPM are considered to be efficient
barometers of firm performance .However the use of operational cash flow and fixed assets ratio
would provide more accuracy of predicting firm performance. The use of cash flow and fixed
assets would lead to the ruction of working capital and thus impact the firm performance
positively.The higher the cash flow the company has, the higher the working capital management
level is. The increase of investments in fixed assets leads to a reduction in working capital as
well. Industry demand would have been a very good control variable as the nature of demand
affects firm profitability(Gorgees and Ali, 2017) .Demand has the impact to change the whole
nature of industry of which the firm is a part of. Considering this variable would form a part of
expanding research and give a better understanding of firm performance. There is also a scope of
expanding the research further by conducting ANOVA analysis .Based on the above findings

21IMPACT OF WCM AND CG ON FIRM PERFORMANCE
there is scope of further research and conducting ratio analysis to assess the financial
performance(Aktas,Croci, and Petmezas 2015). Ratio analysis effectively ascertains the various
positions of the firm in terms of its liquidity, solvency and profitability. It will be the most
suitable indicator of determining future financial performance. The corporate governance is a
good indicator of firm performance, but it can be improved by considering other factors like
institutional ownership and board independence(Gorgees and Ali2017). Board independence
normally has a positive correlation with that of firm performance. It implies the degree of
freedom that is required by the board to make its own decision sand be free from the clutches of
the shareholders. Thus there remains a further scope of research, which accurately will reflect
financial statements. This will be a positive accuracy of providing financial performance and be
a significant step up in predicting the accuracy of providing further detailed financial
performance.The model on working capital management could have incorporated other data
variables like turnover on sales, current assets and current liabilities figures and other financial
estimates to judge a better performance of working capital. In model 2, the data could have
incorporated other variables like number of shareholders and number of executive directors.This
would give a better indication of the firm performance.

22IMPACT OF WCM AND CG ON FIRM PERFORMANCE
References:
Afrifa, G.A., 2016. Net working capital, cash flow and performance of UK SMEs. Review of
Accounting and Finance, 15(1), pp.21-44.
Agha, H., 2014. Impact of working capital management on Profitability. European Scientific
Journal, ESJ, 10(1).
Agustina, W., Yuniarta, G.A., AK, S. and SINARWATI, N.K., 2015. Pengaruh intelectual
capital, corporate social responsibility dan good corporate governance terhadap kinerja keuangan
(studi kasus pada perusahaan bumn yang terdaftar di bursa efek indonesia pada tahun 2011-
2013). JIMAT (Jurnal Ilmiah Mahasiswa Akuntansi) Undiksha, 3(1).
Aktas, N., Croci, E. and Petmezas, D., 2015. Is working capital management value-enhancing?
Evidence from firm performance and investments. Journal of Corporate Finance, 30, pp.98-113.
Baños-Caballero, S., García-Teruel, P.J. and Martínez-Solano, P., 2014. Working capital
management, corporate performance, and financial constraints. Journal of Business
Research, 67(3), pp.332-338.
Bhalla, V.K., 2014. Working capital management. S. Chand Publishing.
Gorgees, H.M. and Ali, B.A., 2017. Employing Ridge Regression Procedure to Remedy the
Multicollinearity Problem. Ibn AL-Haitham Journal For Pure and Applied Science, 26(1),
pp.320-327.
Hasan, M.M., Hossain, M. and Habib, A., 2015. Corporate life cycle and cost of equity capital.
Journal of Contemporary Accounting & Economics, 11(1), pp.46-60.
Katrutsa, A. and Strijov, V., 2017. Comprehensive study of feature selection methods to solve
multicollinearity problem according to evaluation criteria. Expert Systems with Applications, 76,
pp.1-11.

23IMPACT OF WCM AND CG ON FIRM PERFORMANCE
Lins, K.V., Servaes, H. and Tamayo, A., 2017. Social capital, trust, and firm performance: The
value of corporate social responsibility during the financial crisis. The Journal of Finance, 72(4),
pp.1785-1824.
Mathuva, D., 2015. The Influence of working capital management components on corporate
profitability.
Mun, S.G. and Jang, S.S., 2015. Working capital, cash holding, and profitability of restaurant
firms. International Journal of Hospitality Management, 48, pp.1-11.
Shahwan, T.M., 2015. The effects of corporate governance on financial performance and
financial distress: evidence from Egypt. Corporate Governance, 15(5), pp.641-662.
Sraer, D. and Thesmar, D., 2018. A sufficient statistics approach for aggregating firm-level
experiments (No. w24208). National Bureau of Economic Research.