Internet addiction impact on academic performance

Exploring_Internet_Addiction_a2 Does_Resilience_Mediate_the_Li1

For this annotated bibliography, you will be expected and required to document your source(s) according to 8th edition MLA documentation guidelines. Create an annotated bibliography of sources related to your general topic. The annotated bibliography should be 2 pages long. Follow MLA guidelines for formatting your document, including heading, spacing, font, page numbers, and margins. Font should be Times New Roman, or another easily-readable, 12 point. Each item should begin with the MLA-style citation for each source. Under that, write an annotation that does the following:

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Exploring Internet Addiction as a Process Addiction
Carlisle, Kristy L;Carlisle, Robert M;Polychronopoulos, Gina B;Goodman-Scott, Emily;Kirk-Jenkins,…
Journal of Mental Health Counseling; Apr 2016; 38, 2; Research Library
pg. 170

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

114 © The Journal of Negro Education, 2019, Vol. 88, No. 2© The Journal of Negro Education, 2019, Vol. 88, No. 2 114

The Journal of Negro Education, 88 (2), 114-129

Does Resilience Mediate the Link between
Depression and Internet Addiction among African
American University Students?

Seok Won Jin The University of Memphis
Tiffanie-Victoria Jones Southern University at New Orleans
Yeonggeul Lee University of Seoul

The present study aimed to examine risk and protective factors of African American students at
a historically Black university in the southeastern region of the U.S. A total of 326 students
participated in a cross-sectional self-report survey as to scores of the Internet Addiction Test,
depression, resilience, and socio-demographic information. A stepwise multiple-regression
analysis revealed that depression and being a freshman, respectively, were predictors of internet
addiction. Chi-square tests showed that there was significant positive association between
internet addiction and depression, while internet addiction was negatively associated with
resilience. These findings provide useful information for developing culturally tailored
interventions of internet addiction for African American college students especially who are
depressed and not resilient in their first year.

Keywords: internet addiction, depression, resilience, African American university students

INTRODUCTION

The Internet’s rapid and deep integration into daily life has created a public health concern
among young adults (Christakis et al., 2011; Jelenchick, Becker, & Moreno, 2012; Wu et al.,
2015). Building on Young’s (1996, 1998) initial conceptualization of internet addiction, various
researchers have characterized the condition as pathological use of the Internet, leading to
significant impairment or distress (Cash et al., 2012; Elhai et al., 2017; Shaw & Black, 2008).
Despite the variations in its conceptual and operational definitions, internet addiction has been
used interchangeably with internet use disorder (Kardefelt-Winther, 2017), pathological Internet
use (Kaess et al., 2016), and problematic Internet use (Lachmann et al., 2016). Using the term
internet addiction to cover the collective phenomenon, the present study aimed to examine
factors associated with internet addiction among African American university students in the
United States.

VULNERABILITY TO INTERNET ADDICTION IN UNIVERSITY STUDENTS

Research indicates that internet addiction often leads to various negative consequences,
including behavioral, emotional, or relational problems (De Leo & Wulfert, 2013; Kuss et al.,
2014; Kuss & Lopez-Fernandez, 2016). For example, early studies on internet addiction showed
that internet addicts are likely to do less exercise, seek less medical care, skip meals, and sleep
late compared to common internet users (Brenner, 1997; Chou & Hsiao, 2000; Deatherage,
Servaty-Seib, & Aksoz, 2014; Kim et al., 2010). Moreover, internet addiction accompanies
distress in vocational and academic settings, leading to unemployment or poor academic
performance (Kim et al., 2017; Shek, Sun, & Yu, 2013). Previous empirical studies demonstrated
that those with internet addiction frequently experience relational conflicts such as child neglect,
marital discord, failed marriages, and uncommitted friendships and other interpersonal
relationships (Kerkhof, Finkenauer, & Muusses, 2011).
_______________________________
Funding for this research was provided to the first author by a grant (TI-025590) from the Substance Abuse Mental
Health Services Administration’s (SAMHSA) Center for Substance Abuse Treatment (CSAT) and Center for Mental
Health Services (CMHS) through the Historically Black Colleges and Universities-Center for Excellence in Behavioral
Health (HBCU-CFE) at Morehouse School of Medicine. The authors would like to express their gratitude to the Center
and its staff for their generous support.

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Research has consistently found the vulnerability of university students to developing and
experiencing problems with Internet addiction (Derbyshire et al., 2013; Moreno, Jelenchick, &
Breland, 2015; Young, 2015). Many university students leave their home and begin independent
living free from familial interferences, allowing them to spend unlimited amounts of time on
online (Kuss, Griffiths, & Binder, 2013; Li, Garland, & Howard, 2014). Furthermore,
universities encourage students to use the Internet for their assignments and research (Li et al.,
2015; Santos, Boticario, & Pérez-Marín, 2014). Research shows that high accessibility to the
Internet is closely linked to internet addiction (Li et al., 2015; Young, 2010). Lastly, according
to development theories, individuals in younger adulthood are the period during which
individuals tend to seek new social relationships beyond their family members (Committee on
Improving the Health, Safety, and Well-Being of Young Adults et al., 2015; Parker et al., 2012).
Online communication such as texting, chatting, or social media can be a medium that enables
university students to expand their relationships to faculty, social clubs, or organizations
(Anderson, 2014; Parker et al., 2012). In accordance with the expansion of relationships,
university students who experience dissatisfaction with close relationships may leverage internet
contents excessively, including online communication to escape from relational situations (Lam,
2014; Vorderer, Klimmt, & Ritterfeld, 2004; Wang, 2014).

Factors Contributing to Internet Addiction

Researchers have identified several primary factors that predict internet addiction. Time spent
online is a key predictor of internet addiction. Internet addicts spend average 20 to 40 hours per
week, which accounts for three to eight times more internet usage than normal Internet users
(Young, 2010, 2015). This is partly because internet addicts tend to have a distortion of the
amount of time spent online and, therefore, stay online longer than non-Internet addicts do
(Bozoglan, Demirer, & Sahin, 2013). Moreover, prior research showed that increased time on
the Internet decreases time spent with friends and increases family conflicts (Mesch, 2006; Nie,
2002). However, research has also pointed out that an individual’s internet addiction correlated
more significantly to the hours the person spends online for recreational purposes than the total
amount of hours the person spends online (Tokunaga, 2014). Furthermore, according to types of
distinct online addictive behaviors, internet addiction can vary, including addiction pertaining to
cyber-sex, cyber-relationship, obsessive online shopping or trading, information overload, and
obsessive computer game playing (Pontes, Kuss, & Griffiths, 2015).

Regarding demographic variables, age and gender are also factors that influence Internet
addiction. Younger people are more likely to experience Internet addiction compared to older
people (Aboujaoude, 2010; Morrison & Gore, 2010). Furthermore, a systematic review study
revealed that the male gender is associated with internet addiction, indicating that there is no
significant difference in frequency of internet use, while behavioral aspects related to internet
addiction differ by activities on the Internet (Fattore et al., 2014). However, multiple studies
showed that males are at higher risk for internet addiction, with their preferences for online
games and online sex compared to females (Canan et al., 2012; Choi et al., 2015; Çuhadar, 2012;
Lin, Ko, & Wu, 2011). In contrast, recent studies revealed that females are vulnerable to Internet
addiction because of familial conflicts and poorer mental health, with their preferences for online
communicative activities such as chatting, messaging, and blogging (Ciarrochi et al., 2016;
Coyne et al., 2015; Heo et al., 2014).

Researchers have indicated that depression (i.e., major depressive disorder or clinical
depression) is a common but serious mood disorder, being a critical determinant to Internet
addiction among adolescents and university students (Bahrainian et al., 2014; Boonvisudhi &
Kuladee, 2017; Christakis et al., 2011; Orsal et al., 2013). Depression is prevalent especially
among young adults in the United States, with its high rates of recurrence and comorbidity with
other psychiatric illnesses, including anxiety and substance use disorder (Lin et al., 2016). Some
studies showed that depressed university students reported higher levels of internet addiction
compared to students without depressive symptoms (Kim et al., 2014; Moreno et al., 2015;

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116 © The Journal of Negro Education, 2019, Vol. 88, No. 2© The Journal of Negro Education, 2019, Vol. 88, No. 2 116

Torres, 2011). Another study showed that depression is closely connected to initiation and
persistence of internet addiction in adolescents (Chang et al., 2014). Tokunaga and Rains (2010)
found that depression is a strong predictor of internet addiction, with an indirect effect of hours
of internet use. A meta-analysis study found depression along with risky behaviors, including
alcohol use and smoking contributes to internet addiction (Ho et al., 2014). The cognitive-
behavioral theory can explain the link between internet addiction and depression, suggesting that
depressed students’ excessive use of the Internet in order to alter their negative moods results in
internet addiction (Morrison & Gore, 2010). However, the association between internet addiction
and depression is bi-directional, suggesting that depression can be outcomes of Internet addiction
(Ciarrochi et al., 2016; Dong, Kalmaz, & Savides, 2011; Gentile et al., 2011).

Whereas many studies have examined factors contributing to internet addiction, few studies
have paid attention to the factors that potentially function as a buffer to Internet addiction. Social
support, especially from family, correlates reversely to internet addiction (Chen, Chen, & Gau,
2015; Thorsteinsson & Davey, 2014). Additionally, quality relationships correlate negatively to
Internet addiction. For example, Kerkhof and colleagues (2011) found compulsive Internet use
is negatively associated with relationship quality among newlywed couples. Furthermore, Jin
and Berge (2016) reported the potential mediational effect of marital intimacy on the link
between acculturative stress and internet addiction among Asian married couples. Longitudinal
studies demonstrated familial or peer relationships have protective effects on Internet addiction
among adolescents through improving communication skills (Gámez-Guadix et al., 2013; Wang,
Wu, & Lau, 2016; Yu & Shek, 2013).

Moreover, resilience may play a significant role in protecting individuals from developing
Internet addiction. Resilience, originally defined as the ability to cope with negative experiences,
such as acute stress, trauma, or more chronic forms of adversity, permits a person to maintain
psychological well-being (Choi et al., 2015; d’Haenens, Vandoninck, & Donoso, 2013; Jung et
al., 2012). Accordingly, resilience may enable individuals to appropriately deal with factors
contributing to internet addiction, consequently resulting in protecting them from developing the
disorder (Russo et al., 2012). Choi and associates (2015) found resilience correlates negatively
with Internet addiction and smartphone addiction among university students. Resilience has a
partial mediational effect on the relationships between internet addiction and perceived class
climate and alienation, suggesting that improved resilience can lead to reduced internet addiction
(Li et al., 2010). Another study also found that resilience mediates the relationship between stress
and internet addiction among high school males (Jang & Choi, 2012). Moreover, multiple studies
found resilience to have a negative association with depression (Gloria & Steinhardt, 2016;
Holden et al., 2013; Spies & Seedat, 2014). These findings suggest that resilience may mitigate
the effects of depression on Internet addiction (Kuss et al., 2013; Wisniewski et al., 2015).

Research Gaps

Previous studies have evaluated factors at individual and contextual levels that are predicative
of internet addiction. While these studies provide useful information for developing approaches
that focus on reducing negative consequences pertaining to internet addiction, the information is
limited in addressing resilience toward internet addiction. Therefore, there is the need to better
understand how resilience—the ability to recover quickly from difficult situations—mediates the
relationship between the risk factors and internet addiction (Bernardes, Ray, & Harkins, 2009;
Dowling & Brown, 2010; Smith et al., 2008). A clearer understanding of the mediating role
resilience plays can help design strength-based interventions for reducing Internet addiction
based on enhancing resilience (Cash et al., 2012; Kwon, 2011; Vondráčková & Gabrhelík, 2016).

Furthermore, African Americans and, particularly, African American university students are
underrepresented in the literature regarding internet addiction (Jones et al., 2009). The studies
on the link between resilience and Internet addiction utilized samples of Asian or White
adolescents (Jang & Choi, 2012; Kim et al., 2014; Wisniewski et al., 2015). Although there are
a few studies that were concerned with internet usage among African American university
students, their findings are limited because they focused on digital divides by race or the effects

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of internet usage on structure values and social behaviors (Park & Villar, 2015). While Pew
Research Center’s Internet and American Life Project (Fox & Duggan, 2012) revealed that
young African Americans use Twitter the most in the U.S., this did not fully reflect internet
behaviors among African American university students (Smith, 2015; Smith, Rainie, & Zickuhr,
2011). Moreover, this project also reported that cell phone ownership rate is comparable between
Blacks and Whites in the United States (Pew Research Center, 2017), but African American cell
phone users are more likely to seek health information on their phones compared to White cell
phone users (Fox & Duggan, 2012). Therefore, it is necessary to explore internet usage among
African American university students. Such research can lead to the development of
interventions designed to provide mental health information especially regarding depression.
These interventions and information are likely to be easily accessible via a cellphone to African
American university students who suffer from internet addiction. To the authors’ knowledge, the
present study is the first to assess the mediating effect of resilience between depression and
internet addiction among African American university students. The findings of the study will
offer critical implications to mental health practitioners and university professionals, as these
results will illuminate the need for targeted interventions that help tackle internet addiction by
improving resilience among African American university students.

PURPOSE OF THE STUDY

The present study examined the interactions between factors of internet addiction in African
American university students, with a specific focus on resilience. Particularly, this study
investigated the relationships between internet addiction and depression and social-demographic
variables, including gender, age, income, classification, grade point average (GPA.), marital
status, number of children, employment status, and recreational and essential time spent online.
This study also evaluated the mediating effect of resilience between depression and internet
addiction. The following research questions were established:

• Research question 1: Which set of factors best predicts internet addiction?
• Research question 2: Are there statistically significant associations between internet addiction and

gender, depression, and resilience?
• Research question 3: Does resilience mediate the relationship between depression and internet

addiction?

METHODS

Study Design and Sampling Procedure

The study used convenience and purposive sampling to recruit African American university
students at an HBCU in the southeastern region of the U.S. during April 2014. To reduce
sampling bias inherent in nonprobability sampling, the classes for data collection were randomly
selected from a list of the university course tally. The research team contacted instructors of the
selected classes by email to explain the study purpose and data collection procedure and seek
their permission to administer a self-reported cross-sectional survey by the research team during
class. The research team set up a schedule for the survey administration by email with the
instructors who agreed. With class instructors’ permission, staff of the research team briefly
explained the nature and purpose of the study, and then administrated the survey questionnaires
and informed consent documents to students who were interested in the study. Instructors
allowed students who did not want to participate to leave the classroom. The survey took about
15 minutes to complete. Three hundred and twenty-six African American undergraduate and
graduate students from five of the university’s schools participated in this study. At the time of
data collection, the university had about 3,500 students enrolled of which 74% were female and
less than 5% were non-Black students, including international students. Given a small percentage
of non-Black students at the university, the research team decided not to collect information
regarding race or ethnicity of participants in that, the number of potential non-Black participants

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118 © The Journal of Negro Education, 2019, Vol. 88, No. 2© The Journal of Negro Education, 2019, Vol. 88, No. 2 118

has a limited effect on the results of statistical analysis; furthermore, the privacy of information
obtained from these students would remain assured. The Institutional Review Board of the
university approved this study.

Study Participants

Graduate and undergraduate students from the university participated in this study for a sample
of 326. The majority of study participants were female (79.4%, n = 259). The participants’ mean
age was 23.4 years (n = 326, SD = 6.54). The average age of participants in each classification
year includes 18.8 years for freshman (n = 48), 19.9 years for sophomore (n = 41), 21.0 years
for junior (n = 75), 22.2 years for senior (n = 66), and 30.6 years for graduate students (n = 91).
Five of students reported no information for their classification year. About 86% were single (n
= 279). Nearly two-third of participants reported having no child (n = 221). About 43% lived
alone (n = 141). Almost 60% reported having either a full-time (39.6%, n = 129) or a part-time
job (19.9%, n = 65). About 36% reported having a household income below $10,000 (n = 118).
The majority of participants had a GPA of “B” (44.5%, n = 145) or “A” (40.2%, n = 131). With
respect to internet usage, participants reported spending on average seven hours (SD = 5.40) per
day using the Internet for recreational purposes and five hours (SD = 3.96) per day using the
Internet for essential purposes. The sample represented the characteristics of the university
demographics.

Measurements

Internet addiction. To measure the influence of internet use on everyday life and social

interactions, this study used Young’s (1998) Internet Addiction Test (IAT). The IAT consists of
20 items with a six-point Likert scale ranging from 0 = “rarely” to 5 = “always.” A total score
higher than 50 and less than 80 indicates moderate internet addiction—internet users with a score
in the range may experience occasional problems because of excessive internet use. When it is
between 80 and 100, internet users with a score may experience significant problems in their life
because of the Internet. The IAT has been used to assess internet addiction (Aboujaoude, 2010).
With the Cronbach’s alpha of .94 in this study, the internal consistency of the items indicates
excellent reliability.

Depression. This study assessed depressive symptomology using the Center for

Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). The CES-D, a self-reported
20-item measurement, assesses depression across various age ranges on a four-point Likert scale
with 0 indicating “rarely” and 3 indicating “almost all the time.” Its clinical cutoff for depression
is 16 or more; that indicates individuals at high risk of depression. The CES-D’s Cronbach’s
alpha for this study was .85, indicating good internal consistency between items.

Resilience. In this study, the Brief Resilience Scale (BRS) assessed resilience in students.
Smith and colleagues (2008) developed the BRS, which consists of six items with a five-point
Likert scale ranging from “strongly disagree” to “strongly agree.” The content includes items
related to various components of resilience, including the ability to bounce back (e.g., “I tend to
bounce back from quickly hard times”) or recover from stress (e.g., “It does not take me long to
recover from a stressful event,” Agnes, 2005). In this study, the original scale yielded an alpha
coefficient of .36, which suggests poor internal consistency between items. Therefore, a factor
analysis determined which item or items to eliminate. Results from the principal component
analysis showed that item number three had a low loading (.027), which is lower than .30, and
consequently, it was removed from the analysis. Eliminating this item increased the reliability
coefficient to .76, which indicates good internal consistency between the five items.

DATA ANALYSIS

Before conducting analyses, assumptions for all tests were evaluated, including normality of
distributions, linearity, homoscedasticity, normality of residuals, and multicollinearity. Fisher’s

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coefficient, histograms, and q-q normal probability plots revealed that the following variables
were not normally distributed—internet addiction, depression, recreational hours, and essential
hours. Internet addiction and depression were transformed by a square root, while recreational
hours and essential hours were transformed using a logarithm. Additionally, histograms and
normal probability plots revealed that errors were normally distributed. Inspection of the
scatterplot of predicted scores against the residuals validated that variance was the same for all
values of the independent variables. Pearson’s correlation and scatterplots showed a linear
relationship between internet addiction and all factors. As such, all assumptions were satisfied
to conduct statistical analyses.

The first research question used a stepwise multiple-regression analysis to estimate the
model. It was necessary to determine which factors to enter into the analysis; therefore, Pearson’s
correlation evaluated the relationship between internet addiction and all continuous variables,
including age, depression, time spent online for recreational and essential purposes. Independent
t-test evaluated mean differences between males and females with regard to internet addiction.
Lastly, one-way ANOVA determined mean differences between groups of more than three
(socioeconomic status, classification, GPA, marital status, number of children, employment
status, and income), with regard to internet addiction. Depression level and classification
emerged as significant factors. Because it was categorical, classification was recoded into five
dummy variables, and therefore, eleven factors were entered into the analysis. The second
research question used three chi-square tests to examine three sets of relationships: (a) the
association between internet addiction and gender; (b) the association between Internet addiction
and depression; (c) and the association between internet addiction and resilience. The last
research question examined the significance of the effect of resilience as mediating variable. It
used a hierarchal multiple regression analysis, and all assumptions were satisfied to conduct this
analysis.

RESULTS

Which set of factors best predicts internet addiction? (Research Question 1)

Table 1 shows results of multiple regression analysis that examined factors associated with
Internet addiction among participants (F = 30.48, p < .001). Depression was the strongest factor, with a beta of .38 (p < .001). Freshman status emerged as the second strongest predictor, with a beta of .13 (p < .05). This model explains approximately 16 percent (R = .40) of the variance in internet addiction.

Are there statistically significant associations between internet addiction and gender,
depression, and resilience? (Research Question 2)

Chi-square tests examined the association of internet addiction (addicts vs. non-addicts) with
gender (female vs. male), depression (depressed vs. not depressed), and resilience (resilient vs.
not resilient), among a sample of 326 participants. The results of the chi-square tests revealed
internet addiction was significantly associated with depression (χ2 [df = 2] = 4.264, p < .05) and with resilience (χ2 [df = 1] = 4.754, p < .05) while there was no statistically significant association between internet addiction and gender (χ2 [df = 1] = .177, p > .05; see Table 2).

Table 1

Multiple Regression Analysis—Factors of Internet Addiction

Factor R R2 β t F p
Depressiona .383 .147 .380 7.356 54.12 .000

Freshmen .404 .163 .127 2.450 30.49 .015
Note. aSquare Root of Depression; p < .05.

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120 © The Journal of Negro Education, 2019, Vol. 88, No. 2© The Journal of Negro Education, 2019, Vol. 88, No. 2 120

Table 2

Internet Addiction by Gender, Depression, and Resilience

Variable Total IA a Non-IA
N % n % n % x2 p b

Gender .177 > .05
Female 257 80.8 21 6.6 236 74.2
Male 61 19.2 4 1.3 57 17.9
Total 318 100 25 7.9 293 92.1

Depression 4.264 < .05 Yes 122 38.6 14 4.4 108 34.2 No 194 61.4 10 3.2 184 58.2 Total 316 100 24 7.6 292 92.4

Resilience 4.754 < .05 Yes 82 25.7 11 3.4 71 22.3 No 237 74.3 14 4.4 223 69.9 Total 319 100 25 7.8 294 92.2

Note. a Internet addict; b Two-tailed alpha.
_____________________________________________________________________

Furthermore, the Phi-coefficient (ɸ) examined the association between dichotomous
variables. The results showed a non-significant negative association between internet addiction
and gender (ɸ 2 = -.024), a significant positive association between internet addiction and
depression (ɸ 2 = .116), and a significant negative association between internet addiction and
resilience (ɸ 2 = -.122), respectively. These results indicate that depressed people are likely to
have internet addiction (accounting for 1.4 percent of the variance), whereas people who are not
resilient are more likely to have internet addiction (accounting for 1.5 percent of the variance).

Does resilience mediate the relationship between internet addiction and depression?
(Research Question 3)

To test the mediational effect of resilience between depression and internet addiction, it was
necessary to establish the following three conditions: (a) depression significantly correlates with
internet addiction; (b) depression significantly correlates with resilience; and (c) resilience
significantly correlates with internet addiction. The first step of the mediation model revealed
that there was a statistically significant relationship between depression and internet addiction
(R = .383, F [1, 315] = 54.12, p = .000). The second step revealed that depression was also
significantly related to resilience (R = .502, F [1, 318] = 106.9, p = .000). The third step, however,
revealed that resilience was marginally correlated to Internet addiction (R = .272, F [1, 315] =
25.16, p = .077; see Figure 1). Lastly, because of the lack of mediation from resilience, the
relationship between depression and Internet addiction remained statistically significant (R =
.394, F [2, 315] = 28.81, p = .000). Therefore, resilience was not found to mediate the relationship
between depression and internet addiction.

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DISCUSSION

The present study examined factors associated with internet addiction in African American
students at an HBCU. Particularly, this study focused on assessing the mediational effect of
resilience on the association between depression and internet addiction. The findings of the study
shed light on interventions aimed at reducing internet addiction by leveraging its relevant factors.

The study found that levels of depression positively predict levels of Internet addiction,
which are consistent with previous studies (Boonvisudhi & Kuladee, 2017; Christakis et al.,
2011; Torres, 2011). This finding suggests that African American students with higher levels of
depression can be at increased risk of developing internet addiction. This also implies that when
students visit to a university health center complaining of internet addiction, it is important for
mental health practitioners at the health center to assess the coexistence of depression along with
internet addiction. Given some students’ low-income status and academic burdens, stressors
might significantly contribute to depression, which, in turn, influenced their levels of internet
addiction (Orsal et al., 2013; Tang et al., 2014). Therefore, future studies should examine the
relationships among stressors, depression, and internet addiction in African American university
students.

Although this study did not support the mediational model, the findings provide a potential
critical role of resilience in positively influencing internet addiction and its risk factors,
respectively. The findings showed resilience to have significant and marginal reverse
relationships with depression and internet addiction. Other studies concur and offer additional
information regarding the role of resilience on depression. A community-based clinical study on
African American women demonstrated the significant inverse relationship between resilience
scores and depressive symptoms (Holden et al., 2013). A national data set revealed that non-
Hispanic Blacks showed lower risk for depression when compared to non-Hispanic Whites
(Breslau et al., 2006). Furthermore, a nationally representative longitudinal cohort study found
the significant relationship between levels of depressive symptoms as baseline and levels of risks

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122 © The Journal of Negro Education, 2019, Vol. 88, No. 2© The Journal of Negro Education, 2019, Vol. 88, No. 2 122

of subsequent mortality over 25 years among non-Hispanic Whites but not with non-Hispanic
Blacks (Assari et al., 2016). These findings highlight the importance of resilience in attenuating
psychological adversities, including depression and internet addiction among African Americans
(Assari, 2016). This implies that interventions for students with internet addiction should focus
not only on eliminating risk factors such as depression, but also on enhancing protective factors
such as resilience. Therefore, it is worthwhile for university professionals to develop resilience-
enhancing programs for African American students, particularly focusing on ethnicity identity
development, stress coping sessions, on/off-line peer support groups, or physical exercise
activities.

Finally, the findings revealed that freshmen students are more likely than upper class
students are to present with internet addiction. Existing literature has also focused on internet
addiction among freshmen university students, indicating their heightened vulnerability to
internet addiction (Han et al., 2017; Yao et al., 2013). This may be in part because freshmen
typically undergo a more stressful adjustment to new school life and environment than other
classes (Chou et al., 2015; Deatherage, Seraty-Seib, & Aksoz, 2014). Therefore, to escape from
the stressful situations, younger students might increase their internet use that might lead to their
higher levels of internet addiction. Another possible explanation is that younger students (e.g.,
freshman class in this study) might have more experiences of using the Internet than older
students (e.g., graduates in this study). Younger people, referred to as ‘digital natives,’ generally
have earlier exposure to and are more adept at using new technologies and devices than older
people who are referred to as ‘digital immigrants’ (Benotsch et al., 2013; Lee & Coughlin, 2015).
Existing research also has indicated that there is a potential gap in usage of technologies between
digital natives and digital immigrants. Variations exist even within digital natives according to
accessibility and use of technologies for their socialization and learning (Bennett & Corrin, 2018;
Bullen & Morgan, 2016; Kirschner & De Bruyckere, 2017). This implies that universities need
early intervention strategies for students’ internet addiction. For example, during orientation for
freshmen, universities can assess students’ internet behaviors and internet addiction, as well as,
other pertinent risk factors including depression. Counseling centers on campus also need to
track internet addiction and related factors among freshmen students and continue providing
treatment services throughout their matriculation.

LIMITATIONS

This study has some limitations. First, the study employed a cross-sectional survey. Although
the findings of this study provided useful information on factors affecting internet addiction,
longitudinal designs will provide clearer evidence for causal relationships between internet
addiction and risk factors. Additionally, the time of survey administration (i.e., close to final
exam period) might have influenced both depression and internet addiction scores. Given the
link between depression and stressful events and that depression is a major risk factor of internet
addiction, future studies need to consider the possible effect that survey administration timing
has on study variables. Lastly, while this study was the first to investigate internet addiction
among African American students and identify its factors among them, the convenient sampling
used in this study will not allow for generalization to other African American students in the
United States. Comparative studies on internet addiction using a probability sampling method
among various ethnic groups would offer better understanding of culturally competent
interventions for various ethnic student populations.

CONCLUSION

This study examined risk and protective factors of internet addiction in students at an HBCU in
the United States. Depression and freshman classification emerged as risk factors to internet
addiction while resilience was significantly negatively associated with internet addiction. The
findings of this study suggest that interventions for Internet addiction among African American

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students might aim to alleviate depression specifically among younger aged (e.g., freshman)
students. In addition, resilience-enhancing programs for students with internet addiction might
prove useful. Lastly, more culturally appropriate interventions for internet addiction among
African American university students are necessary.

REFERENCES

Aboujaoude, E. (2010). Problematic internet use: an overview. World Psychiatry, 9, 85-90.
Agnes, M. (2005). Webster’s new college dictionary. Cleveland, OH: Wiley.
Anderson, B. (2014). Massive multiplayer online role-playing games and interaction: A

measurable model of interaction for online learning. International Journal of Virtual and
Personal Learning Environments, 5, 28-39.

Assari, S. (2016, September 16). Black Americans may be more resilient to stress than White
Americans. The Conversation. Retrieved from http://theconversation.com/black-americans-
may-be-more-resilient-to-stress-than-white-americans-62338

Assari, S., Moazen-Zadeh, E., Lankarani, M. M., & Micol-Foster, V. (2016). Race, depressive
symptoms, and all-cause mortality in the United States. Frontiers in Public Health, 4.
https://doi.org/10.3389/fpubh.2016.00040

Bahrainian, S., Haji Alizadeh, K., Raeisoon, M., Hashemi Gorji, O., & Khazaee, A. (2014).
Relationship of internet addiction with self-esteem and depression in university students.
Journal of Preventive Medicine and Hygiene, 55, 86-89.

Bennett, S., & Corrin, L. (2018). From digital natives to student experiences with technology.
Igi-Global.Com. https://doi.org/10.4018/978-1-5225-2255-3.ch219

Benotsch, E. G., Snipes, D. J., Martin, A. M., & Bull, S. S. (2013). Sexting, substance use, and
sexual risk behavior in young adults. Journal of Adolescent Health, 52(3), 307-313.
https://doi.org/10.1016/j.jadohealth.2012.06.011

Bernardes, C., Ray, S., & Harkins, D. (2009). An exploratory study of resilience and coping
strategies among Portuguese-speaking immigrant women survivors of domestic violence.
American Journal of Psychological Research, 5, 1-17.

Boonvisudhi, T., & Kuladee, S. (2017). Association between Internet addiction and depression
in Thai medical students at Faculty of Medicine, Ramathibodi Hospital. PLOS ONE, 12(3),
e0174209. https://doi.org/10.1371/journal.pone.0174209

Bozoglan, B., Demirer, V., & Sahin, I. (2013). Loneliness, self-esteem, and life satisfaction as
predictors of internet addiction: A cross-sectional study among Turkish university students.
Scandinavian Journal of Psychology, 54, 313-319.

Brenner, V. (1997). Psychology of computer use: XLVII. Parameters of internet use, abuse, and
addiction: The first 90 days of the internet usage survey. Psychology Reports, 80, 879-882.

Breslau, J., Aguilar-Gaxiola, S., Kendler, K. S., Su, M., Williams, D., & Kessler, R. C. (2006).
Specifying race-ethnic differences in risk for psychiatric disorder in a USA national sample.
Psychological Medicine, 36(1), 57-68. https://doi.org/10.1017/S0033291705006161

Bullen, M., & Morgan, T. (2016). Digital learners not digital natives. La Cuestión Universitaria,
7, 60-68.

Canan, F., Ataoglu, A., Ozcetin, A., & Icmeli, C. (2012). The association between internet
addiction and dissociation among Turkish college students. Comprehensive Psychiatry, 53,
422-426.

Cash, H., Rae, C. D., Steel, A. H., & Winkler, A. (2012). Internet addiction: A brief summary of
research and practice. Current Psychiatry Reviews, 8(4), 292-298.
https://doi.org/10.2174/157340012803520513

Chang, F.-C., Chiu, C.-H., Lee, C.-M., Chen, P.-H., & Miao, N.-F. (2014). Predictors of the
initiation and persistence of internet addiction among adolescents in Taiwan. Addictive
Behaviors, 39(10), 1434-1440. https://doi.org/10.1016/j.addbeh.2014.05.010

This content downloaded from
��198.91.36.79 on Mon, 06 Jul 2020 13:25:38 UTC��
All use subject to https://about.jstor.org/terms

124 © The Journal of Negro Education, 2019, Vol. 88, No. 2© The Journal of Negro Education, 2019, Vol. 88, No. 2 124

Chen, Y.-L., Chen, S.-H., & Gau, S. S.-F. (2015). ADHD and autistic traits, family function,
parenting style, and social adjustment for internet addiction among children and adolescents
in Taiwan: A longitudinal study. Research in Developmental Disabilities, 39, 20-31.
https://doi.org/10.1016/j.ridd.2014.12.025

Choi, S.-W., Kim, D.-J., Choi, J.-S., Ahn, H., Choi, E.-J., Song, W.-Y.,. . . Youn, H. (2015).
Comparison of risk and protective factors associated with smartphone addiction and Internet
addiction. Journal of Behavioral Addictions, 4(4), 308-314.
https://doi.org/10.1556/2006.4.2015.043

Chou, C., &.Hsiao, M. C. (2000). Internet addiction, usage, gratification, and pleasure
experience: The Taiwan college student’s case. Computers and Education, 35, 65-80.

Chou, W.-P., Ko, C.-H., Kaufman, E. A., Crowell, S. E., Hsiao, R. C., Wang, P.-W.,. . . Yen, C.
-F. (2015). Association of stress coping strategies with internet addiction in college students:
The moderating effect of depression. Comprehensive Psychiatry, 62, 27-33.
https://doi.org/10.1016/j.comppsych.2015.06.004

Christakis, D. A., Moreno, M. M., Jelenchick, L., Myaing, M. T., & Zhou, C. (2011).
Problematic internet usage in U.S. college students: A pilot study. BMC Medicine, 9, 1-6.
https://doi.org/10.1186/1741-7015-9-77

Ciarrochi, J., Parker, P., Sahdra, B., Marshall, S., Jackson, C., Gloster, A. T., & Heaven, P.
(2016). The development of compulsive internet use and mental health: A four-year study
of adolescence. Developmental Psychology, 52(2), 272-283.
https://doi.org/10.1037/dev0000070

Committee on Improving the Health, Safety, and Well-Being of Young Adults; Board on
Children, Youth, and Families; Institute of Medicine; National Research Council; Bonnie,
R. J., Stroud, C., & Breiner. H. (Eds.). (2015). Investing in the health and well-being of
young adults. Washington, DC: National Academies Press (U.S.). 3, Relationships.
Available from https://www.ncbi.nlm.nih.gov/books/NBK284783/

Coyne, S. M., Dyer, W. J., Densley, R., Money, N. M., Day, R. D., & Harper, J. M. (2015).
Physiological indicators of pathologic video game use in adolescence. The Journal of
Adolescent Health: Official Publication of the Society for Adolescent Medicine, 56(3), 307-
313. https://doi.org/10.1016/j.jadohealth.2014.10.271

Çuhadar, C. (2012). Exploration of problematic Internet use and social interaction anxiety among
Turkish pre-service teachers. Computers & Education, 59(2), 173-181.
https://doi.org/10.1016/j.compedu.2011.12.029

d’Haenens, L., Vandoninck, S., & Donoso, V. (2013, January). How to cope and build online
resilience? [Monograph]. Retrieved from http://www2.lse.ac.uk/media@lse/research/
EUKidsOnline/Home.aspx

De Leo, J. A., & Wulfert, E. (2013). Problematic internet use and other risky behaviors in college
students: An application of problem-behavior theory. Psychology of Addictive Behaviors,
27, 133-141.

Deatherage, S., Servaty-Seib, H. L., & Aksoz, I. (2014). Stress, coping, and internet use of
college students. Journal of American College Health, 62(1), 40-46.
https://doi.org/10.1080/07448481.2013.843536

Derbyshire, K., Lust, K., Schreiber, L., Odlaug, B., & Christenson, G. (2013). Problematic
internet use and associated risks in a college sample. Comprehensive Psychiatry, 54, 415-
422.

Dong, M. H., Kalmaz, D., & Savides, T. J. (2011). Missed work related to mid-week screening
colonoscopy. Digestive Diseases and Sciences, 56(7), 2114-2119.
https://doi.org/10.1007/s10620-010-1545-2

Dowling, N. A., & Brown, M. (2010). Commonalities in the psychological factors associated
with problem gambling and Internet dependence. Cyberpsychology, Behavior and Social
Networking, 13, 437-441.

Elhai, J. D., Dvorak, R. D., Levine, J. C., & Hall, B. J. (2017). Problematic smartphone use: A
conceptual overview and systematic review of relations with anxiety and depression

This content downloaded from
��198.91.36.79 on Mon, 06 Jul 2020 13:25:38 UTC��
All use subject to https://about.jstor.org/terms

psychopathology. Journal of Affective Disorders, 207, 251-259.
https://doi.org/10.1016/j.jad.2016.08.030

Fattore, L., Melis, M., Fadda, P., & Fratta, W. (2014). Sex differences in addictive disorders.
Frontiers in Neuroendocrinology, 35(3), 272-284.
https://doi.org/10.1016/j.yfrne.2014.04.003

Fox, S., & Duggan, M. (2012). Pew Research Center’s Internet & American Life Project.
Retrieved from http://www.pewinternet.org/files/old-media//Files/Reports/2012/
PIP_MobileHealth2012_FINAL

Gámez-Guadix, M., Orue, I., Smith, P. K., & Calvete, E. (2013). Longitudinal and reciprocal
relations of cyberbullying with depression, substance use, and problematic internet use
among adolescents. Journal of Adolescent Health, 53(4), 446-452.
https://doi.org/10.1016/j.jadohealth.2013.03.030

Gentile, D. A., Choo, H., Liau, A., Sim, T., Li, D., Fung, D., & Khoo, A. (2011). Pathological
video game use among youths: A two-year longitudinal study. Pediatrics, 127(2), e319-
e329. https://doi.org/10.1542/peds.2010-1353

Gloria, C. T., & Steinhardt, M. A. (2016). Relationships among positive emotions, coping,
resilience and mental health. Stress and Health, 32(2), 145-156.
https://doi.org/10.1002/smi.2589

Han, P., Wang, P., Lin, Q., Tian, Y., Gao, F., & Chen, Y. (2017). Reciprocal relationship between
internet addiction and network-related maladaptive cognition among Chinese college
freshmen: A longitudinal cross-lagged analysis. Frontiers in Psychology, 8.
https://doi.org/10.3389/fpsyg.2017.01047

Heo, J., Oh, J., Subramanian, S. V., Kim, Y., & Kawachi, I. (2014). Addictive internet use among
Korean adolescents: A national survey. PloS One, 9(2), e87819.
https://doi.org/10.1371/journal.pone.0087819

Ho, R. C., Zhang, M. W. B., Tsang, T. Y., Toh, A. H., Pan, F., Lu, Y., … Mak, K.-K. (2014).
The association between internet addiction and psychiatric co-morbidity: A meta-analysis.
BMC Psychiatry, 14, 183. https://doi.org/10.1186/1471-244X-14-183

Holden, K. B., Bradford, L. D., Hall, S. P., & Belton, A. S. (2013). Prevalence and correlates of
depressive symptoms and resiliency among African American women in a community-
based primary health care center. Journal of Health Care for the Poor and Underserved,
24(4 Suppl.), 79-93. https://doi.org/10.1353/hpu.2014.0012

Jang, J. N., & Choi, Y. H. (2012). Pathways from family strengths and resilience to internet
addiction in male high school students: mediating effect of stress. Journal of Korean Public
Health Nursing, 26(3), 375-388. https://doi.org/10.5932/JKPHN.2012.26.3.375

Jelenchick, L. A., Becker, T., & Moreno, M. A. (2012). Assessing the psychometric properties
of the Internet Addiction Test (IAT) in US college students. Psychiatry Research, 196(2),
296-301. https://doi.org/10.1016/j.psychres.2011.09.007

Jin, S. W., & Berge, J. (2016). An emerging global concern of Internet addiction: Sociocultural
influences on immigrant families. Global Studies Journal, 9, 15-34.

Jones, S., Johnson-Yale, C., Millermaier, S., & Pérez, F. S. (2009). U.S. college students’
internet use: race, gender and digital divides. Journal of Computer-Mediated
Communication, 14(2), 244–264. https://doi.org/10.1111/j.1083-6101.2009.01439.x

Jung, Y.-E., Min, J.-A., Shin, A. Y., Han, S. Y., Lee, K.-U., Kim, T.-S., . . Positiveness Research
Team of Korea. (2012). The Korean version of the Connor-Davidson Resilience Scale: An
extended validation. Stress and Health: Journal of the International Society for the
Investigation of Stress, 28(4), 319-326. https://doi.org/10.1002/smi.1436

Kaess, M., Parzer, P., Brunner, R., Koenig, J., Durkee, T., Carli, V., . . Wasserman, D. (2016).
Pathological internet use is on the rise among European adolescents. The Journal of
Adolescent Health: Official Publication of the Society for Adolescent Medicine, 59(2), 236-
239. https://doi.org/10.1016/j.jadohealth.2016.04.009

This content downloaded from
��198.91.36.79 on Mon, 06 Jul 2020 13:25:38 UTC��
All use subject to https://about.jstor.org/terms

126 © The Journal of Negro Education, 2019, Vol. 88, No. 2© The Journal of Negro Education, 2019, Vol. 88, No. 2 126

Kardefelt-Winther, D. (2017). Conceptualizing internet use disorders: Addiction or coping
process? Psychiatry and Clinical Neurosciences, 71(7), 459-466.
https://doi.org/10.1111/pcn.12413

Kerkhof, P., Finkenauer, C., & Muusses, L. (2011). Relational consequences of compulsive
internet use: A longitudinal study among newlyweds. Human Communication Research, 37,
147-173.

Kim, J. H., Lau, C. H., Cheuk, K.-K., Kan, P., Hui, H. L. C., & Griffiths, S. M. (2010). Brief
report: Predictors of heavy internet use and associations with health promoting and health
risk behaviors among Hong Kong university students. Journal of Adolescence, 33(1), 215-
220.

Kim, S. M., Huh, H. J., Cho, H., Kwon, M., Choi, J. H., Ahn, H. J.,. . . Kim, D. J. (2014). The
effect of depression, impulsivity, and resilience on smartphone addiction in university
students. Journal of Korean Neuropsychiatric Association, 53(4), 214-220.
https://doi.org/10.4306/jknpa.2014.53.4.214

Kim, S. Y., Kim, M.-S., Park, B., Kim, J.-H., & Choi, H. G. (2017). The associations between
internet use time and school performance among Korean adolescents differ according to the
purpose of internet use. PLOS ONE, 12(4), e0174878.
https://doi.org/10.1371/journal.pone.0174878

Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker.
Teaching and Teacher Education, 67(Supplement C), 135-142.
https://doi.org/10.1016/j.tate.2017.06.001

Kuss, D. J., Griffiths, M. D., & Binder, J. F. (2013). Internet addiction in students: Prevalence
and risk factors. Computers in Human Behavior, 29, 959-966.

Kuss, D. J., Griffiths, M., Karila, L., & Billieux, J. (2014). Internet addiction: A systematic
review of epidemiological research for the last decade. Current Pharmaceutical Design, 20,
4026-4052.

Kuss, D. J., & Lopez-Fernandez, O. (2016). Internet addiction and problematic internet use: A
systematic review of clinical research. World Journal of Psychiatry, 6(1), 143-176.
https://doi.org/10.5498/wjp.v6.i1.143

Kuss, D. J., van Rooij, A. J., Shorter, G. W., Griffiths, M. D., & van de Mheen, D. (2013).
Internet addiction in adolescents: Prevalence and risk factors. Computers in Human
Behavior, 29(5), 1987–1996. https://doi.org/10.1016/j.chb.2013.04.002

Kwon, J. H. (2011). Toward the prevention of adolescent internet addiction. In Young, K. S.,
&de Abreu, C. N. (Ed.), Internet addiction: A handbook and guide to evaluation and
treatment (Vol. 1, pp. 223-243). Hoboken, NJ: Wiley.

Lachmann, B., Sariyska, R., Kannen, C., Cooper, A., & Montag, C. (2016). Life satisfaction and
problematic internet use: Evidence for gender specific effects. Psychiatry Research, 238,
363-367. https://doi.org/10.1016/j.psychres.2016.02.017

Lam, L. T. (2014). Internet gaming addiction, problematic use of the Internet, and sleep
problems: A systematic review. Current Psychiatry Reports, 16, 444-453.

Lee, C., & Coughlin, J. F. (2015). PERSPECTIVE: Older adults’ adoption of technology: An
integrated approach to identifying determinants and barriers. Journal of Product Innovation
Management, 32(5), 747-759. https://doi.org/10.1111/jpim.12176

Li, W., Garland, E. L., & Howard, M. O. (2014). Family factors in Internet addiction among
Chinese youth: A review of English- and Chinese-language studies. Computers in Human
Behavior, 31, 393-411. https://doi.org/10.1016/j.chb.2013.11.004

Li, W., O’Brien, J. E., Snyder, S. M., & Howard, M. O. (2015). Characteristics of internet
addiction/pathological Internet use in U.S. university students: A qualitative-method
investigation. PLoS ONE, 10, 1-19.

Li, Z., Shi, M., Wang, Z., Shi, K., Yang, R., & Yang, C. (2010). Resilience as a predictor of
internet addiction: The mediation effects of perceived class climate and alienation. In 2010
IEEE 2nd Symposium (pp. 66-70). Retrieved from http://www.researchgate.net/

This content downloaded from
��198.91.36.79 on Mon, 06 Jul 2020 13:25:38 UTC��
All use subject to https://about.jstor.org/terms

publication/251963176_Resilience_as_a_predictor_of_internet_addiction_The_mediation
_effects_of_perceived_class_climate_and_alienation

Lin, M.-P., Ko, H.-C., & Wu, J. Y.-W. (2011). Prevalence and psychosocial risk factors
associated with internet addiction in a nationally representative sample of college students
in Taiwan. Cyberpsychology, Behavior and Social Networking, 14(12), 741-746.
https://doi.org/10.1089/cyber.2010.0574

Lin, L. yi, Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., … Primack, B. A.
(2016). Association between social media use and depression among U.S. young adults.
Depression and Anxiety, 33(4), 323-331. https://doi.org/10.1002/da.22466

Mesch, G. S. (2006). Family relations and the Internet: Exploring a family boundaries approach.
Journal of Family Communication, 6(2), 119-138.
https://doi.org/10.1207/s15327698jfc0602_2

Moreno, M. A., Jelenchick, L. A., & Breland, D. J. (2015). Exploring depression and problematic
internet use among college females: A multisite study. Computers in Human Behavior, 49,
601-607.

Morrison, C. M., & Gore, H. (2010). The relationship between excessive internet use and
depression: A questionnaire-based study of 1,319 young people and adults.
Psychopathology, 43, 12-126.

Nie, N., & E., L. (2002). Internet and society: A preliminary report. IT & Society, 1, 275-283.
Orsal, O., Orsal, O., Unsal, A., & Ozalp, S. S. (2013). Evaluation of Internet addiction and
depression among university students. Procedia-Social and Behavioral Sciences, 82, 445-
454. https://doi.org/10.1016/j.sbspro.2013.06.291

Park, D., & Villar, M. (2015). Comparing frequency of TV and Internet use among African-
American students and their effects on material values and sociability. Online Journal of
Communication and Media Technologies, 5, 21-40.

Parker, P. D., Lüdtke, O., Trautwein, U., & Roberts, B. W. (2012). Personality and relationship
quality during the transition from high school to early adulthood. Journal of Personality,
80, 1061-1089.

Pew Research Center. (2017, January 12). Mobile fact sheet. Retrieved from
http://www.pewinternet.org/fact-sheet/mobile/

Pontes, H. M., Kuss, D. J., & Griffiths, M. D. (2015). Clinical psychology of Internet addiction:
A review of its conceptualization, prevalence, neuronal processes, and implications for
treatment. Neuroscience & Neuroeconomics, 4(10.2147/NAN.S60982), 11-23.

Radloff, L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general
population. Applied Psychological Measurement, 1, 385-401.

Russo, S. J., Murrough, J. W., Han, M.-H., Charney, D. S., & Nestler, E. J. (2012). Neurobiology
of resilience. Nature Neuroscience, 15(11), 1475-1484. https://doi.org/10.1038/nn.3234

Santos, O. C., Boticario, J. G., & Pérez-Marín, D. (2014). Extending web-based educational
systems with personalized support through user centered designed recommendations along
the e-learning life cycle. Science of Computer Programming, 88, 92-109.
https://doi.org/10.1016/j.scico.2013.12.004

Shaw, M., & Black, D. W. (2008). Internet addiction: Definition, assessment, epidemiology and
clinical management. CNS Drugs, 22, 353-365.

Shek, P. D. T. L., Sun, D. R. C. F., & Yu, D. L. (2013). Internet addiction. In Pfaff, D. W. (Ed.),
Neuroscience in the 21st century (pp. 2775-2811). New York: Springer. Retrieved from
http://link.springer.com/referenceworkentry/10.1007/978-1-4614-1997-6_108

Smith, A. (2015, April 1). U.S. smartphone use in 2015. Retrieved from
http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/

Smith, A., Rainie, L., & Zickuhr, K. (2011). College students and technology. Retrieved from
http://www.pewinternet.org/2011/07/19/college-students-and-technology/

This content downloaded from
��198.91.36.79 on Mon, 06 Jul 2020 13:25:38 UTC��
All use subject to https://about.jstor.org/terms

Smith, B., Dalen, J., Wiggins, K., Tooley, E., Christopher, P., & Bernard, J. (2008). The brief
resilience scale: Assessing the ability to bounce back. International Journal of Behavioral
Medicine, 15, 194-200.

Spies, G., & Seedat, S. (2014). Depression and resilience in women with HIV and early life
stress: Does trauma play a mediating role? A cross-sectional study. BMJ Open, 4(2),
e004200. https://doi.org/10.1136/bmjopen-2013-004200

Tang, J., Yu, Y., Du, Y., Ma, Y., Zhang, D., & Wang, J. (2014). Prevalence of internet addiction
and its association with stressful life events and psychological symptoms among adolescent
internet users. Addictive Behaviors, 39, 744-747.

Thorsteinsson, E., & Davey, L. (2014). Adolescents’ compulsive internet use and depression: A
longitudinal study. Open Journal of Depression, 03(01), 13-17.
https://doi.org/10.4236/ojd.2014.31005

Tokunaga, R. (2014). A unique problem or the manifestation of a preexisting disorder?: The
mediating role of problematic internet use in the relationships between psychosocial
problems and functional impairment. Communication Research, 41, 531-560.

Tokunaga, R. S., & Rains, S. A. (2010). An evaluation of two characterizations of the
relationships between problematic internet use, time spent using the Internet, and
psychosocial problems. Human Communication Research, 36, 512-545.

Torres, E. (2011). Understanding Internet addiction: Depression and social support. Alliant
International University, San Francisco Bay. Retrieved from
http://gradworks.umi.com/34/91/3491616.html

Vondráčková, P., & Gabrhelík, R. (2016). Prevention of Internet addiction: A systematic review.
Journal of Behavioral Addictions, 5(4), 568-579. https://doi.org/10.1556/2006.5.2016.085

Vorderer, P., Klimmt, C., & Ritterfeld, U. (2004). Enjoyment: At the heart of media
entertainment. Communication Theory, 14(4), 388-408. https://doi.org/10.1111/j.1468-
2885.2004.tb00321.x

Wang, S. S. (2014). Farming not alone: Farmville play and the implications on social capital.
Social Networking, 3(5), 230-239. https://doi.org/10.4236/sn.2014.35028

Wang, Y., Wu, A. M. S., & Lau, J. T. F. (2016). The health belief model and number of peers
with internet addiction as inter-related factors of internet addiction among secondary school
students in Hong Kong. BMC Public Health, 16(1), 272-285.
https://doi.org/10.1186/s12889-016-2947-7

Wisniewski, P., Jia, H., Wang, N., Zheng, S., Xu, H., Rosson, M. B., & Carroll, J. M. (2015).
Resilience mitigates the negative effects of adolescent internet addiction and online risk
exposure. In Proceedings of the 33rd annual ACM Conference on Human Factors in
Computing Systems (pp. 4029-4038). New York: ACM.
https://doi.org/10.1145/2702123.2702240

Wu, C.-Y., Lee, M.-B., Liao, S.-C., & Chang, L.-R. (2015). Risk factors of internet addiction
among internet users: An online questionnaire survey. PLoS ONE, 10(10), e0137506.
doi:10.1371/journal.pone.0137506.

Yao, B., Han, W., Zeng, L., & Guo, X. (2013). Freshman year mental health symptoms and level
of adaptation as predictors of Internet addiction: A retrospective nested case-control study
of male Chinese college students. Psychiatry Research, 210(2), 541-547.
https://doi.org/10.1016/j.psychres.2013.07.023

Young, K. (1996). Addictive use of the Internet: A case that breaks the stereotype—Psychology
of computer use: XL. Psychological Reports, 79, 899-902.

Young, K. (2010). Internet addiction: A handbook and guide to evaluation and treatment.
Hoboken, NJ: Wiley.

Young, K. (2015). The evolution of internet addiction disorder. In Montag, C., & Reuter, M.
(Eds.), Internet addiction (pp. 3-17). New York: Springer International.

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

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Yu, L., & Shek, D. T. L. (2013). Internet addiction in Hong Kong adolescents: A three-year
longitudinal study. Journal of Pediatric and Adolescent Gynecology, 26(3), S10-S17.
https://doi.org/10.1016/j.jpag.2013.03.010

AUTHORS
SEOK WON JIN is Assistant Professor, School of Social Work, at The University of Memphis,
Memphis, Tennessee.
TIFFANIE-VICTORIA JONES is employed at Southern University at New Orleans.
YEONGGEUL LEE is Part-time Lecturer in the Department of Social Welfare, University of
Seoul, Seoul, Korea.

All comments and queries regarding this article should be addressed to sjin1@memphis.edu

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