1

Synergy of Stretch Cost Targets and Concurrent Processes:
Creating Dynamic Tension for Target Cost Managementin Japanese Manufacturing Firms

Eisuke Yoshida ✝
Keita Masuya ☨Abstract
This paper aims to examine how specific management accounting systems create dynamic
tension that contributes to organizational performance. In particular, this study chooses target
cost management and derives two unique elements, stretch cost targets and concurrent processes.
Based on tension management perspective, we assume that their complementary use enhances
dynamic tension that enhances cost reduction. Using hierarchical regression analysis forJapanese manufacturing firms, expected result cannot be gained. However, sub-group analysis
reveals their joint use enhances cost reduction for firms belong to process industries. This result
reflects “Japanese process industries” that are good at products of high quality and technology.

Key Words
Dynamic tension, Target cost management, Stretch cost targets, Concurrent processes,
Cost reduction, Cross-industry analysis✝

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: masuya-keita@keio.jp

2
Introduction
There are two distinct use of management control systems (MCS) (e.g. Ahrens andChapman, 2004; Simons, 1995). One type of control aims to measure deviations and implement
corrective actions to achieve preset performance targets. The other type enables employees tosearch for new opportunities, stimulate dialogue, and new idea generation to enhanceorganizational learning or formulation of new strategy in uncertain environment. These different
types of controls have been contrasted like mechanistic vs organic, negative vs positive lever ofcontrols (Chenhall, 2003; Mundy, 2010; Simons, 1995).
Management control researchers have payed attention to tensions among these opposing
types of controls in recent years (e.g. Henri, 2006; Marginson, 2002; Widener, 2007). Differentrole of controls don’t act independently but in combination and using them jointly creates
tensions because they are used to achieve competitive objectives. Tensions cause anxieties andfear and may result in negative dynamics, however, they are also source of change and creativity(Lewis, 2000). Hence, tension management is needed to enhance creative tensions (Lewis, 2000).
Previous research indicated that balanced use of different roles of MCS creates dynamic tension
which contributes to organizational unique capabilities and performance (e.g. Bedford, 2015;
Chenhall and Morris, 1995; Henri, 2006).
This paper aims to investigate how specific management accounting systems generate
dynamic tension. Particularly, this study focuses on target cost management (TCM). TCM meansa system of profit planning and cost management at earliest stages of product development that is
price led, customer focused, design centered and cross functional because most of the
manufacturing costs are determined at an early stage of product development (Ansari et al..,2007; Kato, 1993a, b). TCM has been developed in Japanese manufacturing industries and3contributed to cost advantage (Hiromoto, 1988; Japan Accounting Association, 1996; Kato,
1993a, b; Monden and Hamada, 1991).
TCM systems are composed of multiple elements such as upstream management, setting
and decomposing cost targets, mile-stone management, value engineering, supplier relationship,
and costs tables (e.g. Okano, 1995; Tani, 1996; Yoshida, 2003). Particularly, this paper focuseson two elements that create tensions in TCM activities: stretch cost targets and concurrent
processes. Setting stretch cost targets creates tensions by creating gaps between present
capabilities and target performance. Also, it creates tensions among other design measures suchas quality and functions. Concurrent process where managers belonging to various departmentsinteract to also accompany tensions because they may have competitive objectives or different
viewpoints. Using stretch cost targets and concurrent processes create tensions, however, theirjoint use enhance knowledge generation and contribute to cost reduction (Iwabuchi, 1992; Koga
and Davila, 1999; Shimizu, 1992b). It can be said that dynamic tension is created if their joint
use enhances TCM performance (Henri, 2006).
Using hierarchical regression analysis with survey data in Japanese manufacturing firms, it
was not found statistical meaningful relationship between their joint effects term and cost
reduction by full sample analysis. However, the following sub-group analysis, it was found that
their joint effects term enhances cost reduction only in process industries. This finding isinterpreted from several aspects. For example, the results reflect characteristics of “Japaneseprocess industries” (Fujimoto and Kuwashima, 2009).
The current study contributes to management accounting literature in three ways. First,
previous literature largely ignored how using specific management accounting systems create
dynamic tension (except for Frow et al., 2010). Therefore, it has been unknown that what or how

4unique elements of individual management accounting systems contribute to create dynamic
tension. Reviewing TCM literature from tension management perspective and testingcombination effects of stretch cost targets and concurrent processes may provide new insights on
how specific management accounting systems create dynamic tension. Second, previous research
only focused on the use of MCS such as diagnostic and interactive (Simons, 1995) that create
tensions. Relatively less attention has been given to the design of them. By considering
combination effects of stretch cost targets and concurrent processes, this paper provides new
evidence about a series of processes about how inherent tensions are created by designing
management accounting systems and how they are submitted to dynamic tension by the use ofthem. Third, previous research were limited to examine tension management at business unit
level by building on Simons (1995)’s framework (e.g. Bedford, 2015; Frow et al., 2002;
Marginson, 2002; Mundy, 2010; Henri, 2006). Evidence about how top-management uses MCS
to influence middle-management to realize competitive objectives such as implementing current
strategy and emerging new strategy have been accumulated, however, relatively little research
examined tension management at specific situation such as new product development (Jørgensen
and Messner, 2009; Lewis et al., 2002; Ylinen and Gullkvist, 2014). This paper proposes
additional insights about tension management in the context of new product development byfocusing on TCM activities, particularly manager’s horizontal collaboration.
The reminder of this paper is structured as follows. The next section provides theoreticalmodel in tension management for TCM and introduces hypothesis. Section 3 indicates researchmethod and variables measurement. In section 4, analyses and results are presented. Subsequent
section interprets the results. Finally, conclusions, limitations and implications for future
research are provided.

5

Literature review and hypothesis development
Tensions among different roles of MCS
Traditionally, it has been considered that using formal control systems is inconsistent with
innovation. Control systems are characterized as mechanistic because they aim to measuredeviations, focus on unfavorable variances, and implement corrective actions to achieve preset
performance targets (Anthony, 1965; Chenhall, 2003). Innovation or creative activities that
accompany uncertainty about causal relationship between manager’s efforts and performance areinconsistent with accounting controls (Abernethy and Brownell, 1997; Rockness and Shields,
1984).
In contrast, Simons (1987) found empirically that prospector firms use formal control
systems highly than defender firms. This result is inconsistent with prior studies, however, heinterpreted the results from types of controls. Later, he classified two types of controls:
diagnostic and interactive control (Simons, 1995). Diagnostic control systems are similar to
traditional mechanistic type of controls that aim to measure deviations and implement corrective
actions to achieve preset performance targets. Interactive control systems are organic type ofcontrols that enable employees to search for new opportunities, stimulate dialogue, and idea
generation to enhance organizational learning and emerge new strategy. In contrast to diagnostic
control, interactive control is characterized as positive lever of control and useful in uncertain
context where strategic change and innovation activities are highly needed (Abernethy and
Brownell, 1999: Bisbe and Otley, 2004).
Recent studies have showed empirical evidence about tensions among different types ofcontrol systems on organizational capabilities and performance (e.g. Chenhall and Morris, 1995;

6
Henri, 2006; Widener, 2007). Chenhall and Morris (1995) considers that combined use of formal
control systems and organic processes is paradoxical, however, control systems can support
translation of ideas that are generated from organic processes into effective innovation that is
consistent with organizational objectives. They reveal empirically that their joint use enhances
organizational performance for entrepreneur firms. Henri (2006) indicates combination ofdiagnostic and interactive controls provides opportunities to enhance organizational dialogue
about strategic objectives and derive new idea generation, thereby enhance organizational
capabilities such as market orientation, entrepreneurship, innovativeness, and organizational
learning. He identifies dynamic tension is created if their joint use enhance organizational
capabilities or performance 1. Following research, Widener (2007) also confirms empirically thatinteractive control systems enhance organizational learning through the formal structure ofdiagnostic control systems. Other studies show combination of controls doesn’t only contributeto organizational capabilities and performance but also contribute to product development andproject performance (e.g. Bedford, 2015; Lewis et al.., 2002; Ylinen and Gullkvist, 2014). Thesestudies provide empirical evidence that opposing types of control systems are not mutually
exclusive but interdependent, and effective organizations combine them in order to create
dynamic tension that contributes to organizational capabilities and performance.
Next, this paper considers how organizations create dynamic tension in TCM activities.
TCM activities accompany various tensions. For example, engineers are held responsible for not
only functions or quality but also costs and design delivery time. Competing needs create
tensions. Also, dual chain of command such as departmental managers and project leaders create
1

Henri (2006, p.533) explains the notion that dynamic tension denotes contradictory but interrelated elements(Lewis, 2000), and two phenomena in a dynamic relationship that involve both competition and complementarity(English, 2001). In addition, Henri (2006, p.534) points out that “the notion of dynamic tension is not necessarily
new in the academic literature, and is related to other terms such as conflict, paradox, dilemma, and contrast(English, 2001)”

7
tensions for engineers. Particularly, this paper chooses two elements that will create inherent
tensions in TCM activities: stretch cost targets and concurrent processes.

Stretch cost targets
The equation of TCM is “Target Cost = Expected Sales Price – Target Profit” (Kato,
1993b). Target price is driven by marketplace and target profit is determined by organizational
medium and long-term profit planning (Japan Accounting Association, 1996; Kato, 1993a, b).
Calculating cost targets from expected sales price reflects customer orientation. TCM
orients to exclude excessive quality and functions completely through thinking customer needs.
(Japan Accounting Association, 1996; Kato, 1993a, b). In order to set price, it needs to analyze
customer needs and forecast how markets evaluate quality and functions accurately (Cooper andSlagmulder, 1999; Japan Accounting Association, 1996; Kato, 1993a, b).
As cost targets reflect medium and long term profit planning, they exhibit formal targets tobe achieved (Japan Accounting Association, 1996). The more TCM systems are sophisticated,
activities of cost reduction are developed to focus on whole products or functions of products.
That is cost targets are calculated from how much each product contribute to organizational
profit (Kato, 1993a, b). This means “infusing target costs into products” (Kato, 1993b, p.42).
In Japan, the level of cost targets set from above equation tend to be stretch seemingly
impossible to achieve without substantial effort (Hiromoto, 1988; Kato, 1993a, b; Tani et al..,1994). The very difficult level is inconsistent with goal setting theory that suggests challenging
but achievable goals are desirable (Locke and Latham, 1990), however, they contribute to drastic
cost reduction in Japan (Kato, 1993a, b; Tani et al.., 1993a).

8
Concurrent processes
Traditional approach of new product development, sequential engineering, known as“throwing it over the wall”, focuses on developing a structured process with clear-defined andsequential phases (Kato, 1993a, b; Takeuchi and Nonaka, 1986). Each department curries out thejobs in isolation and they don’t interact with other departments. The sequential process takes
long development time and has a risk of getting problems on cost, quality at the later stage of
product development (Kato, 1993a, b; Takeuchi and Nonaka, 1986).
Concurrent processes are completely different from traditional approach. Concurrent
processes are also called rugby style product development (Takeuchi and Nonaka, 1986) orsimultaneous engineering, characterized involvement of the managers of product planning,development, design, production preparation and manufacturing as the cross-functional team in
product development processes (Carter and Baker, 1992). The overlapping and parallel processes
which various functional engineers are involved in the early stage of the project shorten time tomarket greatly and contribute to high productivity and performance in Japanese manufacturingfirms (Clark and Fujimoto, 1991; Takeuchi and Nonaka, 1986).
Previous research indicated concurrent processes are also a key component for TCM
systems (e.g. Tani, 1996; Yoshida, 2003). Tani et al. (1993b) provides two features of concurrent
processes for TCM. The first is that drastic cost reduction will not be realized without
cooperation among cross-functional engineers. The second is that collaboration among crossfunctional managers brings creative ideas into product development than interaction among
members belonging to same departments. Co-operation with various managers before drawing a
blueprint triggers many creative ideas because when there are many choices to reduce costs.

9
Yoshida (2003) reveals empirically that interaction among managers from different departments
is more effective on cost reduction than the effect of each tool.

Dynamic tension generated by using stretch cost targets and concurrent processes
Empirical research which investigate the effect of using specific goals and concurrent
processes are scarce. Gopalakrishman et al. (2015)’s experimental study indicate using specificgoals under concurrent processes are less effective on cost reduction than under sequential
processes because high task uncertainty deteriorates manager’s controllability. However, theirstudy ignores dynamics in TCM activities that promote knowledge creation by using specificcost targets under concurrent processes.
Some studies aim to clarify dynamics in TCM activities. Shimizu (1992a) focuses on therole of cost targets information in TCM activities. Building on knowledge creation theory(Nonaka, 1990), he explains how target cost tightness encourage or discourage knowledge
creation activities for each individual. He explains theoretically that as long as the tightness of
cost target is well managed, involved individual can understand existing solutions or previous
experiences that give little help to hit the target, then begin to seek new solutions under quitechaotic environment. Shimizu (1992b) tries to extend his previous discussions of role of cost
targets information at individual level to at group level. As a result, he identifies the roles of cost
targets information as a catalyst of transmission of knowledge and information. As cost targets
information act as a commonly shared objectives for each individual or a team, it becomes easierto drive team efforts, horizontal and vertical interaction, cross-functional activities. He explains
existing solutions or previous experiences are denied and new solutions are developed byknowledge creation at group level in the case of level of cost targets are stretch.

10
Also, Iwabuchi (1992) focuses on the role of shared information among departments froma case study. He explains shared information lead to the cooperative efforts among different
functions, then, collection of expertise and professional experience and knowledge turn into
unique solutions.
Koga and Davila (1999) indicate challenging cost targets initiate intensive interactions
between product and process engineers and frequent monitoring of the gap between the target
and cost estimate. Cost targets act as a catalyst for organizational leaning among the managers.
They also provide a possibility that cost targets cannot generate good actual performance without
cooperation among various functional managers.
From the above, using stretch cost targets under concurrent processes is paradoxical from aviewpoint of controllability principle (Gopalakrishman et al.., 2015), however, their joint use
may enhance TCM performance from a viewpoint of tension management perspective. Buildingon this perspective, concurrent processes enhance TCM performance through the structure of
stretch cost targets that act as a shared objective (Chenhall and Morris, 1995; Henri, 2006;
Shimizu, 1992b; Widener, 2007). And also, setting stretch cost targets not only derive newknowledge by creating chaotic environment at individual level but also enhance new idea
generation at a group level by combined with cooperation among individuals that have different
viewpoints (Iwabuchi, 1992; Koga and Davila, 1999; Shimizu, 1982b). It can be said thatdynamic tension is created if their joint use enhances TCM performance (Henri, 2006). From theabove, following hypothesis are developed.

Hypothesis. Using stretch cost targets and concurrent processes jointly enhances TCM
performance.

11

MethodData collection
A cross-sectional questionnaire survey to executive officer or director of accounting
department of 847 firms in manufacturing industries, which were listed on the first section of theTokyo Stock Exchange, was conducted in 2014. In total, 130 firms responded and an overall
response rate was 15.3%. The total sample we use for analysis is 98 after removing firms that
don’t use TCM and missing data. Table 1 shows the sample for analysis.
In order to test whether respondents and non-respondents have different characteristics
such as industry and size, Chi-square statistics are used. The distribution of respondent firms andnon-respondent firms across the industry is the same (p= .69). In terms of size, respondent firms
have significantly more employees than non-respondent firms and the p value is .05, but nosignificant difference exist in sales volumes (p= .15). In sum, there is a possibility that oursample shows actual condition of bigger firms in the first section of the Tokyo Stock Exchange.

Variable measurement
Environmental complexity and uncertainty moderate the relationship between TCM
elements and performance (e.g. Tani et al.., 1993a; Yoshida, 2001). In order to measure thepotential impact of TCM elements on performance, the effects of these environmental factors arecontrolled. Items are measured by Tani (1995) which has been employed in survey of Japanesefirms. In this research, environmental complexity and uncertainty are measured respectively by
three instruments as follows. The items are degree of (1) diversity of product market, (2)community of technology with competitors, (3) diversity of sales promotion for measuring theconcept of environmental complexity. The items are degree of (4) competitiveness of product12
market, (5) frequency of developing new product and technology, (6) accuracy of estimating
customer demand for measuring the concept of environmental uncertainty.
Stretch cost targets and concurrent processes are composed of one instrument respectively
as follows. The question for measuring stretch cost targets, the extent of difficulty to achieve cost
targets, is (7) Are cost targets set at the challenging level that cannot be achieved easily atstarting point in product development processes? The question for measuring concurrent
processes is (8) Are not only design engineers but also many related cross-functional members
involved in product development processes? The synergy effect, dynamic tension, of stretch cost
targets and concurrent processes is measured by a product term of Question (7) and (8) as
mentioned above. Interaction terms are strongly correlated with independent variables. Hence
prior to the formation of the product term, two independent variables are centered respectively.
TCM performance is measured by (10) the effectiveness of TCM activities to cost reduction. All
items are measured on 1-7 Likert scales. Descriptive statistics of the construct are presented inTable 2. Correlation matrix is in Table 3.

Results
In order to examine the joint effects of stretch cost targets and concurrent processes, using
hierarchical regression analysis is appropriate. This analytical method aims to examine increasein R² by performing multiple regression analysis by explanatory variables that are presumed
causal priority and introducing subsequent variables later (Cohen et al.., 2003). In this paper,
hierarchical regression adds control variables in model 1 and two explanatory variables (stretch
cost targets and concurrent processes) are included in model 2. In model 3, joint effects term is
included to gauge the relative contribution.

13
Table 4 summarizes the results of hierarchical regression analysis. R² of the first model is
not significant (R²= .058, p= .473). In model 2, changes of R² are significant (ΔR²= .167,
p= .000) and both stretch cost targets and concurrent processes enhance cost reduction (β= .333,
p= .006, β= .285, p= .018, respectively). In the third model that introduces the joint effects term,
however, increase in R² is not statistical significant (ΔR²= .004, p= .518). Therefore, it cannot be
said that the joint effect of stretch cost targets and concurrent processes enhance cost reduction
by full sample analysis.
Next, subgroup analysis are implemented. All sample is divided by industries: assemblyand process industries (Table 6, 7) 2. In assembly industries, change of R² of the first model is not
significant (R²= .182, p= .152). Increase of R² in model 2 is significant (ΔR²= .231, p= .001) andit was found using stretch cost targets enhances cost reduction (β= .511, p= .001). But therelationship between concurrent processes and performance are not significant (β= .211, p= .184).
In the third model, the amount of change of R² is not statistical significant (ΔR²= .036, p= .103).
Hence, it cannot be said that hypothesis is supported in assembly industries.
In process industries, increase of R² of the first model is not significant (R²= .149, p= .359).
Changes of R² are significant (R²= .140, p= .036) and concurrent processes enhance performance(β= .437, p= .018). After introducing product term, amount of change of R² is statisticalsignificant (ΔR²= .062, p= .073). Thus, results suggest that dynamic tension, synergy effects ofstretch cost targets and concurrent processes, is created in process industries.

2

The reason why we choose industry as a sub-group is there are differences about TCM capabilities between
assembly and process industries. At first, TCM has been developed in assembly industries such as machinery,electric appliances, transportation equipment, and precision instruments (Japan Accounting Association, 1996;
Sakurai, 1989; Tanaka, 1995).These firms had faced on diversified customer needs and shorter product life cycles
from the 1980s, they had to develop numerous products with quite different characteristics (Sakurai, 1989). In orderto achieve low-cost, high-quality, and timely-introducing new products simultaneously in accordance with changing
various customer needs, it is required to manage costs in the early stages of product development processes. So,
TCM has matured in assembly firms and has been developing in processing firms (Okano and Suzuki, 2007).

14
VIF (Variance Inflation Factor) of all independent variables that are introduced is less than
2.5. This indicates multicollinearity is not likely a problem.

Discussion
The results of this paper suggest that dynamic tension, synergy effects of stretch cost
targets and concurrent processes that contribute to cost reduction, is created in process industries.
Statistical meaningful relationship was not found for full sample and assembly firms. Thissection explains what means this results by interpreting not only the joint effects term on
performance but also each explanatory variables on performance.
First, the positive relationship between stretch cost targets and cost reduction were foundin assembly industries. The reason could be explained by differences in product architecture andcost structure among two industries. Products in assembly industries are composed of many partsand components. Major cost elements of these industries are direct material costs, directconversion costs, and die costs (Tanaka, 1995). In these industries, cost targets are decomposed
into functions, parts, and departments highly (Kato, 1993a; Tanaka, 1995). Also, Koga andDavila (1999) considers performance goals may play as significant coordination role for more
complex products such as automobiles, in which greater number of managers are involved in
product development. In contrast to these assembly industries, it is difficult to achieve cost
targets perfectly in a planning and design phase in process industries, because added-values aremainly developed by production processes. Therefore, setting stretch cost targets is more
effective in assembly firms than in processing firms.
Second, it couldn’t find concurrent processes and the product term improve TCM
performance in assembly industries. It seems like inconsistent with Koga and Davila (1999).

15
However, it may show that the extent of cross-functional collaboration is not a critical
determinant of TCM performance for assembly industries that can achieve cost reduction
constantly. This interpretation is similar to previous literature that revealed control systems areeither used to avoid innovation excess for entrepreneur firms that always pursue innovation andalso used to enhance innovation by increasing information processing for conservative firms thatlack innovation (Bisbe and Otley, 2004; Miller and Friesen, 1982). Also, Gopalakrishnan et al.
(2015) proposes a possibility that too much collaboration among various cross-functional
managers enhance task uncertainty and may cause costly redesign and coordination.
Third, the results can be explained from a historical view point of TCM. Yoshida (2011)shows advanced TCM practices at Toyota from case research. In recently, cost targets are
already achieved when cost targets are decomposed into parts at the beginning of productdevelopment processes. This means that cost targets are mostly equal to estimated costs at thattime because of TCM capabilities; such as databases or know how accumulated through a longtime experience. In this case, collaboration between different departments is used to just adapt tochanges occur afterward. Thus, TCM systems have attained maturity in assembly industries now.
This interpretation is consistent with the view that the more TCM is sophisticated, the role forcost reduction becomes smaller (Kato, 1993a). On the other hand, TCM in process industries
have been developing now (Ansari et al.., 2007). Therefore, there are great potential to reduce
costs in these industries.
Forth, the relationship between joint effects term and performance in process industries
reflects characteristics of “Japanese process industries” (Fujimoto and Kuwashima, 2009).
Previous literature about product development indicated that fine-tuning product development
between parts and functions are needed in assembly industries such as automobile, consumer

16electronics, and computers (Clark and Fujimoto, 1991). In the case of products of processindustries, factors such as epoch-making invention of process technology, investment, amount of
R&D expenses have been considered important extremely (Fujimoto and Kuwashima, 2009).
However, Fujimoto and Kuwashima (2009) proposes that firms in process industries in Japan,
particularly firms that treat industry materials also gain capabilities that achieve intended
functions accurately by customers that propose extremely strict constraints about quality andcosts. In order to achieve severe customer needs, firms in these industries have to realize total
optimization in the steps of operation. In terms of cost reduction, knowledge sharing withcustomers about customizations may lead to reduction of development costs. Also, collaboration
with sales and development departments may lead to better understanding of customer needs
accurately and enable to avoid excessive customization. Hence, concurrent processes at an earlystage of product development are effective on cost reduction.

Conclusion
The current study aimed to investigate how TCM, as a specific management accounting
system, creates dynamic tension that enhances TCM performance. Particularly, this paper choice
two elements that will create tensions in TCM activities: stretch cost targets and concurrent
processes. Based on the data from a questionnaire survey, statistical significant relationshipbetween the combination effects of them and TCM performance were not found by full sample
analysis. However, sub-group analysis divided by assembly and process industries, meaningful
relationship was found only in process industries.
This paper contributes to current management accounting research by three ways. First,
previous research largely ignored how specific management accounting systems create dynamic

17
tension (except for Frow et al.., 2010). Thereby, it has been unknown that what or how peculiar
elements of individual management accounting systems contribute to create dynamic tension.
This paper focused on TCM because TCM activities accompany various tensions and TCM
systems are composed various elements in order to achieve competitive performance.
Particularly, this paper selected two elements, stretch cost targets and concurrent processes, byreviewing previous literature on TCM and restructuring them from a viewpoint of tension
management. That allowed to hypothesize their combined use enhance TCM performance by
creating dynamic tension. Second, previous research only focused on the use of management
accounting systems such as diagnostic and interactive, however, less attention has been given to
the design of them that creates inherent tensions. This paper choice cost targets as a factor which
create tensions. In addition, not focusing on single effect of stretch cost targets but on joint effectwith concurrent processes, a series of processes about how inherent tensions are created bydesigning management accounting systems and how they are submitted to dynamic tension by
the use of them are revealed. Third, previous research were limited to examine tension
management at business unit level by building on Simons (1995)’s framework (e.g. Bedford,
2015; Frow et al., 2010; Henri, 2006; Marginson, 2002; Mundy, 2010). This paper proposes
additional insights about tension management in the context of new product development byfocusing on TCM activities, particularly manager’s horizontal collaboration.
There are also several limitations. First, the numbers of survey instruments are not enough.
Second, this paper have limited the dependent variable in the cost reduction. The purpose of
TCM is not only cost reduction but also high quality and multiple functions (Kato, 1993a, b;
Tani et al.., 1994). Hence, it needs to consider other dependent variables. Third, the results ofthis paper may be unique to Japan. This paper cannot guarantee that the observed relationships

18
generalize for manufacturing firms in other foreign countries. Forth, this paper didn’t consider
strictly which stage cost targets exhibit. As Shimizu (1992a) shows, stretch cost targets act aseither facilitator or constraint for knowledge creation and its function is determined on when to
exhibit cost targets. Thus, results may vary by considering the stages. Finally, results of thispaper are limited to explain the cross-industry influence. Intra-industry differences in cost
reduction processes are not revealed.
Future research about tension management for TCM should consider the effects context
variables on TCM systems and performance. Yoshida (2001) indicated empirically that elements
such as stretch cost targets, concurrent processes don’t always enhance TCM performance andtheir effect is different between computer and air conditioning. This is because they facedifferent business environment such as novelty of technology and market dynamism. Therefore,
it seems like that ideal tension management is dependent upon factors such as technology andmarket environment. Empirical research that guarantee this expectation, Henri (2006) confirmedthat effect of balanced use of diagnostic and interactive control systems on organizational
capabilities depends on degree of environmental uncertainty and organizational culture. In
addition, in order to interpret accurately our results that using stretch cost targets and concurrent
processes enhance TCM performance in process industries, it needs field investigation about
TCM practices in these industries. Unfortunately, there are few studies. By clarifying TCM
activities in these industries, it may enhance our knowledge about current state of Japanesemanufacturing industries and this enables to understand why using stretch cost targets andconcurrent

processes

enhancecost

reduction

inprocess

industries.

19
References
Abernethy, M. A., and Brownell, P. (1997). “Management Control Systems in Research andDevelopment Organizations: The Role of Accounting, Behavior and Personnel Controls,”
Accounting, Organizations and Society, 22 (3/4), 233-248.
Abernethy, M. A., and Brownell, P. (1999). “The Role of Budgets in Organizations Facing
Strategic Change: An Exploratory Study,” Accounting, Organizations and Society, 24,
189-204.
Ahrens, T., and Chapman, C. S. (2004). “Accounting for Flexibility and Efficiency: A Field
Study of Management Control Systems in a Restaurant Chain,” Contemporary Accounting
Research, 21 (2), 271-301.
Ansari, S. L., Bell, J. E., and Okano, H. (2007). “Target Costing: Uncharted Research Territory,”in Handbook of management accounting research, 2, ed. by C. S. Chapman, A. G.
Hopwood, and M. D. Shields. Oxford: Elsevier, 507-530.
Anthony, R. N. (1965). Planning and Control Systems: A Framework for Analysis, Boston, MA:
Harvard University.
Bedford, D. S. (2015). “Management Control Systems across Different Modes of Innovation:
Implications for Firm Performance,” Management Accounting Research, 28, 12-30.
Bisbe, J., and Otley, D. (2004), “The Effects of Interactive Use of Management Control System
on Product Innovation,” Accounting, Organizations and Society, 29, 709-737.
Carter, D. E., and Baker, B. S. (1992). Concurrent Engineering: The Product Development forthe 1990s, Boston, MA: Addition-Wesley.

20
Chenhall, R. H. (2003). Management Control Systems Design within Its Organizational Context:
Findings from Contingency-based Research and Directions for the Future,” Accounting,
Organizations and Society, 28, 127-168.
Chenhall, R. H., and Morris, D. (1995). “Organic Decision and Communication Processes andManagement Accounting Systems in Entrepreneurial and Conservative Business
Organizations,” Omega, International Journal of Management Science, 23 (5), 485–497.
Clark, K., and Fujimoto, T. (1991). Product Development Performance: Strategy, Organizationand Management in the World Auto Industry, Boston, MA: Harvard Business School.
Cohen, J., Cohen, P., West, S. G., and Aiken, L. S. (2003). Applied multiple regression /correlation analysis for the behavioral sciences, 3rd, Mahwah, New Jersey: Lawrence
Erlbaum Associates.
Cooper, R., and Slagmulder, R. (1999). “Developing Profitable New Products with Target
Costing,” Sloan Management Review, 40 (4), 22-33.
English, T. (2001). “Tension Analysis in International Organizations: A Tool for Breaking Down
Communication Barriers,” International Journal of Organizational Analysis, 9 (1), 58-83.
Frow, N., Marginson, D., and Ogden, S. (2010). “Continuous Budgeting: Reconciling Budget
Flexibility with Budgetary Control,” Accounting, Organizations and Society, 35, 444-461.
Fujimoto, T., and Kuwashima, K. (2009). Japanese Process Industries: A Competitive Analysisof Their Manufacturing Systems, Tokyo: Yuhikaku (in Japansese).
Gopalakrishnan, M., Libby, T., Samuels, J. A., and Swenson, D. (2015). “The Effect of Cost
Goal Specificity and New Product Development Process on Cost Reduction Performance,”
Accounting, Organizations and Society, 42, 1-11.

21
Henri, J. F. (2006). “Management Control Systems and Strategy: A Resource-based
Perspective,” Accounting, Organizations and Society, 31, 529-558.
Hiromoto, T. (1988). “Another Hidden Edge: Japanese Management Accounting,” Harvard
Business Review, 66 (4), 22-26.
Iwabuchi, Y. (1992). “Functions of Target Costing: Information Sharing and Knowledge
Creation,” Kigyou Kaikei, 4 (8), 41-47 (in Japanese).
Japan Accounting Association. (1996). The Future Direction of Target Cost Management
Research, The Report of the Special Committee on Target Cost Management, Tokyo:Moriyama Shoten (in Japanese).
Jørgensen, B., and Messner, M. (2009). “Management Control in New Product Development:
The Dynamics of Managing Flexibility and Efficiency,” Journal of Management
Accounting Research, 21 (1), 99-124.
Kato, Y. (1993a). Target Cost Management, Tokyo: Nihon Keizai Shimbun (in Japanese).
Kato, Y. (1993b). “Target Costing Support Systems: Lessons from Leading JapaneseCompanies,” Management Accounting Research, 4 (1), 33-47.
Koga, K., and Davila, A. (1999). “What is the Role of Performance Goals in ProductDevelopment?: A Study of Japanese Camera Manufacturers,” in Dynamic Strategy
Resources: Development, Diffusion and Integration, ed. by M. A. Hitt, P. G. Clifford, R. D.
Nixon, and K. P. Coyne. London: John Wiley & Sons, 403-431.
Lewis, M. W. (2000). “Exploring Paradox: Toward a More Comprehensive Guide,” Academy ofManagement Review, 25 (4), 760-776.

22
Lewis, M. W., Welsh, M. A., Dehler, G.E., and Green, S. G. (2002). “Product Development
Tensions: Exploring Contrasting Styles of Project Management,” Academy of Management
Journal, 45 (3), 546–564.
Locke, E. and Latham, G. (1990). Goal setting and task performance, Englewood Cliffs, NJ:
Prentice Hall.
Marginson, D. W. (2002). “Management Control Systems and Their Effect on Strategy
Formation at Middle-Management Levels: Evidence from a U.K. Organization,” Strategic
Management Journal, 23, 1019-1031.
Miller, D., and Friesen, P. H. (1982). “Innovation in Conservative and Entrepreneurial Firms:
Two Models of Strategic Momentum,” Strategic Management Journal, 3, 1-25.
Monden, Y., and K. Hamada. (1991). “Target Costing and Kaizen Costing in Japanese
Automobile Companies,” Journal of Management Accounting Research, 3, 16-34.
Mundy, J. (2010). “Creating Dynamic Tensions through a Balanced Use of Management Control
Systems,” Accounting, Organizations and Society, 35, 499-523.
Nonaka, I. (1990). A Theory of Organizational Knowledge Creation, Tokyo: Nihon Keizai
Shinbun (in Japanese).
Okano, H. (1995). Japanese Management Accounting: A Historical and Institutional Perspectiveon Target Cost Management, Tokyo: Chuo Keizai (in Japanese).
Okano, H., and Suzuki, T. (2007). A History of Japanese Management Accounting,” in
Handbook of management accounting research, 2, ed. by C. S. Chapman, A. G. Hopwood,and M. D. Shields. Oxford: Elsevier, 1119-1137.
Rockness, H. O., and Shields, M. D. (1988). “Organizational Control Systems in Research andDevelopment,” Accounting, Organizations and Society, 9 (2), 165-177.

23
Sakurai, M. (1989). “Target Costing and How to Use It,” Journal of Cost Management, 3 (2), 3950.
Shimizu, N. (1992a). “The Relationship between Target Costing Information and Knowledge
Creation Activities,” Sangyou Keiri, 51 (4), 132-139 (in Japanese).
Shimizu, N. (1992b). “The Role of Target Costing Information in Target Costing as Knowledge
Creating Activities,” Kigyou Kaikei, 44 (10), 91-96 (in Japanese).
Simons, R. (1987). “Accounting Control and Business Strategy: An Empirical Analysis,”
Accounting, Organizations and Society, 12 (4), 357-374.
Simons, R. (1995). Levers of Control: How Managers Use Innovative Control Systems to Drive
Strategic Renewal, Boston, MA: Harvard Business School.
Takeuchi, H., and Nonaka, I. (1986). “The New Product Development Game,” Harvard Business
Review, 64 (1), 137-146.
Tanaka, M. (1995). Target Cost Management and Profit Engineering, Tokyo: Chuo Keizai (inJapanese).
Tani, T. (1995). “Interactive Control in Target Cost Management,” Management Accounting
Research, 6, 399-414.
Tani, T. (1996). “Elements of Target Cost Management: A Japanese-German Comparison,”Kokumin Keizai Zasshi, 173 (3), 35-49 (in Japanese).
Tani, T., Okano, H., Shimizu, N., Iwabuchi, Y., Fukuda, J., and Cooray, S. (1993a).
“Contingency Theory of Target Costing,” Kigyo Kaikei, 45 (4/5/6), 507-513, 689-695,
797-804 (in Japanese).

24Tani, T., Okano, H., Shimizu, N., Iwabuchi, Y., Fukuda, J., and Cooray, S. (1994). “Target Cost
Management in Japanese Companies: Current State of the Art,” Management Accounting
Research, 5, 67-81.
Tani, T., Shimizu, N., Iwabuchi, Y., and Fukuda, J. (1993b). “Target Cost Management andInteraction among Managers in Meetings,” Kaikei, 144 (3), 80-92 (in Japanese).
Widener, S. K. (2007). “An Empirical Analysis of the Levers of Control Framework,”
Accounting, Organizations and Society, 32, 757-788.
Ylinen, M., and Gullkvist, B. (2014). “The Effects of Organic and Mechanistic Control in
Exploratory and Exploitative Innovations,” Management Accounting Research, 25, 93-112.
Yoshida, E. (2001). “Empirical Study about the Relationship between Organizational
Capabilities for Target Cost Management and Performance: Comparison among Three
Divisions at a Japanese Electric Company,” The Journal of Cost Accounting Research, 25(2), 1-9 (in Japanese).
Yoshida, E. (2003). Organizational Capabilities for Target Costing, Tokyo: Chuo Keizai (inJapanese).
Yoshida, E. (2011). “Target Cost Management as Tension Management,” Mita Business Review,
54 (3), 45-59 (in Japanese).

25
Table 1. Sample for analysis
industry

sending

valid response/rate

assembly industry

use for analysis
52

machinery

120

12

10.0%

10

electrical/electronics

154

27

17.5%

25

transportation equipment

62

16

25.8%

15

precision equipment

28

2

7.1%

2

process industry

46

food

69

13

18.8%

10

textile mill

41

4

9.8%

4

pulp/paper

11

2

18.2%

2

chemical

128

18

14.1%

13

drugs/medicines

38

5

13.2%

4

oil/ coal

11

1

9.1%

0

rubber

11

2

18.2%

1

glass/ clay

33

4

12.1%

3

steel

32

4

12.5%

3

non-ferrous/fabricated metal

24

4

16.7%

2

fabricated metal

37

8

21.6%

4

other manufacturing

48

8

16.7%

0

847

130

15.3%

98

total

Table 2. Descriptive statistics on variables
1. diversity of product market
2. community of technology with competitors
3. diversity of sales promotion
4. competitiveness of product market
5. frequency of developing new product andtechnology
6. accuracy of estimating customer demand
7. stretch cost targets
8. concurrent processes
9. stretch cost targets ×concurrent processes
10. cost reduction

Mean
3.77
3.51
3.85
5.17
4.69

SD
1.572
1.220
1.409
.908
1.327

Median
4
3
4
5
5

Min
1
1
1
2
2

Max
7
6
7
7
7

3.86
3.73
5.23
.2539
5

1.140
1.359
1.275
1.01886
1.176

4
3.50
5
.1172
5

2
1
2
-1.77
2

6
7
7
3.33
7

26
Table 3. Correlation Matrix
1

2

3

4

5

6

7

8

1

1

2

.294**

1

3.351***

.142

1

4

.130

-.053

.069

1

5

.232*

.307**.283**.318**

1

6

.160

.016

.083

.203*

-.036

1

7

-.049

-.036

.183

✝

.013

.006

-.011

1

✝

-.048

.257*

1

9

8

-.044

-.058

-.037

.134

.195

9

.014

-.243*.012.021-.240*

-.108

.250*

-.156

1

10

-.011

-.070

.091

.000

.173

-.066

.352***.344***

-.035

✝

10

1

Notes: p<.1, *p<.05, **p<.01, ***p<.001

Table 4. Results of hierarchical regression analysis for full sample

variableconstant
1.
2.
3.
4.
5.
6.
7.
8.
9.
R²
ΔR²
ΔF

Model 1
β95%CI5.147*** [4.906, .5.388]-.034[-.304, .236]-.169[-.433, .094].078[-.187, .343]-.092[-.378, .195][-.012, .556]
.272✝
-.049
[-.295, .197]

.058
.058
.937

Full sample (N= 98)
Model 2
β95%CI5.162***[4.941, 5.384]
019[-.230, .267]-.133[-.376, .110].019[-.232, .271]-.124[-.387, .140].216[-.051, .483]-.032[-.257, .194].333**[.100, .566].285*[.050, .520]

Notes: ✝p<.1, *p<.05, **p<.01, ***p<.001

.225
.167
9.589***

Model 3
β95%CI5.185***[4.952, 5.417].030[-.222, .283]-.150[-.400, .099].019[-.233, .271]-.113[-.379, .153].199[-.074, .472]-.045[-.275, .185].358**[.112, .604].267*[.025, .509]-.080[-.326, .165]
.229
.004
.422

27
Table 5. Results of hierarchical regression analysis for firms in assembly industries
Model 1βvariableconstant

firms in assembly industries (N= 52)
Model 2
95%CI
β
95%CI

Model 3
β95%CI5.366***[5.033, .5.699]

5.244***[4.949, 5.539]

5.295***[4.999, 5.591]

1.

.167

[-.154, .489].290*[.004, .576].314*[.032, .596]

2.

-.477*[-.852, -.101]-.357*[-.690, -.025]-.380*[-.708, -.053]

3.

.158

[-.162, .479].003[-.288, .294].033[-.255, .321]

4.

-.267

[-.658, .124]-.249[-.591, .094]-.213[-.551, .126]

5.

.386

✝

[-.037, .808].414*[.043, .786]

.359

✝

[-.011, .729]

6.

.152

[-.163, .466].126[-.152, .403].073[-.206, .352]

7.

.511***[.226, .797].556***[.271, .841]

8.

.211

[-.104, .527].232[-.079, .542]-.254[-.561, .054]

9.
R²

.182

.412

.449

ΔR²

.182

.231

.036

1.663

8.434***

2.771

ΔF

✝

Notes: p<.1, *p<.05, **p<.01, ***p<.001

Table 6. Results of hierarchical regression analysis for firms in process industries
Model 1
95%CI

firms in process industries (N= 46)
Model 2
β95%CIvariableβconstant

4.849***[4.510, .5.188]

4.892***

1.

-.182

[-.643, .278]

2.

-.002

3.

Model 3
β95%CI[4.559, 5.225]

4.739***[4.375, 5.103]-.229[-.665, .208]-.331[-.769, .107][-.376, .371].010[-.346, .366].195[-.206, .596].020[-.433, .472].089[-.349, .526].239[-.216, .694]

4.

.026

[-.382, .435]-.131[-.535, .273]-.222[-.626, .182]

5.

-.025

[-.431, .381]-.170[-.574, .234]-.135[-.529, .259][-.781, -.021]✝

[-.674, .054]-.201[-.574, .171]

7.

.019

[-.371, .409]-.236[-.707, .235]

8.

.437*

[.080, .794].668**[.239, 1.097]✝

[-.038, .805]6.

-.401*

-.310

9.

.384

R²

.149

.289

.351

ΔR²

.149

.140

.062

ΔF

1.138

1.138*

3.412

Notes: ✝p<.1, *p<.05, **p<.01, ***p<.001✝