What Drives Cost System Sophistication? Empirical Evidence from the Greek Hotel Industry

Abstract

The increasing complexity of the hotel industry necessitates the implementation of sophisticated cost systems capable of delivering accurate and relevant cost information to support managerial decision-making. Investigating the determinants of cost system design is crucial, given that no single accounting system is universally applicable across all business contexts. This study addresses a critical gap by examining the key drivers of cost system sophistication through the theoretical frameworks of contingency and upper echelons theories, focusing specifically on the Greek hotel sector. Employing multiple regression analysis, the findings reveal that firm size, cost structure, the importance of cost information in decision-making, and the integration of information technology significantly influence the complexity of cost systems. Conversely, factors such as competition, service diversity, business strategy, organizational life cycle, and executive characteristics showed no statistically significant impact. These findings contribute to management accounting and hospitality literature by integrating theoretical perspectives and identifying key determinants of cost system sophistication. Moreover, the study offers practical insights for designing cost systems that meet the specific needs of the hotel industry.

1. Introduction

Traditional costing has been criticized for its simplistic allocation of overhead costs, which can lead to distorted cost calculations and poor managerial decision-making (Hughes & Paulson Gjerde, 2003). These inadequacies have driven the development of more sophisticated cost systems that are capable of enhancing managerial flexibility and supporting both operational and strategic control by providing accurate and relevant cost information (Cohen & Kaimenaki, 2011). According to Brierley (2008), such systems not only improve cost accuracy but also enhance decision-making clarity by better reflecting resource consumption (Al-Omiri & Drury, 2007).

In the hotel industry, the unique cost structure emphasizes the need for accurate and detailed cost systems. Overhead costs represent a substantial proportion of total costs and are incurred both in service delivery and in hotel support functions (Enz & Potter, 1998). Labor costs, which account for 30–40% of total operating costs, further highlight the labor-intensive nature of the sector (Dittman et al., 2009). Additionally, due to the industry’s seasonal nature, fixed idle costs significantly impact cost structures. Hence, the implementation of precise and reliable cost systems is imperative for financial and accounting executives to access accurate information for decision-making purposes.

Furthermore, the appropriateness of cost system design is largely contingent on the specific contextual and organizational environment in which a firm operates. A management accounting system that aligns with contextual demands is more likely to produce high-quality information, thereby enhancing decision-making and improving organizational performance (Haldma & Lääts, 2002). Thus, understanding the factors that influence cost system design is vital for the development of systems that reflect both internal operations and external market conditions.

Research on the determinants of cost system design is a critical area within management accounting literature. Contingency theory, the most prominent framework in both management and cost accounting studies (Roffia et al., 2024), suggests that the effectiveness of management accounting systems depends on their alignment with organizational characteristics and environmental contingencies (Otley, 1980; Drury & Tayles, 2005). Complementing this theory, upper echelons theory posits that the adoption and use of management accounting techniques are shaped by the attributes of top management, given that management accounting serves as an organizational outcome (Hiebl, 2014). By applying these theoretical frameworks, a research stream on the factors influencing cost systems has provided important insights.

However, the empirical results within this domain have been inconsistent (Mazbayeva et al., 2021; Hadid & Hamdan, 2022). Much of the literature has focused on the adoption of Activity-Based Costing (ABC) and its determinants, primarily through the lens of contingency theory (Al-Omiri & Drury, 2007). Studies reveal varied adoption rates across countries (Lee, 2002) and industries (Askarany & Yazdifar, 2012), partly due to differences in research design (Jänkälä & Silvola, 2012). Drury and Tayles (2005) emphasized the need for further empirical research to address these inconsistencies and the prevailing dichotomous measurement of cost systems. In response, recent studies have used the terms complexity, functionality, and sophistication interchangeably to describe cost system design.

This study aims to explore the factors that affect the sophistication of cost systems in the Greek hotel industry. The primary motivation for this research lies in the need to align cost system design with the specific business environment in which it operates. Given that the hotel sector is characterized by high overheads, service heterogeneity, and complex operational structures, it presents a compelling context for examining cost systems. Particularly, the Greek hotel industry was chosen as it offers a relevant empirical setting due to its great contribution to national economy, both in terms of Gross Domestic Product (GDP) and employment, to its exposure to high seasonality, increasing competition and service diversity, and its growing need for accurate and relevant cost information to support decision-making and control. Furthermore, the relationship between cost system design and its determinants remains a promising avenue in management accounting research (Ibrahim et al., 2021). Using multiple regression analysis, the study finds that firm size, the proportion of indirect to total costs, the importance of cost information, and information technology integration significantly influence cost system sophistication. In contrast, competition, service diversity, business strategy, organizational life cycle, and managerial demographics do not show significant effects.

To the best of current knowledge, this is the first study in the hotel industry that combines contingency theory and upper echelon theory to examine cost system sophistication and its determinants. It also addresses the call for further research in management accounting within the service sector (Chenhall, 2003), supports Pavlatos and Paggios’ (2009) suggestion to explore additional contingent factors in the context of the hotel industry, and aligns with Cardoni et al.’s (2023) proposal to examine both organizational and individual effects. Moreover, the empirical findings of the relation between the characteristics of top and middle managers in relation to management accounting are scarce (Liem, 2021; Berisha & Miftari, 2022). Thus, the findings of this study provide valuable insights for financial and accounting professionals who seek to design and implement suitable and effective cost systems.

The remainder of the paper is organized as follows. The next section provides a review of the relevant literature and outlines the development of the research hypotheses. This is followed by a description of the research methodology. The subsequent sections present the empirical results and conclude with a discussion of the findings, the study’s limitations, and directions for future research.

2. Literature Review

2.1. Cost Structure in the Hospitality Industry and Implications for Cost System Design

The use of traditional cost systems in the industrial sector has been criticized for distorting product costs, a problem that is compounded in the service sector due to the intangible nature of services (T. J. Brignall et al., 1991). Additionally, since final goods and services are consumed immediately (Terzioglu & Chan, 2013), inventory valuation becomes largely irrelevant, except in retail, necessitating distinct cost system approaches (T. J. Brignall et al., 1991). Furthermore, the high proportion of fixed costs (Schlissel & Chasin, 1991) and the significant contribution of overhead costs to the total cost of services make traditional systems inefficient for service-oriented firms (Terzioglu & Chan, 2013).

The cost structure of hotels is typically characterized by a high proportion of fixed costs, estimated at approximately 75% of total costs (Kotas, 1999). The mix of services offered can also influence the ratio of fixed to variable costs. Higher-category hotels tend to have greater fixed costs due to more expensive fixed assets and equipment (Seal & Mattimoe, 2011). Concurrently, overhead costs constitute a large portion of total costs, arising from both customer service and support departments (Enz & Potter, 1998).

Traditional cost systems, which often rely on inappropriate allocation bases, distort the allocation of overheads, leading to inaccurate cost calculations. In contrast, ABC is considered more suitable for the service sector, particularly in firms with cost structures dominated by fixed and indirect costs (D. R. Kaplan & Cooper, 1998). The adoption of sophisticated costing systems like ABC allows hotel managers to better understand the cost drivers behind different services, allocate resources more effectively to profit-generating activities, and differentiate between profitable and unprofitable customer segments (Hussain & Gunasekaran, 2001). In support of this view, Brierley et al. (2001) argue that firms operating in industries with high overhead rates, such as the hotel industry, should use complex costing systems to ensure the rational allocation of overheads to products and services.

Despite the need to change the cost systems (Patiar, 2016) and to implement complex and functional ones that provide detailed and accurate cost information, hotels still mainly use traditional cost systems (Alsharari et al., 2020). In the same vein, empirical studies suggest that the adoption of ABC in the hotel industry remains moderate (Pavlatos & Paggios, 2007; Zounta & Bekiaris, 2009; Pavlatos, 2010; Sevima & Korkmaz, 2014; Diavastis et al., 2016). Additionally, Patiar (2016) reports that despite its potential benefits for operational performance, the use of ABC in hotels and the hospitality industry is relatively low. Nevertheless, ABC remains the most suitable cost system for service-based firms like hotels, provided they have the necessary tools and information systems to implement it (Chatzis et al., 2023).

2.2. Advancing Cost System Design

Modern enterprises require sophisticated cost systems that enhance managerial flexibility and enable more effective operational and strategic control (Cohen & Kaimenaki, 2011) by providing relevant and accurate cost information. According to Brierley (2008), highly complex cost systems improve the accuracy of cost estimates and support informed decision-making. Such complexity, which is associated with an increased number of cost pools, contributes to capturing precisely the consumption of firms’ resources by their products and services (Al-Omiri & Drury, 2007). Moreover, a sophisticated cost system will enable firms to implement the appropriate sustainability strategies, which will, in turn, lead to the achievement of the Sustainable Development Goals (SDGs) (Alexopoulou et al., 2024).

Most studies in cost accounting literature assume that there are two distinct systems—traditional costing and ABC—without adequately covering the range of systems applied. However, more recent studies have introduced the terms complexity, functionality, and sophistication to capture the design of the cost systems. For instance, Brierley (2010) defines sophistication as the way that the cost system is designed to allocate overheads to finished products. Drury and Tayles (2006) argue that cost system complexity encompasses more than just causality-based allocations. In contrast, Pizzini (2006) offers a different approach to the cost systems design, focusing on system functionality. According to Pavlatos and Paggios (2009), the functionality of a cost system is determined by the quality of the cost information it provides. In this study, the terms sophistication (Abernethy et al., 2001; Al-Omiri & Drury, 2007; Brierley, 2008; Cinquini et al., 2015), complexity (Schoute, 2009; Ismail & Mahmoud, 2012; Drury & Tayles, 2005; Schoute & Budding, 2017; Klink & Budding, 2025), and functionality (Pizzini, 2006; Pavlatos & Paggios, 2009; Foong & Anak Teruki, 2009; Kuzey et al., 2019; Pavlatos & Kostakis, 2022) of cost systems have been used interchangeably, consistent with prior literature that employs these constructs to describe cost system design (Drury & Tayles, 2005; Foong & Anak Teruki, 2009; Wihinen, 2012).

Recent empirical research has moved beyond the dichotomous approach to examining cost systems, emphasizing the continuum of cost system sophistication. Four key characteristics of sophisticated systems are identified: (1) the number of cost pools, (2) the type of cost pools (e.g., responsibility or activity centres), (3) the number of allocation bases, and (4) the type of allocation bases (e.g., unitary or. hierarchical) (Abernethy et al., 2001; Drury & Tayles, 2005; Al-Omiri & Drury, 2007; Schoute, 2009; Ismail & Mahmoud, 2012). This classification allows systems to be categorized across a range from simple to complex (Al-Omiri & Drury, 2007). Pizzini (2006) further identifies four key features of the design of a cost system: (1) the degree of detail it provides, (2) its ability to categorize costs based on cost behavior, (3) the frequency of providing cost reports, and (4) the degree of analysis of deviations. Furthermore, Krumwiede et al. (2014) associate these characteristics with system functionality, thus expanding the way to measure and evaluate sophistication and broadening its conceptual scope.

2.3. Contingency and Upper Echelons Theories in Cost System Design Research

A key issue in management accounting research concerns the extent to which cost system design is influenced by the environment in which it operates (Schoute & Budding, 2017). Contingency theory has been the predominant theoretical framework in management accounting research, addressing the adoption of sophisticated cost systems and providing insights into the impact of various determinants. It posits that “there is no universally appropriate accounting system that applies in the same way to all enterprises in all statements” (Otley, 1980, p. 413). Therefore, no single cost system can fully satisfy the needs of executives for reporting and strategic planning. Variability in operational contexts necessitates the design of cost systems tailored to specific organizational needs (Fisher & Krumwiede, 2015). As Pavlatos and Paggios (2009) point out, the effectiveness of the design of a cost system is based on its ability to adapt to both external environmental changes and internal factors.

In addition to contingency theory, upper echelons theory posits that the characteristics of senior management significantly influence management accounting systems, which are considered components of organizational structure or business outcomes (Hiebl, 2014). Therefore, top managers’ demographic attributes have been used in management accounting literature to predict strategic choices, as these characteristics often reflect underlying values and psychological traits (Hambrick & Mason, 1984). Moreover, it is suggested that managers differ in the extent to which they are willing to adopt an innovation (Naranjo-Gil et al., 2009), as well as that managerial characteristics explain the transformation of management control systems (Morelli & Lecci, 2014). Therefore, cost systems design is subject to the individual demographic factors such as executives’ education, age, and tenure.

Early empirical research in this domain primarily focused on factors affecting the adoption of ABC. More recently, however, studies have begun to explore how to determine the sophistication of cost systems, as well as the factors that influence it.

Table 1. Empirical Research in the Determinants of Cost System Design.

Study	Sample	Cost System Design	Key Findings
Drury and Tayles (2005)	UK firms	Cost system complexity	Product diversity, firm size, degree of product standardization, and industry type (particularly financial and service sectors) positively influence the complexity of cost systems. In contrast, competition, costing structure, and the importance of costing information for decision-making do not show any statistically significant effect.
Al-Omiri and Drury (2007)	UK manufacturing and service firms	Cost system sophistication	The importance of costing information for decision-making, the firm size, the competition and the industry (specifically financial sector) have a positive effect on the sophistication of cost systems. No significant relationship is found with IT quality, product diversity, or cost structure.
Pavlatos and Paggios (2009)	Greek hotel firms	Cost system functionality	The cost leadership strategy and the degree of use of cost data have a significant impact on the functionality of cost systems. No significant relationship is observed with firm size, competitive intensity, service range, or management status.
Ismail and Mahmoud (2012)	Egyptian manufacturing firms	Cost system sophistication	The importance of cost information for decision-making influences the level of sophistication of cost systems. No statistically significant effects are found for product range, competitive intensity, or cost structure.
Kuzey et al. (2019)	Turkey non-financial firms	Cost system functionality	Information technology usage, use of cost data, and the degree of budget usage positively affect cost system functionality. Competition has a statistically significant negative effect. Production complexity, firm size, and cost leadership strategy do not have any statistically significant effect.
Hadid and Hamdan (2022)	Syrian manufacturing firms	Cost system sophistication	A direct positive relationship exists between firm size and cost system sophistication, although this relationship is negatively moderated by firm age. Additionally, firm size indirectly affects cost system sophistication through product diversity.
Banhmeid and Aljabr (2023)	UK manufacturing firms	Cost system sophistication	The active role of management accountants and advanced manufacturing technology significantly impacts the sophistication of cost systems.

summarizes key empirical findings related to the determinants of cost system design, organized in chronological order. It highlights the relationship between various organizational factors and the cost system design, as presented across different studies.

3. Hypothesis Development

Building upon the assumption that the terms complexity, sophistication, and functionality of cost systems are often used interchangeably in the literature to analyze similar constructs (Drury & Tayles, 2005; Foong & Anak Teruki, 2009; Wihinen, 2012), and drawing on Krumwiede et al.’s (2014) assertion that the functionality features of a cost system enhance the conceptualization and measurement of its complexity, this study adopts the functionality characteristics proposed by Pizzini (2006) as proxies for cost system sophistication. Additionally, as Ibrahim et al. (2021) noted, sophistication is a broader concept encompassing both complexity and functionality.

Accordingly, this research investigates the sophistication of cost systems within the Greek hotel industry, examining its relationship with a set of organizational and contextual variables. These include competition, firm’s size, cost structure, importance of cost information for decision-making, services diversity, business strategy, information technology integration, organizational life cycle, as well as age, experience, and education of the financial and accounting executives (Figure 1). This study applies the selection approach (Drazin & Van de Ven, 1985) of the contingency theory, which suggests that organizational effectiveness depends on the alignment between contextual factors and structural design choices. The selection of the aforementioned variables is based on a comprehensive review of the literature on cost system design, drawing from both contingency theory and upper echelons theory.

3.1. Competition

Hotels operate in markets characterised by intense competition at both the supplier and customer level (Jones & Lockwood, 2002). As firms tend to price their services based on market competition and service differentiation (Sharma, 2002), access to accurate cost information becomes a critical factor in pricing decisions within competitive environments (Guilding et al., 2001). Furthermore, the more intense the competition, the greater the need to control costs and evaluate the firm’s operations against its objectives (Khandwalla, 1972). Consequently, accurate and relevant cost information about cost objects is essential (Haldma & Lääts, 2002).

R. Cooper (1988, p. 43) argues that “competition in general increases the costs of errors because there is a greater chance that a competitor will take advantage of any error that occurs”. Therefore, firms operating under intense competitive pressure require more sophisticated cost systems that allow for more accurate cost allocation to services and customers (Chen et al., 2001; Drury & Tayles, 2005). They also need extensive and detailed cost information to control operations (Van Nguyen & Brooks, 1997) and set appropriate prices (Patiar, 2016). Additionally, when firms face higher competition and experience lower profit margins, more accurate cost systems are needed. The reason is that an inaccurate cost system can result in the miscalculation of the cost of products and services, leading to suboptimal decisions such as discontinuing profitable services or continuing unprofitable ones (Al-Omiri & Drury, 2007).

Empirical findings on the relationship between competition and cost system sophistication are mixed. Pizzini (2006) found that competition is statistically significantly related to the ability of cost systems to provide more detail. Similarly, Al-Omiri and Drury (2007) identified a positive correlation with the adoption of complex cost systems. In contrast, Pavlatos and Paggios (2009), Drury and Tayles (2005), and Ismail and Mahmoud (2012) reported no significant relationship, while Kuzey et al. (2019) found a negative effect of competition on cost system functionality. Accordingly, the following hypothesis is proposed:

H₁: 

Competition has a positive impact on the level of sophistication of the cost system.

3.2. Firm’s Size

According to Cadez and Guilding (2008), larger firms typically require more complex accounting information systems. These firms often engage in diverse activities and offer a wide range of products and services, necessitating sophisticated cost systems capable of capturing the consumption of resources by the cost objects (Drury & Tayles, 2005). Moreover, larger firms typically have enhanced resource availability, which facilitates the adoption of more sophisticated accounting systems (Tran & Tran, 2022). Van Nguyen and Brooks (1997) further argue that larger firms are more likely to incur higher indirect costs, which increases their exposure to cost distortions and drives the demand for more sophisticated cost systems.

Firm size is a key contingent variable in management accounting research, but also one of the most controversial (Askarany et al., 2010). While Pizzini (2006) demonstrated that larger firms use more functional cost systems, the studies of Pavlatos and Paggios (2009) and Kuzey et al. (2019) found no significant relationship. Additionally, Al-Omiri and Drury (2007) and Drury and Tayles (2005) found that firm size positively affects the complexity of cost systems, whereas Ismail and Mahmoud (2012) found no statistically significant correlation. Accordingly, the following hypothesis is proposed:

H₂: 

Firm size has a positive impact on the level of sophistication of the cost system.

3.3. Cost Structure

Cost structure, particularly the proportion of indirect costs relative to total costs, is expected to influence the sophistication of cost systems. Firms with a higher proportion of overhead costs require more refined cost allocation procedures to ensure accurate costing of products and services (Drury & Tayles, 2005). When indirect costs constitute a substantial portion of total costs, the risk of cost distortion increases, thereby necessitating more complex cost systems to trace costs appropriately (Van Nguyen & Brooks, 1997).

Empirical evidence on this relationship is mixed. Pavlatos (2011) found that a higher overhead-to-total cost ratio positively influences the adoption of ABC in hotels. However, studies by Drury and Tayles (2005), Al-Omiri and Drury (2007), and Ismail and Mahmoud (2012) did not find statistically significant correlations between cost structure and cost system sophistication. Accordingly, the following hypothesis is proposed:

H₃: 

A cost structure characterized by a higher proportion of indirect costs in relation to total costs has a positive impact on the level of sophistication of the cost system.

3.4. Importance of Cost Information for Decision-Making

The primary purpose of a cost system is to provide relevant and timely information to support managerial decision-making. Traditional cost systems often fail to meet this objective, necessitating the adoption of more advanced systems (Boyd & Cox, 2002). According to Schoute and Budding (2017), the design of cost systems is influenced by the information needs of internal stakeholders. Therefore, if product costs are important for decision-making, a higher complexity allocation of overheads is required (Brierley, 2010). Kaplan (1988) notes that cost systems used solely for financial reporting, such as inventory valuation, may not necessitate high complexity.

Empirical studies provide partial support to this perspective. Pavlatos and Paggios (2009) and Kuzey et al. (2019) found that the degree of cost data usage positively affects cost system functionality. Similarly, Al-Omiri and Drury (2007) and Ismail and Mahmoud (2012) reported a significant positive relationship between the perceived importance of cost information for decision-making and cost system sophistication. However, Drury and Tayles (2005) found no significant correlation. Accordingly, the following hypothesis is proposed:

H₄: 

The importance of cost information for decision-making has a positive impact on the level of sophistication of the cost system.

3.5. Service Diversity

According to R. Cooper (1988), one of the reasons for the distortion of cost information is the increasing complexity of product production. The range of products influences the number of activities required to produce them (Al-Omiri & Drury, 2007). In addition, the consumption of resources in different proportions, due to the wide portfolio of products and services, can contribute to cost distortions. To accurately capture the resource consumption of different products or services, complex cost systems with more cost pools and allocation bases are required (Malmi, 1999). Thus, the range of products and services is a primary contributor to cost allocation inaccuracies in traditional costing systems (Bjørnenak, 1997). Detailed cost information is needed (Abernethy et al., 2001), which can be obtained through sophisticated and complex cost systems (Drury & Tayles, 2005).

In the empirical research, Drury and Tayles (2005) found that the range of products positively affects the sophistication of cost systems. In contrast, Al-Omiri and Drury (2007), Ismail and Mahmoud (2012), Pavlatos and Paggios (2009), and Daowadueng et al. (2023) found no statistically significant correlation. Accordingly, the following hypothesis is proposed:

H₅: 

Service diversity has a positive impact on the level of sophistication of the cost system.

3.6. Business Strategy

Porter (1985) argues that a comprehensive understanding of cost structures and cost drivers is necessary for implementing both cost leadership and differentiation strategies. Cost leadership demands tight cost control, while differentiation requires sufficient profit margins supported by effective cost management (Banker & Johnston, 2006). However, for the successful implementation of the cost leadership strategy, firms must optimize operational efficiency (Govindarajan, 1989). Therefore, when a firm seeks to maintain its cost leadership strategy, examining internal processes and increasing the activities values involved in the production of products and the provision of services is needed. This can be achieved through the use of sophisticated costing tools such as ABC (Beheshti, 2004).

S. Brignall (1997) notes that service firms pursuing cost leadership place substantial emphasis on resource utilization. In contrast, firms following differentiation strategies tend to utilize simplified costing systems. At the same time, Lucas and Rafferty (2008) argue that accurate cost information is more important in a cost leadership strategy. This is further affirmed by Callahan and Gabriel (1998), who argue that the use of accurate cost information enhances decision-making in cost-driven competitive markets. Additionally, the cost leadership strategy requires cost reduction through tight control of costs and industrial overhead and cost reduction efforts across all functions of the firm (Schaffer, 1986).

Despite these theoretical claims, empirical evidence remains inconclusive. Chenhall and Langfield-Smith (1998) demonstrated that activity-based techniques, such as ABC, are positively related to both cost leadership and differentiation strategy. Pavlatos (2010) and Pavlatos and Kostakis (2018) found a statistically significant relationship between diversification strategy and the use of ABC. Finally, Pavlatos and Paggios (2009) demonstrated that the cost leadership strategy has a positive correlation with the degree of functionality of cost systems, whereas Kuzey et al. (2019) did not find any statistically significant correlation. Accordingly, the following hypothesis is proposed:

H₆: 

The cost leadership strategy has a positive impact on the level of sophistication of the cost system.

3.7. Information Technology Integration

Advances in information technology have contributed to significant improvements in the collection and dissemination of cost data within firms (Ratnatunga et al., 2012). Chenhall (2003) points out that advanced information technology systems facilitate the adoption and use of specific budgeting and cost systems. According to Pavlatos (2012), the use of high-quality information technology has resulted in the extensive use of cost systems in the hotel industry. Additionally, Sheldon et al. (1991) state that the use of software packages can enable the implementation of higher complexity cost systems because of the accuracy, speed, flexibility, better presentation, and consistency they offer to the user.

The level of information technology integration allows for the accurate identification of cost elements and their linkage to firm activities (Maiga, 2015), thereby improving cost driver insights and identifying non-value-adding activities (Maiga, 2017). The positive relationship between information technology and ABC has been highlighted by many researchers (R. Cooper, 1988; Reeve, 1996; Krumwiede, 1998; Nassar et al., 2009; Diavastis et al., 2016). According to Gurses (1999, cited in Sartorius et al., 2007, p. 8), “the application of Activity-Based Costing is easier to implement in firms where information technology has characteristics such as good integration of subsystems, user-friendly search and required data”. Consequently, complex cost systems require access to detailed data related to cost drivers. Furthermore, as Chenhall and Langfield-Smith (1999) state, integrated information systems are related to innovative cost systems. Thus, integrated information technology can enable the implementation of complex cost systems by providing detailed data and information about cost drivers.

In empirical research, Al-Omiri and Drury (2007) did not find a statistically significant correlation between the information technology quality and the degree of sophistication of cost systems. However, Kuzey et al. (2019) found that information technology has a positive effect on the degree of functionality of cost systems, whereas Maiga (2015) suggested that a high level of information technology integration contributes to higher usage of ABC. Accordingly, the following hypothesis is proposed:

H₇: 

Information technology integration has a positive impact on the level of sophistication of the cost system.

3.8. Organizational Life Cycle

Organizational life cycle theory can be used to indicate the differentiation of management accounting systems between the stages of a firm’s development (Silvola, 2008). It describes the stages through which a firm evolves, reflecting the changes to which it is subject (Dodge et al., 1994; Kallunki & Silvola, 2008). Business characteristics, such as environment, structure, strategy, and decision-making, vary between life cycle stages (Miller & Friesen, 1984) and define each stage (Moores & Yuen, 2001). Several models of the organizational life cycle have been developed, and they differ in the number of stages of business development (Su et al., 2013). The most well-known models of the organizational life cycle have been formulated by Greiner (1972), Lewis and Churchill (1983), and Miller and Friesen (1983).

During the growth stage, firms adopt formal systems (Miller & Friesen, 1983) to ensure operational efficiency (Silvola, 2008), as decision-making becomes more analytical and complex (Miller & Friesen, 1984). Thus, the management accounting systems used are to cope with differentiated and more complex structures. Firms in the maturing stage have more formal and bureaucratic structures (Moores & Yuen, 2001) and are characterized by stability (Auzair & Langfield-Smith, 2005), while emphasizing cost control, budgets, and performance evaluation to increase the efficiency and profitability of their operations (Miller & Friesen, 1984). In the revival stage, firms expand their product range and enter new markets to survive in a more competitive and complex environment (Silvola, 2008). Consequently, they increase their reliance on management accounting systems in order to cope with the crisis they are going through (Moores & Yuen, 2001). In contrast, firms that are in the decline stage have low performance, try to hold back their resources rather than focus on the needs of their customers, and become more vulnerable to market changes (Miller & Friesen, 1984). Subsequently, they rely on a smaller variety of management accounting tools (Silvola, 2008).

In empirical research, Kallunki and Silvola (2008) demonstrated that the use of advanced management accounting systems, such as ABC, is greater in firms in the maturity and revival stages, as the need to understand cost causality increases. Furthermore, Silvola (2008) also found a greater emphasis on cost and pricing in these stages. As a firm grows, the processing of information and the complexity of decision-making increases (Miller & Friesen, 1983). Consequently, firms in the maturity and revival stage implement complex and sophisticated cost systems that provide detailed information with the goal of making optimal decisions. Accordingly, the following hypothesis is proposed:

H₈: 

The maturity stage and the revival stage of firms have a positive impact on the level of sophistication of the cost system.

3.9. Characteristics of Executives

According to the upper echelons theory, managers face high complexity and ambiguity, and as a result, cannot achieve the required consistency in their decisions. Subsequently, they rely on their judgments and experiences and also list their own solutions and alternatives (Hambrick, 2016). The core premise of this theory refers to the characteristics of managers that act as determinants of strategic choices. These characteristics include age, job tenure, and education (Hambrick & Mason, 1984).

Regarding age, older managers have less mental and physical stamina, take longer to make decisions, wish to maintain the status quo, and avoid taking any risk (Hambrick & Mason, 1984). They are also less receptive to novel ideas and, due to longer tenure in the profession, may be unfamiliar with contemporary management accounting systems (Naranjo-Gil et al., 2009). In terms of tenure, executives with extensive experience in a firm are likely to have limited knowledge and understanding when confronted with unexpected problems (Hambrick & Mason, 1984). In addition, they have developed specific work routines that they find difficult to change (Hiebl et al., 2017) and rely more on their personal experience and instinct, neglecting information collection and analysis (Reheul & Jorissen, 2014). Therefore, longer-tenured managers are likely to implement less sophisticated management accounting systems (Ahmed & Elmassri, 2025). Educational background also plays a pivotal role. Higher levels of academic education have been positively associated with openness to innovation and the use of more formal and complex management systems (Hambrick & Mason, 1984). Managers with advanced education are typically better equipped with the knowledge and skills required to support innovation (Hiebl et al., 2017) and have a greater need for a comprehensive and complex understanding and control of a situation (Reheul & Jorissen, 2014).

In empirical research, Naranjo-Gil and Hartmann (2007) demonstrated that the use and design of management accounting systems depends on the demographics of top management. Naranjo-Gil et al. (2009) found that age and seniority of financial managers have a negative correlation with the innovation of management accounting systems, while education has a positive correlation. Pavlatos (2012) further demonstrated that age and education have a negative and positive effect, respectively, on the use of the cost systems for decision-making, monitoring, and performance evaluation. Similarly, Vetchagool and Buttarat (2023) found that younger financial executives are more likely to adopt advanced cost systems. Lastly, the empirical findings of Najera Ruiz and Collazzo (2021) suggest that well-educated and older managers are more inclined to implement formal accounting practices.

Based on the extant literature, it is hypothesized that financial and accounting executives in the hotel industry who are younger, possess fewer years of experience, and hold higher educational qualifications are more likely to utilize sophisticated cost systems. This proposition is based on the assumption that cost system sophistication reflects the degree of innovation in management accounting, and on Hiebl’s (2014) claim that younger financial executives with fewer years of experience and higher academic education are associated with innovative and complex management accounting systems. Accordingly, the following hypotheses are proposed:

H₉: 

The age of financial and accounting executives has a negative impact on the level of sophistication of the cost system.

H₁₀: 

The years of job experience of financial and accounting executives has a negative impact on the level of sophistication of the cost system.

H₁₁: 

The education level of financial and accounting executives has a positive impact on the level of sophistication of the cost system.

4. Research Methodology

4.1. Sample and Data Collection

The sample was constructed with the assistance of the Hellenic Chamber of Hotels, which provided a list of its member hotels. After verifying the accuracy and completeness of the list, the final sampling frame included 2508 hotels. The study focused on three-, four-, and five-star hotels, as these are expected to deliver high-quality services and, consequently, require more advanced management accounting systems (Lamminmaki, 2008). Such hotels typically feature more developed managerial and organizational infrastructures (Hiamey & Amenumey, 2013), which facilitate the adoption of sophisticated and functional cost accounting systems. The target respondents were hotel financial and accounting executives, chosen for their informed perspectives on decision-making processes and their influence on the implementation of management accounting innovations (Baines & Langfield-Smith, 2003).

The questionnaire was developed based on an extensive literature review. Following the methodological guidance of Saunders et al. (2012) and D. R. Cooper and Schindler (2003), a multi-stage evaluation process was conducted to ensure its validity. First, a pilot test was carried out with a group of PhD candidates to assess the clarity of questions and estimated completion time. Then, three academic experts and two industry professionals reviewed the instrument and provided critical feedback to improve content validity and relevance. After incorporating their suggestions, a second pilot test was conducted with three hotel financial managers, each from a hotel of a different star category (three-, four-, and five-star), to evaluate wording, structure, and any coding issues. Final adjustments were made based on their input.

The finalized questionnaire was distributed to a simple random sample of 1000 hotels via the SurveyMonkey™ platform. To ensure methodological rigor and reduce sampling bias, simple random sampling was employed (Sekaran, 2003). Of the emails sent, 51 were undeliverable, and 42 hotels opted out of participation. Follow-up reminders were issued to non-respondents. Ultimately, 195 fully completed questionnaires were returned. Following the exclusion criteria outlined by Gerdin (2005), 54 questionnaires were discarded due to more than 20% of the data being incomplete. For the remaining responses with minor missing values, the Hot Deck imputation method was employed. This technique, widely recognized for its accuracy and practicality (Brown & Kros, 2003; Fox-Wasylyshyn & El-Masri, 2005), replaces missing data with observed values from similar cases, thereby preserving the statistical distribution of variables. As a result, the final usable sample comprised 141 responses, yielding an effective response rate of 14.9%.

Given concerns that a low response rate may lead to non-response bias (Dunk, 2001) and threaten the external validity and generalizability of the findings (Mellahi & Harris, 2016), statistical comparisons were conducted between early and late respondents. To assess the external validity and representativeness of the final sample, statistical analyses—including chi-square tests and independent samples t-tests—were conducted to compare the responses of early and late respondents (Armstrong & Overton, 1977). Specifically, the first 20% of the returned questionnaires (n = 28) were classified as early respondents, while the final 20% (n = 28) were designated as late respondents. This method is well-established in management accounting research (Chenhall & Langfield-Smith, 1998; Bedford et al., 2008; Albu & Albu, 2012). Analysis revealed no statistically significant differences in demographic characteristics and key variables between the two groups, suggesting that non-response bias is unlikely to affect the validity of the study.

4.2. Descriptive Statistics of the Sample

Table 2. Demographic Profiles of Respondents.

		Frequency (N)	Percentage (%)
Job Position	Financial Managers	11	7.8
	Internal and Financial Auditors	7	5.0
	Cost Accountants	5	3.5
	Accountants	88	62.4
	Other Roles	30	21.3
Job Experience	1–10 years	68	48.2
	11–20 years	58	41.1
	More than 20 years	15	10.6
Educational Level	Secondary Education (High School)	3	2.1
	Vocational Training Institute	18	12.8
	Bachelor’s Degree	97	68.9
	Postgraduate Degree	23	16.3
	Doctorate (PhD)	0	0
Age Group	Under 35	25	17.7
	35–44	65	46.1
	45–54	34	24.1
	Over 55	17	12.1

presents the demographic profile of the respondents. The majority (62.4%) held positions as accountants or assistant accountants. With respect to professional experience, 51.7% reported more than 11 years of tenure in the field, while 85.2% possessed a higher education degree. These qualifications support the credibility and reliability of the data, as respondents demonstrated substantial professional experience and academic education, thereby enhancing their ability to provide informed and accurate responses.

Table 3. Demographic Characteristics of Hotels.

		Frequency (N)	Percentage (%)
Number of Beds	1–100 beds	6	4.3
	101–150 beds	27	19.1
	151–300 beds	79	56.0
	Over 300 beds	29	20.6
Star Rating	5-star hotels	46	32.6
	4-star hotels	69	48.9
	3-star hotels	26	18.4
Management Status	Privately owned	111	78.7
	Member of domestic hotel chain	19	13.5
	Member of international hotel chain	11	7.8

outlines the characteristics of the participating hotels. Following the classification criteria established by Camisón (2000) and Claver-Cortés et al. (2007), 20.6% of the hotels were categorized as large, and 56.0% were identified as medium-sized establishments. Regarding hotel classification, 32.6% of establishments belonged to the five-star category, 48.9% to the four-star category, and 18.5% to the three-star category. Additionally, 21.3% of the sampled hotels were affiliated with either domestic or international hotel chains.

4.3. Measurement of Variables

The questionnaire was primarily developed by adapting measurement items from prior studies. Where necessary, specific items were modified to reflect the contextual nuances of the hotel industry, ensuring greater relevance and applicability to the research setting.

Cost system sophistication (CSS) was measured using an instrument developed for this study, based on the studies of Pizzini (2006) and Cohen and Kaimenaki (2011). It comprises a four-item 5-point Likert-scaled instrument anchored by (1) ‘not at all‘ to (5) ‘to a very great extent‘. Respondents were asked about the level of detail of cost information (by room, customer, overnight stay, service, and travel agency), the system’s ability to disaggregate costs according to behavior (as fixed/variable, direct/indirect, controllable/uncontrollable), the extent to which variances are calculated and the frequency at which cost information is provided to users. The competition (COMP) was measured based on Sharma’s (2002) study by using a six-item 5-point Likert scale, ranging from (1) ‘strongly disagree’ to (5) ‘strongly agree’. Respondents were asked about competition for supplies, competition for human resources, price competition, the number of services and packages marketed, economic turbulence, and technological turbulence. The importance of cost information for decision-making (ICI) was measured through the instrument developed by Pavlatos (2011) using a five-item 5-point Likert scale anchored by (1) ‘strongly disagree’ to (5) ‘strongly agree’. Lastly, integrated information technology (ITI) was examined through the instrument developed by Maiga (2015), which includes two items on a 5-point Likert scale ranging from (1) ‘not at all’ to (5) ‘to a very great extent’.

Regarding the firm size (SIZE) variable, it is defined by the number of beds (Camisón, 2000; Claver-Cortés et al., 2007) and is coded as follows: 1 (1–50 beds), 2 (51–100 beds), 3 (101–150 beds), 4 (151–200 beds), 5 (201–250 beds), 6 (251–300 beds), and 7 (more than 300 beds). The cost structure (CS) variable is measured as the proportion of indirect costs to the total cost of services (Al-Omiri & Drury, 2007; Pavlatos & Ioakimidis, 2024). The variable services diversity (DIV) is categorical and measured on an ordinal scale, with the value of 1 for accommodation-only services, 2 for several services, and 3 for multiple services. Business strategy (STRA) is measured on a five-point ordinal scale ranging from 1 (cost leadership strategy) to 5 (service differentiation strategy) (Cinquini & Tenucci, 2010; King et al., 2010). The variables AGE and JT represent the age and the job tenure of the financial and accounting executives, respectively, and are measured as continuous variables. Additionally, organizational life cycle (OLC) is treated as a binary categorical variable, taking the value of 0 when the hotel is in the birth, growth, or decline stage, and 1 when it is in the maturity or revival stage. Lastly, the variable EDU, representing educational level, is a categorical variable treated dichotomously by taking value 0 if the executive does not hold a higher education degree (i.e., a high school or vocational training diploma), and 1 if the executive possesses a higher education qualification (i.e., a bachelor’s, master’s, or doctoral degree). The dichotomization of the variables OLC and EDU was based on prior literature and empirical distribution to facilitate interpretation in regression models.

4.4. Data Analysis Techniques

Multiple regression analysis was employed to examine the impact of independent variables on the dependent variable, consistent with its frequent use in contingency theory studies (Chia & Koh, 2007). As noted by Gerdin and Greve (2004), this technique aligns with the congruence approach to contingency fit in management accounting research. Accordingly, the multiple regression model can be expressed as: Υ = b₀ + b₁Χ₁ + b₂Χ₂ + b_kΧ_k + ε, where Y represents the design of management accounting systems and Χ₁, Χ₂, …, Χ_k are the predictor variables. Thus, the following model is employed:

CSS = b₀ + b₁COMP + b₂SIZE + b₃CS + b₄ICI + b₅DIV + b₆STRA + b₇ITI + b₈OLC + b₉AGE + b₁₀JT + b₁₁EDU + ε

where CSS = cost system sophistication; COMP = competition; SIZE = hotel size; CS = cost structure; ICI = importance of cost information for decision-making; DIV = services diversity; STRA = business strategy; ITI = information technology integration; OLC = organizational life cycle; AGE = age of executive; JT = job tenure of executive; and EDU = educational level of executive. ε is the error term.

5. Analysis and Results

5.1. Validity and Reliability Analysis

A comprehensive literature review, along with the validation of the questionnaire by both academics and industry professionals, and a pilot test, confirmed the content validity of the research instrument. The construction of an appropriate sampling frame, the application of random sampling, and the absence of non-response bias further support the external validity of the study. Furthermore, construct validity was assessed through factor analysis using principal component analysis with varimax rotation. The Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity were employed to evaluate the adequacy of the data for factor analysis. KMO values for all multi-item constructs exceeded the recommended threshold of 0.50 (Hair et al., 2014), and Bartlett’s tests were statistically significant, indicating the suitability of the data for factor extraction.

As shown in

Table 4. Results of Factor and Reliability analysis.

Construct	Factor Loadings	Item-Total Correlation	Cronbach’s Alpha
CSS	0.800–0.891	0.775–0.833	0.853
COMP	0.635–0.792	0.557–0.694	0.851
ICI	0.772–0.862	0.851–0.878	0.886
ITI	0.905–0.909	0.800	0.889

, all item loadings surpassed the recommended minimum threshold of 0.50, and each extracted factor exhibited an eigenvalue exceeding 1.00, thereby supporting the validity of the underlying construct structure. The internal consistency of the constructs was assessed using Cronbach’s alpha. Following the guidelines proposed by Hair et al. (2014), a threshold value of 0.70 is considered acceptable. In the current study, the Cronbach’s alpha coefficients for all constructs ranged from 0.851 to 0.889, indicating a high level of reliability. Additionally, all items have Corrected Item-Total Correlation (CITC) scores above 0.30, supporting their retention within their respective scales. Based on these results, both the validity and reliability of the constructs are established, justifying the use of summative scales in subsequent analyses.

5.2. Correlations

Table 5. Correlations, Means, and Standard Deviations for the variables.

	CSS	COMP	SIZE	CS	ICI	DIV	STRA	ITI	OLC	AGE	JT	EDU
CSS	1
COMP	0.168 *	1
SIZE	0.385 **	0.004	1
CS	0.388 **	0.168 *	0.113	1
ICI	0.417 **	0.040	0.211 *	0.318 **	1
DIV	0.317 **	0.093	0.708 **	0.091	0.205 *	1
STRA	−0.059	0.108	−0.199 *	0.044	0.056	−0.193 *	1
ITI	0.415 **	0.113	0.238 **	0.296 **	0.223 **	0.227 **	−0.057	1
OLC	0.215 *	0.034	0.151	0.107	0.167 *	0.125	0.150	0.049	1
AGE	−0.224 **	−0.043	−0.136	−0.108	−0.074	−0.060	0.168 *	−0.028	−0.120	1
JT	−0.280 **	−0.104	−0.133	−0.127	−0.120	−0.049	0.137	−0.102	−0.137	0.783 **	1
EDU	0.214 *	0.192 *	0.290 **	0.162	−0.019	0.242 **	−0.257 **	0.152	0.052	−0.350 **	−0.266 **	1
Mean	2.98	3.28	4.82	41.73	3.05	2.38	2.87	2.45	0.63	42.07	11.84	0.85
Std. Dev.	0.91	0.70	1.54	13.52	0.84	0.58	1.22	0.97	0.48	8.37	6.71	0.36

*, ** indicates significance at 5% and 1% levels, respectively.

presents the Pearson correlation matrix along with descriptive statistics for the study variables. All independent variables—except for the variable STRA—exhibit statistically significant relationships with the dependent variable CSS and display the expected signs as hypothesized. The results provide empirical support for theoretical claims in the literature. Consistent with Auzair and Langfield-Smith (2005), firm size tends to expand as firms advance through the successive stages of the organizational life cycle. The statistically significant negative correlation between business strategy (STRA) and firm size (SIZE) (r = −0.199, p < 0.05) suggests that cost leadership strategies are more frequently adopted by larger hotel firms. This observation supports earlier findings by Jermias (2008) and Pavlatos and Paggios (2009). Additionally, a significant positive correlation was found between integrated information technology (ITI) and firm size (SIZE) (r = 0.238, p < 0.05), in line with Maiga et al. (2014), indicating that the implementation of integrated information systems requires the necessary resources and infrastructure more readily available in larger firms. A similar positive correlation was identified between SIZE and ICI (r = 0.211, p < 0.05), consistent with Hoque (2004), suggesting that as firms grow, reliance on cost information in decision-making intensifies. Lastly, a statistically significant positive relationship was found between the organizational life cycle (OLC) and ICI (r = 0.167, p < 0.05), confirming that firms in the maturity or revival stages of their life cycle tend to place greater importance on cost information (Silvola, 2008).

5.3. Regression Results

Prior to performing the multiple regression analysis, all underlying statistical assumptions were systematically evaluated to ensure the validity of the model. To assess the assumptions of linearity and homoscedasticity, scatter plots of standardized residuals against predicted values were examined. The visual inspection revealed no indication of violation, thereby confirming that these assumptions were met. The assumption of normality was tested through the evaluation of skewness and kurtosis coefficients. The results indicated that the distribution of the residuals did not deviate substantially from normality, supporting the appropriateness of the regression analysis. To examine the independence of observations, the Durbin-Watson (D-W) statistic was calculated. The obtained value of 1.733 falls within the acceptable range, indicating the absence of autocorrelation and thereby satisfying the assumption of independent residuals. Additionally, multicollinearity among the independent variables was assessed using the variance inflation factor (VIF) and tolerance (TOL) statistics. TOL values ranged from 0.355 to 0.908, while VIF values ranged from 1.101 to 2.817, both within acceptable thresholds. These results confirm that multicollinearity does not pose a concern for the regression model. In conclusion, the diagnostic checks support the adequacy of the data for multiple regression analysis, with all relevant assumptions satisfactorily fulfilled.

Based on the results of multiple regression analysis (

Table 6. Regression Results for Cost System Sophistication.

	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity
	B	Std. Error	Beta			Tolerance	VIF
Constant	0.401	0.607		0.659	0.511
COMP	0.104	0.092	0.080	1.127	0.262	0.887	1.127
SIZE	0.118	0.058	0.199	2.021	0.045	0.459	2.177
CS	0.012	0.005	0.178	2.393	0.018	0.808	1.238
ICI	0.248	0.080	0.228	3.085	0.002	0.815	1.228
DIV	0.041	0.152	0.026	0.268	0.789	0.474	2.110
STRA	−0.012	0.055	−0.016	−0.219	0.827	0.835	1.197
ITI	0.213	0.069	0.227	3.102	0.002	0.836	1.196
OLC	0.172	0.132	0.091	1.301	0.195	0.908	1.101
AGE	−0.003	0.012	−0.027	−0.242	0.810	0.355	2.817
JT	−0.018	0.015	−0.129	−1.176	0.242	0.370	2.706
EDU	0.059	0.200	0.023	0.296	0.767	0.726	1.377
R = 0.652; R2 = 0.425; Adj R2 = 0.375;
F = 8.651; Sig. = 0.000; D-W = 1.733

), the regression model is statistically significant. The F-statistic is 8.651, with an associated significance level of p = 0.000. This confirms the overall adequacy of the model in terms of its explanatory power and supports the existence of a statistically significant linear relationship between the independent variables and the dependent variable. The model accounts for 42.5% of the variance in the dependent variable (R² = 0.425), which exceeds the explanatory power reported in comparable studies (Pavlatos, 2012; Ismail & Mahmoud, 2012; Mazbayeva et al., 2021; Humeedat, 2020). Additionally, the adjusted R-squared value is 0.375, further indicating that the model possesses a satisfactory level of predictive accuracy.

The coefficient for competition (COMP) is positive (b₁ = 0.104), yet not statistically significant (p = 0.262 > 0.05), leading to the rejection of hypothesis H₁. This finding aligns with prior empirical studies that also found no significant relationship between competition and cost system complexity (Pavlatos & Paggios, 2009; Drury & Tayles, 2005; Ismail & Mahmoud, 2012). With respect to firm size (SIZE), the coefficient is positive (b₂ = 0.118) and statistically significant at the 5% level (p = 0.045 < 0.05), thereby supporting hypothesis H₂. This result corroborates existing theoretical perspectives and is consistent with earlier empirical findings (Drury & Tayles, 2005; Pizzini, 2006; Al-Omiri & Drury, 2007; Alexopoulou et al., 2024). Moreover, the coefficient for cost structure (CS) is positive (b₃ = 0.012) and statistically significant (p = 0.018 < 0.05), supporting H₃ at the 5% significance level. While earlier studies reported mixed or non-significant results (Drury & Tayles, 2005; Al-Omiri & Drury, 2007; Ismail & Mahmoud, 2012), the current finding is theoretically grounded and offers empirical support for a positive association between cost structure and cost system sophistication.

Regarding the variable importance of cost information for decision-making (ICI), the coefficient is both positive and highly statistically significant (b₄ = 0.248, p = 0.002 < 0.01), confirming hypothesis H₄ at the 1% significance level. This finding is consistent with previous empirical studies (Al-Omiri & Drury, 2007; Pavlatos & Paggios, 2009; Ismail & Mahmoud, 2012; Kuzey et al., 2019; Humeedat, 2020). The coefficient for Service Diversity (DIV) is positive (b₅ = 0.041) but not statistically significant (p = 0.789 > 0.05), leading to the rejection of hypothesis H₅. This outcome contrasts with some theoretical predictions, though it is consistent with several empirical studies that similarly reported non-significant results (Al-Omiri & Drury, 2007; Pavlatos & Paggios, 2009; Ismail & Mahmoud, 2012; Humeedat, 2020). For business strategy (STRA), the coefficient is negative (b₆ = −0.012) and not statistically significant (p = 0.827 > 0.05), leading to the rejection of H₆. While this contradicts most earlier findings (e.g., Pavlatos & Paggios, 2009), it is consistent with the empirical results reported by Kuzey et al. (2019), indicating a potential context-specific relationship that warrants further investigation.

The coefficient for information technology integration (ITI) is positive (b₇ = 0.213) and statistically significant (p = 0.002 < 0.01), thus supporting hypothesis H₇. This result aligns well with both theoretical assertions and previous empirical findings, particularly those of Kuzey et al. (2019), emphasizing the enabling role of information technology in cost system sophistication. In relation to the organizational life cycle (OLC), the coefficient is positive (b₈ = 0.172) but not statistically significant (p = 0.195 > 0.05), and therefore H₈ is rejected. This suggests that the maturity or revival stages of firms do not significantly influence the complexity of cost accounting systems. However, based on a supplementary t-test, the sophistication level of cost systems during these two stages is significantly higher (t = −2.600, p = 0.010).

Finally, regarding the influence of top management characteristics, derived from upper echelons theory, all three related hypotheses (H₉, H₁₀, and H₁₁) are rejected. Specifically, the coefficient for age (AGE) is negative (b₉ = −0.003, p = 0.810 > 0.05), for job tenure (JT) is also negative (b₁₀ = −0.018, p = 0.242 > 0.05), and for education (EDU) is positive (b₁₁ = 0.059, p = 0.767 > 0.05). These results are in line with the findings of Pavlatos and Ioakimidis (2024) but also inconsistent with most of the prior empirical evidence in the management accounting literature (Naranjo-Gil & Hartmann, 2007; Naranjo-Gil et al., 2009; Pavlatos, 2012; Ahmed & Elmassri, 2025). Nonetheless, the direction of the coefficients is in line with theoretical expectations, suggesting that while no statistically significant relationships were observed, further research is necessary to explore the nuanced effects of managerial characteristics on the design of cost systems.

6. Conclusions

As Schoute and Budding (2017) noted, a critical issue in the management accounting literature is the extent to which the design of cost accounting systems aligns with the specific characteristics of the environment in which firms operate. Despite the relevance of this relationship, prior empirical findings remain inconsistent. One potential explanation for this inconsistency is the predominantly dichotomous approach often adopted by researchers when classifying cost accounting systems, which may oversimplify the sophistication inherent in their design (Drury & Tayles, 2005). Therefore, this study aimed to explore the determinants of cost system sophistication in Greek hotels, drawing on theoretical underpinnings from contingency theory and the upper echelons theory and using key features of cost system design. The empirical results offer several insights into how both contextual and organizational factors shape the design and sophistication of cost systems.

The findings of this study indicate that the firm size, a higher proportion of indirect costs relative to total costs, the importance of cost information for decision-making, and the level of integration of information technology constitute significant predictors of cost system sophistication among the surveyed hotel enterprises. First, firm size was found to have a positive and statistically significant effect on cost system complexity. The extensive range of activities required to produce and deliver differentiated products and services, alongside the availability of advanced technological and human resources typically found in larger firms, appears to necessitate the implementation of more sophisticated cost accounting systems. Furthermore, a high ratio of indirect to total costs intensifies the need for advanced cost allocation mechanisms to mitigate potential inaccuracies and misallocations. Although earlier studies produced mixed evidence on this relationship, the present findings highlight its relevance in the hotel industry, where the differentiation of direct and indirect costs is critical for decision-making. The statistically significant influence of firm size and cost structure corroborates prior assertions in the literature that larger firms require cost systems of greater sophistication to ensure accurate cost determination.

In relation to the importance of cost information in managerial decision-making, the extent to which decision-makers rely on such data to make decisions concerning products, services, and customers significantly contributes to the design of more complex and accurate cost systems. Moreover, this finding underscores the strategic role of cost information in guiding managerial actions and resource allocation. The study also identifies information technology integration as a key enabler of advanced cost techniques, enhancing operational and non-financial information availability, accuracy, and timeliness. These technological capabilities support greater system complexity by enhancing both the precision of cost data and the efficiency of reporting processes.

In contrast, variables including competition, service diversity, and business strategy did not exhibit a statistically significant effect on cost system sophistication. Although the extant literature posits that firms operating in highly competitive markets require accurate cost information to inform pricing and strategic decisions (Chen et al., 2001; Patiar, 2016), most empirical studies do not substantiate a statistically meaningful relationship. As suggested by Drury and Tayles (2005), if simplified cost systems are sufficient to meet informational needs under conditions of competitive pressure, then there may be little incentive for firms to pursue further complexity. Similarly, service diversity was not found to be a significant predictor of cost system complexity. This suggests that while the provision of a broad portfolio of services may generate informational demands, such demands do not necessarily translate into more complex cost systems unless accompanied by the requisite organizational resources—resources which, as the findings indicate, are more prevalent in larger firms. The implication is that complexity in service offerings alone is insufficient to drive system sophistication without adequate infrastructural and human capital support. Additionally, although a positive relationship was observed between cost leadership strategy and system complexity, business strategy, in this study, did not function as a robust predictive factor of cost system complexity. However, the findings confirm the assertion that accurate cost information is particularly essential when firms employ a cost leadership strategy (Lucas & Rafferty, 2008). These results suggest that competition, service diversity, and business strategy may not directly influence the complexity of cost systems in the hotel industry, reflecting the specificities of the sector, or that their effects are mediated or moderated by other factors.

Another interesting finding concerns the organizational life cycle. The regression analysis did not identify a statistically significant effect of the maturity and revival stages on cost system complexity. Nonetheless, supplementary statistical tests (t-tests) indicate that cost system complexity is significantly higher during these stages. This suggests a potential shift toward more formalized and elaborate cost control mechanisms as organizations evolve and seek to revitalize operations. Given the limited prior empirical research on this dimension, further investigation is needed to validate and extend these preliminary insights.

Lastly, the findings related to top management characteristics (age, tenure, and education) were not statistically significant. These results diverge from earlier studies suggesting that individual managerial attributes may play a limited role in influencing cost system complexity in this context. However, the direction of the coefficients was consistent with upper echelons theory, indicating that further research is warranted to explore these relationships more deeply, potentially using qualitative methods or refined measurement tools. A plausible explanation for these non-significant findings may lie in the possibility that some financial and accounting executives within the sample do not participate directly in cost system design or strategic decision-making, thereby limiting the observable effect of individual attributes on cost system complexity. Furthermore, these results advocate the claim that the assumptions of upper echelons theory may prove inadequate in dynamic environments, where a variety of factors beyond the managerial characteristics influence decision-making (Chishamba, 2024).

These findings should also be interpreted considering the Greek institutional and cultural context, which may shape the cost system design. The Greek business environment is characterized by economic volatility and regulatory complexity. These factors may lead to a more compliance-focused approach to accounting, influencing firms to adopt more sophisticated cost systems to enhance cost control. Moreover, the dominance of family-owned firms in the Greek hospitality sector may limit the development of complex cost systems. Therefore, there is a need for research in other countries since contextual specificities will help explain variations in cost system sophistication.

In conclusion, the results of the multiple regression analysis showed that the predictors explain significantly the overall variation in the degree of cost system sophistication, validating the appropriateness of variable selection based on contingency theory and upper echelons theory. The design and implementation of complex costing systems is influenced by the need for detailed costing of services provided for the purpose of decision-making, by the costing structure characterized by high indirect costs, by the provision of the necessary resources of the enterprise, and by the integration of information systems. Finally, the fact that several independent variables were found not to be statistically significant indicates that some of them may be either mediators or moderators in this relationship.

This study makes several important contributions to the management accounting literature. First, it responds to Hiebl’s (2014) call for integrating multiple theoretical frameworks to improve explanatory robustness. By combining insights from contingency theory and upper echelons theory, the research develops a multifaceted understanding of the determinants influencing cost system sophistication. This theoretical integration enhances the interpretive depth of the findings and provides a more holistic view of how contextual factors and managers’ demographics shape system design. Second, the study addresses a notable empirical gap by focusing on the hotel industry—an underexplored service context in cost accounting research—and responds to the calls by Hadid and Hamdan (2022), Banhmeid and Aljabr (2023), and Alexopoulou et al. (2024) for further examination of cost system determinants. The sector-specific approach adds contextual richness and contributes to a growing stream of research examining management accounting practices in service-based industries. Third, the study advances the literature methodologically by moving beyond the prevailing dichotomy between ABC and traditional cost systems. It employs a multidimensional construct of cost system sophistication, capturing a broader range of complexity attributes and allows for a more comprehensive and nuanced assessment of system design choices in the hospitality sector. In summary, the study extends the literature on management accounting within service contexts by confirming, refining, and challenging previous findings while providing a solid foundation for future research.

The findings of this study offer several practical implications for accounting and financial executives and policymakers within the hotel industry, particularly concerning the design and implementation of cost accounting systems. First, the significant effect of firm size on cost system sophistication indicates that larger hotel organizations should prioritize the development of more advanced costing systems. As operational scale and complexity increase, larger hotels are encouraged to invest in more sophisticated cost systems that can effectively meet their growing reporting and decision-making needs. Second, the positive relationship between the proportion of indirect costs and cost system sophistication underscores the necessity of aligning cost system design with a hotel’s cost structure. Given the prevalence of overheads in the hotel industry, the inability to accurately allocate indirect costs can result in pricing distortions and inefficient resource allocation. Accordingly, hotel financial and accounting executives should consider implementing advanced cost systems that better capture the cost behavior across services and departments. Third, the positive association between information technology integration and system sophistication highlights its growing role in management accounting systems. The integration of IT systems enhances the availability, accuracy, and timeliness of cost-related data, thereby facilitating decision-making processes. Moreover, the lack of significant effects from managerial demographic characteristics (e.g., age, job tenure, education) suggests that cost system design is primarily driven by structural and operational factors rather than individual managerial traits. This implies that employee training, standardization, and organizational support may be more effective mechanisms for implementing sophisticated cost systems. Finally, the findings underscore the importance of adapting cost systems to the specific operational context and strategies of hotels, thereby contributing to improved managerial decisions and organizational performance.

7. Limitations and Suggestions for Further Research

While the present study provides valuable insights into the determinants of cost system sophistication within the hotel industry, several limitations must be acknowledged, which also open avenues for future research. In an effort to mitigate potential biases arising from conducting research across multiple industries and to enhance the internal validity of the analysis, this study focused specifically on the hotel industry. Although this contributes to the depth and contextual richness of the analysis, it limits the generalizability of the findings to other sectors. Comparative studies across industries could reveal whether the identified relationships are more broadly applicable.

One potential limitation of this study involves endogeneity, particularly the possibility of reverse causality between cost system sophistication and IT integration. While the theoretical framework posits that IT integration facilitates the development and implementation of more sophisticated cost systems, it is also plausible that the adoption of advanced cost systems may, in turn, drive greater investment in IT capabilities. This bidirectional relationship, also suggested by Yigitbasioglu (2017), should be explored further in future research. In addition to addressing endogeneity concerns, future research should consider examining IT integration as a potential moderating variable. In addition to its direct effect on cost system sophistication, IT integration may interact with other contextual variables, potentially affecting their impact.

Even though the research findings are theoretically grounded, the relationships among the variables may evolve over time. Therefore, future studies could benefit from employing longitudinal research designs. Furthermore, the statistical insignificance of the relationship between managerial demographics and cost system sophistication represents another limitation of this study. Future research should delve deeper into this relationship, as upper echelons theory provides a valuable lens through which to interpret the effect of managerial characteristics on cost system design. Additionally, since some independent variables were found to have a non-significant effect on cost system sophistication, the use of more advanced statistical techniques, such as structural equation modeling (SEM), could help uncover any underlying causal relationships (e.g., mediation or moderation effects).

Finally, while this study incorporates variables from contingency and upper echelons theories, other contextual factors—such as organizational culture, regulatory environment, and environmental uncertainty—may further explain variations in cost system design. Future research should consider these factors to enrich the understanding of cost system sophistication and its determinants.