1A Classification of Quality/Cost Competitive Priorities in Japanese Manufacturing Firms:
Empirical evidence based on management accounting practices

Eisuke Yoshida✝
Zhiming Xu☨

Abstract
This study aims to examine whether there is a difference in management accounting practices
among the combination of quality/cost competitive priorities. Using cluster analysis, we explore
the combination of quality/cost competitive priorities. The classification suggests that 4
combinations exist, i.e. Disadvantaged, Cost-emphasis, Quality-emphasis, and Dual-emphasis.
The results of ANOVA show that larger extent regarding usage of cost and performancemeasures in Dual-emphasis group, significant dysfunction of target cost management in Costemphasis firms, and no organization size difference exists between Quality-emphasis andDisadvantaged group. The classification develops operations management theory by providing
possibility of quality/cost priorities’ coexistence based on product development perspective. In
addition, these combinations’ variance proposes the contribution of management accounting
information in investigating competitive priorities’ relationship.

Keywords
Target cost management, quality management, competitive priority, operations performance,cluster analysis, mail survey✝Professor, Faculty of Business and Commerce, Keio University. Email:eyoshida@fbc.keio.ac.jp: 2-15-45 Mita,
Minato-ku, Tokyo 1088345, JAPAN.
☨Doctoral student, Graduate school of Business and Commerce, Keio University. Email: zhiming.xu@keio.jp

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Introduction
This study aims to examine whether there is a difference in management accounting practices
among the combination of quality/cost competitive priorities. According to Skinner (1969, 1974)
and Hayes and Wheelwright (1984), competitive priorities denote a strategy which emphasize on
developing certain manufacturing capabilities that may enhance a plant’s or a firm-level positionin the marketplace. For more than 25 years, researchers from strategic management (e.g., Corbett
and Van Wassenhove, 1993; De Meyer, Nakane, Miller, and Ferdows, 1990; Frohlich and Dixon,
2001; Miller and Roth, 1994; Noble, 1995), operations management (e. g., Boyer and Lweis,
2002; Flynn and Flynn, 2004; Kathuria, 2000) and management accounting (e. g., Chenhall andLangfield-Smith, 1998; Daniel and Reitsperger, 1991; Daniel, Reitsperger and Gregson, 1995;
Daniel, Reitsperger, Morse, 2009) provide the evidence that became proof or disproof of theearly pioneer works’ trade-off perspective. Boyer, Swink, and Rosenzweig (2005) and Swink
and Way (1995) identified the debate involves two theoretical perspectives: the trade-off andcumulative model, which between the supporters of the proposition that trade-offs among quality,
cost, delivery, and flexibility are necessary versus supporters of a cumulative capabilities model
specifying that these priorities can be complementary and built simultaneously over time.
Prior studies of those two models are short of meticulous explanation about the cases thatis not suited to oneself, which is particularly apparent for the problem of quality and cost. Mostof these problems relating achievement of high-quality and low-cost, are closely related to the
management accounting perspective, an indispensable approach to the puzzle of operations
management research (Hansen and Mouritsen, 2007). For example, evidence regarding trade-off
priorities of quality and cost don’t tell us whether the usage of management accounting
information (Fry, Steele, and Saladin, 1995, 1998; Pierce and O’Dea, 2003) such as cost

3
information (Karmarkar, Lederer, and Zimmerman, 1990) and manufacturing measurements(Kaplan, 1983, 1990; Lillis, 2002), or organization size (Kathuria, 2000; Sanjay and Golhar,1996) leads trade-off perceived firms to dual-emphasis, and without enough attention to thecoexistence of high-quality and low-cost from quality-cost researches’ knowledge (Atkinson,
Hamburg, Ittner, 1994; Atkinson, Hohner, Mudt, Troxel, and Winchell, 1991; Fine, 1986; Ittner,
1996; Ittner, Nagar, and Rajan, 2001). Similarly, in spite of cumulative model’s proposition that
quality-first competitive priority should be implemented rather than cost-first (Corbett and Van
Wassenhove, 1993; Noble, 1995), little work has been done to empirically investigate that costfirst will inhibits effort to obtain sustainable competitiveness (Kato, 1993b; Shank and
Govindarajan, 1993; Yoshida, 2003).
Even more important, although product and process design are the most effective levers inquality improvement and cost reduction (Anderson and Sedatole, 1998; Clark and Fujimoto,1991), unfortunately, quality/cost competitive priorities classification that focus on productdevelopment phase remains unclear. Hence, this research classifies quality/cost competitive
priorities using a product development view point of target cost management (TCM) practices.
Using survey data on the setting of TCM from Japanese manufacturing firms, we test that
4 combinations of quality/cost priorities exist, i.e. Disadvantaged, Cost-emphasis, Qualityemphasis, and Dual-emphasis. We provide evidence about features in these four groups. First,
there are differences among Dual-emphasis group and the other two groups, Disadvantaged andQuality-emphasis, in terms of the importance of financial and non-financial performancemeasures, usage of actual costs and physical measure information. Second, the findings suggestthat product engineers’ exhaustion and suppliers’ fatigue, a part of the dysfunction of target costmanagement (Kato, 1993b; Kato and Yoshida, 1998; Yoshida, 2003), is higher in the group of

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Cost-emphasis than that in the others. Furtherly, the organization size of Dual-emphasis group issignificantly larger than other groups’, but no difference exist between Disadvantaged andQuality-emphasis.
As an exploratory analysis, the current research is one of the first step to classify thequality/cost competitive priorities by mean of product development viewpoint and management
accounting practices, which offers two aspects of implications, and intends to contribute toliteratures in some different ways. To start with, we develop an interpretive model exploring
characteristics of quality/cost emphasis firms and find that quality-first cumulative capabilities
model is supported empirically. This model enriches the literatures in strategic management andoperations management research by showing that cost information and performance measures
diversity may be a contribution to Disadvantaged group’s possible development path to Dualemphasis group via Quality-emphasis. On the other hand, cost-first priority forwarding to theachievement of high quality and low cost would meet a potential impediment regarding thepremature capability of target cost management, which offer a new insight into theunderstanding of cost-first type trade-off perspective.
The reminder of this paper is structured as follows. The next section provides theoreticaldevelopment, background and introduces 4 research propositions. Then it moves to indicates
research method and variables measurement. The part of analyses will present the results of thestandard method of cluster analysis and ANOVA (analysis of variance), and subsequent section
regarding discussion. Finally, we provide the conclusions and implications for future research.

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Background and research propositions
Quality/cost competitive priorities’ trade-off vs. cumulative perspective
The perspective of quality/cost competitive priorities’ trade-off relationship is based on thediscipline of linkage between business strategy and manufacturing competitive capabilities. This
stream of research focuses the necessity to limit resources to the single manufacturing capability.
Skinner (1969) and Wheelwright (1978) suggest some important trade-off decisions in
manufacturing include the reliability and quality or low costs, Skinner (1974) also points out that
low-cost plant may be a mistake if the company sacrifice in the way of quality, flexibility anddelivery. Miller and Roth (1994) expand the works of Skinner (1969, 1974), they provide 3groups regarding competitive capabilities which be named as Caretakers, Marketeers andInnovators. Competitive capabilities of the highest rank including low price and highconformance (quality), however, cannot be found in the same group via their cluster analysis.
The reexaminations for Miller and Roth (1994) found mixed results regarding whether
competitive capabilities of low price (low cost) and high quality can be observed in one groupsimultaneously. In these researches, factors such as region (Flynn and Flynn, 2004; Frohlich andDixon, 2001; Zhao, Sum, Qi, Zhang, and Lee, 2006; Zhao, Yeung, and Zhow, 2002) andorganization size (Kathuria, 2000; Sum, Kow, and Chen, 2004) have been discussed to clarify
the consistency of Miller and Roth’s groups, but the coexistence of low-cost and high-quality
received little notice yet. Boyer and Lweis (2002) use transformation method to illustrate that
relationships between competitive priorities are fairly complex. They employ survey datacollected from managers and operators that have recently implemented advanced manufacturing
technology (AMT) and suggest that trade-offs among competitive priorities remain in operations
management decision making . In general, these papers find that most of firms are faced with6
manufacturing competitive capabilities’ trade-offs, but this stream of researches do not provide
systematic analysis to investigate why the trade-offs exists.
As an alternative approach to the trade-off perspective, researches pay close attention to
the relationship regarding quality improvement and cost reduction, and investigate whether andhow this trade-off is perceived by manufacturing firms’ managers. This approach arises from thenotion of “zero-defect” or “quality is free” (Crosby, 1979), which as an element of competitive
advantage (Powell, 1995). Zero-defect is against the concept of “acceptable quality level (AQL)”.
Hayes and Wheelwright (1984) furthered Skinner’s standpoint by investigating differences
between American and Japanese manufacturing firms. They find that the American concept of an“acceptable quality level (AQL)” has not been adopted by the Japanese because the zero-defectconsciousness is acceptable, in spite of no direct evidence that zero-defect with low-cost in these
Japanese firms. Japanese manufacturing firms’ quality management is known as a synonym ofzero-defect consciousness introduced by a large number of literatures in quality management(e.g., Cole, 1998; Flynn, 1992; Garvin, 1984, 1986; Imai, 1986; Ishikawa, 1985; Juran, 1993;
Schonberger, 1982; Takeuchi, 1981; Wheelwright, 1981; Xu, 1999) and management accounting(e. g., Hiromoto, 1988; Johnson and Kaplan, 1987; Kaplan, 1983) literatures since 1980s.
However, Reitsperger and Daniel (1994, 1999) provide empirical evidence that substantial
proportions of Japanese operating managers who either disagree with “quality is free” notions or
are undecided, though their test related to the difference between Japanese and US managers’
quality consciousness. This result is similar to their longitudinal study of Japanese manufacturing
strategies for quality (Daniel et al., 2009), and another multinational investigation (DeMeyer,
Nakane, Miller, and Ferdow, 1989). Daniel and Reitsperger (1991), Daniel et al. (1995) and
Daniel, Lee and Reitsperger (2014) provide the evidence empirically, and explain that

7
management control systems (such as quality goals and feedback) about quality performance(such as reject rate) are more frequently provided to managers adhering to a zero-defect quality
strategy than to managers who are AQL proponents. In addition, Ittner and Larcker (1997)
indicate that organizations placing more emphasis on quality (rather than on low-cost) in thestrategic plan do tend to employ strategic control practices that are consistent with this strategicemphasis. In this way, we can presume that quality-first and cost-first manufacturing firms are
distinct from each other. However, little research of this stream focus on evidence that highquality and low-cost as a performance does exist in manufacturing firms.
In contrast to studies which investigate the relationship between trade-off and zero-defectconsciousness, the other perspective of quality/cost competitive priorities holds that a cumulative
capabilities model (which is also be called “sand cone model”) and specifies that these priorities
can be complementary and built simultaneously over time. In particular, this stream of researchsuggests a model which shows that to build cumulative and lasting manufacturing capability,
management attention and resource should go first toward quality improvement, while all others
efforts (such as flexibility) are further enlarged, direct attention can be paid to cost reduction(Corbett and Wassenhove, 1993; Ferdows and DeMeyer, 1990). As a test of Hayes andWheelwright’s foundation (Hayes and Wheelwright, 1984), Flynn, Schroeder, and Flynn (1999)
demonstrate no trade-offs between dimensions competitive performance, and they find that
quality management process focus practices and world-class manufacturing (Hayes and Pisano,1994) is also simultaneously related to competitive performance. Roth and Miller (1992) findthat superior financial performance firms hold more superior capabilities including quality andprice than poor performance firms, but not all manufacturing capabilities are equal. Theircapability profiles supports cumulative model which predicts that quality management must

8
come first than price. Rosenzweig and Roth (2004) replicate and extend the empirically observed
cumulative capabilities models through committing path analysis on high-tech manufacturingfirms. Noble (1995) finds that quality is not only at the base of the cumulative model but is often
the multiple capabilities in his survey data from American, European and Korean manufacturing
factories, and he demonstrates that quality management is globally important. In addition, thefindings of Kruger (2012) also indicate that cumulative capabilities model can be supported bySouth African businesses’ operation strategies. However, Flynn and Flynn (2004) using datafrom 5 countries and 3 industries to test cumulative capabilities by regression analysis, which did
not find evidence support the sequential progression of cumulative capabilities, in spite of theirresults support the cumulative relationship among capabilities. They suggest that it is highly
unlikely that the same sequence will be optimal under all circumstance. Zatzick, Moliterno, andFang (2012) propose that TQM is an “elaborating element” that achieves internal fit when thecore elements of the activity system are orientated toward a “cost leadership” rather than“differentiation” strategic position. This finding suggests that quality priority will not be a
nostrum in creating sustainable competitiveness when firm forwarding to provide differentiatedproducts.

TCM and quality/cost competitive priorities
Although quality/cost competitive priorities’ trade-off and cumulative perspectives have been
widely discussed, few literature has discussed this issue from the viewpoint of productdevelopment. According to the literature review of Brown and Eisenhardt (1995) which focus on
product-development, they suggest that product advantage include quality and cost should fit

9with core competence. In this paper, we explore the classification of quality/cost competitive
priorities based on TCM.
TCM means a system of profit planning and cost management at earliest stages of productdevelopment that is price led, customer focused, design centered and cross functional (Ansari etal., 1997; 2007). TCM aims to simultaneously achieve a target cost along with planning,development and detailed design of new products (Tani et al., 1994), which may be a support for
the quality/cost competitive priorities in three aspects, regarding setting lower target costs anddeveloping more excellent quality.
First, Target costs are set at an aggressive level not only for the firm itself but also forcompetitors, because target price are determined in the product market and target profit arebroken down from corporate targets (Cooper and Slagmulder, 1999; Kato, 1993a/b). According
to the equation of TCM, “Target cost = Expected sales price – Target profit,” target price isgiven by marketplace and target profit is determined by organizational business strategy andprofit planning (Kato, 1993a/b; Monden and Hamada, 1991).
Second, TCM has multiple objectives including cost reduction, quality assurance, timely
introduction of new products into the market and product development to attract customers (Taniet al., 1994). For example, as one core TCM tool and technique, quality function deployment(QFD) is used in product concept stages of TCM to combine the relationships among
competitive offerings, customer requirements, and design parameters (Ansari et al., 1997), thencontribute to improve design quality (Anderson and Sedatole, 1998).
Third, concurrent engineering (CE), as a key concept of TCM, which is also called rugby
style product development (Takeuchi and Nonaka, 1986), overlaps phases of development (Imai,
Nonaka, and Takeuchi, 1985), or simultaneous engineering (Tani et al., 1994), characterized

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involvement of the managers of product planning, development, design, production preparationand manufacturing as the cross-functional team in product development processes (Carter andBaker, 1992). In addition, CE made up of four parts, i.e. the company assessment questionnaire,the methods matrix, the dimensions map, and the priority roadmap. One of them, priority
roadmap presented here helps engineers “determine priorities for starting to bring the dimensions
into balance – the priorities for implementing the concurrent engineering vision for” (Carter andBaker, 1992: p. 72) their company. In practical terms, quality competitive priories regarding
continuous improvement, CE providing direction for continuous improvement in the product andprocess (Carter and Baker, 1992; Imai et al., 1985), using different managers and teams’ pointsof view at different times (Carter and Baker, 1992; Tani, 1995), revealing production problems
earlier (Clark and Fujimoto, 1991). On the other hand, cost competitive priories mainly improve
the new product development performance, and CE usually contributes cost reduction by
innovation (Valle and Vazquez-Bustelo, 2009).
Different competitive priorities often relay on a product development that reflect firms’
business strategy and realize manufacturing competitive capabilities. In this paper, we proposethat quality/cost competitive priorities’ difference exists in product development phase of
manufacturing firms which use TCM.

Proposition 1. Based on the usage of TCM, manufacturing firms can be classified into different
groups regarding their emphasis on competitive priorities: cost or quality, and both.

Cost information and performance measurement for quality management
It is suggested that traditional cost accounting do not track sources of competitiveness suchas quality and flexibility timely and precisely in the global economy since 1980s (Johnson, 1990;

11
Johnson and Kaplan, 1987; Kaplan, 1983; 1985). Armitage and Atkinson (1990) observed
usefulness of representative traditional cost accounting practices such as process costing andstandard costing, and the outputs from these cost accounting practices are not found to be usefulin measuring or directing productivity improvement. They suggest that effective accounting
systems are not generic and must relate to the particular strategic objectives and opportunities ofeach firm. The effect of cost accounting or the usage of cost information on quality management
has been in doubt in 1980s. According to strategic cost management research (Shank and
Govindarajan, 1993), the role of cost analysis differs in important ways depending on how thefirm is choosing to compete. Manufacturing cost control’s importance is an example and that ishigher in cost leadership strategy than in product differentiation strategy (Shank and
Govindarajan, 1993). However, no direct evidence suggests that the usage of cost informationfrom traditional cost accounting might impede product quality improvement. Some empiricalevidence suggest that usage of cost information even contribute to quality management. For
example, Fry, Steel, and Saladin (1995, 1998) find that users of standard cost systems
manufacture products that compete in the market on quality while non-users compete on price.
Even in the firms that do not focus on low price strategic priorities, evidence show that there arevarious usages of management accounting practices with superior organizational performance,
which including traditional accounting techniques (Chenhall and Langfield-Smith, 1998). The
usages of cost information are likely to be moderated by system and market factors (Tse, 2011),
traditional cost information do not necessarily mean an obstacle of firms’ simultaneous
quality/cost competitive priorities.
Another stream of management accounting researchers began work on explore the effectof non-financial measures (Ittner and Larcker, 1998) to quality improvement. Cooper and Kaplan

12(1999) present a broad array of non-financial measures to monitor and improve the quality of
product when organizations’ operating under the total quality management (TQM). Japanesemanufacturing firms’ physical measure that as opposed to a production unit measured by amonetary term is known as a feature of their excellent manufacturing from the beginning of the
1980s (Hiromoto, 1988; Okano and Suzuki, 2007). Usefulness of non-financial measures (ormanufacturing performance measures) for quality management is also supported in case study(Patell, 1987) and mail survey (Chenhall, 1997). Unfortunately, controversy also exists in theprior studies which focus on usage of non-financial measures’ contribution to quality
management. Perera, Harrison, and Poole (1997) cannot find positive performance effects
between combinations of non-financial measure and TQM. One explanation for these differing
findings is in the use of the performance measures, which is the linkage of measure to reward
and compensation systems (Chenhall and Langfield-Smith, 2007). However, Ittner and Larcker(1995) find no evidence that nontraditional performance measurement and reward systems
improve the performance of organizations with extensive formal quality management programs.
Recently, an alternative approach focuses on the effect of objective and subjective nonfinancial
measures on quality strategy performance, they find that performance measurement diversity
benefits performance, regardless of strategy (Van der Stede, Chow, and Lin, 2006).
The other general argument concerning the usage of cost information and performancemeasurement relates to quality goal or data reporting. Quality data reporting is a critical factor of
quality management (Saraph, Benson, and Schroeder, 1989). Quality priority firms advocate theimportance of quality-related goal, incentives or feedback (Levine and Shaw, 2000). The
reporting of manufacturing performance measures to line personnel is positively related to theimplementation of TQM (Banker, Potter, and Schroeder, 1993; Sim and Killough, 1998). Maiga

13
and Jacobs (2005, 2006) provide empirical evidence about effect of quality goal, quality
feedback and quality incentives on quality performance improvement. However, there was also
little evidence of differences in performance reporting systems between ISO accredited and nonISO accredited companies has been found (Carr, Mak, and Needham, 1997).
In an effort to understand the conflicting conclusions regarding the different cost
information or performance measurement’s contribution in disparate combination of quality/costcompetitive priorities, we propose that:

Proposition 2a. The group orientation — competitive priorities emphasized — isassociated with the usage of cost information.
Proposition 2b. The group orientation — competitive priorities emphasized — isassociated with the usage of performance measurement.

Organization size
Although organization size is an important contingency factor, little attention has been paid to
the influence of size on coexistence of quality/cost competitive priorities. Even though there aresome studies (Katuria, 2000; Sum et al., 2004) focus on the middle and small-size enterprises totest cumulative model or classify the groups of competitive priorities, little studies explore thedifference of size among the clusters. We propose that the effect of organization size on qualityfirst competitive priority is unlike on the cost-first competitive priority.
First, size of the organization has been found to be an important factor influencing the
adoption of cost and accounting system (Otely, 1980). The effect of organization size on theimportance of cost information is significant (Abdel-Kader and Luther, 2008; Al-Omiri and14Drury, 2007). With the expansion of size, firms will improve efficiency, pay effort on focusing
the opportunities for specialization, then larger organizations tend to have more power incontrolling their operating environment (Chenhall, 2003).
Second, the relationship of organization size and quality priority may be more complex
than cost priority. On the one hand, Benson, Saraph, and Schroeder (1991) find that managers’
view of ideal quality management is not affected by their company size. Design management andprocess management on quality do not affected by firm size (Ahire and Dreyfus, 2000). Moreevidence demonstrate that even as small size companies, the firms can also hold zero-defectconsciousness (Daniel et al., 2014; Ross and Klatt, 1986) and TQM (Ahire and Golhar, 1996) aslarger size companies, and use the resource to obtain quality management certification (Sum etal., 2004). On the other hand, effect of firms’ size is a factor that cannot be ignored when firms
focus on quality and cost priority at the same time. Kober, Subraamanniam, and Watson (2012)cannot find evidence that TQM improved financial performance in small and medium enterprises.
Their findings imply that middle and small-size firms can implement TQM actively but suffer
the problem of cost reduction and profits improvement. Larger firms would be best served toapply equal efforts to internal and external failure cost reduction (Rodchua, 2008), which related
to quality-based cost management (Atkinson et al., 1991; 1994; Dale and Plunkett, 1995;
Institute of Management Accountants, 1993; Morse, Roth, and Poston, 1987) that link the quality
performance to profits.
Less literature has investigated whether the organization size is different across various
kinds of combinations of quality/cost competitive priorities. Size may have no effect on qualityemphasis firm. Thus, we propose that:

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Proposition 3a. The group orientation — competitive priorities emphasized — is associatedwith organization size.
Proposition 3b. The group orientation — quality-emphasis and disadvantage in quality— is
NOT associated with organization size.

Dysfunctional aspect of TCM and sustainable competitivenessBecause the organizational capabilities for TCM have a major influence on the sustainable
competitive advantage (Yoshida, 2003), dysfunction of TCM may be a potential impediment inquality/cost competitive priorities’ progress. Kato (1993a/b) and Kato, Boer, and Chow (1995)
demonstrate the dysfunction of TCM including design engineers burn out, suppliers’ fatigue, andmistreatment of customer requirement. It is a challenge for companies to overcome these
problems while maintaining their sustainable competitiveness (Kato, 1993b). We know
remarkably little about dysfunctional aspect of TCM and competitive priorities. As an important
rare empirical evidence, Yoshida (2003) finds that excessive definite directional productdevelopment incurs design engineers’ exhaustion (is also called burn-out), which may impede
the firms to obtain sustainable competitiveness. Perhaps the extent to which TCM dysfunction
exists in what competitive priorities group may be important in understanding relationships
among different combination of competitive priorities. In this paper, a situation that can besummarized in our fourth proposition:

Proposition 4. The group orientation — competitive priorities emphasized — is associated with
dysfunctional aspect of TCM.

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MethodSample selection, survey development
In 2014, we conduct a cross-sectional questionnaire survey (Dillman and Smyth, 2009) toexecutive officer or director of accounting department of 847 firms in manufacturing industries,
which are listed on the first section of the Tokyo Stock Exchange. In total, 130 firms responded
and an overall response rate was 15.3%, we remove the firms that don’t use TCM and drop thesample with missing data. The final sample of this study is 104 firms. Table 1 shows the sample.
We use Chi-square statistics to test whether respondents and non-respondents havedifferent characteristics such as size and industry. We find that the distribution of respondent
firms and non-respondent firms across the industry is the same (p=0.69). In terms of size,
respondent firms have significantly more employees than non-respondent firms and the p value
is 0.05, but no significant differences exist in sales volumes (p=0.15). In sum, our sample might
be subject to a non-respondent bias in terms of size and mainly reflect the actual condition oflarger firms in the first section of the Tokyo Stock Exchange.

Variable measurement
Quality priority. Although the concept or definition of quality is used in a variety of approaches(Garvin, 1987, 1992; Kelemen, 2003; Reeves and Bednar, 1994), according to textbooks ofoperations management (Stevenson, 2015) and management accounting (Horngren, Datar, andRajan, 2012), and academic researches (Anderson and Sedatole, 1998; Freiesleben, 2010; Ittner
et al., 2001), for this study, we limit category of quality in product quality, which includes“design quality” and “conformance quality”. Respondents were asked to indicate the degree of
TCM effect regarding “achievement of quality and function”. And we ask a sticky end of TCM

17regarding “deteriorate product quality” also by 1 (no problem) -7 (a serious problem) then wereverse scoring because the cluster analysis needs all item using the same direction and scale.
Cost priority. We limit “cost” here to product cost, because product design generally offers
the greater potential for achieving high quality performance than process design (Anderson and
Sedatole, 1998) and based on TCM view. Two items regarding types of cost target (Kato, 1993b),“market-oriented cost target (Is target cost set reflecting market prices in product development
processes?)” and “aggressive cost target (Is target cost set at the challenging level that cannot be
achieved easily at starting point in product development processes?)” for measuring cost priority.
Because high-quality will lead to a high price rather than low cost (Phillips, Chang and Buzzel,1983), aggressive cost target here we mean that whether such a target is too high or not based on
the historical experience for similar projects in the same firm, but also based on the point ofviews from their counterparties or competitors. We also measure the “achievement of cost target(the cost target which has been set at starting point in product development process will beachieved frequently)”. And “cost reduction” as effect of TCM be measured.
Concurrent engineering and continuous improvement. CE is measured by the question
regarding “are not only design engineers but also many related cross-functional members
involved in product development processes?”, and our question of continuous improvementmeans that “daily continuous improvement as the main business unit(s)’ feature”. In addition, forclarify simultaneous quality/cost-emphasis, we measure “employees working towards multiple
targets such as cost, quality and product performance autonomously (employees’ autonomy inachieving multiple targets)”.
Cost information and performance measurement. As shown in Table 2, usage of actual cost,
physicalmeasures, financial measurement,

customermeasurement,

business process

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measurement, is asked to measure the usage of cost information and performance measurement.
“Fitness of business strategy and performance objectives” is measured to test whether any
excellent strategy planning and control practice need a precondition about fine quality
performance, which can be used to discuss the cumulative model.
Dysfunctional aspect of TCM. In this study, we limit the dysfunction of TCM to “productengineers’ exhaustion in TCM” and “suppliers’ fatigue in TCM” (Kato, 1993a).
All of questions above are measured by 1-7 Likert scales.
Organization size. According to Kimberly (1976), we use sales, assets, and number of
employees to measure the size (by evaluating natural logarithm).
Descriptive statistics of the construct are presented in Table 2. Correlation matrix is in
Table 3.

Data Analysis
We employ cluster analysis to classify the combination of quality/cost competitive priorities.
Cluster analysis methods provide sophisticated means for determining the way in which
variables combine, and is essentially about discovering groups in data (Everitt, Landau, andLeese, 2001). In the current study, the clustering1 provides the groups of samples that are similar
in terms of a selection of target cost setting, performance of cost reduction or quality andfunction, and continuous improvement.
We consult the application of cluster analysis for strategic group analysis (Harrigan, 1985;
Ketchen and Shook, 1996; Miller and Roth, 1994) and management accounting researches

1
We use standard agglomerative hierarchical clustering methods, Ward’s method (minimum sum of squares). In
Ward’s method, cluster summed variables after fusion, which is sensitive to outliers (Everitt et al., 2001). Thismethod contributes to the exploration of two extremes of competitive priorities or outstanding quality/cost priorities’
coexistence in our survey.

19(Chenhall and Langfield-Smith, 1998; Henri, 2008) to determine the most appropriate number ofclusters. First, the number of clusters is limited to between n/30 to n/60, where n is the sample
size, then two and four clusters are considered. Second, we use multivariate analysis (MANOVA)
and discriminant analysis to statistically validate the robustness of these clusters (Henri, 2008).
We use ANOVA, Tukey’s pairwise comparison, Hedges’s measure of effect size (Hedges’s g)and measure of strength of relationship (η 2) to test differences among the clusters for our
proposition 2 to 4.

Results
Our analysis procedures suggest that a four-cluster combination is the most appropriateclassification for the samples which support proposition 1 (Table 4). The MANOVA shows thatfour clusters are significantly different on dimensions regarding quality/cost competitive priority,
CE and improvement (p< .001). A discriminant model is developed based on these dimensions
and by assuming that four clusters are classified. The three discriminant functions are statisticallysignificant based upon Wilk’s λ (p< .001). Group centroids for each of the four clusters differ
substantially and 96.2% of the originally grouped cases are correctly classified. The four groups
derived from the cluster analysis are labeled as follows: (C1) dual-emphasis, (C2) qualityemphasis, (C3) cost-emphasis, (C4) disadvantaged. Cost targets have been set rigorously andcost reduction effect is well in C1, while this group has almost highest mean scores ofdeteriorated product and continuous improvement. Quality-emphasis has highest scores of TCM
performance regarding achievement of products quality and function, on the other hand, costemphasis group has higher mean scores in cost competitive priority dimension than quality-20emphasis’. The remaining group (C4) has most of the lowest mean scores, this group may bedisadvantaged in product development and competitive edge.
Proposition 2a and 2b predicted the usage of cost information and performancemeasurement is different among competitive priorities emphasized groups. Our results are shownin Table 5 and supports the proposition partly. First, the results of ANOVA show that all cost
information and performance measurement but usage of business process measurement (p= .709)
are different among four clusters significantly. Second, the results of Tukey’s pairwise
comparison between each cluster, and the Hedges’s g for significant difference pairs (g> .6)
show that the mean scores of usage of actual cost, physical measures, financial measurement,
and business process measurement in dual-emphasis are higher than others’ respectively.
Furtherly, fitness of business strategy and performance objective’s mean score in disadvantaged
group (C4) is statistically lower than other three groups’ (g> .6) significantly.
Table 6 shows the results of proposition 3a and 3b. The results of ANOVA regarding all of
the organization size variables reveal statistically significant difference among the four groups(p< .01 when sales and assets as size, p< .001 when number of employees as size). The results
suggest that dual-emphasis group is larger than quality-emphasis and disadvantaged group,
which supports proposition 3a partly. In addition, there is no statistically significant difference
regarding all three types of organization size between quality-emphasis and disadvantage group(p=1.00 when sale and asset as size, p= .99 when number of employees as size). Proposition 3b
has been supported.
The mean score of product engineers’ exhaustion in cost-emphasis group is higher than all
three other groups (p< .01), and the mean score of suppliers’ fatigue in cost-emphasis group ishigher than quality-emphasis and disadvantage groups’, all the effect size of these statistically21significant difference is large (g> .8). Our findings suggest that proposition 4 has been supported
partly (Table 7).

Discussion and conclusions
The main purpose of this research is to explore the combination of quality/cost competitive
priorities based on a product development, and then examine whether the difference inmanagement accounting practices among these combinations.
For the proposition 1, we applied a classification approach and clarified that four very
different clusters exist. Prior studies which support trade-offs perspective demonstrate that highquality cannot be achieved at a low cost level. The mixed results of trade-offs and cumulative
perspective may be derived from the viewpoint which has been limited in manufacturing plants(e. g., Boyer and Lewis, 2002) or a broader business plan (e. g., Miller and Roth, 1994). When
we use product development viewpoint in this study, the Japanese firms successfully achieve
high-quality and low-cost or cannot be found concurrently. In the current study, cluster 1 (dualemphasis group) has all the highest scores in dimension of cost competitive priority andimprovement activities. Meanwhile, this group has a higher performance level of quality, andovercome the deteriorated product quality successfully. We also found that firms of cluster 2 and3, which are skilled quality improvement or cost reduction. Our findings are in line with thoseobtained by other researchers who have examined empirically the relationships between quality
management and firm performance growth. For instance, evidence has been provided to support
that quality management rewarded firms not only have more excellent performance thancompetitors, but also have better results after receiving awards (Zhang and Xia, 2013). TCM as
an important profit planning system that contributes to realizing different possibly conflicting

22
goals, such as low cost, high quality, customer needs, and effective production (Cooper andSlagmulder, 1999; Kato, 1993a, b; Tani et al., 1994), which is neglected for a long time at thestream of competitive priority research.
While the results in term of proposition 2a and 2b were partly significant, the overall
direction of results showed features that could be helpful to know about whether management
accounting information is contribute to quality/cost competitive priorities’ achievement. This
paper, then, highlights the importance of cost information and performance measurement. First,
the contribution of usage of actual cost, physical measures, financial measurement to quality/costcompetitive priority has been documented by researchers who exploring the importance ofmanagement accounting information (Hansen and Mouristen, 2007). Dual and quality-emphasis
pay more attention to the usage of actual cost, which means that they use a simple costaccounting system such as actual costing or normal costing (Horngren et al., 2012). Japanese’s
management accounting practices focus a lot on the usage of physical measures (Hiromoto, 1998;
Okano and Suzuki, 2007), which compares with our test that suggest dual-emphasis’ attention tothese measures. Low level regarding usage of financial measurement exists in disadvantaged
group may be an explanation about conflict conclusions in prior studies, because both quality
and cost-emphasis groups will realize the importance of financial measurement. However, thereasons for these two groups’ usage of financial measurement may be different. Qualityemphasis group can use financial measurement for a “return on quality” (Kroll, Wright, andHeiens, 1999; Rust, Zahorik, and Keiningham, 1995; Rust, Moorman, and Dickson, 2002), while
cost-emphasis use financial measurement for variation analysis (Chenhall and Langfield-Smith,
1998; Kaplan, 1983). Second, as an exception, we cannot find that mean scores of usage ofcustomer measurement is higher in dual or quality-emphasis group. This finding is conflict to the23demonstration of Kaplan and Norton (1996), but similar to the findings of Ittner and Larcker(1995). In particular, in more advanced quality practices, strategic information is communicatedmore broadly throughout the organization, and quality program reflects organization’s overallbusiness strategy (Ittner and Larcker, 1995). In addition, there are also two aspect of customer
satisfaction’s contribution in TQM, one is monitoring and assessing known customer needs, andanother one is scanning for new customers (Sitkin, Sutcliffe, and Schroeder, 1994). Hence, we
propose that quality/cost target will not be achieved if firms focus on customer satisfaction
partially. Third, according to the difference of fitness of business strategy and performance
objectives among clusters, we suggest that the fitness should have to been paid attention, in order
to a more explicit competitive priorities combination. We can also explain this result based on a
cumulative perspective, and demonstrate that quality-priority may be a foundation in any
excellent business strategy management system (where the fitness will be realized). The latter
explanation is still a potential topic of future research. In sum, the diversity of usage of cost
information and performance measurement may be a factor for the disadvantaged group to
develop into dual-emphasis.
Our test also provides an understanding relating the effect of organization size on
quality/cost competitive priority. As our mentioned, studies suggest that small-size firms canimplement quality management preeminently (Ahire and Dreyfus, 2000; Ross and Klatt, 1986),
but researchers cannot find the relationship between quality management practices andperformance growth (Kober et al., 2012). The findings of our proposition 3a and 3b, provide theevidence that no statistically significant difference between cluster 2 (quality-emphasis) and 3(disadvantaged), but cluster 1 (dual-emphasis) is larger than these two group. When smaller size
firms look forward to a path from quality-first to dual-emphasis firms, their problem may be cost

24
management system, organizational capability, or organizational culture, rather than the quality
management technical matter.
Finally, our empirical design also provides a test of proposition 4 relating dysfunctional
aspect of TCM in different combinations of quality/cost competitive priorities. Whereas much of
the research on quality/cost competitive priorities focus on the superiority (Daniel et al., 1995) or
negligence (Fine, 1986) of cost-priority, we have measured major factor relating the sustainable
competitiveness, such as product engineers’ exhaustion or suppliers’ fatigue. These more
important variables provide indirect insight but fine-grained measures of one of the sources ofsustainable competitiveness (Yoshida, 2003). In the cost-emphasis group, product engineers’
exhaustion in TCM is highest among other three groups, even statistically higher than other threegroups’ mean score. The cost-emphasis group has a successful cost reduction performance,
however, which may sacrifice long term growth then lose sustainable competitiveness. This
result is the evidence of the theoretical review of Kato (1993a, b) and empirical work of Yoshida(2003), and is the proof of cumulative perspective in which quality-first has been suggested
rather than cost-first. In addition, cost-emphasis associated with suppliers’ fatigue in TCM, themean score in this group is higher than quality-emphasis and disadvantaged groups. Althoughsupplier-buyer relationship in Japanese manufacturing supports the excellent performance
regarding high-quality and low-cost (Cusumano and Takeshi, 1991), cost-priority sight may
disrupt this relationship. On the other hand, there is no statistically significant difference between
dual and cost-emphasis groups. It may be a tocsin about the Japanese supply chain relations
which has been called as practices of “beyond the contract” (Dekker, Sakaguchi, and Kawai,2013). Target setting and operational reviews as some parts of outstanding Japanese supply chain
management (Dekker et al., 2013), which will make a success relating high-quality and low-cost,25
however, potential risk of suppliers’ fatigue may exist in dual-emphasis group firms or their
suppliers (Kato, 1993b).
This research contributes to understanding the quality/cost competitive priorities based ona product development viewpoint, and the differences which among combinations of these twocompetitive priorities. Evidence from our Japanese manufacturing firms’ practices indicates that
quality/cost competitive priorities can be emphasized simultaneously in product development
phase. And multiple management accounting information or fitness of business strategy andperformance objectives, and organization size can contribute firms’ dual-emphasis. Finally, ouranalysis highlights a serious dysfunction of TCM in cost-first competitive priority that may facewith impediment or no way for their sustainable competitiveness building. We show thisrelationship by an interpretive model in Figure 1. Subsequent work might explore the differenceof quality management practices, cost management system, process management activities, andorganization performance among these competitive priority combinations based on thishypothetical model. We also suggest that case study may be an alternative method to analysis
how and why the different competitive priority combinations emerge.

26
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43
Table 1
SampleIndustry classification
Food and beverages

10

Textile mill

4

Pulp, paper2

Chemical14

Drugs, medicines

4

Rubber1

Glass, clay

3

Steel

3

Non-ferrous, fabricated metal

2

Fabricated metal

4

Machinery

10

Electrical, electronics

25

Transportation equipment

15

Precision equipment

2

Other manufacturing

5

Total sample

104

Size of organizations
No. of employees
200-1000

15

1001-5000

33

5001-10000

20

10001-15000

515001-20000

920001-25000

625001-30000

030001-35000

335001-40000

340001-45000

245001-50000

050001+

8

Total sample

104

44
Table 2
Descriptive statistics on variables
95% CI
Mean (SD)Median

Min

Max

LL

UL

(1)Market-oriented cost target

5.07(1.30)

5

2

7

4.81

5.32

(2)Aggressive cost target

3.67(1.38)

3

1

7

3.41

3.94

(3)Cost target achievement

3.46(1.06)

3

1

6

3.26

3.67

(4)Cost reduction

5.21(1.17)

5

2

7

4.98

5.44

(5)Quality and function

4.40(1.14)

4

2

7

4.18

4.62

(6)Deteriorated product quality (reverse scoring)

2.69(1.12)

2

1

6

2.47

2.91

(7)Concurrent engineering

5.18(1.33)

2

7

5

4.92

5.44

(8)Employees’ autonomy in achieving multiple targets

4.69(1.08)

5

2

7

4.48

4.90

(9)Continuous improvement

5.15(1.34)

2

7

5

4.93

5.38

(10)Usage of actual cost

5.24(1.50)

5

1

7

4.95

5.53

(11)Usage of physical measures4.78(1.62)

5

1

7

4.47

5.10

(12)Usage of financial measurement

6.05(1.16)

6

2

7

5.82

6.27

(13)Usage of customer measurement

4.07(1.50)

4

1

7

3.78

4.36

(14)Usage of business process measurement

3.75(1.45)

4

1

7

3.47

4.03

5.16(1.38)

5

2

7

4.90

5.43

(16)Product engineers’ exhaustion in TCM

3.20(1.06)

3

1

6

3.00

3.41

(17)Suppliers’ fatigue in TCM

3.16(0.95)

3

1

6

2.98

3.35

(18)Sales (natural logarithm of sales volume)

5.24(0.63)

5.24

3.93

6.99

5.12

5.36

(19)Assets (natural logarithm of assets value)

5.29(0.60)

5.30

4.05

7.13

5.17

5.40

8.32

8.93

(15)Fitness
objectivesofbusinessstrategy

andperformance(20)Employees (natural logarithm of number of
8.62(1.56)
8.63
5.53 12.59
employees)
Note. n=104, but “usage of physical measures” sample size=102. CI = confidence interval.

45
Table 3
Correlation Matrix
(1)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)

1

(2)
**

(3)

(4)

(5)

(6)

***

*

(7)

(8)

**

(9)

(10)

(11)
**

(12)

(13)

(14)
*

(15)
***

(16)

(17)

(18)

(19)

(20)

.300

.153

.468

.237

.032

.302

.181

.085

.155

.295

.146

.037

.204

.336

.047

.030

.165

.163

.170

1

-.022

.308**

.178

-.154

.235*

.252**.144.137.313**

.150

-.055

.221*

.224*

.307**.303**.254**.228*.297**

1.265**.263**.222*.319***.303**

.214*

.173

.163

.179

.120

.315**.281**

-.049

-.066

.104

.080

.069

.053

**

.359

***

**

**

**

*

-.041

.151

***

.075

.144

.188

.186

.188

.347

***

.075

*

***

-.028

.001

.082

.064

.091

.086

.084

.049

**

**

.246*

1

.387
1

***

.107
1

.293

.176
*

.235
1

.281

.177

.313

.153

.275

*

.221

.307

.162

.175

.137

.065

.068

.011

*

*

*

**

*

.243
1

.225

***

.206

***

.335

.234

-.011

.318

*

.083

.250

.104

*

.244

-.552

-.068

***

-.495

-.039

.261

.304

.327

.210

.058

-.029

.130

.375

-.047

.050

.263

.233

.245*

1

.080

.231*

-.013

.102

.311**.307**

-.034

.094

.244*

.195*

.281**

1.506**

.244*

-.076

.130

.319***

.036

.020

.011

.026

-.045

.157

**

.041

.034

.072

.085

.137

**

-.056

-.016

.151

.167

.112

.289
1

*

-.069
.076
1

.117
***

***

***

.505

1

*

.103

.220

.515

.260
.354

**

*

.404

.155

.046

.033

.104

.072

.127

1

.352***

.027

-.055

.017

.012

.074

1

-.123

-.043

.128

.129

.101

1

.832***

-.023

.000

.024

1

.150

.135

.185

(17)
(18)

1

(19)
(20)

***

.969

.928***

1.892***
1

Note. Using Pearson correlation coefficient. Sequence number of variable is the same as Table 2. ***p< .001, **p< .01, *p< .05.

46
Table 4
Mean scores of variables within clusters(C1) Dualemphasis(quality/cost)n=21(C2) Qualityemphasis(C3) Costemphasis(4)Disadvantaged

n=32

n=17

n=34

Market-oriented cost target

6.05(1.024)5.22(1.184)

5.59(1.176)4.06(0.919)17.511***Aggressive cost target

5.00(1.000)3.09(1.228)4.41(1.460)3.03(0.834)19.137***

Cost target achievement

4.24(1.044)3.75(1.047)3.29(1.047)2.79(0.592)12.151***

Cost reduction

6.05(0.805)5.50(0.916)

5.53(1.068)4.26(1.024)17.988***

Quality and function

4.62(0.973)

5.09(1.254)4.06(0.966)3.79(0.770)10.070***

Deteriorated
product
quality (reverse scoring)

5.95(0.129)5.88(0.125)3.65(0.270)5.21(0.139)34.568***F

Cost competitive priority

Quality competitive priority

Concurrent engineering and improvement
Concurrent engineering

6.19(0.680)5.53(1.367)

5.24(1.091)

4.21(1.067)15.534***

Employees’ autonomy inachieving multiple targets

5.67(0.966)4.84(0.954)4.47(1.007)4.06(0.814)13.784***

Continuous improvement

6.29(0.784)4.88(1.129)4.82(0.883)4.88(1.038)

11.275***Note. Standard agglomerative hierarchical clustering methods, Ward’s method (minimum sum of squares).
Mean numbers represented as bold means the top rank among four clusters. The higher the “Deteriorated
product quality” score, the less product quality problem. ***p< .001

47
Table 5
Cost information, performance measurement and clusters(C1) Dual(C2) Quality(C3) Costemphasis
emphasisemphasis(quality/cost)
n=21
n=32
n=17
C2
0.33
C3
0.62
0.30
Usage of actual cost
C4
1.27* (g=0.89)
0.94* (g=0.62)
-0.65
Mean
5.86
5.53
5.24
C2
1.22* (g=0.78)
C3-0.03
1.19✝(g=1.10)
Usage of physical measures
C4
2.00* (g=1.54)
0.78
0.81
Mean
6.00
4.78
4.81
C2
0.05
C3
-0.14
-0.19
Usage of financial measurement
C4
0.89* (g=0.71)
0.84* (g=0.70)
1.03* (g=0.85)
Mean
6.33
6.28
6.47
C2
-0.31
C3
-0.06
0.25
Usage of customer measurement
C4
0.12
0.43
0.18
Mean
4.00
4.31
4.06
C2
0.79
C3
0.54
-0.25
Usage
of
business
processC4
1.21* (g=0.90)
0.42
0.68
measurement
Mean
4.48
3.69
3.94
C2
0.17
C3
0.74
0.57
Fitness of business strategy andC4
1.59*** (g=1.44)
1.42*** (g=1.20)
performance objectives
0.85✝(g=0.73)
Mean
5.86
5.69
5.12
✝
Note. One-way Independent ANOVA. Tukey’s post-hoc tests. ***p< .001, **p< .01, *p< .05, < .10(4)Disadvantaged

F

η 2

4.59

4.035**

0.11

4.00

7.842***

0.20

5.44

5.290**

0.14

3.88

0.463

0.01

3.26

3.344*

0.09

4.26

10.461***

0.24

n=34

48
Table 6
Organization size and clusters(C1) Dual(C2) Quality(C3) Cost(4)Disemphasis
emphasisemphasisadvantaged
F(quality/cost)
n=21
n=32
n=17
n=34
C2
0.59** (g=1.03)
C3
0.31
-0.28
Sales
C4
0.60** (g=1.07)
0.00
0.29
Mean
5.67
5.07
5.36
5.07
5.700**
C2
0.54** (g=0.95)
C3
0.29
-0.25
AssetsC4
0.54* (g=0.97)
0.00
0.25
Mean
5.68
5.14
5.38
5.14
4.984*****
C2
1.59 (g=1.06)
C3
0.62
-0.97
employees
C4
1.47**(g=1.00)
-0.13
0.85
Mean
9.69
8.10
9.07
8.23
6.60***
Note. One-way Independent ANOVA. Tukey’s post-hoc tests. All variables have been evaluated by natural logarithm.
***p< .001, **p< .01, *p< .05, ✝< .10

η2

0.15

0.13

0.17

49
Table 7
Dysfunctional aspect of TCM and clusters(C1) Dual(C2) Quality(C3) Costemphasis
emphasisemphasis(quality/cost)
n=21
n=32
n=17
C2
0.35
C3
-0.93** (g=0.89)-1.27** (g=1.13)
Product engineers’
C4
0.10
-2.44
1.03** (g=1.19)
exhaustion in TCM
Mean
3.19
2.84
4.12
C2
0.39
C3
-0.59
-0.98** (g=1.01)
Suppliers’ fatigue in
C4
0.15
-0.24
0.74* (g=0.86)
TCM
Mean
3.24
2.84
3.82
Note. One-way Independent ANOVA. Tukey’s post-hoc tests. ***p< .001, **p< .01, *p< .05, ✝< .10(4)Disadvantaged

F

η2

3.09

6.532***

0.16

3.09

4.508✝

0.12

n=34

50
Figure 1. An interpretive model exploring characteristics of quality/cost emphasis firms

Multiple management accounting
information /Fitness of business strategy
and performance objectives

Dual-emphasis(quality/cost)
Quality-emphasis

Cost-emphasis

Disadvantaged

Organization size
Note. Dotted line presents that cost emphasis group will face with problems because of their immaturecapability of TCM, when this group develops to dual-emphasis.