Psychological Factors Associated With Smartphone Addiction in South Korean Adolescents

Abstract

The smartphone has many attractive attributes and characteristics that could make it highly addictive, particularly in adolescents. The purpose of this study was to examine the prevalence of young adolescents in risk of smartphone addiction and the psychological factors associated with smartphone addiction. Four hundred ninety middle school students completed a self-questionnaire measuring levels of smartphone addiction, behavioral and emotional problems, self-esteem, anxiety, and adolescent-parent communication. One hundred twenty-eight (26.61%) adolescents were in high risk of smartphone addiction. This latter group showed significantly more severe levels of behavioral and emotional problems, lower self-esteem, and poorer quality of communication with their parents. Multiple regression analysis revealed that the severity of smartphone addiction is significantly associated with aggressive behavior (β = .593, t = 3.825) and self-esteem (β = −.305, t = −2.258). Further exploratory and confirmatory studies should consider different sites, demographics, technological mobile devices, platforms, and applications.

Keywords

adolescent smartphone addiction psychological factor self-esteem aggressive behavior

Internet addiction or problematic Internet use has been the focus of attention for several years. Although not yet recognized as an established disorder, it is known that Internet addiction has become a serious public health problem around the world, especially in adolescents (Christakis, 2010). Previous studies suggested that Internet addiction increased the risk to suffer from a number of negative social and health consequences, such as poor academic performance, poor personality relationship, anxiety, depression, and other behavioral problems (Aboujaoude, 2010; Yang & Tung, 2007). However, with the rapid evolving development of technology, Internet addiction does not seem to be the only, nor the most serious, cause of concern.

Most recently, smartphone addiction has been proposed as a diagnostic label to capture problems related to the drastic increase in use of smartphones. A smartphone is a relatively novel and innovative technology that typically combines the features of a mobile phone with those of other popular mobile devices, such as personal digital assistant, media player, Global Positioning System (GPS) navigation unit, and many more. A smartphone can enable ubiquitous Internet access, provide usage of various convenient applications, and offer unique opportunities for maintaining unrestricted and spontaneous contact with others (Sarwar & Soomro, 2013). The use of a smartphone has reached figures over 50% in most developed countries (van Deursen, Bolle, Hegner, & Kommers, 2015). Especially in South Korea, owing to its advanced technological development, over 24 million Koreans owned a smartphone in 2013, and 84% of adolescents owned a smartphone in 2014 (Alam et al., 2014).

Smartphones are becoming a necessity for many people’s daily lives, and they guarantee so much convenience to all the users. Particularly in adolescents, the possession and use of the smartphone acknowledges personal autonomy, provides self-identity, offers entertainment, and favors establishment and maintenance of interpersonal relationships (Oksman & Turtiainen, 2004). It is known that adolescence represents a period of heightened biological vulnerability to addiction (Chambers, Taylor, & Potenza, 2003). The vast majority of people who suffer from addiction encountered the beginnings of their illness as adolescents (Wagner & Anthony, 2002). Adolescents are fascinated in embracing the new technology such as a smartphone. When their greater motivational drives for novelty seeking are coupled with their immature inhibitory control system, smartphones could be potentially addictive to adolescents (Chambers et al., 2003).

Studies have shown that addiction related to mobile or smartphone can lower academic performance, reduce offline activities, and cause family conflicts (Billieux, Van der Linden, & Rochat, 2008; Lobet-Maris, 2003). Also, its correlation with depression, anxiety, and sleep disturbance has been demonstrated (Jenaro et al., 2007). Physically, excessive smartphone use may produce considerable stress on the cervical spine, thus changing the cervical curve and resulting in neck-shoulder pain (J. Park et al., 2015). Also, it has been reported that heightened anticipation about incoming phone calls or messages are associated with motor vehicle crash risk (O’Connor et al., 2013). Additionally, the smartphone could be used as a medium of cyberbullying, and smartphone ownership and frequent use of smartphone significantly elevated the odds of becoming either a cyber-bully or a cyber-victim (Rice et al., 2015).

The purpose of the present study was to examine the prevalence of young adolescents in high risk of smartphone addiction and psychological factors associated with smartphone addiction in South Korea. We hypothesized that adolescents in high risk of smartphone addiction have more emotional and behavioral problems than others.

Method

Subjects

This cross-sectional survey was conducted in all-boys middle school in Gong-ju, a city in South Korea, in 2014. All the students and their parents provided a written informed consent after receiving an explanation on the study and assurance of confidentiality. The students were requested to complete the questionnaires in a classroom under the supervision of a research assistant. A total of 504 students participated in this research, and 14 students were excluded due to incomplete questionnaires, resulting in 490 students.

The average age of the participants was 14 ± 0.89, composed of 131 (24.7%) seventh graders, 182 (34.3%) eighth graders, and 177 (33.3%) ninth graders. Ten (1.9%) reported being a smoker, and 19 (3.6%) had a drinking experience. Four hundred thirty two (81.4%) lived with both parents, 36 (6.8%) lived with one parent, and 22 (4.1%) lived with neither. In regard to paternal education level, parents who had graduated college or more were most prevalent (see Table 1).

Table 1.

Comparison of Sociodemographic Characteristics Between the High-Risk Group and the Normal Control Group.

Variables	High-risk group (n = 128)	Normal control group (n = 353)	p value
Grade, n (%)			.165
7	32(24.8)	97(75.2)
8	41(23.0)	137(77.0)
9	55(31.6)	119(68.4)
Smoking, n (%)			.633
Yes	2(20.0)	8(80.0)
No	126(26.8)	345(73.2)
Drinking, n (%)			.119
Yes	8(42.1)	11(57.9)
No	120(26.0)	342(74.0)
Living with parent(s), n (%)			.226
Both parents	108(25.4)	317(74.6)
One parent	13(38.2)	21(61.8)
None	7(31.8)	15(68.2)
Father education level, n (%)			.965
College or more	63(26.4)	176(73.6)
High school	49(27.7)	128(72.3)
Middle school or less	2(22.2)	7(77.8)
Do not know	14(25.0)	42(75.0)
Mother education level, n (%)			.840
College or more	66(27.6)	173(72.4)
High school	46(25.4)	135(74.6)
Middle school or less	1(14.3)	6(85.7)
Do not know	15(27.8)	39(72.2)

Measurements

Smartphone Addiction Scale–Short Version (SAS-SV) is a self-report on smartphone addiction consisting of 10 items with a 6-point scale (Kwon, Kim, Cho, & Yang, 2013). SAS-SV is designed to identify the level of the smartphone addiction risk and to distinguish the high-risk group. In case of SAS-SV, cutoff value of 31 for boys and 33 for girls distinguished the high-risk group for smartphone addiction. Also, a higher overall score indicates greater severity of smartphone addiction.

Youth Self Report (YSR) is a prominent and widely used self-report measure for the assessment of emotional and behavioral problems, designed for adolescents ages 11 to 18 (Achenbach & Rescorla, 2001). One hundred nineteen items on the YSR measure eight subscale symptoms: Withdrawn, Somatic Complaints, Anxiety/Depressed, Social Problems, Thought Problems, Attention Problems, Aggressive Behavior, and Delinquent Behaviors. An additional scale, Self-Destructive/Identity problems may be scored for boys only. The subscales may be grouped into two broader scales. The Internalizing grouping consists of the sum of the scores of the Withdrawn, Somatic Complaints, and Anxious/Depressed scales. The Externalizing grouping consists of the sum of the scores of the Delinquent and Aggressive Behavior scales. Overall behavioral and emotional functioning is measured by the Total Problem Scale. An adolescent selects his or her response from 0 (not true) to 2 (very true or often true).

The Rosenberg Self-Esteem Scale (RSES) is a widely used self-report instrument for evaluating individual self-esteem (Rosenberg, 1965). A 10-item scale is answered using a 4-point Likert-type scale format ranging from strongly agree to strongly disagree. The total score ranges from 10 to 40, with 10 representing the lowest level of self-esteem.

The Parent-Adolescent Communication Inventory (PACI) is a 20-item instrument that was designed to measure both content and process issues related to communication between adolescents and their parents (Barnes & Olson, 1985). Higher score on the scale reflects better communication with one’s parents.

The Revised Children’s Manifest Anxiety Scale (RCMAS) is a self-reported screening tool to measure anxiety in children aged 6 to 19 years that has demonstrated good reliability and validity (Reynolds & Richmond, 1978). It consists of 37 items, each of which requires a yes or no answer. Higher scores on the RCMAS indicate greater levels of anxiety.

Statistical Analysis

First, we distinguished the subjects into the high-risk group and the normal control group according to the scores of SAS-SV, applying the suggested cutoff score of 31 for boys. Independent t tests and Pearson chi-square tests were used to compare the sociodemographic characteristics and scores of YSR, RSES, PACI, and RCMAS between the two groups.

Second, correlation and multiple regression analyses were conducted to examine the relationship between the risk of smartphone addiction and the psychological factors. In multiple regression analyses, scores of SAS-SV was the dependent variable and the scores of YSR, RSES, PACI, and RCMAS were the predictor variables.

SPSS version 22.0 was used for the analyses, and significance level was set at .05. This study was approved by the ethics committee of Ajou University Medical Center.

Results

Among the 490 participants enrolled in the study, only nine students did not own a smartphone. Thus, the smartphone ownership rate was 98.16%. Of the 481 participants who owned a smartphone, 128 (26.61%) were classified in the high-risk group of smartphone addiction, and the rest (n = 353; 73.39%) were classified in the normal control group. The sociodemographic characteristics between the two groups showed no significant difference (see Table 1).

The mean severity of smartphone addiction on the SAS-SV was 23.92 (SD = 11.30). The average SAS-SV score of the high-risk group was 38.89 ± 7.13 and that of the normal control group was 18.49 ± 6.69. The average YSR total score of the high-risk group was 43.84 ± 26.71 and that of the normal control group was 26.31 ± 19.08. The high-risk group scored higher than the normal control group in all the subscales of the YSR. The RSES score was lower in the high-risk group (32.26 ± 6.09) than in the normal control group (36.09 ± 5.60). The PACI score was also lower in the high-risk group (65.86 ± 13.66) than in the normal control group (73.60 ± 14.42). The RCMAS score was higher in the high-risk group (18.43 ± 7.02) than in the normal control group (12.42 ± 5.84). All the differences were statistically significant (see Table 2).

Table 2.

Comparison of Psychological Factors Between Among the High-Risk Group and the Normal Control Group.

	High-risk group, n = 128 ( $\bar{X}$ ± SD)	Normal control group, n = 353 ( $\bar{X}$ ± SD)	p value
SAS-SV score	38.89 ± 7.13	18.49 ± 6.69	.00
YSR: Total score	43.84 ± 26.71	26.31 ± 19.08	.00
Internalizing Problems	14.03 ± 10.20	8.03 ± 7.57	.00
Withdrawn	3.35 ± 2.94	2.10 ± 2.35	.00
Somatic Complaints	3.54 ± 3.19	1.99 ± 2.70	.00
Anxious/Depressed	7.43 ± 6.21	4.07 ± 4.31	.00
Externalizing Problems	12.30 ± 8.28	7.32 ± 6.30	.00
Delinquent Behaviors	2.80 ± 2.32	1.93 ± 2.19	.00
Aggressive Behaviors	9.52 ± 6.41	5.40 ± 4.65	.00
Thought Problems	2.32 ± 2.13	1.44 ± 1.75	.00
Social Problems	2.84 ± 2.28	1.88 ± 2.07	.00
Attention Problems	6.18 ± 3.46	4.00 ± 2.78	.00
Self-Destructive/Identity Problems	4.74 ± 4.15	2.37 ± 2.85	.00
Self-Esteem score	32.26 ± 6.09	36.09 ± 5.60	.00
PACI score	65.86 ± 13.66	73.60 ± 14.42	.00
RCMAS	18.43 ± 7.02	12.42 ± 5.84	.00

Note. SAS-SV = Smartphone Addiction Scale–Short Version; YSR = Youth Self Report; PACI = The Parent-Adolescent Communication Inventory; RCMAS = Revised Children’s Manifest Anxiety Scale.

Pearson’s correlations between smartphone addiction and subscales of the YSR, RSES PACI, and RCMAS are shown in Table 3. The results indicated that smartphone addiction was positively correlated with withdrawn, somatic complaints, anxiety/depressed, social problems, thought problems, attention problems, delinquent problems, aggressive behavior, self-destructive/identity problems, and RCMAS. Smartphone addiction was negatively correlated with PACI and self-esteem score.

Table 3.

Correlation Between SAS-SV, YSR, RCMAS, PACI, and RSES.

	Wit	Som	Dep	Soc	Tho	Att	Del	Agg	Ide	RCMAS	PACI	RSES
SAS-SV	.287	.273	.345	.234	.265	.384	.257	.411	.406	.398	−.292	−.347
Wit		.489	.733	.563	.525	.606	.394	.475	.669	.609	−.404	−.474
Som			.482	.322	.473	.397	.332	.392	.456	.458	−.283	−.302
Dep				.566	.588	.676	.527	.664	.868	.721	−.375	−.501
Soc					.374	.626	.385	.448	.546	.446	−.268	−.406
Tho						.534	.393	.497	.586	.520	−.250	−.371
Att							.435	.613	.698	.623	−.329	−.493
Del								.679	.561	.423	−.252	−.301
Agg									.704	.597	−.290	−.361
Ide										.669	−.433	−.583
RCMAS											−.461	−.612
PACI												.576

Note. All coefficients had p value < .000. SAS-SV = Smartphone Addiction Scale–short version; YSR = Youth Self Report; RCMAS = Revised Children’s Measured Anxiety Scale; PACI = Parent-Adolescent Communication Inventory; RSES = Rosenberg Self-Esteem Scale; Wit = Withdrawn; Som = Somatic; Dep = Depression/Anxiety; Soc = Social Problems; Tho = Thought Problems; Att = Attention Problems; Del = Delinquent Problems; Agg = Aggressive Behavior; Ide = Self-Destructive/Identity Problems.

The results of multiple regression analysis indicated that more aggressive behavior on the YSR (β = .593, t = 3.825) and lower self-esteem on the RSES (β = −.305, t = −2.258) were significantly associated with more severe smartphone addiction (see Table 4).

Table 4.

Multiple Regression Analysis on Smartphone Addiction.

Variables	R ²	Adjusted R²	F	β	t
Aggressive Behaviors	.243	.219	10.279**	.593	3.825**
Self-Esteem				−.305	−2.258**

p < .05.

Discussion

In this study, 26.61% of the enrolled adolescents were in high risk of smartphone addiction. Among the studies that used the same scale as the present study, SAS-SV, prevalence of young adults in risk of smartphone addiction was estimated as 16.9%, 12.8%, 21.5% in Switzerland, Spain, and Belgium, respectively (Haug et al., 2015; Lopez-Fernandez, 2015). Such higher rate of smartphone addiction in this study could be due to Korea’s high use of smartphone. As South Korea prides itself on being the global leader in high-speed Internet and advanced mobile technology, its rate of smartphone ownership is among the highest in the world (Alam et al., 2014; Lee et al., 2014). Concordantly, the prevalence rates of Internet addiction were relatively high for the countries in the Middle East and Asia, and relatively low for the countries in Northern and Western Europe (Cheng & Li, 2014).

As we hypothesized, adolescents in high risk of smartphone addiction showed significantly more severe levels of psychopathologies in all subscales of YSR including Withdrawn, Somatic Complaints, Anxiety/Depressed, Social Problems, Thought Problems, Attention Problems, Aggressive Behavior, Delinquent Behaviors, and Self-Destructive/Identity Problems. Also, those in high risk of smartphone addiction showed lower self-esteem, higher levels of anxiety, and poorer quality of communication with their parents. Our results of multiple regression analysis revealed that the severity of smartphone addiction is associated with aggressive behavior and self-esteem. However, only 21.9% of the variance in behavioral consequences was explained with the model.

Many studies have revealed a close relationship between aggression and Internet addiction. In terms of specific contents, online chatting, online pornography, online gaming, and online gambling were all found to be associated with aggressive behavior (Ko, Yen, Liu, Huang, & Yen, 2009). Zimbardo proposed that on the Internet, anonymity and loss of individual responsibility results in a deindividuated state of oneself (Zimbardo, 1969). Deindividuation effect would lead to decreased self-observation or self-awareness; minimize the concern for social evaluation; weaken controls based on guilt, shame, and fear; and then, lower the threshold for exhibiting proscribed socially inhibited behaviors (Reicher, Spears, & Postmes, 1995). It can be postulated that since smartphone enables one to access the Internet more easily and more frequently, impact of aggression on smartphone could be even greater than that on the Internet. Moreover, participants in our study were all male. It is known that men are more aggressive than women, possibly due to the differences in social roles and biological features between sexes (Lightdale & Prentice, 1994). Therefore, we may assume that male students would use more aggressive online contents (K. Kim, 2013), which could have affected our study result.

Low self-esteem is one of the characteristics of the addictive personality (Marlatt, Baer, Donovan, & Kivlahan, 1988). Individuals who do not value themselves highly are more likely to bow to peer pressure that makes them vulnerable to engage in addictive behaviors (Zimmerman, Copeland, Shope, & Dielman, 1997). Also, lack of self-worth can keep people trapped in addiction (Marlatt et al., 1988). In case of smartphone addiction, low self-esteem has been proposed to be predictors of mobile phone and smartphone addiction (Ha, Chin, Park, Ryu, & Yu, 2008). In a study that examined the profiles of smartphone addicts, low self-esteem, fear of rejection, and need for approval were found to be important characteristics of smartphone addicts (Lapointe, Boudreau-Pinsonneault, & Vaghefi, 2013). The supportive function of peer relationships is particularly decisive in adolescence (Brown & Larson, 2009). Adolescents with low self-esteem or negative self-image may tend to seek reassurance and intimacy in the virtual world where they would build a new confident self (Lapointe et al., 2013).

The result of this study could also be explained in the context of South Korea’s society that puts great emphasis on academic achievement for adolescents (B. Kim, Lee, Kim, Choi, & Lee, 2015). Korean adolescents are known to have heavy schoolwork, highly competitive social atmosphere, and high expectations for good academic performance. The average amount of time Korean adolescents devote in studying was 7 hours, 50 minutes per day, which was much longer than that of other countries, which ranged from 3 to 6 hour (The State of Korean Children and Adolescents Seoul, 2009). Inevitably, Korean adolescents experience high levels of stress due to problems associated with academic performance, and academic stress is found to be a main predictive factor for aggression (M. Park, Choi, & Lim, 2014). At the same time, Korean adolescents spent less time sleeping (7 hours, 30 minutes per day) and exercising (13 minutes per day) than other adolescents from England, the United States, Holland, Sweden, and Finland (The State of Korean Children and Adolescents Seoul, 2009). Exercise allows for a discharge of aggression, reduces emotional strain, and promotes elevated self-esteem (Gerber, Kellmann, Hartmann, & Pühse, 2010). Low levels of physical activity, poor sleep, and high academic stress are reported to be contributors to the high prevalence of problematic Internet use in Korean adolescents (S. Park, 2014). It could be suggested that we attribute the relatively high prevalence of smartphone addiction in this study and its correlation with aggression and self-esteem to high academic stress and low levels of physical activity of Korean adolescents. Further studies on the relationship between adolescents’ smartphone addiction and academic stress and levels of physical activity are needed.

This study has several limitations that need to be addressed. First, the generalizability of this research may be limited by the characteristics of our participants, since it is a single-site study with all male participants. Second, because of the cross-sectional and correlational nature of the study, the current study alone is insufficient to provide evidence of causal relationships. Especially many sociodemographic factors that could have affected both smartphone addiction and emotional and behavioral problems were not controlled. The implementation of analytical methods, that can examine causal relationships, rather than merely examining the degree of associations, is recommended so that antecedents and consequences of smartphone addiction can be clearly evaluated. Third, we only evaluated the severity of smartphone addiction, but examining the contents and patterns of smartphone use would have been useful. Many insist that problems arise from various activities enabled by the Internet rather than the medium itself (Van Rooij, Schoenmakers, Van de Eijnden, & Van de Mheen, 2010). Fourth, our results based on self-reported questionnaires could suffer from social desirability bias. Finally, the validity of the cutoff score of SAS-SV that we used to distinguish the high-risk group of smartphone addiction could be questioned. The cutoff value of 31 for boys in SAS-SV had a positive value of 62% and a negative predictive value of 97% (Kwon et al., 2013). Figure 1 shows the distribution of SAS-SV scores of the participants in this study, and majority of the participants’ scores are cumulated below 31.

Figure 1.

Distribution of smartphone addiction scale scores of the participants.

Conclusion

The current study provided empirical evidence to increase our understanding of smartphone addiction. We found that young adolescents in risk of smartphone addiction show more severe levels of behavioral and emotional problems, lower self-esteem, and poorer quality of communication with their parents compared with those in normal risk. Particularly, we found association between smartphone addiction and aggressive behavior and self-esteem. We emphasize the importance of continued research not only in the area of smartphone but also in other areas of information technology. Further exploratory and confirmatory studies should consider different sites, demographics, technological mobile devices/platforms, and applications.

Footnotes

Authors’ Note

Everyone who contributed significantly to this study has been listed.

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 research was supported by the grant from Gong-ju National Hospital, and also by a grant of the Korean Mental Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HM14C2603).

Author Biographies

Jeewon Lee is a subspecialist in child and adolescent psychiatry and a clinical assistant professor in Sun Chun Hyang University Hospital in Korea. She received her medical, master’s, and doctoral degree from Ajou University, School of Medicine. She completed her psychiatry residency training and her child and adolescent psychiatry fellowship at Ajou University Hospital.

Min-Je Sung is an associate professor in business at Ajou University in Korea. He received his bachelor’s and master’s from Chung-Ang University, and his MBA and PhD in management decision making from George Washington University. He was formerly an assistant professor in biostatistics and bioinformatics at Duke University School of Medicine.

Sook-Hyung Song is a subspecialist in child and adolescent psychiatry at Yong-in Mental Hospital in Korea. She received her medical and master’s degree from Ajou University and her psychiatry residency training from Ajou University Hospital. She completed her child and adolescent psychiatry fellowship training at Seoul National University Hospital.

Young-Moon Lee is a psychiatrist at the Ajou Good hospital in Korea. He received his medical and master’s degree from Yonsei University, and his psychiatry residency training from Yonsei University Hospital. He also received his master’s degree in public health from both University of Texas and University of Melbourne.

Je-Jung Lee is a psychiatrist in the department of child and adolescent psychiatry at Gong-Ju National Hospital in Korea. She received her medical degree from Chungbuk National University, and her psychiatry residency training from Keyo Medical Center.

Sun-Mi Cho is clinical psychologist and an associate professor in the department of psychiatry and behavioral sciences at Ajou University in Korea. She received her bachelor’s, master’s, and PhD degree in psychology from Korea University.

Mi-Kyung Park is a social welfare expert in Suwon Child-Adolescent Community Mental Health Center. She received her bachelor’s and master’s degree in special education from Ajou University.

Yun-Mi Shin is a subspecialist in child and adolescent psychiatry and a professor at Ajou Medical Center in Korea. She received her medical degree from Ajou University and her psychiatry residency training from Ajou University Hospital. She completed her child and adolescent psychiatry fellowship training at Kangbuk Samsung Hospital.

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