557034 research-article2014

JBSXXX10.1177/0021934714557034Journal of Black StudiesLee

Article

Too Much Information: Heavy Smartphone and Facebook Utilization by African American Young Adults

Journal of Black Studies 2015, Vol. 46(1) 44­–61 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0021934714557034 jbs.sagepub.com

E. Bun Lee1

Abstract This article quantifies the heavy use of smartphone and Facebook among African American college students. It examines the major predictors of smartphone and Facebook overuse, including demographic and personality traits. It further explores the effect of heavy utilization of smartphone and Facebook on the academic performance of African American college students. Younger and female users spent significantly more time on their smartphones. However, excessive Facebook use was not related to gender of our participants. In terms of the prevalence rate, about 11% of the sample showed a high level of smartphone addiction and 10% scored a high level of Facebook addiction. Among personality and psychological traits, social interaction anxiety was the most important predictor of heavy utilization of smartphone and Facebook, not extroversion, agreeableness, neuroticism, or conscientiousness. As expected, multitasking was significantly and positively correlated with excessive smartphone and Facebook use. Surprisingly, multitasking behavior and frequent checking of smartphones did not harm academic performance. Possible reasons for the absence of negative effects on grade point average (GPA) are discussed.

1Texas

Southern University, Houston, USA

Corresponding Author: E. Bun Lee, School of Communication, Texas Southern University, Houston, TX 77004, USA. Email: [email protected]

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Keywords smartphone addiction, Facebook addiction, multitasking, GPA, African American college students

Introduction Mobile phones with the Internet access and multimedia capabilities, also known as “smartphones,” have become ubiquitous. About 168 million people in the United States own smartphones in 2014 and spend over 30 hours each month using an average of 27 applications (“apps”) on their smartphones (comScore.com, 2014; Nielsen.com, 2014b). Among the most frequently used apps are the ones used for social networking. An increasing amount of research has been conducted in the past several years to examine the potential negative effects of heavy smartphone use and concurrent use of social networking apps on the cognitive, social, and mental health of smartphone owners, particularly adolescents and young adults (Andreassen, Torsheim, Brunborg, & Pallesen, 2012; Billieux, 2012; Billieux, Philippot, Schmid, Maurage, & De Mol, 2014; Cheever, Rosen, Carrier, & Chavez, 2014; Griffiths, Kuss, & Demetrovics, 2014; Hong, Chiu, & Huang, 2012; Hong, Huang, Lin, & Chiu, 2014; Kwon, Kim, Cho, & Yang, 2013; Lepp, Barkley, & Karpinski, 2014; Lopez-Fernandez, Honrubia-Serrano, Freixa-Blanxart, & Gibson, 2014; Mok, Choi, Kim, Choi, & Song, 2014; Rosen, Whaling, Rab, Carrier, & Cheever, 2013). These studies have demonstrated that all-day smartphone use by these age groups decrease the time and quality of face-to-face communication and is a distraction that could cause poor academic outcome for some users. However, very little research has focused on African American and Hispanic young adults, despite the recent statistics, which show that they are more likely to possess smartphones than any other demographic group and to spend more time using them to watch photos/videos and access social networking apps (Duggan, 2013; Smith, 2014). A Nielsen study found that 71% of African Americans owned smartphones, outpacing the general population in 2013. They also installed such apps as PayPal for more practical purposes and BibleByLifeChurch.tv for maintaining faith-based connections. Top mobile apps preferred by African American smartphone owners included Instagram, Facebook, Twitter, and Pandora Radio (Nielsen.com, 2013). Smith (2014) found that 85% of African American smartphone owners use text messaging, 72% access the Internet, and 61% listen to music. Latest report by Nielsen found that digitally adapt African Americans use social media as a place to voice opinion

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and join conversations on a range of topics. About 49% of African Americans say they “like” Facebook posts from musical artists and 37% share music through Facebook, Twitter, e-mail, or other platform (Nielsen. com, 2014a). The current study attempts to fill this gap in the research beyond these statistics. It explores the relationship between personality and demographic predictors of smartphone and Facebook addictions among African American young adults. It further explores the effects of these addictions on the academic performance of African American college students.

Literature Review Theoretical Framework Media system dependency (MSD) theory and the uses and gratifications approach offer good understanding of why young adults are heavy users of smartphones and social media. MSD theory examines how audiences, media, and society depend on one another to accomplish their respective goals (BallRokeach & DeFleur, 1976). According to this theory, individual media dependency (IMD) develops the level of involvement depending on the goals of the individual. On the other hand, the uses and gratifications approach assumes that users actively choose media stimuli in order to fulfill specific needs and motives (Katz, Blumer, & Gurevitch, 1974). In their study, Leung and Wei (2000) found two categories of reasons of cell phone use: instrumental and intrinsic factors. The instrumental factors include mobility, immediacy, and instrumentality. The intrinsic factors contain affection and sociability. In line with this study, Skierkowski and Wood (2012) found that texting behavior that is particularly appealing to young people is due to its convenience, speed, and autonomy from parental supervision. A more recent study by Kang and Jung (2014) summarized that major motivations for smartphone use are information, entertainment seeking, relationship development, security, and relaxation. A number of studies on the popularity of social media use, according to Masur, Reinecke, Ziegele, and Quiring (2014), adopted the uses and gratifications approach in order to understand why people use social networks. They have identified a number of basic motive dimensions for using social networks: entertainment, social information seeking, self-presentation, escapism, and meeting new people. Masur et al. suggest that people return to use Facebook to satisfy the same needs again and again. This continuing gratification seeking might slowly turn into compulsive and habitual usage.

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Using MSD and uses and gratifications approach, the current study looks into the possibility of smartphone and social media dependencies among African American college students.

Smartphone Addiction: Personality and Demographic Predictors Heavy use of mobile phones has been studied under different terms such as problematic use (Bianchi & Phillips, 2005; Billieux, Van der Linden, & Rochat, 2008; Lopez-Fernandezet al., 2014), dependence (Choliz, 2012; Toda, Monden, Kubo, & Morimoto, 2004), dysfunctional use (Billieux et al., 2014), and addiction (Kwon et al., 2013). According to Billieux et al. (2014), in psychiatric and clinical psychology literature, problematic use of mobile phones has been viewed as a disorder and conceptualized as addictive behavior marked by symptoms of withdrawal, craving, and loss of control. Billieux (2012) explained the diversity of the terms, instruments, and diagnostic markers. Despite the lack of a uniform scale, similarities have been suggested between smartphone addiction and other sorts of addiction, such as drug, alcohol, Internet, online gambling, and more (Takao, Takahashi, & Kitamura, 2009). Personality traits, including the big five factors, have been examined for excessive use of mobile phones in previous research with mixed findings. Extroversion, for example, was positively correlated to problematic use, with extroverted people spending more time calling and texting (Bianchi & Phillips, 2005; Butt & Phllips, 2008; Hong et al., 2012; Igarashi, Motoyoshi, Takai, & Yoshida, 2008). Neuroticism was also significantly related to heavy use of mobile phone (Butt & Phillips, 2008; Igarashi et al., 2008; Mok et al. 2014), although Bianchi and Phillips (2005) did not find significant relationship between neuroticism and problematic mobile phone use. Major negative health effects of smartphone addiction were associated with anxiety (Billieux et al., 2008; Cheever et al., 2014; Hong et al., 2014; Lepp et al., 2014; Lu, Watanabe, Liu, Uji, Shono, & Kitamura, 2011). Cheever and her team (2014) found that heavy smartphone users who were not allowed to use their devices—whether it was removed from their possession or simply placed out of sight—felt significantly more anxious as time passed. Mok et al. (2014) found that anxiety levels and neurotic personality traits increased with severity levels of smartphone addiction. Depression (Billieux et al., 2008; Lu et al. 2011), stress, and sleep disturbances were positively related to the excessive use of the mobile phone (Thomee, Harenstam, & Hagberg, 2011). Social interaction anxiety was positively related to overuse of the mobile phone (Becker, Alzahabi, & Hopwood, 2013; Hong et al., 2012; Y. K. Lee, Chang, Lin, & Cheng, 2014; Pierce, 2009).

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Demographic variables such as age and gender are related to problematic mobile phone use, with female and younger students exhibiting higher levels of smartphone addiction (Kwon et al., 2013).

Facebook Addiction: Personality and Demographic Predictors Griffiths, Kuss, and Demetrovics (2014) reviewed several scales designed to measure Facebook addiction. According to them, the Bergen Facebook Addiction Scale (“BFAS”) developed by Andreassen, Torsheim, Brunbug, and Pallesen (2012) is regarded as the most psychometrically robust scale, and it is based on the components model of addiction that has been used to develop other psychometrically valid scales to assess behavioral addictions. Personality traits were correlated with Facebook addiction in the previous studies with inconsistent results. High extroversion was correlated to predict the time spent on Facebook and on the number of friends (Andreassen et al., 2012; Ryan & Xenos, 2011). However, other researchers found no significant relationship (Hong et al., 2014; Hughes, Rowe, Batey, & Lee, 2012; Wilson, Fornasier, & White, 2010). Other personality variables such as narcissism (Ljepava, Orr, Locke, & Ross, 2013), self-esteem (Kalpidou, Osten, & Morris, 2011; E. B. Lee, 2012), and loneliness (Bian & Leung, 2014; Y. K. Lee et al., 2014) have been examined. Ross and his research team (2009) concluded that personality factors were not influential as previous research suggested. Detrimental effects of Facebook addiction involved poor sleep (Andreassen et al., 2012; Koc & Gulyagci, 2013; Rosen et al. 2013) and depression (Hong et al., 2014). In a number of studies, social anxiety was also positively related to heavy Facebook use (Y. K. Lee et al., 2014; McCord, Rodebaugh, & Levinson, 2014; Murphy & Tasker, 2011). Studies regarding gender and age differences on Facebook use have suggested different functions served by the social networks. Griffiths et al. (2014) argue that “further research is needed to examine gender differences because there appears to be a higher prevalence of problems among females as opposed to other problematic online behaviors such as gaming addiction, which is more prevalent among males” (p. 138).

Effects of Smartphone, Facebook, and Multitasking on Grade Point Average (GPA) Most young adults use smartphones for a variety of multitasking including texting, checking social networks, listening to music, web surfing, and playing games. According to Judd (2014), Facebook plays an active role in the initiation and promulgation of multitasking behaviors. Other researchers

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found similar activities among college students (Calderwood, Ackerman, & Conklin, 2014; David, Kim, Brickman, Ran, & Curtis, 2014; Grinols & Rajesh, 2014; Mihailidis, 2014; Salehan & Negahban, 2013). The effects of multitasking on academic performance were examined in numerous studies (Becker et al., 2013; Junco & Cotten, 2012; Karpinski, Kirschner, Ozer, Mellott, & Ochwo, 2013; Lepp et al., 2014; Wang & Tchernev, 2012). Most of these studies suggested that multitasking on smartphones are negatively related to academic performance. However, not everyone agrees on the negative effects. David and his colleagues (2014) concluded that previous studies fail to account for emotional, entertainment, or social benefits of multitasking. Lui and Wong (2012) pointed out that a higher degree of multitasking was correlated with better multisensory integration, and heavy multitaskers are not deficient in all kinds of cognitive tasks. Clayson and Haley (2012) as well as Rouis (2012) found no significant relationship between multitasking and GPA. Grinols and Rajesh (2014) argue that productivity may actually increase with multitasking, depending on the nature of tasks themselves. Paucity of research focused on the heavy use of smartphones and Facebook among African American young adults leads to the following research questions for the current study. Research Question 1: What are the significant predictors of smartphone addiction among African American college students? Research Question 2: What are the significant predictors of Facebook addiction among African American college students? Research Question 3: Does multitasking on smartphones affect academic performance of African American college students?

Method Participants A total of 304 African American college students at one of the largest Historically Black College and Universities (HBCUs) in Texas participated in the survey in the spring semester of 2014. The average age of the sample was 22.45 with a range of 17 to 55 (SD = 6.1). Nearly 91% of the sample were between the ages of 17 and 30. The sample consisted of 44% males and 56% females. In terms of academic classification, 38% were freshmen, 30% were sophomores, 21% were juniors, 9% were seniors, and the rest were graduate students. Four students who did not identify themselves as African American were excluded.

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Measures The survey contained demographic information and questions on the ownership of smartphones, monthly charges, number of talking minutes a month, and number of text messages sent and received a month. It also included items on the daily study time outside the classroom and GPA on a 6-point scale with 1 = 1.5 or below, 2 = 1.6 to 2.0, 3 = 2.1 to 2.5, 4 = 2.6 to 3.0, 5 = 3.1 to 3.5, and 6 = 3.6 to 4.0. Smartphone Addiction Scale (SAS-Short Version). In order to avoid a lengthy questionnaire, a short version of the SAS was adopted for the current study. The original SAS by Kwon et al. (2013) contained 33 items grouped into six subscales (daily-life disturbance, positive anticipation, withdrawal, overuse, tolerance, and cyberspace-oriented relationship). The short form of the scale (“SAS-SV”) contains 10 items on a 5-point Likert-type scale, and the total score indicates the severity of smartphone addiction for the current study. According to Kwon’s team, SAS was not designed to diagnose a pathological smartphone addiction, but more to identify the level of the smartphone addiction risk and to distinguish a high-risk group. Facebook Addiction Scale.  The BFAS developed by Andreassen et al. (2012) contained 18 items, made up of three groups reflecting each of the six factors of addiction (salience, mood modification, tolerance, withdrawal, conflict, and relapse) on a 5-point scale with 1 = very rarely to 5 = very often. For the present investigation, six items from each factor with highest item-scale correlation were adopted in the current study (“BFAS-SV”). Personality traits.  In order to measure the personality traits, the Mini-International Personality Item Pool (IPIP) was utilized. This 20-item inventory was developed by Goldberg (1999) as part of the IPIP and had acceptable reliability, according to a study by Donnelland, Oswala, Baird, and Lucas (2006). Four items each measuring extroversion, agreeableness, neuroticism, and conscientiousness were included in the questionnaire. In place of openness, the present study adopted four items of social interaction anxiety from Social Interaction Anxiety Scale (“SIAS”) developed by Mattick and Clarke (1998). Social interaction anxiety is defined as “anxiety and fear at the prospect of being observed or watched by other people, and in particular, where individual expresses distress when undertaking certain activities in the presence of others” (p. 457). This psychological trait measures fear and shyness during situations where one engages in conversations with other people. These personality and psychological traits were measured on a 5-point scale with 1 = very inaccurate to 5 = very accurate.

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Lee Table 1.  Correlations Among Predictor Variables of the Smartphone Addiction Scale-SV (n = 276). Predictor 1. Age 2. Gender 3. Extroversion 4. Agreeableness 5. Neuroticism 6. Conscientiousness 7. Social Anxiety 8. Facebook Addiction 9. Multitasking

M

SD

22.45 1.56 12.49 14.87 11.04 14.65 10.25 9.92 10.8

6.09 0.49 3.69 2.78 2.72 2.97 3.41 4.76 4.35

1

2

3

4

5

6

7

8

—   −.066 —   .001 .028 —   .112* .202** .305** —   −.058 .231** −.057 −.097 —   .102* .021 .086 .209** −.178** —   −.094 .119* −.422** −.15** .279** −.285** —   .096 −.074 −.097 −.072 .088 −.169** .253** — −.207** .063 .084 .059 .022 −.142* .219** .320**

*p < .05. **p < .01.

Multitasking Index.  Finally, four items measuring multitasking behaviors were also included. They asked participants whether they use Facebook/Twitter during classes, text during classes, leave Facebook/Twitter on while studying, and whether their mind distracted from lecture in classes as a result of multitasking.

Procedures for Data Collection and Analysis The researcher and her graduate assistant collected the data with the cooperation of instructors in different areas of the university. All statistical analyses were performed with SPSS 21.0.

Results Research Question 1: What are the significant predictors of the smartphone addiction among African American College Students? Nearly 97% of the participants owned a smartphone. The average talking minutes was 574 minutes a month, and the median number of text messages came about 1,000 a month. Half of the sample indicated texting as the most important reason for using their smartphones, 23.1% for talking, 9.1% for the Internet, 4.8% for music, 4.8% for the social networks, and the rest involved games, chat, and news. Table 1 presents the correlations among predictor variables. Hierarchical multiple regression was performed to assess the effects of personality traits, age, gender, Facebook addiction, and multitasking on the smartphone addiction.

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Table 2.  Hierarchical Regression Analysis for Predictors of the Smartphone Addiction Scale-SV (n = 276). Model 1 2

Predictor

β

t

p

R2

ΔR2

F

Age Gender Age Gender Extroversion Agreeableness Neuroticism Conscientiousness Social Anxiety Facebook Addiction Multitasking

−.153 .137 −.064 .091 .071 −.039 .05 −.056 .308 .114 .297

−2.45 2.196 −1.14 1.617 1.139 −0.670 0.873 −0.993 4.687 1.944 4.974

.015 .029 .255 .107 .256 .504 .384 .322 .000 .053 .000

.045

.037

.335

.31

 5.791   14.881                

Note: Gender was coded Male = 1, Female = 2. *p < .05. **p < .01.

For Step 1, age and gender were entered, and this model was significant, R2 = .45, F(2, 246) = 5.791, p < .01. For Step 2, nine predictors were included, and this model was also significant, R2 = .335, F(7, 239) = 14.881, p < .001. About 33.5% of the variance in the smartphone addiction was explained by the combination of demographics, Facebook addiction, and multitasking behaviors. As presented in Table 2, social interaction anxiety, Facebook addiction, and multitasking were significantly related to smartphone addiction. Mean score for male group was 22.29 (SD = 8.12), for female group 24.75 (SD = 8.24), and for both 23.71 (SD = 8.27). The gender difference was statistically significant (t = 2.47, p < .05), with female students showing a higher level of smartphone addiction. Younger students had higher level of smartphone addiction (r = −.162, p < .01) than the older students. In the current study, the cutoff score of the SAS-SV was tentatively set at the 90th percentile score. Among 276 participants, 31 students (11.23%) had 90th percentile score of 35 or higher. The item-scale correlations, means, and standard deviations of the 10-item scale are presented in Appendix A. Cronbach’s alpha for the SAS-SV was .83. Research Question 2: What are the significant predictors of the Facebook addiction among African American college students?

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Table 3.  Hierarchical Regression Analysis for Predictors of the Bergen Facebook Addiction Scale-SV (n = 269). Model 1 2

Predictor

β

t

p

R2

ΔR2

F

Age Gender Age Gender Extroversion Agreeableness Neuroticism Conscientiousness Social Anxiety Multitasking

.91 −.067 .186 −.103 −.036 −.035 .053 −.076 .155 .324

1.441 −1.063 3.088 −1.662 −E25.53 −0.549 0.838 −1.214 2.167 5.201

.151 .289 .002 .098 .596 .584 .403 .226 .031 .000

.014

.006

.192

.165

1.712   8.798              

Note: Gender was coded Male = 1, Female = 2. *p < . 05. **p < .01.

About 89.7% of the sample had a Facebook account. Nearly 62.8% accessed the account through a smartphone, 9.6% through a personal computer, and 27.6% through both a smartphone and personal computer. Hierarchical multiple regression was performed to identify the significant predictor variables of Facebook addiction. For Step 1, age and gender were entered, and the model was not significant, R2 = .014, F(2, 246) = 1.712, p = .183. For Step 2, the analysis included eight predictor variables. This model was statistically significant, R2 = .192, F(6, 240), p < .01. About 19% of the variance in Facebook addiction score was explained by eight predictors. Table 3 presents the coefficients of these predictors. Age, social interaction anxiety, and multitasking were significantly related to Facebook addiction. None of the personality traits was significantly related to Facebook addiction score for this particular group of college students. There was no significant gender difference on Facebook addiction score, with the mean score for males at 10.00 and for females at 9.63. As shown in Table 3, age was one of the significant predictors of Facebook addiction with older students showing higher level of Facebook use. The cutoff score of the scale (90th percentile score) was 17, and 27 students (10.04%) out of 269 scored 17 or higher. As expected, smartphone addiction and Facebook addiction scores were significantly and positively correlated (r = .284, p < .01). The item-scale correlations, means, and standard deviations of the BFAS-SV are summarized in Appendix B. Cronbach’s alpha for BFAS-SV was .89.

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Research Question 3: Does multitasking on the smartphone affect academic performance of African American college students? About 70% of the respondents indicated that they text, and 49% use Facebook or Twitter during classes. About 57% of the respondents agreed or strongly agreed that they are distracted during lecture in classes due to multitasking. The correlation between multitasking and smartphone addiction scores were significantly related (r = .432, p < .01). The correlation between multitasking and Facebook addiction scores were also statistically significant (r = .320, p < .01). In order to assess the potential effect of smartphone and Facebook addiction as well as multitasking behavior on GPA for this sample, a standard multiple regression was performed with four predictors. The only significant predictor of GPA was the number of hours of studying outside the classroom. Multitasking and checking Facebook on their smartphones do not seem to negatively affect their GPA.

Discussion The purpose of this study was to explore utilization of smartphones and social networking apps and their effect on the academic performance of an underreported group, African American young adults. The short versions of the SAS and BFAS demonstrated robust internal consistencies in our study. Items from SAS such as “I use cell phone longer than I had intended” and “I won’t be able to stand not having a cell phone” showed higher agreement than items indicating negative physical impact such as “I feel pain in the wrists or back of the neck while using a cell phone.” Our survey participants spent an average of 574 minutes per month talking and exchanged an average of 1,000 text messages on their smartphones. Using the 90th percentile as a cutoff point, 11% of the sample showed a high smartphone addiction score, and 10% exhibited a high Facebook addiction score. Social interaction anxiety was a significant predictor of smartphone and Facebook addiction, which is consistent with previous studies (Becker et al., 2013; Hong et al., 2012; Y. K. Lee et al., 2014; Pierce, 2009). Some researchers have speculated that highly anxious people may feel more comfortable by using texting and social networking apps than face-to-face interactions. However, these non-experimental studies do not indicate causality, that is, whether excessive Facebook use causes greater social anxiety or whether social anxiety leads to excessive smartphone and Facebook use. Our data also strongly suggested that multitasking was significantly intercorrelated with excessive smartphone and Facebook usage. Nevertheless,

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multitasking on smartphones does not seem to harm academic outcomes for this group of college students. This finding contrasts with an earlier study that found a negative relationship between Facebook use and math grades among African American and Hispanic teenagers (E. B. Lee, 2014). One of the surprising findings of the study concerns personality traits. None of the personality traits including extroversion, agreeableness, neuroticism, and conscientiousness was a significant predictor of smartphone and Facebook addiction in the current investigation, corresponding with conclusions of Ross et al. (2009). The brief scales employed (Mini-IPIP) may not have captured full dimension of the traits of this particular sample.

Limitations and Implications for Future Research Although this exploratory study examined an important topic and added new information to the growing body of research, it was carried out with several limitations. First, the convenience sampling used in the study does not allow generalization of the results to general population. Future research should be extended to African American college students in different geographical areas of the country and on different campuses as well as age-matched individuals who are not attending college. The lack of research regarding African American smartphone and social media use makes comparison of the present study to other findings less reliable, a deficiency that will only be solved by additional research. Second, only self-reported data were presented here, so there is potential for bias in both the utilization and academic performance data. Future research may look into the nature of tasks and motivations involving multitasking on smartphones, instead of using GPA as a general dependent variable. Listening to music, for example, while working on homework may provide positive affect, but texting during class could cause distraction. Future investigation may benefit from more detailed objective tracking and logging data using experimental designs. Third, shorter version scales were used to decrease survey time, but it is possible that full-length scales are necessary to determine the full impact of personality traits and psychological variables on utilization. Fourth, future research could examine specific smartphone and social networking activities that young adults are involved in because there is such variability and evolution. Ultimately, greater understanding of who uses smartphones and social network excessively and why they do so may help develop ways to harness the positive aspects of these incredible technologies while minimizing the harmful ones.

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Appendix A Mean, SD, and Item-Total Correlations of the SAS-SV (n = 276).

1. Missing planned work due to cell phone use. 2. Having hard time concentrating in class, while doing assignments, or while working due to cell phone use. 3. Feeling pain in the wrists or the back of the neck while using a cell phone. 4. Won’t be able to stand not having a cell phone. 5. Feeling impatient and fretful when I am not holding my cell phone. 6. Having cell phone in my mind even when I am not using it. 7. I will never give up using cell phone even when my daily life is already greatly affected by it. 8. I am constantly checking my cell phone so as not to miss conversations between other people on Twitter or Facebook. 9. Using my cell phone longer than I had intended. 10. The people around me tell me that I use my cell phone too much.

M

SD

Item-scale correlation

Alpha (if item is deleted)

1.95

1.223

.407

.826

2.34

1.368

.585

.809

1.86

1.144

.26

.838

2.99

1.543

.409

.829

2.32

1.321

.638

.804

2.3

1.259

.685

.8

2.43

1.299

.527

.815

2.48

1.384

.596

.808

3.12

1.362

.561

.812

1.92

1.174

.549

.814

Note. Each item was coded 1 = strongly disagree, 2 = disagree, 3 = unsure, 4 = agree, 5 = strongly agree. SAS = Smartphone Addiction Scale.

Appendix B Mean, SD, and Item-Total Correlations of the BFAS-SV (n = 269).

1. Spent a lot of time thinking about Facebook or planned use of Facebook. 2. Felt an urge to use Facebook more and more. 3. Used Facebook to forget about personal problems.

M

SD

Item-scale correlation

Alpha (if item is deleted)

1.84

1.025

.763

.855

1.75

1.007

.801

.849

1.6

1.012

.728

.861 (continued)

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Appendix B  (continued)

4. Tried to cut down Facebook without success. 5. Become restless or troubled if prohibited from using Facebook. 6. Used Facebook so much that it has had a negative impact on studies.

M

SD

Item-scale correlation

Alpha (if item is deleted)

1.82

1.156

.551

.895

1.42

0.837

.784

.859

1.48

0.904

.645

.875

Note. Each item was coded 1 = very rarely, 2 = rarely, 3 = sometimes, 4 = often, 5 = very often. BFAS = Bergen Facebook Addiction Scale.

Declaration of Conflicting Interests The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article

Funding The author received no financial support for the research, authorship, and/or publication of this article.

References Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110, 501-507. Ball-Rokeach, S. J., & DeFleur, M. L. (1976). A dependency model of mass media effects. Communication Research, 3, 3-21. Becker, M. W., Alzahabi, R., & Hopwood, C. J. (2013). Media multitasking is associated with symptoms of depression and social anxiety. Cyberpsychology, Behavior, and Social Networking, 16, 132-135. Bian, M., & Leung, L. (2014). Linking loneliness, shyness, smartphone addiction symptoms, and patterns of smartphone use to social capital. Social Science Computer Review. Advance online publication. doi:10.1177/ 0894439314528779 Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. CyberPsychology & Behavior, 8, 39-51. Billieux, J. (2012). Problematic use of the mobile phone: A literature review and pathways model. Current Psychiatry Reviews, 8, 299-309. Billieux, J., Philippot, P., Schmid, C., Maurage, P., & De Mol, J. (2014). Is dysfunctional use of the mobile phone a behavioral addiction? Confronting symptombased versus process-based approaches. Clinical Psychology & Psychotherapy. Advance online publication. doi:10.1002/cpp.1920

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Author Biography E. Bun Lee (Ph.D., University of Texas at Austin) is a professor of journalism at Texas Southern University in Houston, TX. Her research interests include new media technology, effects of social media, and environmental journalism.