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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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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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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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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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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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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]
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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
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