Effects of social media usage and social media multitasking on the academic performance of university students

Effects of social media usage and social media multitasking on the academic performance of university students

Computers in Human Behavior 68 (2017) 286e291 Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.c...

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Computers in Human Behavior 68 (2017) 286e291

Contents lists available at ScienceDirect

Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Full length article

Effects of social media usage and social media multitasking on the academic performance of university students Wilfred W.F. Lau Department of Curriculum and Instruction, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 August 2016 Received in revised form 5 November 2016 Accepted 22 November 2016

In today's society, social media have become an almost indispensable part of daily life, particularly among university students, who are generally heavy social media users. Social media multitasking has also been increasingly prevalent. Little, however, is known about how social media usage and social media multitasking influence the academic performance of university students. This study examined whether and how these two behaviors predict academic performance among university students. From a sample of 348 undergraduate students at a comprehensive university in Hong Kong, this study found that using social media for academic purposes was not a significant predictor of academic performance as measured by cumulative grade point average, whereas using social media for nonacademic purposes (video gaming in particular) and social media multitasking significantly negatively predicted academic performance. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Social media usage Social media multitasking Academic performance University students

1. Introduction Social media have penetrated the lives of many young adults. The social media usage of American adults aged 18e29 years soared from 12% in 2005 to 90% in 2015 (Pew Research Center, 2015). In education, social media can be used to share information with students, collect information when overseas or while conducting research, share personal academic interests with other people, engage students and understand what they think about during instruction, form student study groups, and enhance e-textbook functions by connecting students with social tools for collaborative purposes (O'Brien, 2012). Social media also develop students' capacity to create and arouse their interests in academic subjects (Lau, Lui, & Chu, 2016), and students more easily communicate with working professionals through social media. Cox and McLeod (2014) found that social media foster communication among teachers, students, parents, and community members, and help create online professional learning communities. In university, students and faculty members have increasingly adopted various social media tools such as Facebook and Twitter to promote teaching and learning both inside and outside the classroom. Empirical studies have shown the following educational benefits associated with the use of social media technologies: (a)

E-mail addresses: wwfl[email protected], [email protected], wflau. [email protected] http://dx.doi.org/10.1016/j.chb.2016.11.043 0747-5632/© 2016 Elsevier Ltd. All rights reserved.

enhanced communication between students and instructors, (b) increased opportunities for networking or collaborations among students, (c) rapid sharing of resources, (d) access to course materials by students after class, (e) provision of an alternative platform to the official learning management systems, and (f) exposure of students to technologies and skills that may improve their employment success (Legaree, 2015). Because students are likely to use more than one medium simultaneously, the potential influence of media multitasking behavior has been under scrutiny for years. Regarding cognition, media multitasking was found to be negatively related to cognitive control ability in adolescents (Ophir, Nass, & Wagner, 2009). Thus, it is believed to be predictive of poor academic performance. Today, a majority of social media tools support the integration of multimedia elements, and this functionality makes media multitasking much easier than was previously possible. Researchers and educators alike are interested in the effects of social media on student academic performance, and numerous empirical studies have explored whether such effects are positive, neutral, or negative (Cheston, Flickinger, & Chisolm., 2013; Glogocheski, 2015). Little, however, is known about how social media usage and social media multitasking (SMM) influence the academic performance of university students. Accordingly, this study examined whether and how these two behaviors predict academic performance among university students.

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2. Social media usage and media multitasking Social media come in a variety of forms including social networking sites, microblogs, blogs, chat platforms, open source mapping, and photo and video sharing (Gastelum & Whattam, 2013). In general, social media can be defined as “applications, services, and systems that allow users to create, remix, and share content.” (Junco, 2014, p. 6). Social media usage refers to “the multiplicity of activities individuals may participate in online” (Smith & Gallicano, 2015, p. 83). This description focuses on numerous online activities that people can engage in with social media and is primarily related to the purposes of using social media. Multitasking is typically understood as the engagement in more than one task within a given period of time. Multitasking may take three forms: dual-tasking, rapid attention switching, and continuous partial attention (Wood & Zivcakova, 2015). Dual-tasking refers to the situation in which individuals complete two tasks simultaneously, rapid attention switching refers to a change of focus between tasks, and continuous partial attention entails partial attention to more than one task continuously. Media multitasking involves simultaneous participation in activities, at least one of which must be media related. Media multitasking may occur between different devices or on a single device (Kononova & Chiang, 2015). Regardless of the forms that multitasking behavior may take, studies have suggested that SMM is prevalent, particularly among youth. Voorveld and van der Goot (2013) showed that people in younger age groups (13e16, 17e19, and 20e24 years) spent more time on media multitasking relative to the total media time compared with their counterparts in older age groups (25e29, 30e39, 40e49, and 50e65 years). For the age groups 13e16 and 17e19 years, social media with music or websites was the second most common media multitasking combination. Voorveld, Segijn, Ketelaar, and Smit (2014) found that in Germany, the United States, the United Kingdom, the Netherlands, France, and Spain, the three most common media multitasking combinations were the concurrent use of social media and new media such as e-mail and mobile phones, the concurrent use of TV and new media, and the concurrent use of the Internet and new media. Age also significantly predicted multitasking with new media, meaning that younger people are more likely to multitask with new media. 3. Social media usage and academic performance Regarding the purposes of using social media, Oye, Adam, and Nor Zairah (2012) indicated that academic performance was adversely affected when social networking sites were used to fulfill social and nonacademic needs only. Ravizza, Hambrick, and Fenn (2014) reported that nonacademic Internet use, including social media, among university students was negatively associated with classroom performance as shown in three examinations during the semester and cumulative final examinations. However, some evidence suggests benefits of social media use in learning. For instance, the use of Twitter for academic and cocurricular discussions was found to have a positive effect on grades for college students (Junco, Heiberger, & Loken, 2011). Students who used Twitter showed higher levels of engagement and obtained a higher semester grade point average (GPA) than students who did not. The positive effect could be explained by the extended engagement between students and faculty via Twitter beyond traditional classroom activities. GreGory, GreGory, and Eddy (2014) demonstrated that the adoption of Facebook as an instructional networkdin that case, the creation of a Facebook group specifically for discussing mathematical course content outside of classdcould


significantly increase undergraduate student engagement, satisfaction, and performance in a calculus course. Therefore, the following hypotheses were proposed: H1. Students who use social media more for nonacademic purposes perform less favorably academically. H2. Students who use social media more for academic purposes perform more favorably academically.

4. Media multitasking and academic performance Studies examining the influence of media multitasking on academic performance have reported that media multitasking has a negative effect on three aspects of academic performance, namely, academic outcomes, study related behaviors and attitudes, and perceived academic learning (van der Schuur, Baumgartner, Sumter, & Valkenburg, 2015). The time displacement hypothesis and the limited information processing capacity hypothesis are frequently used to explain why media use during academic work adversely affects academic performance. The time displacement hypothesis posits that because of the appealing nature of most media today, students are likely to devote far more time to any media than to academic activities. Some may even skip class and choose to use media instead. Media tend to distract student attention from their studies and subsequently decrease their performance and efficiency (Walsh, Fielder, Carey, & Carey, 2013). The limited information processing capacity hypothesis proposes that as multiple tasks are performed simultaneously, a cognitive bottleneck develops because of the limits of cognitive capabilities, and this results in an appreciable disruption in the decision-making process. Multitasking performance in multimedia learning environments can often be explained using cognitive load theory or the cognitive theory of multimedia learning. Cognitive load theory focuses on the role of working memory in the learning process (Sweller, 1988). The theory is premised on the following concepts: (a) working memory is limited in capacity, (b) long-term memory has essentially unlimited capacity, (c) the learning process requires working memory to be actively involved in the processing and comprehension of the instructional materials to encode information into long-term memory, and (d) learning is ineffective if working memory is overloaded. Cognitive load refers to the total amount of mental effort demanded on working memory at any particular instance, and the number of elements requiring attention constitutes the major factor influencing cognitive load. There are three types of cognitive load: intrinsic, extraneous, and germane. Intrinsic cognitive load is induced by the inherent complexity and difficulty of the materials being learned. Extraneous cognitive load is caused by the manner in which the instructional materials are designed and presented. Germane cognitive load is created by effortful learning devoted to the processing, construction, and automation of schemas. The theory predicts an increase in intrinsic or extraneous load that impedes learning when students multitask. For example, when students are engaged in off-task activities using technology, an additional load is imposed on the learning task that must be completed. The cognitive theory of multimedia learning is based on three research-based principles in cognitive science: that learners (a) have two separate channels for handling verbal and pictorial information, (b) can process only a limited number of elements in each channel at a time, and (c) must select, organize, and integrate appropriate information from the instructional materials with existing knowledge into long-term memory for meaningful learning to occur (Mayer, 2010). In other words, learners must pay attention to relevant words and pictures for further processing,


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build internal relationships among selected words and pictures that lead to conceptual understanding in working memory, and connect new understanding with prior knowledge in long-term memory. Media multitasking, however, overloads the limited capacity in the auditory and visual channels and leads to deficits in performance. Based on the relevant research findings and theories, the following hypothesis was formulated in the context of SMM: H3. Students who engage more in SMM perform less favorably academically. 5. Method 5.1. Procedure Mass emails were sent to all undergraduate students at a comprehensive university in Hong Kong to invite them to participate on a voluntary basis (Jupp, 2006). While this nonprobability sampling method may not guarantee the representativeness of the sample collected, it is the most convenient approach to reaching a wide variety of participants from all faculties of the university. To attract more participants, a lucky draw to win HKD100 cash coupons was offered. Anonymous data were collected from the participants through an online survey website. The participants first completed questionnaires measuring their social media usage, SMM, and academic performance. They then provided some demographic information such as gender, age, faculty, degree major, year of study, computer or Internet experience, and information technology (IT) proficiency. 5.2. Participants The participants were 348 undergraduate students from eight university faculties. Informed consent was obtained from all participants prior to data collection. There were 109 males and 232 females in the sample and their ages ranged from 17 to 28 years (mean ¼ 20.252, SD ¼ 1.565). They studied in the faculties of arts (N ¼ 51), business administration (N ¼ 73), education (N ¼ 19), engineering (N ¼ 29), law (N ¼ 8), medicine (N ¼ 53), science (N ¼ 47), and social science (N ¼ 62). There were 123 first-year, 85 second-year, 75 third-year, 57 fourth-year, and 2 fifth-year students. They had, on average, 12e14 years of experience in using computers or the Internet and regarded their IT proficiency to be good. Between 1.4% and 2.0% of the data were missing for the aforementioned demographic variables. 5.3. Measures 5.3.1. Social media usage for nonacademic purposes The Media Usage Subscale of the Media and Technology Usage and Attitudes Scale developed by Rosen, Whaling, Carrier, Cheever, and Rokkum (2013) was used to evaluate social media usage for nonacademic purposes (SMUNAP) among the university students. The authors validated the scale with a large sample of American adults who mainly held college degrees. This study adapted 12 items that measured media sharing (MS; five items), Internet searching (IS; four items), and video gaming (VG; three items) from the subscale. These items represent common online activities among university students when they use social media (Kiedrowski, Mahrholz, Griesbaum, & Rittberger, 2015; Smith & Gallicano, 2015) and were rated on a 10-point frequency scale of 1 (never), 2 (once a month), 3 (several times a month), 4 (once a week), 5 (several times a week), 6 (once a day), 7 (several times a day), 8 (once an hour), 9 (several times an hour), and 10 (all the time). The Cronbach's alpha coefficients of the three subscales were reported

to be 0.84 for MS, 0.91 for IS, and 0.83 for VG. 5.3.2. Social media usage for academic purposes The Social Media Learning Scale developed and refined by Mills, Knezek, and Wakefield (2013) was used to assess student perceptions on the application of social media to support university learning (SMUL). In particular, the scale measures university student perceptions of using social media for online community learning and building, and is a seven-item unidimensional scale with alpha reliability of 0.74. The items were rated on a 5-point Likert-type scale from 1 (strongly disagree) to 5 (strongly agree). 5.3.3. SMM SMM was measured using three items adapted from Ozer's (2014) study, which were “I multitask with my social media account while studying”, “I remain online with my social media site(s) while doing homework”, and “I do not check my social media account if I am doing my work for school.” The items were rated on a 5-point Likert-type scale from 1 (strongly disagree) to 5 (strongly agree) with the last item being reverse coded. Confirmatory factor analysis of the measurement model of the SMM items supported their validity. 5.3.4. Academic performance Academic performance was evaluated with a single item. The participants were required to provide their cumulative GPAs (CGPAs) in an open response format (Paul, Baker, & Cochran, 2012). 6. Results 6.1. Factor structures of the major constructs Fabrigar and Wegener (2012) suggested that the factor structure of a scale is likely to be influenced by factors such as culture. Therefore, exploratory factor analyses using the principal component method with promax rotation were applied to the items of the major constructs described in the Method section. As shown in Table 1, for the construct SMUNAP, four factors with eigenvalues greater than 1 were found instead of three as reported by Rosen et al. (2013). An additional factor with three items, which was labeled “video watching” (VW), was obtained from the original five-item MS factor. Cronbach's alpha coefficients of the four factors were acceptable, ranging from 0.632 to 0.840. The SMUL construct was confirmed to be a seven-item unidimensional scale with a Cronbach's alpha coefficient of 0.756, which is consistent with the finding of Mills et al. (2013). The SMM construct was also found to be unidimensional with acceptable reliability of 0.719, which agrees with the findings of Ozer (2014). All items had factor loadings greater than 0.5 on their respective constructs. 6.2. Descriptive statistics of the major variables As summarized in Table 2, the participants on average engaged in VW several times a week (SD ¼ 1.593), MS almost once a week (SD ¼ 1.909), IS once a day (SD ¼ 1.559), and VG once a week (SD ¼ 2.028). They tended to hold a neutral view toward SMUL. In other words, they were ambivalent about the application of SMUL. They agreed that they multitasked with their social media accounts while doing academic work. The mean CGPA of the participants was 3.178 (SD ¼ 0.349). There was a significant negative correlation between CGPA and VG. The intercorrelations between the variables were mostly statistically significant.

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Table 1 Exploratory factor analyses on the items of the major constructs. Construct VW (a ¼ 0.632) VW1 VW2 VW3 MS (a ¼ 0.709) MS1 MS2 IS (a ¼ 0.840) IS1 IS2 IS3 IS4 VG (a ¼ 0.794) VG1 VG2 VG3 SMUL (a ¼ 0.756) SMUL1 SMUL2 SMUL3 SMUL4 SMUL5 SMUL6 SMUL7 SMM (a ¼ 0.719) SMM 1 SMM 2 SMM 3a


Factor loading

Watch TV shows, movies, etc. on a TV Watch TV shows, movies, etc. on a computer Watch video clips on a computer

0.629 0.861 0.760

Download media files from other people on a computer Share your own media files on a computer

0.761 0.940

Search Search Search Search

0.798 0.962 0.725 0.777

the the the the

Internet Internet Internet Internet

for for for for

news on any device information on any device videos on any device images or photos on any device

Play games on a computer, video game console or smartphone by yourself Play games on a computer, video game console or smartphone with other people in the same room Play games on a computer, video game console or smartphone with other people online

0.822 0.846 0.821

I feel a sense of community learning becomes interactive Posting questions to my peers helps me understand my readings better I am able to get faster feedback from my peers I am able to get faster feedback from my instructor I am able to communicate effectively I am able to connect with peers more easily than face-to-face I increase my participation in classes when I am allowed to contribute through social media

0.670 0.639 0.716 0.576 0.685 0.629 0.564

I multitask with my social media account while studying I remain online with my social media site(s) while doing homework I do not check my social media account if I am doing my work for school.

0.810 0.848 0.741

Note. VW ¼ video watching, MS ¼ media sharing, IS ¼ Internet searching, VG ¼ video gaming, SMUL ¼ social media to support university learning, SMM ¼ social media multitasking. a This item was reverse coded.

Table 2 Descriptive statistics of and intercorrelations between the major variables.

1. 2. 3. 4. 5. 6. 7.











5.489 3.530 6.345 3.652 3.346 3.526 3.178

1.593 1.909 1.559 2.028 0.571 0.806 0.349

e 0.338** 0.329** 0.373** 0.156** 0.133* 0.017

e 0.379** 0.316** 0.075 0.069 0.072

e 0.184** 0.243** 0.146** 0.051

e 0.073 0.017 0.161**

e 0.266** 0.048

e 0.092


Note. VW ¼ video watching, MS ¼ media sharing, IS ¼ Internet searching, VG ¼ video gaming, SMUL ¼ social media to support university learning, SMM ¼ social media multitasking, CGPA ¼ cumulative grade point average. * p < 0.05, **p < 0.01.

6.3. Hierarchical regression analysis Hierarchical regression analysis was conducted with CGPA as the dependent variable and variables from the constructs of VW, MS, IS, VG, SMUL, and SMM as the independent variables. To eliminate the effect of some demographic variables on academic performance, age and gender were entered as the control variables in the first block. In this study, age was reported in an open response format and gender was coded as 1 for males and 2 for females. These variables were shown to be demographic correlates of university students' academic performance in meta-analysis (Richardson, Abraham, & Bond, 2012). The aforementioned independent variables were then entered into the second block. Before the analysis, it is necessary to evaluate the validity of the underlying assumptions of regression analysis, including the absence of outliers and multicollinearity, linearity, normality, independence, and homoscedasticity (Tabachnick & Fidell, 2006). There were four cases with a standardized residual more than 3 standard deviations from the predicted value. This number of cases

is negligible compared with the sample size of 348 and thus they were retained in the regression analysis. All the independent variables had tolerance greater than 0.7, which showed that the variance explained by one independent variable was not well explained by the others, and that therefore the problem of multicollinearity was not apparent. The linearity and homoscedasticity assumptions were tested by examining the residual plot showing the relationship between standardized residuals and standardized predicted values. These assumptions were tenable because the standardized residuals were scattered in a random manner around a horizontal line representing the standardized residuals equaling zero. The DurbineWatson statistic was used to test whether serial correlation between residuals occurs and therefore test the independence assumption. The statistic's value was 1.914, which indicated that the residuals were uncorrelated and that the assumption was valid. The normality assumption was examined using the normal probability plot of the standardized residuals. The points fell mostly on a straight line and thus supported the assumption. From Table 3, for the first block of control variables, age and


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Table 3 Hierarchical regression analysis of the control and independent variables on CGPA. Predictor

CGPA Step 1

Age Gender



0.098 0.115*

0.110* 0.062


Step 2

0.093 0.073 0.116 0.167** 0.040 0.126* 4.187* 0.024 e

3.236** 0.073 0.049

Note. VW ¼ video watching, MS ¼ media sharing, IS ¼ Internet searching, VG ¼ video gaming, SMUL ¼ social media to support university learning, SMM ¼ social media multitasking, CGPA ¼ cumulative grade point average. * p < 0.05, **p < 0.01.

gender together accounted for 2.4% of the variance of CGPA. As the other independent variables were entered into the second block, the variance explained increased by 4.9% with the total variance explained becoming 7.3%. The regression model was significantly predicted by the control and independent variables. Significant predictors of CGPA were gender (b ¼ 0.115, p < 0.05), VG (b ¼ 0.167, p < 0.01), and SMM (b ¼ 0.126, p < 0.05). Therefore, H1 and H3 were supported whereas H2 was not. 7. Discussion The primary objective of this study was to examine whether and how social media usage and SMM influenced the academic performance of university students. Three hypotheses were formulated and tested with online survey data collected from a sample of 348 undergraduate students. After controlling for the effect of age and gender, it was found that SMUL did not significantly predict academic performance as measured by CGPA. SMUNAP (VG in particular) and SMM significantly negatively predicted academic performance. In accordance with the findings of Richardson et al. (2012), a gender difference in academic performance was found in which female students generally attained a higher CGPA than that of male students. There are arguably various cognitive and noncognitive factors that explain academic gender differences (Cooper, 2014). Against this background, it is crucial to explore further how differences in social media usage and SMM between genders may exacerbate or ameliorate the noticeable gender gap in academic performance. Oye et al. (2012) and Ravizza et al. (2014) have empirically demonstrated the negative effect of nonacademic social media usage on academic performance. The present study obtained the same result but further identified VG as the key determinant of poor academic performance. It is beyond the scope of this study to gather more information about how much time students spend on VG every week, the nature of the games they play, and how long students have been involved in VG. These are, however, important issues that will help explain the finding here regarding students who reported that they on average played video games once a week. The present study also found that SMUL had no effect on their academic performance. However, Junco et al. (2011) and GreGory et al. (2014) have shown that the use of Twitter and Facebook can

enhance undergraduate student engagement and performance. The difference may be explained by the fact that the students in the current study used social media mainly for purposes other than learning, as indicated by their ambivalent view toward the related survey items. They possibly have not enrolled in courses that integrate social media as a learning tool and therefore have not experienced the educational benefits arising from such an instructional approach, unlike in the studies of Junco et al. (2011) and GreGory et al. (2014), where social media was utilized as a learning platform. SMM was found to impede student learning, as reflected in students' CGPA. This finding agrees with the existence of ample evidence documenting the negative effect of media multitasking on a range of learning attitudes, behaviors, and outcomes (van der Schuur et al., 2015). The item responses indicate that university students commonly multitask with their social media accounts while studying. A recent online survey conducted with university students in Hong Kong showed that although they used social media for sharing, discussing, and searching for information, they were readily distracted by the entertainment and social functions provided by social media (Tang, Yau, Wong, & Wong, 2015). To reduce this harmful effect on learning, it is necessary to analyze in detail different manifestations of multitasking (Wood & Zivcakova, 2015) and the cognitive learning processes (Mayer, 2010; Sweller, 1988) that may differentially affect student learning trajectories and outcomes. This task calls for more in-depth longitudinal observation and recording of student learning activities over a chosen period of time. Theoretically, the findings of this study provide clear research evidence to guide the investigation of the relationships of the variables concerned (van der Schuur et al., 2015). The study establishes social media usage and SMM as the key variables that negatively influence the academic performance of university students and, specifically, that VG and SMM are detrimental to university student learning. Future studies should also explore how individual difference and contextual factors may moderate these effects on academic performance. Practically, because SMM was found to negatively affect academic performance, educators should consider some measures to mitigate its influence. For example, Bowman, Waite, and Levine (2015) suggested interventions such as technology breaks, self-monitoring, the teaching of metacognitive skills, and the promotion of technological literacy to help students manage their technology use. University administrators should also utilize the findings of this study to set guidelines about the appropriate use of social media (Rowe, 2014). There are a number of limitations in this study that should be further addressed in the future. First, the cross-sectional nature of the study renders making an inference of the causal relationships between the variables impossible, and future researchers should adopt a longitudinal research design to examine causality. Second, the nonprobability sampling method used in this study may limit the generalizability of the findings to the target population. Future investigation should consider employing probability sampling methods such as stratified random sampling to collect random student samples from different faculties. Third, more research is necessary to understand the multitasking activities that students are involved in when using social media. This investigation provides a deeper understanding of how SMM is likely to affect academic performance differentially. Fourth, future research could examine study related behaviors and attitudes, perceived academic learning, and nonacademic performance as outcome variables. 8. Conclusion Because university students participate in various social media

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activities on a daily basis, there are growing concerns about the potential negative impacts of social media on students' social wellbeing. These potential negative impacts include inappropriate interactions between students and teachers online, the influence of an informal relationship with a teacher that disrupts formal instruction during class time, and cyberbullying. Social media may distort the traditional teacherestudent relationship, and the boundaries between students and teachers have become less defined. Social media may also adversely affect students' sense of belonging, psychosocial well-being, and identity development (Allen, Ryan, Gray, McInerney, & Waters, 2014). Other studies have been performed on the effects of social media on students' cognitive development (Ting & Rashied, 2015). This study found that SMUNAP and SMM negatively predicted academic performance. Because the emergence of new social media technologies is anticipated, it is imperative to be more aware of how these technologies may foster or hinder students' psychosocial and academic development, particularly when they are used in a multitasking setting. The present findings shed new light on the understanding of how social media usage and SMM may influence university students' academic performance, and pave the way for future research in this area. Acknowledgments This study was supported by the Direct Grant for Research (2015-16) (Grant number: 4058032) of the Chinese University of Hong Kong awarded to the author of the paper. I would like to thank all the participating undergraduate students for contributing the data for analysis. References Allen, K. A., Ryan, T., Gray, D. L., McInerney, D. M., & Waters, L. (2014). Social media use and social connectedness in adolescents: The positives and the potential pitfalls. The Australian Educational and Developmental Psychologist, 31(1), 18e31. Bowman, L. L., Waite, B. M., & Levine, L. E. (2015). Multitasking and attention: Implications for college students. In Larry D. Rosen, N. A. Cheever, & L. M. Carrier (Eds.), The Wiley handbook of psychology, technology, and society (pp. 388e403). West Sussex, UK: John Wiley & Sons. Cheston, C. C., Flickinger, T. E., & Chisolm, M. S. (2013). Social media use in medical education: A systematic review. Academic Medicine: Journal of the Association of American Medical Colleges, 88(6), 893e901. Cooper, A. D. (2014). Exploring the use of non-cognitive factors in predicting college academic outcomes (Unpublished masters thesis). Chattanooga, Tennessee: University of Tennessee. Cox, D., & McLeod, S. (2014). Social media strategies for school principals. NASSP Bulletin, 98(1), 5e25. Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis. New York, NY: Oxford University Press. Gastelum, Z. N., & Whattam, K. M. (2013). State-of-the-art of social media analytics research. Retrieved from: http://www.pnnl.gov/main/publications/external/ technical_reports/PNNL-22171.pdf. Glogocheski, S. W. (2015). Social media usage and its impact on grade point average and retention: An exploratory study to generate viable strategies in a dynamic higher education learning environment (Unpublished doctoral thesis). USA: St. John's University. GreGory, P., GreGory, K., & Eddy, E. (2014). The instructional network: Using Facebook to enhance undergraduate mathematics instruction. Journal of Computers in Mathematics and Science Teaching, 33(1), 5e26. Junco, R. (2014). Engaging students through social media: Evidence-based practices for use in student affairs. San Francisco, CA: Jossey-Bass. Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27(2), 119e132. Jupp, V. (2006). Volunteer sampling. In V. Jupp (Ed.), The Sage dictionary of social research methods (pp. 322e323). London: SAGE Publications. Kiedrowski, K. v. L., Mahrholz, N., Griesbaum, J., & Rittberger, M. (2015). Social media usage in education related web search: An analysis of the information


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