Low Self-Control Among South Korean Adolescents

Abstract

Theoretical propositions and empirical tests of Gottfredson and Hirschi’s general theory continue to permeate the criminological literature. Nevertheless, the vast majority of studies have been conducted in North America and some European countries. Only a handful of empirical works have been conducted in East Asia. To further test the generality assertion of Gottfredson and Hirschi, the current study examines low self-control’s efficacy in predicting the involvement of South Korean adolescents in typical delinquency, drinking, smoking, Internet addiction, and smartphone addiction. The presented findings largely support the generality hypothesis, although the theorists’ assertion seems to be somewhat overstated.

Keywords

Gottfredson and Hirschi low self-control Internet addiction smartphone addiction

Since Gottfredson and Hirschi’s (1990) landmark publication of A General Theory of Crime, a great deal of theoretical and empirical attention has been paid to their general theory of crime and delinquency. One reason underlying such intensive academic attention toward this theory may be the theorists’ bold and controversial assertion that low self-control is the cause of not only crime and delinquency but also all types of behaviors analogous to crime. This “generality” hypothesis, as their assertion is typically termed,

. . . covers common delinquencies (theft and assault), serious crime (burglary and murder), reckless behaviors (speeding), school and employment difficulties (truancy, tardiness, in-school misbehavior, job instability), promiscuous sexual behaviors, drug use, and family violence (spouse abuse or child abuse), all of which have negative long term consequences. No special motivation for any of these acts is assumed. They all provide immediate, obvious benefits to the actor (as, indeed, do all purposeful acts). (Hirschi & Gottfredson, 1994, p. 16)

Gottfredson and Hirschi firmly hold the notion that many types of deviant behaviors are highly correlated because all of them are manifestations of a common latent trait, namely the tendency to pursue short-term and immediate pleasure, called low self-control (Gottfredson & Hirschi, 1990; Hirschi & Gottfredson, 1994). They further extended their assertion by arguing that low self-control is also responsible for analogous behaviors such as drinking, smoking, gambling, having children out of wedlock, and engaging in illicit sex.

More than two decades of empirical tests have generated an abundance of support for the theory. The robustness of the effects of low self-control can be clearly observed in Pratt and Cullen’s (2000) meta-analytical review, which demonstrated that low self-control, with effect sizes of .20 and over, is among the most powerful and consistent predictors of crime, delinquency, and analogous behaviors across various samples and measurement techniques. Accordingly, Pratt and Cullen (2000) warned that criminological research that fails to include low self-control runs the risk of being misspecified.

Gottfredson and Hirschi’s theoretical propositions and empirical tests continue to permeate the criminological literature. Nevertheless, the vast majority of studies of low self-control theory have been conducted in the West, particularly in North America and some European countries. A fewer number of empirical studies have been conducted in East Asia and most of them examined the association between low self-control and typical criminal and delinquent behaviors (Cheung & Cheung, 2007; Chui & Chan, 2013b; Vazsonyi, Wittekind, Belliston, & Loh, 2004; Wang, Qiao, Hong, & Zhang, 2002). In contrast, there is a paucity of research that investigates the linkage between low self-control and analogous or what Arneklev, Grasmick, Tittle, and Bursik (1993) call “imprudent” behaviors in Asia. Gottfredson and Hirschi (1990) contended that the general theory transcends national or cultural boundaries. If their theory is to be truly general, then, it should be applicable to non-Western contexts and not only to typical crime and delinquency but also to other analogous behaviors. Given this contention, in this study, we examine the extent to which low self-control accounts for the variation in South Korean adolescents’ involvement in typical delinquency, drinking, and smoking. More importantly, we also investigate the extent to which low self-control can predict two emerging forms of analogous behaviors among adolescents, namely Internet and smartphone addiction. These two forms of behavioral addictions are increasingly becoming serious problems among adolescents in South Korea, particularly because of the advanced IT in the country (Kwon, Kim, Cho, & Yang, 2013; Park, Kim, & Cho, 2008). To our knowledge, the general theory has not thus far been applied to the problem of Internet and smartphone addiction. If low self-control proves to be a statistically meaningful predictor of delinquency and analogous behaviors among South Korean adolescents in this study, the validity of Gottfredson and Hirschi’s generality assertion will be further bolstered.

Low Self-Control Theory

Gottfredson and Hirschi’s (1990) general theory posits that variation in criminal and analogous behaviors is rooted in the variation in self-control of individuals. High self-control, they maintained, is a stable individual tendency that allows the individual to avoid acts that afford immediate gratification but that entail negative long-term consequences. People with low self-control, by contrast, tend to seek immediate gratification of their desires. Thus, they are more likely to engage in acts that offer immediate and momentary pleasure but for which the costs exceed the long-term benefits.

Gottfredson and Hirschi (1990) located the sources of variation in self-control in the parental socialization of offspring in early childhood. They argued that individual levels of self-control are determined by three parental management techniques: parents who monitor their children, recognize children’s misbehaviors, and punish them accordingly will raise children with a high degree of self-control. Parents who fail to adopt these three techniques will raise children with low levels of self-control.

In a way, the general theory is akin to traditional criminological theories that emphasize the salience of child-rearing practices. For instance, Glueck and Glueck (1950) observed in their classic study of 500 delinquents and 500 non-delinquents that parental supervision, discipline, and attachment were primary correlates of delinquency. In the same vein, Sampson and Laub (1994) concluded that the fundamental causes of delinquency were “family processes—such as supervision, attachment, and discipline” (p. 539) and that parenting effects on delinquency and crime should be consistent over time and place.

Low self-control theory differs from traditional theories in one important regard, however. The majority of conventional criminological theories find the causes of crime and delinquency primarily in proximal antecedents. Hirschi’s social bonding theory (1969), for example, focuses on the absence of contemporary attachment to conventional social institutions such as the family or school. Sampson and Laub’s (1993) life-course theory emphasizes that turning points in adulthood such as marriage and employment can steer people away from previously established criminal patterns. Differential association (Sutherland, 1947) and social learning theory (Akers, 1998) focus on the definition favorable to or reinforcement of the violation of law stemming from recent or immediate interpersonal relationships. Social disorganization theory underscores the criminogenic influences associated with contemporary residence in disorganized neighborhoods (Sampson, Raudenbush, & Earls, 1997).

On the contrary, the general theory avers that the causes of crime and analogous behaviors―that is, low self-control―are firmly established in early childhood and, therefore, later life experiences, whether it be delinquent peers or turning points, have little or no bearing on the development of crime and delinquency. Gottfredson and Hirschi (1990), for instance, contended that weak social bonds, associating with deviant friends, living in poor neighborhoods, and even marital discord are all in fact the result of having low self-control. As an illustration, while social bonding theory posits that weak attachment to parents is the cause of delinquency, the general theory argues that the association is spurious because weak attachment to parents and delinquency are both caused by low self-control. Likewise, it is not that association with delinquent peers renders one to be delinquent but that those with low self-control are likely to be delinquent to begin with and simply “flock together” with other delinquents who have low self-control. In this regard, their theory is explicitly a theory of social selection or of what Nagin and Farrington (1992) called “persistent heterogeneity” as opposed to theories of social causation or “state dependence.”

The body of empirical research to date supports the main propositions of the general theory. Research has shown that measures of low self-control, whether they be attitudinal, behavioral, or otherwise, are significantly associated with crime (Burton, Cullen, Evans, Alarid, & Dunaway, 1998; Grasmick, Tittle, Bursik, & Arneklev, 1993), self-reported delinquency (Lagrange & Silverman, 1999; Meldrum, Young, Burt, & Piquero, 2013), and a list of analogous or imprudent behaviors (Arneklev et al., 1993; Holt, Bossler, & May, 2012; Quisenberry, 2015; Reisig & Pratt, 2011). Based on the 40-year longitudinal data from the Dunedin Multidisciplinary Health and Development Study, Moffitt, Poulton, and Caspi (2013) offered credible evidence that childhood self-control significantly predicts a variety of outcomes in adulthood, including criminal convictions, health problems, wealth status, and even their own parenting styles. Moreover, the effects of low self-control seem to be robust in that they withstand statistical controls derived from competing theories (Pratt & Cullen, 2000).

Not all of the propositions of the theory, however, have received full empirical support. Contrary to Gottfredson and Hirschi’s contention that parental management techniques are the sole antecedent of the variation in self-control, an emerging line of research compellingly shows that a range of biological factors, including genetics and executive functions of the brain, are also implicated in the development of self-control (Beaver, DeLisi, Vaughn, & Wright, 2010; Beaver, DeLisi, Vaughn, Wright, & Boutwell, 2008; Beaver, Ferguson, & Lynn-Whaley, 2010; Beaver, Wright, & DeLisi, 2007; J. Wright, Beaver, DeLisi, & Vaughn, 2008). Also, despite the general theory’s proposition that the befriending delinquents–delinquency association is spurious, social learning variables remain significant predictors of crime and delinquency even in the presence of low self-control and the effects of peer delinquency typically equal those of low self-control (Evans, Cullen, Burton, Dunaway, & Benson, 1997; Pratt & Cullen, 2000). Furthermore, research on the general theory still has room for geographical expansion into Asia, where corresponding studies are lacking. Gottfredson and Hirschi (1990) devoted an entire chapter to elaborating why their theoretical propositions can be generalized across countries and cultural settings. Thus, testing the major tenets of the general theory beyond the Western context is an effective and useful way of testing the validity of its generality assumption.

Are the Effects of Low Self-Control Culture-Free?

Arguing for the generality of the effects of low self-control, Gottfredson and Hirschi (1990) flatly dismissed the need for a culturally specific theory of crime. Cultural variability, according to them, is not important and therefore researchers “should look for constancy rather than variability in the . . . causation of crime” (p. 175). In line with this contention, cross-national comparative studies largely demonstrate that low self-control is a significant predictor of a number of deviant behaviors. Vazsonyi, Pickering, Junger, and Hessing (2001) examined the effects of low self-control among 8,417 adolescents from Hungary, the Netherlands, Switzerland, and the United States. Using dependent measures that consisted of a list of norm-violating conduct common in most countries, they found that low self-control accounted for 10% to 16% of the total variance explained in these measures. Similar results were garnered from a New Zealand (Caspi et al., 1994) and a Finnish study (Pulkkinen & Hämäläinen, 1995). In addition, Rebellon, Straus, and Medeiros (2008) conducted one of the most comprehensive comparative studies available to date by garnering data from 32 Western and non-Western nations across six continents. The result clearly showed that a measure of self-control was significantly associated with both violence and property crimes across these nations.

Not all research findings, however, provide unqualified support for the culture-free assumption of the theory. A case in point is Arneklev et al.’s (1993) survey of U.S. adults, which examined the effects of low self-control on three types of imprudent behaviors (smoking, drinking, and gambling). In this study, low self-control was shown to significantly predict variation in drinking and gambling but not with regard to smoking. Arneklev et al. (1993) explained that the null finding is reasonable because smoking was not considered to be deviant or imprudent in the United States when the study participants were youths. If the same study were conducted in the present day, low self-control would predict smoking in a statistically meaningful way. Based on such reasoning, these authors argued that the association between low self-control and analogous behaviors should not be considered to be ahistorical or culture-free but that the effect of low self-control may well vary over time and across cultures.

Furthermore, it may be that adolescents from the West and East differ in the extent to which they embrace self-control as an important virtue. East Asian countries, massively influenced by Confucianism, are more collectivistic than Western individualistic societies (Yun, 2008). Collectivistic cultures emphasize group harmony, which does not synchronize with the individualistic Western culture that focuses on the unreserved expression of self. Thus, East Asian parents tend to teach their children to be more disciplined, obedient, and reserved than Western parents do (Reid, 1999). The trait of “conquering self” (克己, pronounced keji in Chinese) is vehemently emphasized at home and in schools. South Korean parents routinely enroll their children at training camps akin to the Marine Corps academy during summer breaks. Such camps in South Korea are ubiquitously called “Conquering Self (克己) Camps.” Such an upbringing that emphasizes self-discipline may render East Asian adolescents to acquire a higher level of self-control than their Western counterparts. In support of this notion, Song (1995) observed that Chinese youths are more considerate, reserved, and self-controlled than Western youths. If the level of self-control is collectively high, thus showing little variance, among East Asian adolescents, then one might expect self-control’s explanatory power to be limited accordingly.

Cheung and Cheung’s (2007) study, one of the few conducted in Asia, is in favor of such an inference. Their survey of 1,015 Chinese adolescents in Hong Kong aged between 14 and 19 showed that low self-control is significantly correlated with a measure of delinquency at the bivariate level. When various statistical controls derived from competing theories including social learning and social bonding theory were included in the multivariate model, however, the previously significant linkage between low self-control and delinquency disappeared. Another study conducted in Mainland China also countered the culture-free assumption of the general theory. Wang et al. (2002) examined the relative applicability of social bonding theory and low self-control theory to delinquency among 527 adolescents in southern China. The results demonstrated that although measures of social bonding were all significant predictors of delinquency, their measure of low self-control was not.

To be fair, there exist some East Asian studies that support the culture-free thesis of the general theory. Vazsonyi et al. (2004) investigated the low self-control–delinquency relation in a sample of 335 Japanese adolescents. This study showed that low self-control is a rather robust predictor of a number of diverse measures of delinquency that range from trivial to more serious behaviors, but it failed to predict alcohol consumption. One limitation of this study is that it did not examine whether the effects of low self-control hold true when other competing theories were simultaneously accounted for. Employing the identical survey instrument of Vazsonyi et al.’s (2004), Kobayashi, Vazsonyi, Chen, and Sharp (2010) reached a similar conclusion based on samples from Japanese and the U.S. college students. Comparable findings were observed in a study of four different national settings including Japan (Vazsonyi & Belliston, 2007). Cheung’s (2014) recent analysis of data from 4,734 high school students in Hong Kong revealed that low self-control was a significant predictor of pathological gambling, heavy use of tobacco and alcohol, and delinquency. Low self-control also predicted perpetration of school bullying behaviors among adolescents in Macau (Chui & Chan, 2013a). Within the South Korean context, low self-control has been shown to be linked to college students’ dating violence (Jennings, Park, Tomsich, Gover, & Akers, 2011), substance use (Hwang & Akers, 2003), and general delinquency (Jo & Zhang, 2014).

Collectively, the results of these East Asian studies provide somewhat mixed evidence on the extent to which the generality assumption of Gottfredson and Hirschi’s theory can be applicable to the East Asian context. These divergent results are perhaps due to differences in sample characteristics, the operationalization of low self-control, and the range of statistical controls considered. Regardless, the mixed findings of the body of research clearly highlight the need for further research in East Asia. Furthermore, the aforementioned studies predominantly employed conventional delinquent behaviors as dependent variables. There is a paucity of research that considers analogous or imprudent behaviors as dependent variables.

Internet Addiction

The Internet has become an efficient and indispensable tool in the current information age. It is a remarkably easy and fast medium through which people obtain information, have fun, and communicate with other people around the globe. However, one of the downsides of Internet use―Internet addiction or excessive Internet use―is becoming an increasingly serious behavioral trait among adolescents and has recently garnered the attention of researchers (Ko, Yen, Yen, & Chen, 2012; Ko, Yen, Yen, Chen, & Weng, 2008; Lam, Peng, Mai, & Jing, 2009). Young (1998) defined Internet addiction as “an impulse-control disorder which does not involve an intoxicant” (p. 238). It is typically characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding computer use and Internet access that lead to impairment or distress (Shaw & Black, 2008).

Existing evidence shows that Internet addiction has become a serious issue for both Western and Eastern adolescents. A recent study demonstrated that 10.8% of Chinese adolescents are moderately to severely addicted to the Internet (Ko et al., 2009). A similar figure (10.7%) was observed among South Korean adolescents (Park et al., 2008). European estimates are reported to range between 1% and 9% (Kaltiala-Heino, Lintonen, & Rimpelä, 2004; Siomos, Dafouli, Braimiotis, Mouzas, & Angelopoulos, 2008). A similar figure for U.S. adolescents is hard to ascertain because of the lack of comparable research. Yet, the prevalence rates for U.S. college students are estimated to range from 9.8% to 15.2% (Anderson, 2001; Lavin, Yuen, Weinman, & Kozak, 2004).

Although debate still exists over whether Internet addiction is a genuine form of addiction (Weinstein & Lejoyeux, 2010), researchers in South Korea, a country with the highest Internet penetration rate and a thriving gaming industry, do not hesitate to term it a type of behavioral addiction (Ko et al., 2009; Kwon, Kim et al., 2013; Park et al., 2008).¹ Gaming and entertainment in North America are largely based on home entertainment systems such as home theater, gaming, and console video game systems. In addition to using such home entertainment systems, South Korean youths frequent to local area network (LAN) gaming centers called “PC-Bangs (literally meaning, PC/Internet rooms),” where they engage in online games and other Internet activities by taking advantage of high-speed Internet connections with low usage charges. PC-Bangs, which operate 24/7, have become an integral part of the majority of South Korean adolescents’ daily lives and peer relations, and these establishments often serve as an extra impetus for excessive Internet use or Internet addiction (Stewart & Choi, 2003). In 2005, a man in his 20s collapsed and died in a PC-Bang after spending 50 consecutive hours playing an online game and a similar incident occurred to a 19-year-old in 2010. In another case, a middle school student committed suicide after killing his own mother because she would not let him play an online game (Park, Kim, & Tak, 2011). Another tragic incident, which was televised worldwide in 2014, involved a 22-year-old online gaming addict who confessed to the murder of his 2-year-old son by suffocation so that he could leave the house to go back to playing online games at a PC-Bang (South Korean online gamer accused of murdering infant son to keep playing, 2014). Realizing the seriousness of the consequences of Internet addiction, South Korean addiction centers are thus increasingly expanding their treatment purview to include Internet addiction, while treatment centers have also been set up across the nation to deal solely with this form of addiction (Stewart & Choi, 2003).

Emerging evidence further indicates that substance abuse and Internet addiction share certain commonalities. Adolescents who are substance users are significantly more likely to suffer from Internet addiction (Ko et al., 2008; Lam et al., 2009). In a similar vein, Ko et al. (2009), in a functional magnetic resonance study, demonstrated that cue-induced gaming urges activate similar brain areas corresponding to substance craving. Research also indicates that adolescents with substance use and those with Internet addiction share personality characteristics, chiefly novelty seeking, a key dimension of low self-control (Ko et al., 2006).

It is also worth noting that the diagnostic criteria of attention deficit hyperactivity disorder (ADHD) such as attention problems, hyperactivity, and impulsivity closely correspond to Gottfredson and Hirschi’s (1990) conception of low self-control. Individuals with ADHD and those with low self-control are both easily bored and have an aversion toward delayed reward (J. Wright & Beaver, 2005). In relation to the present study, research has repeatedly shown that Internet addiction tends to co-occur with ADHD (Bernardi & Pallanti, 2009; Ha et al., 2006; Yoo et al., 2004). As Internet activities are characterized by “rapid response, immediate reward, and multiple windows with different activities” (Ko et al., 2012, p. 5), the Internet may be a palatable medium for those with ADHD, which offers immediate reward and stimulation or assuages the feeling of boredom. Although no research has yet directly examined the linkage between Internet addiction and low self-control, it can be reasonably surmised that, as with ADHD, people with low self-control are likely to be attracted to the immediacy of the response and reward afforded by Internet activities. Moreover, deficiency in self-control may make it difficult for those with low self-control to restrict their Internet use, resulting in them being more vulnerable to Internet addiction.

Smartphone Addiction

In keeping with the current generation’s heavy usage of smartphones, smartphone addiction is garnering scholarly attention as a newer form of behavioral addiction in South Korea (Kwon, Kim, et al., 2013; Lee, Ahn, Choi, & Choi, 2014).² A smartphone today is far more than a mobile phone. Owing to the dramatic improvement in both hardware and software, a smartphone has become a multifunctional device with the capacity of a computer, camera, and mp3/video player. Fitting easily into a pocket or purse, smartphones allow owners to navigate the web at any given time or place. Smartphones have also become a popular gaming device because of fast Internet connections and the staggering number of gaming apps (Sarwar & Soomro, 2013).

Debates may exist about whether smartphone addiction is a genuine addiction according to the conventional diagnostic system. Nevertheless, countries with especially advanced IT such as South Korea are more than likely to define it as a type of behavioral addiction. South Korea has the highest level of smartphone ownership. Over two thirds (67.6%) of mobile phone users in the country had smartphones in 2012 compared with the global average of only 14.8%, and smartphone ownership is expected to reach 88.9% by 2017 (“S. Korea Tops Smartphone Penetration Rate in 2012,” 2013).

A national survey conducted by the National Information Society Agency reported the smartphone addiction rate to be approximately 8% in South Korea (cited in Kwon, Lee et al., 2013). Another study performed by Kwon and colleagues (2013) demonstrated that people judged to be addicted to smartphones exhibited symptoms similar to Internet addiction such as craving, withdrawal, tolerance, preference of cyberspace relationships, and impulse-control disturbance.

As smartphone addiction is a relatively nascent problem concentrated in countries with advanced IT technology, empirical studies have yet to examine the association between low self-control and smartphone addiction. Given that smartphones nowadays function as the Internet plus a variety of entertainment gadgets, the hypothesized association between low self-control and Internet addiction can easily be expanded to smartphone addiction. People with low self-control seek immediate gratification and have minimal tolerance for boredom and discomfort (Gottfredson & Hirschi, 1990). Thus, it can be hypothesized that those with low self-control are likely to submit to the immediate and easy gratification offered by smartphones.

Scope of the Present Study

Evans et al. (1997) noted that the most salient test of the general theory is whether the effects of low self-control are indeed general. The current state of the empirical literature on the theory shows, however, that few studies have tested the generality assumption in East Asia. Moreover, these East Asian studies have examined the effects of low self-control on substance use and typical delinquency. In the current study, we expand the literature by applying the general theory to a sample of South Korean adolescents. Not only do we examine the predictive capacity of self-control on substance use and delinquency, we extend the purview of the research by employing Internet addiction and smartphone addiction as our additional dependent variables.

Method

Procedure and Sample

A sample of 1,852 students was recruited from five middle schools in Gwangju, South Korea. The target population for this study consisted of junior high school students in the seventh through ninth grades in Gwangju. The socioeconomic status (SES) of the districts in which the schools were located was considered to select samples that reflected the average SES in South Korea. Before data collection, the principals of the five middle schools were individually contacted by the researchers. All of them agreed to participate in the study, promising full cooperation. The survey was conducted in July 2013. Trained researchers visited classrooms, explained the procedures, and administered a self-report questionnaire to students at school on the day of the survey. Students were assured of voluntariness, complete anonymity, and confidentiality. The questionnaire developed for this study was written in Korean and it took less than 25 min to complete. The study was approved by the Institutional Review Board of Chosun University. Among the 1,852 students, 57 cases were dropped because of extensive missing data. Furthermore, 166 cases with missing data on key dependent variables were also removed, leaving 1,629 in the final analytical sample. The sample consists of slightly more girls (55%) and the mean age is 13.95 years (SD = .84). Adolescents who live with a single parent constitute 20% of the sample.

Dependent Variables

Five dependent variables were used in the current study. Among them, one represents typical delinquency, two reflect drinking and smoking, and other two reflect Internet and smartphone addiction.

Typical delinquency

As a measure of typical delinquent behaviors, we created a scale by employing 10 acts from the broad spectrum of delinquent and conduct problems committed by South Korean adolescents. Four items on the scale were derived from the Korean version of the Minnesota Multiphasic Personality Inventory–Adolescent (Kim et al., 2005) and six were from the Korea Youth Self-Report (Oh, Ha, Lee, & Hong, 2001). These items reflect common delinquent and conduct behaviors including shoplifting, threatening others, vandalism, running away from home, harassing others, fighting, and physically hurting others. Respondents reported their involvement in each behavior during the past 6 months in a dichotomous manner. These responses were summed to create the typical delinquency scale (α = .70). Typical delinquency was treated as a count variable, where higher scores reflect higher frequencies of delinquent events. Summary statistics for typical delinquency and the other variables used in analysis are provided in Table 1.

Table 1.

Summary Statistics (N = 1,629).

	M or %	SD	Minimum	Maximum
Dependent variables
Typical delinquency	1.37	1.71	0	10
Drinking	.23	1.01	0	18
Smoking	.21	1.38	0	20
Internet addiction^a	5.06	.73	4.35	8.66
Smartphone addiction^b	2.85	.33	2.48	3.87
Independent variables
Low self-control	16.81	4.09	6	30
Male	45%	—	0	1
Age	13.95	.84	12	17
Socioeconomic status	3.01	.77	1	5
Single-parent household	20%	—	0	1
Peer delinquency	.20	.63	0	10
Attachment to parents	8.48	1.60	2	10

Square root transformation.

Natural logarithmic transformation.

Drinking

A single open-ended item was used to measure the frequency at which the respondents drank alcohol over the past 6 months. Specifically, participants were asked “During the past 6 months, how many times have you drunk alcohol to the extent that you felt you were drunk?” The responses to this question were highly skewed (skew = 8.57), and the modal score was 0. Drinking was treated as a count variable, where higher scores reflect increased instances of getting drunk.

Smoking

To measure how many cigarettes respondents regularly smoke, the following open-ended question was used: “During the past 6 months, how many cigarettes have you smoked per day on average?” The distribution of the responses was highly skewed (skew = 8.93), and the modal score was 0. Smoking was treated as a count variable, where higher scores reflect a greater number of cigarettes smoked daily.

Internet addiction

Internet addiction was measured by using the Korean Internet Addiction Proneness Scale–Short Form (KS scale; Korea Agency for Digital Opportunity and Promotion, 2008). The KS scale is a 20-item short version of the original 40-item K scale, which was developed after modeling Young’s (1998) Internet Addiction Scale as a diagnostic scale for Internet addiction among Korean youths. The KS scale examines the degree of compulsive use, preoccupation, withdrawal symptoms, behavioral problems, preference for cyberspace-oriented relationships, and impact on daily life related to Internet use. Example items include “I fall asleep during class due to too much Internet use,” “I become anxious when I cannot use the Internet,” and “People that I met online are more accepting of me than people in the real world.” Adolescents responded to these 20 items on a 4-point Likert-type scale that ranged from strongly disagree (1) to strongly agree (4). The internal consistency coefficient for the 20 items was sufficiently high (α = .93). The KS scale, which was created by summing the responses to the 20 items, was coded so that higher scores reflect higher levels of Internet addiction. The distribution of the scale scores was positively skewed (skew = 2.21) and thus they were transformed by taking the square root of the values of the scale (skew = .72).

Smartphone addiction

To measure the degree of smartphone addiction, 12 items derived from the Korean Smartphone Addiction Proneness Scale for youths were used (Shin, Kim, & Jung, 2011). This scale was developed by the National Information Society Agency, sponsored by the South Korean government, by drawing on existing measurement tools of Internet addiction. The 12 items chosen for the current study primarily tap overuse, daily-life disturbance, and withdrawal related to smartphone use. Example items include “My school grades have dropped due to excessive smartphone use,” “When I cannot use a smartphone, I feel like I have lost the entire world,” and “I panic when I cannot use my smartphone.” Students responded to the 12 items on a 4-point Likert-type scale that ranged from never (1) to always (4). The internal consistency of the 12-item smartphone addiction scale was .93. The distribution of the scale was slightly skewed to the right (skew = 1.43) and thus it was transformed by taking the natural log (skew = .72).

Independent Variables

Low self-control

Gottfredson and Hirschi’s (1990) conceptualization of low self-control is largely derived from their view of crime. Crime typically involves a simple and easy act to perform, requiring little planning and minimal verbal skills. Crime is also exciting and provides an immediate and short-lived gratification. Based on this characterization of crime, Gottfredson and Hirschi (1990) describe individuals with low self-control as being “impulsive, insensitive, physical (as opposed to mental), risk-taking, short-sighted, and non-verbal” (p. 90). Furthermore, they maintain that behavioral measures of self-control should be preferred to attitudinal measures. The use of behavioral measures of low self-control, however, has exposed the general theory to the risk of tautology (i.e., predicting the dependent variable with independent variables that are not distinct from the dependent variable, see Akers, 1991). Even so, Pratt and Cullen (2000) advise criminologists not to take the charge of tautology as an impregnable threat to the viability of the general theory. For one thing, they show that studies employing attitudinal measures of self-control consistently support the main tenets of the theory. For another, the effect size estimates for behavioral and attitudinal measures of self-control are largely similar.

In the current study, we used 6 items that represent these six traits of low self-control. By using a five-category response set, adolescents were asked to indicate how strongly they agreed or disagreed with the following six statements: “I jump into exciting things even if I have to take a test tomorrow (impulsivity),” “I abandon a task soon if it becomes laborious to do (preference for simple tasks),” “I enjoy risky activities (risk taking),” “I fight more frequently than others (preference for physical activities),” “I enjoy teasing and harassing other people (self-centeredness),”³ and “I lose my temper whenever I get angry (temper).” The responses to these items were summed to create the self-control scale. The scale was coded in a way that higher scores reflect lower levels of self-control. The reliability coefficient for the scale was .65. Considering that this scale consists only of 6 items, the magnitude of its reliability seems to be acceptable (Cronbach, 1951). In addition, it compares favorably with those found in other studies. For instance, B. Wright, Caspi, Moffitt, and Silva’s (1999) 7-item measure of self-control had a reliability of .64, whereas Cheung and Cheung’s (2007) 10-item scale used in Hong Kong had an alpha of .61. In addition, principal component factor analysis showed that all the items loaded on only one factor, suggesting a unidimensionality of the scale. This single factor explained 63% of the variance across the 6 items. An almost identical measure of self-control has been used in previous studies of South Korean adolescents (Yun & Walsh, 2011).

Control variables

In multivariate analysis, statistical controls are used to account for potential spuriousness. Prior scholars have noted that social learning and social bonding variables predict crime and delinquency even in the presence of low self-control (Burton et al., 1998; Grasmick et al., 1993; Pratt & Cullen, 2000). Given this finding, first, a measure that represented the influence of delinquent peers was employed as a statistical control. Specifically, respondents were asked how many of their three closest friends had engaged in the following six delinquent behaviors over the past 6 months: drinking, smoking, unexcused absence from school, beating others, stealing, and robbing. The responses to these six items were summed and then the average was used as a measure of delinquent peer influence or, simply, peer delinquency (α = .76). Second, attachment to parents was used as a measure of social bonding. Respondents were asked to report on a 5-point response category how close they felt to their father and to their mother. The responses to these two items were summed to reflect attachment to parents (α = .66).

Four variables that reflected the socio-demographic attributes of respondents were also included. Male is a dichotomous variable (female = 0, male = 1). Age is measured in years. SES is captured by using five categories ranging from high (5) to low (1), as reported by participants. Finally, whether the respondent is living with a single parent was measured (living with both parents = 0, living with single parent = 1).

Missing Data

Subjects with missing data on the variables of interest were excluded from the analysis. Missing data were most commonly observed with regard to drinking (4.1%) and smartphone addiction (4.9%). In comparing students with missing data to those with complete data, we found that adolescents with missing data were slightly younger (mean ± SD = 13.68 ± .75 vs. 13.95 ± .84 years; p < .001) and more likely to be boys (79% vs. 45% boys; p < .001). The two groups, however, did not vary with respect to SES and single-parent household. More importantly, no difference was observed between the groups with regard to all the dependent variables. Thus, we judged the missing data to be immaterial to our analysis and chose not to impute values for them. Our final analytical sample thus consisted of 1,629 cases with complete information, after deleting the cases with missing values.

Analytic Strategy

We estimated a series of multivariate regression models to examine low self-control’s predictive efficacy for the five dependent variables. Stata’s countfit procedure was employed in regards to count dependent variables (typical delinquency, drinking, and smoking) to select the most appropriate Poisson-based regression model for a dependent variable of interest (Long & Freese, 2014).⁴ Through this procedure, negative binomial regression was chosen as the most appropriate statistical technique for the three count variables in the current study. Thus, a negative binomial regression equation was first estimated, where the count variable, typical delinquency, was regressed on low self-control and the six control variables.

This equation for typical delinquency essentially served two purposes. First, it showed whether our low self-control measure performs well with South Korean adolescents, as expected. Second, the estimate for low self-control in this model served as a point of reference against which the estimates from the ensuing models could be compared. In the next stage of analysis, two count variables―drinking and smoking―were regressed on low self-control and the control variables. The final stage of the analysis involved ordinary least squares (OLS) equations for the two transformed dependent variables: Internet addiction and smartphone addiction. In the current sample, students were nested in classrooms. To guard against bias that might stem from clustering, we employed the Huber–White robust standard error correction for clustering for all regression equations (White, 1980). Multicollinearity was not a concern among the independent variables in all analyses (variance inflation factors (VIFs) < 1.12; tolerances > .89).

Results

Table 2 shows the results of the negative binomial regression with typical delinquency as the dependent variable. Our purpose is not only to examine whether low self-control performs well in predicting the typical delinquency of South Korean adolescents but also to test whether the effects of low self-control endure when holding constant the salient variables from competing theories. The finding shows that our measure of low self-control demonstrates significant predictive power, even controlling for peer delinquency, parental attachment, and socio-demographic factors. As the negative binomial regression uses a log link function between the dependent variable and independent variables, the interpretation of the regression coefficient is not intuitive. To allow for a direct comparison of the relative predictive power of the predictors, we calculated the percentage change in expected count for a standard deviation increase in each predictor (%change/SD). The percentage changes were calculated by employing Long and Freese’s (2014) Spost estimation suite, which is a post-estimation algorithm for regression models for Stata. As shown in Table 3, for a standard deviation increase in low self-control, the expected count of students’ delinquency increases by 66.7%. This value is considerably greater than that for delinquent peers (15.6%) and for attachment to parents (−15.5%), indicating that low self-control outperforms the variables from other theories in predicting typical delinquency.⁵

Table 2.

Negative Binomial Regression Model for Typical Delinquency.

	B	SE	%change/SD
Low self-control	.12***	.01	66.7
Male	.20**	.05	10.7
Age	−.01	.03	−.7
Socioeconomic status	−.03	.04	−2.6
Single-parent household	.01	.07	.3
Delinquent peers	.23***	.04	15.6
Attachment to parents	−.11	.15	−15.5
Wald χ² = 552.35***
McFadden’s R² = .091

Note. Robust standard errors that adjust for clustering are used.

p < .01. ***p < .001 (two-tailed test).

Table 3.

Negative Binomial Regression Models for Drinking and Smoking.

	Drinking			Smoking
Variable	B	SE	%change/SD	B	SE	%change/SD
Low self-control	.12***	.02	64.2	.26***	.05	189.9
Male	.41	.25	22.9	1.28**	.47	90.0
Age	.38*	.15	37.9	1.28***	.22	196.8
Socioeconomic status	−.02	.13	−1.7	−.17	.22	−12.5
Single parent	.17	.27	.6	.31	.63	11.8
Peer delinquency	1.04***	.18	93.8	1.26***	.28	123.2
Attachment to parents	−.19**	.06	−27.2	.01	.11	1.3
Wald χ² = 141.41***				Wald χ² = 20.34***
McFadden’s R² = .078				McFadden’s R² = .109

Note. Robust standard errors that adjust for clustering are used.

p < .05. **p < .01. ***p < .001 (two-tailed test).

Recall that two of the six items constituting the low self-control scale―preference for physical activities and temper―are of a behavioral nature. To assuage the charge of tautology, we estimated an auxiliary model by using a low self-control measure that excluded these two items (α = .60). The results are presented in Appendix B. We found the pattern of the results to be remarkably similar to that presented in Table 2, demonstrating that the results in Table 2 should not be written off as merely tautological outcomes.

In the next step, drinking and smoking were regressed onto low self-control and the other control variables (Table 3). The regression coefficients show that low self-control is again a significant predictor of drinking and smoking behaviors by South Korean adolescents. The results under the drinking model in Table 3 shows that, for a standard deviation increase in low self-control, the expected count of students’ drinking increases by 64.2%. This value is lower than that for delinquent peers (93.8%). With regard to smoking, a standard deviation increase in low self-control equates to a 189.9% increase in the expected count of students’ smoking, which is substantially greater than that for delinquent peers (123.2%). The effect of attachment to parents was significant only in the drinking model; yet, the magnitude was relatively weak (−27.2%). In short, the results from these two negative binomial regression models demonstrate that low self-control is a persistent predictor of drinking and smoking, net of peer influences, parental attachment, and other statistical controls.

Last, Table 4 presents the results of the OLS estimation for the Internet and smartphone addiction scales. As with all the previous regression models, low self-control stands as a significant predictor of these two dependent measures. In the Internet addiction model, the magnitude of the effect of low self-control (β = .21) was far greater than that for peer delinquency (.03) and attachment to parents (−.09). The low self-control scale accounted for 35% of the total explained variance in Internet addiction. Turning our attention to the smartphone addiction model, low self-control demonstrates the greatest magnitude of the standardized regression coefficient (.29) among all predictors, accounting for 39% of the total variance explained by the model. Again, the effects of peer delinquency (β = .08) and attachment to parents (β = −.09) are significant, but the magnitudes are weak relative to low self-control. Gender is a significant predictor in both models. Interestingly, being male is significantly linked to a higher tendency toward Internet addiction, while being female is significantly associated with a higher tendency toward smartphone addiction. It may be that boys use the Internet far more frequently than girls for playing violent Internet games, whereas girls are more likely to use smartphones for talking, texting, and playing non-violent games offered through various apps.

Table 4.

OLS Models for Internet and Smartphone Addiction.

	Internet addiction			Smartphone addiction
Variable	B	SE	β	B	SE	β
Low self-control	.04***	.01	.21	.02***	.01	.29
Male	.34***	.03	.23	−.18***	.02	−.28
Age	.03	.02	.04	.02*	.01	.05
Socioeconomic status	−.05*	.03	−.05	.01	.01	.01
Single parent	−.06	.05	−.03	−.03	.02	−.04
Peer delinquency	.03	.03	.03	.04***	.02	.08
Attachment to parents	−.04***	.01	−.09	−.02***	.01	−.09
F test = 33.67***				F test = 56.83***
R² = .129				R² = .199

Note. Robust standard errors that adjust for clustering are used. OLS = ordinary least squares.

p < .05. **p < .01. ***p < .001 (two-tailed test).

Discussion

The essence of Gottfredson and Hirschi’s claim is that their theory is general. In their view, low self-control explains not only crime and delinquency but also a wide range of behaviors analogous to crime. In addition, the predictive efficacy of low self-control holds true irrespective of national and cultural boundaries. The validity of their theoretical position, therefore, can be strengthened by showing that low self-control can predict crime, delinquency, and analogous behaviors in new geographical settings where previous research has rarely touched upon. The current study extended previous works by examining the linkage of self-control to typical delinquency, drinking, and smoking by South Korean adolescents. This is also the first study to examine the relationships of self-control with Internet and smartphone addiction.

Taken together, the empirical results presented herein support Gottfredson and Hirschi’s generality claim. The central findings of our analyses show that low self-control exerts a positive and statistically significant effect on typical delinquency, drinking, and smoking among South Korean adolescents. It is important to note that these effects are significant even when accounting for peer delinquency, parental attachment, and other statistical controls. Furthermore, in contrast to Pratt and Cullen’s (2000) observation that the effects of low self-control and peer delinquency are about equal, the magnitudes of low self-control’s effects were found to be considerably greater than those of peer delinquency in typical delinquency and smoking; yet, peer delinquency’s effects outperformed those of low self-control in predicting drinking. Overall, the current results support Gottfredson and Hirschi’s generality claim. Nevertheless, their assertion that low self-control is the cause of crime and delinquency seems to be somewhat overstated given that the two control variables derived from social learning and social bonding theory largely remain significant in spite of the presence of low self-control.

The salience of low self-control is particularly pronounced in the prediction of the two forms of analogous behaviors. Compared with previous models, the effects of low self-control seem to be even greater than the effects of peer delinquency and parental attachment in models that predict Internet and smartphone addiction. Low self-control accounts for 35% and 39% of the total explained variance in each model. The current findings thus lend unique support to the generality claim because low self-control is shown to be implicated in two emerging forms of analogous behaviors that have thus far not been examined. Nonetheless, as with previous models, low self-control does not seem to be the cause of Internet and smartphone addiction. Despite the significant effects exerted by low self-control, however, the total amount of variance explained by these two equations did not live up to Gottfredson and Hirschi’s bold claim (12.9% and 19.9%, respectively), although one might still judge them to be sizable given social science standards.

All things considered, our test seems to be both supportive and challenging for the general theory. On the supportive note, low self-control’s effects are significant and persistent in that it continues to exert meaningful effects on five forms of deviant or analogous behaviors among South Korean adolescents. Given that the magnitudes of these effects are mostly larger than the effects of measures from competing theories, the failure to include low self-control in studies of crime and delinquency among South Korean samples may run the risk of misspecification. It should also be noted that the significant association between low self-control and delinquency observed in the current study does not agree with the null findings garnered from some the Chinese studies (Cheung & Cheung, 2007; Wang et al., 2002), whereas they are more in line with Vazsonyi et al.’s (2004) Japanese study as well as most Western studies.⁶

On the challenging note, it seems as though Gottfredson and Hirschi’s claim that self-control theory predominates over other competing theories was somewhat overstated. They theorized that other sociological variables favored by typical criminologists such as peer delinquency and attachment to parents are merely products of self-control and are likely to lose their predictive efficacy once low self-control is accounted for. Our analysis showed that peer delinquency and attachment to parents, for the most part, remain significant predictors, net of low self-control’s effects. This observation is largely in consonance with previous self-control research that has employed Western samples (Arneklev et al., 1993; Evans et al., 1997; Pratt & Cullen, 2000).

The current findings should be interpreted with caution in light of the following limitations. First, our measure of low self-control used only six items and its validity was not sufficiently tested. It remains to be seen whether the findings remain valid when more sophisticated measures with proven construct validity such as Grasmick et al.’s (1993) scale are used. Second, in undertaking the current study, we did not incorporate measures of opportunity for deviant or analogous behaviors. Gottfredson and Hirschi (1990) stated that the criminal manifestation of low self-control is contingent upon opportunity. Yet, it can be argued that opportunities for delinquency such as vandalism, fighting, and drinking are prevalent across South Korea as they are in other industrialized countries. South Korea also has the fastest Internet connections and highest smartphone ownership rate (Meurant, 2010). As such, opportunities for Internet and smartphone addiction seem to be almost ubiquitous. Third, we are cautious about generalizing the findings because of our school-based sampling procedure. Dropouts and absentees on the day of the survey, who might display more delinquent behavior, were not represented in our sample. Finally, our study did not include predictors of self-control. Gottfredson and Hirschi (1990) argued that the inadequate supervision and discipline of children by parents are the main causes of low self-control. This omission is unfortunate given that East Asian parents are known to be generally more supervising and controlling of their children than their Western counterparts (Chua, 2011; Yun, 2008).

In summary, the results presented herein are largely consistent with Gottfredson and Hirschi’s general theory of crime and delinquency. Our research contributes to the literature by demonstrating that the theory is applicable to South Korean adolescents, who may be qualitatively different from those in North America and Europe on socio-demographic and cultural grounds. A further contribution of the current study is that it examined low self-control’s effects on Internet and smartphone addiction, two forms of imprudent behavior that have not been empirically tested.

The current study has two implications for future research. On one hand, international research on crime delinquency may not safely omit low self-control without the possibility of potential misspecification. On the other hand, provided that the most salient feature of Gottfredson and Hirschi’s theory is the generality assumption (Evans et al., 1997), future researchers are advised to look beyond typical crime and delinquency and examine deviant or analogous behaviors that have not been tested thus far.

Footnotes

Appendix

Appendix B

Auxiliary Negative Binomial Regression Model for Typical Delinquency Employing a Low Self-Control Measure Excluding Behavioral Items.

	B	SE	% Change/SD
Low self-control	.16***	.01	60.6
Male	.20***	.05	11.0
Age	−.02	.03	−1.7
Socioeconomic status	.02	.04	−1.2
Single-parent household	.03	.07	1.1
Delinquent peers	.26***	.04	18.3
Attachment to parents	−.66	.02	−15.7
Wald χ² = 488.21***
McFadden’s R² = .083

Note. Robust standard errors that adjust for clustering are used.

***

p < .001 (two-tailed test).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by research fund from Chosun University, 2015.

Notes

References

Akers

(1991). Self-control as a general theory of crime. Journal of Quantitative Criminology, 7, 201-211.

Akers

(1998). Social learning and social structure: A general theory of crime and deviance. Boston, MA: Northeastern University Press.

Anderson

(2001). Internet use among college students: An exploratory study. Journal of American College Health, 50, 21-26.

Arneklev

Grasmick

Tittle

Bursik

(1993). Low self-control and imprudent behavior. Journal of Quantitative Criminology, 9, 225-247.

Beaver

DeLisi

Vaughn

Wright

(2010). The intersection of genes and neuropsychological deficits in the prediction of adolescent delinquency and low self-control. International Journal of Offender Therapy and Comparative Criminology, 54, 22-42.

Beaver

DeLisi

Vaughn

Wright

Boutwell

(2008). The relationship between self-control and language: Evidence of a shared etiological pathway. Criminology, 46, 939-970.

Beaver

Ferguson

Lynn-Whaley

(2010). The association between parenting and levels of self-control: A genetically informative analysis. Criminal Justice and Behavior, 37, 1045-1065.

Beaver

Wright

DeLisi

(2007). Self-control as an executive function: Reformulating Gottfredson and Hirschi’s parental socialization thesis. Criminal Justice and Behavior, 34, 1345-1361.

Bernardi

Pallanti

(2009). Internet addiction: A descriptive clinical study focusing on comorbidities and dissociative symptoms. Comprehensive Psychiatry, 50, 510-516.

10.

Burton

Cullen

Evans

Alarid

Dunaway

(1998). Gender, self-control, and crime. Journal of Research in Crime & Delinquency, 35, 123-147.

11.

Caspi

Moffitt

Silva

Stouthamerloeber

Krueger

Schmutte

(1994). Are some people crime-prone: Replications of the personality-crime relationship across countries, genders, races, and methods. Criminology, 32, 163-195.

12.

Cheung

(2014). Low self-control and co-occurrence of gambling with substance use and delinquency among Chinese adolescents. Journal of Gambling Studies, 30, 105-124.

13.

Cheung

(2007). Self-control, social factors, and delinquency: A test of the general theory of crime among adolescents in Hong Kong. Journal of Youth and Adolescence, 37, 412-430.

14.

Chua

(2011). Battle hymn of the tiger mother. New York, NY: Penguin Books.

15.

Chui

Chan

(2013a). Association between self-control and school bullying behaviors among Macanese adolescents. Child Abuse & Neglect, 37, 237-242.

16.

Chui

Chan

(2013b). The gendered analysis of self-control on theft and violent delinquency: An examination of Hong Kong adolescent population. Crime & Delinquency. Advance online publication. Retrieved from https://dx-doi-org.web.bisu.edu.cn/10.1177/0011128712470992

17.

Cronbach

(1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.

18.

Evans

Cullen

Burton

Dunaway

Benson

(1997). The social consequences of self-control: Testing the general theory of crime. Criminology, 35, 475-504.

19.

Glueck

(1950). Unraveling juvenile delinquency. New York, NY: Commonwealth Fund.

20.

Gottfredson

Hirschi

(1990). A general theory of crime. Palo Alto, CA: Stanford University Press.

21.

Grasmick

Tittle

Bursik

Arneklev

(1993). Testing the core empirical implications of Gottfredson and Hirschi’s general theory of crime. Journal of Research in Crime & Delinquency, 30, 5-29.

22.

Yoo

Cho

Chin

Shin

Kim

(2006). Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. The Journal of Clinical Psychiatry, 67, 821-826.

23.

Hirschi

(1969). Causes of delinquency. Berkeley: University of California Press.

24.

Hirschi

Gottfredson

(1994). The generality of deviance. In Hirschi

Gottfredson

(Eds.), The generality of deviance (pp. 1-22). New Brunswick, NJ: Transaction.

25.

Holt

Bossler

May

(2012). Low self-control, deviant peer associations, and juvenile cyberdeviance. American Journal of Criminal Justice, 37, 378-395.

26.

Hwang

Akers

(2003). Substance use by Korean adolescents: A cross-cultural test of social learning, social bonding and self-control theories. In Akers

Jensen

(Eds.), Social learning theory and the explanation of crime: A guide for the new century (pp. 39-63). New Brunswick, NJ: Transaction.

27.

Jennings

Park

Tomsich

Gover

Akers

(2011). Assessing the overlap in dating violence perpetration and victimization among South Korean college students: The influence of social learning and self-control. American Journal of Criminal Justice, 36, 188-206.

28.

Zhang

(2014). Parenting, self-control, and delinquency: Examining the applicability of Gottfredson and Hirschi’s general theory of crime to South Korean youth. International Journal of Offender Therapy and Comparative Criminology, 58, 1340-1363.

29.

Kaltiala-Heino

Lintonen

Rimpelä

(2004). Internet addiction? Potentially problematic use of the Internet in a population of 12–18 year-old adolescents. Addiction Research & Theory, 12, 89-96.

30.

Kim

Han

Lim

Min

Lee

Moon

(2005). 다면적 인성검사-청소년 매뉴얼 [MMPI-A Manual]. 서울 [Seoul, Korea]: 마음사랑 [Maumsarang].

31.

Liu

Hsiao

Yen

Yang

Lin

. . . Chen

(2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43, 739-747.

32.

Yen

Chen

Yen

(2006). Tridimensional personality of adolescents with internet addiction and substance use experience. Canadian Journal of Psychiatry, 51, 887-894.

33.

Yen

Chen

(2012). The association between Internet addiction and psychiatric disorder: A review of the literature. European Psychiatry, 27, 1-8.

34.

Yen

Chen

Weng

(2008). The association between Internet addiction and problematic alcohol use in adolescents: The problem behavior model. CyberPsychology & Behavior, 11, 571-576.

35.

Kobayashi

Vazsonyi

Chen

Sharp

(2010). A culturally nuanced test of Gottfredson and Hirschi’s “General Theory”: Dimensionality and generalizability in Japan and the United States. International Criminal Justice Review, 20, 112-131.

36.

Korea Agency for Digital Opportunity and Promotion. (2008). 청소년 인터넷중독 자가진단 척도 고도화 연구 [The follow-up study on the internet addiction proneness scale]. 서울 [Seoul, Korea]: Author.

37.

Korea Communications Commission. (2010). Statistical data. Available from http://www.kcc.go.kr

38.

Kwon

Kim

Cho

Yang

(2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE, 8(12), e83558.

39.

Kwon

Lee

Won

Park

Min

Hahn

Choi

Kim

(2013). Development and validation of a smartphone addiction scale (SAS). PLoS ONE, 8, e56936.

40.

Lagrange

Silverman

(1999). Low self-control and opportunity: Testing the general theory of crime as an explanation for gender differences in delinquency. Criminology, 37, 41-72.

41.

Lam

Peng

Mai

Jing

(2009). Factors associated with Internet addiction among adolescents. CyberPsychology & Behavior, 12, 551-555.

42.

Lavin

Yuen

C. N.

Weinman

Kozak

(2004). Internet dependence in the collegiate population: The role of shyness. CyberPsychology & Behavior, 7, 379-383.

43.

Lee

Ahn

Choi

(2014). The SAMS: Smartphone addiction management system and verification. Journal of Medical Systems, 38, 1-10.

44.

Long

Freese

(2014). Regression models for categorical dependent variables using Stata (3rd ed.). College Station, TX: Stata Press.

45.

Meldrum

Young

Burt

Piquero

(2013). Maternal versus adolescent reports of self-control: Implications for testing the general theory of crime. Journal of Criminal Justice, 41, 24-32.

46.

Meurant

(2010). Providing every student with an iPad as a means of helping develop Korean EFL digital literacy. Paper presented at the 6th International Conference on Networked Computing and Advanced Information Management, Seoul, Korea.

47.

Moffitt

Poulton

Caspi

(2013). Lifelong impact of early self-control: Childhood self-discipline predicts adult quality of life. American Scientist, 101, 352-359.

48.

Nagin

Farrington

(1992). The onset and persistence of offending. Criminology, 30, 501-523.

49.

Lee

Hong

(2001). K-YSR 청소년 자기행동 평가척도 [K-YSR Youth Self-Report]. 서울 [Seoul, Korea]: 중앙적성출판사 [Chungang Aptitude Publishing].

50.

Park

Kim

Cho

(2008). Prevalence of Internet addiction and correlations with family factors among South Korean adolescents. Adolescence, 43, 895-909.

51.

Park

Kim

Tak

(2011). Influence of parents, friends, moral disengagement and relational efficacy on cyber delinquency. The Korean Journal of Educational Psychology, 25, 617-645.

52.

Pratt

Cullen

(2000). The empirical status of Gottfredson and Hirschi’s general theory of crime: A meta-analysis. Criminology, 38, 931-964.

53.

Pulkkinen

Hämäläinen

(1995). Low self-control as a precursor to crime and accidents in a Finnish longitudinal study. Criminal Behaviour and Mental Health, 5, 424-438.

54.

Quisenberry

(2015). Texting and driving: Can it be explained by the general theory of crime? American Journal of Criminal Justice, 40, 303-316.

55.

Rebellon

Straus

Medeiros

(2008). Self-Control in global perspective: An empirical assessment of Gottfredson and Hirschi’s general theory within and across 32 national settings. European Journal of Criminology, 5, 331-361.

56.

Reid

(1999). Confucius lives next door. New York, NY: Random House.

57.

Reisig

Pratt

(2011). Low self-control and imprudent behavior revisited. Deviant Behavior, 32, 589-625.

58.

Sampson

Laub

(1993). Crime in the making: Pathways and turning points through life. Cambridge, MA: Harvard University Press.

59.

Sampson

Laub

(1994). Urban poverty and the family context of delinquency: A new look at structure and process in a classic study. Child Development, 65, 523-540.

60.

Sampson

Raudenbush

Earls

(1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918-924.

61.

Sarwar

Soomro

(2013). Impact of smartphones on society. European Journal of Scientific Research, 98, 216-226.

62.

Shaw

Black

(2008). Internet addiction: Definition, assessment, epidemiology and clinical management. CNS Drugs, 22, 353-365.

63.

Shin

Kim

Jung

(2011). Development of a Korean smartphone addiction proneness scale. Seoul, South Korea: Korean National Information Society Agency.

64.

Siomos

Dafouli

Braimiotis

Mouzas

Angelopoulos

(2008). Internet addiction among Greek adolescent students. CyberPsychology & Behavior, 11, 653-657.

65.

S. Korea tops smartphone penetration rate in 2012. (2013, June 25). The Korea Herald. Available from http://www.koreaherald.com

66.

Song

(1995). A preliminary study of the character traits of the Chinese. In Tseng

(Eds.), Chinese culture and mental health (pp. 47-55). New York, NY: Academic Press.

67.

South Korean online gamer accused of murdering infant son to keep playing (2014, April 15). Reuters. Available from http://www.reuters.com/article/2014/04/15/

68.

Stewart

Choi

(2003). PC-bang (room) culture: A study of Korean college students’ private and public use of computers and the Internet. Trends in Communication, 11, 63-79.

69.

Sutherland

(1947). Principles of criminology (4th ed.). Philadelphia, PA: Lippincott.

70.

Vazsonyi

Belliston

(2007). The family → Low self-control → Deviance: A cross-cultural and cross-national test of self-control theory. Criminal Justice and Behavior, 34, 505-530.

71.

Vazsonyi

Pickering

Junger

Hessing

(2001). An empirical test of a general theory of crime: A four-nation comparative study of self-control and the prediction of deviance. Journal of Research in Crime & Delinquency, 38, 91-131.

72.

Vazsonyi

Wittekind

Belliston

Loh

(2004). Extending the general theory of crime to “The East”: Low self-control in Japanese late adolescents. Journal of Quantitative Criminology, 20, 189-216.

73.

Wang

Qiao

Hong

Zhang

(2002). Adolescent social bond, self-control, and deviant behavior in Chins. International Journal of Contemporary Sociology, 39, 52-68.

74.

Weinstein

Lejoyeux

(2010). Internet addiction or excessive Internet use. The American Journal of Drug and Alcohol Abuse, 36, 277-283.

75.

White

(1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817-838.

76.

Wright

Caspi

Moffitt

Silva

(1999). Low self-control, social bonds, and crime: Social causation, social selection, or both? Criminology, 37, 479-514.

77.

Wright

Beaver

(2005). Do parents matter in creating self-control in their children? A genetically informed test of Gottfredson and Hirschi’s theory of self-control. Criminology, 43, 1169-1202.

78.

Wright

Beaver

Delisi

Vaughn

(2008). Evidence of negligible parenting influences on self-control, delinquent peers, and delinquency in a sample of twins. Justice Quarterly, 25, 544-569.

79.

Yoo

Cho

Yune

Kim

Hwang

. . . Lyoo

(2004). Attention deficit hyperactivity symptoms and internet addiction. Psychiatry and Clinical Neurosciences, 58, 487-494.

80.

Young

(1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1, 237-244.

81.

Yun

(2008). Wengu zhisxin: Review the old and know the new. Asia Pacific Journal of Police & Criminal Justice, 6(1), 3-23.

82.

Yun

Walsh

(2011). The stability of self-control among South Korean adolescents. International Journal of Offender Therapy and Comparative Criminology, 55, 445-459.