Essential Factors When Designing a Cost Accounting System in Greek Manufacturing Entities

Abstract

We examine the extent to which basic factors, such as the structure, complexity, and usefulness of a cost system, affect the design of cost systems and the resulting satisfaction and help companies make the right decisions. Moreover, we examine the relationship between the structure and complexity of cost systems with (a) a company’s demographic data, such as the volume of its activities, the number of years it has been operating, its sector, its size, and the gender, age, level of training, and position of its employees; and (b) information concerning production and competition, such as the number of products that a company produces, the number of a company’s production lines, the level of competition, and the extent to which competition affects a company’s pricing policy. Empirical research was conducted via a questionnaire in which a sample of 114 industrial companies in Greece took part. The findings revealed that the structure and the usefulness of a cost system, but not its complexity, significantly affect the satisfaction users get from the system when they are called to make fast and correct decisions. The results point out a positive correlation between the satisfaction a user gets from a cost system and the range of information (R), the calculation of deviations (CS), the provision of accurate information (CS), the quality of information (CS), the number of cost pools (C), the number of allocation bases (C), and the cost information (U). Companies that produce more goods and have a complex production process have cost systems that not only have a more detailed structure and provide more detailed information with the calculation of deviations as well as accurate information but also have more cost pools and cost allocation bases. The more competition affects a company’s pricing policy, the more a company seeks systems that categorize costs based on behavior (structure) and more cost allocation bases (complexity). The larger a company is, with a long (>20 years) and international presence, the higher the probability a company will have a system with a more detailed cost information structure.

1. Introduction

Over the past few years, efforts have been made to study the level of complexity of cost systems. Increased competition in terms of quality, price, and services increases a company’s need to make decisions based on accurate cost information (Ammar 2017). Understanding the design of cost systems through establishing an appropriate basis for the allocation of general production costs can help companies make better, more thoughtful decisions (Gunasekaran et al. 1999; Shea et al. 2018; Humeedat 2020). Companies and organizations need to introduce optimum management accounting techniques in order to improve the quality of information and the decision-making process (Cohen and Kaimenaki 2011). Earlier studies support that a cost system’s efficiency is a good indicator that shows how well cost systems significantly help companies and their management make better decisions (Nicolaou 2003). Companies have always needed to continuously improve the complexity of their cost systems as a result of rapid technological developments, increasing global competition, the cost of information, and the ever-increasing demands of customers for a wider range of products. From the end of the 1980s, researchers began to extensively study the design, use, and effectiveness of cost systems. Initially, the studies focused on the decisive factors that impact the design of cost information and the consequences on performance that arise due to the adoption of advanced ABC systems (Bjørnenak 1997; Cagwin and Bouwman 2002), while more recent studies have focused on the complexity of the procedures that are used for the absorption of general expenses, according to the number of cost pools and the cost allocation bases that are used in cost systems (Abernethy et al. 2001; Drury and Tayles 2005; Al-Omiri and Drury 2007; Schoute 2009) where the design of the cost system is based on the number of cost pools and the bases of allocation of indirect costs that each company applies to its production. The people who design cost systems need to create systems that provide more accurate information so that companies can quickly determine costs related to product/service support activities (Pavlatos and Paggios 2009). Furthermore, designers of costing systems should consider that technology is evolving daily, presenting new challenges. It is evident that costing systems must provide higher-quality information. The quality of this information is assessed based on various criteria, such as accuracy, validity, timeliness, reliability, the level of cost analysis, and the extent to which the system meets a company’s costing requirements. Balios et al. (2020) note that Big Data and Data Analytics are already impacting nearly all aspects of decision-making and business strategies of modern organizations. Big Data and Data Analytics represent a revolution in how businesses operate and are audited today. It appears that large data analysis is a critical tool for both organizations and auditors. Businesses and organizations around the world are utilizing Big Data to refine their strategies and make informed decisions. Additionally, Big Data presents an opportunity to further improve the auditing process by incorporating both financial and non-financial information. Data analytics are transforming the audit process at both the transaction and general-ledger levels. Auditors now have access to advanced tools that enable them to extract and visualize data, allowing for the exploration of larger, non-traditional datasets and the execution of more complex analyses. Big Data and Data Analytics can enhance audit effectiveness by providing an abundance of information in various formats and massive quantities, offering unique, timely, and real-time insights to auditors. They also assist auditors in identifying and assessing risks, such as bankruptcy risks and inaccuracies in financial statements. Moreover, large data analysis facilitates the detection of fraud and enables continuous auditing rather than annual assessments. Therefore, it is essential for businesses to develop tools to better handle large volumes of information and design costing systems capable of supporting the new technologies of Big Data and Data Analytics. Big Data and Data Analytics in auditing ensure the quality of the audit and the detection of fraud (Balios 2021). Upgraded information systems and the automation of business processes reduce the need for staff involvement. Inevitably, accountants’ skills and knowledge must be aligned with Big Data and Data Analytics, and modern accountants need to develop an analytical mindset by becoming proficient in data and technologies (Balios 2021). In conclusion, the integration of Big Data and Data Analytics into costing systems enhances the accuracy, reliability, and effectiveness of cost analysis, while simultaneously improving strategic decision-making and transparency. This approach enables businesses to adjust their strategies according to market needs and trends, providing significant advantages in an ever-changing business environment.

Moreover, the emergence of numerous global environmental, social, and economic problems, including climate change, water crises, gender equality, the wars in Gaza and Ukraine, global poverty, the COVID-19 pandemic, and the energy crisis, has heightened concerns about the necessity of corporate sustainability. The COVID-19 pandemic fundamentally altered the way organizations conduct business. Presently, there is a need for companies to adapt to new strategies in order to counter the negative effects that arose from this crisis and reduce their losses (Humeedat 2020). Christopoulos et al. (2019) examined the ability to predict financial distress using a survival model based on dynamic logit as the financial crisis of 2007–2008 created liquidity problems. The primary assumption of this research was that liquidity and profitability are the main factors determining a firm’s financial distress status. They found that two variables were significant at the 5% level, the gross profit margin and operating expenses to revenues, related to financial distress based on profitability. They also found that three variables that were significant at the 5% level, the inventory turnover ratio, the current liabilities turnover ratio, and the net change in cash to current liabilities, related to financial distress based on liquidity. They also found that four variables were significant at the 5% level, the inventory turnover ratio, the current liabilities turnover ratio, gross profit margin, and operating expenses to revenues relating to the financial distress based on liquidity and profitability. Consequently, utilizing data derived solely from financial statements continues to play a significant role in predicting financial distress. Based on liquidity criteria, the majority of distressed firms were observed in 2010. In the following years, the number of distressed firms increased at a decreasing rate. This outcome was anticipated, as a lack of liquidity was a primary feature of the financial crisis. Taking into account the aforementioned consequences caused by the above-mentioned crises, it is widely accepted that businesses are facing huge pressure to succeed as markets have become more intense and unforeseen. In this way, sustainability is becoming necessary for organizations. Georgakopoulos et al. (2022) found a strong correlation between key capital structure and corporate governance parameters and firm performance. Managers need to take into account sustainability in their decisions, and this requires support from a cost accounting system as it will help managers to determine the cost of production and set the price of a product considering the above factors with accuracy, validity, timeliness, and reliability. A more sophisticated cost system, in addition to providing higher-quality information supporting managerial decision-making during crises, will help businesses to optimize their capital structures, strengthen the credibility of their financial or non-financial reporting, implement effective systems for corporate governance, improve their profitability, and reduce their cost of capital. A more functional system will help companies to develop a more integrated approach to corporate sustainability management, supported by accounting and detailed reporting, creating value for all stakeholders including its customers, suppliers, employees, investors, and others who have a stake in the organization. Already, many companies have been obliged to meet a number of important requirements according to the guidelines of GRI sustainability reports, CSR, and ESG reporting, and the adoption of more functional systems would be fundamental for corporations. A functional, sophisticated cost system will support the sustainability strategies providing appropriate, reliable, and transparent information on the sustainability results of the company. In this way, it acts comprehensively as a responsible enterprise by applying modern business practices and actively contributing, through its business model, to the achievement of the Sustainable Development Goals (SDGs).

Extensive attention has been given to studying cost system design in manufacturing companies worldwide as a response to the increase in the level of competition in local and global markets, customers’ demand for greater product diversity, and crises. There is a great need to investigate how Greek manufacturing companies are designing their cost systems to meet managers’ demands for reliable cost information to make profitable decisions, especially during crises. Thus, the present study will provide a more detailed picture in this regard. This paper contributes to the current body of literature on the design of cost systems in several ways. First, it extends the literature on how the extent to which more sophisticated cost systems provide detailed cost information, classify costs according to behavior, provide high-quality information, and calculate more variances, providing the satisfaction that users derive from cost systems when they are called to make the right decisions during a crisis. Second, the specific aim of the paper is to examine the extent to which different explanatory variables influence the level of complexity of product cost system design during crises. Prior studies have examined, in isolation, the relationship between cost system design and the main factors influencing a cost system’s complexity, usage for decision making, and effectiveness.

The methodology employed in the present study provides a more comprehensive understanding, using a combination of the main factors that affect the cost system design to ascertain whether findings from previous studies (Pizzini 2006; Al-Omiri and Drury 2007; Brierley 2008; Schoute 2009; Cohen and Kaimenaki 2011; Ismail and Mahmoud 2012; Schoute and Budding 2017a, 2017b; Humeedat 2020) are applicable, in practice, to many business fields in Greece. The findings indicate the level of Greek companies that are familiar enough with the implementation of sophisticated cost systems to make fast and accurate decisions and highlight the importance for Greek corporations to adopt more sophisticated cost systems to provide more useful and qualitative information. The findings will be useful for management teams in making better financing decisions by revealing how a more sophisticated cost system can affect profitability and improve the overall firm performance.

The remainder of the paper is organized as follows. The next section briefly sets out the review of the literature. The research hypotheses are presented in Section 3. Section 4 analyzes the research methodology. Section 5 and Section 6 critically discuss major empirical findings and detail our robustness checks. Finally, Section 7 concludes with a summary of implications, limitations, and ideas for further research.

2. Literature Review

Accounting and costing systems are essential tools used by businesses and organizations to manage their financial resources, analyze costs, and support decision-making processes. These systems must be reliable, flexible, and meet standards of transparency and security, while also addressing the needs of all stakeholders (Freeman 1984). Shareholders are concerned with the accuracy and transparency of financial reports, as these affect the value of their investments and future returns. Managers utilize information from accounting and costing systems to make strategic decisions, plan, and control performance. Employees may be interested in how costing policies affect their working conditions and salaries. Regarding customers and suppliers, accounting information can influence relationships with these parties, particularly regarding pricing and transaction reliability. Regulatory authorities are interested in the company’s compliance with laws and regulations and the integrity of financial reports (Freeman 1984). Below is a brief literature review of the main studies concerning the design of costing systems and their main role in business operations. A summary of these studies in table form is presented in Appendix A.

Abernethy et al. (2001) studied how and the extent to which product diversity affects complexity and the production process and how it, in turn, affects the amount and nature of general expenses, which constitute the basic characteristics of the design of cost systems. They believed that there were two main reasons for maintaining cost systems: (a) they help companies make and review decisions and (b) the satisfaction that users get from the systems. The findings showed that not only product diversity but also the way in which technology is used to manage various products affects the design of cost systems (Abernethy et al. 2001).

Drury and Tayles (2005) examined the additional factors of the external environment such as the structure of the cost, the level of competition, product diversity, the system’s adaptability, the size of the organization, the importance of and the extent to which the cost information can contribute to the decision-making process, and the sector in which the organization is active that affect the design of cost systems. Their findings revealed that the proportion of indirect costs to total costs, the level of competition, and the important role that cost information plays in the decision-making process are not statistically important variables that affect the choice of cost systems. The following factors were found to be statistically important: the variety of the products, the system’s adaptability, and the organization’s size and sector (Drury and Tayles 2005).

Pizzini (2006) found that more functional cost systems can provide detailed information, better categorize costs depending on behavior, calculate more deviations, and provide information more frequently. Pizzini examined the relationship between a cost system’s functionality and four characteristics of its structure that determine the level of detail, namely design, relevance, usefulness of the cost information, and the financial performance of hospitals. These four characteristics relate to the level of detail that is provided, the possibility of separating costs according to behavior, the frequency at which information is provided, and the degree to which deviations are calculated (Pizzini 2006). Pizzini determined that the relevance and usefulness of cost information correlate positively with the degree to which the system can continuously provide detailed cost information, better categorize costs depending on behavior, and provide cost information on a more frequent basis.

Al-Omiri and Drury (2007) researched factors that affect the level of complexity of product cost systems. Their findings revealed that there was a positive correlation between the importance of the cost information, the degree to which advanced management accounting techniques are used, the level of competition, the size of and the extent to which JIT systems are used, and more complex cost systems. Conversely, they found no positive correlation between a cost system’s complexity and the cost structure, the diversity of products, and the quality of the information (Al-Omiri and Drury 2007).

Van Triest and Fathy Elshahat (2007) studied cost systems that are used to apply cost information to strategic planning and decision-making processes in Egypt. Their study showed that cost information was used primarily for external purposes (pricing) rather than internal purposes (performance) (Van Triest and Fathy Elshahat 2007).

Brierley (2008) researched whether the level of complexity varies among the various definitions of complexity and if there is a correlation between a cost system’s complexity and the satisfaction financial users derive from cost systems concerning the accuracy of the cost information. Brierley found that the satisfaction derived from how accurate the cost information is does not correlate with the number of cost groups and drivers used in calculating the product cost (J. A. Brierley 2008).

Schoute (2009) researched the correlations between a cost system’s complexity and effectiveness. An earlier study (Pizzini 2006) showed that cost systems that are more detailed provide more relative and more useful cost information, which, in turn, leads to better financial performance, but it did not take into consideration the purposes for which cost systems are used. This omission in the bibliography was covered by Schoute (2009), who, in determining the purposes for which cost systems are used, used nine purposes that had been developed in earlier research (Innes and Mitchell 1995), namely cost modeling, measurement of performance, cost reduction, decisions concerning product design, budget, valuation of inventories, evaluation of client profitability, product pricing, and design of new products. Schoute took two dimensions into consideration: (a) functionality (the number and nature of cost groups and allocation bases) and (b) effectiveness (the extent to which cost systems are used and the satisfaction that is derived from their use). The findings revealed that when a cost system is used primarily for the purposes of product planning, the cost system’s complexity negatively affects the extent to which it is used, while conversely, when a cost system is used primarily for purposes of cost management, the cost system’s complexity has a positive effect on the extent to which it is used and the satisfaction that is derived from its use (Schoute 2009).

Pavlatos and Paggios (2009) examined the link between a cost system’s functionality and the potential factors of its design in Greece’s hospitality sector. Pavlatos and Paggios proved that a cost system’s functionality is statistically significantly positively linked to a low-cost strategy and the extent to which the cost information is used. They found that the level of competition, the diversity of the services that are provided, and a hotel’s management policy are not positively correlated with a cost system’s design (Pavlatos and Paggios 2009). Pavlatos and Paggios ascertained that a cost system’s functionality was statistically significantly correlated with the extent to which cost information is used and there is a link between a cost system’s design and the extent to which cost information is used for the purposes of making and monitoring decisions. These findings are consistent with the studies of Al-Omiri and Drury (2007), who stated that a cost system’s complexity is affected by the extent to which cost information is used for the purposes of making pricing decisions and controlling costs. Hotels that have detailed information have more advanced systems and are more efficient according to Pizzini (2006). The level of a cost system’s functionality is significantly positively linked to a low-cost strategy. These research findings were consistent with earlier research of Chenhall and Langfield-Smith (1998), Hill (2000), and Pizzini (2006), which found that companies that apply a low-cost strategy adopt and design more sophisticated cost systems (Pavlatos and Paggios 2009).

Cohen and Kaimenaki (2011) examined whether a cost system’s structure and the quality of information (expressed in utility, relevance, timeliness, usefulness, compatibility with underlying needs, reliability, and contribution to the decision-making process) affect the system’s design and use. Specifically, they found that the existence of detailed information, the capability of allocating costs on the basis of behavior, the capability of calculating a range of deviations, and the frequency at which cost information is provided were what defined a cost system’s structure (Cohen and Kaimenaki 2011). Their research revealed that the majority of the characteristics of a cost system’s structure have a statistically significant positive effect on the quality dimensions of the cost information. Their findings were consistent with those of Pizzini (2006) in that more functional cost systems provide higher-quality information that helps companies make better decisions. Cohen and Kaimenaki found that a user’s needs do not correlate significantly with the quality of information (Cohen and Kaimenaki 2011).

Ismail and Mahmoud (2012) examined the extent to which the design of cost systems is affected by organizational factors such as product diversity, cost structure, and the importance of the cost information and environmental factors such as the level of competition. Their findings revealed that Egyptian manufacturing companies did not use very advanced cost systems. They found a positive correlation between the level of a cost system’s complexity and the importance of the cost information, and no correlation between a cost system’s complexity and product diversity, the level of competition, and cost structure (Ismail and Mahmoud 2012).

Uyar and Kuzey (2016) studied the effect that management accounting practices (MAPs) have on the design and performance of cost systems. Their research was based on the findings of Pizzini (2006) and Pavlatos and Paggios (2009) and supplemented the bibliography that existed on the design of cost systems based on a sample of different industries. Uyar and Kuzey’s research follows earlier research that was conducted in Turkey concerning cost accounting and management accounting practices (Uyar 2010; Uyar and Bilgin 2011; Yalcin 2012). Their findings revealed that the design of a cost system on its own does not affect a company’s performance, but that there is a mediating correlation between design and performance. Moreover, they found that the design of cost systems (CSD) had a positive impact on the use of management accounting practices and that management accounting practices contribute positively to a company’s performance. Uyar and Kuzey proved that a cost system that has a relatively high cost should be weighed against the benefits the company derives from it, provided the cost information will be used by management as a decision-making tool (Uyar and Kuzey 2016).

Schoute and Budding (2017a) examined three aspects of a cost system’s design and effectiveness in the local self-government sector based on the research of J. A. Brierley (2008), who determined that cost systems had three main characteristics: complexity (on the basis of the number of cost groups and cost allocation bases), the content of the system’s information (the degree to which general expenses are allocated to the main cost centers—the inclusiveness of cost systems), and the quality of the cost information. Their findings revealed that the more a municipal service provides detailed information to private individuals, the more complex the system becomes. These findings were consistent with those of Geiger and Ittner (1996) and Lapsley and Wright (2004). Schoute and Budding found that there is a positive correlation between a cost system’s complexity and the extent to which it is used for the purpose of conducting operational controls, as well as a positive correlation between the content of the cost information and the extent to which it is used for the purpose of product costing. Their findings were consistent with earlier research that revealed that the above two purposes support the importance of use purposes as decisive factors of a cost system’s design (Cooper 1988; Schoute 2009).

Schoute and Budding (2017b) examined how different types of uncertainties (environmental, financial, and structural) affect the design and complexity of cost systems, as well as the extent to which cost systems are used for the purpose of conducting operational controls or product costing. Schoute and Budding found positive correlations between the uncertainty and changes in a cost system’s complexity, between financial uncertainty and a change in the extent to which a cost system is used for the purpose of product costing, and between a change in the cost system’s complexity and changes in the extent to which the cost system is used for the purposes of conducting functional controls and product costing (Schoute and Budding 2017b). Schoute and Budding also found that organizations will use more sophisticated systems when the level of competition is higher. The findings of Schoute and Budding are consistent with those of Khandwalla (1972), Gordon and Miller (1976), and Al-Omiri and Drury (2007).

Zuriekat (2020) examined whether the use of management practices, such as TQM and JIT, and product diversity affect the design of a company’s cost system. The study adopted the model that was developed by Drury and Tayles (2005) for measuring the design of cost systems by determining the number of activities, as well as the number of activity groups and cost drivers. The model is considered a relative and valid model by several people who research management accounting methods (Brierley 2008; Schoute and Budding 2017a; Ammar 2017). Moreover, earlier research suggested that environmental factors may affect the design of cost systems (Dent 1990; Chenhall 2003). The findings of this research revealed that the Just in Time (JIT) system, the Total Quality Management (TQM) framework, and product diversity significantly affect the design of cost systems (Zuriekat 2020). In addition, product diversity was considered a significant factor that affects the design of cost systems, which entails more cost pools and cost drivers (Zuriekat 2020) due to a greater combination of production lines with different cost structures, which leads to a greater range of cost factors and that are required for the accurate allocation of manufacturing overhead (Schoute and Budding 2017a). Finally, researchers found that there was a positive correlation between management practices and the design of cost systems (Bjørnenak 1997; Malmi 1999; Drury and Tayles 2006; Boerema et al. 2018).

Humeedat (2020), following the global crisis caused by the COVID-19 pandemic, examined the impact of certain aspects of the design of cost systems that had already been studied by other researchers who are cited in the bibliography, as well as new factors such as technological developments and operating losses that had arisen as a result of the change in the manner in which companies operated during the COVID-19 pandemic. His findings revealed that the design of cost systems is positively correlated with technological developments, the operating losses that arose, and the relevance of the cost information, in contrast to product diversity, which did not have a positive correlation. Humeedat’s findings were consistent with those of Al-Omiri and Drury (2007) and Ismail and Mahmoud (2012) in that cost systems do not positively correlate with product diversity. The study found that manufacturing companies need to redesign their cost systems with the aim of reducing their short-term costs and weathering the economic consequences that arose from the COVID-19 pandemic (Humeedat 2020).

Dokas et al. (2021) provided a critical assessment of conventional studies on earnings management, with a particular emphasis on its influence on the decision-making process. Accounting information is the primary tool that contributes to decision-making processes in the business world. Ensuring its reliability aligns with compliance with accounting frameworks and sustainability standards. However, companies can influence this information through their accounting policies, thereby impacting the quality of the accounting data they receive from costing systems.

Barkas et al. (2022) declared that since the financial crisis of 2009 and continuing through 2020, the banking sector, along with other sectors, has experienced significant impacts on income, output, and employment variables. Greek financial institutions have been characterized by anomalies that, as anticipated, impacted their performance. Additionally, bank profitability and efficiency were affected by the volatile environment. Financial crises have now created the need for a costing system that serves as a tool to help organizations survive during such periods.

Dokas (2023) found that when the CEO also holds the position of board chairman, it promotes the manipulation of discretionary expenses while decreasing the manipulation of real earnings through sales and production costs. The personal motivations of CEOs constitute a significant parameter of earnings management (Dokas et al. 2021). In countries with high levels of corruption, large boards are linked to greater manipulation of production costs and discretionary expenses. In low-corruption contexts, longer board tenure negatively impacts accruals and discretionary expenses but tends to increase manipulation through production costs. Therefore, it is crucial for costing systems and accounting information systems to provide accurate information and prevent any opportunities for earnings manipulation.

3. Hypothesis Development

3.1. Importance and Usefulness of Cost Information for Decision Making

Cost information has become crucial for companies given that there is a growing need for accurate information. According to the current bibliography, companies—depending on their needs and decisions concerning strategic plans, reducing costs, pricing, adding, redesigning, or deleting products, and analyzing client profitability—need to adopt very sophisticated cost systems so that they can have more accurate cost information. This finding is consistent with the findings of Krumwiede (1998), Baird et al. (2004), Al-Omiri and Drury (2007), Pavlatos and Paggios (2009), Ismail and Mahmoud (2012), Schoute and Budding (2017a, 2017b), and Humeedat (2020). These findings supported the idea that the importance of a cost system depends on whether it provides the information that companies need to make the right decisions and analyze the level of profitability, where the importance of accurate cost information increases if a product’s profit margin is limited or if the production capacity has been reduced (Drury and Tayles 2005).

3.2. Product Diversity

The complexity of a company’s production process affects its decision concerning the cost system it will adopt. Companies that produce diversified products require more activities and costs for the existence of more activity allocation bases. Allocation bases require an accurate design so that they include both common manufacturing overhead and adjusted manufacturing overhead for every type of product (Cinquini et al. 2015; Pavlatos and Kostakis 2015). Product diversity requires different support from different departments as regards the product’s design, construction, and distribution. The more complex a production process is, the greater a company’s need to have a more sophisticated cost system (Cooper 1988; Estrin et al. 1994; Malmi 1999; Abernethy et al. 2001; Drury and Tayles 2005; Zuriekat 2020). However, other research has shown that product diversity is insignificantly linked to the level of a cost system’s complexity. Consequently, companies that produce highly diversified products or that have a complex production process do not need to adopt a complex cost system (Bjørnenak 1997; Krumwiede 1998; Al-Omiri and Drury 2007; Ismail and Mahmoud 2012; Humeedat 2020).

3.3. Cost Structure

Highly complex cost systems accurately allocate indirect costs to cost objects (Drury and Tayles 2005). A cost’s structure is determined by the level of the cost’s details, the capability to group the cost based on its behavior, the capability to categorize the cost into direct, indirect, fixed, and variable costs, the frequency at which cost information is provided, the accuracy of the cost information, and the degree to which deviations are calculated. If the cost system cannot determine and aggregate the cost for the cost objects and provide adequate detailed cost information, managers will not be able to make well-informed decisions (Covaleski et al. 1993; Comerford and Abernethy 1999). According to the research of Kaplan and Cooper (1998), companies that have high indirect costs need to use sophisticated cost systems in order to correctly allocate indirect expenses. In order for the cost to be aggregated per procedure, the direct cost must be identified in the procedure and the indirect fixed and variable cost must be allocated to the procedure. In order for this to happen, the cost system must be designed in such a manner that it categorizes the cost as direct/indirect and as fixed/variable. If this categorization is performed arbitrarily, the detailed cost information will not be useful (Swenson 1995; McGown 1998). In earlier research, the importance of cost information was significantly positively linked to the level of the cost system’s complexity. Respectively, as cost information becomes more and more important for companies, the need for more accurate information increases. As a result, companies will adopt more sophisticated cost systems (Pizzini 2006; Brierley 2008; Cohen and Kaimenaki 2011; Schoute and Budding 2017a, 2017b). However, there are other studies that claim that if indirect costs represent a small percentage of the total cost, companies do not need to have a complex cost system (Brierley et al. 2001; Cooper 1988).

3.4. Competitive Environment

Various studies have researched the link between the design and use of cost systems and the level of competition (Khandwalla 1972; Simons 1990; Libby and Waterhouse 1996; Malmi 1999; Al-Omiri and Drury 2007). The findings of these studies suggest that companies that operate in a highly competitive market tend to use more sophisticated cost systems with higher levels of complexity, thus increasing the probability that costs are more accurately allocated to products, services, and clients (Cooper 1988). Kaplan and Bruns (1987) claimed that competition is the most important external factor that leads companies to redesign their cost systems. Companies that are active in highly competitive sectors are in constant need of differentiating their products and controlling costs, which, in turn, leads to the adoption of sophisticated cost systems (Guilding and McManus 2002). In sectors that are particularly competitive, possible errors that may arise due to inaccurate cost information give competitors an advantage. Companies that operate with a cost strategy face greater competition and lower profit margins. Consequently, sub-costing products may bring about considerable losses. Nevertheless, Drury and Tayles (2005) and Ismail and Mahmoud (2012) ascertained that the level of competition is not a statistically significant factor that affects a company’s decision when it is called to choose a cost system.

3.5. Size of the Organization

The size of an organization (measured in annual turnover) and the sector in which an organization is active are considered important variables in deciding whether or not to adopt the ABC system (Innes and Mitchell 1995; Bjørnenak 1997; Malmi 1999; Drury and Tayles 2005; Al-Omiri and Drury 2007; Schoute and Budding 2017a). Larger organizations, in contrast to smaller ones, have more resources that allow them to adopt innovative cost systems, a wider and differentiated range of activities, more production departments, and a wider range of products; therefore, they need cost systems that are highly complex. Soras and Christopoulos (2024) concluded that larger companies are more likely to adhere to ESG reporting requirements, making company size an indicator of effective governance. It is evident from the above facts that the sizes of companies play a significant role in the adoption of a complex system that provides accurate, detailed, and reliable financial information, contributing to the sustainability of large enterprises and the disclosure of trustworthy financial and non-financial data. Several research studies have found that a very important factor that hinders the adoption of sophisticated cost systems is the very high cost that is associated with their application, which only large organizations can support (Innes and Mitchell 1995; Shields 1995). However, there are researchers who believe that the size of an organization does not affect its choice of more sophisticated cost systems (Libby and Waterhouse 1996; Xiong et al. 2008; Ahmadzadeh et al. 2011).

3.6. Business Sector

The design and efficiency of cost information and cost systems depend on an organization’s characteristics. There is a positive correlation between the business sector in which an organization is active and higher costing levels (Shields 1997; Drury and Tayles 2005; Al-Omiri and Drury 2007). In contrast, some research supports that the sector in which an organization is active does not have a statistically important effect on the cost system’s complexity (Innes and Mitchell 1995; Anand et al. 2005).

Based on the above factors that affect the design of cost systems, the following hypotheses arise for which statistical analysis will follow:

Hypothesis 1.

The structure (Questions 16–20), complexity (Questions 21–23), and utility of the cost (Questions 24–27) have a statistically significant effect on the satisfaction that users derive from cost systems when they are called to make right decisions (Questions 25 and 26).

Hypothesis 2.

There is a statistically significant relationship between demographic data and production and competition data (Questions 1–12) with the structure (Questions 16–20) and complexity (Questions 21–23) of cost systems.

4. Research Design and Data Collection

This research is based on an exploratory statistical analysis of the factors that affect the design of cost systems that companies and organizations use in Greece, as well as an examination of the level of satisfaction that users derive from cost system design. In testing the hypotheses, empirical quantitative research was conducted with the aim of gathering data through the use of predetermined tools that, subsequently, produced the research’s statistical data. The demographic data of the respondents’ company representatives and the production and competition data were summarized with absolute and relative frequencies (N, %), while the results were also presented in the form of bar charts. The same logic was also followed in the case of the cost system’s variables, which are characterized as categorical and/or hierarchal. Certain questions of the questionnaire constitute scales with multiple-choice options on the basis of the 5-point Likert scale. The reliability of these scales was assessed with Cronbach’s alpha reliability coefficient (

Table 1. Cronbach’s Alpha coefficients.

	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

* Cronbach’s alpha reliability coefficients for each individual section of the questionnaire. The minimum acceptable level is 0.60 for exploratory research suggested by Hair et al. (1998).

), and aggregated variables were calculated based on the mean value of the data of each scale (A. Field 2016). Initially, these new variables were summarized with descriptive statistical data of the mean, median, standard deviation, and range. The normality tests that were conducted using the Shapiro–Wilk test (A. Field 2016), which is suitable for small samples of up to 2000 persons, revealed that the data did not follow a normal distribution and, thus, in the analysis that followed, non-parametric tests were conducted. Specifically, Spearman’s non-parametric correlation coefficient was used to measure the relationship between the individual variables. Hypothesis H1 was tested with a multiple linear regression model (A. Field 2016), while Hypothesis H2, which concerns differentiation between the demographic data and the production and competition data as regards the structure and complexity of cost systems, was tested with the Mann–Whitney and Kruskal–Wallis tests, as well as with Spearman’s correlation coefficients, due to the data not following a normal distribution. As shown in Table 1, the results of the reliability test reveal that Cronbach’s Alpha for the questionnaire was over 0.60, which is the minimum acceptable level suggested by Hair et al. (1998), meaning that the questionnaire is highly reliable.

4.1. Sample of Survey

A well-structured questionnaire was used to gather the survey’s data, which was distributed in two ways. Due to the particular characteristics of the survey, it was necessary to ensure that the questionnaire was completed by qualified persons to be validated and tested for its reliability. The research was realized in two phases. In the first phase, a participation form was sent to the selected Greek companies accompanied by a cover letter via electronic mail (e-mail) or via direct contact on the social media platform LinkedIn, which included a brief reference of the main goals of the study. Financial managers were asked to indicate the type(s) of cost and management accounting practice(s) used by their companies, as well as to state correspondence information to address the survey questionnaire, in case they were interested. In the second phase of the research, the survey questionnaire was designed and sent to the sampled companies. Before the finalization of the questionnaire, a pilot test took place. More specifically, interviews were conducted with four Chief Accountants who had extensive experience in cost and management accounting practices to make sure that the questionnaire’s content was easy to understand. This testing enabled us to account for omissions or vagueness in the expressions used to formulate the questions. The survey aimed to examine the factors that affect the design of cost systems of manufacturing companies and industrial organizations. The questionnaire was sent to a total of 332 manufacturing companies and industrial organizations of various sectors. The companies and organizations were found with the help of 28 chambers of Greece, via the Federation of Industries of Greece (SBE) and collaborating companies that are active in the researcher’s sector. The individuals who were contacted were chosen on the basis of their job title, while the aim was to choose individuals who had specialized knowledge of the information that was requested in the questionnaire. Individuals who were familiar with the design of cost systems and the management practices of the study were chosen. This reinforces the reliability of the data that were gathered, given that these individuals were familiar with the cost systems of their companies. Of the 332 questionnaires that were sent, 114 individuals responded positively, which represents a positive response of 34.34%. As the questionnaire was long and its subject was particularly specific, an accompanying letter was sent along with the questionnaire that explained the subject, purposes, and goals of the survey, and which individuals should complete the questionnaire. The respondents were asked to send confirmation via post or message on LinkedIn that the questionnaire was completed.

4.2. Measurement of the Variables

The questionnaire consisted of 29 questions divided into 8 sections. The questions were closed dichotomous type, closed-ended preset options with single and multiple responses, and 5-point Likert scale questions ranging from 1 (very low-intensity/no intensity) to 5 (very high-intensity/extent). Similar questionnaires have been widely used in the literature in research on the level of complexity of cost systems (Drury and Tayles 2005; Al-Omiri and Drury 2007; Van Triest and Fathy Elshahat 2007; Brierley 2008; Schoute 2009). Some survey questions were adopted in whole or in part from several previous studies as follows: Questions 6–7 (Schoute 2009), Question 12 (Cohen and Kaimenaki 2011; Schoute 2009; Uyar 2010), Questions 13–15, 17, and 25 (Cohen and Kaimenaki 2011), Questions 18–19, 22–23, and 24 (Schoute 2009), and Questions 16 and 26 (Uyar 2010).

4.3. Variables

4.3.1. Variables of H1

The dependent variable is satisfaction with the cost system design and its use for decision making. The independent variables are as follows: Cost structure (cost classification based on behavior, range of cost information, calculation of variances, provision of accurate information, and evaluation of costing system quality—accurate, timely, reliable, tailored to the needs of each firm, and contributes to decision making), Complexity of costing information (number of cost pools, number of cost allocation bases, and overhead allocation bases), and Usefulness of cost information (costing information on break-even point, unit participation margin, net participation margin, and quick decision making to maintain profitability during in crises).

4.3.2. Variables of H2

The dependent variables are as follows: Cost structure (cost classification based on behavior, range of cost information, calculation of variances, provision of accurate information, and evaluation of costing system quality—accurate, timely, reliable, tailored to the needs of each firm, and contributes to decision making) and Complexity of costing information (number of cost pools, number of cost allocation bases, and overhead allocation bases). The independent variables of this hypothesis are as follows: Demographic data (gender, age, education, position in the company, sector of activity of the company, size of the company using annual turnover in millions (€), years of operation, and level of activity) and Production and Competition data (product diversity, production lines, intensity of competition, and the competition effect on price policy formation).

5. Descriptive Statistics

5.1. Descriptive Data of the Sample Manufacturing Companies

A large part (72.8%) of the survey’s respondents who represented the 114 companies and organizations were male, 35 years of age or older (>80%), and had completed postgraduate studies (53.5%). Concerning the respondents’ positions, 58.8% were Cost Accounting Managers, Chief Accountants, or Financial or General Managers. Concerning the sector in which the companies are active, there was a wide dispersion: 36.8% were active in the food sector, 7.9% were active in the construction sector, 7.9% were active in the pharmaceutical sector, and the remaining companies were active in other sectors (see also Appendix A-

Table A1. Descriptive data of sample companies.

		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%

).Concerning the sizes of the companies, 21.9% of the companies were small or very small companies (≤50 employees, ≤10 million euros in sales), 29.8% were medium-sized companies (51–250 employees, ≤50 million euros in sales), and 48.2% were large companies (>250 employees, >50 million euros in sales). Concerning their years of operation, 78.1% of the companies had been operating for more than 20 years, while 62.3% are active outside of Greece and 17.5% are active both in Greece and abroad (see also Appendix A-Table A1).

5.2. Production–Competition Data

Relating the number of products or services that the companies produce or provide, as well as their production lines, we can see (see also Appendix A-

Table A2. Product diversity and number of production lines.

		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%

) that 63.16% of the companies produce or provide more than 40 products or services and only 20.2% has 1–2 production lines. Concerning the level of competition, the respondent companies stated that they have a moderate to positive effect on their activities (Mean value = 3.68; Std. Deviation = 0.75), with the most significant effect on sales prices (Mean value = 3.80; Std. Deviation = 1.03). Concerning the extent to which competition affects their pricing policy, 83.33% of the companies stated that competition considerably affects their pricing policy (see also Appendix A-

Table A3. Intensity of competition.

	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

).

5.3. Application of Cost Accounting

Ninety-eight (98) of the one hundred and fourteen companies of the sample stated that they have a cost system (86%). The cost systems that are applied for the purpose of determining unit costs are as follows (the systems have been placed in order of preference as determined by the companies): Continuous Production Costing (17.82%), Customized Production Costing (17.24%), Standard Costing (16.67%), Full Costing (13.79%), Activity-Based Costing (13.22%), Target-Based Costing (8.05%), Benchmarking (7.47%), and Direct-Marginal Costing (5.75%). According to the results of this research, the importance of cost information is considered high (Mean value = 3.94, ΤA = 0.63). The highest evaluations were recorded for the following information: valuation of inventories, budget preparation, pricing, and cost control, (see also Appendix A-

Table A4. Importance of cost accounting information.

	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

).

5.4. Cost Structure

All of the companies recognize, to a considerable extent, the range of cost information and, specifically, of the cost per product (Mean value = 4.41; Std. Deviation = 0.73)1. In addition, the companies recognized, to a considerable extent, that cost systems provide accurate information and, specifically, information concerning direct materials (Mean value = 4.23; Std. Deviation = 0.91) and direct labor (Mean value = 4.15; Std. Deviation =0.88). The provision of accurate information (Mean value = 3.98; Std. Deviation = 0.86) and the quality of the cost system (Mean value = 3.86; Std. Deviation = 0.70) present the highest mean values, which suggests that companies believe that cost systems provide accurate and quality information (see also Appendix A-

Table A5. Descriptive statistics of cost structure.

	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

).

5.5. Complexity of Cost Information

Regarding the complexity of costing information, the proportion of firms stating that they use more than 32 cost pools was 30.6%, 1–2 cost pools was 19.4%, and 3–4 cost pools was 14.3%. According to the survey, the proportion of enterprises declaring that they use 1–2 cost allocation bases was 22.5%, 3–4 cost allocation bases was 30.6%, and 5–8 cost allocation bases was 16.3%. Firms stated that the most used type of overhead allocation bases to calculate product costs is direct labor hours (27.1%), followed by direct labor costs (25.5%), production units (24.7%), and machinery operations hours (22.7%).

5.6. Cost Information and Decision Making

Relating the variables of the cost information/usefulness and the effectiveness of cost systems for making quick decisions to the purposes of maintaining profitability during times of crises, we can see that companies have a moderate to positive view on whether cost systems provide information on break-even points, unit participation margins, and net participation margins (Mean Value = 3.58; Std. Deviation = 0.92) and a moderate to positive view on the extent to which a cost system helps users make fast decisions during times of crises (Mean Value = 3.56; Std. Deviation = 0.71). In addition, companies are satisfied with their cost systems (Mean Value = 3.62; Std. Deviation = 0.86) and believe that users use cost systems to make decisions (Mean Value = 3.87; Std. Deviation = 0.86 (see also Appendix A-

Table A6. Descriptive statistics of cost information/usefulness and effectiveness of cost system for making quick decisions to maintain business profitability during crises.

	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

and

Table A7. Descriptive statistics of satisfaction with cost accounting system and the use of cost system for making quick decisions.

		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

Scale: 0 Not at all–5 To a very great extent.

).

5.7. Frequency of Cost Information

We can see that companies, to a considerable extent, declared that cost information was provided to users whenever it was requested (Mean Value = 3.74; Std. Deviation = 0.92) and, to a lesser extent, that cost information was provided systematically (per day, per week) (Mean Value = 3.47; Std. Deviation = 1.06), (see also Appendix A-

Table A8. Descriptive statistics of cost information’s frequency.

	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

). According to the research, the difficulties that the companies faced in applying product costing methods are the following: complexity in production (32.4%), lack of qualified executives (21.6%), lack of suitable ERP software(Softone PBS ONE, Epsilon Hyper/Pylon/Smart or equivalent) (17.6%), lack of required know-how (14.9%), and high application costs (13.5%).

6. Inferential Statistics

According to the results of the normality tests of the Shapiro–Wilk distributions for small samples shown in

Table 2. Test of normality using Shapiro–Wilk test.

	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

, it is demonstrated that the variables cannot be considered to be normally distributed due to the p value being less than the chosen alpha level (p < 0.05). As a result, the null hypothesis is rejected (H₀ = normal distribution), and it is considered that the sample has not been generated from a normal distribution. Based on this outcome, non-parametric tests were used for further statistical analysis.

6.1. Correlations

Table 3. Spearman’s rank correlation coefficient.

	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 **	--

Note: The table notes moderate and strong correlations by darker-shaded areas, ** p < 0.01, * p < 0.05 (2-tailed).

presents Spearman’s correlation coefficients between the variables of the analysis regarding cost systems (the table notes moderate and strong correlations by darker-shaded areas). The more a company maintains that cost systems include a classification of costs based on their behavior, the more it recognizes the range of the cost information, calculation of variances, provision of accurate information, and quality of the cost system. In addition, a substantial number of cost centers is positively linked to the classification of costs based on their behavior. The range of cost information is positively linked to the calculation of variances, the provision of accurate information, and the quality of the cost system. In addition, a substantial number of cost centers is positively linked to the range of cost information. The range of cost information is positively linked to costing information, the satisfaction that is derived from the cost system, to use of the cost system for the purposes of making decisions, the making of fast decisions for the purposes of maintaining profitability during times of crises, and the frequency at which cost information is provided. The calculation of variances is positively linked to the provision of accurate information and the quality of the cost system. The calculation of variances is positively linked to the number of hours that a company’s machinery operates and the number of cost centers. The calculation of variances is positively linked to the satisfaction that is derived from cost systems, the use of cost systems for the purposes of making decisions, and the making of fast decisions for the purposes of maintaining profitability during times of crises. The provision of accurate information is positively linked to the quality of the cost system. In addition, having a substantial number of cost centers and cost allocation bases is positively linked to the provision of accurate information. The provision of accurate information and the quality of cost systems are positively linked to the cost information, the satisfaction that is derived from the cost system, the use of the cost system for the purposes of making decisions, the making of fast decisions for the purposes of maintaining profitability during times of crises, and the frequency at which cost information is provided. A substantial number of cost centers is positively linked to the satisfaction that is derived from the cost system and the use of the cost system for the purposes of making decisions. Moreover, cost information is positively linked to the satisfaction that is derived from the cost system, the use of the cost system for the purposes of making decisions, the making of fast decisions for the purposes of maintaining profitability during times of crises, and the frequency at which cost information is provided. The satisfaction that is derived from the cost system is positively linked to the use of the cost system for the purposes of making decisions, while the making of fast decisions for the purposes of maintaining profitability during times of crises is positively linked to the frequency at which cost information is provided.

6.2. Multiple Regression Model of H1

We conducted a multivariate regression analysis to take into consideration the simultaneous effects of all organizational and environmental variables on the satisfaction level of cost system design. The following model was used to test the hypotheses2:

$$\begin{gathered} \mathrm{Y}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{C}\mathrm{L}\mathrm{A}\mathrm{S}\mathrm{S}\mathrm{I}\mathrm{F}\mathrm{Y}+{\mathsf{\beta }}_2\mathrm{R}\mathrm{A}\mathrm{N}\mathrm{G}\mathrm{E}+{\mathsf{\beta }}_3\mathrm{V}\mathrm{A}\mathrm{R}\mathrm{I}\mathrm{A}\mathrm{N}\mathrm{C}\mathrm{E}\mathrm{S}+{\mathsf{\beta }}_4\mathsf{A}\mathrm{C}\mathrm{C}\mathrm{U}\mathrm{R}\mathrm{A}\mathrm{C}\mathrm{Y}+{\mathsf{\beta }}_5\mathrm{Q}\mathrm{U}\mathrm{A}\mathrm{L}\mathrm{I}\mathrm{T}\mathrm{Y}+{\mathsf{\beta }}_6\mathrm{C}\mathrm{O}\mathrm{S}\mathrm{T}\mathrm{I}\mathrm{N}\mathrm{F}+{\mathsf{\beta }}_7\mathrm{C}\mathrm{R}\mathrm{I}\mathrm{S}\mathrm{E}\mathrm{S}+{\mathsf{\beta }}_8\mathrm{P}\mathrm{O}\mathrm{O}\mathrm{L}\mathrm{S}+{\mathsf{\beta }}_9\mathrm{B}\mathrm{A}\mathrm{S}\mathrm{E}\mathrm{S} \\ +{\mathsf{\beta }}_{10}\mathrm{D}\mathrm{I}\mathrm{R}\mathrm{L}\mathrm{A}\mathrm{B}\mathrm{H}\mathrm{O}\mathrm{U}\mathrm{R}+{\mathsf{\beta }}_{11}\mathrm{D}\mathrm{I}\mathrm{R}\mathrm{L}\mathrm{A}\mathrm{B}\mathrm{C}\mathrm{O}\mathrm{S}\mathrm{T}+{\mathsf{\beta }}_{12}\mathrm{M}\mathrm{A}\mathrm{C}\mathrm{H}\mathrm{H}\mathrm{O}\mathrm{U}\mathrm{R}+{\mathsf{\beta }}_{13}\mathrm{U}\mathrm{N}\mathrm{I}\mathrm{T}+\mathrm{e} \end{gathered}$$

The regression model, as shown in

Table 4. Model summary ^b.

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

^a Predictors: (Constant): Production units, classify costs according to behavior, Machinery operating hours, Direct labor cost, Quick decision making to maintenance of profitability during crisis, Nr of cost allocation bases, Direct labor hour, Cost information, Range of cost information, Quality of cost information, Accuracy of cost information, Nr of cost pools, Calculation of variances. ^b Dependent Variable: Satisfaction from cost system design.

and

Table 5. Three-way ANOVA ^a.

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

^a Dependent Variable: Satisfaction from cost system design. ^b Predictors: (Constant): Production units, classify costs according to behavior, Machinery operating hours, Direct labor cost, Quick decision making to maintenance of profitability during crisis, Nr of cost allocation bases, Direct labor hour, Cost information, Range of cost information, Quality of cost information, Accuracy of cost information, Nr of cost pools, Calculation of variances.

(F = 10.82, p < 0.001), is statistically significant (rejection of H₀), and the adjusted R Square shows that the regression model accounts for 62.9% in explaining satisfaction level variations with the costing system. The variable Costing System Quality Rating (βeta = 0.634, p < 0.05) and the variable Costing Information (βeta = 0.248, p < 0.05) are statistically significant predictors of satisfaction with the costing system, as shown in Table 4.

The results of the regression analysis led to the development of the following regression formula regarding the satisfaction that is derived from cost systems (dependent variable $\widehat{\mathsf{{\rm Y}}}$): $\widehat{\mathsf{{\rm Y}}}=-0.453+0.791\ast \mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}+0.238\ast \mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t} \mathrm{i}\mathrm{n}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}$.

These results, as shown in

Table 6. Multiple regression analysis: predictors for the variable Satisfaction from costing system design.

	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

, are interpreted as follows: The more a company is satisfied with the quality of the information that is provided (part of the cost’s structure) and the more a cost system provides detailed cost information on the break-even point, unit participation margin, and net participation margin (usefulness of the cost), the greater the satisfaction that the company derives from the cost system will be. Therefore, the H1 hypothesis is partially confirmed, whereas both the structure and usefulness but not the complexity of the cost positively affect the satisfaction that is derived from the cost system and its use in making decisions. The relationship between the quality of cost information that is provided by the cost system and the satisfaction that is derived from the cost system shows an increase in the latter and is linked to more positive perceptions about the quality of cost systems and cost information usefulness.

6.3. Kruskal–Wallis and Mann–Whitney—H2

In this hypothesis, due to the data not following a normal distribution and containing some strong outliers, the tests for differences in the rank distribution of the data were checked by the non-parametric Kruskal–Wallis and Mann–Whitney tests (Field 2009; Field 2016; Xia 2020) for differences between independent groups of categorical variables (level of business activity, operating Years, business sector, company size, gender, age, educational level, and position in the enterprise).

Table 7. Results of non-parametric Kruskal–Wallis and Mann–Whitney tests.

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

Note: The significance level is 0.050.

presents the results of the Kruskal–Wallis and Mann–Whitney tests, which are non-parametric statistical tests for the relationship between variables of demographic data and cost structure.

Table 8. Spearman’s rank correlation coefficient H2.

	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	--

Note: The table notes moderate and strong correlations by darker-shaded areas, ** p < 0.01, * p < 0.05 (2-tailed).

presents the correlation matrix of Spearman’s correlation coefficients between the production and competition data and the structure and complexity of cost systems. According to this table (darker-shaded area), companies that produce more goods and services have cost systems that provide more detailed information and have more cost centers and allocation bases. Companies with more production lines have cost systems that provide more detailed information, more cost centers and allocation bases, a wider range of cost information, and better-quality and more qualitative information concerning direct labor costs and the hours their machinery operates. The more that the level of competition affects a company’s pricing policy, the more a company will have a cost system that classifies costs based on their behavior and has more cost allocation bases. In addition, the higher the level of competition, the more a company will have a cost system that classifies costs based on their behavior.

7. Discussion and Conclusions

The hypotheses were tested based on the answers that were given by 114 manufacturing companies in Greece. In general, 62.30% of the companies were found to be active outside of Greece, while 63.16% of the sample produces more than 40 products. The companies operate in a highly competitive market, which obliges them to produce a wide range of products to keep pace with their competitors. Many of the companies (83.33%) stated that the competition that they face affects, to a certain or great extent, their pricing policy. This requires them to turn to the cost system that they use for information concerning the cost per product that they produce, as well as the cost of direct materials and direct labor costs, which constitute the basic parameters of the production cost. Companies, in an effort to weather the recent crises such as the COVID-19 pandemic, the war in Ukraine, high inflation, the energy crisis, and the increase in transport costs, made more use of their cost systems when they were called to make decisions. In general, the data provide supportive evidence that there are positive correlations between the characteristics of a cost system’s structure (classification based on behavior, range, calculation of variances, accuracy, and quality of information), as well as a corresponding correlation with the usefulness, cost information, and frequency at which cost information is provided. These findings are consistent with those of Pizzini (2006). It was found that companies with diverse products have cost systems that provide detailed information with the calculation of variances and the provision of accurate information and have more cost centers and allocation bases (complexity of the system). Companies that have more production lines and a complex production process have cost systems that provide more detailed information, have more cost centers and allocation bases, have a wider range of cost information, have better quality, and provide information on the hours of operation of their machinery. The above finding is consistent with certain previous research (Abernethy et al. 2001; Drury and Tayles 2005; Zuriekat 2020) and contrasts the findings of other researchers (Al-Omiri and Drury 2007; Ismail and Mahmoud 2012; Humeedat 2020) who did not find a positive correlation between a system’s complexity and product diversity. In addition, the more that competition affects a company’s pricing policy, the more a company seeks cost systems that classify costs on the basis of their behavior (structure) and have more cost allocation bases (complexity). Moreover, the higher the level of competition, the more a company will adopt a system that classifies costs on the basis of their behavior (direct, indirect, fixed, variable, controllable, and non-controllable), a finding that is consistent with Al-Omiri and Drury (2007) and contrasts the findings of Drury and Tayles (2005) and Ismail and Mahmoud (2012). The examination of the first hypothesis found that both structure and usefulness, but not complexity of the cost, positively affect the satisfaction users derive from a cost system and the cost system’s usefulness when they need to make decisions during times of crises. Specifically, the structure concerning the quality of cost information has a statistically significant effect on the satisfaction users derive from the system when they are called to make fast and accurate decisions, a finding that is consistent with Pizzini (2006) and Cohen and Kaimenaki (2011). The quality of the information is analyzed in terms of accuracy, validity, timeliness, reliability, the level at which the cost is analyzed, and the extent to which the system responds to a company’s costing needs. Cost systems that are more functional provide higher-quality information, a finding that is consistent with those of Cohen and Kaimenaki (2011). Concerning a cost system’s usefulness (analysis of the break-even point, unit participation margin, and net participation margin), it has a statistically significant effect on the satisfaction users derive from the system. Consequently, companies will adopt a more sophisticated, more complex cost system. The above findings are consistent with those of previous researchers (Pizzini 2006; Al-Omiri and Drury 2007; Brierley 2008; Schoute 2009; Ismail and Mahmoud 2012; Schoute and Budding 2017a, 2017b; Humeedat 2020). Respectively, as cost information becomes important for a company and satisfies a company’s needs, the need for more accurate information will increase, a finding that is consistent with previous research (Pizzini 2006; Al-Omiri and Drury 2007; Brierley 2008; Schoute 2009; Cohen and Kaimenaki 2011; Ismail and Mahmoud 2012; Schoute and Budding 2017a, 2017b; Humeedat 2020). Conversely, this research found that the level of a cost system’s complexity, which is analyzed in terms of the number of cost centers and the number and type of cost allocation bases, does not have a statistically significant effect on the satisfaction users derive from the system, and, consequently, it does not constitute an important factor concerning a cost system’s design. This is a finding that is consistent with J. A. Brierley (2008). Regarding the second research hypothesis concerning the relationship between, on the one hand, demographic data and competition and production data, and, on the other hand, the structure and complexity of cost systems, it was found that the following variables affect the structure and complexity of cost information: (a) the size and the number of years a company has been operating, a finding that is consistent with those of previous researchers (Drury and Tayles 2005; Al-Omiri and Drury 2007; Schoute and Budding 2017a); and (b) the volume of a company’s activities, the level of competition, and its effect on a company’s pricing policy, a finding that is consistent with the findings of certain previous researchers (Al-Omiri and Drury 2007; Schoute and Budding 2017a, 2017b; Humeedat 2020) but conflicts with others (Drury and Tayles 2005; Ismail and Mahmoud 2012). It was found that a company’s sector does not have a statistically significant effect on the system’s complexity, a finding that conflicts with Al-Omiri and Drury (2007) and Drury and Tayles (2005). It was also found that the respondent’s age and level of training are statistically significant concerning the structure. Large companies that have been operating for many years have cost systems that provide a wider range of cost information and a calculation of variances, assess the quality of the information that the cost system provides more positively, and realize that their cost systems provide accurate cost information. In addition, large companies that have been operating for many years and have an international presence believe that their systems provide quality, accurate information, as well as information on variances. The more that competition affects a company’s pricing policy, the more companies will seek systems that classify costs based on their behavior (structure) and have more cost allocation bases (complexity) (Al-Omiri and Drury 2007; Humeedat 2020). Finally, the higher the level of competition, the more companies will adopt more sophisticated systems.

Based on our research, it is evident that cost analysts must design cost systems in a manner that not only aligns with the characteristics of their organization but also enhances decision-making and operational efficiency. Cost managers should first understand business processes and activities. They have to identify which processes are critical and require detailed cost information. They have to be aware of the effect of competition on the formation of the company’s pricing policy. They have to ensure that the cost system supports the organization’s strategic goals. For example, if cost leadership is a strategic objective, the cost system should provide detailed insights into cost-saving opportunities. They have to know the goals of each department in the company in order to design a cost system that meets the specific needs of different departments They have to evaluate the complexity of their operations. They must identify which costing methodologies they will use to determine the cost unit. Organizations with more complex operations may benefit from more sophisticated cost systems like Activity-Based Costing (ABC). They must know how many different products are produced by their company and how many production lines their firm has. Moreover, they have to consider industry requirements and align cost system design with industry standards and regulations. For example, manufacturing firms may require detailed cost tracking at various stages of production. Cost system managers should design a system that classifies costs based on their behavior (direct, indirect, fixed, variable, controllable, and non-controllable). They must distinguish between direct and indirect costs. They must implement methods to accurately allocate indirect costs to the appropriate cost objects. Moreover, they must know how many cost pools they will use and how many cost allocation bases they will need. Cost systems must calculate the direct material price variances, direct material quantity variances, direct labor rate variances, direct labor efficiency variances, variable manufacturing overhead variances, fixed manufacturing overhead variances, and activity cost variances. They must design the cost system in such a way that it is scalable so it can grow with the organization. This includes the ability to handle increased data volume and additional business. They have to know the importance of their cost accounting system for the measurement and evaluation of managers’ performance, the measurement and evaluation of departmental performance, the measurement of activities’ productivity, the recognition of value-adding activities, the recognition of non-value-adding activities, cost control, pricing, benchmarking, the evaluation of special orders’ profitability, product design, short-term decision making (e.g., make-or-buy decisions), the introduction or discontinuation of products, the valuation of inventories, sales and production strategy, budget preparation, negotiation with suppliers, discount policy, the preparation of customer profitability analyses, and the analysis of differences between budgeted and actual results. They must design a sophisticated cost system that enhances transparency and accessibility.

Therefore, it is crucial that sophisticated costing systems support sustainability strategies. These systems must provide appropriate, reliable, and transparent information regarding the company’s sustainability outcomes, ensuring compliance with guidelines such as the Sustainable Development Goals (SDGs), the Global Reporting Initiative (GRI), Corporate Social Responsibility (CSR), and Environmental, Social, and Governance (ESG) reporting.

7.1. Limitation of Research

One of the limitations of this research is that it is based on research where the relationships between the variables that were studied concern only a given point in time. They do not provide information about the cost systems before and after a time period, what urged the companies’ executives to choose a particular system design instead of another design, and what the actual results were from the application of these designs over a period of time. To be able to answer these questions, we need longitudinal research that will examine the interaction of the potential variables over time and will be based on a broader spectrum of theoretical frameworks. In addition, this research did not include telephone interviews with the respondents who otherwise could have possibly provided additional and more reliable information regarding the factors that affect the design of a cost system. Regarding the first hypothesis and the factors that affect the satisfaction that is derived from a cost system, it should be noted that the limited sample of 114 companies does not allow us to identify many significant effects. As a result, the only important variables that are distinguished are the assessment of the cost system’s quality and the cost information. Another limitation that affects the results of the regression model is that the dependent variable of satisfaction was measured in a simple manner (only one question on the 5-point Likert scale), which perhaps did not take into consideration the exact ways in which the level of satisfaction that is derived from the cost system is affected (Al-Omiri and Drury 2007). According to Foster and Swenson (1997), the existence of multiple questions leads to a more complex score, which is clearly preferable to a single question for the measurement of variables that also relate to other multifaceted aspects that require different questions for the registration of the multidimensional aspects that this variable may concern. In the second hypothesis, a respondent’s position in the company and gender did not affect their perceptions about the cost structure and complexity. However, these results will have to be considered with a degree of caution as the limitation here was that the sample was comprised mainly of men and that there was an uneven distribution and significant diversification of the respondents in the company’s various positions (Blanca et al. 2018). Finally, the same observation also applies to the sector in which the company is active.

7.2. Future Proposals

The findings of this research may constitute a strong incentive for future research efforts. This study proposes various directions that future research can take. Future research should examine other significant variables, like the effect of a company’s organizational culture, the contribution of a company’s top management, and a company’s business strategy, which were not examined in this study.