（19）1KEIO BUSINESS REVIEW
No. 54, 2020
pp.19-38.

Final version received on 12th September, 2020

Complementary Effects of Stretch Target Costs andConcurrent Processes on Cost Reduction:
A Dynamic Tension Perspective
By

Eisuke Yoshida†
Keita Masuya‡
Abstract
Recent empirical evidence has not supported anecdotal evidence of the effectiveness of joint use of stretch target costs and concurrent processes. The goal of thisresearch is to examine the joint effects of stretch target costs and concurrent processeson cost reduction. Specifically, this study builds on the dynamic tension perspective to
explain how and why joint use of stretch target costs and concurrent processes areassociated with superior performance. Applying this perspective, we assume that theirjoint use is accompanied by dynamic tensions that drive creative generation of cost
reduction ideas. Nevertheless, we find no statistically meaningful relationship using
multiple regression analysis for a sample of large Japanese manufacturing firms.
However, supplementary analysis shows that the joint effects enhance cost reduction forfirms in process industries but not those in assembly industries. Moreover, ad hoc analysis
shows that concurrent processes enhance cost reduction when target costs are set atstretch levels. These results reflect the characteristics of Japanese process industries that
manufacture products of high quality and technology while achieving low cost. This
study extends previous research by building on the dynamic tension perspective to
explain how and why the joint use of stretch target costs and concurrent processes
enhances cost reduction. Moreover, it extends the existing literature by suggestingtarget cost management is effective in process industries.

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

1. Introduction
Japanese manufacturing firms have achieved cost competitiveness by using target costmanagement (TCM) to manage costs in the product development stage (Ansari et al., 2007;
Hiromoto, 1988; Kato, 1993b; Tani et al., 1994). TCM refers to a system of profit planning andProfessor, Faculty of Business and Commerce, Keio University
Assistant Professor, Faculty of Commerce, Takachiho University

†
‡

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KEIO BUSINESS REVIEW No.54

cost management in the earliest stages of product development, such as during productplanning or design. Given that the market determines product prices and most productcosts are determined when product specifications are defined, TCM is used to generatelarger profits through effective cost management in these stages (Sakurai, 1989). Since
Toyota’s development of 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).
Target costs are calculated by subtracting target profit from the expected sales price(Kato, 1993b), and when price competition intensifies, the market determines sales prices.
In this situation, firms need to effectively manage costs to earn their expected profits. One
characteristic of TCM in Japan is that stretch levels of target costs are extremely difficult toachieve; they are virtually unattainable goals that can be achieved only with revolutionarychanges in operations. Japanese manufacturing firms, especially those in assembly
industries, have attempted to set stretch level target costs to achieve higher profits (Sakurai,
1989; Tani et al., 1994). Furthermore, in Japan, these extremely difficult to achieve target
costs contribute to drastic cost reductions (Kato, 1993a; Tani et al., 1993a).
Although setting stretch target costs is an important step in TCM, it is difficult torealize effective cost reduction without support tools. Prior studies indicate concurrent
processes play a role (Iwabuchi, 1992; Koga and Davila, 1999). Concurrent processes areoverlapping and parallel processes in product development that involve various departmental managers (Carter and Baker, 1992; Takeuchi and Nonaka, 1986). Concurrent processes
contribute to effective new product development in Japanese manufacturing firms by significantly shortening time to market and achieving high productivity (Clark and Fujimoto,1991).
Previous research suggests that joint use of stretch target costs and concurrent
processes enhances cost reduction; however, there is little empirical evidence to support
this. Specifically, the Japanese TCM literature based on knowledge creation theory orinteractive control theory theoretically suggests that their joint use enhances cost reduction(Iwabuchi, 1992; Koga and Davila, 1999). On the contrary, Gopalakrishnan et al.’s (2015) experimental study based on Hirst’s (1987) theory suggests their joint use is ineffective for cost
reduction because overlapping processes result in unexpected changes in product specifications or readjusting designs; as a result, costs might increase. However, there is little
empirical evidence of their joint effects, and different theoretical bases predict opposing
effects of their joint use.
This study aims to fill this gap. Specifically, to explain how and why their joint effects
influence cost reduction, this study relies on the dynamic tension perspective developed inmanagement control research (Chenhall and Morris, 1995; Henri, 2006; Simons, 1995). This
perspective was developed to explain why highly innovative firms combine organic
processes with formal control systems when prior research based on contingency theory
assumes their combined use is inconsistent or paradoxical (Chenhall, 2006). The dynamic
tension perspective assumes that formal control systems can support translation of ideas
generated from organic processes such as horizonal communication into effective
innovations consistent with organizational objectives. Furthermore, new information, ideas,
and strategies developed via organic processes can be effectively monitored by formal
controls. Joint use of formal control systems and organic processes creates dynamic tensions
that enhance performance by acting complementarily (Chenhall and Morris, 1995; Henri,2006). This perspective is useful for explaining how and why joint use of stretch target costs

Complementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction（21）
3and concurrent processes are associated with superior performance.
Using ordinary least squares (OLS) and Tobit regression analysis with survey data fromlarge Japanese manufacturing firms, no statistically meaningful relationship between the
joint effects and cost reduction is found in the full sample analysis. However, supplementary
analysis shows the joint effects enhance cost reduction for firms in process industries but
not those in assembly industries. Furthermore, the results indicate that concurrent
processes enhance cost reduction in process industries when target costs are set at highstretch levels.
This study makes several contributions to the TCM literature. First, it theoreticallyexplains how and why joint use of stretch target costs and concurrent processes enhances
cost reduction. The dynamics of TCM activities are associated with superior productdevelopment performance is not well understood (Ansari et al., 2007). The dynamic tension
perspective is useful for explaining how the dynamic nature of TCM activities that
accompany tensions or conflicts affect performance. This study explains the mechanism byrelying on the dynamic tension perspective developed in management control research.
Second, this study provides empirical evidence of the effect of joint use of stretch target
costs and concurrent processes on cost reduction. There is much anecdotal evidence about
their interaction effects, and some studies indicate their joint use is effective. Contrary tothese findings, this study’s empirical evidence suggests their joint use is not always effective.
This result is important for future research as it highlights the need to explore contextual
variables that determine the effectiveness of their joint use.
Third, this study empirically demonstrates the possibility that the cost reduction
effects of TCM activities depend on the adopting firm’s industry. The study’s results indicate
joint use of stretch target costs and concurrent processes enhances cost reduction for firms
in process industries but not for firms in assembly industries. This result is importantbecause prior studies have concentrated on TCM practices in assembly industries, leaving
a lack of evidence regarding how TCM practices work for firms in process industries.
The remainder of this paper is structured as follows. Section 2 provides a theoreticalmodel of dynamic tensions for TCM and introduces the hypotheses. Section 3 describes thedata collection and variable measurement. Section 4 presents the analyses and results,
which are interpreted in Section 5. Finally, Section 6 provides the conclusions, limitations,and implications for future research.

2. Theory and hypotheses
2.1 Dynamic tension perspective
The dynamic tension perspective is useful for explaining the joint effects of stretch
target costs and concurrent processes on cost reduction. This perspective has beendeveloped as disproof of the perceived inconsistency between formal control systems andinnovation.
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 relationship between managers’
efforts and performance. In this situation, formal control systems inhibit innovation because
they force managers to focus excessively on short-term performance targets, which may

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KEIO BUSINESS REVIEW No.54

prevent new idea generation that would contribute to future performance. Hence, it has
been considered that formal control systems are inconsistent with innovation (Abernethy
and Brownell, 1997; Lövstal and Jontoft, 2017; Rockness and Shields, 1984).
Contrary to traditional theory, Simons (1987) empirically finds that highly innovative
firms use formal control systems more than conservative firms. Although this result seems
inconsistent with prior studies, he interprets it from the viewpoint of types of controls. Later,
he classifies controls using two types: diagnostic and interactive (Simons, 1995). Diagnostic
controls are similar to traditional mechanistic types of controls that aim to measuredeviations and implement corrective actions to achieve preset performance targets. By
contrast, interactive controls are organic types of controls that enable employees to search
for new opportunities, stimulate dialogue, and generate ideas to enhance organizational
learning or the emergence of new strategies (Simons, 1995). Interactive controls are characterized as positive control levers and are useful in highly uncertain environments wherestrategic change and innovation are greatly needed (Abernethy and Brownell, 1999 ; Bisbe andOtley, 2004).
Since types of control systems were classified, empirical research has examined theperformance effects of the tensions created by joint use of opposing types of controls(Chenhall and Morris, 1995; Henri, 2006; Tessier and Otley, 2012; Widener, 2007). From a
traditional viewpoint, the combined use of formal control systems and organic processes
that pursue organizational learning or innovation seems to be inconsistent or paradoxical.
The dynamic tension perspective assumes their joint use offers benefits rather than disadvantages. Specifically, this perspective assumes that formal control systems can support
the translation of ideas generated by organic processes into effective innovations that areconsistent with organizational objectives (Chenhall and Morris, 1995). Furthermore, newinformation, ideas, and strategies developed by organic processes can be effectively
monitored by formal (diagnostic) controls (Henri, 2006). The relationship between formal
controls and organic processes is not substitutionary but complementary (Heinicke et al.,2016; Widener, 2007). Hence, joint use of formal control systems and organic processes
stimulates dynamic /creative tensions that enhance innovation, organizational learning,
and ultimately, organizational performance (Chenhall and Morris, 1995; Henri, 2006; Widener,2007). Later studies support the certainty of this perspective by confirming the positiveeffects of joint use of opposing types of controls on product development or project
performance (Bedford, 2015; Ylinen and Gullkvist, 2014). This perspective suggests joint useof opposing types of controls enhances performance by creating dynamic tensions (Henri,2006).1,2
2.2 Stretch target costs
Stretch target costs are virtually unattainable goals that can be achieved only throughrevolutionary operational changes. Target cost is calculated as “target cost = expected sales
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 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).
2
Although previous empirical research has focused not only on diagnostic and interactive use but also the belief orboundary systems of Simons’ (1995) framework (Bedford, 2015; Heinicke et al., 2016; Widener, 2007), this study
focuses only on diagnostic and interactive controls because the interest is in how a specific management accounting
system, TCM, enhances cost reduction by creating dynamic tensions, and beliefs and boundary systems are not thecentral concern. Grafton et al. (2010) and Ylinen and Gullkvist (2014) are relevant studies that focus only on the
diagnostic and interactive use of control systems.

Complementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction（23）
5

price − target profit” (Kato, 1993b). Theoretically, the expected sales price is driven by themarketplace and target profit is determined through medium or long-term organizational
profit planning (Kato, 1993b; Sakurai, 1989).
In the target cost calculation using the expected sales price, one aspect of the equationreflects a customer orientation (Cooper and Slagmulder, 1997). As the market environment
becomes more competitive, the market determines product prices, forcing firms to set
almost the same price as their competitors. In this situation, Japanese manufacturing firms
use a pricing method called “pricing by functions” (Kato, 1993b, p. 38). Creating products
requires many functions, and the total value of each function is combined to determine thesales price (Kato, 1993b). To manage costs effectively, firms need to completely exclude
functions that are unnecessary from the viewpoint of customer needs (Kato, 1993b; Sakurai,1989).
The other aspect of the equation is the strong linkage between profit planning andtarget costs. Because of this, target costs come to mean a commitment agreed upon by
every person who participates in TCM activities (Kato, 1993b). In the early phase of TCM
practices, before they mature, specific products are linked to profit planning. Later, as more
TCM activities mature, profit planning becomes linked not just to specific products but toall products. In other words, target costs are calculated based on how much each productcontributes to the organization’s overall profit (Kato, 1993b).
In Japan, the level of target costs calculated using these processes tends to be extremely
difficult to achieve (Hiromoto, 1988; Kato, 1993b; Tani et al., 1994). According to Thomas et al.
(1997), stretch targets are virtually unattainable goals that force organizations to significantly alter their processes in a way that often involves an entirely new operational paradigm.
Without such revolutionary change, stretch targets cannot be achieved. An extremely
difficult to achieve target is not supported by goal-setting theory (Thomas et al., 1997), whichsuggests challenging but achievable goals are desirable for enhancing goal commitment, inturn contributing to desirable performance (Locke and Latham, 1990). There are enormous
gaps to close between target costs and drifting costs, which cannot be achieved with thecurrent way of thinking; capabilities and substantial effort are required for a new way of
thinking. Prior studies suggest stretch target costs contribute to drastic cost reduction in
Japan (Kato, 1993b). 3 For example, Tani et al. (1993a) find that the tightness of target costs is
positively correlated with their achievement. Similarly, Koga and Davila (1999) find that themore difficult stretch target costs are to achieve, the greater the reduction in actual costs.
This discussion leads to the following preliminary hypothesis:
Hypothesis 1. Stretch target costs enhance cost reduction.
2.3 Concurrent processes
Sequential engineering is the traditional approach to new product development; known
as “throwing it over the wall,” it focuses on developing a structured process with clearly
defined and sequential phases (Takeuchi and Nonaka, 1986). The job classification of eachdepartment that participates in product development, such as product planning,
3
According to goal-setting theory, which focuses on individual recognition, the specificity and difficulty of goals
enhance performance by directing attention to goals and motivating greater efforts (Locke and Latham, 1990). The
dynamic tension perspective focuses on organizational states or processes and indicates that tensions or conflicts are
beneficial to organizational performance but avoiding or suppressing them leads to undesirable consequences(Henri, 2006; Lewis, 2000).

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KEIO BUSINESS REVIEW No.54

development, design, production preparation, and manufacturing, is rigorous, and each one
acts independently. Sequential processes take a long time to develop and carry the risk ofcreating problems on cost or quality for the later stages of product development (Takeuchi
and Nonaka, 1986).
Concurrent processes are completely unlike the traditional approach. Concurrent
processes refer to overlapped activities among the department managers in the productdevelopment process (Carter and Baker, 1992). They are also called “rugby-style productdevelopment” or “simultaneous engineering.” For example, detailed design engineers
communicate with basic design engineers before the basic design is determined.
Furthermore, production engineering managers interact with basic design engineers
before the basic design is determined to infuse ideas about the new product’s manufacturability to secure considerable cost savings in the manufacturing stage (Tani, 1995).
Overlapping these processes earlier prevents problems that might occur in later productdevelopment stages and contributes significantly to high productivity and performance inJapanese manufacturing firms (Clark and Fujimoto, 1991; Takeuchi and Nonaka, 1986).
Previous research indicates 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 amongcross-functional managers brings more creative ideas into product development thaninteraction solely among members belonging to the same department. The cooperation ofvarious managers before a blueprint is prepared triggers more creative ideas because many
options for cost reduction exist. Furthermore, sharing thoughts and ideas triggers new idea
generation, contributing to cost reduction. Yoshida (2003) empirically finds that interaction
among managers from different departments is more effective for cost reduction than theindividual performance effects of each TCM tool, supporting these suggestions.
Hypothesis 2. Concurrent processes enhance cost reduction.
2.4 Joint effects of stretch target costs and concurrent processes on cost reduction
Some studies attempt to explain the cost-reduction effects of the joint use of stretch
target costs and concurrent processes. Based on knowledge-creation theory (Nonaka, 1990),
Shimizu (1992a) theoretically explains how tight target costs encourage or discourage
individual knowledge-creation activities. Shimizu (1992a) states that as long as target cost
tightness is well-managed, participants can understand which existing solutions or previous
experiences contribute little to meeting the target and then begin to seek new solutions.
Subsequently, Shimizu (1992b) extends his discussion about the role of target cost
information at the individual level to the group level, identifying target cost information’srole as a catalyst for transmitting knowledge and information. That is, because target cost
information acts as a commonly shared objective for each individual or team, it becomeseasier to drive team efforts, horizontal and vertical interaction, and cross-functional
activities. Consequently, existing solutions or previous experiences are rejected, and new
solutions are developed. Similarly, Iwabuchi (1992) focuses on the role of sharing information
among departments using a case study. He explains that shared information leads tocooperative efforts among functions, and the collection of expertise, professional experience,and knowledge then turns into unique solutions. Furthermore, Koga and Davila (1999)
suggest that stretch target costs initiate intense interactions between product and process

Complementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction（25）
7engineers, as well as frequent monitoring of the gap between the target cost and costestimate. Thus, target costs act as a catalyst for organizational learning among managers
and contribute to good actual performance.
Contrary to these studies, Gopalakrishnan et al.’s (2015) experimental study based on
Hirst’s (1987) theory explains that using specific goals for cost reduction under concurrent
processes is less effective than under sequential processes. Based on Hirst’s theory,
Gopalakrishnan et al. (2015) assume the effects of specific target costs decrease underconcurrent processes because they enhance task uncertainty. Because of this, changes inor readjustment of designs increase, ultimately increasing costs. However, their study
ignores the dynamics in concurrent processes that create new and useful ideas, which
contribute to product development performance by jointly using specific target costs.
The dynamic tension perspective is useful for explaining how the dynamics of TCM
activities such as joint use of stretch target costs and concurrent processes is associatedwith superior performance. Building on this perspective, these two notions—stretch target
costs and concurrent processes—can be regarded as formal control systems and organic
processes, respectively. Concurrent processes can be assumed to enhance cost reduction
through stretch target costs, which act as a shared objective (Chenhall and Morris, 1995;
Henri, 2006; Shimizu, 1992b; Widener, 2007). Setting stretch target costs not only triggers new
knowledge by creating a chaotic environment at the individual level but also enhances new
idea generation at the group level through cooperation among individuals with different
viewpoints (Iwabuchi, 1992; Koga and Davila, 1999; Shimizu, 1992b). Joint use creates dynamic
tensions that enhance performance (Henri, 2006). This leads to the following hypothesis:
Hypothesis 3. The dynamic tension resulting from joint use of stretch target costs andconcurrent processes enhances cost reduction.

3. Method
3.1 Data collection
A cross-sectional questionnaire survey was administered among large Japanese manufacturing firms. Japanese manufacturing firms are appropriate as respondents because thestudy’s hypotheses are developed based on Japanese TCM practices and literature. Questionnaires were mailed to executive officers or directors of firms’ accounting departments.
Because accounting managers are assumed to be quite familiar with TCM practices, they
are appropriate respondents. Specifically, previous research indicates that accounting
managers in Japan frequently participate in cost meetings (Tani, 1995; Tani et al., 1994).
Furthermore, the TCM office is often located within the accounting department (Kato,
1993a; Okano and Suzuki, 2007). The questionnaire, in Japanese, was pre-tested among morethan 30 academics and controllers in several companies.
The questionnaire was sent by mail on January 14, 2014 to 847 manufacturing firms
listed on the First Section of the Tokyo Stock Exchange, with January 31, 2014 the deadline
for response. It instructed respondents to answer all questions regarding their core business
unit’s practices. To improve the response rate, postcard reminders were sent to those who
had not responded.
In total, 130 firms responded, an overall response rate of 15.3%. After removing firmsthat did not use TCM (23) and questionnaires with missing data (9), the final sample for

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KEIO BUSINESS REVIEW No.54
Table 1. Sample characteristics
Industry

Assembly industry
Machinery
Electrical/electronics
Transportation equipment
Precision equipment
Process industry
Food
Textile mill
Pulp/paper
ChemicalPharmaceuticals
Oil/coal
RubberGlass/clay
SteelNon−ferrous/non−fabricated metal
Fabricated metal
Other manufacturing
Total

Sent

Valid responses/rate (%)

Sample

120
154
62
28

12
27
16
2

10.0
17.5
25.8
7.1

69
41
11
128
38
11
11
33
32
24
37
48

13
4
2
18
5
1
2
4
4
4
8
8

18.8
9.8
18.2
14.1
13.2
9.1
18.2
12.1
12.5
16.7
21.6
16.7

52
10
25
15
2
46
10
4
2
13
4
0
1
3
3
2
4
0

847

130

15.3

98

analysis includes 98 firms. Table 1 shows the details of the response rate.
3.2 Variable measurement
3.2.1 TCM usage, stretch target costs, concurrent processes, and cost reduction
Respondents were first asked whether their business unit uses TCM. Since firms orbusiness units may use techniques similar to TCM without realizing they are TCM
techniques, a general description of TCM was provided (Dekker and Smidt, 2003). Specifically, respondents were asked whether their business units set or manage target costs at the
product planning, development, and design stages of new product development (yes or no).
Respondents who answered “yes” were asked to answer other questions regarding TCM
practices.
The survey constructs of stretch target costs and concurrent processes were composedof one instrument each, as follows. The stretch target cost question (STC) relates to thedifficulty of achieving target costs: “Are target costs set at a challenging level that cannot be
achieved easily at the starting point of product development processes?” The concurrent
processes (CP) question is: “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 by theirproduct term. Prior to forming the product term, the two independent variables were meancentered because the product term is strongly correlated with each independent variable.
The dependent variable, cost reduction, was measured by how effective TCM activities
are in cost reduction. Respondents were asked to rate the effectiveness of TCM activities inComplementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction（27）
9

cost reduction using a scale of 1-7, where 1 indicates “not effective” and 7 indicates “veryeffective.” 4
3.2.2 Control variables
Environmental complexity and uncertainty moderate the relationship between TCM
elements and performance (Yoshida, 2001). The effects of these environmental factors
should be controlled to measure the potential impact of TCM elements on cost reduction.
Tani’s (1995) items are used because they are suitable for examining business environments
in Japan. The items for environmental complexity are the degree of product market diversity(Diversity), community of technology with competitors (Community), and variety of sales
promotions (Variety). The items for environmental uncertainty are the degree of productmarket competitiveness (Competitiveness), frequency of developing new products andtechnology (Frequency), and inaccuracy in estimating customer demand (Inaccuracy).
Respondents were asked to rate their perceived environmental complexity and uncertainty
on a scale of 1-7.
Organizational size (Size) is also included, measured by the natural logarithm of sales. 5

4. Results
4.1 Descriptive statistics and variable correlations
The descriptive statistics of the survey constructs are presented in Table 2, and thecorrelation matrix is presented in Table 3. Table 3 shows statistically significant positive
correlation coefficients for STC and CP (r = .257, p = .011). This result demonstrates they areused complementarily.
4.2 Hypotheses tests
Multiple regression analysis is performed to examine the joint effects of stretch target
costs and concurrent processes on cost reduction. Two models are used; model 1 examines
the main effects of STC and CP and includes control variables. Model 2 includes the joint
effect term of STC and CP. Equation (1) is estimated to test the hypotheses.
Cost reduction = α + β1STC + β2 CP + β3 STC*CP + ΣControls + ε(1)Table 4 summarizes the estimation results of the OLS regression. The results of model
1 show positive coefficients of STC and CP on cost reduction (β = .313, p = .011 and β = .273,
p = .025, respectively). Hence, hypotheses 1 and 2 are both preliminarily supported. The
positive effects of STC on cost reduction are consistent with Tani et al.’s (1993a) results,
which indicate a correlation between tightness and achieving target costs. In addition, thepositive association between CP and cost reduction is consistent with previous literaturesuggesting or confirming that interaction among functional managers effectively reduces
cost (Tani et al., 1993b; Yoshida, 2003).
4
The key variables of stretch target costs, concurrent processes, and cost reduction are measured using one item
because the questionnaire survey’s purpose is to investigate current Japanese management accounting practices.
The questionnaire includes 138 items, such as items about budgets and performance measurement. Because of thisstudy’s exploratory nature, its key variables are measured using one item.
5
Harman’s single factor test is used to examine common method bias (Podsakoff et al., 2003). Confirmatory factor
analysis with no rotation performed on nine questionnaire items reveals four factors. As the contribution ratio offactor 1 is sufficiently low (16.84%), common method bias might not be a serious problem.

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KEIO BUSINESS REVIEW No.54
Table 2. Variable descriptive statistics

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

Stretch target costs (STC)
Concurrent processes (CP)Diversity of product market (Diversity)
Community of technology with competitors (Community)
Variety of sales promotions (Variety)
Competitiveness of product market (Competitiveness)
Frequency of developing new products and technology (Frequency)
Inaccuracy in estimating customer demand (Inaccuracy)
Organizational size (Size)
Cost reduction

Mean

SD

Min

Max

3.73
5.23
3.77
3.51
3.85
5.17
4.69
3.86
5.27
5.17

1.36
1.28
1.57
1.22
1.41
.91
1.33
1.14
.63
1.18

1.00
2.00
1.00
1.00
1.00
2.00
2.00
2.00
3.93
2.00

7.00
7.00
7.00
6.00
7.00
7.00
7.00
6.00
6.99
7.00

8.

9.

Notes: Sample size is 98.

Table 3. Variable correlation
1.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

2.

3.

4.

STC
CP
.257*
−.044
Diversity−.049.294**−.058
Community−.036.351*** .142
Variety.183† −.037
.130
.134
−.053
Competitiveness .013
.006
.232*
.195†
.307**
Frequency
−.048
−.011
.160
.016
Inaccuracy.261** .087
.224*
.030
Size
.352*** .344*** −.011−.070
Cost reduction

5.

.069
.283**
.083
.123
.091

6.

7.

.318**.203* −.036.226*.391*** −.191†
−.066
.000
.173†

.246*

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

In model 2, the coefficient of the joint effects term of STC and CP is not statisticallysignificant (β = −.087, p = .487). Furthermore, the change in the coefficient of determination(R²) is not statistically significant (ΔR 2 = .004, ΔF2 = .488, p = .487). This result indicates thatthe change in explanatory power might not differ if the joint effects term is included.
A Tobit model is also tested because the range of the dependent variable, cost reduction,is restricted. Table 4 reports the results, which are similar to the OLS results; the costreduction effects of STC*CP are again not statistically significant (p > .10). Hence, it cannot
be said that joint use of STC and CP enhances cost reduction.
4.3 Supplementary analysis
To examine the effects of firm industry, an industry dummy variable is created. Thisvariable is included because differences in the maturity or sophistication of TCM between
assembly and process industries seem to create differences in how TCM elements affectperformance. 6 The industry dummy variable equals 1 for firms in assembly industries and 0

Complementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction

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11

Table 4. Estimation results of multiple regression analysis
OLS
STC
CP
STC*CP
Controls
Diversity
Community
VarietyCompetitivenessFrequency
Inaccuracy
SizeIntercept
R²
Adj. R²F
Pseudo−R²
Number of censored observations
Total number of observations
Log likelihood ratio

TOBIT

Model 1Model 2

.313*
.273*

.339**.252*−.087.390**
.265*
−.094

.012
−.126
.020
−.139
.182
−.010
.167

.024
−.145
.019
−.129
.161
−.023
.175

.029
−.166
.006
−.130
.226
−.020
.208

4.290***.231.152
2.936**

4.268***.235.147
2.676**

4.165***

.086
12
98
−145.897

Notes: P-values are two-sided tests. Unstandardized. *p<.05, **p<.01, ***p<.001.

for firms in process industries. Interaction variables, STC*Industry, CP*Industry, andSTC*CP*Industry, are also included. Each industry is sorted using the classification table ofindustries developed by the Securities Identification Code Committee of the Japan ExchangeGroup.7
Multiple regression analysis using Equation (2) is conducted to examine the combined
cost-reduction effects of STC, CP, and Industry.
Cost reduction = α + β1STC + β2 CP + β3 Industry + β4 STC*Industry +β5CP*Industry + β6 STC*CP + β7STC*CP*Industry+ ΣControls + ε(2)Table 5 presents the OLS regression estimation results. In Model 1, the effects ofIndustry are significantly positive at the 10 percent level (β = .457, p = .073). Although the6
TCM was first developed in assembly industries like machinery, electric appliances, transportation equipment,
and precision instruments (Monden and Hamada, 1991; Sakurai, 1989; Tanaka, 1995). Beginning in the 1980s, thesefirms faced diversified customer needs and shorter product life cycles, and thus, had to develop numerous productswith unique characteristics (Sakurai, 1989). To simultaneously achieve low-cost, high-quality products introduced
timely based on changing customer needs, costs must be managed in the early stages of product development
processes. Therefore, TCM is relatively mature in assembly firms but only developing in processing firms (Okano andSuzuki, 2007).
7
A test is conducted to examine differences in this sample’s industry distribution and that of the First Section of theTokyo Stock Exchange. The results indicate there are no statistically significant differences in industry distribution
between respondents and firms belonging to the First Section of the Tokyo Stock Exchange ( χ2 = 11.821, df = 15, p =.693). Hence, this study’s sample industry distribution is representative of the First Section of the Tokyo Stock
Exchange.

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Table 5. Results of multiple regression analysis including industry dummy variable
Model 1
Coefficient
90% CI

Model 2
Coefficient
90% CI

Model 3
Coefficient
90% CI

Model 4
Coefficient
90% CI

STC.279*[ .078, .481].018[−.317, .353]

−.152

CP

.282*

[ .085, .480].360*[ .067, .653].510**[ .204, .816]

STC*Industry.449 †[ .038, .859].672*[ .239, 1.105]

CP*Industry−.128[−.549, .293]−.262[−.684, .161]

STC*CP−.024[−.239, .190].269[−.031, .569]−.539*[−.934, −.145]

STC*CP*Industry[−.502, .199]

Diversity−.015[−.236, .207].030[−.179, .239].068[−.147, .282].055[−.155, .265]

Community−.153[−.369, .063]−.128[−.331, .075]−.113[−.322, .096]−.070[−.276, .136]

Variety.061[−.155, .277].021[−.188, .230].008[−.201, .217].039[−.166, .244]

Competitiveness−.121[−.358, .116]−.132[−.353, .090]−.142[−.371, .086]−.165[−.388, .059]

Frequency.154[−.092, .400].150[−.086, .386].139[−.101, .379].125[−.109, .360]

Inaccuracy−.031[−.239, .178]−.036[−.231, .159]−.029[−.236, .177]−.037[−.239, .164]

Size

.274

[−.090, .637].087[−.263, .437].083[−.273, .438].073[−.274, .420]

Industry.457†[ .038, .875].359[−.040, .758].387[−.014, .788].550*[ .140, .959]

Intercept

3.484**

4.525***

4.494***

4.421***

R²

.125

.250

.281

.323

Adj. R²

.046

.164

.170

.209

F

1.585

2.903**

2.530**

2.834**

Notes: Estimated with OLS-regression analysis. CI means confidence interval. P-values are two-sided tested. Unstandardized.
†

p<.1, *p<.05, **p<.01, ***p<.001, VIF< 4.0.

interaction terms of Industry and STC or CP are not statistically significant in Models 2 and3, STC*CP*Industry is statistically significant in Model 4 (β = −.539, p = .026).
A Tobit regression is also performed, and the results are reported in Table 6. Similar to
the OLS regression results, the negative coefficient of STC*CP*Industry is statisticallysignificant (β = −.671, p = .007). Moreover, the positive coefficient of STC*CP is statisticallysignificant at the 10 percent level if the industry dummy variable is added (β = .320, p = .090).
The results from the OLS and Tobit regressions suggest that the cost-reduction effects ofjoint use of stretch target costs and concurrent processes differ depending on the firm’s
industry.
A simple slope analysis is performed to examine which industries have statisticallysignificant coefficients of STC*CP. Following Aiken and West (1991), the regression line of
CP is estimated when STC takes ±1 standard deviations (SD).
The results shown in Figure 1 indicate that CP enhances Cost reduction when STC ishigh (+1SD) in process industries (β = .748, p = .007). The coefficient of CP when STC is low(−1SD) is not statistically significant (β = .247, p = .247). In contrast, no statistically significant
relationship is found for firms in assembly industries (p > .10). Hence, the results suggestthat the complementary use of STC and CP enhances Cost reduction for firms in processindustries, thereby partially supporting hypothesis 3.

Complementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction
Table 6. Results of Tobit analysis including the industry dummy variable

STC
CP
STC*Industry
CP*Industry
STC*CPSTC*CP*Industry
Diversity
Community
VarietyCompetitivenessFrequency
Inaccuracy
Size
Industry
Intercept

CoefficientStandard error−.123.545**.714**−.291.320 †−.671**
.057
−.083
.027
−.166
.188
−.041
.101
.587*
4.318***

.217
.191
.269
.264
.189
.250
.133
.130
.129
.141
.146
.125
.218
.254
1.119

Pseudo−R²
Number of censored observations
Total number of observations
Log likelihood ratio

.123
12
98
−139.974

Notes: Method is Tobit (lower limit=1, upper limit=7). †p<.1, *p<.05, **p<.01, ***p<.001

Figure 1. Interaction between STC and CP for firms in process industries
7

Cost reduction

6
5
4
3
2

－2

0

－1

1

CP
High STC

Low STC

2

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5. Discussion
The estimation results indicate that the joint use of stretch target costs and concurrent
processes enhances cost reduction for firms in process industries; however, no statisticallymeaningful relationship is found for the full sample or assembly firms alone.
A positive main effect of stretch target costs is found in assembly industries; however,no significant effect is found for concurrent processes and the interaction effect. These
results may indicate that excessive use of concurrent processes is not effective for cost
reduction. Today, firms in assembly industries benefit from TCM support tools, such as
TCM case studies or three-dimensional computer-aided design (3D-CAD). These tools are
effective for cost reduction because TCM case studies encourage idea generation based on
previous TCM product development cases, and 3D-CAD solves component interference
problems. Although these tools are useful for engineers in assembly industry firms, moremeetings because of concurrent processes might create excess workload and hamperautonomy. Busy engineers do not have enough time to use these support tools (Yoshida,2003; 2007); thus, effective ideas for cost reduction may not be developed and further cost
reduction may not be realized.
The estimation results for the positive performance effects of the joint effect term forfirms in process industries reflect the characteristics of Japanese process industries(Fujimoto and Kuwashima, 2009). According to Fujimoto and Kuwashima (2009), previous
product development literature indicates that fine-tuning product development between
parts and functions is needed in assembly industries, such as automobiles, consumer
electronics, and computers. For process industry products, such factors as epoch-making
process technology inventions, investment, and R&D expense amounts have been
considered extremely important. On the contrary, the authors propose that Japanese firms
in process industries, particularly firms that treat industrial materials, also gain capabilities
that accurately achieve the required functions for customers with extremely strict
constraints on quality and costs. To meet these strict customer needs, firms in theseindustries must totally optimize operational steps. Specifically, sharing knowledge withcustomers about product specifications might lead to reduced development costs by avoiding
excessive customization. Furthermore, collaboration between sales and development
departments might lead to better and more accurate understanding of customer needs andhelp avoid excessive customization. This study’s results indicate that concurrent processes
effectively reduce costs for process industry firms. Hence, stretch target costs set to achieve
strict customer needs might strengthen the cost-reduction effects of concurrent processes.

6. Conclusion
This study aims to clarify the cost-reduction effects of the joint use of stretch target
costs and concurrent processes. To examine their joint effects, the 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 is found when the full sample isanalyzed. However, supplementary analysis using an industry dummy variable shows thatthe joint effects enhance cost reduction for firms in process industries but not those inassembly industries. Moreover, ad hoc analysis of process industry firms indicates that

Complementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction

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15

concurrent processes enhance cost reduction when target costs are set at high stretch
levels.
This study contributes to the growing body of TCM literature. The study theoreticallyexplains 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 stretch target costs and concurrent processes positively affect cost
reduction. The study’s dynamic tension perspective is useful for explaining how the dynamic
nature of TCM activities that accompany tensions or conflicts affect performance, which
has not been adequately studied (Ansari et al., 2007).
In addition, this study provides empirical evidence about these interaction effects,
which have primarily been suggested theoretically in prior studies. Contrary to expectations, no statistically significant relationship is found when the full sample is analyzed.
However, supplementary analysis finds the expected results for firms in process industries.
These results imply that different product development processes require different TCM
practices, as Messner (2016) indicated theoretically. This study’s results are important fordeveloping future research because differences in TCM practices between industries are
not well known.
This study also has implications for managerial practices. The results suggest effective
tools for cost reduction differ by firm or business unit industry. This is important for practice,especially for firms in process industries, because successful TCM factors for cost reductionin these industries have not been demonstrated before. The study’s results suggest
concurrent processes among departments are effective for cost reduction at early stages of
product development. By contrast, for firms in assembly industries, the results suggest thatthe effective means of reducing costs might have changed since the 1990s. In other words,
firms in assembly industries should avoid excessive use of concurrent processes that erodeengineers’ autonomy and lead to exhaustion. The results suggest that setting stretch target
costs and decomposing target costs by function, part, department, and so on are important
cost reduction factors, because decomposing target costs enhances controllability andenables target achievement.
This study has several limitations. First, the sample size and number of survey
instruments are small. Specifically, only one questionnaire item is used for the survey
constructs. Although each survey item captures the construct’s content, supporting content
validity, using more items helps ensure the reliability and validity of the survey constructs.
Second, endogeneity concerns cannot be eliminated because of the cross-sectional survey.
Several factors related to TCM cannot be controlled, such as how target costs aredecomposed and certain tools such as cost tables and VE (Tani et al., 1994). These uncontrolled omitted variables may lead to bias. Third, the study results are limited to explaining
cross-industry influences. Thus, intra-industry differences in cost-reduction processes
cannot be explained. The 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 or project team capabilities. These overlooked factors might lower the validity of thefindings, although some level of homogeneity in TCM practices within industries can beassumed, as Messner (2016) explains. Finally, Japanese characteristics might influence thestudy’s results. The relative maturity of TCM practices in Japan compared to other countries(Ansari et al., 2007) might influence the cost-reduction effects of stretch target costs andconcurrent processes; results in other countries where TCM maturity is low might differ
from these results.

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Despite these limitations, this study provides insights into the effectiveness of the joint
use of stretch target costs and concurrent processes and establishes the basis for several
directions in future research. First, research should consider context variables thatmoderate the cost-reduction effects of joint use. Yoshida (2001) empirically finds thatelements such as stretch target costs and concurrent processes do not always enhance cost
reduction, and their effect is different for businesses such as computers and air conditioning. This is because different business environments, such as those with novel technology
and market dynamism, require different approaches to cost reduction. Hence, it seems thatthe effectiveness of joint use of stretch target costs and concurrent processes is determinedby these contextual factors. Future research should explore and examine their effects.
Second, future research should consider the stage at which target costs are identified. As
Shimizu (1992a) and Yoshida (2003) show, stretch target costs act either as a facilitator foror constraint to knowledge creation, depending on when target costs are identified. It is
possible that statistically meaningful effects of joint use of stretch target costs andconcurrent processes on cost reduction will be found by considering different stages. Third,
accurately interpreting the result that using stretch target costs and concurrent processes
enhances cost reduction for firms in process industries requires field investigation of TCM
practices in these industries. Unfortunately, there are few such investigations. Clarificationof TCM activities in these industries might enhance knowledge about the current state ofJapanese manufacturing industries and enable an understanding of the cost reduction
generated by joint use in process industries.

References
Abernethy, M. A., and P. Brownell (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 P. Brownell (1999). “The Role of Budgets in Organizations Facing Strategic
Change: An Exploratory Study”, Accounting, Organizations and Society, 24, 189-204.
Aiken, L. S., and S. G. West (1991). Multiple Regression: Testing and Interpreting Interactions.
London: Sage.
Ansari, S. L., J. Bell, and H. Okano (2007). “Target Costing: Uncharted Research Territory”, in
Handbook of Management Accounting Research (Vol. 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 D. Otley (2004). “The Effects of Interactive Use of Management Control Systems on
Product Innovation”, Accounting, Organizations and Society, 29, 709-737.
Carter, D. E., and B. S. Baker (1992). Concurrent Engineering: The Product Development Environment
for the 1990s. Boston, MA: Addison-Wesley.
Chenhall, R. H. (2006). “Theorizing Contingencies in Management Control Systems Research”, in
Handbook of Management Accounting Research (Vol. 1), ed. by C. S. Chapman, A. G. Hopwood,and M. D. Shields. Oxford: Elsevier, 163-205.
Chenhall, R. H., and D. Morris (1995). “Organic Decision and Communication Processes andManagement Accounting Systems in Entrepreneurial and Conservative BusinessComplementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction

（35）
17

Organizations”, Omega, International Journal of Management Science, 23(5), 485-497.
Clark, K., and T. Fujimoto (1991). Product Development Performance: Strategy, Organization andManagement in the World Auto Industry. Boston, MA: Harvard Business School.
Cooper, R., and R. Slagmulder (1997). Target Costing and Value Engineering. Portland, OR:
Productivity Press.
Dekker, H., and P. Smidt (2003). “A Survey of the Adoption and Use of Target Costing in Dutch
Firms”, International Journal of Production Economics, 84, 293-305.
English, T. (2001). “Tension Analysis in International Organizations: A Tool for Breaking Down
Communication Barriers”, The International Journal of Organizational Analysis, 9(1), 58-83.
Fujimoto, T., and K. Kuwashima (2009). Nihongata Purosesu Sangyo: Monozukuri Keieigaku ni yoru
Kyosoryoku Bunseki [Japanese Process Industries: A Competitive Analysis of Their Manufacturing
Systems]. Tokyo: Yuhikaku.
Gopalakrishnan, M., T. Libby, J. A. Samuels, and D. Swenson (2015). “The Effect of Cost Goal
Specificity and New Product Development Process on Cost Reduction Performance”,
Accounting, Organizations and Society, 42, 1-11.
Grafton, J., A. M. Lillis, and S. K. Widener (2010). “The Role of Performance Measurement and
Evaluation in Building Organizational Capabilities and Performance”, Accounting,
Organizations and Society, 35, 689-706.
Heinicke, A., T. W. Guenther, and S. K. Widener (2016). “An Examination of the Relationship
Between the Extent of a Flexible Culture and the Levers of Control System: The Key Role ofBeliefs Control”, Management Accounting Research, 33, 25-41.
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.
Hirst, M. K. (1987). “The Effects of Setting Budget Goals and Task Uncertainty on Performance: A
Theoretical Analysis”, The Accounting Review, 774-784.
Iwabuchi, Y. (1992). “Genka Kikaku no Kino: Jyoho-kyoyu-ka to Chishiki Sozo no Kanten kara
[Functions of Target Costing: Information Sharing and Knowledge Creation]”, Kigyo Kaikei,
4(8), 41-47.
Kato, Y. (1993a). Genka Kikaku: Senryakuteki Kosuto Manejimento [Target Costing: Strategic Cost
Management]. Tokyo: Nihon Keizai Shimbun.
Kato, Y. (1993b). “Target Costing Support Systems: Lessons from Leading Japanese Companies”,
Management Accounting Research, 4(1), 33-47.
Koga, K., and A. Davila (1999). “What is the Role of Performance Goals in Product Development?
A Study of Japanese Camera Manufacturers”, in Dynamic Strategy Resources: Development,
Diffusion and Integration, ed. by H. A. Hitt, P. G. Clifford, R. D. Nizon, and K. P. Coyte. London:
John Wiley & Sons, 407-431.
Lewis, M. W. (2000). “Exploring Paradox: Toward a More Comprehensive Guide”, Academy ofManagement Review, 25(4), 760-776.
Locke, E. A., and G. P. Latham (1990). A Theory of Goal Setting and Task Performance. Englewood
Cliffs, NJ: Prentice Hall.
Lövstal, E., and A. M. Jontoft (2017). “Tensions at the Intersection of Management Control andInnovation: A Literature Review”, Journal of Management Control, 28, 41-79.
Messner, M. (2016). “Does Industry Matter? How Industry Context Shapes Management
Accounting Practices”, Management Accounting Research, 31, 103-111.
Monden, Y., and K. Hamada (1991). “Target Costing and Kaizen Costing in Japanese Automobile

18
（36）

KEIO BUSINESS REVIEW No.54

Companies”, Journal of Management Accounting Research, 3, 16-34.
Nonaka, I. (1990). Chishiki-Sozo no Keiei [A Theory of Organizational Knowledge Creation]. Tokyo:
Nihon Keizai Shinbun.
Okano, H., and T. Suzuki (2007). “A History of Japanese Management Accounting”, in Handbook ofManagement Accounting Research (Vol. 2) ed. by C. S. Chapman, A. G. Hopwood, and M. D.
Shields. Oxford: Elsevier, 1119-1137.
Podsakoff, P. M., S. B. MacKenzie, J. Y. Lee, and N. P. Podsakoff (2003). “Common Method Bias in
Behavioral Research: A Critical Review of the Literature and Recommended Remedies”,
Journal of Applied Psychology, 88, 879-903.
Rockness, H. O., and M. D. Shields (1984). “Organizational Control Systems in Research andDevelopment”, Accounting, Organizations and Society, 9(2), 165-177.
Sakurai, M. (1989). “Target Costing and How to Use It”, Journal of Cost Management, 3(2), 39-50.
Shimizu, N. (1992a). “Genka Kikaku Katsudo ni okeru Mokuhyo Genka Jyoho to Chishiki-Sozo
Katsudo no Kankei [The Relationship Between Target Costing Information and Knowledge
Creation Activities]”, Sangyo Keiri, 51(4), 132-139.
Shimizu, N. (1992b). “Syudanteki Chishiki-Sozo Katsudo ni okeru Mokuhyo Genka Jyoho no
Yakuwari [The Role of Target Costing Information in Target Costing as Knowledge Creating
Activities]”, Kigyo Kaikei, 44(10), 91-96.
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 I. Nonaka (1986). “The New Product Development Game”, Harvard Business
Review, 64(1), 137-146.
Tanaka, M. (1995). Genka Kikaku no Riron to Jissen [Target Cost Management and Profit Engineering].
Tokyo: Chuo Keizai.
Tani, T. (1995). “Interactive Control in Target Cost Management”, Management Accounting
Research, 6, 399-414.
Tani, T. (1996). “Genka Kikaku no Eremento: Niti-Doku Hikaku [Elements of Target Cost
Management: A Japanese-German Comparison]”, Kokumin Keizai Zasshi, 173(3), 35-49.
Tani, T., H. Okano, N. Shimizu, Y. Iwabuchi, J. Fukuda, and S. Cooray (1993a). Genka Kikaku no
Jittutai Chosa: Genka Kikaku no Kontinjenshi Riron [Contingency Theory of Target Costing]”,
Kigyo Kaikei, 45(6), 797-804.
Tani, T., H. Okano, N. Shimizu, Y. Iwabuchi, J. Fukuda, and S. Cooray (1994). Target Cost
Management in Japanese Companies: Current State of the Art”, Management Accounting
Research, 5, 67-81.
Tani, T., N. Shimizu, Y. Iwabuchi, and J. Fukuda (1993b). “Genka Kikaku to Kaigitai deno Sogo-Sayo[Target Cost Management and Interaction among Managers in Meetings]”, Kaikei, 144(3),80-92.
Tessier, S., and D. Otley (2012). “A Conceptual Development of Simons’ Levers of Control
Framework”, Management Accounting Research, 23, 171-185.
Thomas, K. R., W. A. Hochwarter, and N. J. Mathys (1997). “Stretch Targets: What Makes Them
Effective?”, Academy of Management Executive, 11(3), 48-60.
Widener, S. K. (2007). “An Empirical Analysis of the Levers of Control Framework”, Accounting,
Organizations and Society, 32, 757-788.
Ylinen, M., and B. Gullkvist (2014). “The Effects of Organic and Mechanistic Control in Exploratory
and Exploitative Innovations”, Management Accounting Research, 25, 93-112.

Complementary Effects of Stretch Target Costs and Concurrent Processes on Cost Reduction

（37）
19

Yoshida, E. (2001). “Genka Kikaku Katsudo wo Shien Suru Soshikinoryoku to Pafuomansu to no
Kankei: Bo Denki Meka ni okeru Jigyo kan Hikaku [Empirical Study About the Relationship
Between Organizational Capabilities for Target Cost Management and Performance:
Comparison among Three Divisions at a Japanese Electric Company]”, Cost Accounting
Research, 25(2), 1-9.
Yoshida, E. (2003). Genka Kikaku Noryoku [Organizational Capabilities for Target Costing]. Tokyo:
Chuo Keizai.
Yoshida, E. (2007). “Ko Hinshitsu to Tei Kosuto no Jirenma: Jidosha Rikoru Genin Bunseki ni yoru
Kosatsu [Dilemmas Between High Quality and Low Cost: An Analysis of Japanese Automobile
Recall Data]”, Mita Business Review, 49(7), 47-61.