




































American Research Journal of Economics, Finance and Management 

Volume 11 Issue 4, October-December 2023 

ISSN: 2836-9416 

Impact Factor: 5.57 

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EXPLORING THE RELATIONSHIP BETWEEN BREWERY FIRM 
SIZE AND FINANCIAL PERFORMANCE IN NIGERIA 

 
 

Dr. Chika Inyiama and Prof. Adaobi Okafor 
Department of Accountancy, Enugu State University of Science and Technology, Enugu State, Nigeria. 
Department of Accountancy, Enugu State University of Science and Technology, Enugu State, Nigeria. 

 
Abstract: The brewery industry, characterized by complex production processes and heavy fixed 
asset requirements, presents a unique accounting system known as process cost accounting. This 
system plays a pivotal role in assessing the financial performance of companies within the industry. 
However, despite their shared industry and external environment, individual firms exhibit varying 
financial performances due to a range of internal factors. These factors include firm size, age, debt 
ratio, quick ratio, inventory level, sales growth, physical capital intensity, and capital turnover. This 
study delves into the influence of these internal factors on firm performance within the brewery 
industry. Understanding these dynamics is crucial for brewers to optimize their financial standing in 
an industry where competitiveness is paramount. 
Keywords: Brewery industry, process cost accounting, financial performance, internal factors, firm 
size, debt ratio, inventory level, capital turnover. 
 
1.0 Introduction  
The nature of production processes in the brewery industry demands a heavy fixed asset base. This is 
because they have a complex production process that requires the installation of machineries, plants, 
equipment and sometimes complete automation of the production line. Process cost accounting in the 
brewery industry reveals the peculiar nature of its accounting system that facilitates the determination 
of its financial performance. The financial performance of firms within the industry is influenced by a 
number of factors. Chandrapala and Knapkova (2013) stated that even though all firms operate in the 
same industry and interact with same external variables, their financial performances are not the same 
as a number of internal factors could be responsible for firm performance such as firm size, age, debt 
ratio, quick ratio, inventory level, sales growth physical capital intensity and capital turnover as 
suggested by Pavelkova and Knápková (2009).  
Yegon, Mouni and Wanjau (2014) citing  Kamar, Rajan and Zingales (2001) suggested that what 
determines a firm size is the ownership of physical assets which are critical resources. The neoclassical 
theory of firm size supported by Lucas (1978) also looked at the firm size in terms of per capita capital 
in form of investment return and research and development. Pervan and Višić (2012) emphasized on 
the conceptual framework that advocates a negative relationship between firm size and profitability 
which is noted in the alternative theories of the firm. The theory, as stated, suggests that large firms 
come under the control of managers pursuing selfinterested goals and therefore profit maximization as 
the firm’s objective function which may be replaced by managerial utility maximization function. Akbas 
and Karaduman (2012) citing  Athanasoglou, Brissimis and Delis (2008) claimed that size could impact 

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21 | P a g e  

the profitability negatively, for firms that become extremely large due to bureaucratic and other 
reasons.  
The nature of the relationship between firm size and economic performance has received considerable 
attention in the literature but has provoked vigorous debate as existing literatures provide conflicting 
results (Symeou, 2012). Some industries, organizations and sectors link large firms to better 
performance in line with the neoclassical theory of firm size while some research findings support the 
conceptual framework that advocates a negative relationship between firm size and profitability. 
This study, therefore, aims at examining the interactions between firm size and financial performance 
of selected firms in the Nigeria brewery sector; considering the contribution of the sector to national 
economy. The remaining part of the paper is arranged into four sections. Section 2, x-rays the existing 
related literature, section 3 documents the methodology for data analysis, section 4 discusses the 
empirical results while section 5 summarizes and concludes.  
2.0 Review of Related Literature   
Pavelkova and Knápková (2009) posits that when a firm becomes larger, it enjoys economics of scale 
and its average cost of production is lower and operational activities are more efficient. Yang and Chen 
(2009) opines that large firms face less difficulty in getting access to credit facilities from financial 
institutions for investment, have broader pools of qualified human capital, and may achieve greater 
strategic diversification. Akbas and Karaduman (2012) while citing Hardwick (1997), stated that larger 
firms have some advantages such as greater possibility of taking advantage of scale of economies which 
can enable more efficient production, a greater bargaining power over both suppliers and distributors 
or clients, exploiting experience curve effects and setting prices above the competitive level.  While 
citing Weiner and Mahoney (1981), Ravenscraft and Scherer (1987), Akbas and Karaduman (2012) also 
argued that larger firms are more stable and mature and they can generate greater sales because of the 
greater production capacity and finally, those firms have the chance of capital cost savings with the 
economies of scale.  
The understanding of the relationship between firm size and performance was advanced by Symeou 
(2012) when he examined whether firms enjoying higher growth potential are better performers, 
arguing that small economy size could contain firm growth potential and by extension firm 
performance. Controlling for the effects of competition, firm governance structure, and institutional 
risk, inter alia, the findings suggest that firm growth potential is not necessarily a limiting factor as both 
firms in small and large economies can operate efficiently.  
On the financial performance of Jordanian Insurance Companies, Almajali, Alamro and Al-Soub (2012) 
examined the factors that mostly affect financial performance. The findings revealed that Leverage, 
liquidity, Size, Management competence index have a positive statistical effect on the financial 
performance.   
The effect of firm size on profitability of virtually all the branches of Bank of Ceylon (BOC) and 
Commercial Bank of Ceylon Ltd (CBC) with 10 years accounting period was studied by Velnampy and 
Nimalathasan (2010). The correlation analysis conducted on the secondary data indicates that there is 
a positive relationship between Firm size and Profitability in Commercial Bank of Ceylon Ltd, while 
there is no relationship between firm size and profitability in Bank of Ceylon.  

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The relationship between the capital structure and financial performance as evidenced from 21 
industries in Karachi Stock Exchange in Pakistan was investigated by Javed and Akhtar (2012) using 
correlation and regression test on the financial data. The findings of the study show a positive 
relationship between the leverage, financial performance, growth and size of the companies.  Bashir,  
Abbas,  Manzoor and  Akram (2013) identifies the factors significantly affecting the firm’s performance 
in food sector of Pakistan using one-way fixed effect model due to the presence of cross-sectional fixed 
effect. In the sector, long term leverage, size, risk, tangibility and non-debt tax shield were found to be 
the factors significantly affecting the firm’s financial performance.  
An examination of the impact of firm specific factors on company financial performance of 974 firms 
in the Czech Republic over the period 2005 to 2008, using data in the Albertina database was conducted 
by Chandrapala and Knápková (2013). Their research found that the firm size, sales growth and capital 
turnover are having significant positive impact on financial performance of firms, while debt ratio and 
inventory reflect significant negative impact on financial performance of firms.  
Taani and Banykhaled (2011) examined the effect of accounting information such as profitability, 
liquidity, debit to equity, market ratio, size which is derived from firm’s total assets, and cash flow from 
operation activities on earning per share (EPS) by using a sample of 40 companies listed in the Amman 
Stock Market. The findings reveal that profitability ratio (ROE), market ratio (PBV), cash flow from 
operation/sales, and leverage ratio (DER) has significant impact on earnings per share. A related study 
by Martani, Mulyono and Khairurizka (2009) reveals that profitability, turnover and market ratio has 
significant impact on the stock return. 
An examination of the effect of firm size and profitability on the extent of corporate social disclosures 
by Oil and Gas firms in Nigeria was done by Ebiringa, Yadirichukwu, Ogbu, and Ogochukwu (2013).   
A sample of twenty quoted companies was selected using the simple random sampling technique. The 
findings among others show that an insignificant negative correlation exists between CSR disclosure 
and firm size, while profitability is significantly and positively related to CSR disclosure of the 
companies.  
An investigation into the impact of capital structure on the financial performance of companies listed 
in the Tehran Stock Exchange was carried out by Pouraghajan, Malekian,  Lotfollahpour and Bagheri 
(2012).They tested a sample of 400 firms among the companies listed in the Tehran Stock Exchange. 
Results suggest that there is a significant negative relationship between debt ratio and financial 
performance of companies, and a significant positive relationship between asset turnover, firm size, 
asset tangibility ratio, and growth opportunities with financial performance measures. However, the 
relationship between ROA and ROE measures with the firm age is not significant. Hendricks and 
Singhal (2000) examined firm characteristics such as firm size, the degree of capital intensity, the 
degree of diversification, the timing of TQM implementation, and the maturity of the program and 
found that smaller firms do significantly better than larger firms.  
Memon, Bhutto and Abbas (2012) investigated the impact of capital structure on firm financial 
performance in textile sector of Pakistan with 141 textile firms from 2004-2009. The results indicate 
that all the determinants of capital structure such as size, tangibility, debt to equity ratio, amount of 
annual tax, growth of firm and risk associated with business entity were significant and that Pakistan 
textile sector is performing below the optimum capital structure level and textile firms of large size have 

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failed to achieve the economies of scale. The impact of firm level characteristics (size, leverage, 
tangibility, Loss ratio (risk), growth in writing premium, liquidity and age) on performance of insurance 
companies in Ethiopia was examined by Mehari and Aemiro (2013).  The results of regression analysis 
reveal that insurers’ size, tangibility and leverage are statistically significant and positively related with 
return on total asset; however, loss ratio (risk) is statistically significant and negatively related with 
ROA.   
The above review of relevant works reveals that studies on the relationship between firm size and 
financial performance is still scanty and unbalanced amongst the leading sectors of the economy.   
Most of the existing studies on the subject centres on financial and allied institutions and not one of 
such studies considered the brewery sector of the Nigeria economy. Hence, this study aims at examining 
the causality, magnitude and nature of the interactions, with emphasis on relationship and effect, 
between firm size and financial performance in Nigeria brewery industry.  
3.0 Methodology  
The order of interaction and integration was studied using the two-step error correction procedure of 
Engle and Granger (1985). This was adopted in consonance with the work done in Abraham (2013). 
The formation of the relevant models to facilitate analysis of data is as stated below:  
  
EPSt  a0  a1LogTAt  
………………………………………………………….(1)  

 a2Ut-1  εt                       

EPSt  a0  a1LogTAt  a2  RESt-1  εt  

………………………………………………………….(2)                                                      
Where:                                                      
 a1 denotes the coefficient indicating the short run equilibrium relationship linking the two 
variables;  
 a2 denotes the coefficient indicating the long run relationship linking the variables with a priori 
expectation of -1;  
 Ut-1 or RESt-1 is the residual obtained from the linear regression of variables. The residual is 
lagged by one to fulfill the requirement of the granger representation theorem.   
 εt is the disturbance term for the model. 

Table 1:    Description of Variables  
Unit Root Test  

  
Acronym   
EPS   

Details   
Earnings Per Share   

Mathematical Expression   
   Net earnings available for common    
   stock   
   Average number of outstanding    
    shares   

LogTA   Log of Total Assets   Fixed Assets + Current Assets   
     

Source: Author’s   Arrangement   

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The Unit root test was conducted on the time series data obtained from annual report and accounts of 
Nigerian Breweries Plc and Guinness Nigeria Plc, which represent the Nigerian brewery industry. Data 
series with unit root issues produces spurious regression when used for analysis. A graphical 
representation was made to initially ascertain the existence of unit root in the time series data. The 
trend of the line graphs reveals that the data series were not stationary and needs to be disinfected to 
avoid spurious regression. This is evident from the fact that the line graph did not cross the zero line 
even at an instance as shown below:   
Figure 1: Graphical Representation of the Variables with Unit Root Issues 

 
8.6 
8.4 
8.2 
7.6 Source: Author’s EView 8.0 Computation   
Figure 1 reveals that the time series data for total assets and earnings per share were non-stationary as 
the line graphs have wide disparity from zero and did not cross the zero line severally.   
The Augmented Dickey Fuller (ADF) procedure was applied in testing for existence of unit root or 
stationarity of time series data and the order of integration of the two variables under study.  
Table 2: Augmented Dickey Fuller (ADF) Unit Root Test Results   

0  

1  

2  

3  

4  

5  

6  

2000 2002 2004 2006 2008 2010 2012  

EPS  

7.4 
2000 2002 2004 2006 2008 2010 2012 

L O G T A  

  

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Variables                                Test Critical 
Values  

Test Statistics              
Status  

           1 %             5 %          10 %         ADF  (Stationarity)  

EPS  -
3.808546  

-
3.020686  

-
2.650413  

-
5.287424  

I(2)  

LogTA  -
2.816740  

-1.982344  -
1.601144  

-
3.683826  

1(2)  

Source: Researcher’s EView 8.0 Computation   
Table 2 reveals that both Earnings Per Share and Total Assets data series have unit root but were found 
to be stationary at second difference. There integration of the same order I(2) is an indication that the 
variables could cointegrate in line with the opinion of Engle and Granger (1985). They opined that when 
time series data are integrated of the same order, the data series tend to cointegrate. This means that 
their short term characteristics are sustainable at the long term. They listed the consequences of such 
cointegration to include that;   
• Time series data that are integrated of the same order I(2), share a stochastic component and a 
long run equilibrium relationship.  
• Wide disparities from the zero line of equilibrium as a result of volatilities will be     corrected 
over a period of time.  
• ΔYt is believed to be responding to shocks to X under a state of cointegration over the short and 
long term.  
However, after subjecting the time series data to unit root test, a new set of data series were generated 
through the Augmented Dickey Fuller (ADF) procedure. The line graphs that resulted from the new 
series were found to be closer to the equilibrium, indicating that the data series have attained 
stationarity after the repair.  
  
  
  
  
  
  
Figure 2: Graphical Representation of the Variables without Unit Root Issues  
  
1.5 
1.0 
0.5 
0.0 
-0.5 
-1.0 
-1.5 

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DLOGTA 
.24 
.20 
.16 
.12 
.08 
.04 
.00 
-.04 
-.08 
Source: Author’s EView 

8.0 Computation  
  
Table 3 explains the characteristics of the research variables.  It reveals the mean, median, standard 
deviation and other frequency distribution indices for the study, as well as the maximum and minimum 
values of the time series data under study.   
Table 3:   Descriptive Statistics  
DETAILS  EPS  LOGTA  
 Mean   2.865000   7.989534  
 Median   2.450000   7.943398  
 Maximum   5.700000   8.404207  
 Minimum   0.770000   7.557553  
 Std. Dev.   1.630676   0.244133  
 Skewness   0.393132   0.316478  
 Kurtosis   1.837475   2.580741  
 Jarque-Bera   1.148978   0.336239  
 Probability   0.562993   0.845253  
 Sum   40.11000   111.8535  
 Sum Sq. Dev.   34.56835   0.774812  
 Observations   14   14  

Source: Author’s EView 8.0 Computation  
The coefficient of skewness for EPS and Total Assets have values below one  
(1) signifying a normal frequency distribution. Kurtosis coefficient is 1.837475 and 2.580741 for EPS 
and Total Assets respectively. Jarque-Bera statistic shows that EPS and Total Assets have insignificant 
p-values of 0.562993 and 0.845253 respectively. Both Kurtosis and Jarque-Bera statistic confirm that 
the time series data were normally distributed.  The standard deviation of EPS is more volatile than 
that of Total Assets.  
Granger-Causality test is conducted in the context of linear regression models and specified in bivariate 
linear autoregressive model of two variables X1 and X2 based on lagged values of EPS and Total Assets 
as applied by Pasquale (2006) and cited in Inyiama (2013): 
             P                        p  

2000 2002 2004 2006 2008 2010 2012  

D E P S  

2000 2002 2004 2006 2008 2010 2012   

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X1(t) =∑ A11,jX1(t−j) + ∑ A12,jX2(t−j) + E1 (t) ………………………………….(5)          j =1                       j =1  
  
             P                        p  
X2(t) =∑ A21,jX1(t−j) + ∑ A22,jX2(t−j) + E2 (t)……………………………………(6)           j =1                       j =1   
Where;  
  
 p is the maximum number of lagged observations included in the equation, the matrix A contains the 
coefficients of the equation (i.e., the contributions of each lagged observation to the predicted values of 
X1(t) and X2(t) ,   
X1 is the Earnings Per Share which is constant while X2 takes the form of Total  
Assets index and value and,   
E1 and E2 are residuals (prediction errors) for each time series data.  
Table 4:  Pairwise Granger Causality Tests  
Date: 10/16/14   Time: 20:08  
Sample: 2000 2013  Lags: 2  

  
  
  

  
  
  

 Null Hypothesis:   
  

Obs   
  

F-
Statistic   
  

Prob.    
  

 DLOGTA does not Granger 
Cause DEPS   

 11    
2.92233   

0.1300   

 DEPS does not Granger Cause DLOGTA    0.26709  
 0.7742   
        

  
Source: EView 8.0 Computation  
Table 5:   Pairwise Granger Causality Tests  
Date: 10/16/14   Time: 20:11  
Sample: 2000 2013   
Lags: 1   
    

  
    
    

 Null Hypothesis:   
  

Obs   
  

F-Statistic 
Prob.    
    

 DLOGTA does not Granger Cause 
DEPS   

 12    1.89060 
 0.2024   

 DEPS does not Granger Cause DLOGTA   1.67404 0.2279    
      

         

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Source: EView 8.0 Computation   
Tables 4 and 5 indicate that there is no causality running from either earnings per share to total assets 
or from total assets to earnings per share, both at lagged periods 1 and 2. This implies that earnings per 
share does not granger cause total assets and vice versa.   
1.5 

 
The time series graph of fitted observations as shown in Figure 3 is very close to the graph of the 
corresponding observed values. 
Table 6: Residual Test for Stationarity  
  
Null Hypothesis: RES has a unit root    
Exogenous: Constant      
Lag Length: 0 (Automatic - based on SIC, maxlag=1)  
          

      
      

t-Statistic   
  

  Prob.*   
  

Augmented Dickey-Fuller test statistic   -3.299552    0.0473   
Test critical values:  1% level     -4.420595     

  5% level     -3.259808     
  10% level     
      

-2.771129   
  

  
  

*MacKinnon (1996) one-sided p-values.   
Warning: Probabilities and critical values calculated for 2 0  
observations  and may not be accurate for a sample size of 9   
Augmented Dickey-Fuller Test Equation   
Dependent Variable: D(RES)     
Method: Least Squares     
Date: 10/22/14   Time: 12:30     
Sample (adjusted): 2005 2013     
Included observations: 9 a fter adjustmen ts   

  
  
  
  
  
  
  
  

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Variable   
  

Coefficient   
  

Std. Error  t-Statistic   
    

Prob.     
  

RES(-1)   -1.136048   0.344304  -3.299552   0.0131   

C   
  

0.007662   
  

0.027982  0.273811   
    

0.7921   
  

R-squared   0.608655       Mean dependent var   0.013442   

Adjusted R-squared   0.552749       S.D. dependent var   0.125277   
S.E. of regression   0.083781       Akaike info criterion   -1.928084   
Sum squared resid   0.049135       Schwarz criterion   -1.884256   
Log likelihood   10.67638       Hannan-Quinn criter.   -

2.022664   
F-statistic   10.88704       Durbin-Watson stat   2.227577   
Prob(F-statistic)   
  

0.013129   
  

    
    

  
  

  
Source: Author’s EView 8.0 Computation   
Table 6 reveals that the variables are co-integrated at 5 percent significance level. According to the 
Granger Representation Theorem, when the variables under study are integrated of the same order and 
are found to be cointegrated, an error correction model could be estimated. Abraham (2013) supports 
that if the variables are found to cointegrate, then the second step of the Engle and Granger (EG) 
procedure which involves specifying an error correction model (ECM) for each equation in the system 
could be done.   
He emphasized that the multivariate EG two-step procedure for estimating ECM however, requires that 
there are only two variables in the system. Therefore, the output of the regression analysis, after the 
estimation, is then presented in Table 7.   
Table 7:  Regression Analysis Result  
       
Dependent Variable: DEPS        
Method: Least Squares        
Date: 10/22/14   Time: 09:55        

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Sample (adjusted): 2005 2013        
Included observations: 9 after adjustments      
          
 Variable   Coefficient  Std. Error  t-Statistic   Prob.     
          
C  0.418502  0.108770  3.847585  0.0085   
DLOGTA  1.868122  1.148158  1.627061  0.1548   
DRES  2.106872  0.760097  2.771847  0.0323   

         
 R-squared   0.652150       Mean dependent var    0.547778  
Adjusted R- 
squared  0.536199      S.D. dependent var 0.393820  
S.E. of regression 0.268203      Akaike info criterion 0.467057  
Sum squared resid 0.431597      Schwarz criterion  0.532798     Hannan-Quinn  
Log likelihood  0.898244  criter.  0.325187  
F-statistic  5.624396      Durbin-Watson stat 1.620818  
Prob(F-statistic)  0.042090        

         
Source: Author’s EView 8.0 Computation                
Table 7 reveals that Total Assets has a positive but insignificant short run effect on financial 
performance as proxied by EPS. It further reveals that the long term effect of total assets on financial 
performance is positive and significant. The error correction mechanism suggests that deviations from 
equilibrium are corrected at approximately 187% per annum. This implies that the distortions affecting 
EPS in the long run could be corrected in approximately six months while adjusted R2 stood at 54%.  
Table 8: Correlation Results  

Variables  EPS  CAR  

Earnings Per Share (EPS)   1.000000     

Firm Size (LogTA)   0.324590    1.000000  

 Source: Author’s EView 8.0 Computation   
Table 8 reveals a positive correlation between EPS and Total Assets. The relationship between EPS and 
Total Assets is not a strong one. This signifies that an increase in Total Assets could result to an increase 
in EPS, holding other factors constant. The strength of the relationship is estimated at approximately 
32.5%. This is in line with the insignificant effects which Total Assets exerts on EPS as revealed by the 
regression analysis. 
5.0 Summary and Conclusion  
The study aims at determining the extent to which Earnings Per Share is influenced by the level of Total 
Assets maintained by firms in the Nigeria brewery industry, as well as the nature and magnitude of 
their causalities. The researcher applied the 2-step cointegration and error correction model of Engle 
and Granger (1985) in a simple regression framework. Firm Size has both short and long term positive 
effect on EPS. However, the long run relationship is significant at 5%. On causalities, there is no 

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causality running from either EPS to Total Assets or otherwise, both at 1 year and 2 years lagged periods. 
The implication is that EPS does not granger cause Total Assets and vice versa.    
The long term positive effect of Total Assets on EPS is in line with our a priori expectation that an 
increase in asset base of a brewery firm will lead to a positive shift in the firm’s financial performance. 
The finding is consistent with the outcome of the studies carried out by Pavelkova and Knápková 
(2009), Memon,  Bhutto and  Abbas (2012), Chandrapala and Knápková (2013),  Pouraghajan, 
Malekian, Lotfollahpour and Bagheri (2012), Bashir,  Abbas,  Manzoor and  Akram (2013), Velnampy 
and Nimalathasan (2010), Almajali, Alamro and Al-Soub (2012). This could be attributable to the 
capital intensive nature of the brewery industry. The production lines of most of the big firms within 
the industry are highly automated which results in more quality output and production level that 
guarantees customer satisfaction through supplies at very short notice.   
Under this situation, there is no stock out cost. Hence, brewery firms should strive to attain this height 
of a sound asset base in order to meet, on a timely basis, their responsibilities towards the customers 
and by extension, improve on their financial performance; especially at the long term.    
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Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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