Prevalence of internet addiction and correlations with family factors among South Korean adolescents.

Park, Soo Kyung ; Kim, Jae Yop ; Cho, Choon Bum 等

Over the past two decades, the South Korean government has strongly promoted the establishment of a nationwide Internet network. As a result, by 1999 some 22.4% of South Koreans used the Internet and by 2005, Internet use had more than tripled to 71.9% (National Internet Development Agency of Korea, 2006).

In South Korea more adolescents use the Internet than do any other age group. For these youths, the Internet is not only the most common activity of daily life but also a major recreational activity. By 2005, some 97.3% of South Korean adolescents between the ages of 6 and 19 years used the Internet (National Internet Development Agency of Korea, 2006).

Generally, the concept of addiction has been applied to excessive use of the Internet. Young (1999) claimed, "Internet addiction" is a broad term that covers a wide variety of behaviors and impulse control problems. The term adopted in describing this behavior in which some people's involvement can become so intense as to be pathological has varied, including Internet addiction, problematic Internet use, and pathological Internet use (Charlton & Danforth, 2007).

Previous studies have indicated that Internet accessibility is one of the most decisive factors for overuse by college students (Morahan-Martin & Schumacher, 2000; Anderson, 2001; Lin & Tsai, 2002). When access is free and easy, college students tend to be vulnerable to becoming addicted to the Internet (Kandell, 1998). In South Korea, adolescents have easy Internet access, similar to college students, due to the nationwide Internet infrastructure and may be vulnerable to pathological Internet use.

As elsewhere, problems related to Internet overuse in South Korea include addiction, circulation of undesirable content, dissemination of private information, extreme entertainment-oriented use, grammar problems, diminished vision, and lack of sleep (Song, 1999). The addictive aspect of the Internet is of special interest because it can lead to more serious problems such as mental illness, lying, kleptomania, lessened concentration, lower school grades, poor school attendance, dropping out of school, running away from home, and other family crises (Kim & Kim, 2003).

Despite public and private efforts for preventing and resolving this growing problem in South Korea, most adolescents fail to recognize how detrimental excessive Internet use can be to their physical and mental health.

Previous studies have documented that an adolescent's family environment is highly predictive for adolescent Internet addiction (Young, 1999; Nam, 2002). Moreover, a number of studies in South Korea have found family factors that influence Internet addiction among adolescents. Most studies have focused on the relationships between psychological characteristics and Internet addictions (Yun, 1998; Lee, 2000; Song & Lee, 2002; Choi & Han, 2006) or on the relationships between protective factors such as parenting attitude, communication, and cohesion within families and Internet addiction among adolescents (H. W. Kim, 2001; Cho, 2001; Nam, 2002; Hwang, 2000; Kim & Kim, 2003).

On the other hand, there has been little consideration of relationships between exposure to family violence and Internet addiction among South Korean adolescents. However, previous studies indicated that adolescents exposed to family violence tend to seek relief from the associated tension and stress through such risky behaviors as substance abuse or running away (Kim, 1997; Clark, Lesnick, & Hegedus, 1997; Johnson, Whitbeck, & Hoyt, 2005). In addition, Goldberg (1996) borrowed from substance-dependence criteria of the DSM-IV and defined Internet Addiction Disorder as a behavioral addiction that acts as a coping mechanism. In accord with this definition and prior study results, we predicted that adolescents subjected to family violence would be' more likely to overuse the Internet.

Accordingly, after taking the scarce information on factors associated with adolescent Internet addiction into consideration, we conducted an exploratory examination of relations between risk factors (e.g., exposure to family violence) and Internet addiction as well as relations between protective factors such as parenting attitude, family cohesion, and communications and Internet addiction among adolescent in South Korea.

METHOD

Participants and Procedures

This study included middle and high school students residing in Seoul, the largest metropolitan area in South Korea and the area with the largest concentration of adolescents.

To select the sample, we divided Seoul into four areas and randomly chose one or two schools in each area. We personally visited the schools, explained the purpose of the study, and made clear that participation was voluntary and that answers were confidential. We subsequently distributed surveys to schools that agreed to participate.

The survey was self-administered by students and data were collected in December 2005. Of the 950 returned surveys, 903 were chosen for analyses after 47 were discarded that were improperly completed.

About 69.6% of the subjects were male adolescents, 60.5% were middle school seniors, and the rest (39.5%) were high school students (12.4% freshman, 27.1% juniors). One of four adolescents surveyed (24.9%) said they were of high economic status, 57.9% was of middle economic status, and 17.1% low economic status (Table 1).

Measurements

To measure the level of Internet addiction, we used Young's Internet Addiction Scale (IAS) after reconstruction to fit this research (Young, 1998). This scale includes the following components: obsessive behavior related to Internet or chatting, withdrawal symptoms, tolerance, slump in school performance, negligence of family and school life, personal relationship problems, behavioral problems, health trouble, and emotional problems. In all, 20 questions measure addiction. Answers were scored as 1, totally inapplicable; 2, inapplicable; 3, so-so; 4, applicable; and 5, totally applicable. Total scores for Internet use ranged from 20 to 100.

Together with the measurement of Internet addiction level, this study assessed the distribution (range) of Internet addiction by group, by subdividing groups as possibly Internet addicted and non-addicted. The group division of Internet addiction was carried out by Young's standard using of the following scores:

20-39: Average Internet user, capable of controlling online usage

40-69: History of having problems with Internet usage

70-100: Serious life problems related to Internet usage

As noted, we classified respondents with scores of 70 and higher as Internet addicted and those with scores of 40 to 69 as possibly addicted. Persons with scores of 39 and lower were considered not having problems with regard to Internet use. In this study, Chronbach's coefficient alpha was .884.

For parenting attitude, we used the scale of Lee (1997) developed by conducting factor analyses of each mother and father, and adjusting for our research conditions. This scale is divided into sub-dimensional types: authoritarianism, democracy, and blind obedience. However, we did not use the sub-dimensions separately and scored parenting attitude level by using "positive" and "negative" concepts. In this instance, a higher score means a more positive parenting attitude. Chronbach's coefficient alpha was .927.

To measure the effect of exposure to marital violence, we modified the Conflict Tactics Scale (CTS) developed by Straus (1979). The CTS is designed to measure the degree of verbal, minor physical and serious physical violence, and the time of exposure ("within the past year" or after the time the inquiry starts). This scale also can measure parent-to-child violence.

Verbal violence was measured by one question: "Said something insulting and made you feel ill." Four questions addressed minor physical violence: "Broke or kicked something," "Threw something," "Pushed you hard," and "Slapped you on the cheek." Five questions were concerned with serious physical violence: "Kicked, bit, or hit you with a fist," "Beat you with objects (such as a belt, stick, or golf club)," "Beat you up pitilessly," "Choked you," and "Threatened or hurt you with a knife (scissors) or gun." Marital violence exposure and children's battering were measured as follows: 0, "Have no experience"; 1, "Once or twice a year"; 2, "Once or twice a month"; 3, "More than once a week"; and 4, "Almost every day." For exposure to marital violence Chronbach's coefficient alpha was .942, Chronbach's coefficient alpha for parent-to-child violence was .890.

To measure family cohesion and communication, this study applied the scale modified and verified by M. O. Kim (2001), based on the theory of Walsh (1998). Family cohesion and family communication were measured by 10 questions with each scale including reverse questions. Answers were scored on a 5-point Likert scale, with 5 "Always," 4 "Frequently," 3 "Normally," 2 "Hardly," and 1 "Never" according to the degree of harmony in thinking and feeling within the family. Chronbach's coefficient alpha for family cohesion was .781 and for family communication, .807.

After coding and data-cleaning procedures, the data were analyzed by frequency, one-way ANOVA, and cross-tabulation analysis using SPSS version 12.0

RESULTS

Socio-demographic Characteristics and Internet Use

Cross-tabulation analysis was used to examine demographic characteristics of three groups divided according to the IAS score of Young (1998). Among the participants in the present study, one often (10.7%) reached the criteria for IAs (Internet addicts), 73.7% were PAs (possible Internet addicts), 15.0% were NAs (non-addicts).

Males were more likely to be Internet addicts than females, but the difference was not statistically significant. Among male adolescents, 14.4% were NAs, 73.7% were PAs, and 11.9% were IAs; rates for females were 16.4%, 75.5%, and 8.2%, respectively. By grade level, 13.0% of high school students and 9.6% of middle school students met the criteria for IAs, but this difference was also not statistically significant. In terms of economic status, 13.8% of those who identified themselves as of high economic status, 9.5% of middle economic status, and 9.4% of low economic status met the criteria for IA, but again the difference between groups was not statistically significant.

Family Protective Factors and Internet Addiction

Our next consideration was the relationship between family factors and Internet addiction. The results are shown in Tables 2 and 3 and are divided into family protective factors and family risk factors.

The mean scores for family protective factors, i.e., parenting attitude, conversation time with parents, communication within the family, and family cohesion by IAS group are presented in Table 2. When father's parenting attitude was assessed by IAS group, the mean scores were 3.45, 3.16, and 3.04 for the NA, PA, and IA groups, respectively, and there were significant differences in the father's parenting attitude scale among the three groups (F = 22.637, p = .000). When we conducted post hoc testing on father's parenting attitude, differences were found between the NA and PA groups and between the NA and IA groups, but there was no difference between PA and IA groups. In terms of mother's parenting attitude by IAS group, the mean scores were 3.54, 3.26, and 3.13 for the NA, PA, and IA groups, respectively, and there were significant differences in the father's parenting attitude scale among the three groups (F = 25.409, p = .000). When post hoc testing was conducted on the father's parenting attitude, differences were found between the NA and PA groups and between the NA and IA groups, but no difference between PA and IA groups. Post hoc analysis of the mother's parenting attitude produced significantly different scores for all three IAS groups.

However, no statistically significant differences were found between IAS groups for conversation time between parents and adolescents (conversation time with father F = 1.578, p = .207, conversation time with mother F = 1.154, p = .316).

When the relationship between family communication and Internet addiction was analyzed, the NA, PA, and IA groups had mean scores of 3.64, 3.34, and 3.25, respectively, and there were statistically significant differences among all three groups (F = 15.189, p = .001). Post hoc analysis showed differences between NAs and PAs and between NAs and IAs, but not between PAs and IA.

The relationship between family cohesion and Internet addiction was also explored. The three groups (NA, PA, and IA) had mean scores of 3.75, 3.44, and 3.23, respectively, and there were statistically significant differences among three IAS groups (F = 20.934, p = .000). Post hoc analysis showed that the mean score of family cohesion differed significantly among all three IAS groups.

Family Risk Factor and Internet Addiction

Table 3 shows the relationship between Internet addiction level and family risk factors as the opposite of family protective factors. As noted above, family risk factors were primarily related to domestic violence: marital violence exposure and parent-to-child violence.

First, we searched for the relationship between Internet addiction and the adolescent's exposure to marital violence. Fewer than half of the participants (41.6%) had been exposed to parental violence (father-to-mother 38.9% or mother-to-father 30.6%) but these students tended to be more addicted to the Internet than were participants without this exposure ([chi square] = 11.895, p < .001). Also, both the subjects who had witnessed father-to-mother violence ([chi square] = 10.639, p < .001) and those exposed to mother-to-father violence ([chi square] = 13.005, p < .001) were more likely to meet the criteria for Internet addiction than those who lived in families without parental violence.

Second, nearly two out of three participants in the study sample (62.3%) were subjected to physical violence by their parents: 53.0% by their fathers and 48.8% by their mothers. These adolescents had higher rates of Internet addiction than those not subjected to parental violence ([chi square] = 8.426, p < .05). Also, both the adolescents whose fathers behaved violently toward them ([chi square] = 7.422, p < .05) and those with violent mothers ([chi square] = 10.471, p < .01) were more likely to meet the criteria for Internet addiction than were those without this exposure.

DISCUSSION

South Korea has an Internet-based society that provides numerous middle and high school adolescents as well as college students with easy Internet access--which may lead those most prone to Internet addiction. In terms of prevalence of addiction, slightly more than one in 10 adolescents (10.7%) in the present study scored at least 70 on the Internet Addiction Scale, indicating that they were at high risk for Internet addiction and in need of further assessment and intervention. This figure is similar (10.3%-24%) to those reported in most prior South Korean studies which used the Addiction Diagnostic Questionnaire designed by Young (Song, 1998; Cho, 2001; Ahn & Kim, 2000; Lee, 2000; Hwang, Hwang, & Lee, 2001; H. W. Kim 2001; Information Culture Center of Korea, 2001), although some researchers reported much lower prevalence rates of Internet addiction (1.6%-4.2%) than we found (Kim et al., 2002; Kim et al., 2006). In two studies of Taiwanese high school adolescents, 11.69% (Yang & Tung, 2007) and 13.8% of the students (Lin & Tsai, 2002) were identified as addicts by use of the eight-item Internet Addiction Diagnostic Questionnaire designed by Young (online Internet addiction survey) or as problematic Internet users, respectively. However, previous studies reported that 4.6% of girls and 4.7% of boys among 12- 18-year-olds Finnish youth (Kaltiala-Heino, Lintonen, & Rimpela, 2004) and a total of 1.98% (2.42% for boys and 1.51% for girls) among Norwegian youth (12-18 years) met the criteria of Internet addiction (Johansson & Gotestam, 2004). In the United States, only approximately 5.7% of Internet users meet the criteria for serious, compulsive Internet use (Greenfield, 1999), and 6-13% among college students were classified as Internet dependent or pathological users (Scherer, 1997; Morahan-Martin & Schumacher, 2000; Anderson, 2001; Kubey, Lavin, & Barrows, 2001). These differences between countries may be partly attributable to different data collection methods, including samples, time, and measures. However, even after taking these differences into consideration, our results demonstrate that Internet addiction among South Korean adolescents is serious.

Bivariate analyses were conducted concerning the relationships between family factors and Internet addiction among South Korean adolescents. The results show that not only protective factors (e.g., parenting attitudes, family communication, and family cohesion) but also risk factors of family violence (e.g., marital violence and parent-to-child violence) are strongly associated with Internet addiction. These results are consistent with previous findings that parenting attitudes (H. W. Kim, 2001; Cho, 2001; Nam, 2002) and communication with parents (Cho, 2001; Park, 2001; Hwang, 2000) are related to Internet addiction among South Korean adolescents. In fact, adolescents who receive more support from parents tend to participate in fewer negative and anti-social behaviors (Hawkins, Catalono, & Miller, 1992; Ahn, 2000), while adolescents whose parents provide insufficient attention and support are more likely to be psychologically unstable, leading to Internet overuse to escape their home situations (Davis, 2001).

However, interestingly, conversation time with parents was not associated with Internet addiction, suggesting that the quality of a relationship between parents and youths is more important than the quantity of time spent together and that the quality of the relationship may have more significant influences on adolescent negative behaviors.

Adolescents who are physically abused tend to seek relief from associated tension and stress through substance abuse (Clark et al., 1997; Kim, 1997; Resnick et al., 1997). and adolescents exposed to marital violence and abuse by a parent are more likely to meet problematic criteria for alcohol use (Caetano et al., 2003) and persons abused as children are more likely to report substance abuse in adulthood (Cohen & Densen-Gerber, 1982; Schaefer, Sobieraj, & Hollyfield, 1988; Wilsnack et al., 1997; Call, 2001; Walsh, 2002; McGettigan-Savarese, 2001). Thus, considering that Goldberg (1996) defined an Internet Addiction Disorder as a behavioral addiction such as substance dependence, we were not surprised by the relationship found in this study between Internet addiction and family violence. Our study confirms that exposure to family violence plays a key role in Internet addiction among adolescents.

An unexpected finding was that gender and grade level are not statistically significant in relationship to Internet addiction. These results were consisted with findings in another South Korean study of Internet use that surveyed middle and high school students (Nam, 2002) but were dissimilar to findings for Taiwanese youths. In Taiwan, significantly more boys (a ratio of 4 boys to 1 girl) and more 11th or 12th graders (compared to 10th graders) exhibited Internet dependence (Lin & Tasi, 2002). This indicates that on the whole, Internet addiction among South Korean adolescents was more commonplace regardless of gender or grade than in Taiwanese adolescents with similar cultures.

Our study results have several important implications. First, the high prevalence of Internet addiction among South Korean adolescents suggests that policymakers need to be aware that adolescent Internet addiction is becoming associated with escalating problem behaviors and that family environment is associated with Internet addiction. In addition, professional intervention programs based in school or community mental health centers should be developed in order to prevent higher risk-taking behaviors.

Some school and community agencies in South Korea have identified potential Internet addicts and have provided them with education about Internet use. However, these prevention programs related to Internet addiction have had only partially positive effects because prevention services have been primarily focused on superficial education about negative effects of Internet overuse rather than on intervention to decrease risk factors and increase protective factors affecting Internet addiction. Thus, policymakers and social service agency workers need to enhance community-based service systems for identifying potential Internet addicts and then to link prevention programs with treatment.

Second, we identified family factors that were associated with Internet addiction among adolescents. This suggests that family risk factors as well as protective factors need be considered by policymakers and social workers. There is a need for school- or community-based parent education and intervention programs to improve parenting and communication skills to enhance family relationships. As our study noted, the more adolescents experience stressful events such a physical violence, the more likely they are to overuse the Internet as a means of coping with family-induced stress. Thus, policymakers and practitioners must provide services that include stress-coping skills, counseling, referral for addiction treatment, and active recreational activities.

Future studies should seek to identify causal relations between family-related factors and Internet addiction by multivariate analysis and investigate the relationship between Internet addiction and other addictive behaviors such as substance abuse and game addiction.

The present study has some limitations. Since the data were obtained by the self-reporting method, the results may suffer from the general weakness related to this methodology. Also, because all study participants were enrolled in schools in Seoul, the results may not be generalized to the entire South Korean adolescent population.

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Soo Kyung Park, Associate Professor, Department of Social Welfare, Daejin University.

Jae Yop Kim, Professor, Department of Social Welfare, Yonsei University.

Choon Bum Cho, Researcher, Center for Social Welfare Research, Yonsei University.

Reprint requests should be sent to Soo Kyung Park, Department of Social Welfare, Daejin University, San 11-1 Sundah-dong, Pocheon City, Kyunggi-Do, Rep of Korea. E-mail: [email protected]

Table 1
Socio-demographic characteristics in the three IAS groups

Socio- Total NA PA
demographic N (%) N (%)
characteristics

 Male 616(69.6) 89(14.4) 454(73.7)
Gender Female 269(30.4) 44(16.4) 203(75.5)
 Total 885(100.0) 133(15.0) 657(74.2)

 Middle 458(60.5) 72(15.7) 342(74.7)
Grade high 299(39.5) 39(13.0) 221(73.9)
 Total 757(100.0) 111(14.7) 563(74.4)

 Low 149(17.1) 27(18.1) 108(72.5)
Economic Middle 504(57.9) 72(14.3) 384(76.2)
status High 217(24.9) 33(15.2) 154(71.0)
 Total 870(100.0) 132(15.2) 646(74.3)

Socio- IA
demographic N (%) df Chi-Square P
characteristics

 Male 73(11.9)
Gender Female 22(8.2) 2 2.885 0.236
 Total 95(10.7)
 Middle 44(9.6)
Grade high 39(13.0) 4 7.797 0.099
 Total 83(11.0)
 Low 14(9.4)
Economic Middle 48(9.5) 4 4.638 0.327
status High 30(13.8)
 Total 92(10.6)

Note: Not all adolescents surveyed answered all questions.

NA=Non-addicts, PA=Possible Internet addicts, IA=Internet addicts

Table 2
Family protective factors and Internet addiction

 Level of
 Internet
 Use Mean SD

Father's parenting NA(n=131) 3.45 0.45
attitude PA (n=663) 3.16 0.50
 IA (n= 93) 3.04 0.53
 NA (n=127) 3.54 0.33

Mother's parenting PA (n=664) 3.26 0.47
attitude IA (n= 95) 3.13 0.53
 NA (n=123) 74.45 82.57

Conversation time PA (n=629) 63.23 74.98
with father IA (n= 91) 57.34 64.18
 NA (n=123) 130.91 121.34

Conversation time PA (n=629) 114.68 120.70
with mother IA (n= 92) 108.75 112.01
 NA (n=134) 3.64 0.71

Family PA (n=668) 3.34 0.58
communication IA (n= 95) 3.25 0.66
 NA (n=134) 3.75 0.67

Family cohesion PA (n=670) 3.44 0.58
 IA (n= 95) 3.23 0.67

 Scheffe
 F P test

Father's parenting 22.637 0.000 a [not equal to] b,
attitude a [not equal to] c

Mother's parenting 25.409 0.000 a [not equal to] b
attitude [not equal to] c

Conversation time 1.578 0.207
with father

Conversation time 1.154 0.316
with mother

Family 15.189 0.000 a [not equal to] b,
communication a [not equal to] c

Family cohesion 20.934 0.000 a [not equal to] b
 [not equal to] c

Note: Not all adolescents surveyed answered all questions.

NA=Non-addicts(a), PA=Possible Internet addicts(b), IA=Internet
addicts(c)

Table 3
The relationships between family risk factors and Internet addiction

 Total NA
 N (%) N (%)

Exposure to marital violence 903(100.0)
 No 552(61.1) 98(17.8)
 Father-to-mother violence Yes 351(38.9) 36(10.3)
 No 627(69.4) 109(17.4)
 Mother-to-father violence Yes 276(30.6) 25(9.1)

Parent-to-children violence 903(100.0)
 No 424(47.0) 69(16.3)
 Father-to-children violence Yes 479(53.0) 65(13.6)
 No 462(51.2) 79(17.1)
 Mother-to-children violence Yes 441(48.8) 55(12.5)

 PA IA Chi-
 N (%) N (%) square p

Exposure to marital violence
 402(72.8) 52(9.4) 10.639 0.005
 Father-to-mother violence 271(77.2) 44(12.5)
 460(73.4) 58(9.3) 13.005 0.001
 Mother-to-father violence 213(77.2) 38(13.8)

Parent-to-children violence
 322(75.9) 33(7.8) 7.422 0.024
 Father-to-children violence 351(73.3) 63(13.2)
 347(75.1) 36(7.8)
 Mother-to-children violence 326(73.9) 60(13.6) 10.471 0.005

Note: NA=Non-addicts, PA=Possible Internet addicts, IA=Internet addicts