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

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.
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
© The Journal of Negro Education, 2019, Vol. 88, No. 2 115
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;
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��

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
��198.91.36.79 on Mon, 06 Jul 2020

13:25:38 UTC��
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. Eliminati ng
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
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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.
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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.
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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 i nternet

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
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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/blac k-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
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��

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/[email protected]/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
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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.yfr ne.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.pdf
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
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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/j knpa.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/
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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/
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��

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.

��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��
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 [email protected]
��198.91.36.79 on Mon, 06 Jul 2020
13:25:38 UTC��

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

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

Recommended

Recommended

More Related Content

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

Similar to 114 © The Journal of Negro Education, 2019, Vol. 88, No. 2© Th (20)

More from BenitoSumpter862

More from BenitoSumpter862 (20)

Recently uploaded

Recently uploaded (20)

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