Essential Factors When Designing a Cost Accounting System in Greek Manufacturing Entities
Abstract
:1. Introduction
2. Literature Review
3. Hypothesis Development
3.1. Importance and Usefulness of Cost Information for Decision Making
3.2. Product Diversity
3.3. Cost Structure
3.4. Competitive Environment
3.5. Size of the Organization
3.6. Business Sector
4. Research Design and Data Collection
4.1. Sample of Survey
4.2. Measurement of the Variables
4.3. Variables
4.3.1. Variables of H1
4.3.2. Variables of H2
5. Descriptive Statistics
5.1. Descriptive Data of the Sample Manufacturing Companies
5.2. Production–Competition Data
5.3. Application of Cost Accounting
5.4. Cost Structure
5.5. Complexity of Cost Information
5.6. Cost Information and Decision Making
5.7. Frequency of Cost Information
6. Inferential Statistics
6.1. Correlations
6.2. Multiple Regression Model of H1
6.3. Kruskal–Wallis and Mann–Whitney—H2
7. Discussion and Conclusions
7.1. Limitation of Research
7.2. Future Proposals
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Ν | % | ||
---|---|---|---|
Gender | Male | 83 | 72.8% |
Female | 31 | 27.2% | |
Age | 25–34 | 22 | 19.3% |
35–44 | 53 | 46.5% | |
45–54 | 30 | 26.3% | |
54 and up | 9 | 7.9% | |
Educational Level | Secondary/post-secondary school graduate | 7 | 6.1% |
University graduate | 46 | 40.4% | |
MSc ή PhD graduate | 61 | 53.5% | |
Position of respondent | Cost manager | 33 | 28.9% |
Accounting manager | 16 | 14.0% | |
Financial director | 4 | 3.5% | |
General manager | 14 | 12.3% | |
Other | 47 | 41.2% | |
Industry sector | Food and drink Industry | 42 | 36.8% |
Clothing and footwear sector | 1 | 0.9% | |
Energy | 2 | 1.8% | |
Furniture and Furnishings | 1 | 0.9% | |
Cosmetics | 4 | 3.5% | |
Construction sector | 9 | 7.9% | |
Wood and cork | 4 | 3.5% | |
Oil and coal derivatives | 3 | 2.6% | |
Pharmaceutical | 9 | 7.9% | |
Chemical products | 3 | 2.6% | |
Other | 36 | 31.6% | |
Size (annual sales and nr of employees) | Very Small or Small (≤50 employees, ≤10 m.€ annual sales) | 25 | 21.9% |
Medium (51–250 employees, ≤50 m.€ annual sales) | 34 | 29.8% | |
Heavyweights (>250 employees, >50 m.€ annual sales) | 55 | 48.2% | |
Operating Years | <10 | 9 | 7.9% |
10–20 | 16 | 14.0% | |
>20 | 89 | 78.1% | |
Level of business activity | International | 71 | 62.3% |
National | 23 | 20.2% | |
National, International | 20 | 17.5% |
Ν | % | ||
---|---|---|---|
Product diversity | Till 4 | 11 | 9.6% |
5–10 | 11 | 9.6% | |
11–20 | 11 | 9.6% | |
21–40 | 9 | 7.9% | |
41–80 | 16 | 14.0% | |
Over 80 | 56 | 49.1% | |
Production lines | 1–2 | 23 | 20.2% |
3–4 | 26 | 22.8% | |
5–8 | 20 | 17.5% | |
9–16 | 22 | 19.3% | |
17–32 | 10 | 8.77% | |
>32 | 13 | 11.4% |
N | Mean Value | Std. Deviation | Median | IQR | |
---|---|---|---|---|---|
Intensity of competition | 114 | 3.68 | 0.75 | 3.80 | 1.00 |
Price competition | 114 | 3.80 | 1.03 | 4.00 | 1.25 |
The quality and product diversity | 114 | 3.59 | 1.01 | 4.00 | 1.00 |
Market share | 114 | 3.64 | 1.00 | 4.00 | 1.00 |
Access to raw materials | 114 | 3.40 | 1.05 | 4.00 | 1.00 |
Promotion and Distribution channel | 114 | 3.61 | 1.06 | 4.00 | 1.00 |
N | Mean Value | Std. Deviation | Median | IQR | |
---|---|---|---|---|---|
Importance of cost accounting information | 98 | 3.94 | 0.63 | 4.00 | 0.68 |
Measurement and evaluation of managers’ performance | 98 | 3.51 | 1.00 | 4.00 | 1.00 |
Measurement and evaluation of departmental performance | 98 | 3.76 | 0.87 | 4.00 | 1.00 |
Measurement of activities’ productivity | 98 | 4.01 | 0.91 | 4.00 | 1.00 |
Recognition of value-adding activities | 98 | 3.85 | 0.93 | 4.00 | 0.25 |
Recognition of non-value-adding activities | 98 | 3.48 | 1.10 | 4.00 | 1.00 |
Cost control | 98 | 4.47 | 0.78 | 5.00 | 1.00 |
Pricing | 98 | 4.35 | 0.81 | 5.00 | 1.00 |
Benchmarking | 98 | 3.97 | 0.97 | 4.00 | 2.00 |
Evaluation of special orders profitability | 98 | 3.83 | 1.01 | 4.00 | 2.00 |
Product design | 98 | 3.84 | 1.02 | 4.00 | 2.00 |
Short-term decision making (e.g., make-or-buy decisions) | 98 | 3.88 | 0.92 | 4.00 | 2.00 |
Introduction or discontinuing of products | 98 | 3.91 | 0.95 | 4.00 | 1.00 |
Valuation of inventories | 98 | 4.10 | 0.95 | 4.00 | 1.00 |
Sales and production strategy | 98 | 4.02 | 0.81 | 4.00 | 1.00 |
Budget preparation | 98 | 4.12 | 0.93 | 4.00 | 1.00 |
Negotiation with suppliers | 98 | 3.96 | 0.96 | 4.00 | 2.00 |
Discount policy | 98 | 3.77 | 0.99 | 4.00 | 1.25 |
Preparation of customer profitability analyses | 98 | 4.04 | 1.03 | 4.00 | 1.00 |
Analysis of differences between budgeted and actual results | 98 | 4.01 | 0.98 | 4.00 | 2.00 |
N | Mean Value | Std. Deviation | Median | IQR | |
---|---|---|---|---|---|
Classify costs according to behavior | 98 | 3.72 | 0.87 | 4.00 | 1.00 |
Direct and indirect | 98 | 3.91 | 1.02 | 4.00 | 2.00 |
Fixed and variable | 98 | 3.93 | 0.98 | 4.00 | 1.25 |
Controllable and non-controllable | 98 | 3.34 | 1.01 | 3.00 | 1.00 |
Range of cost information | 98 | 3.71 | 0.82 | 3.80 | 1.00 |
Cost per client | 98 | 3.64 | 1.20 | 4.00 | 2.00 |
Cost per product | 98 | 4.41 | 0.73 | 5.00 | 1.00 |
Cost per order | 98 | 3.56 | 1.10 | 4.00 | 1.00 |
Cost per business activity | 98 | 3.83 | 1.12 | 4.00 | 2.00 |
Calculation of variances | 98 | 3.75 | 0.83 | 3.86 | 1.04 |
Direct raw materials price variances | 98 | 3.93 | 1.06 | 4.00 | 2.00 |
Direct raw materials quantity variances | 98 | 3.89 | 1.01 | 4.00 | 1.00 |
Direct labour rate variances | 98 | 3.79 | 1.10 | 4.00 | 2.00 |
Direct labour efficiency variances | 98 | 3.56 | 1.12 | 4.00 | 1.00 |
Variable manufacturing overhead variances | 98 | 3.80 | 1.07 | 4.00 | 2.00 |
Fixed manufacturing overhead variances | 98 | 3.73 | 1.03 | 4.00 | 1.00 |
Operating expenses | 98 | 3.58 | 1.05 | 4.00 | 1.00 |
Accuracy of cost information | 98 | 3.98 | 0.86 | 4.00 | 1.50 |
Direct materials cost | 98 | 4.23 | 0.91 | 4.00 | 1.00 |
Direct labour cost | 98 | 4.15 | 0.88 | 4.00 | 1.00 |
Variable manufacturing overhead | 98 | 3.93 | 1.00 | 4.00 | 2.00 |
Indirect labour cost | 98 | 3.94 | 1.04 | 4.00 | 2.00 |
Depreciations | 98 | 3.93 | 1.13 | 4.00 | 2.00 |
Other indirect cost | 98 | 3.70 | 1.12 | 4.00 | 2.00 |
Quality of cost information | 98 | 3.86 | 0.70 | 4.00 | 0.67 |
Cost accounting system provides accurate information (ACCUR) | 98 | 3.87 | 0.81 | 4.00 | 1.00 |
Cost accounting system provides information in a timely manner (TIME) | 98 | 3.93 | 0.82 | 4.00 | 0.00 |
Cost accounting system provides up-to-date information (DATE) | 98 | 3.86 | 0.77 | 4.00 | 1.00 |
Cost accounting system provides information that meets decision makers’ needs (NEED’S) | 98 | 3.66 | 0.92 | 4.00 | 1.00 |
Cost accounting system provides reliable information (REL) | 98 | 3.89 | 0.84 | 4.00 | 0.00 |
Cost accounting system provides information at an appropriate level of analysis for decision-making purposes (APPR) | 98 | 3.96 | 0.86 | 4.00 | 1.25 |
N | Mean Value | Std. Deviation | Median | IQR | |
---|---|---|---|---|---|
Cost information/usefulness | 98 | 3.58 | 0.92 | 3.83 | 0.75 |
Break -even point | 98 | 3.45 | 1.10 | 4.00 | 1.00 |
Unit profit margin participation (Price less variable cost) | 98 | 3.69 | 1.00 | 4.00 | 1.00 |
NET profit margin participation (Unit profit margin x units Q-fixed cost) | 98 | 3.59 | 1.08 | 4.00 | 1.00 |
Effectiveness of cost system for making quick decisions to maintain business profitability during in crises | 98 | 3.56 | 0.71 | 3.67 | 0.67 |
Covid 2019 | 98 | 3.28 | 0.96 | 3.50 | 1.00 |
Ukraine war | 98 | 3.29 | 1.00 | 3.00 | 1.00 |
Inflation | 98 | 3.54 | 0.95 | 4.00 | 1.00 |
Energy crisis | 98 | 3.93 | 0.92 | 4.00 | 1.00 |
Increases of transport cost and freight | 98 | 3.83 | 0.94 | 4.00 | 0.25 |
Technological developments | 98 | 3.50 | 0.89 | 4.00 | 1.00 |
Satisfaction with Cost Accounting System | Use of Cost System for Making Quick Decisions | ||
---|---|---|---|
N | Companies with Cost system | 98 | 98 |
Companies without Cost system | 16 | 16 | |
Mean value | 3.62 | 3.87 | |
Std. Deviation | 0.856 | 0.857 | |
Median | 4.00 | 4.00 | |
IQR | 1.00 | 1.25 |
N | Mean Value | Std. Deviation | Median | IQR | |
---|---|---|---|---|---|
Frequency of cost information | 98 | 3.61 | 0.83 | 3.50 | 1.00 |
Information upon request | 98 | 3.74 | 0.92 | 4.00 | 1.00 |
Frequent reports on a systematic, regular basis | 98 | 3.47 | 1.06 | 4.00 | 1.00 |
Author(s) | Variables | Findings |
---|---|---|
(Abernethy et al. 2001) | Dependent variables: Cost system design Independent variables: Product diversity, Production process complexity, Manufacturing overhead, Costing system characteristics | Cost system design is influenced by:
|
(Drury and Tayles 2005) | Dependent variables: Level of cost system complexity Independent variables: Indirect cost, competition, product diversity, Size, cost importance Dummy variable: Service sector, Finance sector, Retail sector, Miscellaneous category | Three variables were not statistically significant:
|
(Pizzini 2006) | Dependent variables: (a) cost-system functionality, firm performance Independent variables: (a) Detail (the level of detail provided by the system), Classify (the system’s ability to classify costs according to behaviour), Frequent (the frequency with which cost information is disseminated throughout the organization), and Variance(the type and number of variances calculated) Control Variables: Beds, Case mix, for Profit, System, competition, wage, MCO penetration, and managed. Association between cost-system functionality and financial performance (Profit margin, Cash flow 2, Administrative expense, Expense per admit) Association between absolute level of cost-system functionality and managers’ evaluations of the relevance and usefulness of cost data | The relevance and usefulness of cost data are positively correlated with:
The coefficients for CLASSIFY and FREQUENT are positively and significantly associated with data relevance (p < 0.10 and p < 0.01, two-sided, respectively), but neither is associated with usefulness. DETAIL and CLASSIFY are highly correlated (r = 0.68). The capability to provide more detail (DETAIL) is positively and significantly associated with operating margin (p < 0.05, two-sided) and cash flow per bed, (p < 0.01, two-sided). DETAIL is negatively associated with the administrative expense ratio (p < 0.10, two-sided) Neither CLASSIFY nor FREQUENT is significantly associated with performance CLASSIFY is positively and significantly associated with cash flow per bed (p < 0.05, two-sided). The variable, FREQUENT, is not associated with actual financial performance No relation between the extent to which systems calculate variances and managers’ evaluations of the relevance and usefulness of cost information. |
(Van Triest and Fathy Elshahat 2007) | Dependent variables: Cost Systems- Specificity, Accuracy, Satisfaction, Development plans independent variables: firm size, overhead, the average number of cost pools. They analysed the relationship between firm size and overhead, the correlations of the costing systems, and the average number of cost pools, the overhead percentage and firm size. | Positive correlation: Specificity of the costing system with satisfaction Negative correlation: Specificity of the costing system with development plans and with the number of cost pools with accuracy. No significant difference is found between the score on using costing information for customer profitability calculations. No correlation between firm size and costing system characteristics such as specificity, accuracy or number of cost pools Activity-based costing is virtually unknown, and accounting concepts such as cost pools and resource consumption seem unfamiliar to the respondents. The cost accounting information in Egypt is available at a basic level at the most, and used more for external (pricing) purposes than for internal (performance) purposes. |
(Al-Omiri and Drury 2007) | Dependent variables:
|
Intensity of the competitive environment (p < 0.01 for both dependent variables). Size (p < 0.01 for both dependent variables). Financial sector (p < 0.05 for both dependent variables). Results indicate 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. |
(Brierley 2008) | Three types of sophistication (1) the assignment of indirect overhead costs to product costs, (2) the inclusion of all costs in product costs, and (3) the understandability of product costs by nonaccountants were measured by the number of cost pools and the number of cost drivers. | The main definitions were discovered:
The correlations between satisfaction with the overall accuracy of product costs and the number of cost pools and cost drivers are both low and not significant (p > 0.10), which indicates there is no relationship between them. |
(Pavlatos and Paggios 2009) | Dependent variables: the dichotomous variable of more functional cost systems(More functional cost system are those that provide greater detail, better classify costs according to behavior, report cost information more frequently, provide accurate cost data to a great extent, and calculate more variances.) and less functional cost systems Independent variables: Extent of the use of cost data, Level of competition, Size (annual log sales in s million), Low cost strategy, Number of services variants, Member of multinational chain | Hotel costing systems do not provide high-quality cost data. They do not calculate variances or provide detailed cost information by cost object. Statistically significant variables affecting system complexity include: the extent of the use of cost data (p < 0.01) and low-cost strategy (p < 0.01). The correlation between low cost strategy and use of data (r = 0.45, p 0.01) is not sufficient. |
(Schoute 2009) | Regression analysis results for the associations between cost system complexity, purposes of use, and cost system effectiveness. | At higher levels of usage for product planning purposes, the complexity of the system negatively affects the intensity of cost system usage. At higher levels of usage for cost management purposes, the complexity of the cost system positively affects:
|
(Cohen and Kaimenaki 2011) | Dependent variables: RELEV, the extent to which cost information is relevant for decision making; ACC, the extent to which cost information is accurate; TIME, the extent to which cost information is provided in time; DATE, the extent to which cost information is up-to-date; NEEDS, the extent to which cost information meets users’ needs; APPR, the extent to which cost information has the appropriate level of analysis; REL, the extent to which cost information is reliable; USE, the extent to which cost information is used to make decisions Independent variables: DET_1 is the extent to which the system analyses costs by cost centre, product and activity; DET_2 is the extent to which the system allows the preparation of customized reports according to users ‘specification; DISAGG is the extent to which the system classifies costs according to behaviour; VAR is the extent to which the system calculates variances; FREQ_1 is the extent to which the system provides frequent reports on a systematic basis; FREQ_2 is the extent to which the system provides information upon request; | Positive and Significant: Τhe accuracy of cost accounting information (ACC) as well as the system’s ability to meet users ‘needs (NEEDS) are positively and significantly associated with the extent to which costs are analysed by cost centre, product and activity (DET_1), the degree to which variance analysis is conducted (VAR) and the extent to which information is provided upon request (FREQ_2). The more frequent the cost information and the more variances are calculated (VAR) the greater the extent to which cost information is provided in time (TIME) and is up to date (DATE). Positive association between the degree to which variances are calculated (VAR) and the extent to which cost information has the appropriate level of analysis (APPR). The greater the extent to which a cost accounting system analyses costs by cost centre, product and activity (DET_1) and permits variance calculation (VAR) the more reliable the information that it provides to users (REL). A positive and statistically significant association between the extent to which cost information is used for decision making (USE) and frequency of information provision No significant Neither the extent to which the cost accounting system allows the preparation of customized reports according to users specifications (DET_2) nor the degree to which costs are classified according to behavior (DISAGG) are found to be significant predictors of any of the dependent variables. |
(Ismail and Mahmoud 2012) | Dependent variable: (a) Cost system sophistication level (ABC/Non-ABC, number of cost pools and number of cost drivers). (b) Manufacturing performance (quality, time and cost) Independent variables: (a) product diversity, cost structure, intensity of competition and the importance of cost information. (b) Cost system sophistication level, Firm size, Industry type Control variables: Firm size, Industry type | Positive significant
|
(Uyar and Kuzey 2016) | Dependent variable: (a) MAPs. (b1&2) Firm performance Independent variables: (a) Cost system design. (b1) Management accounting practises. (b2) Cost system design |
|
(Schoute and Budding 2017a) | Variables: external information needs and internal information needs, cost system complexity, cost system inclusiveness, cost system understandability, Cost system intensity of use, Cost system satisfaction Control Variables. The organizational size (ORG_SIZE) Two dummy variables are used to capture this construct (SECT_Dum and CONC_Dum) | It was found that the greater the need for information in a municipality, the more complex the costing system becomes. There is a positive correlation between:
|
(Schoute and Budding 2017b) | Variables: Changes in Contextual Factors, Changes in Cost System Design, Changes in Cost System Intensity of Use changes in cost system design during the years 2008–2010 | During the 2010 crisis, on average, uncertainty regarding the environment and financing had indeed increased significantly, while cost system design and intensity of use had shown little change. There was a positive correlation between:
|
(Zuriekat 2020) | Dependent variable: the design of costing systems Independent variables: Total quality management, Just-in-time, Product diversity | The following factors have a statistically significant impact on system design:
|
(Humeedat 2020) | Dependent variable: the design of costing systems Independent variables: product diversity, relevant cost information, technological changes, triggered exception operational losses | A positive correlation was found between cost system design and:
It is recommended to immediately redesign cost systems to mitigate the economic impacts resulting from the COVID-19 pandemic. |
(Dokas et al. 2021) | A first step for criticism concerns the reliability of these models in the estimation of manipulation level. A basic element in the construction of these models is the definition of accruals. This process varies among the firms because of the structure of corporate activities, while the accounting policies implementation plays a significant role. The individual characteristics of each company, such as the governance status, the quality of auditing and the effect of fees, the share of the firm in the market, and the status of the competition, are some of the figures with an important role in the intensity of earnings manipulation. The literature shows that the rapid changes in the structure of the economies and the firms, especially at an institutional level, revise the corporate strategy relevant to the available tools which are used in the manipulation of the accounting information. Qualitative factors such as accounting knowledge and business ethics play a significant role in detecting earnings manipulation. | |
(Barkas et al. 2022) | Dependent variable: ROA Independent variables:(Total) capital adequacy ratio, Annual percentage change in deposits, Provision for credit losses ratio, Natural logarithm of public debt Pseudo-variable for the year that banks were affected by the haircut of Greek government bonds |
|
(Dokas 2023) | Dependent variable: earnings management. The four alternative dependent variables are the discretionary accruals (DACC), the abnormal level of discretionary expenses, the abnormal production cost, and the abnormal cash flows from operations Independent variables: the board size, the natural logarithm of the number of annual board meetings, the portion of independent members of the board, the tenure of the board members, skills, CEO duality Control variables concerning financial performance, the scrutiny level, and earnings management incentives: the quality of auditors (BIG4), the return on assets (ROA), The firm size, The leverage ratio Dummies: Year, Country, Industry |
|
1 | Likert scale: 1—Not at all, 2—To a little extent, 3—To neutral extent, 4—To a considerable extent, 5—To a very great extent. |
2 | Υ: Satisfaction from cost system, : Classify costs according to behavior, : Range of cost information, : Calculation of variances, : Accuracy of cost information, : Quality of cost information, : Cost information/usefulness, : Quick decision making to maintenance of profitability during crisis, : Number 0f cost pools, : number of cost allocation bases, : Direct labor hour, : Direct labor cost, : Machinery operating hours, : Production units. |
References
- Abernethy, Margaret A., Anne M. Lillis, Peter Brownell, and Paul Carter. 2001. Product diversity and costing system design choice: Field study evidence. Management Accounting Research 12: 261–79. [Google Scholar] [CrossRef]
- Ahmadzadeh, Taha, Etemadi Hossien, and Ahmed Pifeh. 2011. Exploration of factors influencing on choice the activity-based costing system in Iranian organizations. International Journal of Business Administration 2: 61–70. [Google Scholar] [CrossRef]
- Al-Omiri, Mohammed, and Colin Drury. 2007. A survey of factors influencing the choice of product costing systems in UK organizations. Management Accounting Research 18: 399–424. [Google Scholar] [CrossRef]
- Ammar, Sameh. 2017. Enterprise systems, business process management and UK-management accounting practices: Cross-sectional case studies. Qualitative Research in Accounting and Management 14: 230–81. [Google Scholar] [CrossRef]
- Anand, Manoj, Bidya Sahay, and Subhashish Saha. 2005. Activity-based cost management practices in India: An empirical study. Decision 32: 123–52. [Google Scholar]
- Baird, Kevin M., Graeme L. Harrison, and Robert C. Reeve. 2004. Adoption of activity management practices: A note on the extent of adoption and the influence of organizational and cultural factors. Management Accounting Research 15: 383–99. [Google Scholar] [CrossRef]
- Balios, Dimitris. 2021. The Impact of Big Data on Accounting and Auditing. International Journal of Corporate Finance and Accounting 8: 14. [Google Scholar] [CrossRef]
- Balios, Dimitris, Panagiotis Kotsilaras, Nikolaos Eriotis, and Dimitrios Vasiliou. 2020. Big Data, Data Analytics and External Auditing. Journal of Modern Accounting and Auditing 16: 211–19. [Google Scholar] [CrossRef]
- Barkas, Panagiotis, Theodoros Kounadeas, and Nikolaos Spatharakis. 2022. Explaining the first effects of COVID-19 on Greek banks’ profitability. Bulletin of Applied Economics 10: 67–88. [Google Scholar] [CrossRef]
- Bjørnenak, Trond. 1997. Diffusion and accounting: The case of ABC in Norway. Management Accounting Research 8: 3–17. [Google Scholar] [CrossRef]
- Blanca, Mena, Rafael Alarcón, and Jaume Arnau. 2018. Effect of variance ratio on ANOVA robustness: Might 1.5 be the limit? Behavior Research Methods 50: 937–62. [Google Scholar] [CrossRef] [PubMed]
- Boerema, Annelies, Steven Van Passel, and Patrick Meire. 2018. Cost-Effectiveness Analysis of Ecosystem Management with Ecosystem Services: From Theory to Practice. Ecological Economics 152: 207–18. [Google Scholar] [CrossRef]
- Brierley, John A. 2008. Toward an Understanding of theSophistication of Product Costing Systems. Journal of Management Accounting Research 20: 61–78. [Google Scholar] [CrossRef]
- Brierley, John A., Christopher J. Cowton, and Colin Drury. 2001. Research into product costing practice: A European perspective. European Accounting Review 10: 215–56. [Google Scholar] [CrossRef]
- Cagwin, Douglass, and Marinus J. Bouwman. 2002. The association between activity-based costing and improvement in financial performance. Management Accounting Research 13: 1–39. [Google Scholar] [CrossRef]
- Chenhall, Robert H. 2003. Management control systems design within itsorganizational context: Findings from contingency-basedresearch and directions for the future. Accounting, Organizations and Society 28: 127–68. [Google Scholar] [CrossRef]
- Chenhall, Robert H., and Kim Langfield-Smith. 1998. The relationship between strategic priorities, management techniques and management accounting: An empirical investigation using a systems approach. Accounting, Organizations and Society 3: 243–64. [Google Scholar] [CrossRef]
- Christopoulos, Apostolos G., Ioannis G. Dokas, Petros Kalantonis, and Theodora Koukkou. 2019. Investigation of financial distress with a dynamic logit based on the linkage between liquidity and profitability status of listed firms. Journal of the Operational Research Society 70: 1817–29. [Google Scholar] [CrossRef]
- Cinquini, Lino, Paolo Collini, Alessandro Marelli, and Andrea Tenucci. 2015. Change in the relevance of cost information and costing systems: Evidence from two Italian surveys. Journal of Management & Governance 19: 557–87. [Google Scholar] [CrossRef]
- Cohen, Sandra, and Efrosini Kaimenaki. 2011. Cost accounting systems structure and information quality properties: An empirical analysis. Journal of Applied Accounting Research 12: 5–25. [Google Scholar] [CrossRef]
- Comerford, Sue E., and Margaret A. Abernethy. 1999. Budgeting and the management of role conflict in hospitals. Behavioral Research in Accounting 11: 93. [Google Scholar]
- Cooper, Robin. 1988. The rise of activity-based costing-part three: How many cost drivers do you need, and how do you selectthem? Cost Management 2: 34–46. [Google Scholar]
- Covaleski, Mark A., Mark W. Dirsmith, and Jeffrey Michelman. 1993. An institutional theory perspective on the DRG Frame-work: Case-mix accounting systems health-care organizations. Accounting, Organizations and Society 18: 65–80. [Google Scholar] [CrossRef]
- Dent, Jeremy F. 1990. Strategy, organization and control: Some possibilities for accounting research. Accounting Organizations and Society 15: 3–25. [Google Scholar] [CrossRef]
- Dokas, Ioannis. 2023. Earnings Management and Status of Corporate Governance under Different Levels of Corruption—An Empirical Analysis in European Countries. Journal of Risk and Financial Management 16: 458. [Google Scholar] [CrossRef]
- Dokas, Ioannis, Christos Leontidis, Nikolaos Eriotis, and Konstantinos Hazakis. 2021. Earnings Management. An overview of the relative literature. Bulletin of Applied Economics 8: 25–55. [Google Scholar] [CrossRef]
- Drury, Colin, and Mike Tayles. 2005. Explicating the design of overhead absorption procedures in UK organizations. British Accounting Review 37: 47–84. [Google Scholar] [CrossRef]
- Drury, Colin, and Mike Tayles. 2006. Profitability analysis in UK organizations: An exploratory study. The British Accounting Review 4: 405–25. [Google Scholar] [CrossRef]
- Estrin, Teviah L., Jeffrey Kantor, and David Albers. 1994. Is ABC suitable for your company? Management Accounting 75: 40–45. [Google Scholar]
- Field, Andy. 2009. Discovering Statistics Using SPSS, 3rd ed. London: Sage Publications. [Google Scholar]
- Field, Andy. 2016. Investigating Statistics Using IBM SPSS. Athens: Propompos. [Google Scholar]
- Foster, George, and Dan W. Swenson. 1997. Measuring the success of activity-based cost management and its determinants. Journal of Management Accounting Research 9: 109–42. [Google Scholar]
- Freeman, Edward R. 1984. Strategic Management: A Stakeholder Approach. Pittman and Marshfield: Pitman Boston. [Google Scholar]
- Geiger, Dale R., and Christopher D. Ittner. 1996. The influence of funding source and legislative requirement on goverment cost accounting practices. Accountfng Organizations and Society 21: 549–67. [Google Scholar] [CrossRef]
- Georgakopoulos, George, Toudas Kanellos, Evangelos I. Poutos, Theodoros Kounadeas, and Stefanos Tsavalias. 2022. Capital Structure, Corporate Governance, Equity Ownership and Their Impact on Firms’ Profitability and Effectiveness in the Energy Sector. Energies 15: 3625. [Google Scholar] [CrossRef]
- Gordon, Lawrence A., and Danny Miller. 1976. A contingency framework for the design of accounting information systems. Accounting, Organizations and Society 1: 59–69. [Google Scholar] [CrossRef]
- Guilding, Chris, and Lisa McManus. 2002. The Incidence, Perceived Merit and Antecedents of Customer Accounting: An Exploratory Note. Accounting Organizations and Society 27: 45–59. [Google Scholar] [CrossRef]
- Gunasekaran, Angappa, Hussain Bux Marri, and Yahaya Yusuf. 1999. Application of activity-based costing: Some case experiences. Managerial Auditing Journal 14: 286–93. [Google Scholar] [CrossRef]
- Hair, Joseph F., Rolph E. Anderson, Ronald L. Tatham, and William Black. 1998. Multivariate Data Analysis, 5th ed. Englewood Cliffs: Prentice-Hall. [Google Scholar]
- Hill, Nancy. 2000. Adoption of costing systems in US hospitals: An event history analysis 1980–1990. Journal of Accounting and Public Policy 19: 41–71. [Google Scholar] [CrossRef]
- Humeedat, Mohammad M. 2020. New environmental factors affecting cost systems design after COVID-19. Management Science Letters 10: 3777–82. [Google Scholar] [CrossRef]
- Innes, John, and Falconer Mitchell. 1995. A survey of activity-based costing in the U.K.’s largest companies. Management Accounting Research 6: 137–153. [Google Scholar] [CrossRef]
- Ismail, Tariq H., and Nancy Mahmoud. 2012. The Influence of Organizational and Environmental Factors on Cost Systems Design in Egypt. British Journal of Economics, Finance and Management Sciences 4: 31–51. [Google Scholar]
- Kaplan, Robert S., and Robin Cooper. 1998. Cost and Effect: Using Integrated Systems to Drive Profitability and Performance. Boston: Harvard Business School Press. [Google Scholar]
- Kaplan, Robert S., and William J. Bruns. 1987. Accounting and Management: Field Study Perspectives. Brighton: Harvard Business School Press. [Google Scholar]
- Khandwalla, Pradip N. 1972. The Effect of Different Types of Competition on the Use of Management Controls. Journal of Accounting Research 10: 275–85. [Google Scholar] [CrossRef]
- Krumwiede, Kip R. 1998. The Implementation Stages of Activity-Based Costing and the Impact of Contextual and Organizational Factors. Journal of Management Accounting Research 10: 239–77. [Google Scholar]
- Lapsley, Irvine, and Elisa Wright. 2004. The diffusion of management accounting innovations in the public sector: A research agenda. Management Accounting Research 15: 355–74. [Google Scholar] [CrossRef]
- Libby, Theresa, and John. H. Waterhouse. 1996. Predicting change in management accounting systems. Journal of Management Accounting Research 8: 137–50. [Google Scholar]
- Malmi, Teemu. 1999. Activity-based costing discusion across organizations: An exploratory empirical analysis of Finnish firms. Accounting, Organizations and Society 24: 649–72. [Google Scholar] [CrossRef]
- McGown, Annie S. 1998. Perceived benefits of ABCM implementation. Accounting Horizons 12: 31–50. [Google Scholar]
- Nicolaou, Andreas. I. 2003. Manufacturing strategy implementation and cost management systems effectiveness. European Accounting Review 12: 175–99. [Google Scholar] [CrossRef]
- Pavlatos, Odysseas, and Hara Kostakis. 2015. Management accounting practices before and during economic crisis: Evidence from Greece. Advances in Accounting 31: 150–64. [Google Scholar] [CrossRef]
- Pavlatos, Odysseas, and Ioannis Paggios. 2009. Activity-based costing in the hospitality industry: Evidence from Greece. Journal of Hospitality and Tourism Research 33: 511–27. [Google Scholar] [CrossRef]
- Pizzini, Mina J. 2006. The relation between cost-system design, managers’ evaluations of the relevance and usefulness of cost data, and financial performance: An empirical study of US hospitals. Accounting, Organizations and Society 31: 179–210. [Google Scholar] [CrossRef]
- Schoute, Martijn. 2009. The relationship between cost system complexity, purposes of use, and cost system effectiveness. British Accounting Review 41: 208–26. [Google Scholar] [CrossRef]
- Schoute, Martijn, and Tjerk Budding. 2017a. Chapter 1: Changes in cost system design and intensity of use in times of crisis: Evidence from Dutch local government. Advances in Management Accounting 29: 1–31. [Google Scholar] [CrossRef]
- Schoute, Martijn, and Tjerk Budding. 2017b. Stakeholders’ Information Needs, Cost System Design, and Cost System Effectiveness in Dutch Local Government. Financial Accountability & Management 33: 77–101. [Google Scholar] [CrossRef]
- Shea, Vincent, Bobby Waldrup, Helen Xu, and Steven Williamson. 2018. Error Rate Impacts on Decision Efficacy: Activity-Based Costing Systems in Small Business. Quarterly Review of Business Disciplines 5: 59–71. [Google Scholar]
- Shields, Michael D. 1995. An Empirical Analysis of Firms’ Implementation Experiences with Activity-Based Costing. Journal of Management and Accounting Research 7: 148–66. [Google Scholar]
- Shields, Michael D. 1997. Research in management accounting by North Americans in the 1990’s. Journal of Management Accounting Research 9: 3–62. [Google Scholar]
- Simons, Robert. 1990. The role of management control systems in creating competitive advantage: New perspectives. Accounting, Organizations and Society 15: 127–43. [Google Scholar] [CrossRef]
- Swenson, Dan. 1995. The benefits of activity-based cost management to the manufacturing industry. Journal of Management Accounting Research 7: 167–80. [Google Scholar]
- Soras, Evangelos, and Apostolos G. Christopoulos. 2024. Compliance with the Requirements of the Greek Legislation for Reporting on ESG Issues: The Case of the Paper Processing Sector. Risk Financial Management 17: 14. [Google Scholar] [CrossRef]
- Uyar, Ali. 2010. Cost and Management Accounting Practices: A Survey of Manufacturing Companies. Eurasian Journal of Business and Economics 3: 113–25. [Google Scholar]
- Uyar, Ali, and Necdet Bilgin. 2011. Budgeting practices in the Turkish hospitality industry: An exploratory survey in the Antalya region. International Journal of Hospitality Management 30: 398–408. [Google Scholar] [CrossRef]
- Uyar, Ali, and Cemil Kuzey. 2016. Does management accounting mediate the relationship between cost system design and performance? Advances in Accounting 35: 170–76. [Google Scholar] [CrossRef]
- Van Triest, Sander, and Mohamed Fathy Elshahat. 2007. The use of costing information in Egypt: A research note. Journal of Accounting & Organizational Change 3: 329–43. [Google Scholar] [CrossRef]
- Xia, Yinglin. 2020. Correlation and association analyses in microbiome study integrating multiomics in health and disease. Progress in Molecular Biology and Translational Science 171: 309–491. [Google Scholar] [CrossRef]
- Xiong, Yanren, Wenbin Su, and Thomas Lin. 2008. Activity-based costing popularity in China. Cost Management 22: 40–48. [Google Scholar]
- Yalcin, Selcuk. 2012. Adoption and benefits of management accounting practices: An inter-country comparison. Accounting in Europe 9: 95–110. [Google Scholar] [CrossRef]
- Zuriekat, Maidy I. 2020. Management practices and cost systen design: Evidence from Jordanian manufacturing companies. Humanities & Social Sciences Reviews 8: 920–29. [Google Scholar] [CrossRef]
Question | Data | Cronbach’s Alpha * | |
---|---|---|---|
Intensity of the competitive environment | Q11 | 5 | 0.807 |
Relevance/Importance of the information in the cost accounting system | Q15 | 19 | 0.929 |
Classify costs according to behavior | Q16 | 3 | 0.842 |
Range of cost information | Q17 | 5 | 0.791 |
Calculation of variances | Q18 | 7 | 0.895 |
Accuracy of cost information | Q19 | 6 | 0.918 |
Quality of cost information | Q20 | 6 | 0.917 |
Cost information/usefulness | Q24 | 3 | 0.829 |
Quick decision making to maintenance of profitability during crisis | Q27 | 6 | 0.847 |
Frequency of cost information | Q28 | 2 | 0.556 |
Kolmogorov-Smirnov | Shapiro-Wilk | |||||
---|---|---|---|---|---|---|
Statistic | df | p | Statistic | df | p | |
Intensity of the competitive environment | 0.114 | 98 | 0.003 | 0.963 | 98 | 0.008 |
Importance of the information in the cost | 0.114 | 98 | 0.003 | 0.944 | 98 | <0.001 |
Classify costs according to behavior | 0.157 | 98 | <0.001 | 0.896 | 98 | <0.001 |
Range of cost information | 0.113 | 98 | 0.003 | 0.959 | 98 | 0.004 |
Calculation of variances | 0.093 | 98 | 0.035 | 0.947 | 98 | <0.001 |
Accuracy of cost information | 0.138 | 98 | <0.001 | 0.915 | 98 | <0.001 |
Quality of cost information | 0.146 | 98 | <0.001 | 0.944 | 98 | <0.001 |
Cost information/usefulness | 0.177 | 98 | <0.001 | 0.897 | 98 | <0.001 |
Satisfaction with cost accounting system | 0.303 | 98 | <0.001 | 0.830 | 98 | <0.001 |
Use of cost system for making quick decisions | 0.235 | 98 | <0.001 | 0.859 | 98 | <0.001 |
Quick decision making to maintenance of profitability during crisis | 0.181 | 98 | <0.001 | 0.905 | 98 | <0.001 |
Frequency of cost information | 0.173 | 98 | <0.001 | 0.943 | 98 | <0.001 |
Information upon request | 0.303 | 98 | <0.001 | 0.851 | 98 | <0.001 |
Frequent reports on a systematic, regular basis | 0.243 | 98 | <0.001 | 0.894 | 98 | <0.001 |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Classify costs according to behavior | -- | |||||||||||||||
2. Range of cost information | 0.415 ** | -- | ||||||||||||||
3. Calculation of variances | 0.542 ** | 0.680 ** | -- | |||||||||||||
4. Accuracy of cost information | 0.442 ** | 0.615 ** | 0.722 ** | -- | ||||||||||||
5. Quality of cost information | 0.425 ** | 0.525 ** | 0.505 ** | 0.654 ** | -- | |||||||||||
6. Nr of cost pools | 0.308 ** | 0.367 ** | 0.516 ** | 0.557 ** | 0.369 ** | -- | ||||||||||
7. Nr of cost allocation bases | 0.245 * | 0.207 * | 0.250 * | 0.405 ** | 0.235 * | 0.726 ** | -- | |||||||||
8. Direct labor hour | 0.123 | 0.213 * | 0.166 | 0.147 | 0.121 | 0.171 | 0.057 | -- | ||||||||
9. Direct labor cost | 0.117 | 0.06 | 0.043 | −0.051 | −0.199 * | 0.042 | 0.162 | 0.214 * | -- | |||||||
10. Machinery operating hours | 0.19 | 0.252 * | 0.342 ** | 0.294 ** | 0.138 | 0.383 ** | 0.199 * | 0.334 ** | −00.01 | -- | ||||||
11. Production units | 0.032 | 0.009 | −0.12 | 0.136 | 0.043 | 0.026 | 0.138 | −0.093 | 0.201 * | 0.04 | -- | |||||
12. Cost information | 0.272 ** | 0.389 ** | 0.291 ** | 0.302 ** | 0.422 ** | 0.128 | 00.117 | −0.037 | 0.124 | 0.126 | 0.079 | -- | ||||
13. Satisfaction from cost system | 0.197 | 0.404 ** | 0.313 ** | 0.423 ** | 0.667 ** | 0.303 ** | 0.235 * | 0.043 | −0.035 | 0.076 | 0.043 | 0.474 ** | -- | |||
14. Use of cost system for making quick decisions | 0.197 | 0.365 ** | 0.340 ** | 0.430 ** | 0.581 ** | 0.356 ** | 0.254 * | 0.098 | −0.095 | 0.183 | 0.104 | 0.330 ** | 0.716 ** | -- | ||
15. Quick decision making to maintenance of profitability during crisis | 0.256 * | 0.374 ** | 0.335 ** | 0.405 ** | 0.424 ** | 0.185 | 0.206 * | −0.021 | −0.066 | 0.074 | −0.041 | 0.463 ** | 0.346 ** | 0.403 ** | -- | |
16. Frequency of cost information | 0.118 | 0.406 ** | 0.248 * | 0.329 ** | 0.495 ** | 0.096 | 0.072 | −0.049 | −0.11 | 0.136 | 0.05 | 0.537 ** | 0.361 ** | 0.275 ** | 0.341 ** | -- |
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson |
---|---|---|---|---|---|
1 | 0.793 a | 0.629 | 0.571 | 0.556 | 1.981 |
Model | Sum of Squares | df | Mean Square | F | Sig. | |
---|---|---|---|---|---|---|
1 | Regression | 43.463 | 13 | 3.343 | 10.819 | <0.001 b |
Residual | 25.650 | 83 | 0.309 | |||
Total | 69.113 | 96 |
B | 95% CI | Beta | t | p | |
---|---|---|---|---|---|
(Constant) | −0.453 | −1.313, 0.407 | −1.048 | 0.298 | |
Cost structure | |||||
Classify costs according to behavior | −0.147 | −0.311, 0.016 | −0.144 | −1.791 | 0.077 |
Range of cost information | −0.01 | −0.234, 0.213 | −0.009 | −0.093 | 0.927 |
Calculation of variances | 0.045 | −0.228, 0.317 | 0.041 | 0.325 | 0.746 |
Accuracy of cost information | 0.14 | −0.114, 0.394 | 0.133 | 1.095 | 0.277 |
Quality of cost information | 0.791 | 0.559, 1.023 | 0.634 | 6.782 | <0.001 |
Usefulness of cost structure | |||||
Cost information | 0.238 | 0.079, 0.398 | 0.248 | 2.979 | 0.004 |
Quick decision making to maintenance of profitability during crisis | 0.018 | −0.194, 0.23 | 0.014 | 0.169 | 0.866 |
Complexity of cost | |||||
Nr of cost pools | −0.035 | −0.134, 0.064 | −0.081 | −0.708 | 0.481 |
Nr of cost allocation bases | 0.037 | −0.068, 0.142 | 0.071 | 0.705 | 0.483 |
Direct labor hour | −0.121 | −0.406, 0.165 | −0.066 | −0.841 | 0.403 |
Direct labor cost | 0.145 | −0.138, 0.428 | 0.082 | 1.019 | 0.311 |
Machinery operating hours | −0.011 | −0.279, 0.257 | −0.007 | −0.083 | 0.934 |
Production units | −0.078 | −0.342, 0.186 | −0.044 | −0.586 | 0.560 |
Variable group “Cost structure” | Demographic data where the distribution differs statistically significantly | Test results of Kruskall-Wallis and Mann-Whitney for differences between groups of categorical variables Demographics data and Cost Structure |
Classify costs according to behavior | Age of the respondent (p = 0.011) | 45–54 respond more positively that their costing system classify costs according to behavior |
Range of cost information | Operating Years (p = 0.011) Size (p = 0.004) | Companies operating for more than (>20) years as well as large companies apply systems with a wider range of cost |
Calculation of variances | Level of business activity (p = 0.011) Operating Years (p = 0.006) Age of the respondent (p = 0.045) Size (p = 0.002) | Companies that operate internationally, large companies, companies that operating for more than (>20) years and those whose participants are older than 54+ years old, claim that their costing system provide variance calculations. |
Accuracy of cost information | Level of business activity (p = 0.014) Educational level (p = 0.036) Operating Years (p < 0.001) Size (p < 0.001) | Companies that operate internationally, large companies, companies that operating for more than (>20) years and those whose participants have Master’s and PhD degrees, argue that their costing system provides information with high cost accuracy |
Quality of cost information | Level of business activity (p = 0.005) Operating Years (p = 0.026) Size (p = 0.011) | Companies that operate internationally, large companies, companies that operating for more than (>20) years evaluate the quality of information provided by the cost accounting system more positively |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Product diversity | -- | ||||||||||||||
2. Production lines | 0.436 ** | -- | |||||||||||||
3. The competition effect on price policy formation | 0.061 | 0.172 | -- | ||||||||||||
4. Intensity of the competitive environment | 0.185 * | 0.218 * | 0.368 ** | -- | |||||||||||
5. Classify costs according to behavior | 0.068 | 0.154 | 0.234 * | 0.207 * | -- | ||||||||||
6. Range of cost information | 0.152 | 0.325 ** | −0.007 | 0.061 | 0.415 ** | -- | |||||||||
7. Calculation of variances | 0.211 * | 0.395 ** | 0.041 | 0.107 | 0.542 ** | 0.680 ** | -- | ||||||||
8. Accuracy of cost information | 0.204 * | 0.408 ** | −0.021 | 0.168 | 0.442 ** | 0.615 ** | 0.722 ** | -- | |||||||
9. Quality of cost information | 0.165 | 0.271 ** | 0.042 | 0.09 | 0.425 ** | 0.525 ** | 0.505 ** | 0.654 ** | -- | ||||||
10. Number of cost pools | 0.335 ** | 0.520 ** | 0.07 | 0.129 | 0.308 ** | 0.367 ** | 0.516 ** | 0.557 ** | 0.369 ** | -- | |||||
11. Number of cost allocation bases | 0.232 * | 0.383 ** | 0.203 * | 0.112 | 0.245 * | 0.207 * | 0.250 * | 0.405 ** | 0.235 * | 0.726 ** | -- | ||||
12. Direct labor hour | 0.067 | 0.087 | 0.052 | −0.007 | 0.123 | 0.213 * | 0.166 | 0.147 | 0.121 | 0.171 | 0.057 | -- | |||
13. Direct labor cost | −0.131 | −0.215 * | 0.012 | −0.166 | 0.117 | 0.06 | 0.043 | −0.051 | −0.199 * | 0.042 | 0.162 | 0.214 * | -- | ||
14. Machinery operating hours | 0.075 | 0.235 * | −0.123 | −0.077 | 0.19 | 0.252 * | 0.342 ** | 0.294 ** | 0.138 | 0.383 ** | 0.199 * | 0.334 ** | −0.01 | -- | |
15. Production units | −0.042 | 0.092 | −0.107 | −0.126 | 0.032 | 0.009 | −0.12 | 0.136 | 0.043 | 0.026 | 0.138 | −0.093 | 0.201 * | 0.04 | -- |
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Alexopoulou, S.; Balios, D.; Kounadeas, T. Essential Factors When Designing a Cost Accounting System in Greek Manufacturing Entities. J. Risk Financial Manag. 2024, 17, 366. https://doi.org/10.3390/jrfm17080366
Alexopoulou S, Balios D, Kounadeas T. Essential Factors When Designing a Cost Accounting System in Greek Manufacturing Entities. Journal of Risk and Financial Management. 2024; 17(8):366. https://doi.org/10.3390/jrfm17080366
Chicago/Turabian StyleAlexopoulou, Sofia, Dimitris Balios, and Theodoros Kounadeas. 2024. "Essential Factors When Designing a Cost Accounting System in Greek Manufacturing Entities" Journal of Risk and Financial Management 17, no. 8: 366. https://doi.org/10.3390/jrfm17080366