Melco Management Accounting Research
Discussion Paper Series
No.MDP2016-006
Joint Effects of Stretch Target Costs and Concurrent Processes on Cost
Reduction: An Empirical Test from a Dynamic Tension Perspective

December 2016

Keita Masuya*
Graduate School of Business and Commerce, Keio University
2-15-45 Mita, Minato-ku, Tokyo
108-8345, Japane-mail:masuya-keita@keio.jp
Eisuke Yoshida
Faculty of Business and Commerce, Keio University
2-15-45 Mita, Minato-ku, Tokyo
108-8345, Japane-mail:eyoshida@fbc.keio.ac.jp*Corresponding author

The Melco Foundation
Nagoya, Japan
Discussion Paper Series of the Melco Management Accounting Research, in order to promote
the study of management accounting, has published this unfinished paper onto the Web. Pleaseobtain the permission of the author when citing this paper.

Joint Effects of Stretch Target Costs and Concurrent Processes on
Cost Reduction: An Empirical Test from a Dynamic Tension
Perspective

Keita Masuya*
Graduate School of Business and Commerce, Keio University, Japan
E-mail address: masuya-keita@keio.jp

Eisuke Yoshida
Faculty of Business and Commerce, Keio University, Japan
E-mail address: eyoshida@fbc.keio.ac.jp*Corresponding author

Abstract
This study examines joint effects of stretch target costs and concurrent
processes on cost reduction. There is no consensus in previous research
about cost-reduction effects of their joint use. This study builds on the
dynamic tension perspective, which has been developed in the
management control literature. Specifically, this study assumes that
concurrent processes enhance cost reduction through the structure of
stretch target costs that act as a shared objective for managers.
Furthermore, using stretch target costs under concurrent processes triggers
new idea generation among managers who have different viewpoints.
Their joint use is accompanied by tensions among multiple design targets
or different departmental managers, and it enhances cost reduction if
tensions are dynamic or creative. Multiple regression analysis for large

1Japanese manufacturing firms is conducted, but no statisticallymeaningful relationship can be found. Further analysis, which divides the
sample into assembly and process industries, shows that their joint use
enhances cost reduction for firms belonging to process industries.
Moreover, ad hoc analysis shows that concurrent processes enhance cost
reduction when target costs have set stretch levels. These results areinterpreted from several aspects. For example, the results reflect thecharacteristics of Japanese process industries that manufacture products ofhigh quality and technology. This study extends previous research about
the cost reduction-effects of the joint use of stretch target costs andconcurrent processes by building on the dynamic tension perspective. In
addition, this study extends the existing literature by providing thepossibility of the effectiveness of target cost management in processindustries.

Keywords
Target cost management, Dynamic tension perspective, Stretch target costs,
Concurrent processes, Cost reduction, Cross-industry analysis

1. Introduction
Cost competitiveness is one way to gain competitive advantage (Porter, 1980).
One means to achieve effective cost reduction is to manage costs in the productdevelopment stage. Larger profits can be gained by managing these costs at an earlystage of product development, such as product planning or design, because mostproduct costs are determined when specification of products are determined (Drury,2012).
Japanese manufacturing firms manage costs under the product development
stage by using target cost management (TCM) (Ansari et al., 2007; Hiromoto, 1988;
Kato, 1993b; Tani et al., 1994). TCM refers to a system of profit planning and cost2

management at the earliest stages of product development. Given that the market
determines product prices, TCM is used to gain larger profits by effective costmanagement at these stages (Sakurai, 1989). Since Toyota developed TCM in the
1960s, it has spread not only to assembly industries, such as transportation equipment
and electrical machinery, but also to process-oriented industries (Ansari et al., 2007;
Tani et al., 1994).
One characteristic of TCM in Japan is that stretch levels of target costs that arevery difficult to achieve. Target costs are calculated from expected sales prices lesstarget profit (Kato, 1993b). When price competition becomes intense, the market
determines sales prices. In this situation, firms need to manage costs effectively toearn expected profits. Japanese manufacturing firms, especially those in assembly
industries, have attempted to set target costs at levels that are very difficult to achieve
in order to gain larger profits (Sakurai, 1989; Tani et al., 1994). Furthermore, thesetarget costs that are very difficult to achieve contribute to drastic cost reduction in
Japan (Kato, 1993a; Tani et al., 1993a).
Although setting stretch target costs is an important step for TCM, it is difficult
to realize effective cost reduction without any support tools. Prior studies indicate that
concurrent processes may play a role (Iwabuchi, 1992; Koga & Davila, 1999).
Concurrent processes mean overlapping and parallel processes by which various
departmental managers are involved in product development (Carter & Baker, 1992;
Takeuchi & Nonaka, 1986). Concurrent processes contribute to effective new productdevelopment by significantly shortening the time to market and realizing highproductivity in Japanese manufacturing firms (Clark & Fujimoto, 1991).
Previous research provides the possibility that joint use of stretch target costs
and concurrent processes enhances cost reduction; however, empirical evidence tosupport this possibility is scarce and inconsistent. Specifically, the Japanese TCM
literature suggests that their joint use enhances cost reduction (Iwabuchi, 1992; Koga
& Davila, 1999). On the contrary, Gopalakrishnan et al.’s (2015) experimental study
based on goal-setting theory suggests that their joint use is ineffective for cost
reduction, because overlapping processes unexpectedly change product specifications
or design readjustment. As a result, costs might increase.
Based on the abovementioned research, this study aims to examine the joint
effects of stretch target costs and concurrent processes on cost reduction. In order toexplain their joint effects, this study uses the dynamic tension perspective developedin the management control literature (Chenhall & Morris, 1995; Henri, 2006). This
perspective is developed to explain why highly innovative firms combine organic
processes with formal control systems, although prior research based on contingency

3

theory assumes their combined use is inconsistent or paradoxical (Chenhall, 2006).
The dynamic tension perspective assumes that formal control systems can support thetranslation of ideas that are generated from organic processes into effective innovationthat is consistent with organizational objectives. Furthermore, new information, ideas,
and strategies that are developed via organic processes can be monitored effectively byformal controls. Tensions accompany the joint use of formal control systems andorganic processes, although they act complementarily and their interaction enhances
organizational performance if tensions are dynamic or creative (Chenhall & Morris,
1995; Henri, 2006). Regarding the objective of this research, it can be assumed that thejoint use of stretch target costs and concurrent processes is accompanied by tensions
among multiple design targets or different departmental managers. However, it can besaid that the tensions caused by joint use are dynamic or creative if they enhance cost
reduction (Chenhall & Morris, 1995; Henri, 2006).
Using multiple regression analysis with survey data from large Japanesemanufacturing firms, a statistically meaningful relationship between the joint effects
and cost reduction cannot be found by the full sample analysis. However, thefollowing sub-group analysis based on assembly and process-oriented industries
shows that the joint effects enhance cost reduction for firms in process industries.
Moreover, ad hoc analysis for firms in process industries indicates that concurrent
processes enhance cost reduction when target costs are set at high stretch levels. Thesefindings can be interpreted from several aspects. For example, the results reflect thecharacteristics of Japanese process industries that manufacture products of high quality
and technology (Fujimoto & Kuwashima, 2009).
This study contributes to the growing body of TCM literature. The study
theoretically explains how and why joint use of stretch target costs and concurrent
processes enhances cost reduction. Contrary to Gopalakrishnan et al.’s (2015) study
based on goal-setting theory, this study, using a dynamic tension perspective, assumes
they have positive effects on cost reduction. The dynamic tension perspective used inthis study is useful for explaining the dynamic nature of TCM activities that
accompany tensions or conflicts on performance, which has not been examinedsufficiently (Ansari et al., 2007).
In addition, the current study provides empirical evidence about their interaction
effects, which are suggested mainly theoretically by prior studies. Contrary to the
expectations of this study, a statistically meaningful relationship cannot be foundusing the full sample analysis. However, the expected results can be obtained using
subgroup analysis along industry division. These results could indicate that different
methods of cost reduction are needed by assembly and process industries because of4

maturation or sophistication of TCM, such as database or knowhow. The results of thisstudy are important because the differences regarding TCM practices between theseindustries are not well known.
The remainder of this paper is structured as follows. Section 2 provides atheoretical model of dynamic tension for TCM and introduces the hypothesis. Section
3 presents the data collection and variable measurement. Section 4 presents theanalyses and results. Section 5 interprets the results. Finally, section 6 provides theconclusions, limitations, and implications for future research.

2. Theory and hypothesis
This section presents a literature review of the development of the dynamic
tension perspective, stretch target costs, concurrent processes. Subsequently,
hypothesis about the effects of the joint use of stretch target costs and concurrent
processes on cost reduction is developed.

2.1

Dynamic tension perspective

The dynamic tension perspective is useful for explaining the joint effects ofstretch target costs and concurrent processes on cost reduction. This perspective has
been developed as disproof against the perception of inconsistency among formal
control systems and innovation.
Traditionally, formal control systems are characterized as mechanistic because
they aim to measure deviations, focus on unfavorable variances, and implement
corrective actions to achieve preset performance targets (Anthony, 1965). Behavior
that pursues innovation is accompanied by uncertainty about the causal relationshipbetween managers’ efforts and performance. In this situation, formal control systems
inhibit innovation because they force managers to focus on short-term performancetargets and new idea generation that will contribute to future performance. Hence, it
has been considered that formal control systems are inconsistent with innovation(Abernethy & Brownell, 1997; Rockness & Shields, 1984).
Contrary to traditional theory, Simons (1987) empirically finds that highly
innovative firms use formal control systems more than conservative firms do.
Although this result seems to be inconsistent with prior studies, he interprets this result
from the viewpoint of types of controls. Later, he classifies two types of controls:
diagnostic and interactive (Simons, 1995). Diagnostic control is similar to traditional5mechanistic type of control that aims to measure deviations and to implement
corrective actions to achieve preset performance targets. By contrast, interactive
control is an organic type of control that enables employees to search for newopportunities, stimulate dialogue, and generate ideas for the enhancement oforganizational learning or the emergence of new strategy (Simons, 1995). Interactive
control is characterized as positive lever of control and is useful in a highly uncertain
environment in which strategic change and innovation are highly needed (Abernethy& Brownell, 1999; Bisbe & Otley, 2004).
Since the use of control systems has been classified, empirical research has been
undertaken to examine the performance effects of tensions created by joint use of
opposing types of controls (Chenhall & Morris, 1995; Henri, 2006; Widener, 2007).
The combined use of formal control systems and organic processes that pursue
organizational learning or innovation seems to be inconsistent or paradoxical from a
traditional view, as mentioned above. On the contrary, the dynamic tension perspective,
developed based on Simons’s (1987) study, assumes that their joint use brings benefits
rather than disadvantages. Specifically, this perspective assumes that formal control
systems can support the translation of ideas that are generated from organic processes
into effective innovation that is consistent with organizational objectives. Furthermore,
new information, ideas, and strategies that are developed by organic processes can bemonitored effectively by formal (diagnostic) controls. As a result, joint use of formal
control systems and organic processes stimulates dynamic or creative tensions thatenhance innovation, organizational learning, and ultimately, organizational
performance (Chenhall & Morris, 1995; Henri, 2006; Widener, 2007). In addition,
later studies support the certainty of this perspective by confirming the positive effectsof the joint use of opposing types of controls on product development or project
performance (Bedford, 2015; Ylinen & Gullkvist, 2014). Based on this perspective,
tensions that are created by the joint use of control systems are dynamic or creative if
it enhances performance (Henri, 2006)1.

2.2

Stretch target costs

1

Henri (2006, p.533) explains the notion that “dynamic tensions denote contradictory
but interrelated elements.” (Lewis, 2000) In addition, Henri (2006, p.534) points outthat “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)

6

The formula to calculate target cost is “Target Cost = Expected Sales Price −Target Profit.” (Kato, 1993b) Theoretically, the expected sales price is driven by themarketplace and target profit is determined by organizational medium- or long-term
profit planning (Kato, 1993b; Sakurai, 1989).
In calculating target costs from the expected sales price, one aspect of thecharacteristics of the equation reflects customer orientation (Cooper & Slagmulder,1997). As the market environment becomes more competitive, the market comes todetermine the price of products and thereby, firms have to set almost the same price as
their competitors do. In this situation, Japanese manufacturing firms use a pricing
method called “pricing by functions.” (Kato, 1993b, p.38) Specifically, products
consist of many functions and the total values of each function come to determine thesales price (Kato, 1993b). In order to manage costs effectively, firms need to exclude
excessive functions completely from the viewpoint of customer needs (Kato, 1993b;
Sakurai, 1989).
The other aspect of the characteristics of the equation is the strong linkage
between profit planning and target costs. Because of this linkage, target costs come tomean the commitment agreed upon by every person who participates in TCM
activities (Kato, 1993b). At the beginning of TCM practices, when they are not mature,
specific products are linked to profit planning. Later, as more TCM activities become
mature, profit planning comes to be linked not only to specific products but also to all
products. In other words, target costs are calculated from how much each productcontributes to organizational profit (Kato, 1993b).
The level of target costs that are calculated via the abovementioned processes
tends to be very difficult to achieve in Japan (Hiromoto, 1988; Kato, 1993b; Tani et al.,1994). Stretch target costs cannot be achieved using the current way of thinking or
capabilities. It requires substantial effort to accompany the new way of thinking. A
level that is very difficult to achieve seems to be inconsistent with goal-setting theory,
which suggests that challenging but achievable goals are desirable to enhance goalcommitment, which would contribute to desirable performance (Locke & Latham,1990). However, prior studies suggest stretch target costs contribute to drastic cost
reduction in Japan (Kato, 1993b; Koga & Davila, 1999; Tani et al., 1993a). For
example, Tani et al. (1993a) find that tightness of target costs is positively correlatedwith their achievement. Similarly, Koga and Davila (1999) find that the more stretch
target costs are difficult to achieve, the more actual costs are reduced.

2.3

Concurrent processes

7

The traditional approach to new product development is sequential engineering,known as “throwing it over the wall,” which focuses on developing a structured
process with clearly defined and sequential phases (Takeuchi & Nonaka, 1986). Eachdepartment that participates in product development, such as product planning,development, design, production preparation, and manufacturing, acts independently.
The sequential processes take a long time to develop and carry the risk of creatingproblems on cost or quality for the later stage of product development (Takeuchi &Nonaka, 1986).
Concurrent processes are completely different from the traditional approach.
Concurrent processes, also called “rugby-style product development” or “simultaneous
engineering,” are characterized by the involvement of the managers of eachdepartment as a cross-functional team in product development processes (Carter &Baker, 1992). The overlapping and parallel processes by which various functional
managers are involved in the early stage of the project significantly shorten the time tomarket and contribute to high productivity and performance in Japanesemanufacturing firms (Clark & Fujimoto, 1991; Takeuchi & Nonaka, 1986).
Previous research indicates that concurrent processes are a key component of
TCM activities (Tani, 1996; Tani et al., 1993b). Tani et al. (1993b) provide two
features of concurrent processes for TCM. The first is that drastic cost reduction
cannot be realized without cooperation among cross-functional engineers. The second
is that collaboration among cross-functional managers brings creative ideas into
product development compared to interaction among members belonging to the samedepartments. The co-operation of various managers before drawing up a blueprint
triggers many creative ideas, because there are many options for cost reduction.
Furthermore, sharing different kinds of thoughts or ideas triggers new idea generationthat will contribute to cost reduction. These suggestions are empirically supported by
Yoshida (2003), who finds that interaction among managers from different
departments is more effective for cost reduction than the performance effect of eachtool used for TCM.

2.4

Joint effects of stretch target costs and concurrent processes on cost reduction

Some studies aim to explain cost-reduction effects caused by the joint use of
stretch target costs and concurrent processes. Shimizu (1992a) focuses on the role oftarget cost information in TCM activities. Based on knowledge-creation theory

8

(Nonaka, 1990), Shimizu (1992a) theoretically explains how tightness of target costs
encourages or discourages knowledge-creation activities for each individual. Shimizu(1992a) explains that as long as the tightness of a cost target is well managed,
participants can understand existing solutions or previous experiences that contributelittle to meet the target, and then, can begin to seek new solutions. Subsequently,
Shimizu (1992b) attempts to extend his previous discussions about the role of target
cost information at the individual level to the group level. He identifies the roles oftarget cost information as a catalyst for the transmission of knowledge and information.
That is, as target cost information acts as commonly shared objectives for eachindividual or team, it becomes easier to drive team efforts, horizontal and verticalinteraction, and cross-functional activities. As a result, existing solutions or previous
experiences are rejected and new solutions are developed. Similarly, Iwabuchi (1992)
focuses on the role of shared information among departments using a case study. He
explains that shared information leads to cooperative efforts among different functions,and then, the collection of expertise and professional experience and knowledge turns
into unique solutions. Furthermore, Koga and Davila (1999) provide the possibility
that stretch target costs initiate intensive interactions between product and processengineers, as well as frequent monitoring of the gap between the target and costestimate. Then, target costs act as a catalyst for organizational leaning am ong
managers and contribute to good actual performance.
Contrary to the abovementioned studies, Gopalakrishnan et al.’s (2015)
experimental study based on goal-setting theory explains that using specific goals
under concurrent processes is less effective for cost reduction than doing so undersequential processes. The authors assume the effects of specific target costs that cantell managers how to achieve targets decrease under concurrent processes, because
they enhance task uncertainty. Because of this task uncertainty, change orreadjustment of design increases and ultimately enhances costs. However, their study
ignores the dynamics in TCM activities that promote knowledge-creation by using
specific target costs under concurrent processes.
Cost-reduction effects of joint use of stretch target costs and concurrent
processes can be explained from the dynamic tension perspective. Building on thisperspective, two notions—stretch target costs and concurrent processes—can be
regarded as formal control systems and organic processes, respectively. Specifically, it
can be assumed that concurrent processes enhance cost reduction through the structureof stretch target costs that act as a shared objective (Chenhall & Morris, 1995; Henri,
2006; Shimizu, 1992b; Widener, 2007). Furthermore, setting stretch target costs not
only triggers new knowledge by creating a chaotic environment at the individual level9but also enhances new idea generation at the group level through cooperation amongindividuals that have different viewpoints (Iwabuchi, 1992; Koga & Davila, 1999;
Shimizu, 1992b). Based on this perspective, it can be assumed that joint use of stretch
target costs and concurrent processes is accompanied by tensions among multiple
design targets or different departmental managers. However, it can be said that thetensions caused by joint use are dynamic or creative if it enhances cost reduction(Chenhall & Morris, 1995; Henri, 2006). From the above, the following hypothesis isdeveloped.
Hypothesis. The joint use of stretch target costs and concurrent processes enhances
cost reduction.

3. Method
3.1

Data collection

A cross-sectional questionnaire survey was undertaken among large Japanesemanufacturing firms. Japanese manufacturing firms are appropriate as respondents
because the hypothesis of this study is developed based on Japanese TCM practices
and literature. Questionnaires were mailed to executive officers or directors of firms’
accounting departments. Accounting managers are appropriate as respondents because
it can be assumed that they know TCM practices well. Specifically, previous research
indicates that accounting managers in Japan frequently participate in cost meetings(Tani, 1995; Tani et al., 1994). Furthermore, there are many instances in which theTCM office is located within the accounting department (Kato, 1993a; Okano &Suzuki, 2007).
The questionnaire was sent to 847 manufacturing firms that are listed on theFirst Section of the Tokyo Stock Exchange in January 2014. The survey period lasted
about two weeks. In total, 130 firms responded and the overall response rate was
15.3%. The final sample for analysis comprises 98 firms after removing those firmsthat do not use TCM and samples containing missing data. Table 1 shows the details of
the response rate of the questionnaire survey.
Several tests to assess nonresponse bias were conducted. First, tests were
undertaken to examine differences in sales and employment between responding andnonresponding firms. Although the mean of employment was larger for responding
firms than for nonresponding firms (t= 2.005, p= .047), there were no statistically10significant differences in sales (t= 1.453, p= .146). Next, tests were undertaken tocompare early versus late respondents on organizational size. The results indicate nostatistically significant differences in sales (t= .016, p= .987) and employees (t=−1.136, p= .258).
Finally, tests were undertaken to compare early versus late respondents on all
variables used in this research. As a result, there are no significant differences in any
variables (p> .10). Hence, it seems that there is no serious nonresponse bias.
Table 1. Sample characteristics
Industry

Sent

Valid response/rate (%)

Assembly industry

Sample52

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

Pharmaceuticals

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/non-fabricated metal

24

4

16.7

2

Fabricated metal

37

8

21.6

4

Other manufacturing

48

8

16.7

0

Total

847

130

15.3

98

3.2

Variable measurement

3.2.1

TCM usage, stretch target costs, concurrent processes, and cost reduction
First, respondents were asked whether their business unit uses TCM. It is
possible that firms or business units use similar techniques to TCM, without realizing
they are using TCM (Dekker & Smidt, 2003). In order to examine whether they use

11

TCM, a general idea of TCM should be provided (Dekker & Smidt, 2003). Specifically,
respondents were asked whether their business units implement the setting or
managing of target costs at product planning, development, and design stages of new
product development (yes or no). Respondents who answered “yes” were required toanswer other questions regarding TCM practices.
The survey constructs of stretch target costs and concurrent processes werecomposed of one instrument each, as follows. The question of stretch target costs(STC) relates to the extent of difficulty in achieving target costs, and is as follows:“Are target costs set at a challenging level that cannot be achieved easily at the starting
point of product development processes?” The question of concurrent processes (CP)is as follows: “Are design engineers as well as many related cross-functional members
involved in product development processes?” These two items were measured on
Likert scales of 1–7, where 1 indicates “not at all” and 7 indicates “absolutely correct.”
The joint use of stretch target costs and concurrent processes was measured bytheir product term. Prior to the formation of the product term, two independent
variables were mean-centered because the product term is strongly correlated witheach independent variable.
Cost reduction as a dependent variable was measured by the effectiveness of
TCM activities in cost reduction. Respondents were asked to rate the effectiveness of
TCM tools in cost reduction on a scale of 1–7, where 1 indicates “not effective” and 7
indicates “very effective.”
3.2.2

Control variables
Environmental complexity and uncertainty moderate the relationship between
TCM elements and performance (Yoshida, 2001). In order to measure the potential
impact of TCM elements on cost reduction, the effects of these environmental factors
should be controlled. Tani’s (1995) items are used because they are suitable for
examining business environments in Japan. The items for environmental complexity
are degree of diversity of product market (Diversity), community of technology withcompetitors (Community), and variety of sales promotion (Variety). The items forenvironmental uncertainty are degree of competitiveness of product market(Competitiveness), frequency of developing new product and technology (Frequency),
and inaccuracy of estimating customer demand (Inaccuracy). Respondents were asked
to rate their perceived environmental complexity and uncertainty on a scale of 1 –7,
where 1 indicates “very predictable” and 7 indicates “very unpredictable.”
In addition, organizational size (Size) is included. Organizational size ismeasured by the natural logarithm of sales in 2013.

12

4. Results
4.1

Descriptive statistics and variable correlation

The descriptive statistics of the survey constructs are presented in Table 2 andthe correlation matrix is presented in Table 3. In this sample, 107 firms (82.7%) use
TCM. Considering that the adoption rate of TCM in 1991 was about 60.6% (Tani,
1995), the results of this study suggest that TCM has spread widely to Japanesemanufacturing firms in the last 25 years, as explained by Ansari et al. (2007). Variablecorrelation in Table 3 shows positive correlation coefficients of STC and CP, which
are statistically significant (r= .257, p= .011). This result indicates they are usedcomplementarily. Furthermore, both are positively correlated with cost reduction(r= .352, p= .000 and r= .344, p= .001, respectively). The positive correlation between
STC and cost reduction is consistent with the results of Tani et al. (1993a), which showcorrelation between tightness of target costs and achievement of target costs. In
addition, positive correlation between CP and cost reduction is consistent with theprevious literature suggesting or confirming that interaction among various functional
managers is effective for cost reduction (Tani et al., 1993b; Yoshida, 2003).

Table 2. Descriptive statistics of variables
Mean

SD

Min

Max

1.

Stretch target costs (STC)

3.73

1.36

1

7

2.

Concurrent processes (CP)

5.23

1.28

2

7

3.

Diversity of product market (Diversity)

3.77

1.57

1

7

3.51

1.22

1

6

Communityoftechnology

withcompetitors

4.
(Community)
5.

Variety of sales promotion (Variety)

3.85

1.41

1

7

6.

Competitiveness of product market (Competitiveness)

5.17

.91

2

7

7.

Frequency of developing new product and technology

4.69

1.33

2

7

(Frequency)
8.

Inaccuracy of estimating customer demand (Inaccuracy)

3.86

1.14

2

6

9.

Organizational size (Size)

5.27

.63

3.93

6.99

10.

Cost reduction

5.17

1.18

2

7

13

Table 3. Variable correlation
1.

2.

3.

4.

5.

6.

7.

8.

9.

1.

STC

1

2.

CP

.257*

1

3.

Diversity

-.049

-.044

1

4.

Community

-.036

-.058

.294**

1

5.

Variety

.183✝

-.037

.351***

.142

1

6.

Competitiveness

.013

.134

.130

-.053

.069

1

7.

Frequency

.006

.195✝

.232*

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

1

8.

Inaccuracy

-.011

-.048

.160

.016

.083

.203*

-.036

1

9.

Size

.224*

.261**

.087

.030

.123

.226*

.391***-.191✝

1

-.066

.246*

10.

Cost reduction.352***.344***

-.011

-.070

.091

.000

.173

✝

Notes: ✝p<.1, *p<.05, **p<.01, ***p<.001 (two-sided test).

4.2

Hypothesis test

In order to examine the joint effects of stretch target costs and concurrent
processes on cost reduction, multiple regression analysis is performed. In this study,
two models are set. Specifically, model 1 includes the main effect of STC and CP. In
addition, control variables are included. In model 2, the joint effect term of STC and
CP is included.
It should be noted that small sample size reduces the statistical power of the tests.
The sample size of 98 firms in this study is small relative to previous management
accounting research (Van der Stede et al., 2005). Because of the small sample size,
there is a greater possibility of a type-2 error occurring (this error occurs when thefalse null hypothesis is not rejected). In order to increase statistical power, this study
follows Lindsay’s (1993) recommendation to increase the significance level to .10.
This approach is also adopted by a related study, Dekker and Smidt (2003).
Table 4 summarizes the estimation results of the multiple regression analysis. Inmodel 1, a positive coefficient of STC and CP on cost reduction is found (β= .313,
p= .011 and β= .273, p= .025, respectively). In model 2, which introduces the joint
effects term of STC and CP, the coefficient is not statistically significant (β= −.087,
p= .487). Furthermore, the change in the coefficient of determination (R²) is notstatistically significant (ΔR²= .004, ΔF²= .488, p= .487). This result indicates that the
explanatory power might not change significantly when the joint effect term is not

14

10.

1

added. Hence, it cannot be said that the joint use of STC and CP enhances cost
reduction by the full sample analysis.

Table 4. Estimation results of multiple regression analysis for full sample
Model 1
Coefficient

Model 2
90% CI

Coefficient

90% CI

STC.313*[.11, .51]

.339**[.13, .55]

CP

.273*

[.07, .47]

.252*[.05, .46]−.087[−.29, .12]

STC*CP
Controls
Diversity.012[−.20, .22]

.024[−.19, .24]

Community−.126[−.33, .08]−.145[−.36, .07]

Variety.020[−.19, .23]

.019[−.19, .23]

Competitiveness−.139[−.36, .08]−.129[−.35, .10]

Frequency.182[−.05, .42]

.161[−.08, .40]

Inaccuracy−.010[−.20, .18]−.023[−.22, .17]

Size

.167

[−.17, .51]

.175[−.17, .52]

Intercept

4.290***

4.268***

R²

.231

.235

Adj. R²

.152

.147

ΔF

.488

Estimated with ordinary least squares. CI means confidence interval. P-values are two-sided tested.
Unstandardized. *p<.05, **p<.01, ***p<.001. VIF< 2.0.

4.3

Supplementary analysis

Next, subgroup analysis is performed. The whole sample is divided by industry:
assembly and process industries. The reason that industry as a sub-group is selected isthat differences in the maturation or sophistication of TCM between assembly andprocess industries seem to create differences in TCM elements on performance 2. In
2

TCM was first developed in assembly industries, such as machinery, electric
appliances, transportation equipment, and precision instruments (Monden & Hamada,
1991; Sakurai, 1989; Tanaka, 1995). These firms faced diversified customer needs andshorter product life cycles from the 1980s, and thus, they had to develop numerous

15

order to examine the cost-reduction effects of the joint use of STC and CP, samples are
divided by classification table of industries developed by the Securities Identification
Code Committee of the Japan Exchange Group 3.
Table 5 shows the descriptive statistics of variables in the assembly and processindustries. The mean scores of variables for TCM practices (STC, CP, and Cost
reduction) are higher for firms in assembly industries than for firms in processindustries. In particular, the mean differences for STC and Cost reduction are
statistically significant (t= 2.41, p= .018, t= 2.66, p= .009, respectively). Furthermore,
Frequency is higher for firms in assembly industries (t= 2.33, p= .022). Hence, it canbe said that firms in assembly industries are required to develop new products more
frequently, use TCM tools more often, and achieve cost reduction more than are firms
in process industries. Therefore, it can be said that TCM practices are matured forfirms in assembly industries compared to firms in process industries, only with respect
to STC, CP, and Cost reduction.
Table 6 shows the variable correlation in assembly industries (Panel A) andprocess industries (Panel B). The results show that correlation between STC and CP isnot statistically significant in assembly industries (r= .186, p= .187); however, STC
and CP are positively correlated in process industries (r= .308, p= .037). These results
indicate the possibility that the relationship between STC and CP is complementary infirms in process industries.

products with quite different characteristics (Sakurai, 1989). In order tosimultaneously achieve low-cost, high-quality products that are introduced timeouslyin accordance with changing customer needs, it is necessary to manage costs in theearly stages of product development processes. Therefore, TCM has matured inassembly firms but is only developing in processing firms (Okano & Suzuki, 2007).
3

The test is conducted to examine differences in industry distribution in this sample
and in the First Section of Tokyo Stock Exchange. The results indicate there are no
statistically significant differences in industry distribution between respondents andfirms belonging to the First Section of the Tokyo Stock Exchange (χ²= 11.821, df= 15,
p= .693). Hence, the industry distribution of the sample of this study is representativeof the First Section of the Tokyo Stock Exchange.

16

Table 5. Descriptive statistics of variables for firms in assembly and process industries
Firms in assembly industries

Firms in process industries
Mean

Mean

SD

Min

Max

Mean

SD

Min

Max
differences

1.

STC

4.04

1.40

1

7

3.39

1.24

1

7

.65*

2.

CP

5.35

1.15

3

7

5.11

1.40

2

7

.24

3.

Diversity

3.73

1.74

1

7

3.80

1.38

2

7

-.07

4.

Community

3.56

1.24

1

6

3.46

1.21

2

6

.10

5.

Variety

3.96

1.55

1

7

3.72

1.24

1

6

.24

6.

Competitiveness

5.25

.95

3

7

5.09

.87

2

6

.16

7.

Frequency

4.98

1.31

2

7

4.37

1.29

2

6

.61*

8.

Inaccuracy

3.92

1.17

2

6

3.78

1.11

2

6

.14

9.

Size

5.46

.62

4.22

6.99

5.05

.57

3.93

6.20

.41***

10.

Cost reduction

5.46

1.15

2

7

4.85

1.14

2

7

.61**

Notes: *p<.05, **p<.01, ***p<.001 (two-sided test).

17

Table 6. Variable correlation for firms in assembly and process industries
Panel A: Firms in assembly industries
1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

1.

STC

1

2.

CP

.186

1

3.

Diversity

−.197

−.168

1

4.

Community

−.182

−.165

.415**

1

5.

Variety

.164

−.058

.324*

.348*

1

6.

Competitiveness

−.022

−.045

.101

−.004

.100

1

7.

Frequency

−.118

−.009

.274*

.405**.320*.480***

1

8.

Inaccuracy

−.034

.122

.163

.098

.129

.248✝

.179

1

.180

.253

✝

.084

.036

.113

.185

.284*

−.155

1

.453***

.278*

.123

−.157

.154

.000

.163

.144

.245✝

1

2.

3.

4.

5.

6.

7.

8.

9.

10.

9.

Size

10.

Cost reduction

Panel B: Firms in process industries
1.
1.

STC

1

2.

CP

.308*

1

3.

Diversity

.202

.103

1

4.

Community

.131

.036

.122

1

5.

Variety

.175

−.033

.409**

−.164

1

6.

Competitiveness

.009

.304*

.183

−.124

.003

1

7.

Frequency

.033

.359*

.205

.189

.206

.090

1

8.

Inaccuracy

−.018

−.226

.160

−.090

.003

.135

−.330*

1

9.

Size

.130

.243

.128

−.007

.081

.242

.418**−.315*

1

10.

Cost reduction.122.388**

−.190

.003

−.047

−.054

.070

−.361*.087

Notes: ✝p<.1, *p<.05, **p<.01, ***p<.001 (two-sided test).

Multiple regression analysis is conducted in order to examine the cost-reduction
effects of the joint use of STC and CP. Panel A in Table 7 presents the estimation
results for firms in assembly industries. The estimation results for model 1a indicatethat STC enhances Cost reduction significantly (β= .494, p= .001). On the contrary, themain effect of CP on Cost reduction is not statistically significant (β= .180, p= .282).
Model 2a shows no statistically significant relationship between the joint effect termand Cost reduction (β= −.256, p= .103). Hence, the expected results are not achieved
for firms in assembly industries.

18

1

Panel B of Table 7 shows the estimation results for firms in process industries.
Model 1b indicates that CP enhances Cost reduction significantly (β= .437, p= .020).
On the contrary, a statistically significant relationship between STC and Cost
reduction cannot be found (β= .020, p= .921). Model 2b indicates that the joint effectterm is positively associated with Cost reduction (β= .384, p= .077). Furthermore,model 2b has greater explanatory power than Model 1b does because the increase of R²
is statistically significant (ΔR²= .062, ΔF²= 3.322, p= .077). These results suggest thatthe joint use of STC and CP enhances Cost reduction for firms in process industries.
Table 7. Estimation results for firms in assembly and process industries
Panel A: Firms in assembly industries

Panel B: Firms in process industries

Model 1aModel 1bModel 2aModel 2b

Coefficient

90% CI

Coefficient

90% CI

Coefficient

90% CI

Coefficient

90% CI

STC.494**[.25, .74]

.539***[.30, .78].020[−.31, .35]−.234[−.63, .17]

CP

.180

[−.10, .46]

.199[−.07, .47]

.437*[.14, .74]

.667**[.30, 1.03]

STC*CP✝

−.256[−.51, .00]

.301*[.06, .54]−.229[−.60, .14]−.330[−.70, .04]−.378*[−.65, -.10].010[−.29, .31]

.194[−.15, .53].384[.03, .74]

Controls
Diversity.278✝

Community-.355*[.04, .52][−.63,-.08]

Variety.003[−.24, .25]

.033[−.21, .28]

.088[−.28, .46]

.238[−.15, .62]

Competitiveness-.262[−.55, .03]−.227[−.51, .06]−.130[−.48, .22]−.219[−.57, .13]

Frequency.387*[.07, .71]

.329✝

[.01, .65]−.168[−.53, .19]−.131[−.48, .22]

Inaccuracy.153[−.09, .40]

.102[−.14, .35]−.311[−.63, .01]−.205[−.53, .12]

Size

.161

[−.26, .58]

.169[−.24, .58]−.006[−.57, .56]−.022[−.57, .53]

Intercept

4.379**

4.388**

4.922**

4.851**

Observations (n)

52

52

46

46

R²

.418

.455

.289

.351

Adj. R²

.293

.322

.111

.165

ΔF

3.322✝

2.779

Estimated with ordinary least squares. CI means confidence interval. P-values are two-sided tested. Unstandardized. ✝p<.1,*p<.05, **p<.01, VIF< 2.5.

In order to examine the content of interaction, simple slope analysis isperformed. Following Aiken and West (1991), the regression line on CP is estimated19when STC takes ±1 standard deviation (SD)4. Figure 1 highlights the regression line on
cost reduction.
The estimation results shown in Figure 1 indicate that CP enhances Cost
reduction when STC is high (+1SD) (β= .748, p= .007). The coefficient of CP when
STC is low (−1SD) is not statistically significant (β= .247, p= .247). Hence, the results
suggest that the complementary use of STC and CP enhances Cost reduction for firms
in process industries.

Figure 1. Interaction between STC and CP for firms in process industries

5. Discussion
The estimation results indicate that the joint use of stretch target costs andconcurrent processes enhances cost reduction for firms in process industries. On thecontrary, a statistically meaningful relationship cannot be found for the full sample
and assembly firms. This section interprets these results.
In assembly industries, positive main effect of stretch target costs can be found;however, no significance can be found for concurrent processes and the interaction
effect. These results can be explained by engineer’s loss of autonomy or excess
workload caused by excessive use of concurrent processes. An increase of meetings
4This analysis chooses STC as the moderator variable, because it can be assumed thatthe cost-reduction effects of CP might be enhanced through the structure of STC, as
Widener (2007) empirically shows.

20

caused by concurrent processes might result in excess workload and hamper autonomy.
It is difficult for busy engineers to effectively use support tools for TCM, such as
TCM-case studies or 3DCAD (three-dimensional computer-aided design), which areused mainly in assembly industries (Yoshida, 2003, 2007). As a result, effective ideas
for cost reduction will not be developed. Because of the negative effects of tensions
caused by excessive use of stretch target costs and concurrent processes, statisticallymeaningful relationships between concurrent processes or the interaction effect andperformance cannot be found in assembly industries 5.
Furthermore, the estimation results in assembly industries can be explained by arecent advanced practice of TCM in assembly industries. Yoshida (2011) showsadvanced TCM practices at Toyota from case research. Recently, target costs havebeen achieved when they were decomposed into parts at the beginning of productdevelopment processes. This means that target costs are mostly equal to estimatedcosts at that time because of maturation of TCM capabilities, such as databases or
know-how accumulated through long time experience. In this case, collaboration
between different departments is used to adapt to changes that occur afterward. These
practices suggest that the cost-reduction effects of concurrent processes become
smaller in assembly industries, although not to the same extent as in Toyota.
The estimation results about the positive performance effects of the joint effectterm in process industries reflect the characteristics of Japanese process industries(Fujimoto & Kuwashima, 2009). According to Fujimoto and Kuwashima (2009),
previous literature on product development indicates that fine-tuning productdevelopment between parts and functions is needed in assembly industries, such as
automobiles, consumer electronics, and computers. In the case of the products ofprocess industries, such factors as epoch-making inventions of process technology,
investment, and amount of R&D expenses have been considered extremely important.
On the contrary, the authors propose that firms in process industries in Japan,
particularly firms that treat industry materials, also gain capabilities that achieveintended functions accurately by customers who propose extremely strict constraints
on quality and costs. In order to meet these strict customer needs, firms in theseindustries have to realize total optimization in the steps of operation. Specifically,knowledge sharing with customers about product specifications might lead to
reduction of development costs by avoiding excessive customization. Furthermore,collaboration with sales and development departments might lead to better and more
5

According to Lewis (2000), tensions act as a double-edged sword. That is, theymight serve as a trigger for change or they might inhibit for change.

21

accurate understanding of customer needs and help avoid excessive customization.
The results of this study indicate that concurrent processes are effective for cost
reduction for firms in process industries. Hence, it is possible that stretch target costs,
which are set to achieve strict customer needs, strengthen the cost-reduction effects ofconcurrent processes, as the results of simple slope analysis show.

6. Conclusion
This study aims to clarify the cost-reduction effects of the joint use of stretch
target costs and concurrent processes. In order to examine their joint effects, this study
builds on a dynamic tension perspective. Based on data from a questionnaire survey,
no statistically meaningful relationship between the joint effects and cost reduction arefound when analyzing the full sample. However, the results of sub-group analysis byassembly and process industries indicate that joint use enhances cost reduction forfirms in process industries.
This study contributes to the growing body of TCM literature. This study
theoretically explains how and why joint use of stretch target costs and concurrent
processes enhances cost reduction. Contrary to Gopalakrishnan et al.’s (2015) study
based on goal-setting theory, this study assumes positive effects of stretch target costs
and concurrent processes on cost reduction. The dynamic tension perspective used inthis study is useful for explaining the dynamic nature of TCM activities that
accompany tensions or conflicts on performance, which has not been examinedsufficiently (Ansari et al., 2007).
In addition, this study provides empirical evidence about their interaction effects,
which are suggested mainly theoretically by prior studies. Contrary to the expectationsof this study, statistically significant relationship cannot be found when analyzing thefull sample. However, expected results are found by subgroup (industry) analysis.
These results might indicate that differences in product development processes requiredifferent TCM practices, as Messner (2016) suggested. The results of this study areimportant because differences regarding TCM practices between these industries are
not well known.
This study has several limitations. First, sample size and the number of survey
instruments might not be sufficient. Specifically, the survey constructs of this study
contain only one questionnaire item. In order to support content validity, it ispreferable to use more items. Second, this study does not strictly consider the stages inwhich target costs are shown. As Shimizu (1992a) and Yoshida (2003) show, stretch

22

target costs act as either a facilitator or constraint for knowledge-creation, dependingon when target costs are shown. It is possible that statistically meaningful effects of thejoint use of stretch target costs and concurrent processes on cost reduction will befound by considering different stages. Hence, it cannot be said strictly that joint use
does not enhance cost reduction in assembly industries. Finally, the results of thisstudy are limited to explaining cross-industry influence. Thus, this study cannot
explain intra-industry differences in cost-reduction processes. It can be assumed thatthe effects of the joint use of stretch target costs and concurrent processes on cost
reduction might vary with more micro factors, such as product architecture orcapabilities of project teams. These overlooked factors might lower the validity of thefindings, although some level of homogeneity in TCM practices within industries canbe assumed, as Messner (2016) explains.
Future research should consider context variables that will moderatecost-reduction effects of the joint use of stretch target costs and concurrent processes.
Yoshida (2001) empirically indicates that such elements as stretch target costs andconcurrent processes do not always enhance cost reduction and their effect is different
for such businesses as computers and air conditioning. This is because different
business environments, such as those with novelty of technology and market
dynamism, need different ways to reduce costs. Hence, it seems that the effectivenessof the joint use of stretch target costs and concurrent processes is determined in thesecontextual factors. Future research should explore and examine their effects. In
addition, in order to accurately interpret the result that using stretch target costs andconcurrent processes enhances cost reduction for firms in process industries, field
investigation about TCM practices in these industries is needed. Unfortunately, there
are few such investigations. Clarification of TCM activities in these industries mightenhance knowledge about the current state of Japanese manufacturing industries andthis enables understanding of why the use of stretch target costs and concurrent
processes enhances cost reduction in process industries.

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