An investigation into the mixed reported adoption rates for ABC: Evidence from Australia, New Zealand and the UK

Int. J. Production Economics 135 (2012) 430–439

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An investigation into the mixed reported adoption rates for ABC: Evidence from Australia, New Zealand and the UK Davood Askarany a,n, Hassan Yazdifar b a b

Business School, School of Accounting and Finance, The University of Auckland, New Zealand University of Glasgow, Glasgow, G12 8QQ, Scotland, UK

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 February 2010 Accepted 21 August 2011 Available online 31 August 2011

An accumulated body of the literature confirms that the adoption of activity-based costing (ABC) can lead to a substantial improvement in organisational performance, productivity and profitability, and therefore encourages further adoption of the technique. However, studies investigating the diffusion of ABC have reported inconsistent and mixed results. This could cause uncertainty for many potential adopters of ABC (especially for those who follow the fashion and fads approaches) and influence their tendencies towards the adoption of ABC in the future. Addressing the diffusion process as a contextual factor, this study simultaneously investigates the adoption of ABC from the perspectives of different diffusion processes. Using two commonly adopted diffusion processes (the stages of adoption and the levels of adoption), this study examines the relationship between the reported adoption rates for ABC and the diffusion process approaches chosen to measure its adoption rates in three western countries: Australia, New Zealand and the UK. A similar questionnaire was used and more than 2000 qualified CIMA members (via a survey study and follow-up interviews) were targeted. The findings suggest a significant association between the reported adoption rates for ABC and the diffusion process approaches chosen to measure the adoption rates. The findings further suggest that the lack of a common understanding of ABC systems may have also contributed to the mixed reported adoption rates for ABC, as many ABC adopters have considered themselves adopters of traditional accounting systems by mistake (especially when they are dealing with ‘facility costs’ as one of the main cost hierarchies under ABC systems). & 2011 Elsevier B.V. All rights reserved.

Keywords: Activity-based costing Diffusion of innovation Contextual factors Adoption processes

1. Introduction The positive role of activity-based costing (ABC) in improving organisational performance, productivity and profitability is well demonstrated in the literature (Baykasoglu and Kaplanoglu, 2008; Ben-Arieh and Qian, 2003; Gunasekaran and Sarhadi, 1998; Kee, 2008; Qian and Ben-Arieh, 2008; Singer and Donoso, 2008; Tornberg et al., 2002; Tsai et al., 2008). However, research on the diffusion of ABC has produced inconsistent results, ranging from less than 10% up to 78% both within and between countries (Al-Omiri and Drury, 2007a; Baird, 2007; Baird et al., 2007; Cobb et al., 1993; Innes and Mitchell, 1991, 1995; Innes et al., 2000; Langfield-Smith, 1997; Pierce, 2004). This inconsistency could become more complex by the fact that some adopters of ABC decided to stop the implementation after a short period (Innes and Mitchell, 1991; Madison and Power, 1993). This situation could cast doubt on ABC’s capability as a suitable technique for improving organisational performance, productivity

n Corresponding author. Tel.: 64 9 9235785; fax: 64 9 3737406 or 64 93737444 or 64 9 3737019. E-mail address: [email protected] (D. Askarany).

0925-5273/$ - see front matter & 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2011.08.017

and profitability especially in organisations that follow the fashion and fads approaches (Abrahamson, 1991, 1996; Røvik, 1996) and might influence their willingness towards the adoption of ABC in the future. A number of metaphors like translation, imitation and fashion have been used to describe the processes by which new ideas (innovations) travel between the members of a social system (Røvik, 1996). As with the fashion perspective, the diffusion innovation theory (Rogers, 2003) has been used to describe the diffusion of innovations (including ABC) in organisations. This theory suggests that a wide range of contextual factors (such as organisational strategy, organisational culture, organisational structure, characteristics of innovations, communication channels, environmental factors, etc.) may have an impact on the diffusion of innovations (Adam and Fred, 2008; Al-Omiri and Drury, 2007a; Askarany and Yazdifar, 2009b; Berling, 2008; Englund and Gerdin, 2008; Qian and Ben-Arieh, 2008; Yazdifar et al., 2005). However, the results are not consistent. However, the majority of studies which have reported mixed results have either failed to provide a clear definition for ABC or used different adoption processes (e.g. considering ABC as a practice, see Cobb et al., 1993; Innes and Mitchell, 1991, or as a process, see Baird et al., 2004; Gosselin, 1997). So, it is not clear

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if these studies have examined and measured the same thing. This is the main impetus for the current study to examine if there is a relationship between the adopted diffusion processes and the reported diffusion rates for ABC. No study has been reported to examine the above relationship; therefore, current study is the first to address this gap in the literature. Addressing the gap in the literature identified above, this study simultaneously investigates the diffusion of ABC both as a ‘practice’ and as a ‘process’ through two different diffusion approaches in three western countries: Australia, New Zealand and the UK. It targets (more or less similar respondents) 2041 qualified members of Chartered Institute of Management Accountants (CIMA) and then examines the level of association between adopted diffusion processes and the reported diffusion rates for ABC. It also examines the level of association between the reported diffusion rates for ABC and the country in which ABC is implemented. The remainder of the paper is organised as follows: Section 2 provides a background to the research questions, followed by the diffusion of ABC from theoretical perspectives (Section 3). Section 4 explains the adopted research method, Section 5 discusses our empirical results and Section 6 concludes the study.

2. Background While an accumulated body of the literature highlights the important role of ABC in improving organisational performance, productivity and profitability, the adoption of ABC has been said to be inconsistent and relatively behind that of the traditional ones over the past two decades (Al-Omiri and Drury, 2007a; Baird, 2007; Baird et al., 2007; Cobb et al., 1993; Innes and Mitchell, 1991, 1995; Innes et al., 2000; Langfield-Smith, 1997; Pierce, 2004). This is despite the fact that the ABC literature provides convincing evidence for the significant role of ABC in improving organisational performance, productivity and profitability including the following:

 To help managers make important strategic business decisions

 

 

(Lin et al., 2001) or to assist them with: ‘cost reduction’ (Baykasoglu and Kaplanoglu, 2008; Charles and Hansen, 2008b,a; Homburg, 2005; Qian and Ben-Arieh, 2008; Satoglu et al., 2006; Thyssen et al., 2006). To facilitate optimal joint product mix decisions (Tsai et al., 2008); pricing, product mix and capacity expansion decisions (Kee, 2008). To offer cost-estimation models (Kingsman and de Souza, ¨ zbayrak et al., 2004; Qian and Ben-Arieh, 2008), to 1997; O provide more accurate product-cost information and to improve decision quality (Charles and Hansen, 2008b). To improve efficiency by identifying and eliminating areas of non-value added activities in supply chain processes (Whicker et al., 2009). To improve performance measurement systems (Kim et al., 1997) and the quality of the products’ profitability information (Pirttil¨a and Sandstrm, 1996) and to predict the economic consequences of production and processes’ actions (Salafatinos, 1996).

However, despite the above contributions of ABC to organisational performance (which lead to relatively high expectation and consistent adoption for ABC), research on the diffusion of ABC has reported mixed and generally low adoption rates (commonly less than 30% with a few exceptions of over 70% adoption rates) both in manufacturing and service sectors (Al-Omiri and Drury, 2007a; Askarany et al., 2010; Baird, 2007; Baird et al., 2007; Innes et al.,

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2000; Langfield-Smith, 1997; Pierce, 2004). There is also some evidence that a number of firms which had started to implement ABC decided to stop the implementation after a short period (Innes and Mitchell, 1991; Madison and Power, 1993). This situation may cast doubt on ABC’s capability as a suitable technique and might have an impact on the attitudes of potential adopters of ABC and reduce their willingness towards the adoption of the technique in the future. Given the above, investigations into the adoption of ABC have been popular during the past two decades. While many studies have investigated the impact of different contextual factors on the diffusion of ABC in different industries, the implemented adoption processes and the findings (especially in terms of adoption rates) have not been consistent (Al-Omiri and Drury, 2007a; Anderson, 1995; Askarany and Smith, 2004; Baird et al., 2007; LangfieldSmith, 1997; Yazdifar et al., 2008b). Until now, the speed and scope of the diffusion of ABC have been the major focus and concern in most of the published studies in the field of ABC (Al-Omiri and Drury, 2007b; Anderson and Young, 1999; Askarany, 2003; Baird, 2007; Gosselin, 1997; Langfield-Smith, 1997; Yazdifar et al., 2008a). One of the common messages of these published studies is that the adoption of ABC still lags behind those of traditional management accounting techniques. For example, Pierce (2004) reports that in terms of the extent of adoption of management accounting techniques in Ireland, the adoption of ABC ranks behind most traditional accounting techniques such as ‘budgeting’, ‘cost modelling’ and ‘product/service pricing’. As another example, Chenhall and Langfield-Smith (1998) have found that besides ABC, the level of adoption of most other new management accounting techniques in Australia were relatively lower than those of the traditional techniques. For instance, the ranking in terms of the extent of adoption of some of the new techniques were as follows: ‘activity-based costing ranked’ (24), ‘activity based management’ (21), ‘product life cycle analysis’ (20) and ‘target costing’ (27). Given the above, they found the following rankings in terms of the extent of adoption of some of traditional management accounting techniques: ‘analysis for budgeting and for planning financial position’ (1), ‘capital budgeting’ (2) and ‘performance evaluation using return on investment’ (3). Other studies have also reported relatively low adoption rates for ABC in Australia such as 10% (Warwick and Reeve, 1997), 12% (Booth and Giacobbe, 1997) and 28% (Askarany et al., 2007a). Likewise, the published studies on the diffusion of management accounting innovation in the UK depict a similar picture. For example, Abdel-Kader and Luther (2006) suggest that in terms of ranking, the adoption of ABC ranks 32nd in the British food and drinks industry. Similarly, another survey suggests that the adoption rate for ABC by UK organisations is still fairly low, at approximately 15% (Al-Omiri and Drury, 2007a). As with Australia and the UK, the adoption of ABC in New Zealand is reported to be low (Cotton et al., 2003a). Conducting a comparative analysis, Cotton et al. (2003a) report an average of a 20.3% adoption rate for ABC users (both in manufacturing and non-manufacturing sectors) in New Zealand and 17.5% for the UK companies. Recent studies on the diffusion of ABC in New Zealand indicate the adoption of ABC in New Zealand is still lower than 23% (Askarany et al., 2010). Nevertheless, some studies suggest that the majority of ABC users are still using traditional management accounting techniques. For example, Innes et al. (2000) report that while 17.5% of the UK’s largest companies have adopted ABC, only 6.2% have replaced their traditional accounting techniques with ABC and the rest of ABC users are using ABC alongside their traditional accounting techniques. This could imply that the adopters of ABC are not quite convinced that ABC is a perfect replacement for

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traditional management accounting techniques. This may discourage potential adopters of ABC who follow the fashion and fads approaches to replace their traditional accounting systems with ABC (Abrahamson, 1991, 1996; Røvik, 1996). Given the above, some studies suggest that any further significant increase in the number of ABC adopters is unlikely (Cotton et al., 2003a; Pierce, 2004). Other studies have gone even further and suggested that there has been a reduction in the adoption of ABC after the first decade of its introduction. For example, Innes et al. (2000) argued that from 1987 to 1994 there was a considerable growth (from nil to over 20%) in ABC adoption in the U.K.’s largest companies but this rate of growth had not been maintained. According to their findings, the adoption rate for ABC in the U.K.’s largest companies has dropped to 17.5% (from 20% in 1994) by 1999. However, while many published studies on the adoption of ABC have reported a relatively lower adoption rate for ABC (compared with those using traditional costing systems), the findings are inconsistent. The majority of reported adoption rates range from 10% to 30% with a few exceptions of over 70% (Al-Omiri and Drury, 2007a; Baird, 2007; Baird et al., 2007; Innes et al., 2000; Langfield-Smith, 1997; Pierce, 2004). For instance, the reported adoption rates for ABC in Australia vary from 10% (Warwick and Reeve, 1997) to 56% (Chenhall and Langfield-Smith, 1998) in almost the same period and up to 78% after 6 years (Baird et al., 2004). However, others reported an adoption rate of less than 30% for ABC in Australia at the same time that a 78% adoption rate was reported (Askarany et al., 2007a; Booth and Giacobbe, 1997; Clarke and Mia, 1995; Corrigan, 1996). It might be argued that such variations could be normal as some of the studies with the low adoption rates of 10% and 12% (such as those of Warwick and Reeve and Booth and Giacobbe) occurred in 1997, whereas Baird’s studies that reported higher adoption rates were published in 2004 and 2007. While this could be a legitimate argument for comparing studies that occurred in very different time periods, reporting very different adoption rates for the same countries in the same time-period somehow needs justification. For instance, different reported adoption rates of 10% (Warwick and Reeve, 1997) and 56% (Chenhall and Langfield-Smith, 1998) in almost the same time period (1997–8) in Australia followed by other quite different adoption rates of less than 20% (Askarany and Smith, 2004; Booth and Giacobbe, 1997; Clarke and Mia, 1995; Corrigan, 1996) to 78% (Baird et al., 2004) in the same country (Australia) in the same time period (2004) raise an important question: Are these studies examining the same thing and is there any relationship between adopted diffusion processes and the reported diffusion rates for ABC? A careful examination of the published results in the literature suggests that part of the above variation (mixed results) could be related to the following factors: using different diffusion process approaches for studying the adoption of ABC such as referring to ABC as a whole practice in some studies (Al-Omiri and Drury, 2007a; Anderson, 1995; Pierce, 2004) but breaking down ABC into a set of different processes and activities in other studies (Baird et al., 2004; Brown et al., 2004; Gosselin, 1997; Krumwiede, 1998); using different questionnaires, or targeting different populations with different understanding of ABC; or comparing the findings of different studies carried out in different periods. However, the majority of these studies fail to provide a clear explanation to show how they have measured the process of the diffusion of ABC. In other words, they have either failed to provide a clear definition or diffusion process for ABC or they have used different adoption processes (e.g. considering ABC as a practice, see Cobb et al., 1993; Innes and Mitchell, 1991, or as a process, see Baird et al., 2004; Gosselin, 1997). So, it is unclear if the targeted respondents had a common understanding regarding the

diffusion of ABC and whether these studies have examined and measured the same thing. What is clear is that they have produced inconsistent and mixed results for the adoption of ABC in different countries over the past two decades. Table 1 provides a summary of the variations for the diffusion of ABC in the three targeted countries (Australia, New Zealand and the UK) and some other countries for almost the past two decades (1991–2009). The main impetus for the current study is to examine if there is a relationship between adopted diffusion processes and the reported diffusion rates for ABC. No study has been reported to examine the above relationship; therefore, the current study is the first to address this gap in the literature. In this study we examine the diffusion of ABC both as a practice and as a process at the same time by targeting the same respondents. Furthermore, this study compares the adoption rates of ABC among the three surveyed countries; Australia, New Zealand and the UK to see whether (or not) there is any association between the diffusion of ABC and the countries where the diffusion of ABC is investigated. The following section discusses the diffusion of ABC from theoretical perspectives.

Table 1 A summary of the diffusion of ABC during the past two decades. Author, date

Adoption rate (%)

Country

Drury and Tayles (2000) Cobb et al. (1993) Innes and Mitchell (1991) and Innes and Mitchell (1995) Warwick and Reeve (1997) Nicholls (1992) Cinquini et al. (1999) Armitage and Nicholson (1993) Chen et al. (2001) Sartorius et al. (2007) Clarke et al. (1999) Chongruksut (2002) Booth and Giacobbe (1997) Corrigan (1996) Groot (1999) Nguyen and Brooks (1997) Clarke and Mia (1995) Armitage and Nicholson (1993) Al-Omiri and Drury (2007a,b) Askarany and Yazdifar (2009a,b) Cotton et al. (2003a,b) Innes et al. (2000) Groot (1999) APQC/CAM (1995) Askarany and Smith (2004) Kip and Augustin (2007) Hosseini et al. (1997) Joshi (2001) Cotton et al. (2003a,b) Innes and Mitchell (1995) Kip and Augustin (2007) Askarany and Yazdifar (2009a,b) Askarany and Yazdifar (2009a,b) Pavlatos and Paggios (2009) Ittner et al. (2002) Fawzi (2008) Pierce and Brown (2004) Askarany et al. (2007a,b) Hosseini et al. (1997) Maelah and Ibrahim (2007) ¨ Bjornenak (1997) Cohen et al. (2005) Gosselin (1997) Yakhou and Dorweiler (1995) Duffy and McCahey (2002) Chenhall and Langfield-Smith (1998) Baird et al. (2004)

4 6 6

UK UK UK

10 10 10 11 11 11.60 11.80 11.90 12 12 12 12.50 13 14 15 15.20 17.50 17.50 17.70 18 19 19 20 20 20.30 21 21 22.50 23.40 23.50 26 26.30 27.90 28 28 36 40 40.90 47.8 48 55 56 78

Australia UK Italy US Hong Kong South Africa Ireland Thailand Australia Australia Netherlands Australia Australia Canada UK UK UK UK US US Australia Germany Canada India New Zealand UK US New Zealand Australia Greek US Ireland Ireland Australia US Malaysia Norway Greek Canada UK Ireland Australia Australia

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3. The diffusion of ABC from theoretical perspectives According to diffusion theory (Rogers, 2003), an innovation can be an idea, practice, or object that is perceived as new by an individual or other unit of adoption. Likewise, Damanpour and Gopalakrishnan (1998) define innovation as ‘the adoption of an idea or behaviour new to the organisation’. Wolfe (1994) explains the diffusion of an innovation as a way the new ideas are accepted (or not) by those to whom they are relevant. Rogers (2003) extends this definition to consider diffusion as a process by which an innovation is communicated through certain channels over time among the members of a social system. The members of a social system could be organisations, societal sectors or nations. According to Rogers (2003), the diffusion of an innovation in a particular population is usually measured by its rate of implementation. Diffusion rates are often measured in terms of the proportion of firms using a new technique (an innovation) as compared with those using the old ones. As with the diffusion of innovation theory (Rogers, 2003), imitation, fashion and fads have also been introduced to explain the diffusion process of innovations (Abrahamson, 1991, 1996; Røvik, 1996). Røvik (1996) introduces ‘fashion’ as an influencing factor, which can play an important role in the diffusion process of an innovation. He argues that the process of the diffusion of an innovation follows a selective perception, which adjusts to the social environment and copes with what is in fashion and what is out of fashion. This implies that usually the innovations chosen are those which seem to be more fashionable. According to Røvik (1996), ‘fashion’ is a human made and dynamic phenomenon that spreads by drawing attention to it. Fashion can present itself in many ways: as ideas, social organisations, specific structures and processes in organisations, etc. Røvik refers to fashion as an institutionalised standard for implementing a new idea, and change/innovation, in order to organise successfully, be up-todate and be efficient. According to Røvik (1996, p.159), fashion also refers to the notion that organisations are torn between ‘‘signalling a common identity and belonging to a group of organizations’’ and ‘‘the motive of distinguishing themselves from the other organisations and attracting attention’’. From this perspective, fashionable ideas and innovations/changes spread by imitation, but, after a while, they will be so common, that some organisations may wish to demonstrate their uniqueness by developing new ideas or implementing new innovations, which in turn become fashionable, and so the process starts all over again. The fashion perspective leads to imitating certain technologies promoted by ‘fashion-setting organisations’ or ‘fashion setters’, such as consultants, irrespective of whether or not such technologies are efficient (Malmi, 1999). Finally, the fad perspective explains that innovations are adopted for legitimacy rather than rational purposes. In line with the above argument, we can assume that some organisations may follow fashion and fads approaches in pursuing ABC adoption. In this case, the low level of acceptance and the low diffusion rate of ABC may have an impact on its further diffusion in organisations, especially for organisations that follow fashion and fads approaches (Abrahamson, 1991, 1996; Røvik, 1996). So, from this perspective, an investigation into the mixed and relatively low adoption rates of ABC in organisations may contribute to the further diffusion of ABC in practice. Proposing the diffusion of innovation theory, Rogers (2003) identifies a wide range of contextual factors influencing the diffusion of innovations in organisations. Addressing the major contextual factors identified by Rogers, Askarany (2003) classifies all influencing factors into three main categories: characteristics of innovations, characteristics of adopters, and factors external to both innovations and adopters (such as social and environmental factors).

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Adopting the diffusion of innovation theory, an accumulated body of literature has examined and confirmed the impact of different contextual factors on the diffusion of management accounting innovations including ABC (Adam and Fred, 2008; Al-Omiri and Drury, 2007a; Askarany and Yazdifar, 2009b; Berling, 2008; Englund and Gerdin, 2008; Qian and Ben-Arieh, 2008; Yazdifar and Askarany, in press). According to the diffusion of innovation theory (Rogers, 2003), there are a number of contextual factors which could influence the diffusion of innovations: characteristics of innovations (e.g. relative advantage, complexity, compatibility, observability and trialibility of innovation/s), characteristics of adopters (e.g. organisational structure, organisational culture, organisational strategy, etc.) and other influential factors related to a particular country or environment such as the level of development of the country, local regulations and policies, communication channels, roads, networks, etc. Given the above, it can be argued that the variation of the above factors in different countries or societies may influence the diffusion of ABC and lead to different adoption rates for ABC in different countries. So, in line with the diffusion of innovation theory, we may suggest the following proposition: There is a significant association between the reported adoption rates for ABC and the country where the diffusion of ABC is investigated. Studies investigating the diffusion of ABC have used different approaches to examine the status and the extent of the diffusion of ABC in organisations (Askarany and Yazdifar, 2009b; Baird, 2007; Chenhall and Langfield-Smith, 1998; Gosselin, 1997; Innes and Mitchell, 1991; Yazdifar and Askarany, 2009). Some studies have examined the level of the diffusion of ABC based on the purpose for which it has been used: ‘activity analysis’, ‘activity cost analysis’ and the allocation of costs to costs objects (Baird et al., 2007, 2004; Gosselin, 1997). Some studies have examined the extent of the adoption of ABC based on the following scales: ‘relatively high adoption’, ‘relatively moderate adoption’ and ‘relatively low adoption’ (Chenhall and Langfield-Smith, 1998), or ‘small extent’, ‘moderate extent’ and ‘great extent’ (Baird, 2007; Baird et al., 2007), Other studies have used further scales to examine the status and the extent of the diffusion of ABC in organisations including: ‘adoption’ and ‘implementation’ processes (Gosselin, 1997; Rogers, 2003); ‘initiation’, ‘adaption’, ‘adoption’, ‘acceptance’ processes (Anderson, 1995; Cooper and Zmud, 1990); ‘not considered’, considering’, ‘considered then rejected’, ‘approved for implementation’, ‘analysis’, ‘getting acceptance’, ‘implemented then abandoned’, ‘acceptance’, ‘routine systems’ and ‘integrated system’ (Krumwiede, 1998); ‘no consideration/decision or introduction’, ‘decided not to adopt or decided to reject’, ‘considering adoption in the future or given some consideration into the introduction of ABC’, ‘implemented on a trial basis’ and ‘implemented and accepted’ (Al-Omiri and Drury, 2007a; Askarany et al., 2007a; Pierce, 2004). Given the above, we can classify all implemented diffusion processes in the literature (for examining the diffusion of ABC) into two main groups: studies that look at ABC as one whole practice (Al-Omiri and Drury, 2007a; Anderson, 1995; Pierce, 2004) and studies that look at ABC as a process or as a set of events or activities (Baird et al., 2007, 2004; Gosselin, 1997) and therefore identify two diffusion approaches. Depending on the selected approach, the diffusion of ABC follows different steps. Considering the diffusion of ABC as a practice may include studies which use any of the following stages or scales addressed in the literature: ‘implementation’, ‘initiation’, ‘adaption’, ‘adoption’, ‘acceptance’, (Anderson, 1995; Cooper and Zmud, 1990)‘not considered’, considering’, ‘considered then rejected’, ‘approved for

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implementation’, ‘analysis’, ‘getting acceptance’, ‘implemented then abandoned’, ‘used as routine systems’, ‘used as integrated system’, ‘decided not to adopt’, or ‘decided to reject’, ‘considering adoption in the future’, or ‘given some consideration into the introduction of ABC’, ‘implemented on a trial basis’ and ‘implemented and accepted’. Considering the diffusion of ABC as a process (as a sequence of events) does not look at the adoption of ABC as a ‘yes’ or ‘no’ option but makes a distinction between full adoption and partial adoption of ABC by dividing the adoption process of ABC into a few levels. The main commonly adopted diffusion levels reported in the literature for this approach can be summarised as follows: activity analysis (AA), which is the first level used for identifying the activities and procedures performed to make the final products/services; activity cost analysis (ACA), which is the second level used for identifying the costs of each activity and cost drivers and finally activity-based costing (ABC), which is the allocation of the costs of different activities and cost hierarchies to cost objects (Baird et al., 2007, 2004; Gosselin, 1997). Considering the diffusion of ABC as a process, Baird et al. (2004) report relatively higher adoption rates for all three levels of ABC in private sectors in Australia as follows: 86.2% for AA (activity analysis) users, 82.1% for ACA (activity cost analysis) users and 78.1% for ABC users. In a comparative study, Baird (2007) reports similarly higher adoption rates for all three activity levels of ABC in public sectors in Australia. However, considering the diffusion of ABC as a practice (looking at ABC as one whole practice), some studies have (generally) recorded relatively much lower adoption rates for ABC, such as 10% (Warwick and Reeve, 1997), 12% (Booth and Giacobbe, 1997) and 28% (Askarany et al., 2007a). Furthermore, as discussed earlier, while some studies report a positive trend (an increase) in the adoption rates for ABC over the years, other studies suggest the opposite (Innes and Mitchell, 1991, 1995; Innes et al., 2000). The literature review presented in this study highlights the possible impact of using different diffusion process models on the reported adoption rates of ABC and suggests further investigation to examine the level of association between the reported adoption rates for ABC and the implemented diffusion models. However, no study has been reported to examine the above relationship; therefore, the current study is the first to address this gap in the literature. Given the above, we may suggest the following proposition: There is a significant association between the reported adoption rates for ABC and the adopted diffusion approaches to investigate the diffusion of ABC.

4. Research method A survey questionnaire was mailed to 2041 registered CIMA members in Australia, New Zealand and the UK in 2007 (1175 in Australia, 366 in New Zealand and 500 in the UK) followed by 56 interviews. The selection of the total number of CIMA members for each country and also the selection of each individual member in each country was based on the total numbers of registered and qualified CIMA members in each country who were working in managerial accounting sections of organisations in 2007. The head office of CIMA in the UK provided the authors with a list of names and addresses of qualified and registered CIMA members who were working in a managerial accounting position in Australia, New Zealand and the UK in 2007. Hard copies of similar questionnaires were sent out to all targeted members in these three countries followed by a general announcement on CIMA website (after three weeks) encouraging those CIMA members who had received the hard copies of the questionnaires but did

not complete them to complete an online version of the questionnaire. Although the limitations of surveys are well documented in the literature (Birnberg et al., 1990; Runkel and McGrath, 1972; Young, 1996), a survey instrument was considered appropriate at this first stage as it could provide the large amount of crosssectional data needed for this study. It was followed by interviewing those who expressed their willingness for this in the survey. The interviews aimed at eliminating uncertainty, validating responses and examining answers to open ended questions in detail, as well as gathering additional qualitative interpretations. The respondents were the CIMA qualified management accountants who had expressed their interest in participating in an interview by checking a box in the questionnaire and provided the researchers with their contact details. Consequently, 56 interviews were conducted with CIMA members: 34 in Australia, 13 in New Zealand and 9 in the UK (face-to-face and over the phone interviews). These interviews took place in 2008 and 2009. The comments received from respondents to the initial, openended questions drew our attentions to an important, but unexplored issue in the ABC literature (e.g. the misunderstanding regarding the concept of ABC). Although semi-structured questions were set, the interviews took a flexible form along with follow-up questions aimed at clarifying some of the practices. All but six of the interviews lasted between 1 and 2 h. For validity purposes, these were also followed-up by some telephone calls and emails to clarify some issues that had emerged subsequently. Apart from three, all the interviews were tape recorded with the permission of the interviewees, and then transcribed (confidentiality was assured both externally and internally). Examining the diffusion process approaches, the two most commonly used diffusion processes in the ABC literature that were identified and implemented follows: the stages of diffusion and the levels of diffusion.

 The stages of diffusion approach looks at ABC as one whole



practice and examines the diffusion of ABC through different stages such as ‘decided not to use ABC’, ‘decided to use ABC’, etc. (Al-Omiri and Drury, 2007a; Anderson, 1995; Pierce, 2004). The levels of diffusion approach looks at ABC as a process or a set of different activity levels such as AA, ACA and ABC (Baird et al., 2004; Gosselin, 1997; Krumwiede, 1998).

Under the first diffusion approach, respondents were asked to indicate the extent to which ABC was used in their organisations, using a 5-point Likert-type scale (Abdel-Kader and Luther, 2006; Innes et al., 2000) as follows: with anchors of 1. ‘‘discussions have not taken place regarding the introduction of ABC’’; 2. ‘‘a decision has been taken not to introduce ABC’’; 3. ‘‘some consideration is being given to the introduction of ABC in the future’’; 4. ‘‘ABC has been introduced on a trial basis’’ and 5. ‘‘ABC has been implemented and accepted’’. Under the second diffusion approach, the respondents were asked to identify the level of the adoption of ABC in their organisations as follows: activity analysis—AA (identification of activities and procedures performed in their organisations to make the final products/services); activity cost analysis—ACA (identification of cost drivers and allocation of costs to cost pools) and activity-based costing—ABC (allocation of costs of activities to products/services). However, we have also provided respondents with a definition for ABC as follows: an approach to costing that focuses on activities as the fundamental cost objects and uses the cost of these activities as the basis for assigning costs to other cost objects such as products, services, or customers.

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5. Findings The final number of useable survey responses (both hard copies and online replies) was 584 for all three countries (310 completed questionnaires plus 88 not-completed or not delivered for Australia; 142 completed questionnaires plus 10 not-completed or not delivered for New Zealand and 132 completed questionnaires plus 45 not-completed or not delivered for the UK). The final completed questionnaires provided the authors with the satisfactory response rates of 28.5%, 39.5% and 29% for Australia, New Zealand and the UK, respectively. According to Krumwiede (1998), the normal response rate for these surveys is approximately 20% though there are many published surveys with lower response rates such as 12.5% (Brown et al., 2004) or 19.6% (Al-Omiri and Drury, 2007a). Non-response bias was examined both using the aggregated data provided by CIMA (such as the total number of CIMA members working in manufacturing and non-manufacturing organisations, the average length of experiences of CIMA members and their average ages as qualified CIMA members) and comparing them with the same information gathered by the surveys, and through a comparison between early and late responses. The former showed responses to be representative, the latter that there was no perceived difference between these responses, suggesting that non-response bias would not influence the outcomes. Besides using two different diffusion processes at the same time, the study used a similar questionnaire (for all 3 surveyed countries) and targeted similar respondents. 5.1. The diffusion of ABC based on stages model Using the first diffusion approach, the diffusion of ABC in all three targeted countries: Australia, New Zealand and the UK, is shown in Table 2 under the following stages: ‘discussions have not taken place regarding the introduction of ABC’; ‘a decision has been taken not to introduce ABC’; ‘some consideration is given to the introduction of ABC’; ‘ABC has been introduced on a trial basis’ and finally ‘ABC has been implemented and accepted’. According to Table 2, a significant number of organisations in all three surveyed countries have not even discussed the introduction of ABC in their organisations (39.6% in Australia, 45.1% New Zealand and 34.8% in the UK). Furthermore, the findings show a considerable number of organisations have decided not to introduce ABC in their organisations (13.0% in Australia, 15.5% in New Zealand and 18.2% in the UK). The sum of the two figures shows that the adoption of ABC has not been an option for the majority of organisations (more than 50%) in all three surveyed countries. However, some organisations have given some consideration to the introduction of ABC in their organisations (18.8% in Australia, 12.7%

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in New Zealand and 19.7% in the UK), and some introduced it on a trial basis (5.2% in Australia, 4.2% in New Zealand and 12.1% in the UK). Nevertheless, the percentage of organisations which have implemented and accepted the adoption of ABC in their organisations is still low (23.4% in Australia, 22.5% in New Zealand and 15.2% in the UK). If we add to these (implemented and accepted category) the percentages of organisations which have introduced ABC on a trial basis, the total adoption rates are still lower than 29% for all three countries (28.6% in Australia, 26.7% in New Zealand and 27.3% in the UK). Given the above, the findings of the current study are confirmatory of the results of the majority of previous studies which have reported relatively low adoption rates (of less than 30%) in the above countries (Al-Omiri and Drury, 2007b; Askarany et al., 2007b; Booth and Giacobbe, 1997; Cotton et al., 2003a; Warwick and Reeve, 1997). 5.2. The diffusion of ABC based on levels model Using the second diffusion process approach, the latest ‘levels of adoptions’ of ABC in all three targeted countries: Australia, New Zealand and the UK are shown in Table 3. Examining the extent of the adoption of ABC (using the ‘levels of adoption’ rather than the ‘stages of adoption’), Table 3 indicates a much higher adoption rate for ABC in all three countries. According to Table 3, 42.6% (6.5þ24.5 þ11.6%) of organisations in Australia are adopting some of the levels of ABC. This figure is nearly twice as much as the percentage (23.4%) of the firms which have implemented and accepted the ABC (as a practice). This is the case for New Zealand and the UK as well. Using the second approach, the extents of the adoption of ABC in New Zealand and the UK are as follows: 38% (4.2þ16.9 þ16.9%) and 36.4% (1.5þ15.2þ 19.7%), respectively. A comparison between the above reported adoption rates for ABC (resulted from two diffusion approaches) for the three targeted countries in this study can help us to explain some variations between reported adoption rates for ABC in the literature as follows: according to the findings, more than 19% (42.6–23.4%) of the variation in adoption rates for ABC in Australia, more than 15% (38–22.5%) of the variation in the adoption rates for ABC in New Zealand, and finally more than 21% (36.4–15.2%) variation in adoption rates for ABC in the UK are due to the implementation of different diffusion approaches (using the ‘stages’ of adoption and the ‘levels’ of adoption). These findings may provide us with some insights to explain how and why some studies (Baird, 2007; Baird et al., 2004; Chenhall and Langfield-Smith, 1998) have reported relatively high adoption rates for ABC in the past. According to Vincent (2005), Chi-Square is one the best statistics for the analysis of categorical (ordinal) and nominal

Table 2 The diffusion process of ABC in Australia, New Zealand and the UK (using the stages approach). Countries

Discussions have not taken A decision has been taken Some consideration is being given to the introduction of not to introduce this place regarding the this practice introduction

This practice has been introduced on a trial basis

This practice has been implemented and accepted

Total

Count Australia 122 New Zealand 64 UK 46

Pearson Chi-Square Likelihood ratio

%

Count

%

Count

%

Count

%

Count

%

Count %

39.6 45.1 34.8

40 22 24

13.0 15.5 18.2

58 18 26

18.8 12.7 19.7

16 6 16

5.2 4.2 12.1

72 32 20

23.4 22.5 15.2

308 142 132

100 100 100

Value

df

Asymp. sig. (2-sided)

17.500 16.826

8 8

0.025 0.032

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Table 3 The diffusion process of ABC in Australia, New Zealand and the UK (using the levels approach). Countries

Australia New Zealand UK

Activity analysis (AA)

Allocation of costs to activities (ACA)

Allocation of costs of activities to products/service (ABC)

Total ABC adopters

Total

Count

%

Count

%

Count

%

Count

%

Count

%

20 6 2

6.5 4.2 1.5

76 24 20

24.5 16.9 15.2

36 24 26

11.6 16.9 19.7

132 54 48

42.6 38 36.4

308 142 132

100 100 100

Pearson Chi-Square Likelihood ratio

Value

df

Asymp. sig. (2-sided)

15.436(a) 16.387

6 6

0.017 0.012

Table 4 The extent of association between two diffusion approaches for ABC in Australia, New Zealand and the UK. The levels of the diffusion of ABC The stages of the diffusion of ABC

Activity analysis (AA)

Total

Allocation of costs to cost pools (ACA2)

Allocation of cost pools to products/services (ABC)

Not used

Discussions have not taken place regarding the introduction Count 0 % 0.0

0 0.0

0 0.0

232 39.9

232 39.9

A decision has been taken not to introduce this practice Count 0 % 0.0

0 0.0

0 0.0

86 14.8

86 14.8

Some consideration is being given to the introduction of this Count 14 % 2.4

48 8.2

10 1.7

30 5.2

102 17.5

This practice has been introduced on a trial basis Count 6 % 1.0

24 4.1

8 1.4

0 0.0

38 6.5

This practice has been implemented and accepted Count 8 % 1.4

48 8.2

68 11.7

0 0.0

124 21.3

120 20.6

86 14.8

348 59.8

582 100.0

Count %

Pearson Chi-Square Likelihood ratio

28 4.8

Value

df

Asymp. sig. (2-sided)

578.523 702.039

12 12

0.000 0.000

data. Given the nature of data in this study, we would consider our data as categorical (in terms of the levels and the stages of adoption of ABC) and nominal (in terms of the names of countries) rather than ‘interval’ and ‘ratio’ data. Confirming our first stated proposition, descriptive information as well as the data analysis presented in Tables 2 and 3 suggest a significant association between the reported adoption rates for ABC (both the stages model and the levels model) and the countries where the diffusion of ABC was investigated (significant at 0.025 level-Pearson Chi-Square for the stages model and significant at 0.017 level-Pearson Chi-Square for the levels model). These findings suggest a need for further studies to investigate the impact of contextual factors (constituting the individual characteristics of the country where the study is done, such as the extent of the development in the country, government’s support for the diffusion of innovations, the levels of

education, communication channels and networks, etc.) on the diffusion of ABC. Examining the impact of such hindering and facilitating factors on the diffusion of ABC is expected to contribute to the adoption of ABC in the future. Supporting our second stated proposition, descriptive information as well as the data analysis presented in Table 4 show a significant association (significant at 0.000 level-Pearson Chi-Square) between the adopted diffusion models (for measuring the diffusion of ABC) and the reported adoption rates. In interpreting the above findings, (especially in terms of the variation between the adoption rates for ABC when using two different diffusion models), we may suggest a few possible scenarios as follows:

 Some organisations (e.g. those who have given some consideration to the introduction of ABC at primary levels) may have

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considered themselves as ABC adopters under the second diffusion model (the levels of adoption model) but not under the first diffusion model (the stages of adoption model). This could be the case if some organisations have actually started practising some kinds of ‘activity analysis’ or ‘allocation of costs to cost pools’, which are considered the first and the second ‘levels’ of ABC adoption under the levels diffusion model but would not make them ABC adopters under the stages diffusion model. Some organisations might have been using ‘Time-Driven ABC’ but not considered themselves as ABC adopters. As with traditional accounting systems, time-driven ABC uses a predetermined rate for allocations of costs during a specified period. This rate is fixed for the whole period and depends only on two parameters: (1) the unit cost of supplying capacity and (2) the time required performing a transaction or an activity (Askarany, 2012). Having a predetermined rate (similar to those of traditional allocation systems) may mislead some adopters of time-driven ABC to think that they are using traditional accounting systems rather than ABC. There is also a possibility that some respondents might have answered yes for ABC adoption while thinking of the ABC classification of goods stocked in warehouses. In the case of reporting higher adoption rates (when using the levels diffusion model), it is also possible that the higher adoption rates are representing the organisations that are using ABC on a trial basis as well as those that are using ABC as an accepted practice and even some of the organisations which have indicated giving consideration to the introduction of ABC in the future.

In order to verify our findings, we further performed some interviews (face-to-face and telephone interviews with CIMA qualified management accountants) following our surveys and after some data analyses. While questionnaires proved to be very economic in collecting a large volume of primary data, we realised that we would gather some significant insights through a number of follow-up interviews. Our interviews with 56 accountants in organisations revealed a very important key factor explaining part of the reason why the adoption of ABC has always been reported to lag behind those of traditional systems and why the majority of ABC adopters have not replaced traditional systems with ABC. For example, Innes et al. (2000) report that while 17.5% of the UK largest companies have adopted ABC, only 6.2% have replaced their traditional accounting techniques with ABC and the rest of ABC users are using this technique along with their traditional accounting techniques. We selected our interviewees form those who had adopted management accounting innovations including ABC. We asked our interviewees who had adopted ABC if they were using traditional cost accounting alongside ABC or whether their old costing systems were replaced by ABC. Surprisingly, the majority of interviewees (72%) stated they were using traditional cost accounting alongside ABC. When we asked our interviewees to provide us with more details about their use of ABC alongside traditional costing systems, it became clear that they were considering the allocation of ‘facility costs’ (which is one of the cost hierarchies under ABC and uses an arbitrary allocation base the same as traditional accounting systems—rather than activity bases) as the adoption of traditional accounting systems. This may be regarded as a significant addition to the current literature that can explain an important part of the reason why the literature suggests that the majority of adopters of ABC have not replaced ABC with their traditional accounting techniques and use ABC alongside their traditional accounting techniques. In other words, given that using arbitrary allocation bases for assigning ‘facility costs’ is part of ABC, many

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adopters of ABC may have considered themselves as adopters of traditional accounting systems by mistake.

6. Conclusion and limitations The present study first highlights the positive role of ABC in improving organisational performance, and then identifies different diffusion stages/scales (used in the literature to examine the diffusion process of ABC) and classifies them into two main diffusion approaches: ‘the stages model’ (which looks at the diffusion of ABC as one whole practice), and ‘the levels model’ (which breaks down the process of adoption of ABC into different levels). It also contributes to the literature by examining the levels of association between the diffusion of ABC and the implemented diffusion models and explains part of the reason for mixed adoption rates for ABC. The study further identifies the lack of a common understanding of ABC systems as a key factor contributing to the mixed reported adoption rates for ABC. The particular feature of this investigation is that besides using a similar questionnaire and targeting more or less similar respondents (qualified CIMA members who are working in a managerial accounting position in organisations) in the same period (2007), it provides information on the extent of the adoption of ABC both in terms of ‘the stages of adoption’ (usually used in studies which have reported low adoption rates for ABC) and ‘the levels of adoption’ (usually used in studies which have reported high adoption rates for ABC) at the same time. The findings suggest that examining the diffusion of ABC using the ‘levels model’ (rather than the ‘stages model’) may lead to results suggesting a higher adoption rate. Using the ‘stages model’, the findings suggest that the percentage of organisations which have implemented and accepted the ABC systems in their organisations is still lower than 24% in all three surveyed countries: Australia, New Zealand and the UK by 2007. However, using the ‘levels model’, the findings suggest a much higher adoption rate for all three countries. According to the findings, 42.6% of organisations in Australia are adopting ABC at some level and the extent of adoption of ABC in New Zealand and the UK is 38% and 36.4% respectively. A comparison between the reported adoption rates for ABC (resulting from two diffusion approaches) for the three targeted countries in this study can help us to explain some variations between reported adoption rates for ABC in the literature as follows: according to the findings, more than 19% of the variation in the adoption rates for ABC in Australia, more than 15% of the variation in the adoption rates for ABC in New Zealand and finally more than 21% of the variation in the adoption rates for ABC in the UK are likely to be due to the variation between using ‘stages’ of adoption and using ‘levels’ of adoption to study the diffusion of ABC. Confirming our first stated proposition, both adopted diffusion approaches (the stages model and the levels model) suggest a significant association between the reported adoption rates for ABC and the countries where the diffusion of ABC was investigated (significant at 0.025 level-Pearson Chi-Square based on the ‘stages model’ and significant at 0.017 level-Pearson Chi-Square based on the ‘level process model’). Given the above, the current paper recommends further studies into the impact of contextual factors (composing the characteristics of individual countries) on the diffusion of ABC in organisations. Examining the impact of such hindering and facilitating factors on the diffusion of ABC is expected to contribute to the adoption of ABC in the future. Confirming our second stated proposition, the findings suggest a significant association (significant at 0.000 level Pearson ChiSquare) between the reported adoption rates for ABC and the implemented diffusion models.

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Another important finding of the current study is explaining an important part of the reason why the literature suggests that the majority of adopters of ABC have not replaced their traditional accounting techniques with ABC and use ABC alongside their traditional accounting techniques. We found that the majority (72%) of ABC users classify themselves (by mistake) as the adopters of traditional systems when they are dealing with the allocation of ‘facility costs’ (which is one of the cost hierarchies under ABC and uses an arbitrary allocation base – like traditional accounting systems – rather than activity bases). Although statistical tests were performed to look for evidence of non-response bias, there was no way to directly test whether the non-respondents were systematically different than the respondents. The respondents were mostly controllers and accounting managers who were members of the CIMA and may thus have exhibited a bias toward reporting ABC adoption or implementation. Thus, generalising the results of this study to all organisations in Australia, New Zealand and the UK should be done with caution.

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