INTRODUCTION
Working capital management, which deals with the management of current assets
and current liabilities, is very important in corporate finance because it directly
affects the liquidity and profitability of the firm (Appuhami,
2008; Christopher and Kamalavalli, 2009; Dash
and Ravipati, 2009; Deloof, 2003; Eljelly,
2004; Raheman and Nasr, 2007). For example, the current
assets of a typical manufacturing firm or even a distribution firm account for
more than half of the firm’s total assets. Deloof (2003)
held the same proposition that the accounts receivables and inventories comprise
a substantial percentage of the total assets of a firm. Excessive levels of
current assets can easily result in a firm’s realizing a substandard return
on investment. However, firms with too few current assets may incur shortages
and difficulties in maintaining smooth operations (Van Horne
and Wachowicz, 2005).
Profitability is the rate of return on firm’s investment. An unwarranted
high investment in current assets would reduce this rate of return (Vishnani,
2007). The purpose of working capital management is to manage the firm’s
current accounts so as to attain a desired balance between profitability and
risk (Ricci and Vito, 2000). Shin
and Soenen (1998) found that efficient working capital management is an
integral component of the overall corporate strategy towards creating shareholder
value. The various components of working capital management are described as
follows:
The Average Collection Period (ACP) is the time taken to collect cash from
customers. The average collection period was used as a proxy for the collection
policy as an independent variable as used in previous studies (Deloof,
2003; GarciaTeruel and MartinezSolano, 2007; Lazaridis
and Tryfonidis, 2006; Raheman and Nasr, 2007).
The Inventory Conversion Period (ICP) refers to the time taken to convert inventory
held in the firm into sales. Consistent with previous studies such as Deloof,
(2003), GarciaTeruel and MartinezSolano (2007),
Lazaridis and Tryfonidis (2006) and Raheman
and Nasr (2007), the inventory conversion period was used as a proxy for
the inventory policy.
The Average Payment Period (APP) is the time taken to pay the firm’s suppliers.
Consistent with Deloof, (2003), GarciaTeruel
and MartinezSolano (2007), Lazaridis and Tryfonidis
(2006) and Raheman and Nasr (2007) the average collection
period was used as a proxy for the firm’s collection policy.
The various interrelationships among the working capital components can be shown in Fig. 1.
Efficient working capital management involves planning and controlling the
current assets and current liabilities in a manner that eliminates the risk
of inability of a firm to meet due short term obligations and to avoid excessive
investment in these assets on the other hand (Eljelly, 2004).
Managers spend considerable time on daytoday problems that involve working
capital decisions (Raheman and Nasr, 2007). One reason
for this is that current assets are shortlived investments that are continually
being converted into other asset types (Rao, 1989). With
regard to current liabilities, the firm is responsible for paying these obligations
on a timely basis. Taken together, decisions on the level of different working
capital components become frequent, repetitive and time consuming (Appuhami,
2008).
Working capital management is a very sensitive area in the field of financial
management. It involves the decision on the amount and composition of current
assets and the financing of these assets. Current assets include all those assets
that in the normal course of business return to the form of cash within a short
period of time, ordinarily within a year and such temporary investment as may
be readily converted into cash upon need (Raheman and Nasr,
2007). Smith (1980) and Raheman
and Nasr (2007) observed that working capital management is important because
of its effects on the firm's profitability and risk and consequently its value.
The way in which working capital is managed can have a significant impact on
both the liquidity and profitability of the firm (Deloof,
2003). For example, decisions that tend to maximize profitability tend to
minimize the chances of adequate liquidity. Conversely, focusing almost entirely
on liquidity will tend to reduce the potential profitability of the firm. A
firm can have larger sales with a generous credit policy, which extends the
cash cycle. In this case, the longer cash conversion cycle may result in higher
profitability. However, the traditional view of the relationship between the
cash conversion cycle and corporate profitability is that, ceteris paribus,
a longer cash conversion cycle hurts the profitability of a firm (Deloof,
2003; Smith, 1980). This study aimed at examining
the influence of working capital management components on corporate profitability
on Kenyan listed firms.
MATERIALS AND METHODS
Data for this study was collected from the listed firms on the NSE for the
period 19932008. The reason as to why this market was chosen is primarily due
to the availability and the reliability of the financial statements in that
they are subject to the mandatory audit by internationally recognized audit
firms (mainly the big four). Furthermore, firms listed on the stock exchange
market have an incentive to present profits if those exist in order to make
their shares more attractive (Lazaridis and Tryfonidis, 2006).
For the initial sample, data was obtained for all firms for the years ending
1993 through 2008. Consistent with Barako et al.
(2006), data was obtained from the NSE handbooks and the Kenya Capital Markets
Authority. Consequently, the sample data begins in 1993 and ends in 2008. Consistent
with Deloof (2003), firms in banking and financial institutions,
insurance, some commercial and service firms and some firms listed under the
industrial and allied segment were omitted. This is because the definition of
working capital is different for these firms from the definition being investigated
in this study (Lazaridis and Tryfonindis, 2006).
Of the 36 sample firms in the sample, the final sample contains 30 firms. In
order to ensure accuracy of the collected data, a number of filters were applied.
Observations of firms with anomalies such as negative values in their total
assets, current assets, fixed assets, capital, depreciation or the interest
paid were eliminated. Observations of items from the balance sheet and profit
and loss accounts showing signs contrary to reasonable expectations were removed.
Since the panel data being analyzed had a number of influential observations
and data errors as pointed out by Fama and French (1998),
each year was treated as having missing values 1% of the observations in each
tail of the distribution for each variable. This is consistent with previous
studies (Deloof, 2003; GarciaTeruel
and MartinezSolano, 2007; Raheman and Nasr, 2007;
Shin and Soenen, 1998). As a result of eliminating 1%
of the extreme values, the final sample of 468 firmsyear observations over
the period from 1993 through 2008 was arrived at.
The variables used were as follows. The study followed Deloof
(2003) in computing the NOP, which is the net operating profit ratio obtained
as (salescost of sales+depreciation and amortization)/(total assetsfinancial
assets). ACP is the accounts collection period obtained as accounts receivables/salesx365.
ICP refers to the inventory conversion cycle derived as inventory/cost of salesx365.
APP is the average payment period/purchasesx365. The cash conversion cycle,
CCC is obtained by adding the ACP to ICP and then subtracting the APP. Firm
size, CS is the natural logarithm of the total turnover (sales). The leverage
ratio, Lev is obtained by (shortterm loans+longterm loans)/total assets. The
fixed financial assets ratio, FFAR, is obtained as fixed financial assets/total
assets. Consistent with Deloof (2003), variability is
obtained by computing the standard deviation of net operating profit over the
19932008 period. The growth in gross domestic product, GDPGrow, is the GDP
growth rate in nominal terms as obtained from the Central Bureau of Statistics
website. Finally, the age of the firm is obtained by the natural logarithm of
the number of years the firm has existed since its inception.
RESULTS AND DISCUSSION
In Table 1, the summary statistics of the variables included
in the regression models are presented. Overall, the mean (median) net operating
profit lagged by total assets less financial assets is 38.2% (27.5%). The mean
(median) accounts collection period is 64.68 (65.23) days (approximately two
months), with the first quartile (third quartile) of 41.18 days (approximately
one and half months) (83.33 days (approximately three months). On average, firms
take 97.26 (median 82.17) days (approximately three months) to convert their
inventories into sales, with the first quartile (third quartile) of 40.63 days
(approximately one and half months) (138.59 days (approximately five months).
An average firm takes 95.58 (median 81.13) days (approximately 3 months) to
pay its creditors, with the first quartile (third quartile) of 53.97 days (approximately
three months) (121.74 days (approximately four months). The mean (median) cash
conversion cycle is 69.35 (63.66) days (approximately two months), with the
first quartile (third quartile) of 19.90 days (approximately half a month) (115.08
days (approximately four months). The table shows that an average firm has a
size of 7.637 (median 7.608) as measured by the natural logarithm of its total
turnover. The mean (median) leverage ratio is 12.70% (7.30%) lagged by total
assets. The typical firm in the sample has a fixed financial assets ratio of
1.57%. The mean variability of net operating profit is 12%. The mean (median)
growth in GDP is 3.28% (3.33%) in nominal terms. The average (median) age of
a firm in the sample was 2.052 (2.284) as measured by the natural logarithm
of the number of years since the firm was founded. All variables share a common
sample size of 468 firmyears.
Pearson and Spearman’s Correlations
Consistent with Shin and Soenen (1998), Table
2 shows both the Pearson and Spearman’s correlations among the observed
variables. The Spearman’s rank correlation coefficients are on the upper
right triangle while the Pearson product moment correlation coefficients are
on the lower left triangle. The industry, ownership control and firmyear dummy
variables are estimated but not reported.
Table 1: 
Summary statistics 

The industry, ownership control and firmyear dummy variables
are estimated but not reported. Source: 19932008 survey data, author’s
computation 
Table 2: 
Pearson and Spearman’s correlation coefficients 

The pvalues are in parentheses, with * and **Denoting significance
at the 5 and 1% levels, respectively. Source: 19932008 survey data, author’s
computation 
Table 2 shows that the NOP is negatively related to ACP and
CCC. The negative relation of NOP and ACP is consistent with the view that the
less the time taken by customers to pay their bills, the more cash is available
to replenish the inventory hence leading to more sales which result to an increase
in profitability. The Table also shows that the NOP is positively related to
ICP and APP. The negative relationship between NOP and ICP can be explained
by the fact that firms which maintain high inventory levels reduce the cost
of possible interruptions in the production process. This helps in preventing
loss of business due to the scarcity of products and reducing the cost of supplying
the goods. In so doing, firms are protected against price fluctuations (Blinder
and Maccini, 1991). The positive relation between NOP and APP can be explained
by the fact that lagging payments to suppliers ensures that the firm has some
cash to purchase more inventory for sale thus increasing its sales levels hence
boosting its profits. The negative relationship between NOP and CCC is consistent
with the view that the time lag between the expenditure for the purchases of
raw materials and the collection of sales of finished goods can be too long
and that decreasing this time lag increases profitability (Deloof,
2003). Firm size is positively related to NOP. This means that larger firm
report higher profits compared to smaller firms. This may be due to larger firm’s
ability to exploit their economies of scale.
Although, the Pearson linear and Spearman rank correlations give proof of an
inverse relationship between NTC and profitability, these measures do not allow
us to identify causes from consequences (Shin and Soenen, 1998). It is hard
to say whether a shorter accounts collection period leads to higher profitability
or a higher profitability is as a result of the short accounts receivable period.
This means therefore that, care must be exercised when interpreting the Pearson
correlation coefficients because they cannot provide a reliable indicator of
association in a manner which controls for additional explanatory variables.
Examining a simple bivariate correlation in a conventional matrix does not take
account of each variable’s correlation with all other explanatory variables
(Padachi, 2006). The main analysis will be derived from
appropriate multivariate models estimated using the overall least squares regression
models.
To assess the effects of working capital management on the firm’s profitability,
the firm’s profitability is modeled as a function of the three core working
capital management measures in addition to other firm characteristics. Consistent
with Deloof (2003) and Raheman and
Nasr (2007), the NOP ratio has been used as a proxy for firm’s profitability.
Table 1 reports that the mean NOP in the sample is 0.382 (38.2%),
which is greater than the median NOP of 0.275 (27.5%), suggesting that the distribution
of NOP is skewed to the left. To control for this skewness, the variability
in the NOP and the natural logarithm of turnover (as represented by CS) are
incorporated in the least squares estimations. This is consistent with Huang
et al. (2009). Five regression models were run and the results represented
in a tabular form. Consistent with previous studies, the impact of working capital
management on profitability was modeled using the following regression equations:
Model 1
Model 2
Model 3
Model 4
Model 5
Where, NOP denotes the net operating profit ratio, ACP is the average collection
period, ICP is the inventory conversion period, APP is the average payment period,
CS is the firm (firm) size, Lev is the leverage ratio, FFAR is the fixed financial
assets ratio, Variability represents the variability of the NOP, GDPGrow is
the nominal GDP growth rate, Age is the age of the firm, Ind is the industry
dummy, Ctrl is the ownership dummy and λ denotes firm year controls. Subscripts
i denotes firms (crosssection dimension) ranging from 1 to 468 and t denotes
years (timeseries dimension) ranging from 1 to 16.
Following GarciaTeruel and MartinezSolano (2007)
and Deloof (2003), the determinants of corporate profitability
are estimated both with the pooled OLS and the fixed effects models as presented
in Table 3 and 4. These two models were
used for comparison purposes. The fixed effects model excludes all variables
that are time invariant from the model (Deloof, 2003).
Table 3: 
Relationship between WCM and profitability (19932008) pooled
OLS models 

The pvalues are in the parentheses with *, **and ***Denoting
significance at 10, 5 and 1% levels, respectively. The results are obtained
using the pooled OLS estimation model. Source: 19932008 survey data, author’s
computation 
Table 4: 
Relationship between WCM and profitability: Fixed effects
regression models 

The pvalues are in the parentheses, with *, ** and ***Denoting
significance at the 10, 5 and 1% levels, respectively. The results are obtained
using the fixed effects estimation model. Source: 19932008 survey data,
author’s computation 
The OLSmodel includes all variables of the fixed effects model in addition to the dummy variables. The fixed effects model explains the variations in profitability within firms while the pooled OLS explain the variations in profitability between firms.
Consistent with GarciaTeruel and MartinezSolano (2007),
this methodology presents important benefits. These include the fact that panel
data methodology assumes that individuals, firms, states or countries are heterogeneous.
Timeseries and crosssection data studies not controlling for this heterogeneity
run the risk of obtaining biased results. Furthermore, panel data give more
informative data, more variability, less collinearity among variables, more
degrees of freedom and more efficiency (Baltagi, 2001).
Estimating models from panel data requires the researchers first to determine
whether there is a correlation between the unobservable heterogeneity of each
firm and the explanatory variables of the model. If there is a correlation (fixed
effects), it would be possible to obtain the consistent estimation by means
of the withingroup estimator.
In the first regression model, the ACP has been regressed against the NOP.
In the second regression model, the ICP has been regressed against the NOP.
The third regression model involves a regression of the APP against the NOP.
In the fourth regression model, the CCC is regressed against the NOP. Finally,
the three working capital measures (ACP, ICP and AP) have been regressed together
against the NOP. The CCC was not included in the last regression model because
its inclusion results to a high degree of multicollinearity among the working
capital management variables as shown by the variance inflation factors (VIFs)
(Montgomery et al., 2007).
Because a firm’s working capital management practices are affected by
the macroeconomic factors, the growth in the Gross Domestic Product (GDP) in
its nominal terms has been included. Since, good economic conditions tend to
be reflected in a firm’s profitability, this variable controls for the
evolution of the economic cycle (GarciaTeruel and MartinezSolano,
2007). The GDP also controls for the inflationary pressures that affect
the key components of working capital management. It also controls for other
economic changes in the macroeconomic environment. The firm size (CS), leverage
(Lev), Fixed Financial Assets Ratio (FFAR), the variability in sales (Variability)
and the natural logarithm of the firm years of existence (Age) have been incorporated
to control for firm characteristics.
The agency theory suggests that managers may manage the working capital towards
set targets hence deceiving the shareholders of the firm. Again, as Deloof
(2003) posits, developing countries have underdeveloped capital markets
in the sense that information and agency problems are particularly pronounced.
La Porta et al. (1997), who find that countries
with poorer investor protection have smaller and narrower external capital markets,
shows that a developing country has weak legal protection of corporate shareholders
and creditors, making bank financing and trade credit more attractive. Fisman
and Love (2003) argue that trade creditors mitigate weak creditor protection
and imperfect information better than formal lenders and find that firms in
countries with less developed financial markets use informal credit provided
by their suppliers to finance growth.
Finally, the controls for industry, ownership and time effects (not reported) using indicator variables have also been incorporated in the regression models. Table 3 and 4 report the pooled OLS and fixed effects regression results of the overall relationship which exists between WCM and profitability. The analysis on this study is based on Table 3 since the results in this table are obtained using the pooled OLS model.
Relationship Between Accounts Collection Period (ACP) and Profitability
Consistent with Deloof (2003), Raheman
and Nasr (2007), Shin and Soenen (1998) and Garciateruel
and MartinezSolano (2007), a negative relationship exists between the ACP
and profitability (p<0.01). This result suggests that firms can improve their
profitability by reducing the number of days accounts receivable are outstanding.
The result can also be interpreted as the less the time it takes for customers
to pay their bills, the more cash is available to replenish inventory hence
the higher the sales realized leading to higher profitability of the firm. The
negative coefficient on the ACP suggests that an increase in the number of days
accounts receivable by 1 day is associated with a decline in profitability.
Consistent with Lazaridis and Tryfonidis (2006), this
finding implies that managers can improve profitability by reducing the credit
period granted to their customers. This finding implies that a more restrictive
credit policy giving customers less time to make their payments improves performance.
The coefficients on the other variables are significant. The model shows that
the net operating profit increases with firm size (as measured by natural logarithm
of sales) and this is highly significant at 1% level. The NOP increases with
an increase in the variability of net operating profit (p<0.01). The NOP
decreases with an increase in leverage (p<0.05). Consistent with Deloof
(2003), net operating profit also increases with fixed financial assets
(p<0.01). Again, net operating profit increases with an increase in a firm’s
age (as measured by the natural logarithm of years of existence) (p<0.01).
The model’s adjusted R^{2} is 53% with an Fvalue of 22.08 which
is highly significant (p<0.01). The Durbin Watson statistic is 1.213.
Relationship Between Inventory Conversion Period (ICP) and Profitability
In model 2, the coefficient on the inventory conversion period is positive
and highly significant at 1%. This means that there exists a positive relationship
between the ICP and profitability. This finding is consistent with studies carried
out on conservative working capital policies. This means that maintaining high
inventory levels reduces the cost of possible interruptions in the production
process and the loss of business due to scarcity of products. Maintaining high
levels of inventories also helps in reducing the cost of supplying the products
and protects the firm against price fluctuations as a result of adverse macroeconomic
factors as observed by Blinder and Maccini (1991). Most
studies have not found the expected negative relationship between ICP and profitability
to be significant (Lazaridis and Tryfonidis, 2006; Padachi,
2006). The other variables in model 2 are also significant. The firm size
is positively related to profitability and this is highly significant at 1%.
The use of leverage is negatively related to firm’s profitability (p<0.10).
The fixed financial assets ratio is positively related to the firm’s profitability
and is highly significant at 1%. Both the variability of net operating profit
and the age of the firm (as measured by the natural logarithm of sales) are
positively related to the firm’s profitability at 1%. The model’s
adjusted R^{2} is 55.6% with an Fvalue of 24.43 which is highly significant
(p<0.01). The Durbin Watson statistic is 1.256.
Relationship Between the Average Payment (APP) and Profitability
In model 3, the coefficient on the average payment period is positive and
highly significant at 1%. This suggests that an increase in the number of days
accounts payable by 1 day is associated with an increase in profitability. The
positive relationship can be explained in two ways. First, contrary to Deloof
(2003) and Raheman and Nasr (2007), this finding
holds that more profitable firms wait longer to pay their bills. This implies
that they withhold their payment to suppliers so as to take advantage of the
cash available for their working capital needs. Second, this result makes economic
sense in that the longer a firm delays its payments to its creditors, the higher
the level of working capital levels it reserves and uses in order to increase
profitability. This finding is in line with the working capital management rule
that firms should strive to lag their payments to creditors as much as possible,
taking care not to spoil their business relationships with them. The other variables
in the model are significant with firm size being positively related to profitability
(p<0.01). Consistent with the other models, an increase in the use of debt
decreases profitability (p<0.05). The fixed financial assets ratio is positively
related with profitability and this is highly significant at 1% level. The firm’s
profitability is positively related with both the variability of the net operating
profit and the age of the firm at 1% level of significance. The model’s
adjusted R^{2} is 56.8% with an Fvalue of 25.52 which is highly significant
(p<0.01). The Durbin Watson statistic is 1.365.
Relationship Between the Cash Conversion Cycle (CCC) and Profitability
In model 4, there exists a negative relationship between the cash conversion
cycle and profitability. This finding is significant at 10% level. This supports
the notion that the cash conversion cycle is negatively related with profitability.
Shin and Soenen (1998) argued that the negative relation
between profits and the cash conversion cycle could be explained by the market
power or the market share, i.e., a shorter CCC because of bargaining power by
the suppliers and/or the customers as well as higher profitability due to market
dominance. The negative relationship between the firm’s CCC and profitability
can also be explained by the fact that minimizing the investment in current
assets can help in boosting profits. This ensures that liquid cash is not maintained
in the business for too long and that it is use to generate profits for the
firm. The other variables in the model are also statistically significant. The
firm size is positively related to profitability and this is highly significant
at 1%. The use of leverage is negatively related to firm’s profitability
(p<0.05). The fixed financial assets ratio is positively related with profitability
and it is highly significant at 1%. Both the variability of net operating profit
and the age of the firm are positively related to firm’s profitability
at 1%. The model’s adjusted R^{2} is 52.4% with an Fvalue of 21.56
which is highly significant (p<0.01). The Durbin Watson statistic is 1.220.
Model 5 acts as a control model for the variables under study. The model was
run so as to provide an indicator as to the most significant variables affecting
the study. The model shows that all the variables included are highly significant
at 1% level with an exception of leverage (significant at 10%) and GDP Growth
rate which is not significant. In this model, the ACP and the leverage are negatively
related with the firm’s profitability while all the other variables exhibit
a positive relationship. The model’s adjusted R^{2} is 59.7%% with
an Fvalue of 26.64 which is highly significant (p<0.01). The Durbin Watson
statistic is 1.383.
In this research, an examination of the relationship between working capital
management and corporate profitability is carried out. While prior research
documents that managers can create value for their shareholders by reducing
the ACP and the ICP to a reasonable minimum (Deloof, 2003;
Raheman and Nasr, 2007; Padachi,
2006; GarciaTeruel and MartinezSolano, 2007),
this study holds that managers can actually create value for their shareholders
by decreasing the ACP and increasing the ICP. This finding is consistent with
prior research such as Blinder and Maccini (1991). Contrary
to findings by Deloof (2003), the negative relationship
between the accounts payable and profitability is consistent with the view that
more profitable firms wait longer to pay their bills since they have a greater
bargaining power with their suppliers.
CONCLUSION
Based on the key findings from this study, the following conclusions can be derived:
The management of a firm can create value for their shareholders by reducing the number of days accounts receivable.
The management can also create value for their shareholders by increasing their inventories to a reasonable level. Firms can also take long to pay their creditors in as far as they do not strain their relationships with these creditors.
Firms are capable of gaining sustainable competitive advantage by means of effective and efficient utilization of the resources of the organization through a careful reduction of the cash conversion cycle to its minimum. In so doing, the profitability of the firms is expected to increase.
ACKNOWLEDGMENTS
The author wishes to thanks the research workshop participants at Strathmore University on 31st July 2009 for their comments on this study. Finally, the author wishes to thank the two anonymous reviewers who have contributed in their comments on this study.