1. Introduction

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 future expansion

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 than others.

Table 3.2 ANOVA table of factor highly related with number of technical personnel

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 Island 0.858

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 0.791

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. Implications

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 page

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created: 07/01/95
last updated:
author: Tetsuo Karaki