ANALYSIS OF LOCATION DECISIONS OF THE COMPUTER INDUSTRY IN THE
These days, because of the recent strong Yen and the trading conflict
between Japan and the United States, the necessity of making manufacturing
factories and other facilities in the United States has become
greater and greater for the Japanese companies. There are many
problems to start business in foreign countries, and one of the
most important problems is the selection of location. Although
there have been much research on this matter so far, not enough
studies of location decisions for the computer industry have been
done. The purpose of this paper is 1) to examine the locational
behavior of computer manufacturers in the U.S., and 2) construct
an operational location evaluation framework for the computer
industry. In order to solve the above problem through the quantitative
analysis, we have done the questionnaire survey in the United
states. The analysis started from the basic analysis like the
means of the location factor question. Then, we have determined
the important latent location factors out of 41 location questions.
The final step of our analysis was to evaluate the U.S. 48 states
as an application of the result of our survey. We finished our
survey with indicating the suitable states for the computer industry
to be located. The problem of location is very difficult problem.
However, the real problem occurs after they start their business.
Therefore, this survey is just the first step of the whole survey
about the foreign launch, and it is important that further work
be done to continue studies in this field.
2. Data Collection Method
380 computer related companies in the United States were selected
and the questionnaires were mailed at the middle of June 1987.
By the end of July, 115 out of 380 came back to the home address
in Japan. After omitting incomplete questionnaires, 104 replies
were decided to be used for the following quantitative analysis.
Figure 2.1 shows the locational distribution of the respondents.
In the figure, the number indicates the percentage of the respondents
out of 104 replies which are used for this analysis.
Figure 2.1 Locational distribution of the respondents 3. Data
Analysis of Location Decision Making - Based on the Questionnaire
The raw data was put into SAS (Statistic Analysis System) software
package to administer the following analyses .
3.1. Basic Analysis of Locational Characteristics
Looking at the characteristics of the respondents, newly operating
companies (less than 4 years) are only 10 percent. About 70 percents
of the companies have less than 200 production workers and less
than 200 technical personnel. However, there are only few manufacturing
companies with less than 10 production workers. Almost 65 percents
of respondents are manufacturing related companies. Almost 50
percents of respondents are using their facilities for R&D center
and for a head office. Interestingly, the average price is divided
into 5 groups almost equally. As shown in the Table 3.1, communication
(Q06) was the highest (6.29), although in the United States, there
is not much difference on this point. Next to the communications,
the rating for availability of technical personnel (Q28) and availability
of professional and office staff (Q29) were high (6.06, 5.90).
This indicates that for the computer companies, it is very important
to have excellent engineers and staff. The rating on cost of local
capital (Q10) and availability of local capital (Q11) were the
lowest and the second lowest (3.27, 3.04). This seems to mean
either the respondents are not interested in local capital because
they can prepare their own capital, or there is no difference
on this matter in any place in the United States.
Table 3.1 Location questions with high average
standard mean deviation Q06 Communication 6.29 1.47 Q28 Availability
of technical 6.06 1.66 personnel Q29 Availability of professional
5.90 1.55 and office staff Q26 Labor attitude 5.57 2.12 Q08 Availability
and 5.55 1.68 quality of utilities Q20 Ample space for 5.53 1.41
3.2. Relationship between segmentation categories and factor questions
Then the relationships between segmentation categories (P01-P06)
and each factors (Q01-Q41) are examined and the following combination
shows interesting difference. As the number of technical personnel
- neither production workers nor office staff - increases, the
companies are more interested in housing (Q16). The following
table is the result of ANOVA test. This fact means that technical
personnel are very much concerned with the quality of housing
Table 3.2 ANOVA table of factor highly related with number of
One-way ANOVA model: Factor = f(P03) + Ei Factor F value Prob>F
R-Square Q16 2.27 0.0539 0.114
3.3. Identification of Latent Factors for Business Location Decisions
- Factor Analysis
To make a better understanding of 41 location factors, factor
analysis was performed. Factor analysis is a procedure that takes
a large number of variables or objects and searches to see whether
they have a small number of factors in common. Through this technique,
we would like to find out some latent factors which influence
the location decision making for the computer manufacturers in
the United States. Since there are many location factors as many
as 39, we have decided to proceed the factor analysis by 2 steps.
Firstly the factor analysis was performed by each category. Then
for the next step, factor analysis using factor scores calculated
at the first step was performed to identify the relationship.
This yielded 4 factors shown on Figure 3.1.
Figure 3.1 Location decision making flow of computer companies
First, they think this matter from two aspect, environment and
capital and cost. Each of them can be divided into two aspects.
They are general industrial environment, computer industrial environment,
capital, and cost. The general industrial environment factor includes
matters like communications, police and fire protection, availability
and cost of utilities, and cost of land and construction. The
computer industry environment factor is related to existence of
other high tech companies and high tech zone, quality of business
- local government relationship, availability of engineers and
professional staff, housing and convenience of living, and quality
of schools and universities. The next factor - capital - has to
do with availability and cost of local capital, and incentives
from government. The cost factor includes transportation cost
of raw material and finished goods, availability of labor, labor
union, labor attitude, and wage rate. This result shows that respondents
are examining locations from these four factors. Next, we would
like to examine American 48 states from these four points.
4. Evaluation of the U.S. 48 States - Based on the results from
Basic Analysis and Factor Analysis
In this section, we would like to do an evaluation of the 48 United
States states applying the results of Section 3. In the previous
section, by the factor analysis, we have calculated figures how
each location questions are related. We will use these figures
- factor scoring coefficients - for computing factor scores for
each individual state. This factor score means how the states
are superior in terms of the factor. For example, if one state
has high factor score on environment, it means that the state
has good environment for the computer companies. In order to induce
these results, we will combine the factor scoring coefficients
with statistic figures1 for each state in the United States. As
the result of computation explained above, we have received the
following result shown on Table 4.1, which shows the top 10 states
for each factors from one to four.
Table 4.1 States with high factor scoring
1. Factor 1 (general industrial environment) rank state Factor
1 scores 1 Mississippi 1.682 2 South Carolina 1.666 3 North Dakota
1.386 4 Kentucky 1.361 5 Arkansas 1.300 6 Tennessee 1.292 7 Louisiana
1.256 8 Kansas 1.161 9 Delaware 1.114 10 South Carolina 1.058
2. Factor 2 (computer industrial environment)
rank state Factor 2 scores 1 Kansas 1.526 2 Tennessee 1.501 3
Iowa 1.480 4 Arkansas 1.155 5 Oklahoma 1.085 6 Nebraska 1.032
7 Idaho 1.020 8 Montana 0.941 9 Alabama 0.917 10 Mississippi 0.842
3. Factor 3 (capital)
rank state Factor 3 scores 1 California 2.304 2 Connecticut 1.966
3 New Jersey 1.848 4 Maine 1.305 5 Oregon 1.234 6 Ohio 1.209 7
Michigan 1.097 8 Massachusetts 1.079 9 Maryland 0.908 10 Rhode
4. Factor 4 (cost)
rank state Factor 4 scores 1 Florida 2.804 2 South Carolina 2.018
3 New Mexico 1.556 4 North Carolina 1.331 5 Arizona 1.239 6 Colorado
1.146 7 Georgia 1.114 8 Texas 0.907 9 Mississippi 0.898 10 Virginia
The final step of this research - to find out the most suitable
state for the computer companies - was then induced by adding
the four factors for each state, after the weighting for each
factor has calculated. The results are shown in Table 4.2 and
Figure 4.1. Except South Dakota and Kansas, they are concentrated
in so-called South. They are rather shifted to east from the current
Sunbelt high tech zone. Ironically, the results of this survey
show that the current high tech states are not necessary the ideal
place for the respondents.
Table 4.2 States with high factor scoring (total)
rank state total scores 1 Tennessee 1.019 2 Mississippi 0.988
3 Arkansas 0.892 4 Kansas 0.787 5 Alabama 0.746 6 North Carolina
0.698 7 Kentucky 0.668 8 South Dakota 0.606 9 Louisiana 0.555
10 South Carolina 0.509
Figure 4.1 States with high score on total
5.1. The last section says that the current high tech states are
not necessary the ideal place for the respondents. On first sight,
it seems to be that there is something wrong in our survey. However,
if we look at the movement of high tech companies, our result
shows high fitness with the trend of high tech companies.
Table 5.1 Number of high tech plant change by state 1972 - 772
rank state % change
1 Tennessee +31.0 2 Mississippi +27.3 3 Arkansas +43.3 4 Kansas
+27.5 5 Alabama +28.7 6 North Carolina +31.2 7 Kentucky +26.1
8 South Dakota +39.0 9 Louisiana +24.9 10 South Carolina +48.0
U.S. average +19.8
As it is shown on Table 5.1, all the states with high total factor
score of our survey had increased the number of high tech plant
more rapidly than the U.S. average. On the other hand, the percentage
change in the number of plants for some of the current high tech
states are less than the U.S. average. This indicates that although
the states listed here are not necessary the current high tech
states, they are becoming to be high tech states. Therefore, for
the Japanese computer companies, it might be a good idea to be
located in the states which has high scores in our survey. The
reasons for this trend of shifting high tech states are partly
due to the fact that the condition of states had changed through
the passage of time. For example, the cost of land and housing
will increase if many people want to move there. Therefore, the
situation for the computer companies always changes.
Another reason for shifting of high tech states is due to the
fact that the important factor always varies from time to time
Of course, the classic and fundamental criteria of location will
continue to be applied in the future. However, we always have
to catch up the recent new factors important for locations. Therefore,
our list of suitable location for the computer companies will
change from time to time.
For our questionnaire, we have asked 41 elements from 8 categories
as possible influences on location decisions. These factors and
categories may be used as a check list by companies for evaluating
locations from their proposed sites. Also, the method we have
used to evaluate the U.S. 48 states can be applied for the actual
site selection. In our survey, we have used statistic data for
the U.S. states. If we used statistic data for some cities, we
could have evaluated these cities. Therefore, this evaluation
method can be applied for company's location choices. Suppose,
one company has a plan to open a facility in the United States.
For reasons of some limitation or preference, they have chosen
several proposed sites for the first step. The second step is
to choose one location from the preferred candidates. In this
case, we can apply the evaluation method we have used in our survey.
After collecting statistical data for these sites, they put the
data in the algebra and can have the most suitable site from their
proposal. As it is stated in this example, this evaluation method
can be applied for the company's location selection.
1The sources are as follows:
U.S.Department of Commerce Statistical Abstract of the United
States. Washington DC: U.S. Government Printing Office, 1986.
U.S.Department of Commerce Statistical Abstract of the United
States. Washington DC: U.S. Government Printing Office, 1987.
Georgia Department of Transportation. Georgia - The State of Business
Today. 1987. "The Fifty Legislative Climates" Industrial
Development, Jan/Feb, 1983.
2Markusen, Hall and Glasmeier.High Tech America. Boston: Allen
& Unwin, Inc., 1986, pp.107-109 * footnote continues to the next
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author: Tetsuo Karaki