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JOURNAL OF RESEARCH IN NATIONAL DEVELOPMENT VOLUME 8 NO 2, DECEMBER, 2010


 

PRODUCT COSTING SYSTEMS IN NIGERIAN COMPANIES

 

Chijoke Oscar Mgbame and Egbon Osamuyimen

 Department of Accounting,University of Benin, Nigeria

 E-mail-chijokem@yahoo.com

 

Abstract

 The paper examined the factors influencing product costing systems in Nigerian manufacturing organizations.Following Al-Omiri and Drury (2007) framework where alternative proxy measures were used to identify the features of product costing systems,  a questionnaire was used to gather data, while OLS regression technique was applied to evaluate how importance of cost information, product diversity, cost structure, intensity of competition, size of organization, and quality of information technology, affect the sophistication of cost system of manufacturing companies whose shares are quoted in the floor of the Nigerian Stock Exchange. Results indicate a positive relationship between cost system sophistication and all observed independent variables. However, the relationships between cost system sophistication and cost structure, product diversity and quality of information technology are significant while the relationship between cost system sophistication and size, level of competition, and importance of cost information are not significant.

 

Keywords: Costing; competition, product diversity, pools; cost drivers

 

 


Introduction

Studies have assumed contingency theory to show how particular aspects of an accounting system are reflected with various contextual variables. This is in an attempt to explain the diversity of management accounting practices. Al-Omiri and Drury (2007) observe that a considerable amount of contingency-based research has been undertaken relating to management accounting control systems. However, little attention has been given to identifying the factors that influence the content of product costing systems, meanwhile vast amount of publicity is given to developing a sophisticated product costing systems (Cooper, 1988a; Cooper, 1988b; Cooper and Kaplan, 1992; Kaplan and Cooper, 1998). The need to improve the sophistication of product costing systems has been motivated by changes in manufacturing technology, global competition, information costs and customers’ demands or greater product diversity. These changes have prompted criticisms of the ability of traditional management accounting systems to sufficiently report accurate product costs. ABC systems were therefore promoted as the solution to overcome the distortions in the product costs reported by traditional costing system (Cooper, 1988b; Kaplan, 1994).

 

Research evidence in the United Kingdom suggests that the ABC system adoption rate has been fairly low, being approximately 15% of the companies surveyed (Innes et al., 2000; Drury and Tayles, 2000). This low rate of adoption has prompted this study to concentrate on the factors influencing the adoption or non-adoption of ABC systems. This research has generally been inconclusive and has been unable to establish strong links between the adoption of ABC systems and those contextual factors that have been identified in the literature that are conducive to the adoption of ABC systems. Two possible reasons may account for this situation. First, there may be no relations between the constructs of interest and therefore any significant findings may have been spurious and not reproducible. Second, the methods used by previous studies may have flaws relating to poor measures, measurement error, bias etc. Due to the above reasons, there is a need to continue empirical research on this subject matter.

 

Instead of using the measure adopted by previous research (i.e. the adoption or non-adoption of ABC systems), this study adopted the approach of Al-Omiri and Drury (2007), (i.e. the use of alternative proxy measure for identifying the features of product costing systems). The paper looks at the degree to which different contextual factors influence the choice of product costing systems using the alternative proxy measure.

 

Literature review

Product costing system in it simplest term refers to a set of procedures that accounts for an organization’s product cost and provides timely and accurate unit cost information for pricing, planning and control, inventory valuation and financial statement preparation. It can also be seen as a system that accumulates the cost of a production process and assign them to the products that constitute the organization’s output. There are various product costing systems varying along a continuum from the direct costing system to the ABC systems. While the most simplified of these systems is the direct costing system, the most complex is the ABC system.

 

 According to Al-Omiri and Drury (2007), the principle in contingency based research has been the concept of ‘fit’ whereby contextual factors and aspects of an accounting system must fit together for an organization to be effective. Drazin and Van de Ven (1985) identify two forms of fit relating to Structural Contingency Theory – the selection and interaction approaches. The selection approach examines the relationship between contextual factors and organization structure without examining whether this relationship affects performance, while the focus of the interaction approach is to explain variations in performance from the interaction of organizational structure and context. Only particular designs are expected to give high performance in a given context. Departures from such designs are expected to give lower performance. Given that organizations are believed to have varying degrees of fit, the challenge is to prove that a higher degree of fit between context and structure is associated with higher performance.

 

Research on management accounting control systems have had a vast majority adopting the selection approach to fit (Chenhall, 2003; Luft and Shields, 2003) whereby features of the accounting system are represented as the dependent variable. Researchers in accounting have justified the selection approach on the basis that rational managers are not likely to use accounting systems that do not assist in improving performance (Chenhall, 2003). Where the interaction approach to fit has been adopted, with a measure of organizational performance as the dependent variable, outcome measures such as satisfaction or usefulness of the management control system have been widely used as proxy measures of desired organizational performance. (Al-Omiri and Drury, 2007)

 

Previous studies relating to product costing showed that virtually all of the researches undertaken have concentrated on contextual factors influencing the adoption or non-adoption of ABC systems. Al-Omiri and Drury (2007) identify seven survey-based studies published in the major journals (Bjornenak, 1997; Gosselin, 1997; Krumwiede, 1998; Malmi, 1999; Clarke et al., 1999; Hoque, 2000 and Cagwin and Bouwman, 2002). The first six studies as observed by Al-Omiri and Drury (2007) adopted the selection approach to fit and the later adopted the interaction approach. The most widely used contextual variable has been product diversity, being included in four of the six studies that have adopted the selection approach. The effect of cost structure and size has been examined in three of the six studies. Other variables that have been examined by only one or two of the above studies include the level of competition, quality of information technology, the extent of advanced technologies/practices and competitive strategy. Only size has consistently been identified as a significant variable. Product diversity was identified as a significant variable in two of the four studies as a significant variable while cost structure was not a significant variable at the 5% significance of the three studies that examined this variable (Al-Omiri and Drury, 2007). Study by Cagwin and Bouwman (2002) observe a positive relationship between the interactions of ABC system with business complexity and the use of other initiatives employed concurrently with ABC system (e.g. JIT, TQM, BPR etc.) and improvements in return on investment.

 

This research has identified two studies that focused on classifying product cost systems by characteristics rather than the discrete alternatives of traditional and ABC systems. The first by Abernethy, Lillis, Brownell, and Carter (2001) adopted an interactive approach to fit. Based on case study research they classified product costing systems by their level of sophistication using data collected from five divisions within two firms in Australia. Four divisions had a low level of sophistication but there was a reasonable level of satisfaction with the information provided by the costing systems at three of the four divisions. The authors linked this to the ‘fit’ between the levels of sophistication of the costing system and the contextual factors of cost structure and product diversity. All three divisions had low product diversity and low overhead costs. In the fourth division, overhead costs and product diversity were high. Management was dissatisfied with the costing system and the authors linked this to the lack of ‘fit’ between the contextual factors and the existing costing system. The fifth division operated a sophisticated traditional costing system and users were very satisfied with the costing system. Product diversity was high but this was facilitated by investment in advanced manufacturing technology (AMT) resulting in overhead costs being mainly associated with investment in AMT, which represented facility-sustaining costs. In these situations the authors argued that there was little justification for sophisticated ABC system because the batch-related and product sustaining costs associated with product diversity were low.

 

The second study that adopted a wider perspective to group costing systems was a survey undertaken by Drury and Tayles (2005). A measure of cost system complexity represented the dependent variable. An 8-point scale was used to obtain information relating to the number of cost pools and different types of cost drivers. The two scales were merged to subjectively determine a measure of cost system complexity. The contextual variables, derived mostly from our questionnaire, were incorporated into a multiple regression model with the dependent variable being the measure of cost system complexity. Four variables were statistically significant – product diversity, degree of customization, size and corporate sector (the financial and service sectors had significantly higher levels of cost system complexity compared with companies operating in the manufacturing sector).

 

Existing literature confirms that the following factors are important in determining the level of cost system sophistication: Importance of cost information, Product diversity, Cost structure, Business sectors, Top management support intensity of competition, Quality of information technology, Size of organization, Use of innovation management accounting technique, e.t.c

 

In their study, Al-Omiri and Drury, (2007) attempted to establish a relationship between cost system sophistication and the following contextual factors for UK organizations: Importance of cost information, Product diversity, Cost structure, Intensity of the competitive environment, Size of the organization, The quality of information technology, Extent of the use of innovative management accounting techniques, Extent of use of lean production techniques (including JIT techniques), Business sector. The study showed that higher levels of cost system sophistication are positively associated with the importance of cost information, extent of use of other innovative management accounting techniques, intensity of the competitive environment, size, extent of the use of JIT/lean production techniques and the type of business sector. No association was found between the level of cost system sophistication and cost structure, product diversity and quality of information technology. However, we shall attempt to establish the relationship between cost system sophistication and the following: Importance of cost information, Product diversity, Cost structure, Intensity of Competition, Size of organization and Quality of information technology.

 

 Research hypotheses

To establish the relationship between cost system sophistication and the contextual factors understudy, we formulated the following hypotheses:

Hypothesis 1 (H1): There is a positive relationship between the importance of cost information and the level of cost system sophistication.

Hypothesis 2 (H2): There is a positive association between higher levels of product diversity and the level of cost system sophistication.

 Hypothesis 3 (H3): There is a positive association with the proportion of indirect costs within an organization’s cost structure and the level of cost system sophisticated.

Hypothesis 4 (H4): There is a positive association between the intensity of competition and the level of cost system sophistication.

Hypothesis 5 (H5): There is a positive relationship between the size of the organization and the level of cost system sophistication.

Hypothesis 6 (H6): There is a positive relationship between the quality of an organization’s information technology and the level of cost system sophistication.

 

Research design and data collection

A questionnaire survey was used to gather the data. A random sample consisting of 40 manufacturing companies was chosen from the list of companies quoted in the Nigerian Stock Exchange. Only firms with annual sales greater than N2 billion were selected since the focus was on larger companies (small scale companies are expected to have at most N2 billion as sales, CAMA 1990) that are likely to have established management accounting system.

A total of 34 questionnaires were returned from the sample of 40 companies which represent about 89% of manufacturing companies quoted on the stock exchange Respondents (19 males and 15 females, all within top and middle level management) were asked to complete the questionnaire from the perspective of the business unit where they were employed. The reason for this being that the features of costing systems and contextual factors may differ between business units in large companies.

 

Measurement of the variables

The independent variables of interest required the use of perceptive measures and Likert-type five point scales were used to derive composite scores for each variable. The five point scales ranging from strongly agree (SA) to strongly disagree (SD) were assigned the following values: Strongly disagree (1), Disagree (2), Not sure (3), agree (4), Strongly agree (5). The dependent variable (cost system sophistication) was represented using a single proxy measure of the number of cost pools used in the first stage of the two stage allocation process. The OLS method was employed in the analysis being a method suitable in testing the relationship between two variables, where one is dependent and the other independent. Furthermore, the choice of the OLS regression technique was based on the unbiased and efficient estimate that can be obtained from using the technique.

 

Model specification

In order to test our hypotheses, we develop the model below being an adaptation of the model used by Al-Omiri and Drury.

 

 

 

 

 

Y= b0+b1ICI+b2PD+b3CS+b4SIZE+b5IT+b6COMPET+e

Y= b0+b1ICI+e…………… (2)

Y= b0+b2PD+e……………. (3)

Y= b0+b3CS+e……………. (4)

Y= b0+b4SIZE+e…………. (5)

Y= b0+b5IT+e…………….. (6)

Y=b0+b6COMPET+e……… (7)

Where:

 

Y= Cost system sophistication (represented by number of cost pools).

ICI= Importance of cost information.

PD= Product diversity.

CS= Cost structure.

SIZE= Size of organization.

IT= Importance of information Technology.

COMPET= Intensity of competition.

 

Research findings

This study examines some factors that influence product costing system sophistication in Nigerian manufacturing companies. In other to test our hypotheses, the model below was applied with respect to the variables in question.

Y= b0+b1ICI+b2PD+b3CS+b4SIZE+b5IT+b6COMPET

 

The cost system sophistication of manufacturing companies (Y) was related to the importance of cost information (ICI), product diversity (PD), cost structure (CS), size of the organization (SIZE), quality of information technology (IT) and the intensity of competition (COMPET).

 

In evaluating how these variables affect the sophistication of cost system of manufacturing companies, an OLS regression technique was adopted. The simple OLS regression results provide information on the one on one relationship between the dependent variable (Y) and each of the explanatory variables while the OLS multiple regression result show how all the explanatory variables jointly explain the sophistication of cost systems. The choice of the OLS regression technique was based on the unbiased and efficient estimate that can be obtained from using the technique. The regression results are presented in the table below

 


 

 

 

 

 

 

 

 

 

Table 1

 

 Ordinary least squares regression result

 

Explanatory Variables

EQN 1

EQN 2

EQN 3

EQN 4

EQN 5

EQN 6

EQN 7

 

Cost system               

ICT only

PD only

CS only

SIZE only

IT only

COMPET only

C

-4.41

(-2.58)

-3.20

(3.38)

1.63

(1.98)

2.39

(2.44)

3.06

(2.62)

2.34

(2.40)

5.14

(5.25)

ICI

0.29

(1.41)

0.30

(1.30)

 

 

 

 

 

PD

0.87

(3.44)

 

0.96

(3.80)

 

 

 

 

CS

0.46

(2.04)

 

 

0.49

(2.31)

 

 

 

Size

0.38

(1.60)

 

 

 

0.28

(1.09)

 

 

IT

0.59

(2.22)

 

 

 

 

0.53

(0.24)

 

COMPET

0.14

(0.51)

 

 

 

 

 

-0.35

(-1.49)

R2

0.65

0.19

0.28

0.28

0.17

0.26

0.20

Adjusted R2

0.57

0.14

0.23

0.23

0.12

0.21

0.51

F

8.19

3.52

5.88

5.88

3.22

5.18

3.75

DW

`1.65

2.03

2.06

2.06

2.01

2.03

1.98

Note: The t-values are in parenthesis and are statistically significant at the 5% levels.

 


Table 1 reveals from the simple OLS regression that the importance of cost information (ICI), product diversity (PD), cost structure (CS), size of the organization (SIZE), quality of Information Technology (IT) and the Intensity of Competition (COMPET) contribute positively to the sophistication of manufacturing companies cost system. Among these variables, product diversity, quality of information technology and cost structure are the only three factors that are significantly related to the cost system sophistication. The F-statistics for the simple- regressions (equation 2 to 7)) also reveals that the dependent variable is significantly related to each explanatory variable since their respective F-Statistics values are significant at 5% levels.

 

In the  case of their co-efficient of determination (R2) and adjusted (R2), it can be deduced that the systematic variations in cost sophistication (Y) from a simple regression approach is explained as follows;  14% by ICI, 23% by PD, 23% by CS, 12% by SIZE, 21% by IT and 15% by COMPET. In testing for spatial autocorrelation in equations 2 to 7, the Durbin-Watson (DW) statistic was adopted. The values as reflected in table 1 show that there is not enough evidence to accept the presence of autocorrelation in each of the models. This means that equations 2 to 7 are valid models that can be used to predict the sophistication of cost systems.

 

The multiple OLS regression results as shown in equation (1) are also presented in table 1. The results reveal that about 65% of the systematic variations in cost sophistication (Y) are jointly explained by the explanatory variables. This, when adjusted for the degree of freedom yields 57%. The F- statistic value of 8.19 also supports the existence of linear significant relationship between the dependent variables (Y) and all the explanatory variables. Specifically, product diversity (PD), cost structure (CS) and the quality of Information Technology (IT) with coefficient of 0.87, 0.46 and 0.59 respectively and t-values of 3.44, 2.04 and 2.22 are the only variables that statistically and significantly contribute to the sophistication of manufacturing cost systems.

 

The Durbin Watson Statistic of 1.65 also shows that there is not enough evidence to accept the presence of auto correlation in the model. This means that equation 1 can also be used to explain the significance of cost systems in manufacturing companies with minimum bias.

 

Discussion and conclusion

Prior research has provided inconsistent findings relating to factors influencing the nature of product costing systems. Weak measures have been used for both the dependent and the independent variables. This study has sought to overcome these weaknesses by using composite scores derived from multiple questions. Instead of using the adoption or non-adoption of ABC systems as a dependent variable as previous studies have done, this study has used a single proxy measure.  Multiple and simple regression, rather than bivariate statistical tests, was used to test the hypotheses.

 

 The results of this study are different from that of Al-Omiri and Drury, (2007) to the extent that they (Al-Omiri and Drury) did not find any association between the level of cost system sophistication and cost structure, product diversity and quality of information technology. However the evidence we gathered shows all the independent variables are positively associated with our dependent variable hence, our acceptance of the six hypotheses presented. However, only three of these proved very significant. Besides attempting to improve the methods of measuring the variables, future research should consider incorporating other variables that might have been omitted from this and other studies which are likely to influence cost system design. The notable omitted variables are organizational variables such as top management support, resistance to change by those that prepare and use accounting information.

 

 

 

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