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280

How Should Suppliers Respond to Economic
Crises? Lessons from the Japanese Auto Parts
Industry
Atsushi Inuzuka
Nagoya University

ABSTRACTThe aim of this paper is to describe the effect of suppliers’ choices of either limiting or
broadening their customer base as well as their product lines. To identify the choices
made, I used information on the transaction volume of auto parts shipped to each of theJapanese automakers over 2005-2012, a period that included the financial crises of theLehman Crash (the bankruptcy of Lehman Brothers) and the Great East Japan
Earthquake. The effects on current profit ratios showed that suppliers’ choices todiversify their customer base worked positively in general, although this effectdisappeared during crises. Handling many product lines had almost no effect on
suppliers. Additional analysis showed that suppliers that changed their main customer
during a crisis failed to recover their profit ratios after the crisis. These findings teach us
how suppliers should maintain relationships during economic crisis.
Keywords: Automobile Industry, Supplier, Transaction Volume, Economic Crisis.

1. INTRODUCTION
Contingency theory advises us that a company should seek a proper “fit” to its
environment in order to survive (Burns and Stalker, 1961). This ideal has not been soeasy to achieve for most companies after economic crises such as the Lehman Crash(the bankruptcy of Lehman Brothers) in 2008; it has been especially hard for mostJapanese companies, because they experienced the further crisis of the Great East Japan
Earthquake in 2011.
The Japanese automobile industry was one of the sectors that was badly damagedby these crises. Toyota and Nissan both fell into deficit and their downsizing by laying
off part-time and temp workers became a public issue. A major tsunami that hit the
Hokuriku region in Japan right after the earthquake forced many automakers to close
their operations. Despite this series of hardships, Japanese automakers recovered theirpositions among the top-selling vehicle manufacturers in the world. Behind this feat,there must have been tremendous efforts by auto-parts suppliers; however, little has
been shed light on how these suppliers survived these crises.
Literature on handling business crises stresses the importance of “better
management” (Champion 1999; Goad 1999), but does not specify what this might
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Review of Integrative Business and Economics Research, Vol. 5, no. 4, pp.280-292, October 2016281

entail. Of the many managerial actions the suppliers could have taken, this paper will
focus on their choices in transactions. Japanese auto-parts suppliers are known tomaintain close ties with their respective automakers. These cohesive ties often play arole in creating high barriers to entry, leaving automakers few choices for substitutions.
For this reason, suppliers have found it difficult to take conspicuous actions like adding
other product lines for rival automakers or increasing shipping volumes to them.
However, during the crises in question, suppliers would have felt relatively free totake strategic actions, because their main customers could not pressure them, due totheir relatively small current orders. In other words, these times of crisis might provide
suppliers with unique opportunities to change their main customers. Under crisis, how
did suppliers built transactional relationships with automakers and how well did thiswork? Is this process different from situations when there is no crisis? This paper seeks
to answer these questions.

2. BACKGROUND
Japan’s automobile industry is unique in that major automakers have developed theirown keiretsu system. Under this system, auto-parts suppliers maintain long-term andcorporative ties with their customers, the automakers. Making transactions in thissituation does not merely mean shipping products. The suppliers’ ties with automakers
contribute to their accumulation of knowledge, know-how, and technology. This
process can be promoted by suppliers making strategic choices in limiting theirtransactions to deal only with their main automakers. Research has demonstrated theadvantages of suppliers having these long-term corporative relationships with
automakers in the Japanese automobile industry (Dore, 1983; Asanuma, 1985; Clarkand Fujimoto, 1991).
This notion of keiretsu sometimes creates misunderstandings. Despite thelong-term and corporative ties with automakers, the number of automakers with whichtier-1 auto-parts suppliers transact is not usually limited to one. Put in another way,
normal suppliers have built long-term and corporative relationships not only with theirmain automaker, but also with its rival automakers. Automakers sometimes try tointervene in various ways to exclude their suppliers from transacting with otherautomakers, for fear of leaking their specific know-how or trade secrets, etc.
Porter (1980) argued that too much dependence on a few large customers, which
tend to stress low transaction prices, will contribute to low profitability. According tothis idea, suppliers can gain high profitability by diversifying their transaction volume
in order to avoid negotiation pressure from their customers, the automakers. As long as
they have superior technology, some suppliers can become “independent” bydiversifying their customer base so as to avoid such harsh conditions, particularly interms of price. Successful suppliers can then earn better profits than others.

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282

H1: The less concentrated the ratio of transaction volume a supplier has with
automakers, the higher its profit ratio will be.
The condition mentioned in the paragraph above—that the suppliers have superior
technology—implies that H1 could be supported only when suppliers havetechnologies that are not easily substituted by others. One way for suppliers to gain thistechnological competitiveness is to achieve economies of scope (Panzar and Willig,
1977; 1981). Having a variety of product lines would allow them to achieve this.
H2: The more product lines a supplier has with automakers, the higher its
profit ratio will be.
Suppliers having many product lines can easily take action to be independent fromtheir automakers, which may in turn result in higher profits. In other words, suppliers
having many product lines, as well as diversifying transaction volumes with
automakers, are likely to gain better profits.
H3: The effect on a supplier’s profit of having many product lines is mediatedby the concentration ratio of its transaction volume with automakers.
That is, a better profit ratio is achieved by a supplier when it has many
product lines and low concentrations of transaction volumes with
automakers.
The process of hypothesis testing will be outlined in the following section.

3. METHOD
3.1 Data Source
For hypothesis testing, I relied on the data book series, “Survey on Production andDistribution of 200 Main Automobile Parts (Jidosya buhin 200 hinmoku no seisanryutsu chousa [in Japanese]” published by IRC Co., Ltd. Books in this series (hereafter,“the books of 200-parts”) are issued every two or three years. In this paper, I used thebooks published in 2005, 2008, 2010, and 2012.
The books of 200-parts contain information on transaction volumes for each of
200 auto parts sold to 12 major Japanese automakers: Toyota, Nissan, Mitsubishi
Motors, Honda, Mazda, Suzuki, Daihatsu, Fuji Heavy Industries (Subaru), Isuzu, Hino,
Nissan Diesel, and Mitsubishi-Fuso (Fuso). The last four automakers mainly produce
commercial vehicles only (buses, trucks, etc.). All the suppliers in this book areso-called tier-1 suppliers for the automakers. When referring to the suppliers’ corporate
history, I regard a supplier that changed its company name only (not including
corporate merger, acquisition, or integration) as the same supplier.

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Table 1. List of the 181 auto parts tracked.
■ENGINE BODY PARTS■ENGINE VALVE SYSTEM■ENGINE FUEL SYSTEM

engine block (cylinder block)cylinder liner
crankshaft
connecting rod
crank-shaft bearing metal
pistonpiston pin
piston ringcylinder head
cylinder head covercylinder head gasket
Vee belts
crankshaft pulleyflywheel
drive platering gear
cylinder head bolt
engine a'ssy

camshaftrocker arm
engine valve
valve spring
valve sheet
valve guidevalve lifter
timing belt
timing chain
timing crank pully
timing cam pully
variable valve timing unit
timing auto tensioner
timing belt (chain) coverfuel injector (gasoline)
throttle body (gasoline)pressure regulator
electric fuel indector (diesel)
fuel tube
fuel filter
fuel gasoline pumpfuel tankcanister■ENGINE INTAKE AND EXHAUSTSYSTEM
air cleaner
intake manifold
turbo charger
intercooler
exhaust manifold
exhaust pipe
EGR valve
O2 sensor
catalyst
catalystic convertermuffler■ENGINE LUBRICATION /COOLINGoil panoil strainer
oil pump
oil filter
oil seal (engine)radiatorwater pumpthermostat
cooling fancooling fan driving device■ENGINE ELECTRICAL
EQUIPMENT
ignition coiligniterspark plug
glow plug
starteralternator■POWER TRAIN■STEERING COMPONENT■SUSPENSION■BRAKE■WHEEL TIRE

coil springleaf spring
shock absorberstabilizer
suspension ball joint

brake disc rotor
disc brake caliper
disc brake padbrake shoe a'ssy
brake drumbrake lining
brake master cylinder
brake wheel cylinder
brake booster
proportioning valve
brake hosebrake tube
ABS
ESC
parking brake level pedal

steel wheel
aluminum foil
full wheel covertire■EXTERIOR EQUIPMENT■INTERIOR EQUIPMENT■BODY ELECTRICAL EQUIPMENT■SUPPLIES

engine mountglass
front window molding
resin bumper
rear roof spoiler
sunroofroof rail
outside mirror
radiator grillemark emblem
wiper a'ssy
wiper blade
window washer
door handle
door hinge
door weather strip
door lock (for side doors)
wind reglator

sheet
haedrestseat track
seat recliner
seat belt
seat belt pretensioner
electric pretensioner
instrument panel
glove box
ashtray
door trimmolded header
sun visor
inner mirror
rear package tray
accelerator pedal
clutch pedal
brake pedal
air bag module (for sides)
air bag module (for driver's seats)
air bag module (for passenger seats)
air bag module (for curtain rail sides)

keyless entry systemsmart key
immobilizer system
head lamp
rear combination lamp
high-mount stop lamp
flasherhorn
meter
lever combination switch
wire harness

car air conditioner
car heater
car audio
navigation systemclutch cover
hydraulic power steering
clutch disk
electric power steering
clutch facing
hydraulic power steering pumpclutch master cylinder
electric power steering motor
clutch release cylinder
power sterring hoseMTsteering wheel
MT shift leversteering column
shift folk
steering column coversynchronizer ringsteering shaftAT
steering joint
CVTsteering link system
torque converter
steering knuckle
AT shift lever
AT control cabletransferdifferential
mechanical LSD
electric 4WD motor
propellter shaft
propellter shaft joint yoke
constant velocity universal joints
boots for constant velocity universal
jointsNote: A part, retarder, was eliminated because it is the part only used for commertial commercial vehicles.

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283

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In terms of auto parts, I tracked 181 such parts that appeared in common in all
issues of the book of 200-parts. They are listed in Table 1.

3.2 Dependent Variables
Suppliers’ profit ratios were obtained from three resources. The first is “Current Statusof the Japanese Automobile Part Suppliers Industry (Nihon no Jidosya Buhin Sangyo
no Jittai [in Japanese])” by IRC Co., Ltd (hereafter, “the books of current status”). This
series includes information on over 1,000 auto-parts suppliers, including their sales
figures and current profits.
I used the books of current status issued in 2004, 2007, 2010, and 2013 (this series
is also published periodically, appearing every three to four years) and linked theinformation in these books with corresponding data in the books of 200-parts bycompany name. Around 70% of the suppliers appeared in both book series. For those
suppliers whose sales and profit figures were not indicated in the books of current status,
I turned to another data series, “Japanese Automotive Parts Industry (Nihon no Jidosya
Buhin Kogyo [in Japanese]), published almost every year by the Japan Auto Parts
Industries Association. Additionally, for suppliers listed on the stock exchange,
missing figures were substituted by a commercial database, the EOL. This being done,
I limited the sample to suppliers with at least one year’s profit in the publication year ofa particular book of 200-parts or its successive year. For example, when I used thebooks of 200-parts in 2005 for an analysis, current profit ratios of the suppliers in 2005
and 2006 were collected and then averaged (a few suppliers with either extremely highprofit ratios of 40% or above, or very low ratios of −40% or below, were eliminated inadvance). The final numbers of suppliers I obtained were 296 in 2005, 261 in 2008, 238
in 2010, and 209 in 2013.

3.3 Independent Variables
The information for independent variables was all taken from the books of 200-parts.
The first independent variable is the concentration of transaction volume of
suppliers (for testing H1). Suppose a supplier ships two different parts to Toyota andDaihatsu. If it ships part A to Toyota only, with a volume of 200, the transaction
volume ratio of this part is 100% with Toyota. If it ships part B to Toyota with a volumeof 300 and to Daihatsu with a volume of 100, the transaction volume ratio of this part
becomes 75% for Toyota and 25% for Daihatsu. Each transaction volume ratio is thenaveraged by the number of auto parts the supplier shipped. In this case, it becomes
0.875 (= (1 + 0.75) / 2) for Toyota and 0.125 (= (0 + 0.25) / 2) for Daihatsu. The HHI(Herfindahl- Hirschman Index) is then made from these numbers, resulting in 0.78125(=0.8752 + 0.1252).
The next variable is the number of product lines (for testing H2). It is made bysimply counting the number of product lines a supplier produced among the 182 parts
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that are tracked in this study. Since the number obtained was highly skewed (mean:
2.86, min: 1, max: 33), a natural log was taken before the analysis.
The interaction term, the final variable, is made with standardized numbers of theabove two variables.

3.4 Controls
Since the suppliers’ profit ratios could be affected by the characteristics of theparticular automakers with which they mainly transacted, their average ratios oftransaction (shipping) volume in the above process were input as control variables. In
order to take the economic situation into consideration, year dummies were also
inserted as control variables.
In addition, because the number of auto parts produced by a supplier was assumed
to correlate with company size, a natural log of sales in the same period for the
dependent variables was added into the analysis (I did not use the number of employees
for this measure, because not all suppliers supplied this figure).

4. RESULTS
Table 2 shows the correlation matrix among the variables. No strong correlations over
0.5 were found among independent variables. I then set regression models with dataover the whole duration, as well as in each observation year. The results are
summarized in Table 3.
From the column “all periods,” it is clear that the coefficient of the HHI was
negative and significant, showing that diversifying transaction volume amongautomakers results in higher profit (H1 is supported). The results show that diversifying
the transaction volume among automakers generally worked well, which is consistent
with the findings of Nobeoka (1996) and Ikeuchi et al. (2005). On the other hand, thecoefficient of the number of product lines (different auto parts treated) was negative,
rejecting H2. This implies that economies of scope are not likely to be established bysimply increasing the number of product lines. This should rather be accomplished bybroadening the customer base. H3 is also confirmed, because the coefficient of theinteractive term was negative and significant.
However, the effect on the interactive term was limited. Looking into the details,
the interaction term between the HHI and the number of parts treated is significant only
in 2005. Although the signs on the coefficients are all negative, it is safe to say that H3
is not always supported, maybe due to the rejection of H2 (the absolute value on theinteractive term was slightly large when the ones on the number of product lines are
positive). H1 is also not universal. The negative effect on the HHI is once mitigated in2008 and 2010, and reappears in 2012.

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* <.05, ** p<.01.

novariables
1 current profit ratio
2 HHI (concentration of shipping volume)
3 LN (number of product lines)
4 average ratio of parts shipped to Toyota
5 shipping volume ratio to Nissan
6 shipping volume ratio to Mitsubishi
7 shipping volume ratio to Honda
8 shipping volume ratio to Mazda
9 shipping volume ratio to Suzuki
10 shipping volume ratio to Daihatsu
11 shipping volume ratio to Subaru
12 shipping volume ratio to Isuzu
13 shipping volume ratio to Hino
14 shipping volume ratio to Nissan-Diesel
15 shipping volume ratio to Mitsubishi-Fuso
16 LN (sales/million yen)st.dev
3.612
0.416
0.753
0.279
0.265
0.250
0.254
0.284
0.233
0.193
0.208
0.178
0.154
0.163
0.178
1.594

mean
3.629
0.394
0.712
0.135
0.125
0.104
0.106
0.124
0.092
0.064
0.073
0.051
0.036
0.040
0.050
10.357

2

3

4

5

6

7

8

9

10

11

12

-0.10 **
0.02
-0.26 **
0.01
-0.07 ** 0.06 **-0.16 **
0.06
-0.07 ** 0.01
-0.05
-0.01
0.00
-0.14 ** -0.14 **
0.02
0.02
0.08 ** -0.13 ** -0.11 ** -0.13 **0.04
0.10 ** -0.06 ** -0.16 ** -0.15 ** -0.13 ** -0.13 **
-0.02
0.02
-0.02
-0.11 ** -0.11 ** -0.11 ** -0.09 ** -0.11 **
0.01
0.01
-0.01
-0.02
-0.13 ** -0.09 ** -0.10 ** -0.10 ** -0.07 **-0.07 * -0.02
0.01-0.13 ** -0.06 ** -0.09 ** -0.08 ** -0.11 ** -0.09 ** -0.06 **
0.01
-0.02
0.00
-0.11 ** -0.09 ** -0.10 ** -0.09 ** -0.10 ** -0.10 ** -0.07 ** -0.07 **
0.00
0.00
0.02
-0.06 ** -0.09 ** -0.08 ** -0.09 ** -0.09 ** -0.08 ** -0.06 ** -0.07 ** 0.04
-0.09 ** -0.09 ** -0.10 ** -0.08 ** -0.07 ** -0.06 ** -0.01
0.05
0.02
-0.08 ** -0.11 ** -0.03-0.09 ** 0.04
-0.11 ** -0.12 ** -0.09 ** -0.08 ** -0.09 ** 0.00
-0.05 * -0.12 ** -0.11 ** 0.02
-0.06 * -0.05 * -0.06 *0.13 ** -0.23 ** 0.47 ** 0.31 ** 0.14 ** -0.10 ** 0.09 ** -0.11 ** -0.05

1

-0.01
-0.02
-0.01

13

15

-0.01
-0.14 ** -0.18 **14

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Table 2. The correlation matrix.

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Table 3. Results of multiple regression analysis.
novariables1
HHI (concentration of shipping volume)2
LN (number of product lines)
3
interaction term (1 x 2)
4shipping volume ratio to Toyota
5shipping volume ratio to Nissan
6
shipping volume ratio to Mitsubishi
7shipping volume ratio to Honda
8shipping volume ratio to Mazda
9shipping volume ratio to Suzuki
10
shipping volume ratio to Daihatsu
11
shipping volume ratio to Subaru
12
shipping volume ratio to Isuzu
13shipping volume ratio to Hino
14
shipping volume ratio to Nissan-Diesel
15 shipping volume ratio to Mitsubishi-Fuso
16
LN (sales)
17
dummy on year 2005
18
dummy on year 2008
19
dummy on year 201020
dummy on year 2012
R2
F
number of observations

all periods-0.123 ***0.010-0.098 **―0.044-0.031
0.055
0.051-0.0200.021-0.0500.023
0.020
0.064 *
-0.0600.100 **
-0.019-0.330 ***-0.154 ***―0.155
10.0 ***
1,004

2005
-0.187 **0.048-0.181 **―-0.041-0.145 *
0.014
0.005
-0.069-0.067-0.1070.012-0.0580.074-0.136 *
0.037
―
―
―
―
0.126
2.7 **
296

2008-0.0460.067-0.096
―
0.055
0.117
0.137
0.126
0.081
0.107
0.039
0.111
0.138 *
0.134 *
-0.0730.182 *
―
―
―
―
0.101
1.8 *
261

2010
-0.095-0.021-0.075―0.121-0.0180.064
0.071-0.0150.060-0.019-0.036-0.040-0.036
0.005
0.075
―
―
―
―
0.058
0.9
238

2012
-0.190 **-0.077-0.035―0.078-0.0780.030
0.016-0.0810.001-0.136-0.0070.046
0.112-0.030
0.125
―
―
―
―
0.123
1.8 *
209

Note: Numeric numbers represent standadized partial coefficients (* p<.05, ** p<.01, *** p<.001). The maximum value of VIF is 1.726.

5. DISCUSSION
5.1 Additional Analysis
I would like to consider the fact that the effect on the HHI disappeared in the years 2008
and 2010. The implies that the Lehman Crash in 2008 triggered modifications of thetransaction process, but that the Great East Japan Earthquake in 2011 did not have astrong impact on suppliers’ choices in transactions. Rather, in facing the earthquake,Japanese automakers and their auto-parts suppliers appear to have acted to strengthen
their unity to survive the hardships.
By contrast, right after the Lehman Crash, there seems to be no clear strategy thatworked for the suppliers. Reflecting this, indicators of the fitness of the regression
models (i.e., R2 or F) in these periods are quite low. Thinking about it in another way,
the crisis could have provided a chance for many suppliers that wanted to change theirmain customers. Successful suppliers might have gained relatively better profits bychanging their customers during this crisis. I will continue this discussion by putting
forward the following hypothesis with an additional analysis.
H4: The more a supplier changed its transaction volume or product lines
during the crisis, the better its profits after the crisis will be.

5.2 Dependent Variable
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From Figure 1, as I will show later, the suppliers’ profits after the crisis seemed torecover during and after 2011. I then averaged current profit ratios from 2011 to 2013 toobtain a dependent variable.

5.3.1 Change of customer concentration
The HHI index was used to construct the change of customer concentration. The
variables were set as the difference between the HHI in a given year from that in thenext observation year. Using the HHI in 2005, 2008, 2010, and 2012, the following
variables were made:Change of customer concentration( 2005 − 2008)i = HHI ( 2008)i − HHI ( 2005)i
Change of customer concentration( 2008 − 2010)i = HHI ( 2010)i − HHI ( 2008)i
Change of customer concentration( 2010 − 2012)i = HHI ( 2012)i − HHI ( 2010)i
where HHI ( y )i : number of the HHI index of a firm _ i in the year _ y

5.3.2 Change of number of product lines
The changes of the number of product lines were made by subtracting the number of
product lines a supplier had in a year from the one in the next observation year.
Change of num _ of _ product lines( 2005 − 2008)i = pl ( 2008)i − pl ( 2005)i
Change of num _ of _ product lines( 2008 − 2010)i = pl ( 2010)i − pl ( 2008)i
Change of num _ of _ product lines( 2010 − 2012)i = pl ( 2012)i − pl ( 2010)i
where pl ( y )i : LN of the number of product lines of a firm _ i in the year _ y

5.3.3 Absolute change of transaction volume ratio
The change of transaction volume ration was determined by the absolute value ofsubtraction of the difference between the transaction volume ratios in a given year fromthe ones in the next observation year.
Absolute change of transaction volume ratio( 2005 − 2008)i = ∑ tra( 2008) car ,i − tra( 2005) car ,icar

Absolute change of transaction volume ratio( 2008 − 2010)i = ∑ tra( 2010)car ,i − tra( 2008)car ,icar

Absolute change of transaction volume ratio( 2010 − 2012)i = ∑ tra( 2012)car ,i − tra( 2010)car ,icarwhere tra( y )car ,i : ratio of transaction volume to a customer ( car ) of a firm _ i in the year _ y
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5.3.4 Dummy for the change of main customer
Using the average ratios of transaction volume, the main customers for each supplier(those automakers having the highest transaction volume ratio for a supplier) weredetected. When two or more automakers had the same highest ratio, all of them wereregarded as the supplier’s main customers. Comparing the main customers in a yearwith the ones in the next observation year, for cases where the former included at least
one of the latter (i.e., when a supplier retained at least one of its main customers), a
dummy variable was set to 0 (otherwise, 1). Like the former variables, these dummies
were constructed over 2005-2008, 2008-2010, and 2010-2012.
5.3.5 Control
As in the former analysis, a natural log of sales during 2011-2013 (the same period forthe dependent variable) was added to the analysis as a control variable.
5.4 Results of the additional analysis
Since these variables require transaction volume in successive observation years, I limit
the analysis to suppliers that had shipped at least one auto part in all observation years(2005, 2008, 2010, and 2012). The results of the additional analysis are summarized inTable 4.
These results show that there is no strong positive effect of any these variables on
profits. The only variable with a significant level except the control variable was thedummy on the change of main customer in 2008-2010, in the midst of the Lehman
Crash. This shows that changing main customers during the crisis was harmful to them.
Table 4. Results of the analysis (predicting average current profit ratios in 2011-13).
depandent variables
Change of customer concentration (2005-2008)
Change of customer concentration (2008-2010)
Change of customer concentration (2010-2012)
Change of number of product lines (2005-2008)
Change of number of product lines (2008-2010)
Change of number of product lines (2010-2012)
Absolute change of transaction volume ratio (2005-2008)
Absolute change of transaction volume ratio (2008-2010)
Absolute change of transaction volume ratio (2010-2012)
Dummy for the change of main customer (2005-2008)
Dummy for the change of main customer (2008-2010)
Dummy for the change of main customer (2010-2012)
LN average sales (2011-2013)
R2
F

std coeff
0.009
0.008
-0.070-0.021-0.056-0.034-0.103
0.135
0.139
0.153
-0.165-0.046
0.173
0.085
1.5

p
0.902
0.910
0.351
0.759
0.421
0.629
0.308
0.093
0.148
0.152
0.039 *
0.589
0.011 *

Note: N=223. The maximum value of VIF = 2.574.

5.5 Interpretation
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Review of Integrative Business and Economics Research, Vol. 5, no. 4, pp.280-292, October 2016

290

Contrary to expectations, taking strategic actions (changing their main customers)
during the crisis was not a good choice for the suppliers. To confirm this, I compared
the average profit ratios of the suppliers that changed their main customers between
2008 and 2010 with ones that did not.
The main reason for suppliers to change their main customers in the crisis may havebeen that they could earn near-term profits in the crisis. Seeing the average profit ratio
in 2009 in Fig 1, this tactic seemed to work in the midst of the crisis (the average profit
ratio of the suppliers that changed their main customers is higher than that of the
suppliers that did not). However, this group experienced a slower recovery after thecrisis.
These findings suggest that suppliers’ opportunistic behavior, especially in a crisis,
ended up in ruining confidence from their customers. They might not have been able
regain sufficient support from their former main customers; or changing main
customers might have required them to acquire new knowledge about the new customer.
The background of this phenomenon remains to be seen, but damaging the faith ofcustomers, especially in hard times, did not work in the long run in the Japanese
automobile industry.
6
5
4

averagecurrent3
profitratio (%) 2
not changed

1changed

0
2005

2006

2007

2008

2009

2010

2011

2012

2013

year

Fig 1. Current profit ratios of suppliers that either changed or did not change theirmain customers between 2008-2010.

6. CONCLUSION
Previous studies have reported beneficial effects for suppliers that diversify theircustomer base. In this respect, this paper added a new perspective in viewing the same
effect longitudinally. The results revealed that this effect worked only when theeconomic situation was good. This paper also revealed that the number of product linesthat suppliers carried had almost no effect on their performance, which was contrary to
our expectations. Suppliers’ economies of scope are not likely be achieved by simply

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Review of Integrative Business and Economics Research, Vol. 5, no. 4, pp.280-292, October 2016291

adding more product lines. These findings show that we need to pay more attention to
suppliers’ strategic choices.
Focusing especially on the Lehman Crash, the suppliers seem to have searched foralternative strategies to gain near-term income. However, the additional analysis
showed us that such suppliers should not take opportunistic actions that could
undermine the confidence that had been built up with their customers.
This paper has several limitations. The most serious one may arise from the data
sources. Although I endeavored to collect as many samples as possible using the four
resources, some suppliers that did not appear in these sources could not be taken into
account. In addition, among the suppliers in this paper, there were some big companies
like Panasonic whose main domain was not in the auto parts industry. To get a more
precise perspective, I have to consider carefully which suppliers should be tested.
Given these limitations, I am now planning to add further information to this data
set. When this process is complete, I will undertake the next inquiry.

ACKNOWLEDGEMENT
This research is partially supported by the Nagai Foundation for Science & Technology.
I would like to express my gratitude to a fellow researcher, Dr. Tomomi Hamada for her
support throughout the project.

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