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Publication Date: August 25, 2020
DOI: 10.14738/abr.88.8729.
Etale, L. M., & Sawyerr, A. E. (2020). Liquidity Management And Financial Performance Nexus: A Micro Analysis With Glaxosmithkline
Consumer Nigeria PLC. Archives of Business Research, 8(8). 138-150.
Liquidity Management and Financial Performance Nexus: A Micro
Analysis With Glaxosmithkline Consumer Nigeria PLC
Lyndon M. Etale
Dept. Accounting, Faculty Management Sciences,
Niger Delta University, Wilberforce Island,
Bayelsa State, Nigeria
Ayaundu E. Sawyerr
Dept. Accounting, Faculty Management Sciences,
Niger Delta University, Wilberforce Island,
Bayelsa State, Nigeria
ABSTRACT
This study was set to investigate the link between liquidity
management and financial performance of GlaxoSmithKline a leading
pharmaceutical manufacturing company in Nigeria with a strong
multinational background. Current ratio (CUR), quick ratio (QUR) and
cash ratio (CAR) were used to represent liquidity management (the
independent variables), while return on assets (ROA), proxy for
financial performance was adopted as the dependent variable.
Secondary data for the study was obtained from the financial
statements of GlaxoSmithKline for the eight year period covering 2011
to 2018. Statistical tools employed for the analysis of data include
descriptive statistics and OLS multiple regression technique applying
the E-view 10 software. The results revealed that current ratio and cash
ratio had significant positive effect on return on assets, while quick
ratio had a significant negative link with return on assets. The study
concluded that liquidity management had mixed significant economic
connection with financial performance in the case of GlaxoSmithKline
Consumer Nigeria PLC. The study recommended that the management
of the company should pay close attention to its liquidity position by
putting in place policies for efficient management of its current assets,
especially inventory, accounts receivable and cash to reduce the
incidence of excess liquidity in the last few years.
Keywords: Current Assets, Current Liabilities, Financial Performance,
Liquidity Ratios, Management
INTRODUCTION
Liquidity management refers how well a firm manages it cash holding and near cash items because
cash is the most liquid asset or resource of any firm or organization. It deals with the ability of a
company to meet its maturing debt obligations (Pandey, 2010). It is concerned with when liquid
assets would be needed and when they would be available for the business to operate smoothly. A
proper management of a firm’s liquidity position involves making cash projections or forecasts as
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well as preparing statements of cash and fund flows on a regular basis as is desired by the firm.
Cash flow statements or forecasts provide information regarding when cash receipts would be
realized from sales or debtors and when payments would be required to be made to creditors
(suppliers of materials or goods) and to other holders of claims against the firm. As liquidity
management is to ensure that the firm has just enough cash available to meet claims that may fall
due.
The three most common ratios used for gauging liquidity include current ratio, quick ratio and cash
ratio. Current ratio is the ratio of current assets to current liabilities. In an ideal situation this ratio
should be 2:1. The quick ratio considers current assets that can easily be converted to cash (the
most liquid asset). Quick ratio is the ratio of current assets less inventory (or stock) to current
liabilities. Stock is not included in the computation because it takes a longer time to covert stock to
cash (inventory conversion cycle). Besides, stock may deteriorate in value if stored for a long time.
However, a ratio of 1:1 is generally acceptable. In terms of Naira currency of Nigeria, a ratio of 1:1
indicates that one Naira of quick asset is available as cover for one Naira of current debt obligation.
The third ratio used to measure liquidity is the cash ratio. It is the ratio of cash plus near cash items
(usually short term investments or marketable securities) to current liabilities. A firm is considered
highly liquid if the value of the numerator used in computing the ratio is far greater than the
denominator value, with some caution to guard against the risk of excess liquidity.
Efficient management of liquidity means that holding too little cash is bad just as holding too much
cash is poor business management. There has to be a balance. According Pandey (2010) the
inability of a firm to meet its maturing obligations due to insufficient funds will lead to poor credit
worthiness, loss of creditors’ confidence or legal pressures that may end up in insolvency or
bankruptcy (in the extreme case). On the other hand, excess liquidity in the case of too much cash
or near cash items would be holding idle assets which would earn no income but reduce profits. In
practice therefore, business managers need to look out for a balance between liquidity and
profitability, because being liquid is not equal to being profitable. This why a good financial
manager should strive to achieve a balance between profitability and liquidity, because lack of
sufficient liquidity may lead to pressure from suppliers resulting in the firm’s closure; while excess
liquidity (say of holding idle liquid current assets) would rather lead to incurring unnecessary cost
that would negatively impact on profitability.
Profitability and performance have often been used inter-changeably to mean one and the same.
Profits, sales growth, assets growth, earning growth, among others are measures of how well a
company is doing. These growth indicators can be used to determine the long term survival of
firms. But simply put performance explains how well a company’s resources are being managed to
generate income for the company over and above the costs incurred in making such income, and
this includes managing the firm’s current or liquid assets. This is where most accounting and
finance themes such liquidity management, working capital management, inventory management
and so on finds their usefulness: as in when a company is set-up; capital structure determined;
funds are made available; plants, machinery and other fixed assets are in place and the business is
up and running; the day-to-day management of the current assets and liabilities becomes key to
the survival of the business.
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Etale, L. M., & Sawyerr, A. E. (2020). Liquidity Management And Financial Performance Nexus: A Micro Analysis With Glaxosmithkline Consumer
Nigeria PLC. Archives of Business Research, 8(8). 138-150.
The motivation for this study came from the fact that the review of previous studies conducted in
both developed and developing economies on the liquidity management and performance link
(available for review) have not produced any consensus in the findings of previous scholars.
Srbinoska (2018), Majakusi (2016), Molefe and Muzindutsi (2016), and Sheikdon and Kavale
(2016) in their works reported that liquidity management was positively linked to performance.
But the studies conducted by Ali and Jameel (2019), Onyekwelu, Chukwuani and Onyeka (2018),
and Lartey, Antwi and Boadi (2013) revealed negative association between liquidity management
and profitability. Whereas, the studies of Tamunosiki, Giami and Obari (2017), Akinwumi, Michael
and Raymond (2017), Svrtinov, Trajkovska and Koleva (2017), Bibi and Amjad (2017), Etale and
Bingilar (2016), Alshatti (2015) and Ben-Caleb, Olubukunola and Uwuigbe (2013) showed mixed
effects, while Murthy, Ramakrishna, Madhavi, Muniraja and Naik (2018), Salimand Bilal (2016) and
Ibe (2013) in their respective studies reported that liquidity management had no effect on
performance of profitability.
This shows a research gap, and this study aimed to further investigate the liquidity management- performance link to help address that gap. To do that this study applied a case study approach and
adopted current ratio, quick ratio and cash ratio as components of liquidity management, while
financial performance was measured by return on assets using data from GlaxoSmithKline for the
period 2011 to 2018; with the objective of determining the effect of liquidity management on
financial performance.
The rest of this paper is divided into four parts. Part two covered review of empirical studies. Part
three treated the research methodology; part four contains the results of data analysis and
discussion of the findings; while the summary, conclusion and recommendations are finally
presented in part five.
REVIEW OF EMPIRICAL STUDIES
Ali and Jameel (2019) examined the role of liquidity management on profitability of banks listed
on Iraq Stock Exchange for the period 2006 to 2016. The study adopted current ratio as proxy for
liquidity management and ROA and ROE were used as measures of profitability. Secondary time
series data was collected from the annual financial statements of sampled 5 banks. They conducted
unit root test, co-integration test as well as pooled effect, mixed effect and random effect regression
analysis based on E-views software in evaluating their study data. They found that there was no
long run relationship among the variables, and that liquidity management had an insignificant
negative effect on profitability. Onyekwelu, Chukwuani and Onyeka (2018) evaluated the effect of
liquidity management on financial performance of banks in Nigeria working with a sample of 5
banks for the period 2007 to 2016. Secondary data was collected from the annual reports of
sampled banks. The study employed descriptive statistics and multiple regression techniques as
methods of data analysis. The results indicated that liquidity management had negative effect on
bank performance.
Srbinoska (2018) examined the relationship between liquidity and profitability of non-financial
companies listed on the Macedonian Stock Exchange for the period 2014 to 2017. The study
adopted current ratio and quick ratio (as measures of liquidity), while ROA, ROE and ROCE were
used as proxies for profitability. Secondary data were collected from audited annual reports of
sampled 76 companies. The study employed descriptive statistics and correlation analysis based
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on windows SPSS computer software as methods of data evaluation. The results indicated that
liquidity had positive significant association with profitability. Murthy, Ramakrishna, Madhavi,
Muniraja and Naik (2018) investigated the link between liquidity and profitability of 3 Tyre
manufacturing companies listed on the Indian Stock Exchange for the period 2013 to 2017. They
adopted current ratio and quick ratio as the independent variables representing liquidity, while
net profit margin and return on capital employed among others were used as proxy for
profitability. Secondary data for the study was obtained from annual reports of sampled companies
and the website of Indian Stock Exchange. The study employed correlation analysis as the
statistical technique for the evaluation of data. The results showed that liquidity management had
no significant impact on profitability.
Waleed, Pasha and Akhtar (2017) examined the trade-off between liquidity and profitability in the
banking sector of Pakistan for the period 2010 to 2015. Secondary data was obtained from financial
statements of all banks listed on the Pakistan Stock Exchange. They employed OLS regression
method for data analysis. The results revealed that bank liquidity ratio had significant association
with ROA, ROE and net profit margin. Tamunosiki, Giami and Obari (2017) examined the
relationship between liquidity and corporate performance of banks in Nigeria for the period 1984
to 2014. Secondary data for the study were collected from annual reports of selected banks and
CBN Statistical Bulletin. The study employed OLS regression, Johansen co-integration test,
Granger-causality test and error correction model as tools for data analysis. The results indicated
that cash reserve ratio had significant negative relationship with ROE (corporate performance).
Loan to deposit ratio had significant positive link with ROE, while liquidity ratio was positively
related to ROE but the relationship was weak.
Akinwumi, Michael and Raymond (2017) examined the relationship between liquidity
management and the performance of banks in Nigeria for the period 2007 to 2016. The study
involved a sample of 4 banks. Secondary data was collected from the annual financial statements
of the selected banks. The study employed Pearson’s correlation technique for data analysis. The
results revealed that return on equity and return on assets both had negative relationship with
current ratio proxy for liquidity management, but only return on equity was significantly related
to current ratio. Svrtinov, Trajkovska and Koleva (2017) examined the relationship between
liquidity, bank capital and profitability of commercial banks in 8 Southeast European countries
using quarterly data for the period 2010 to 2016.
Variables adopted for the study include return on assets (proxy for profitability) and liquid assets
ratio among others. Secondary data was obtained from IMF database using financial soundness
indicators, and the National Bank of Serbia in the case of data relating to banks in Serbia. They
employed descriptive statistics and correlation analysis to analyze data. The results indicated that:
liquidity had statistically significant relationship with profitability in all countries except Greece
and Serbia; liquidity had negative effect on profitability in Macedonia, Greece, Romania, Bosnia,
Herzegovina and Serbia; and liquidity had positive influence on profitability in Bulgaria and
Slovenia.
Bibi and Amjad (2017) investigated the relationship between liquidity and profitability of firms
listed on Karachi Stock Exchange for the period 2007 to 2011. Secondary data for the study was
collected from 50 non-financial business entities included in the study sample. They employed
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Etale, L. M., & Sawyerr, A. E. (2020). Liquidity Management And Financial Performance Nexus: A Micro Analysis With Glaxosmithkline Consumer
Nigeria PLC. Archives of Business Research, 8(8). 138-150.
descriptive statistics, correlation and multiple regression analysis for the evaluation of data. The
findings showed that cash gap had significant negative association with profitability, while current
ratio had significant positive link with profitability. Fagboyo, Adeniran and Adedeji (2016)
evaluated the impact of liquidity management on profitability for deposit money banks in Nigeria
for the period 2007 to 2016. Secondary data for the study variables were collected from the annual
reports of 5 banks namely; Access Bank, Ecobank, First Bank, UBA and Zenith Bank. The study
employed pooled regression analysis for data analysis. The results provided evidence that liquidity
management represented by quick ratio, cash ratio and liquidity coverage ratio had considerable
impact on bank performance measured by ROA and ROE.
Etale and Bingilar (2016) examined the impact of liquidity management on profitability of food and
beverage companies in Nigeria for the period 2011 to 2015. The study involved 5 food and
beverage companies quoted on the Nigerian Stock Exchange. Secondary data was collected from
financial statements of sampled companies. The study employed descriptive statistics and multiple
regression technique based on E-view 7 computer software for the analysis of data. The results
revealed that cash ratio and quick ratio had positive significant relationship with return on capital
employed, but cash conversion cycle had an insignificant negative association with ROCE proxy for
profitability. Majakusi (2016) investigated the effect of liquidity management on the performance
of commercial banks in Kenya for the period 2010 to 2014. Secondary data for the study was
obtained from annual financial statements of 43 banks available at the Central Bank of Kenya. The
study employed descriptive statistics and OLS regression techniques for data analysis. The findings
of the study showed that liquidity management measured by cash and cash equivalents scaled by
total assets and return on assets proxy for banks’ performance were positively correlated.
Duruechi, Ojiegbe and Otiwu (2016) examined the effectiveness of liquidity management on bank
performance in Nigeria for the period 1990 to 2014. Secondary data for the study were obtained
from CBN Statistical Bulletin. The study employed OLS regression technique based on E-view 7.1
software for data analysis. Their findings indicated that all the CBN liquidity management
measures except foreign claims of banks had significant positive effect on banks’ performance.
Salim and Bilal (2016) investigated the impact of liquidity management on financial performance
of banks listed on Muscat Securities Market in Oman for the period 2010 to 2014. The study
involved 4 local banks, and secondary data was collected from annual reports of the sampled banks.
They employed multiple regression techniques for data analysis. The study found that liquidity
management was significantly related to bank performance, but liquidity management had no
significant relationship with net interest margin of banks.
Molefe and Muzindutsi (2016) analyzed the effect of liquidity management on profitability in South
Africa using a sample of 5 banks namely; Barclays, First Rand, Investec, Nedbank and Standard
Bank. The study adopted capital and quick ratios as measures of liquidity management, while ROA
and ROE were used as proxies for profitability. Secondary data obtained from the annual financial
statements of sampled banks were retrieved from McGregor INETBFA database for the period
2004 to 2014. They employed panel unit root test and co-integration test as the statistical tools for
data analysis. The results which showed no long run relationship between profitability and
liquidity/capital management indicated that capital ratio had significant positive effect on
profitability, but liquidity had no effect on banks’ profitability. Sheikdon and Kavale (2016)
examined the effect of liquidity management on financial performance of commercial banks in
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Mogadishu, Somalia. Primary data for the study was collected through the use of a questionnaire
administered on a sample of 87 commercial bank employees in Mogadishu. The study employed
Cronbach Alpha coefficient and multiple regression analysis based on windows SPSS version 22
computer software for data analysis. The results provided evidence that liquidity management had
significant positive influence on financial performance.
Ahmad (2016) examined the relationship between liquidity and profitability in Pakistan using
Standard Chartered Bank as a case study. The study adopted current ratio, quick ratio and net
working capital to represent liquidity, while profitability measures considered in the study
included gross profit, net profit, ROA and ROE. Secondary data for the study was obtained from
annual financial statements of Standard Chartered Bank for the period 2004 to 2013. The study
employed correlation and regression analysis as the statistical tools for data analysis. The results
showed a weak positive relationship between liquidity and profitability. Alshatti (2015)
investigated the effect of liquidity management on profitability of Jordanian commercial banks for
the period 2005 to 2012.
The study adopted quick ratio, liquid assets ratio among others as proxy for liquidity management,
while ROE and ROA were used to represent profitability. The study involved 13 commercial banks
trading on the Amman Stock Market. Secondary data for the study were obtained from Amman
Stock Exchange for the 8 years covered by the study. The study employed ADF unit root test and
regression analysis as tools for data analysis. The results showed that quick ratio and investment
ratio had positive relation with profitability, while liquid assets ratio and capital ratio were
negatively associated with profitability.
Lartey, Antwi and Boadi (2013) examined the relationship between liquidity and profitability of
banks listed on the Ghana Stock Exchange for the period 2005 to 2010. The study involved 7 banks,
and secondary time series data was collected from annual financial reports of sampled banks. They
employed correlation and regression analysis as the statistical tools for data evaluation. The results
indicated that liquidity management had a very weak relationship with profitability of banks in
Ghana. Ben-Caleb, Olubukunola and Uwuigbe (2013) investigated the relationship between
liquidity management and profitability using a sample of 30 manufacturing companies listed on
the Nigerian Stock Exchange.
The study adopted return on capital employed representing profitability as the dependent variable,
while cash conversion cycle, current ratio and quick ratio were used as proxies for liquidity
management and the independent variables. Secondary data was collected from annual reports of
sampled companies for the period 2006 to 2010. They employed descriptive statistics, Pearson’s
correlation and multiple regression techniques based on windows SPSS version 15.0 as tools for
data analysis. The results showed a not significant mixed relationship between liquidity
management and profitability represented by ROCE.
Priya and Nimalathasan (2013) investigated the effect of liquidity management on profitability of
manufacturing companies listed on the Colombo Stock Exchange in Sri Lanka for the period 2008
to 2012. Secondary data for the study was collected from annual reports of sampled manufacturing
firms. The study employed correlation technique and multiple regression analysis as the statistical
tools for data evaluation. The results suggested that there was a significant relationship between
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Etale, L. M., & Sawyerr, A. E. (2020). Liquidity Management And Financial Performance Nexus: A Micro Analysis With Glaxosmithkline Consumer
Nigeria PLC. Archives of Business Research, 8(8). 138-150.
liquidity management and profitability. Ibe (2013) investigated the impact of liquidity
management on profitability of banks in Nigeria for the period 1995 to 2010. Secondary data for
the study was collected from the annual reports of sampled 3 banks and CBN Statistical Bulletin.
The study employed multiple regression analysis as the method for data evaluation. The study
concluded that liquidity management was a critical problem in the Nigerian banking industry.
METHODOLOGY
The research methodology used in carrying out this study is covered in the section; and these
include the research design, source of data, model specification and data analysis techniques.
Research Design
The type of research design adopted in this study is the export facto research design in a case study
approach using GlaxoSmithKline. The export facto research design was used because the study was
done long after the observed events had been recorded. Thus the reliability of data is assured as
the researchers had no powers to manipulate the study data.
Source of Data
This study made use of financial performance as the dependent variable and liquidity management
as the independent variable. Performance was represented by return on assets (ROA) while the
components liquidity management adopted included current ratio (CUR), quick ratio (QUR) and
cash ratio (CAR). Secondary data for these variables were compiled from Glaxo’s statements of
financial position for relevant years covering 2011 to 2018 (as available from the company’s
website).
Data Analysis Techniques
The study used descriptive statistics and multiple regression analysis based on the E-view 10
software as the statistical techniques for data analysis following the regression specified below.
The statistical tool of OLS regression technique is reliable, consistent, efficient, and possesses the
unique properties of best linear unbiased estimator when compared to other techniques of data
analysis.
Model Specification
To facilitate the analysis of data, the study adopted the following model which has been widely
used by previous researchers such as Salim and Bilal (2016) and Etale and Bingilar (2016) to
mention a few:
ROA = ƒ (CUR, QUR, CAR)
The above model was translated into a regression equation as follows;
ROA = α + β1CUR + β2QUR + β3CAR + е Eq:1
Where,
ROA = Return on assets, the dependent variable.
CUR = Current ratio, current assets divided by current liabilities.
QUR = Quick ratio, current assets less inventory divided by current liabilities.
CAR = Cash ratio, cash plus short-term investments or marketable securities divided by current
liabilities.
α = is the intercept or constant
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β1 to β3 = are the coefficients of the independent variables to be determined, which defined the
extent of the link between the dependent variable and the independent variables that were used in
answering the research questions.
е = is the error term of the equation.
RESULTS OF DATA ANALYSIS AND DISCUSSION OF FINDINGS
Presentation of Data
Annual data for the study variables obtained from the content analysis of GlaxoSmithKline financial
statements for the eight years period 2011 to 2018 are presented in Table 1. The data represent
annual ratios of the study variables, return on assets (ROA), current ratio (CUR), quick ratio (QUR)
and cash ratio (CAR) compiled from GlaxoSmithKline’s financial reports.
Table 1: Annual Ratios of the Study Variables
Dependent Variable Independent Variables
Year ROA CUR QUR CAR
2011 0.15 1.40 0.78 0.50
2012 0.13 1.35 0.87 0.48
2013 0.11 1.18 0.70 0.36
2014 0.07 1.09 0.51 0.14
2015 0.03 1.07 0.61 0.21
2016 0.15 2.27 1.87 1.35
2017 0.02 2.58 2.21 1.47
2018 0.04 14.16 9.98 3.75
Source: Compiled from Financial Statements of GlaxoSmithKline
Results of Data Analysis
Descriptive Statistics
The summary of the descriptive statistics of the variables are shown in Table 2. Table 2 shows that
ROA, CUR, QUR and CAR have mean of 0.088, 3.138, 2.191 and 1.033 respectively. On the other
hand, the maximum values of ROA, CUR, QUR and CAR are 0.150, 14.160, 9.980 and 3.750
respectively. While there minimum values are 0.020, 1.070, 0.510 and 0.140 respectively.
Table 2 further shows that the standard deviation of ROA, CUR, QUR and CAR are 0.054, 4.489,
3.208 and 1.207 respectively. This indicates that CUR is the most dispersed variable among the
variables in the study, while ROA is the least dispersed among the variables. The calculated P- values shows that ROA and CAR are normally distributed with probabilities of 0.633 and 0.138
(which are greater than 5 per cent), respectively. CUR and QUR with probability value of 0.009 and
0.015 which are less than 0.05 is not normally distributed, though their effect not significant.
Discussion of Findings
On the basis of the regression results in Table 3, the regression Equation 1 can be expressed as
follows:
ROA = -0.032 + 0.384CUR - 0.704QUR + 0.444CAR + 0.038 Eq2
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Etale, L. M., & Sawyerr, A. E. (2020). Liquidity Management And Financial Performance Nexus: A Micro Analysis With Glaxosmithkline Consumer
Nigeria PLC. Archives of Business Research, 8(8). 138-150.
Equation 2 shows the extent of the association between the dependent variable ROA and the
independent variables CUR, QUR and CAR. The regression results in Table 3 were used to test the
hypotheses of the study. The results shown in Table 3, indicates the independent variables
combined explained 72% of changes in the dependent variable with probability of F-statistic value
of 0.129. At 5% or 0.05 acceptance rule, this is not significant. Secondly, the coefficient of
determination (R-squared) value of 0.723 indicates that 72% of changes in the dependent variable
are accounted for by the combined effect of changes in the independent variables.
Also, the adjusted R- squared value of 0.516 indicates that the model used is a proper and good fit
for testing the hypotheses of the study. This provides a confidence level (at approximately 52% for
acceptance of the goodness of the study model. Furthermore, the Durbin- Watson statistics value
of 2.495 is approximately equal to the 2.0 benchmark, which indicates there is no serial auto
correlation among the independent variables.
Table 2: Descriptive Statistics
ROA CUR QUR CAR
Mean 0.088 3.138 2.191 1.033
Maximum 0.150 14.160 9.980 3.750
Minimum 0.020 1.070 0.510 0.140
Std. Dev. 0.054 4.489 3.208 1.207
Probability 0.633 0.009 0.015 0.139
Observations 8 8 8 8
Source: E-views 10 output
Regression Results
Table 3: Multiple Regression Results
Dependent Variable: ROA
Method: Least Squares
Date: 05/19/20 Time: 15:22
Sample: 2011 2018
Included observations: 8
Variable Coefficient Std. Error t-Statistic Prob.
C -0.032 0.049 -0.656 0.547
CUR 0.384 0.134 2.854 0.046
QUR -0.704 0.241 -2.916 0.043
CAR 0.444 0.154 2.888 0.044
R-squared 0.723 Prob.(F-statistic) 0.129
Adjusted R-squared 0.516 Durbin-Watson stat 2.495
S.E. of regression 0.038
Source: E-views 10 Output
Overall, the regression results used to examine the link between liquidity management, (CUR, QUR
and CAR) and financial performance (ROA) indicated that: CUR has significant positive link with
ROA with coefficient of determination and P-value of 0.383 and 0.046 respectively; QUR has
significant negative association with ROA with coefficient of determination and P-value of -0.704
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and 0.043 respectively; and CAR also has statistically positive significant effect on ROA with
coefficient of determination and P-value of 0.444 and 0.044 respectively.
Research questions
The coefficients of the independent variables in Equation 2 were used to answer the research
questions.
1. What is the effect of current ratio (CUR) on return on assets (ROA)?
The coefficient of CUR in Equation 2 is 0.384. This means that a unit increase in CUR will
lead to 0.38 units increase in ROA.
2. What is the effect of quick ratio (QUR) on return on assets (ROA)?
The coefficient of QUR in Equation 2 is -0.704. This means that a unit increase in QUR will
bring about a 0.70 units decrease in ROA.
3. What is the effect of cash ratio (CAR) on return on assets (ROA)?
The coefficient of CAR in Equation 2 is 0.444. This means that a unit increase in CAR will
result to 0.44 units increase in ROA.
Test of hypotheses
The calculated values of the co-efficient determination and their related P-values of the
independent variables were used to address the research questions and test the study hypotheses
in the following sections.
1. Current ratio (CUR) has no significant effect on return on assets (ROA)
From Table 3, the coefficient of determination of CUR is 0.384 with a P-value of 0.046. This
means that CUR has a positive significant effect on ROA (significant at 5% level). Therefore,
the null hypothesis above is rejected. Implying that a unit increase in CUR will lead to 0.38
units increase in ROA. This finding is supported by the study result of Tamunosiki et al
(2017).
2. Quick ratio (QUR) has no significant influence on return on assets (ROA)
Again, from Table 3 the coefficient of determination of QUR is -0.704 with P-value of 0.043.
This means QUR has a negative significant influence on ROA (significant at 5% level). So the
null hypothesis stated above is rejected. The economic implication being that a unit increase
in QUR will bring about a 0.70 units decrease in ROA. This finding agrees with the result of
Tamunosiki et al (2017), but contradicts the findings of Akinwumi et al (2017), Fagboyo et
al (2017) and Etale and Bingilar (2016).
3. Cash ratio (CAR) has no significant impact on return on assets (ROA)
The coefficient of determination of CAR is 0.444 with P-value of 0.044 as shown in Table 3.
This also means that CAR has a significant positive effect on ROA. Here again the null
hypothesis which stated that cash ratio (CAR) has no significant impact on return on assets
(ROA) is rejected. This finding also implies that a unit increase in CAR will result to 0.44
units increase in ROA. This finding supports the findings of Fagboyo et al (2017), Etale and
Bingilar (2016) and Majakusi (2016), but not consistent with the findings of Bibi and Amjad
(2017) and Alshatti (2015).
Overall this study revealed that liquidity management has significant mixed economic link with
financial performance for GlaxoSmithKline Consumer Nigeria PLC. From the test of the hypotheses
above, CUR and CAR (two measures of liquidity) had significant positive link with ROA (the
performance measure); while QUR ratio (the third measure of liquidity) had significant negative
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Etale, L. M., & Sawyerr, A. E. (2020). Liquidity Management And Financial Performance Nexus: A Micro Analysis With Glaxosmithkline Consumer
Nigeria PLC. Archives of Business Research, 8(8). 138-150.
association with ROA. However, the high liquidity ratios in 2016, 2017 and 2018 shown in Table 1
indicates that the company is having excess liquidity with consequently low ROA figures of 0.02
and 0.04 in 2017 and 2018 respectively. In addition, the abnormally high liquidity ratios in 2018
(CUR 14.16:1; QUR 9.98:1), and CAR 3.75:1) points to the fact that the company is likely having
weaknesses in managing its inventory, accounts receivables and cash holdings. An efficient
management of these items of current assets would possibly set the position right.
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Summary
On the basis of the regression results and test of hypotheses the following summarizes the findings
of this study:
1. Current ratio (CUR) had positive significant effect on return on assets (ROA) with p-value
of 0.046 and co-efficient of determination of about 0.38;
2. Quick ratio (QUR) had negative significant relationship with return on assets (ROA), with p- value of 0.043 and co-efficient of determination of -0.70;
3. Cash ratio (CAR) had a positive significant association with return on assets (ROA) with p- value of 0.044 and co-efficient of determination of 0.44; and
4. Liquidity management had significant mixed effect on financial performance. Besides, the
high liquidity ratios in 2016, 2017 and 2018 indicates that the company is having excess
liquidity with consequently low ROA figures of 0.02 and 0.04 in 2017 and 2018 respectively.
Particularly in 2018, the abnormally high liquidity ratios (CUR 14.16:1; QUR 9.98:1), and
CAR 3.75:1) means the company is likely having weaknesses in managing its inventory,
accounts receivables and cash holdings.
CONCLUSION
This study was set to investigate the link between liquidity management and financial performance
of GlaxoSmithKline a leading pharmaceutical manufacturing company in Nigeria with a strong
multinational background. Current ratio (CUR), quick ratio (QUR) and cash ratio (CAR) were used
to represent liquidity management (the independent variables), while return on assets (ROA),
proxy for financial performance was adopted as the dependent variable. Secondary data for the
study was obtained from the financial statements of GlaxoSmithKline for the eight year period
covering 2011 to 2018. Statistical tools employed for the analysis of data include descriptive
statistics and OLS multiple regression technique applying the E-view 10 software. The results
revealed that current ratio and cash ratio had significant positive effect on return on assets, while
quick ratio had a significant negative link with return on assets. The study concluded that liquidity
management had significantly mixed economic connection with financial performance in the case
of GlaxoSmithKline Consumer Nigeria PLC.
RECOMMENDATIONS
1. This study recommended that the management of GlaxoSmithKline should pay close attention
to the company’s liquidity position and take immediate steps to reverse the increasing excess
liquidity position in 2016, 2017 and 2018. This can be achieved by putting in place policies for
efficient management of its current assets, especially inventory, accounts receivable and cash.
2. It was also recommended that the company should take steps to efficiently manage its
inventory and accounts receivables order to reduce its inventory conversion cycle and
receivables collection period.
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3. Also, the company should put in place efficient cash management policies to ensure that any
idle cash as observable in 2018 is promptly invested in short term marketable securities to
income for the company.
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