An Empirical Analysis of Nexus between Working Capital
Management, Policy and the Corporate Profitability of Listed Non-
financial Firms in Nigeria
AKINTO ADETOLA AJIKE, UMAR ABBAS IBRAHIM, MURITALA TAIWO ADEWALE
Department of Business Administration,
Nile University of Nigeria,
Cadastral Zone C-OO, Research & Institution Area, Airport Rd, Jabi 900001, Abuja
NIGERIA
Abstract: - The economic recession in Nigeria and the effect of COVID-19 on quoted companies has brought to
fore the need for an effective working capital management. Managers need to understand the dynamics of
investing, financing and managing of working capital in achieving business sustainability and maximization of
shareholders’ wealth. It is against this backdrop that this study assessed the link between working capital
management (WCM), working capital policy (WCP) and corporate profitability of listed non-financial firms in
Nigeria. We used a sample of 109 Listed Non-financial Firms on the Nigeria Exchange Group from 2011
through 2020. The purpose of this paper is to establish a relationship that is statistically significant between
profitability, Working Capital Management Policy and its components for listed firms in the NSE which was
estimated by the static panel regression model computed in STATA 14 statistical software. The results of our
research showed that there is statistical significance between profitability, measured through return on capital
employed, and the components of working capital management (WCM): cash conversion cycle and working
capital investment policy. Although, a negative and no statistical relationship is observed between profitability,
measured through return on capital employed and working capital financial policy which is the proxy for
working capital policy (WCP). Managers should take advantage of the effect observed by keeping CCC in a
well-controlled and reasonable period as the level of impact achievable is based on management strategy.
Furthermore, the study has shown that if firms invest more in current assets their profit will be significantly
affected. Managers should however put into account the nature of their business and identify the optimal level
that brings the highest return as the associated cost of holding current assets may outweigh the gain later..
Key-Words: - Working Capital Management, Working Capital Policy, Return on Capital Employed
Received: September 17, 2021. Revised: May 23, 2022. Accepted: June 15, 2022. Published: July 22, 2022.
1 Introduction
From 2011 and 2020, Nigeria experienced an erratic
economic trend, recession and negative economic
impact of both insecurity and COVID-19 that put
businesses in financial crises ([1]; [2]; [3]).
According to PwC Nigeria, Economic [4]
challenges of foreign exchange illiquidity and
insecurity in the country inhibited the growth of
many business sectors, and companies tend to fail in
financial distress due to inadequate working capital
[5]. Reduction in the available working capital of
some listed non-financial firms (Guinness Nigeria
PLC, UACN Plc, Nigeria Breweries PLC,
Afromedia PLC, GlaxoSmithKline PLC, Chams
PLC, 11 PLC and Chellerams PLC) by 36%, 49%,
111%, 11%, 2%, 23%, 29%, and 162% respectively
between 2019 and 2020 contributed to the grossly
lower profit before tax of these firms by
140%,204%, 49.81%, 7.08%, 14%.364%, 31% and
128% respectively. This has now made it expedient
to take a deeper look at the management of working
capital to maintain the liquidity, survival, solvency
and profitability of listed firms, especially during
the economic downturn ([6]; [7], [8]; [9].
Likewise, discourse on working capital and
profitability has been ongoing for years but either
working capital management or working capital
policy have been the determinants [10], [11]; [12];
[13]; [10]. However, working capital management
and capital structure are two scopes that academics
frequently explore in order to predict a firm's
profitability. There have been a variety of
approaches to working capital management.. Other
studies that confirmed a significant effect and
positive relationship between working capital
management, working capital policy and financial
performance include [14], [15], [16], [17] and [18].
Conversely, studies by Azeez (2017), [19], [20],
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[21], [22] and Likewise, [23] amongst others posited
that a negative and significant relationship between
the working capital management and policy on
cooperate profitability and profitability.
While WCM and WCP which have a
complementary effect on firm performance are yet
to be jointly thoroughly explored. Also, many
companies are still out of business as a result of
their poor working capital management and weak
working capital investment and financing strategies
[24], because the maintenance of adequate solvency
and maximization of profitability requires the right
balance between short-term sources of financing and
short-term investments in current assets [25]. This
has necessitated the need for further research on the
relationship between corporate profitability, WCP
and WCM to acquire knowledge to curb the decline
of working capital level and reduce the chances of
businesses failing.
Furthermore, the focus of this study is listed non-
financial firm in Nigeria which consists of several
sectors involved in manufacturing, production,
telecoms and provision of utilities that require large
capital investment. However, extant studies
scarcely considered a comprehensive profitability
measure like return on capital employed (ROCE)
which shows how well a firm is generating profits
from invested asset [26]. ROCE is measured by
profit before interest and tax to total capital
employed ratio. It eliminates the influence of tax
rates in determining corporate financial
performance, allowing for more equitable horizontal
comparisons between enterprises, especially capital-
intensive industries [27]. The ROCE can help
discover firms that have the potential to make more
money and management that can efficiently allocate
capital and resources.
2 Problem Formulation
It is based on these realities that this study focused
on analysing the nexus between WCM, WCP, and
the corporate profitability of listed non-financial
firms in Nigeria by adopting the accounting-based
measure of profitability- return on capital employed
(ROCE). Therefore, the study explicitly examines
the effect of the cash conversion cycle, working
capital investment policy and working capital
financial policy on the return on capital employed
by listed non-financial firms in Nigeria. The study
also considers that the non-financial firms in Nigeria
consist of 10 sectors with varying operational
procedures and different working capital
requirements, hence will determine the individual
influence of each sector, to check if the relationship
observed is sector bias. This therefore
diagrammatically illustrated in figure 1 below.
Fig. 1: Study Conceptual Model and Hypotheses
Path
2.1 Study Theoretical Framework
The cash conversion cycle (CCC) theory was
chosen because it provided a comprehensive
approach to determining the ideal working capital
level for firms and encompasses the other three
components of working capital management,
accounts payable period, accounts receivable period,
inventory period and; and directly affects the
liquidity and profitability of the firm [28]; [29],
[30]. CCC represents the flow of cash within the
company as it predicts how long cash is tied up in
its operations [16]. According to the CCC theory, all
things being equal, a short CCC will lead to an
increase in the firm’s profitability, liquidity and firm
value; while a longer one will cause a lower firm
value and profitability [31], [32]; [33]; [34].
Figure 2 is a pictorial representation of the cash
conversion cycle theory. It shows the relationship
among the components of CCC (inventory period,
accounts receivable period and accounts payable
period) and a pictorial presentation of the difference
between the operating cycle and CCC. The length of
the average payment period partly indicates how
much current liabilities is used in financing current
assets investments. The cash conversion cycle can
be shortened by increasing the average payment
period and reducing the inventory and average
collection periods.
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Akinto Adetola Ajike, Umar Abbas Ibrahim,
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Fig. 2: Cash Conversion Cycle
This choice of theory is inconsonant with the studies
of [35], [31], [1], [36], [37], [38] and [39] who have
used the CCC theory as the foundation for their
studies.
2.1.1 Methodology
A secondary method of data collection was used for
this study as the relevant data were extracted from
the financial statements of all 109 non-financial
firms from 2011 to 2020. These firms are
categorized into 10 different sectors: Agriculture
(5), Conglomerates(5), Construction/Real Estate(8),
Consumer Goods(19), Healthcare(10), ICT(9),
Service(25), Industrial Goods(13), Oil & Gas(11)
and Natural Resources(4). Outside the identified
variables, some other variables could affect the
outcome of this study. To achieve a fair result, the
outcome is then protected from other possible
impacts by using control variables [40]. Following
the examples of previous research work like [41],
[42], [14], [8], [10], [16], [22] Firm age, size and
leverage are chosen as the control variables for this
study.
2.1.2 Model Specification
ROCEit = β0+ β1(CCC)it + β2(WCIP)it+
β3(WCFP)it+ β4(AGE)it+ β5(FS)it+ β6(LVG)it+
Sector (Dummy.i) + eit
Where:
ROCE = Return on capital employed
CCC = Cash Conversion Cycle
WCIP = Working Capital Investment Policy
WCFP = Working Capital Financing Policy
AGE = Firm Age
FS = Firm Size
LVG = Leverage
Sector (Dummy.i) = Dummy Sector. It is a
vector of the sectors that captures all the ten sectors
(Agriculture, Conglomerate, Construction & Real
Estate, Consumer Goods, Health Care, ICT,
Industrial Goods, Natural Resources, Oil & Gas
and Services). ei = random error term which takes
care of the effects of other factors which are not
fixed in the model, on dependent variable
β0 = Intercept/Regression Constant;
i =Firm
t = time
β1, β2, β3, β4, β5, β6 are the regression coefficient
associated with independent variables. The
dependent and independent variables are computed
variables and ratios, the measure of the variables is
stated in Table 1.
Table 1. Measure of Variables
Variables
Measure
Abbreviation
Return on
Capital
Employed
Profit before
Interest and
Tax
Capital
Employed
ROCE
Cash
Conversion
Cycle
Inventory
Period +
Average
Receivable
Period -
Average
Payment
Period
CCC
Working
Capital
Investment
Policy
Total
Current
Assets
Total Assets
WCIP
Working
Capital
Financing
Policy
Total
Current
Liabilities
Total Assets
WCFP
Firm Age
Number of
years from
incorporation
to 2020
AGE
Firm Size
Total Asset
SIZE
Leverage
Total Debt
Total Assets
LVG
Source: Author’s Computation, 2022
2.1.3 Method of Data Analysis
Panel Data Regression Analysis which was adopted
by [43], [44] and [45], [46] is also used for this
study to give an estimation result that is Best Linear
Unbiased (BLUE) [41]. Panel Data Regression is a
combination of cross-section data and time series,
the same individual/cross-section unit is measured at
different times [47]. This approach can predict the
effect more than one independent variable (CCC,
WCIP and WCFP) has on the dependent variable
Received
ddddevc
Cash Paid for Inventory
Av. Payment Period
Inventory Conversion Period
Average Collection Period
Inventory Purchased
Inventory Sold
Cash Conversion Cycle
Operating Cycle
Cash
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(ROCE). The computation device used for this
estimation is the STATA 15 software [48].
In employing panel data to estimate the regression
model, three approaches can be used [49]; [50]:
Common Effect Model or Pooled Least Square
(PLS), Fixed Effect Model (FE) and the Random
Effect Model (RE). The Hausman test is used to
determine the more appropriate method between
Fixed Effect (FE) and Random Effect (RE). All tests
are carried out at five per cent (5%) level of
significance. Hence all null hypotheses were only
rejected where the p-value was less than 0.05.
Where p-value was greater than 0.05, the
hypotheses were not rejected.
3 Empirical Results and Discussions
3.1 Summary of Descriptive Statistics
The subsection reports the summary statistics. The
population of this study is made up of all the one
hundred and nine (109) non-financial firms listed in
the Nigeria Exchange Group for the period of ten
years, 2011 -2020. Table 4 displays their descriptive
statistics for Return on Capital Employed (ROCE),
Working Capital Financing Policy (WCFP),
Working Capital Investment Policy (WCIP) Cash
Conversion Cycle (CCC) and the control variables,
Firm Age, Firm Size and Leverage The summary
statistics revealed that the data set in the panel is
balanced since we were able to obtain equal
timeframe (10 years each) for all the cross-sections
(firms).
Table 2. Summary of Descriptive Statistics
ROCE
CCC
WCIP
WCFP
AGE
FS
LEV
Mean
1.083
16.716
10.363
8.884
42.491
22.626
0.199
Median
1.187
13.125
9.045
7.255
40.000
22.535
0.112
Maximu
m
2.640
438.620
43.810
43.380
142.000
29.360
1.923
Minimu
m
-0.952
0.010
0.000
0.000
1.000
0.000
0.000
Std.
Dev.
0.326
19.651
7.722
6.626
24.611
3.120
0.269
Skewness
-2.380
10.986
0.862
1.099
1.069
-3.429
2.931
Kurtosis
11.140
212.265
3.561
4.409
4.950
26.310
13.968
Jarque-
Bera
4038.7
85
2010797.
000
149.17
4
309.63
2
380.165
26813.
150
7023.7
03
Probabilit
y
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Sum
1180.4
73
18220.96
0
11296.
050
9683.4
30
46315.0
00
24662.
190
216.54
1
Sum Sq.
Dev.
115.67
6
420526.0
00
64928.
660
47818.
390
659582.
400
10602.
930
78.643
Observati
ons
1090
1090
1090
1090
1090
1090
1090
Source: Author's Computation (2022)
The total is expected to be 1090 data points
(from 10 years for all the 109 non-financial firms
listed in Nigeria used for the analyses). Each
company is unique and has varying data, thus no
variable is repeated for any company and all the
variables are in natural logarithm. Mean is the
average value of the sequence resulting from the
division by the number of measurements of the total
value of the variable. In the panel, all the series
which make up the panel are taken into account; it
also includes the average panel. From table 4.1 the
mean of Return on Capital Employed (ROCE),
WCIP, CCC, WCFP, firms age (AGE), firms size
(FS) and Leverage (LEV) are 1.083. 16.716. 10.363,
8.884. 42.491. 22.626 and 0.199 respectively.
Also, the Standard deviation is how the
dispersion or spread in the series is measured. Table
4.1, shows the standard deviation for ROCE, Cash
Conversion Cycle (CCC), Working Capital
Investment Policy (WCIP) are 0.326, 19.651, 7.722,
6.626, 24.611, 3.120 and 0.269 respectively. This
shows that the rate of spread of the variables over
the period under study is on average similar but
CCC is more widely spread than any other variable
while ROCE has comparatively a minimal spread of
all the independent variables.
The Jarque-Bera normality test which is a
precondition for fitting the panel regression model is
stated. Although, the normality test for all the
variables return a p-value less than 0.05 (5%) level
of significance, thus implying that none of the
variables is normally distributed and as such the
variable natural logarithm transformation was used
to correct for the non-normality seen in the series
before fitting the panel model.
Pre-Estimation Test
In order not to undermine the accuracy of outcomes,
pre-estimation tests are done to check for the
probability of the presence of conditions and biases.
They are done to establish that the data meet the
panel regression model's essential assumptions.
Table 3. Unit Root Test Result
Variable
Statistic
(Adjusted
t*)
p-
value
Levin-Lin-Chu unit-root test for
ROCE
-11.43
0.000
Levin-Lin-Chu unit-root test for
CCC
-18.8563
0.000
Levin-Lin-Chu unit-root test for
WCIP
-15.8067
0.000
Levin-Lin-Chu unit-root test for
WCFP
-20.9606
0.000
Levin-Lin-Chu unit-root test for FS
-16.4904
0.000
Levin-Lin-Chu unit-root test for Lev
-13.8718
0.000
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Source: Author’s Computation (2022)
In testing for stationarity, with a p-value less
than 5%, the null hypothesis can be rejected. The
outcome of the Levin-Lin-Chu unit-root test for
panel data in table 4.2 shows a p-value of 0.000<
0.05, which implies that all the variables are not
stationary at level. Since the variables are not
stationary, they are therefore transformed by taking
their natural logarithm before fitting the panel
regression for optimal model parameter estimation.
Table 4. Test of Multicollinearity Result
Model
Collinearity Statistics
Tolerance
VIF
1
CCC
0.989
1.011
WCIP
0.990
1.010
WCFP
0.995
1.005
AGE
0.970
1.031
FS
0.967
1.034
Lev
0.998
1.002
a. Dependent Variable: ROCE
From the multicollinearity test shown in Table 4.4,
it was observed that all the variables return a low
VIF value that does not exceed the minimum
condition (>5) for no collinearity stated by the VIF.
This implies that the variables do not collinear, the
Panel data Regression (Generalized Least Square
GLS) model can be applied with an expectation of a
robust inference since the multicollinearity
assumption is not violated.
Table 5. Hausman Test for Model Selection
As observed from the Hausman test p-value
(0.9009) which is greater than the 0.05 (5%) level of
significance, which in turn implies that the random
effect model is the most appropriate and thus better
than both the Pooled OLS and the fixed Effect
model. Therefore, this study will be based on it for
the test of hypothesis with ROCE as the dependent
variable.
Table 6. Model Parameter Estimate
ROCE
Random Effect Model
Fixed Effect Model
Coef.
P>|z|
Coef.
P>|t|
CCC
0.0032124
0.000
0.0032063
0.000
WCIP
0.0025067
0.000
0.0024905
0.000
WCFP
-0.000054
0.852
-0.0000568
0.845
AGE
-0.0000867
0.359
-0.0000775
0.414
FS
-0.0001109
0.865
-0.0000882
0.893
Lev
-1.077709
0.000
-1.077925
0.000
SEC
Conglomerates
-0.0147059
0.271
-0.0152031
0.257
Construction/Real
Estates
0.0024912
0.840
0.0018534
0.881
Consumer Goods
0.0174744
0.093
0.0170896
0.101
Healthcare
0.0090501
0.427
0.0083269
0.466
Ict
-0.0336835
0.003
-0.0336343
0.003
Industrial Goods
0.0069812
0.513
0.0064129
0.549
Oil And Gas
-0.0048345
0.657
-0.0057502
0.598
Natural Resources
-0.0062986
0.649
-0.0072367
0.603
Services
0.0086757
0.393
0.0078328
0.442
_cons
1.221939
0.000
1.22195
0.000
Number of groups
109
109
Number of obs
1,090
1,090
F(15, 1074)
24165.98
1604.72
Prob > F
0.000
0.000
R-squared
0.7975
0.7974
Adj R-squared
NA
NA
Root MSE
NA
NA
Discussion of Findings
As seen from the table 6 above, the variable Cash
Conversion Cycle (CCC) has a coefficient of
0.0032124. This implies that the Cash Conversion
Cycle (CCC) has a positive impact on the return on
capital employed (ROCE) as a measure of the
corporate profitability of the firms. This, suggests
that with a percentage increase in the Cash
Conversion Cycle (CCC) of the selected non-
financial firm could result in about a 0.0032124-unit
increase in profitability as explained by their ROCE.
However, Cash Conversion Cycle (CCC) has a p-
value of 0.0000 which is less than the 0.05 (5%)
level of significance which implies that the
coefficient is statistically significant. Hence, the null
hypothesis is rejected. We, therefore, conclude that
the relationship observed between the Cash
Conversion Cycle (CCC) and the return on capital
employed is generalisable.
Contrary to [51] that changes in the CCC metric
are not related to changes in company performance
and also [25], [26], [47]and [37] who also found that
cash conversion cycle and its components have no
major impact on profitability; the inference from
hypothesis one is consistent with previous studies
that there is likely to be a strong link between CCC
and profitability [41], [42] and that the CCC is the
central theory and dynamic indicator of working
capital management and is a very crucial component
of WCM as it directly affects the liquidity and
profitability of the firm [22], [39]. It agrees with
Prob>chi2 = 0.9009
= 8.53
chi2(15) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
10 .0078328 .0086757 -.0008429 .0008119
9 -.0072367 -.0062986 -.0009382 .0010702
8 -.0057502 -.0048345 -.0009157 .0007928
7 .0064129 .0069812 -.0005683 .0007539
6 -.0336343 -.0336835 .0000492 .0008322
5 .0083269 .0090501 -.0007232 .0007964
4 .0170896 .0174744 -.0003848 .0007584
3 .0018534 .0024912 -.0006378 .0010771
2 -.0152031 -.0147059 -.0004973 .0009648
SEC
Lev -1.077925 -1.077709 -.000216 .000498
FS -.0000882 -.0001109 .0000227 .000048
AGE -.0000775 -.0000867 9.18e-06 7.60e-06
WCFP -.0000568 -.000054 -2.76e-06 .0000195
WCIP .0024905 .0025067 -.0000162 .0000162
CCC .0032063 .0032124 -6.05e-06 7.15e-06
fixed random Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
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[14] and [15]; [4]; [18] whose results highlighted a
positive and significant effect of CCC on
profitability.
The Working Capital Investment Policy (WCIP)
has a panel regression coefficient of 0.0025067
which implies that WCIP has a positive impact on
the firms' ROCE as a measure of profitability. Thus,
suggesting that a unit increase in the WCIP will
result in about a 0.0025067-unit increase in its
profitability as explained by its return on capital
employed, which is also seen to be significant since
the p-value 0.000 is less than the 5% level of
significance. Hence, the null hypothesis is rejected.
We, therefore, conclude that there is a significant
and generalizable impact of the Working Capital
Investment Policy (WCFP) on the firms’ return on
capital employed (ROCE) as a measure of corporate
profitability.
A conservative approach are posited to come
with low risk and low return [14] and [15which does
not align with the positive relationship inferred from
this estimation. But it is consistent with [16], [17]
and [18] who posited positive significant
relationships. Which is contrary to [20] who
submitted that working capital investment does not
predict the profitability.
The variable Working Capital Financing Policy
(WCFP) has a panel regression coefficient of -
0.00054 which implies that the Working Capital
Financing Policy (WCFP) has a negative impact on
the firms' return on capital employed (ROCE) as a
measure of corporate profitability. Thus, suggesting
that a percentage increase in the WCFP of the firm
will cause approximately -0.00054-unit decrease in
its performance as explained by its ROCE however,
not statistically significant. Hence, the null
hypothesis is not rejected. We, therefore, conclude
that there is no significant and generalisable impact
of the Working Capital Financial Policy on the
return on capital employed as a measure of
profitability.
This result agrees with studies by [10] and [52]
who posited that financing policy does not have any
significant impact on profitability, which infers that
being aggressive in working capital financing policy
may not be able to improve its profitability. There
could be varying influence depending on the
industry as posited by Ajaya and Swagatika (2018)
who indicated that WCF had a convex relationship
with profitability for firms in the chemical,
construction and consumer goods sector but a
concave relationship was observed for textile, metal
and machinery sector. However the findings is not
consistent with findings by [53] and [54] which
found WCFP to have a significant effect on
profitability and that following a conservative
financing policy by using more long-term debt to
fund the company's operating activities has a
positive effect on company profitability and [55]
that firm performance is enhanced/reduced with a
reduction/increase in working capital financing
through short term debt.
Control Variables: The firms’ age, size and
leverage are used as control variables to mediate the
effect of WCM and WCP on firms' corporate
profitability. The firm age, and firm size return with
negative coefficients and p-values of 0.359 and
0.865 respectively which are greater than 0.05 (5%)
when modelled with ROCE as a dependent variable;
this shows both firm age and size not to have a
significant controlling effect on the relationship
observed between ROCE and WCM/WCP.
Conversely, leverage returned with a p-value of
0.000 which is less than 0.05 (5%), implies that it
has a significant effect on the relationship observed
between ROCE and WCM/WCP but the negative
type.
Controlling effect of the Sector as Dummy: The
sector dummy was plugged into the model to check
if the distinct nature and varying operational
procedures and different working capital
requirements of each sector could influence the
outcome of this study, thus making the observed
relationship between WCM/WCP and the firms’
profitability sector biased. The results are contained
in the overall estimated panel regression model for
ROCE in Tables 4.4.
It was observed that only the ICT sector with a p-
value of 0.003 which is less than 0.05(5%) is
significant when estimated with the ROCE model.
This indicates that the effect of working capital
management and policy on corporate profitability
measured by ROCE is biased to the ICT sector
because the ICT sector influences its outcome.
4 Conclusion
This study has been able to analyze the nexus
between working capital management, working
capital policy and the corporate profitability of all
109 listed non-financial firms in Nigeria as a whole
and per sector to determine the individual influence
of each sector. In conclusion, with regards to the
general objective of this study, there is an observed
positive and significant Impact of working capital
management proxied by CCC and working capital
investment policy on return on capital employed of
listed non-financial firms in Nigeria. While working
capital financing policy has a negative but not
significant effect on return on capital employed of
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.111
Akinto Adetola Ajike, Umar Abbas Ibrahim,
Muritala Taiwo Adewale
E-ISSN: 2224-2899
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Volume 19, 2022
listed non-financial firms in Nigeria. Also, only
leverage as a control variable had an effect on
ROCE while firm size and firm age had no effect.
Furthermore, the outcome of the effect of WCM and
WCP on Return on Capital Employed (ROCE) is
biased to the ICT sector because the ICT sector
influences its outcome.
From the findings and conclusion drawn from this
study, the researcher recommends thus:
Listed companies should take advantage of
the effect CCC has on ROCE by paying attention to
the three components that make up CCC: Inventory
period accounts receivable period and accounts
payable period. CCC should be kept in a well-
controlled and reasonable period as the level of
impact achievable is based on management strategy.
As seen from the results of this study, if
firms invest more in current assets their profit will
be significantly affected. Managers should however
put into account the nature of their business and
identify the optimal level that brings the highest
return as the associated cost of holding current
assets may outweigh the gain later.
This study has shown that WCFP is
insignificant in predicting the profitability of listed
non-financial firms in Nigeria should therefore be
consistent in managing the level of its working
capital such that there is not too much available cash
which can lose its value in harsh economic
conditions and there is no shortage of cash flow that
can make the firm seek additional long-term debts
under unfavorable conditions.
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Muritala Taiwo Adewale
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Muritala Taiwo Adewale
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Akinto Adetola Ajike: Conceptualization,
Methodology, Formal analysis, Software, Writing -
original draft.
Dr Ibrahim Umar Abbas and Dr Muritala Taiwo
Adewale: Conceptualization, Methodology and
Supervision.
Authors’ Declarations
The Authors have no conflict of interests to declare.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research received no specific grant from any
funding agency in the public, commercial, or non-
for-profit sectors.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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DOI: 10.37394/23207.2022.19.111
Akinto Adetola Ajike, Umar Abbas Ibrahim,
Muritala Taiwo Adewale
E-ISSN: 2224-2899
1264
Volume 19, 2022