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Volume 12 Numbers 1 & 2, 2002 THE
DIGITAL DIVIDE IN SOUTH KOREA:
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|
|
|
No or very
few |
Reasonable
|
Good
|
|
All |
|
45.6 |
41.0 |
13.4 |
|
Gender |
Male |
41.3 |
42.3 |
16.3 |
|
Female |
51.8 |
39.2 |
9.0 |
|
|
Age |
13-19 |
47.5 |
41.3 |
11.2 |
|
20-29 |
31.3 |
50.4 |
18.3 |
|
|
30-39 |
51.4 |
34.6 |
14.0 |
|
|
40-49 |
63.2 |
30.5 |
6.3 |
|
|
50-59 |
72.9 |
27.1 |
0.0 |
|
|
60-64 |
100.0 |
0.0 |
0.0 |
|
|
Occupation
|
Farmer/fisher
|
66.7 |
25.0 |
8.3 |
|
Self employer
|
61.9 |
28.5 |
9.6 |
|
|
Blue collar
|
52.2 |
37.4 |
10.3 |
|
|
White collar
|
27.2 |
51.8 |
21.0 |
|
|
Housewife
|
68.5 |
26.6 |
4.8 |
|
|
Middle/high
student |
49.2 |
39.8 |
11.0 |
|
|
College
student |
27.8 |
53.3 |
18.9 |
|
|
Unemployed
|
39.4 |
48.5 |
12.1 |
|
|
Education
|
Low |
93.3 |
6.7 |
0.0 |
|
Low middle
|
59.7 |
33.2 |
7.1 |
|
|
High middle
|
42.0 |
44.0 |
13.9 |
|
|
High |
28.1
|
50.0
|
21.9
|
Poor
digital skills may lead to little use of the computer. A number of 30.5 percent
of the non-computer users answered that they found it difficult to use. A
large proportion of the total group of nonusers were women, elderly people,
non-white collar workers and people with the low education and annual income.
Among people not connected to the Internet this figure reaches 42.1 percent.
Table
2. Reasons for not using a computer in South Korea in 2000
|
Items
|
Percent
|
|
Do
not know what for |
7.6
|
|
Complex
and hard to use it |
30.5
|
|
Do not need
to use it |
26.8
|
|
Have no
time |
26.0
|
|
Because
of cost |
7.2
|
|
Do not want
to learn how to use it |
1.6
|
|
Do not have
computer |
0.3
|
|
Out of order
|
0.1 |
*
Source: ICC (2000). N = 1487
==============================================================
==============================================================
Table
3. Reasons for not using the Internet in South Korea in 2000
|
Reasons
|
Percent
|
|
Do
not know what for using |
8.4
|
|
Do not know
how to use |
42.1
|
|
Do not need
to use |
32.2
|
|
Do not have
useful content |
1.4
|
|
Can not
speak English |
1.4
|
|
Because
of cost |
9.1
|
|
Do not want
to learn how to use |
1.4
|
|
Do not have
time to use |
2.4
|
|
Do not have
computer |
0.8
|
|
Because
of connection speed |
0.5
|
|
Because
of capacity of computer |
0.2
|
|
Out of order
computer |
0.1
|
|
Do not have
modem |
0.1 |
*
Source: ICC (2000). N = 1888
==============================================================
There
are not only Internet illiterate but also "want-nots" (32.2 percent).
But this figure should be interpreted very carefully. They should not be regarded
as simply "choose-nots"as 77.1 percent of them have no or few digital
skills. Thus, they may not really be “want-nots”. When people view the use
of computer technology as a difficult job, their perceptions affect the person's
self-efficacy and they may avoid computing. Self-efficacy is the belief "in one's capabilities to organize and
execute the courses of action required to produce given attainments"
(Bandura, 1997, p. 3). In relation to a technology, self-efficacy
is the confidence of an individual that it can use (or control) a particular
technology in performing its activities.
==============================================================
Table
4. Off-liners
Reasons and their digital skills in South Korea (2000)
|
Reasons
|
No
or Very few |
Reasonable
|
Good
|
|
Do
not know what for using |
81.8
(0)
|
18.2
(2)
|
0.0
(0)
|
|
Do
not know how to use |
86.1
(124)
|
12.5
(18)
|
1.4
(2)
|
|
Do
not need to use |
77.1
(81)
|
21.0
(22)
|
1.9
(2)
|
|
Do
not have useful content |
68.8
(11)
|
31.3
(5)
|
0.0
(0)
|
|
Can
not speak English |
87.5
(7)
|
12.5
(1)
|
0.0
(0)
|
|
Because
of cost |
76.0
(73)
|
19.8
(19)
|
4.2
(4)
|
|
Do
not want to learn how to use |
100.0
(6)
|
0.0
(0)
|
0.0
(0)
|
|
Do
not have time to use |
33.3
(1)
|
66.7
(2)
|
0.0
(0)
|
|
Do
not have computer |
0.0
(0)
|
100.0
(1)
|
0.0
(0)
|
|
Because
of connection speed |
75.0
(6)
|
25.0
(2)
|
0.0
(0)
|
|
Because
of capacity of computer |
100.0
(4)
|
0.0
(0)
|
0.0
(0)
|
|
Do
not have modem |
0.0
(0)
|
100.0
(1)
|
0.0
(0)
|
* Source: ICC (2000). N = 403
* Row Percent (Count)
In
addition to incompetent digital skills, a usage gap also constitutes a divide.
According to Van Dijk and Hacker (2000), a usage
gap is defined as the unbalanced use of digital technologies between parts
of the population. Some people use and benefit from advanced technologies,
more difficult applications and services, but other people tend to use basic
and simple applications such as word processing or a computer card game.
Table
5. Usage of software among South Koreans in 2001 (percentages)
|
|
|
Word
processing |
Spread
sheet |
Database
|
Graphic/Music
|
Internet/
Online Network |
Utilities
(WinZip etc.) |
Game
/Enter-tainment
|
Learning/ Education
|
|
|
All |
86.9
|
49.0
|
18.2
|
58.6
|
80.2
|
35.4
|
77.6
|
48.5 |
||
|
Gender |
Male |
87.7
|
52.9
|
21.2
|
61.2
|
81.9
|
43.7
|
81.2
|
46.1 |
|
|
Female |
86.0
|
44.2
|
14.5
|
55.2
|
78.2
|
25.1
|
73.2
|
51.5 |
||
|
Age |
7-13 |
73.5
|
23.8
|
6.8
|
43.8
|
63.0
|
16.0
|
93.0
|
68.7 |
|
|
14-19 |
90.6
|
45.0
|
15.5
|
72.3
|
86.0
|
40.2
|
90.2
|
59.4 |
||
|
20s |
90.8
|
61.4
|
22.2
|
71.3
|
87.8
|
46.0
|
79.2
|
38.4 |
||
|
30s |
88.6
|
55.8
|
22.4
|
55.1
|
80.5
|
37.8
|
71.8
|
49.6 |
||
|
40s |
87.3
|
47.8
|
19.1
|
49.5
|
79.3
|
30.5
|
63.8
|
42.3 |
||
|
Over 50
|
81.8
|
39.5
|
15.7
|
34.9
|
69.2
|
19.6
|
51.3
|
23.2 |
||
|
Occupation
|
Farmer/fisher
|
88.2
|
35.3
|
14.7
|
41.2
|
64.7
|
26.5
|
64.7
|
38.2 |
|
|
Self employer
|
83.1
|
44.7
|
19.7
|
48.6
|
76.8
|
30.5
|
70.9
|
33.2 |
||
|
Blue collar
|
77.2
|
38.2
|
15.0
|
47.6
|
72.4
|
27.3
|
74.7
|
31.3
|
||
|
White collar
|
95.7
|
72.9
|
27.3
|
62.0
|
88.0
|
47.7
|
67.8
|
41.9
|
||
|
Housewife
|
83.6
|
38.9
|
13.3
|
50.2
|
77.4
|
22.6
|
67.1
|
51.1
|
||
|
Primary
Student |
73.3
|
23.9
|
6.9
|
43.8
|
63.2
|
16.2
|
93.2
|
69.2
|
||
|
Middle/high/
College student |
92.4
|
51.3
|
18.9
|
74.9
|
87.2
|
45.7
|
87.8
|
55.7
|
||
|
Unemployee/
Others |
88.1
|
53.2
|
19.0
|
63.4
|
84.5
|
40.3
|
78.4
|
40.7
|
||
|
|
|
|
|
|
|
|
|
|
|
|
*
Source: ICC (2001). n = 10351
* Multiple responses
For
example, as summarized in Table 5, word processing is broadly used across
various sectors of Korea (ICC, 2001). However, there are substantial differences in the
use of other applications among people of different gender, age and occupation.
Women use all applications significantly less than males except for educational
applications. People above 50 use all applications less than people under
50 are using them. The same goes for blue-collar workers as compared to white-collar
workers. The differences between blue-collar and white-collar workers are
the largest with the most difficult or advanced applications for work: spreadsheets,
databases and utilities. They are the smallest with relatively simple or educational
applications: graphic/music, Internet, game or entertainment applications
and some applications of learning. This may be due to scanty opportunities.
Blue collars usually get less exposure to the new media at their workplace.
On the other hand, the wide use of new technology in the office helps white-collar
workers remain active users of those applications. They will easily access
and use a large number of different software applications. The greater the
chance, the more likely it is that the people will access to digital applications
and services. This reveals that there exists a digital divide in skills as
well as in usage.
Scientific
explanation
For
a scientific explanation of the impact of digital skill on home computer possession
and Internet connection, the 2000 data provided by the ICC were analyzed.
Five background variables (gender, age, occupation, income, education) and
digital skill were entered into a logistic regression analysis to determine
if they were significant predictors of computer possession and Internet connection.
Two separate regressions were performed using an enter method.
In
the first model, the dependent variable was the ownership of a home computer.
Of the original 3,000 cases, 1,494 were deleted due to missing data. As seen
in Table 6, the Wald test shows that, of the independents, only digital skill
had significant relationship with the ownership of home computer (p < .05).
Income, occupation, and age were significant variables, but its component
dummy variables were not all significant when taken individually. On the whole,
the pseudo R square (Nagelkerke R2) indicates that 16.4 percent
of the variance in dependent variable is accounted for by the independent
variables.
Table
6. Logistic regression of background variables of computer possession in South
Korea in 2000
|
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
|
Digital
Skills |
|
|
32.26 |
2.00 |
0.00 |
|
|
Skills (No or Very few=1) |
-1.44 |
0.31 |
21.46 |
1.00 |
0.00 |
0.24 |
|
Skills (Reasonable=1) |
-0.66 |
0.31 |
4.56 |
1.00 |
0.03 |
0.52 |
|
Education
|
|
|
4.55 |
3.00 |
0.21 |
|
|
Education
(Low=1) |
-0.66 |
0.61 |
1.20 |
1.00 |
0.27 |
0.51 |
|
Education
(Low Middle=1) |
-0.46 |
0.23 |
4.01 |
1.00 |
0.05 |
0.63 |
|
Education
(High Middle=1) |
4.84 |
9.45 |
0.26 |
1.00 |
0.61 |
126.67 |
|
Income |
|
|
16.79 |
3.00 |
0.00 |
|
|
Income (Low=1) |
-1.05 |
0.37 |
8.16 |
1.00 |
0.00 |
0.35 |
|
Income (Low Middle=1) |
-0.43 |
0.27 |
2.46 |
1.00 |
0.12 |
0.65 |
|
Income (High Middle=1) |
0.06 |
0.28 |
0.04 |
1.00 |
0.84 |
1.06 |
|
Gender (Male=1) |
-0.29 |
0.17 |
2.92 |
1.00 |
0.09 |
0.75 |
|
Occupation
|
|
|
20.06 |
7.00 |
0.01 |
|
|
Occupation
(Farmer/Fisher=1) |
-0.89 |
0.94 |
0.89 |
1.00 |
0.34 |
0.41 |
|
Occupation
(Self employer=1) |
-0.70 |
0.51 |
1.87 |
1.00 |
0.17 |
0.49 |
|
Occupation
(Blue Collar=1) |
-1.25 |
0.48 |
6.84 |
1.00 |
0.01 |
0.29 |
|
Occupation
(White Collar=1) |
-1.16 |
0.48 |
5.84 |
1.00 |
0.02 |
0.31 |
|
Occupation
(Housewife=1) |
0.59 |
0.66 |
0.79 |
1.00 |
0.37 |
1.80 |
|
Occupation
(Middle/High School Student) |
-5.73 |
9.46 |
0.37 |
1.00 |
0.54 |
0.00 |
|
Occupation
(Collegian=1) |
-5.69 |
9.45 |
0.36 |
1.00 |
0.55 |
0.00 |
|
Age |
|
|
30.94 |
5.00 |
0.00 |
|
|
Age (13-19=1) |
-6.19 |
17.61 |
0.12 |
1.00 |
0.73 |
0.00 |
|
Age (20-29=1) |
-6.32 |
17.60 |
0.13 |
1.00 |
0.72 |
0.00 |
|
Age (30-39=1) |
-5.75 |
17.60 |
0.11 |
1.00 |
0.74 |
0.00 |
|
Age (40-49=1) |
-4.06 |
17.61 |
0.05 |
1.00 |
0.82 |
0.02 |
|
Age (50-59=1) |
-4.15 |
17.62 |
0.06 |
1.00 |
0.81 |
0.02 |
|
Constant |
10.20 |
17.61 |
0.34 |
1.00 |
0.56 |
26886.03 |
Nagelkerke
R2=0.164
Hosmer & Lemeshow's Goodness of Fit=6.781 (df=8, N=1506), p= .560.
The
second model included Internet connection as a dependent variable instead
of home computer ownership. As indicated by the significance levels of the
Wald test, among independent variables, only digital skill was significant
(p = .00). In addition, income and education were significant variables, but
their dummy variables were not all significant when entered individually.
These results are very similar to the first model. The pseudo R square was
18.8 percent.
Table
7. Logistic regression of Internet connection in South Korea in 2001
|
|
B |
S.E. |
Wald |
Df |
Sig. |
Exp(B) |
|
Digital
Skills |
|
|
112.34 |
2.00 |
0.00 |
|
|
Skills (No or Very few=1) |
-1.84 |
0.23 |
64.51 |
1.00 |
0.00 |
0.16 |
|
Skills (Reasonable=1) |
-0.67 |
0.23 |
8.54 |
1.00 |
0.00 |
0.51 |
|
Education
|
|
|
28.89 |
3.00 |
0.00 |
|
|
Education
(Low=1) |
-0.79 |
0.43 |
3.42 |
1.00 |
0.06 |
0.45 |
|
Education
(Low Middle=1) |
-0.89 |
0.17 |
28.40 |
1.00 |
0.00 |
0.41 |
|
Education
(High Middle=1) |
-0.09 |
0.70 |
0.02 |
1.00 |
0.90 |
0.91 |
|
Income |
|
|
9.22 |
3.00 |
0.03 |
|
|
Income (Low=1) |
-0.80 |
0.31 |
6.79 |
1.00 |
0.01 |
0.45 |
|
Income (Low Middle=1) |
-0.36 |
0.20 |
3.26 |
1.00 |
0.07 |
0.70 |
|
Income (High Middle=1) |
-0.15 |
0.20 |
0.58 |
1.00 |
0.45 |
0.86 |
|
Gender (Male=1) |
-0.13 |
0.13 |
0.92 |
1.00 |
0.34 |
0.88 |
|
Occupation
|
|
|
4.66 |
7.00 |
0.70 |
|
|
Occupation
(Farmer/Fisher=1) |
-0.69 |
0.70 |
0.99 |
1.00 |
0.32 |
0.50 |
|
Occupation
(Self employer=1) |
-0.21 |
0.34 |
0.39 |
1.00 |
0.53 |
0.81 |
|
Occupation
(Blue Collar=1) |
-0.34 |
0.33 |
1.07 |
1.00 |
0.30 |
0.71 |
|
Occupation
(White Collar=1) |
-0.46 |
0.32 |
2.05 |
1.00 |
0.15 |
0.63 |
|
Occupation
(Housewife=1) |
-0.26 |
0.37 |
0.48 |
1.00 |
0.49 |
0.77 |
|
Occupation
(Middle/High School Student) |
-0.72 |
0.80 |
0.81 |
1.00 |
0.37 |
0.49 |
|
Occupation
(Collegian=1) |
-0.31 |
0.73 |
0.18 |
1.00 |
0.67 |
0.73 |
|
Age |
|
|
8.38 |
5.00 |
0.14 |
|
|
Age (13-19=1) |
-0.33 |
1.09 |
0.09 |
1.00 |
0.76 |
0.72 |
|
Age (20-29=1) |
-0.54 |
1.04 |
0.27 |
1.00 |
0.61 |
0.59 |
|
Age (30-39=1) |
-0.33 |
1.03 |
0.10 |
1.00 |
0.75 |
0.72 |
|
Age (40-49=1) |
0.02 |
1.04 |
0.00 |
1.00 |
0.98 |
1.02 |
|
Age (50-59=1) |
0.19 |
1.07 |
0.03 |
1.00 |
0.86 |
1.20 |
|
Constant |
3.23 |
1.10 |
8.58 |
1.00 |
0.00 |
25.38 |
Nagelkerke
R2=0.188
Hosmer & Lemeshow's Goodness of Fit=17.379 (df=8, N=1506), p= .026.
Therefore,
digital skill had a significant effect on the prediction of home computer
possession and Internet connection. The other variables did not discriminate
looking at the ownership of home computer and Internet connection among people.
In other words, the important finding of the logistic regression is that digital
skill was a strong factor in predicting the ownership of personal computer
and Internet connection at home.
This
paper examined the digital divide in South Korea in the 1990s. The main findings
are that the gaps in digital skill and usage opportunities are widening among
different sector of the Korean population, while the mental and material divides
have been closing over time.
In
this situation, enhancing computer literacy is discussed among Korean policymakers
as an integral element of closing the newly emerging digital divide such as
insufficient computer skill. Computer literacy is defined as the degree of
being adept at using computer appliances and services for work and play. Learning
how to use computer technology requires intellectual and economic ability
so that the poor and the uneducated may not have educational opportunities.
They are not able to obtain adequate digital skills to operate more advanced
digital equipment and services.
After
the inauguration of President Dae-Jung Kim in February 1998, the Ministry
of Information and Communication prepared "the Cyber Korea 21" (MIC,
1999). Covering a four-year period from 1999 to 2002, the Cyber Korea
21 itemizes informatization programs for a knowledge-based information society.
Perhaps the most important policy in terms of the digital divide is computer
education targeting the entire population. It expresses a strong willingness
of the government to make Koreans the best computer users in the world to
strengthen human resource infrastructure. More specifically, the government
provides various programs tailored for each group of citizens: students, civil
servants, senior citizens, housewives, the self-employed, the disabled, and
the military. The necessary environment will be created by the transformation
of public institutions such as post offices into open training centers for
the public, and encouraging private institutions to offer free computer courses
by providing subsidies.
But
computer education should not be regarded as an omnipotent solution to a number
of gaps in accessing and using computer equipment and services. Social access
is "know-how, a mix of professional knowledge, economic resources, and
technical skills, to use technologies in ways that enhance professional practices
and social life" (Kling, 1999). Thus, the Korean
policymaker needs to consider a wide range of social factors. In the case
of computer literacy, the following recommendations are made.
Computer
literacy or ability should not be viewed too narrowly. It is more than the
number of computer-related courses. Computer literacy is "an understanding
of computer characteristics, capabilities, and applications, as well as an
ability to implement this knowledge in the skillful, productive use of computer
applications suitable to individual roles in society" (Simonson,
Maurer, Montag-Torardi, & Whitaker, 1987, p. 233). Next, in a similar
vein, without motivation and usage opportunity, computer literacy will remain
useless and peripheral in the process of solving the digital divide. Computer
education or training plans can not be separated from such issues as how a
society increases the use of digital information throughout various sectors
of society. The program must motivate the disadvantaged to maintain the appropriate
level of digital skills. In fact, real practice and motivation are decisive
factors. However, unfriendly interfaces may diminish people’s motivation and
prevent them from using computer and going online. Thus, the South-Korean
government’s endeavor to close the digital divide by providing training programs
should be coupled with developing user friendly software.
Han Woo Park
received his B.A. and M.A. from Hankuk University of Foreign Studies and Seoul
National University of South Korea respectively, in Communication and Information,
and his Ph.D. in the School of Informatics from State University of New York
at Buffalo (2002). He has served as a Research Associate at Royal Netherlands
Academy of Arts and Sciences (KNAW). He has published his papers in international
journals such as the Journal of American Society for Information Science and
Technology. His research focuses on collaboration, hyperlink networks, information
society policy, virtual community, international communication, and communications
via new media such as the Internet.
[1] The author is grateful to Ji Yeol Yoo, George Barnett, Doo Jin Choi, and the Information Culture Center of South Korea for their help while gathering the data for the article. Also, my thanks to Jan van Dijk for his insightful suggestions on the earlier version of the paper.
[2] For example, the International
Data Corporation (IDC), a research and consulting company on information technology,
rates annually the situation of informatization around worldwide countries
according to Information Society Index (ISI). Just as GDP measures economic
wealth, the ISI measures computer infrastructure, Internet infrastructure,
information infrastructure, and social infrastructure. In the 2,000 survey,
Sweden ranked the first. See, the IDC web site at. http://www.idc.com/
Theoretically, the term "informatization"
is different from the term "information society." According to Webster
(1995), the information society is used to emphasize
a decisive break with past eras. On the other hand, informatization is often
adopted to mean that present society is in the process of digitizing information,
maintaining past social relations. However, this distinction is believed to
be irrelevant because the term informatization , the most frequently used
in Korea, tends to mean the same. Thus, information society and informatization
are interchangeably used here. The "Basic Act on Informatization Promotion,"
basic guiding principles on building the Korean Information Infrastructure
(KII) and creating an information society, defines informatization as "making
each sector of society work or accelerate their efficiency through the production,
distribution or utilization of information" (Sub-Section 2 of section
2). In addition, to explain that the usage of informatization is influenced
by the term "modernization" may be more convincing. In the last
few decades, in order to be developed, countries including Korea needed to
be modernized (Lerner, 1962; Rogers, 1976). Similarly,
at present, they need to be informatized.
[3] It should be noted
that this does not always say that there exists a causal link between the
Southern Korean governments informatization policy and people’s awareness
and adoption rate. In theory, this paper assumes the correlation.
[4] The digital skill
index was constructed by summing the four items measuring word processing,
Excel, utilities such as WinZip, and information searching/retrieval on the
Internet. Reliability coefficient (Cronbach’s Alpha) for the index, a = .82.
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