Online ISSN 1347-4456 Print ISSN 1347-4464

ABAS: Annals ofBusiness Administrative
ScienceVol. 1, No. 3, October 2002

SHIMIZU, Takashi

39

FUJITA, Hideki

47

IGARI, Eijiro

57

The longevity of the Japanese big businesses

The reproduction of Hofstede Model

Learning process of core technology in sporting gun industry of Japan

http://www.gbrc.jp© 2002 Global Business Research CenterAnnals of Business Administrative ScienceVol. 1, No. 3, October 2002

CHIEF EDITOR

Junjiro Shintaku

University of TokyoEDITORIAL BOARD

Makoto Abe
University of Tokyo
Takahiro Fujimoto University of Tokyo
Makoto Kasuya
University of Tokyo
Hotaka Katahira
University of Tokyo
Nobuo Takahashi University of Tokyo
Technical Editor: Maki Nishida, Global Business Research Center

Annals of Business Administrative Science is published quarterly in January, April,
July, and October, by Global Business Research Center, Marunouchi, Chiyoda-ku, Tokyo,
JAPAN. http://www.gbrc.jp

Annals of Business Administrative Science Vol.1, No.3 (October 2002)

The Reproduction of Hofstede Model
FUJITA Hideki
Graduate School of Economics, University of Tokyo
E-mail: fujita-hideki@mc.neweb.ne.jp

Abstract: G. H. Hofstede (1980) Culture’s Consequences is a result of an extensive
research on the international comparison of corporate culture in 40 countries through1967 to 1973. Hofstede identified four dimensions of culture from the investigation
data of IBM’s 40 subsidiaries. He developed four indices, but did not explicitly
present the exact computation formulas for Individualism index and Masculinity index.
This paper reproduces the formulas and proves how to conduct a factor analysis that
Hofstede employed for development of these indices. Hereafter, supplementary
analysis could be done using all four indices of Hofstede. Also a factor analysis could
be done in Hofstede's method.
Keyword: Hofstede, dimensions of culture, factor analysis

1. Introduction

indices of which the exact computation formulas are

During 1967 to 1973, Hofstede (1980) conducted a

not explicitly presented.

70,000Virtually, Culture's Consequences (1984) of

employees in total at the sales and administration

Hofstede is an abridged edition, in which all the

departments of IBM in 40 countries, to conduct

descriptions on the statistical analysis and the simple

international comparison between corporate culture.

totals of IBM data are omitted. However, by aThough four dimensions of culture appeared in thedetailed analysis on these two data which appear ininvestigation data of IBM, only two of them, Power1980 version, calculating method of the rest two

Distance Index and Uncertainty Avoidance Index,

indices unexplained so far and the factor analysis

were given explanation on calculation methods. Thisoutline in developing the indices were mostly

paper reproduces the rest two of Hofstede’s four

discovered. Hereafter, analyses using all four indicesquestionnaire

surveytargeting

about

47

Fujitaformula is reviewed hereafter.

of Hofstede are possible, and also, factor analyses
could be done in the Hofstede method of developing

PDI (Power Distance Index)

indices.

Power Distance is a measure of the interpersonal

2. Multinational Comparison of

power distance between B (boss) and S (subordinate),

Corporate Culture

which is defined as “the difference between the

Hofstede (1980) Culture’s Consequences is a result

extent to which B can determine the behavior of S

of an extensive research on the international

and the extent to which S can determine the behavior

comparison between the corporate culture in 40of B” (Hofstede, 1984, p. 72). In actual analysis, PDI

countries through 1967 to 1973. The survey wasis employed so that it becomes higher when thecomposed of two phases. Phase 1 was conductedfollowings become high:during 1967 to 1969 to obtain 31,218 workers’ data.

Phase 2, while the latter were recommended for(1) Frequency of employees’ perception that they
are afraid to disagree with their managers.
(2) The ratio of those subordinates who prefer not
to work for superiors with a consultative styleof decision making.
(3) The ratio of those subordinates who perceive
their boss as autocratic or persuasive.

optional usage. Phase 2 was conducted during 1971

By definition, the higher PDI is, the higher

As its result, questionnaire was modified, which wasreportedly composed of 60 core questions (A1-A60)
and 66 optional ones (B1-B66). The former
questions were used through the entire process of

to 1973 to obtain 40,997 workers’ data.

Power Distance becomes. The formula used is as

In total, 72,215 workers’ data were selected forfollows:

analysis. Finally, sample was limited to employees at

PDI=135

sales and administration departments dropping−25×B46(mean)

production and product development departments.

Employee afraid todisagree withAccording to Hofstede, four dimensions ofmanagers

culture were identified as a result of extensive– A54 (3: %)surveys, that is, UAI (uncertainty avoidance index),

Preferred managers

Perceived

consultative

managers as

PDI (power distance index), IDV (individualism+ A55 (1 + 2: %)autocratic orindex), MAS (masculinity index). As for the latter

persuasive

two indices, 14 questions were employed for factor

The constant 135 has been added to give theanalysis, but Hofstede (1980) did not explicitly

index value a certain range. The actual range of PDI

present the exact computation formula (Takahashi,

is 11≦PDI≦94, while theoretical range is –90≦

1995, chap. 5). The detail on the computation

PDI≦210. The full question of B46, A54, and A55,
48

Reproduction of Hofstede Model
which is relative to the former two variables.

which compose the formula, are shown in Appendix
1 (Hofstede, 1980, pp. 403-410).

IDV (Individualism Index) and MAS
UAI (Uncertainty Avoidance Index)(Masculinity Index)

As for Avoidance of Uncertainty, UAI is employed

As for IDV and MAS, though a factor analysis waswhich is designed to become higher when theused for the development of the indices, how tofollowings become high:calculate them has not practically been explained(Takahashi, 1995). Though the research of Hofstede(1) The degree the employees considering thatcompany rules should not be broken even if
they think it is not in the company’s bestinterests.
(2) The ratio of those who believe to continue
working with the present company more thananother five years.
(3) Frequency they feel nervous or tense at work.

is known very well, a supplementary examination by
third party researchers in the strict meaning has notbeen done so far. We intend to follow the analysis of
Hofstede as requested in Fujita (1999) which gave
the first explanation on how to calculate these two
indices.

“Stress” question is introduced in (3) since

Hofstede conducted a factor analysis on thefollowing relations are observed in which (3) relates

basis of the mean scores of every country computedto (1) and (2); two means to avoid uncertainties, via

from 14 “work goals” questions. Then he focused on

the anxiety level and the desire for safety:

two main factors that could explain 46% of the(3)←anxiety level→desire for safety→(1) and (2)variance of the mean scores, and related the first

The used formula is as follows:
UAI=300

factor to the degree of individualism, and second
factor to the degree of masculinity. The first factor–30×B60 (mean)

was named “individual-collective” factor because it

Rule orientation–A43 (1+2: %)–40×A37 (mean)

opposed goals that did not stress independence from

Continue working
less than five years

Nervous/ tense atwork

the organization to goals that stressed independence(Hofstede, 1980, pp. 220-221). The second factor

The actual range of UAI is 8≦UAI≦112, while its

was named “social-ego” factor because it wastheoretical range is –150≦UAI≦230. UAI wascharacterized by high importance of manager andcorrelated to the average age of the respondents.

cooperation, and low importance of earnings

UAI has been compiled on the basis of the country(Hofstede, 1980, p. 277). By substituting the factor

mean scores for three questions, that is, B60 (rulescores (INV, SOC) of these two factors for theorientation) and A43 (continue working less thanfollowing formulas, IDV and MAS are calculated so

five years) as well as A37 (nervous/tense at work)that the value of the indices may be within range
49

Fujitabetween 0 and 100 (Hofstede, 1980, pp. 242, 299).

Consequences, one is 1980 version, which is the firstedition, and the other is 1984 version, which is the

IDV = 50 + 25 INV
MAS = 50 – 20 SOC

abridged edition. Descriptions on the statistics and soon used for the analysis of IBM data are removed

However, minus 1 is multiplied to the factor

from 1984 version. Even though the analysis madescore of the second factor to reverse the sign,in Cultures and Organizations (Hofstede, 1991), tobecause the respondents of the questionnaire werewhich data in 13 countries were added newly, is the

mainly men and factor loadings of the goals that men

same, descriptions on statistics is much simpler.

take seriously are minus. Formula of each index

Four dimensions of culture were identified inappears only in 1980 and 1991 versions.

IBM data, that is, PDI, UAI, IDV, and MAS. How to

However, in 1980 and 1984 versions, Hofstede

calculate PDI and IDV was made clear, and also thepartly mentions 14 factor loadings respectively ofcomputation formulas were mentioned explicitly.

the first factor and the second factor (only factor

However, from reasons stated above, not only theloadings over 0.35; 9 factor loadings respectively ofcomputation formulas of the factor scores needed forthe first and second factor in 1980 version, and 6 andcalculating IDV and MAS but also the method of

8 factor loadings respectively in 1984 version; see

factor analysis used to develop the indices were not

Table 3). Therefore the factor structure in “workexplained. Table 1 shows the degree of disclosures ofgoals” questions is not clear. And standardized

the processing and statistical analysis of IBM data

scoring coefficients (or factor score coefficients) toneeded to calculate these two indices in eachcalculate the estimated value of factor scores (INV,

version.

SOC) from the matrix of standardized variables are

1980 version is most particular with thementioned only in the section of statistical analysis

explanation on the processing of IBM data. In thisin the 6th chapter which was omitted from 1984

version, Hofstede mentions the factor analysis usedversion. Nonetheless, practically, Hofstede made it

for the development of each index and the simple

impossible for third party researchers to calculate

totals of IBM data. These descriptions are omitted

factor scores by the data of supplementaryfrom 1984 version. In Cultures and Organizations,

examinations, because “the former process” of thethere is hardly explanation on statistical analyses

analysis was made a black box (i.e., the proceduresuch as factor analysis. Yet, even in 1980 version

was in such a way of the standardization of the data

where the descriptions are most detailed, it ismatrix).

difficult for following researchers to compute IDV
and MAS from originally collected data. And it is

3. Reproduction of IDV and MAS

impossible to reproduce the factor analysis. In 1980

As mentioned above, two versions exist in Culture’s
50

Reproduction of Hofstede Model
Likert scale, that is, from “1. ofTable 1. The State of Disclosures on Processing andStatistical Analysis of IBM Data
Information needed
for reproduction of
IDV and MAS

utmost importance to me” to “5. ofBook title/Published date
Culture’s Consequences

Cultures andOrganizations

1980

1984

1991

○

×

×

△

△

×

○

×

○

○

×

×

×

×

×

△

×

×

(1) Simple totals of IBM
data(2) Factor loadings(3) Formulas of IDV andMAS(4) Standardized scoring
coefficients(5) Computing formulas offactor scores(6) Processing of raw data
(7) Method of factor
×××
analysis(8) Interpretation of factor△
××
structure
Note: ○: mentioned, ×: not mentioned, △: mentioned insufficiently.

very little or no importance.” Themean scores (or the standard scores)of every country are used to compute
IDV

and

MAS.

However,thenumerical values which appeared inTable A3.1 are not the mean scores(raw data) of these answers but ones
that

are

given

the

following

operations.
First, five-point Likert scale ofraw data is run in reverse so that thenumerical value of the question may
grow larger when respondents reply

version,

Hofstedementioned

thestandardizedthat it is an important goal. Practically, the value ofscoring coefficients, but the explanation on how to
calculatefactorscoresisinsufficient

the answer is subtracted from 6 and made as theandvalue of that variable. Though Hofstede did not refer

computation formulas are not clear (Hofstede simply

to this operation at all, this point was confirmed fromclaims to take the summation of product of standard

the author’s factor analysis, which will be explained

scores and standardized scoring coefficients).

later. Second, on the basis of this reversed values, theWhat Hofstede did not refer is (1) how to readmean scores of every country is calculated andthe simple totals of “work goals” questions, (2) the

standardized (calculation of standard scores). Third,summary of factor analysis on “work goals”

the standard scores are changed to have an averagequestions, (3) the interpretations of the factors

500 and standard deviation 100 to avoid negative

extracted from the factor analysis. These three points

numbers and decimals. Therefore, it is decided thatare cleared in the following.

the goal of high importance takes the value beyond
500 and the goal of low importance takes 500 and

How to Read the Simple Totals

under (Hofstede, 1980, pp. 79-80).

The simple totals of 14 “work goals” questions

As a matter of fact, the latter half of thisappear on Table A3.1 of Appendix 3 in 1980 version.

operation is substantially the same as the calculation

Respondents replied to these questions in five-pointof deviation value. Though deviation value is 50
51

Fujitaadded to 10 times standard score (standard score isprocess are used in factor analysis. First, the values

modified to mark an average 50, standard deviationof “work goals” questions are averaged by seven10), Hofstede designed a similar calculation totypes of job in every country, then they are

deviation value on his own.

standardized. Every country’s standard scores aremean of these seven standard scores (Hofstede, 1980,

Therefore, the reversed standard scores are 500

pp. 78, 237, note 19 in p. 90).

subtracted from the numerical values in Table A3.1
and divided by 100. The whole mean and variance of

Hofstede conducted a factor analysis on theeach question are used to acquire the country meanbasis of 40 countries×14 questions matrix (Hofstede,scores of raw data. The values (MEA) in the second

1980, p. 241), but did not refer to whether or not to

row from the bottom of Table A3.1 are the wholestandardize and reverse Likert scale of the values ofmean scores of each question yet standardized scores,

answer. Therefore, the author’s factor analysis wasthus it is needed that standardization is removed byactually done on both the 40×14 matrix composedusing the whole mean scores and the variances of allof raw data’s mean scores and the 40×14 matrix

the questions (IMP and SD on the bottom left ofcomposed of the reversed standard scores. The factor

Table A3.1, respectively 1.91 and 0.28). Because

analysis adopted two factors criterion and principal

IMP and SD are raw data values, reversed mean

components method in accordance with Hofstede

scores are what IMP is subtracted from 6. To remove(1980). Because an initial factor method was notstandardization of the whole mean scores of eachpresented clearly, the factor analysis was done withquestion and the country mean scores, 0.28 and 4.09

both principal components method and principal(= 6 – 1.91) are used, standard deviation and whole

factors method. As a result of the factor analysis, notmean score of each question are used respectively.

only factor scores but also factor loadings andEach mean score is reversed Likert scale, thus it isstandardized scoring coefficients of all the questions

possible to get raw data mean by subtracting from 6.

were computed (Table 2: but only the factor score
SOC is multiplied by minus 1 in accordance with

Summary of Factor Analysis

Hofstede).

The factor scores calculated from factor analysis on

Signs were opposite when factor scores were

14 “work goals” questions are used to compute IDV

compared before and after the reverse of Likert scale.

and MAS. The procedure for this factor analysis is

Though the signs of standard scores and factor

mentioned in the statistical analysis section of thescores are reversed by the reverse of Likert scale, it

5th chapter (Hofstede, 1980, pp. 237-259).

is confirmed that Hofstede conducted a factor

Not the numerical values themselves of theanalysis on the standard scores of raw data because

answer but the standardized values from following

the factor scores before reverse are same as those in52

Reproduction of Hofstede Model

Reinterpretation of Two
Table 2. Factor Loadings and Standardized Scoring Coefficients of“Work Goals” Questions
Questions

Abbreviation

factor loadings

Factors in “Work Goals”

standardized scoring
coefficients

Questions

1st factor

2nd factor

1st factor

2nd factor

Hofstede regarded the two

A5

Challenge

-0.45

-0.54

-0.13

-0.17

factors extracted from theA6

Desirable area

-0.36

0.59

-0.11

0.19

factor

A7

Earnings

-0.03

-0.70

-0.01

-0.22

A8

Cooperation

0.37

0.69

0.11

0.22

A9

Training

0.83

0.02

0.25

0.00

of

A10

Benefits

0.40

0.09

0.12

0.03

“masculinity.” However, theA11

Recognition

-0.24

-0.59

-0.07

-0.19

A12

Physical conditions

0.70

0.00

0.21

0.00

A13

Freedom

-0.49

-0.03

-0.15

-0.01

between the factor loadings

A14

Employment security

0.05

0.48

0.01

0.15

and

A15

Advancement

0.18

-0.55

0.06

-0.18

questions) calculated from theA16

Manager

-0.16

0.69

-0.05

0.22

A17

Use of skills

0.63

-0.40

0.19

-0.13

A18 Personal time
Note: appeared in Hofstede (1980).

-0.86

0.01

-0.26

0.01

analysis

on

“work

goals” questions as the factors“individualism”

factorstructuretheauthor’s

and(relations

contents

factoroftheanalysisindicated that the first factor
was related to “collectivism”
and the second factor toTable A3.1.

“femininity.” The factor structure of these two

As for the factor loadings and the standardized

factors is reinterpreted here.

scoring coefficients of the first factor, signs wereFirst, the factor loadings of the first factor

reverse to the calculation result of Hofstede. Because(Table 2) are examined. The questions that havefactor loadings are, so to speak, the weight

positive factor loadings are “Training” (A9),coefficients of the common factor in each variable or“Physical conditions” (A12), “Use of skills” (A17),

the correlation coefficients between standard scores“Benefits”

and factor scores, the signs of data do not determine(A10),“Cooperation”

(A8),

and

“Advancement” (A15). The factor scores of the firstthe signs of factor loadings. Therefore, there are no

factor rise as much as these goals are taken seriously.

relations between the reverse of Likert scale and

The questions that have negative factor loadings areinconsistency of the signs of the factor loadings and“Personal

the standardized scoring coefficients of the first
factor; they are multiplied by minus 1 for some otherreason.

time”(A18),“Freedom”“Challenge”(A5),“Desirable“Recognition”(A11),and(A13),

area”

(A6),

“Manager”(A16).

Therefore the first factor’s characteristics become
stronger as the degree of dependence to the company
53

Fujitaincreases and cooperation is placed importance,

Table 3. Gender Difference in “Work Goals”

while weaker as personal interest and respect forMore important for men

More important for women

Advancement

Friendly atmosphereconfrontation structure of “collective-individual”

Earnings

Position security

because strong loyalty to company organization andTraining

Physical conditions

Up-to-datenessManager

privacy are valued. The first factor has theemphasis on group harmony are the expressions of

Cooperation
Source: Hofstede (1980, p. 274)

collectivism. The higher the degree of collectivism is,
the higher the factor scores of the first factor are. It
was revealed that Hofstede multiplied factorof skills” (A17), absolute value of factor loading

loadings and standardized scoring coefficients bybeing higher respectively. When this is comparedminus 1 and interpreted the first factor as a factor ofwith Table 3, it is understood that the higher theindividualism.

scores of “work goals” which women place

Second, the factor loadings of the second factor

importance, the higher the factor scores of the(Table 2) are examined. Hofstede conducted thesecond factor. It is proved that Hofstede formulated

following analysis prior to the interpretation that thethe index as “masculinity” by multiplying factor

second factor was related to “masculinity.” Thisscore by minus 1 in consideration of the larger

analysis aims at confirming whether there is any

number of male respondents (Hofstede, 1980,

difference between men and women concerning

p. 277). However, essentially, the second factor

importance placed on different work goals. He

should be related to “femininity.”

selected occupations that have sufficient numbers of
employees of both gender to allow statistical

4. For Further Supplementarytreatment of data, and where employees of bothExamination

gender engage in equal tasks. A t-test between men

Based on the above analysis, data treatment process

and women was done on the basis of the mean scoresin supplementary examination by using IDV andof “work goals” questions (Hofstede, 1980, pp.

MAS will be explained. Analysis can be performed

271-275). Result is in Table 3.

in two ways. One is to compare the culture

The questions that have positive factor loadinginternationally or to examine diachronic change ofare “Cooperation” (A8), “Manager” (A16),

culture in comparison with IBM data by using the“Desirable area” (A6), and “Employment security”

calculation formulas of two indices that Hofstede(A14). The questions that have negative factor

developed. Another is to verify the validity of theseloading are “Earnings” (A7), “Recognition” (A11),

two indices or to develop a new index of culture by“Advancement” (A15), “Challenge” (A5), and “Useusing factor analysis as Hofstede’s.
54

Reproduction of Hofstede Model

Using Two Indices in Original Formand “×” shows multiplication. INV is INV’

When using the computation formulas of IDV andmultiplied by minus 1. These estimated factor scores,

MAS developed by Hofstede in original form,

INV and SOC, are substituted into the computation

answers for “work goals” questions must be

formulas shown in Section 2 to calculate the indices’collected and factor scores should be estimated fromnumerical values. In other words, IDV = 50 – 25

that data. First, it is needed to calculate the following

INV’. SOC is multiplied by minus 1 within thetwo kinds of mean scores and standard deviations ofcomputation formula.

each “work goals” question. One kind is the meanscores of each question calculated for the country.

Conducting Factor Analysis

These mean scores compose the matrix of country

When conducting the same factor analysis as

number×14. Another kind is the mean score of the

Hofstede, it is necessary to calculate the correlation

whole sample calculated for each question. Standard

matrix of “work goals” questions. Then calculate thedeviations are calculated for every question as well.

eigenvalues of this matrix, and conduct a factor

Second, country means are standardized by using

analysis using the number of eigenvalues above 1 as

whole means and standard deviations. Simply deduct

factor number criterion. In fact, this correlation

whole means from country means and divide themmatrix is computed in the process of principal

by standard deviations of each question.

component analysis. Therefore conduct a factor

The estimated values of the factor scores areanalysis employing principal component method as

calculated from above standard scores by using theinitial factor method. That is, the factor analysis ofstandardized scoring coefficients shown in Table 2.

principal component method using varimax rotation

Though Hofstede did not explicitly show theas rotation method is needed.

computation formulas, they are proved to be as

The data used in factor analysis of Hofstede

follows from the author’s factor analysis.

(1980) is standardized. Yet it is required to conduct a
factor analysis with raw data because standardization

INV’ = – 0.13×A5 – 0.11×A6 – 0.01×A7 +0.11×A8 + 0.25×A9 + 0.12×A10 –0.07×A11 + 0.21×A12 – 0.15×A13 +0.01×A14 + 0.06×A15 – 0.05×A16 +0.19×A17 – 0.26×A18
SOC = – 0.17×A5 + 0.19×A6 – 0.22×A7 +0.22×A8 + 0.00×A9 + 0.03×A10 –0.19×A11 + 0.00×A12 – 0.01×A13 +0.15×A14 – 0.18×A15 + 0.22×A16 –0.13×A17 + 0.01×A18

is automatically processed in general factor analysis
programs.
Next, compare the rotated factor patterncomputed from the factor analysis with the factor
loadings shown in Table 2. It can be said that thefactor of “collectivism” and “femininity” wereextracted if the number of factors extracted and thesigns/amount of factor loadings were about the same

Values from A5 to A18 are the standard scores,
55

Fujita(1st/Abridged eds.). Beverly Hills: Sage.

as in Table 2. In that case, simply multiply the

Hofstede,second factor by minus 1 and substitute the factor

G.

H.

(1991/1997).

Culturesandscores into the computation formulas shown inorganizations: Software of the mind (1st ed.).

above section .

London: McGraw-Hill. (Rev. ed.). New York:
McGraw-Hill.

When the signs/amount of factor loadings do

Honda, M., & Shimada, K. (1977). Keiei no tame no

not align with Table 2, interpret what the factors
extracted are. The way of analyzing a factor

tahenryou

kaisekihou[Multivariate

analysisstructure is to pick out in order from the higher

method for administrative science]. Tokyo: Sanno

factor loadings and classify them by plus and minus,

University Press. (In Japanese)
Ichikawa, S., & Ohashi, Y. (1987). SAS ni yoru datathen examine what kind of confrontation structure

kaiseki nyumon [An introduction to data analysis

they have.

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56