1
Chapter 1: Introduction to the Study
Social media permeates many facets of life; for many, it has become routine to
check social media feeds, share posts, and interact with others (Zheng & Lee, 2016).
Though social media can benefit people in terms of sharing information and building
social capital (Imran et al., 2019; Kim et al., 2016), it can also result in maladaptive use
with negative consequences. Excessive social media engagement is related to depression
(Lin et al., 2016), anxiety, loneliness, low self-esteem (Bhagat, 2015), and poor social
adaptation (Bodroža & Jovanović, 2016). Social media can also affect students’ academic
outcomes. In school, social media use that is not directly associated with classroom
activities, known as nonacademic social media use (Ravizza et al., 2014), can undermine
academic performance. Nonacademic social media use may occur as social media
multitasking and is associated with poor academic performance on standardized tests
(Ravizza et al., 2014) and low self-reported grade point average (GPA; Lau, 2017).
The fear of missing out can perpetuate heavy social media use, contributing to
reduced academic performance (Rozgonjuk et al., 2019). However, it is unclear whether
fear of missing out is correlated with nonacademic social media multitasking or whether
it moderated the relationship between social media multitasking and academic
performance. Accordingly, the current quantitative investigation involved an examination
of the potential relationships between nonacademic social media multitasking, academic
performance (measured as self-reported, overall GPA), and fear of missing out. Findings
from this research have potential social change implications for policies regarding social
media use in schools and ultimately improvements in student academic performance.
2
The aim of this chapter is to orient the reader to the current investigation. It begins
with a discussion of the background of the problem, followed by the problem and
purpose statements. The research questions, theoretical framework, and nature of the
study are discussed next. Key terms, assumptions, delimitations, and limitations are also
detailed. The chapter ends with a statement of the study’s significance and a concise
summary.
Background
Social media refers to online platforms that allow users to create public profiles,
curate a list of online friends or followers, and interact with others (Kircaburrun et al.,
2018). Social media can help individuals develop social capital (Roberts & David, 2020),
garner feelings of support (Domahidi, 2018), reduce loneliness (Seo et al., 2016), and
pursue professional opportunities (Utz, 2016). However, though social media has many
benefits, maladaptive use or abuse can create problems for users. For example, excessive
social media use is associated with depression (Lin et al., 2016), anxiety, loneliness, and
low self-esteem (Bhagat, 2015).
Social media also has the potential to serve as an education tool (Edwards et al.,
2015), but it can also create distractions that undermine learning. When social media use
is nonacademic and occurs concurrently with academic work (i.e., nonacademic social
media multitasking), the effects can be harmful. Social media multitasking involves
simultaneously engaging in several activities, at least one of which is social media
(Konova & Chiang, 2015). In a classroom setting, nonacademic social media
multitasking could involve taking lecture notes while checking social media feeds. Social
3
media multitasking is particularly prevalent among youth and young adults between the
ages of 13 and 24 (Voorveld & van der Goot, 2013). Fear of missing out can propagate
social media multitasking, as students fear disengagement with social media while
working on academic activities may cause them to miss out on important social events or
information. A growing body of literature suggests that social media multitasking may be
primarily associated with the fear of missing out (Beyens et al., 2016; Duman & Ozkara,
2019).
Although researchers have studied the effects of social media use and
multitasking behaviors (Chen & Yan, 2016; Hwang et al., 2014; Kirschner & De
Bruyckere, 2017; Kononova & Chiang, 2015), little is known about the effects of
nonacademic social media multitasking. Accordingly, additional investigation was
needed to understand the relationships between these constructs. The current study aimed
to address this gap in the literature by examining the potential relationships between
nonacademic social media multitasking, academic performance (measured as self-
reported, overall GPA), and fear of missing out. The current research was needed to
better understand the ways nonacademic social media multitasking and fear of missing
out may affect academic performance.
Problem Statement
Social media has become a ubiquitous aspect of everyday life in U.S. society
(Dempsey et al., 2019). There are many benefits of social media, such as the development
of social capital (Kim et al., 2016), knowledge sharing (Imran et al., 2019), and social
support (Cole et al., 2017). When use is distracting, however, social media can have a
4
number of negative effects on users, including the development of distress (Muench et
al., 2015), depression (Marino et al., 2018), alcohol abuse (Hormes, 2016), anxiety
(Casale & Fioravanti, 2015; Seabrook et al., 2016), and poor learning (Rozgonjuk et al.,
2019).
In educational settings, social media use that is not directly associated with
classroom activities and can distract students from learning is referred to as nonacademic
social media use (Ravizza et al., 2014). Nonacademic social media use is associated with
poor academic performance on standardized tests (Ravizza et al., 2014) and low self-
reported GPA (Lau, 2017). The negative effect of social media multitasking on academic
performance may be the result of cognitive overload. According to the cognitive theory
of multimedia learning (Mayer & Moreno, 2003), the human information processing
system has two channels (visual and auditory), is limited in capacity, and is used to
process incoming information (Mayer, 2010). Nonacademic social media multitasking
overloads the limited capacity of the visual and auditory channels, creating deficits in
learning and performance (Lau, 2017).
Another factor to consider in the potential relationship between nonacademic
social media multitasking and poor academic performance is the fear of missing out,
which may perpetuate high levels of social media use (Bright & Logan, 2018). Fear of
missing out describes an apprehension that others are having rewarding experiences that
one is missing out on, when absent (Rozgonjuk et al., 2019). Fear of missing out can have
a number of negative effects such as anxiety, depression (Krasnoa et al., 2015), and
smartphone addiction (Chotpitayasunondh & Douglas, 2016), but it is unknown whether
5
fear of missing out is correlated with nonacademic social media multitasking, or if it
moderates the relationship between social media multitasking and academic performance.
Purpose of the Study
The purpose of this quantitative investigation was to examine the potential
relationships between nonacademic social media multitasking, academic performance
(measured as self-reported, overall GPA), and fear of missing out. The examined
variables included nonacademic social media multitasking (independent variable),
academic performance (dependent variable), and fear of missing out (independent
variable). Although some research indicated social media use was associated with poor
academic outcomes (Rozgonjuk et al., 2019), the mechanism of this relationship was less
understood. If social media use is non-academic and distracting, as in the case of
nonacademic social media multitasking, the negative effects on academic performance
may be the result of reduced cognitive processing, as described in the cognitive theory of
multimedia learning (Mayer & Moreno, 2003). In addition, it is possible that fear of
missing out may prompt nonacademic social media multitasking, as students fear they
will miss out on important information if they abstain from social media while in class. In
this way, the fear of missing out may promote nonacademic social media multitasking,
thereby overloading cognitive processing abilities and impeding academic performance.
Research Questions and Hypotheses
Research Question 1: To what degree is nonacademic social media multitasking
(as assessed by Ozer’s items for measuring social media multitasking) related to
6
academic performance (as assessed by self-reported GPA) among U.S. undergraduate
students?
H
0
1: Nonacademic social media multitasking is not a significant predictor of
academic performance.
H
1
1: Nonacademic social media multitasking is a significant predictor of
academic performance.
Research Question 2: To what degree is fear of missing out (as assessed by the
Fear of Missing Out Scale) related to nonacademic social media multitasking (as assessed
by Ozer’s items for measuring social media multitasking), among U.S. undergraduate
students?
H
0
2: Fear of missing out is not a significant predictor of nonacademic social
media multitasking.
H
1
2: Fear of missing out is a significant predictor of nonacademic social media
multitasking.
Research Question 3: To what degree does fear of missing out moderate the
relationship between nonacademic social media multitasking and academic performance?
H
0
3: Fear of missing out does not significantly moderate the relationship between
nonacademic social media multitasking and academic performance.
H
1
3: Fear of missing out does significantly moderate the relationship between
nonacademic social media multitasking and academic performance.
7
Theoretical Framework
The framework for the current study was based on Baumeister and Leary’s (1995)
belonging hypothesis and van dur Schuur et al.’s (2015) scattered attention hypothesis.
The belonging hypothesis was useful for understanding how the need to belong prompts a
fear of missing out, which may influence social media use (Beyens et al., 2016). The
scattered attention hypothesis was helpful for understanding how social media
multitasking, prompted by the need to belong, predicted academic performance.
Together, the belonging hypothesis and scattered attention hypothesis were useful in
understanding fear of missing out and its relationships with social media multitasking and
academic performance. An illustration of the theoretical model is provided in Figure 1.
Figure 1
Theoretical Model
Belonging Hypothesis
Baumeister and Leary (1995) proposed that the need to belong was a fundamental
driver of human behavior. People have an innate drive to form and maintain lasting,
meaningful relationships, which transcends cultures has biological roots that foster
reproduction, competition for resources, and survival of the human species. From an
evolutionary perspective, social exclusion was life threatening (Roberts & David, 2019).
Belonging hypothesis
(fear of missing out)
Scattered attention
hypothesis
(from social media
multitasking)
Academic
performance
(as self-reported GPA)
8
In this way, the need to belong is a biological instinct to foster survival. To develop and
maintain valuable interpersonal relationships, humans must engage in frequent and
pleasant interactions with others, and the interactions must occur within a context of
concern for another’s welfare (Baumeister & Leary, 1995). A lack of belonging, which
can occur when frequent interactions are not present, can create a sense of deprivation.
The need to belong is a powerful and pervasive human motivation that influences
individuals’ behaviors and emotions (Wang et al., 2017). In modern societies, behaviors
reflective of the need to belong may involve the use of modern communication
technologies, such as social media and personal digital devices (Beyens et al., 2016).
Because the role of social media and smart devices is to foster communication, it may
follow that the need to belong inspires engagement with these technologies (Roberts &
David, 2019). Further, the need to belong may prompt a fear of missing out, which
fosters social engagement with others through social media (Abel et al., 2016; Beyens et
al., 2016). Accordingly, the need to belong may help explain fear of missing out and
social media multitasking among students.
The belonging hypothesis has been employed to understand social media use and
fear of missing out in a number of contexts. For example, Yin et al. (2019) examined the
relationships between social media addiction, fear of missing out, the need to belong, and
envy. Results indicated that social media addiction was positively associated with fear of
missing out, and that envy mediated this relationship. Wang, Zhao, et al. (2017) used the
framework to explore smartphone addiction prompted by the need to belong, among a
sample of Chinese adolescents. More recently, Wang, Xie, et al. (2018) studied the need
9
to belong in relation to authentic self-preservation and found fear of missing out was a
significant moderator in this relationship. Roberts and David (2019) also used the
belonging hypothesis to examine social media use, social connection, and fear of missing
out. The researchers found that fear of missing out was positively correlated with the
intensity of social media use and that it affects well-being through its effects on social
connection and social media intensity.
Scattered Attention Hypothesis
In addition to the belonging hypothesis (Baumeister & Leary, 1995), the scattered
attention hypothesis (van dur Schuur et al., 2015) was used in the framework for the
current study, as this hypothesis may help explain relationships between social media
multitasking and academic performance. Approaching cognitive function from a
theoretical angle, the scattered attention hypothesis states that the brain utilizes and
allocates cognitive resources to complete tasks, as necessary. Because attention is a
limited resource, factors that distract one’s focus could undermine the performance of
primary tasks (May & Elder, 2018).
According to the scattered attention hypothesis, social media multitasking can
create disruptions in cognitive control, encouraging individuals to gravitate toward
preferred tasks rather than resist distractions and focus on required tasks. Because
attention is a limited resource (van dur Schuur et al., 2015), the simultaneous engagement
in multiple tasks can result in distractions and errors that reduce performance on primary
tasks. In the context of social media multitasking and academic performance, the
scattered attention hypothesis would suggest that social media multitasking can allocate
10
an individual’s limited attention resources to secondary tasks (social media), thus
reducing performance on primary, academic tasks.
Researchers have used the scattered attention hypothesis to understand the
cognitive effects of multitasking behaviors. For example, van der Schuur et al. (2019)
used the scattered attention hypothesis to examine the relationship between media
multitasking and academic achievement among adolescents. Although no significant
relationships were detected between media multitasking and academic achievement, the
researchers did detect an association between multitasking and academic attention issues.
Ophir et al. (2009) also used the hypothesis to study the correlation between media
multitasking and poor academic outcomes, arguing that media multitasking creates
cognitive control deficits that undermine academic performance. Finally, May and Elder
(2018) conducted a meta-analysis of research on multitasking behaviors and academic
performance and found that media multitasking interfered with working memory and
attention, undermining self-regulation, efficiency, test performance, reading
comprehension, and GPA.
Nature of the Study
The nature of this research was quantitative with a nonexperimental design.
Because the aim was to explore relationships between quantifiable variables, a
quantitative method was selected. A qualitative investigation would not support the
research questions, nor the data collection strategy (online survey). This study was
nonexperimental because the sample was not randomized, nor were there control or
intervention groups.
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The target population for this study included all U.S. undergraduate students who
were currently attending public, 4-year institutions in the United States. The sample
consisted of at least 77 U.S. undergraduate college students. To be eligible for this study,
individuals had to be currently enrolled in a 4-year, post-secondary U.S. institute, be at
least a sophomore, and be at least 18 years old. Data were collected via online survey,
which was distributed through SurveyMonkey. The survey was used to measure the three
study variables (fear of missing out, nonacademic social media multitasking, and
academic performance). The online survey consisted of the Fear of Missing Out Scale
(Przybylski et al., 2013), items from Ozer’s (2014) study on social networking and
academic performance, and a demographic questionnaire that gathered descriptive
statistics, including respondents’ GPA. The first two research questions were assessed via
simple linear regressions. To address Research Question 3, Baron and Kenny’s (1986)
method for moderation was planned to test whether fear of missing out moderated the
relationship between nonacademic social media multitasking and academic performance.
Definitions
Academic performance: Academic performance describes outcomes in
educational settings, such as test scores, course grades, or GPA (Womack & McNamara,
2017). In the current study, academic performance was operationalized as participants’
current self-reported GPA. Self-reported GPA is one of the most commonly used
measures of academic performance (York et al., 2015).
Fear of missing out: Fear of missing out describes an apprehension that others are
having rewarding experiences that one is missing out on when absent (Rozgonjuk et al.,
12
2019). Fear of missing was assessed in this study using the Fear of Missing Out Scale
(Przybylski et al., 2013; Appendix A).
Media multitasking: Media multitasking describes simultaneously participating in
multiple activities—at least one of which involves media use (Kononova & Chiang,
2015).
Nonacademic social media multitasking: In a classroom setting, nonacademic
social media multitasking could involve simultaneously taking lecture notes while
checking social media feeds.
Social media: Social media are “web-based services that allow individuals to (a)
construct a public or semi-public profile within a bounded system, (b) articulate a list of
other users with whom they share a connection, and (c) view and traverse their list of
connections and those made by others within the system” (Kircaburrun et al., 2018, p.
526).
Assumptions
The current study was based on a few assumptions, which refer to elements that
researchers must assume as true in order to perform an investigation (Simon & Goes,
2013). First, it was assumed that only eligible individuals would receive the study
invitation and have the opportunity to participate in this research. SurveyMonkey only
distributed the study invitation and survey link to registered users who met the stated
inclusion criteria. It was also assumed that only eligible individuals would complete the
survey because no incentives were offered for participation, and ineligible individuals
would have little motivation to complete a survey for which they were not eligible. I
13
assumed that all participants possessed the cognitive and literacy skills required to read,
understand, and complete the online survey. Because all participants were college
students, it was safe to assume they all possessed these basic skills.
Scope and Delimitations
The scope of the study was bound by certain delimiting factors. Delimitations are
study aspects selected by researchers that narrow the scope of an investigation (Simon &
Goes, 2013). Delimitations of the current investigation included the population, which
only included students who were currently enrolled in a 4-year, post-secondary U.S.
institute, were at least a sophomore, and were at least 18 years old. The theoretical
framework and selected method and design were also delimiting factors. Finally, the
instruments selected to assess the study variables were delimitations.
Limitations
The main limitation of this study was the use of self-reported GPA to measure
academic performance. Although a number of metrics may be used to assess academic
performance, self-reported GPA is most convenient for a student survey. Further, other
metrics such as test scores may not be standardized across all students, making it difficult
to accurately compare performance across a sample. However, GPA is a standardized
metric for all college students, reported along the same 4-point scale. Further, most
students are aware of their cumulative GPA. Another limitation was factors other than
nonacademic social media multitasking may influence academic performance. These
factors could include individual characteristics such as socioeconomic status or race; the
current research was limited in that it did not account for other factors that could
14
influence academic performance. But the aim of this research was not to prove causation;
the aim was to assess for the presence of correlations.
Findings were also limited to undergraduate students enrolled at traditional, brick-
and-mortar institutions. Graduate students or those enrolled in online program may have
different experiences that would produce different results. Finally, I had little control over
who completed the survey. Although I used screening questions to help ensure all
respondents were eligible, I had no way of confirming eligibility. However, because I did
not offer incentives for participation, I did not anticipate ineligible individuals would be
motivated to complete the survey.
Significance
Findings from this study may shed light on the ways social media multitasking
can affect student academic performance. Though nonacademic social media
multitasking is associated with poor academic outcomes (Lau, 2017; Ravizza et al.,
2014), the mechanism behind this relationship is less clear. This study also revealed
whether fear of missing out was correlated with nonacademic social media multitasking.
In addition to filling important gaps in the existing research regarding the relationships
between social media multitasking, fear of missing out, and academic performance,
findings from this study may also be useful to educational practitioners seeking to
improve academic outcomes, such as GPA and college graduation, among postsecondary
students.
Findings from this research also have potential social change implications. As a
society, education and academic success are critical to a well-informed and successful
15
public (Mackey, 2019). An educated society is also essential for maintaining a
competitive foothold in an increasingly globalized society (Wilgus, 2019). In recent
decades, policymakers and educational leaders have become increasingly concerned with
the declining academic performance of U.S. students (Jain, 2019). Although technology
has provided teachers and learners with powerful educational tools, social media
multitasking may impede academic performance via cognitive overload. Findings from
this study revealed ways social media multitasking and fear of missing out may affect
academic performance (assessed as students’ self-reported GPA). Findings could inform
the development policies regarding social media use in schools. Ultimately, this research
could result in positive social change via improvements in student academic performance.
Summary
It was unknown whether the fear of missing out was correlated with nonacademic
social media multitasking or whether it moderated the relationship between social media
multitasking and academic performance. Accordingly, the current quantitative
investigation examined the potential relationships between nonacademic social media
multitasking, academic performance (measured as self-reported, overall GPA), and fear
of missing out. Data for this study were collected via online survey, distributed by
SurveyMonkey to eligible college students. Findings shed new light on the ways social
media multitasking can affect student academic performance.
This chapter provided an introduction to the current investigation. A
comprehensive review of recent, related research is provided in Chapter 2.
16
Methodological details are outlined in Chapter 3, and study results are provided in
Chapter 4, followed by a discussion of findings in Chapter 5.
17
Chapter 2: Literature Review
Nonacademic social media use such as social media multitasking is associated
with poor academic performance on standardized tests (Ravizza et al., 2014) and low
self-reported GPA (Lau, 2017). Another factor that can affect academic performance is
the fear of missing out. Because fear of missing out can perpetuate high levels of social
media use, it is an important factor to consider in the relationship between nonacademic
social media multitasking and poor academic performance (Bright & Logan, 2018).
However, it is unclear whether fear of missing out is correlated with or moderates the
relationship between nonacademic social media multitasking and academic performance.
Accordingly, the purpose of this quantitative investigation was to examine the potential
relationships between nonacademic social media multitasking, academic performance
(measured as self-reported, overall GPA), and fear of missing out.
This chapter provides a review, analysis, and synthesis of findings from previous
researchers on the topics of social media, multitasking, fear of missing out, and academic
performance. The chapter begins with a discussion of the strategy used to locate the
journal articles, books, and other scholarly sources discussed in this review of the
literature. Next, the theoretical framework is expanded on to illustrate the alignment of
belonging hypothesis and scattered attention hypothesis to the current study. Next, a
review of the related body of literature is presented, beginning with a discussion of the
history of social media and its potential benefits and harms. Research on social media
multitasking, the fear of missing out, and academic outcomes are also discussed. The
chapter closes with a brief summary and conclusion.
18
Search Strategy
A number of online databases were used to locate the research discussed in this
chapter, including Academic OneFile, JSTOR, Academic Search Premier, Gale InfoTrac,
Digital Commons, Education Source, ProQuest, SAGE, Taylor & Francis Online, Project
MUSE, Wiley, and Google Scholar. When possible, included research was limited to that
which was published within the last 5 years. Older sources were selectively included
when they were seminal or particularly relevant. Several combinations of the following
search terms were used: social media, social networking, SNS, Facebook, Twitter, social
networking sites, academic outcomes, college, fear of missing out, social media overload,
multitasking, media, social media multitasking, and cognitive overload.
Theoretical Framework
The theoretical framework for the current study was based on Baumeister and
Leary’s (1995) belonging hypothesis as well as van dur Schuur et al.’s (2015) scattered
attention hypothesis. Together, the belonging hypothesis and scattered attention
hypothesis were useful in understanding fear of missing out and its relationships with
social media multitasking and academic performance. Each of these hypotheses is
discussed in relation to the current investigation in the following sections.
Belonging Hypothesis
Baumeister and Leary (1995) proposed that the need to belong is a fundamental
driver of human behavior. According to the researchers, “Human beings have a pervasive
drive to form and maintain at least a minimum quantity of lasting, positive, and
significant interpersonal relationships” (p. 497). The need to belong is an innate
19
motivation that prompts much of individuals’ interpersonal behaviors (Baumeister &
Leary, 1995). In order to develop and maintain valuable interpersonal relationships,
humans must engage in frequent and pleasant interactions with others, and the
interactions must occur within a context of concern for another’s welfare (Baumeister &
Leary, 1995). A lack of belonging, which can occur when frequent interactions are not
present, can create a sense of deprivation.
The need to belong may help explain fear of missing out and social media
multitasking among students. Modern communication technologies, such as social media
and personal digital devices (Beyens et al., 2016), can exacerbate the need to belong. The
need to belong may prompt a fear of missing out, which fosters social engagement with
others over social media (Abel et al., 2016; Beyens et al., 2016). Fear of missing out has
been positively correlated with social media intensity and negatively correlated with
social connection (Roberts & David, 2019). Essentially, fear of missing out affected
subjective well-being in both positive and negative ways, depending on the intensity of
social media use and users’ social connections (Roberts & David, 2019). Further, fear of
missing out can mediate the relationship between authentic self-presentation and the need
to belong (Wang, Xie, et al., 2018). The current study expanded on the belonging
hypothesis by using it to examine the potential moderating effect of fear of missing out in
the relationship between nonacademic social media multitasking and academic outcomes.
Scattered Attention Hypothesis
The scattered attention hypothesis was also used in the framework for the current
study, as this hypothesis helped explain relationships between social media multitasking
20
and academic performance. According to the scattered attention hypothesis, social media
multitasking can disrupt focus on required tasks because attention is limited, which can
lead to errors and reduced performance on tasks (van dur Schuur et al., 2015). In the
context of this study, social media multitasking may reduce academic performance.
Previous research based on the scattered attention hypothesis has shown that
social media can have negative effects on users. Research has indicated that multitasking
with academic media results in attention problems (van dur Schuur et al., 2015).
Multitasking with academic media can scatter users’ attention and undermine focus on
academic tasks. Further, media multitasking interferes with working memory, which has
a negative effect on academic outcomes (Mary & Elder, 2018; Ophir et al., 2009). In
addition to undermining academic performance, social media has been associated with
psychological distress (May, 2017).
Literature Review Related to the Key Variables and Concepts
Social Media: A Historical View
Humans a typically desire a sense of belonging and membership in social groups
(Abel et al., 2016). Historically, bonding social interactions occurred through face-to-face
interactions; however, modern technologies have drastically changed the way humans
interact, socialize, and build relationships. Much of today’s communication and
socialization take place via social media, also known as social networking sites. Social
media sites allow people to connect with others and present themselves through a profile
(Kircaburrun et al., 2018, p. 526). The most popular social media sites in the world
include Facebook, Twitter, Instagram, Snapchat, and YouTube (Alexa, n.d.). Although
21
individuals may be motivated to use social media by a number of factors, the primary
reason for engaging with the platforms is to establish and maintain relationships, both
online and offline (Kuss & Griffiths, 2017). The social aspect of social media is apparent
in the different ways various platforms can be used. For example, Instagram can be used
for self-expression and social interaction (Lee et al., 2015); Facebook can satisfy users’
need to belong (Kircaburrun et al., 2018); and Snapchat is ideal for quick communication
that integrates users’ immediate environments (Piwet & Joinson, 2016).
A discussion on the effects of social media can begin with a review of the origins
of these popular social networks. Researchers often credit the Bulletin Board System
(BBS) of the 1980s as the beginning of social media (Bialy, 2017). The BBS was a
rudimentary online meeting room that allowed users to leave messages for one another
without knowing with whom they were communicating. A growing interest in online
interaction was sparked by the BBS, which eventually led to the development of other
emerging social platforms, such as classmates.com and sixdegrees.com in the 1990s
(Bialy, 2017).
In the early 2000s, online platforms evolved further to become the social media
platforms so many users are now familiar with, two decades later. LinkedIn was founded
in 2003 (Kaur, 2016), followed by the development of Facebook by Mark Zuckerburg
and his classmates in 2004 (Scott et al., 2018). As of 2020, Facebook remained the most
popular online platform, used by 2.7 billion individuals each month (Statista, 2020b).
Facebook allows users to become “friends” online, giving them opportunities to view and
“like” one another’s posts. Twitter emerged in 2006, unique in its use of hashtags and
22
short, 140-character messages that users could “tweet” to their online followers (Blank &
Lutz, 2016). With the growing popularity of social media, several other social media
platforms emerged in the early 2000s, including Myspace, Google+, YouTube,
Instagram, and Snapchat (Bialy, 2017). By the end of 2019, over 2.5 billion people used
social media platforms worldwide (Statista, 2020a).
Over the years, social media has evolved to become a tool for more than just
online connection and social media interaction. Today’s users also engage social media to
obtain information, such as news and current events (Oeldorf-Hirsch, 2017). Social media
platforms allow users to absorb and repost articles, share their opinions, and engage in
debates. Social media thus plays a prominent role in the flow of information across
societies (Bialy, 2017). Social media has become such a prolific information source that
mainstream media networks often use the platforms to share information and learn about
new stories through users. Social media has also changed the face of journalism, allowing
regular people to become citizen journalists, sharing stories of their own and offering
alternatives to mainstream media (Saka, 2018). However, with the protection of
anonymity and the absence of gatekeepers, risks for misinformation run high on social
media platforms (Bialy, 2017).
The growing popularity of social media should not be underestimated. Between
2010 and 2016, the number of social media users in the world increased from .97 billion
to 2.34 billion (Statista, 2020a). Other estimates include that two-thirds of internet users
and one-third of all people in the world use social media (Kempy, 2017). Social media
dominates many people’s lives, and nearly one-quarter of Americans’ days are spent on
23
social media (Abel et al., 2016). Apart from connection and information-sharing, social
media has also emerged as a tool for mobilizing support, waging information operations,
disseminating narratives, and even coordinating military operations (Bialy, 2017).
Although there are many beneficial uses of social media, its potential for harm has also
increased, in the form of social cyberattacks. Common types of social cyberattacks waged
on social media include crowd manipulation, hysteria propagation, escalation of rumors,
violence, panic, confusion, and chaotic mass behaviors (Goolsby, n.d.).
Benefits of Social Media
Though much of the research on the effects of social media focuses on the
potential harms of problematic levels of engagement, many researchers report a number
of benefits associated with social media use. For example, social media has the potential
to help users build relationships and bonds while developing social capital (Roberts &
David, 2020). Social media engagement can also increase users’ perceptions of social
resources and support (Domahidi, 2018) while reducing feelings of loneliness (Seo et al.,
2016). Social media also has a number of potential benefits from a professional
perspective. For example, individuals can use the career oriented social media site,
LinkedIn, to create connections or build social capital to boost their professional
endeavors (Utz, 2016). The number of connections users had on professional social
networking sites is predictive of informational benefits, such as receiving work-related
information and opportunities for career advancement (Utz & Breuer, 2016).
Marketers and organizations have also tapped into the power of social media,
using the platforms to create targeted ads and campaigns based on user preferences
24
(Chiang et al., 2019). Small businesses can use social networking sites to advertise, build
communities, grow brands, leverage influence, and turn fans into customers (He et al.,
2017). Social media sites such as Facebook allow businesses to directly communicate and
interact with customers and potential customers in ways not previously possible (Morales
et al., 2017).
In an educational context, many potential benefits of social media exist. For
example, the use of social media increases content sharing and the exchange of ideas
(Edwards et al., 2015). In classroom settings, social media can also help increase
engagement (Edwards et al., 2015). Through the collaboration fostered by social media,
these platforms have the potential to improve learning (Dumpit & Fernandez, 2017).
Finally, social media has reconceptualized the boundaries of formal and informal
education, creating opportunities for distance learning and educational attainment
previously impossible for many individuals (Greenhow & Lewin, 2016).
Potential Harms of Social Media
Controlled use of social media has many benefits; however, maladaptive or
addictive use can have a number of harmful effects on users. For example, compulsively
checking social media feed can have harmful psychological consequences that undermine
the well-being of users (Brooks, 2015; Fox & Moreland, 2015; Oberst et al., 2017).
Research indicates that excessive social media engagement is related to depression (Lin
et al., 2016), anxiety, loneliness, and low self-esteem (Bhagat, 2015). Researchers have
also reported on the association between social media engagement and poor social
adaptation (Bodroža & Jovanović, 2016).
25
The directions of the relationships between maladaptive social media engagement
and negative affect, such as depression, is less clear. For example, it is unclear whether
depressed individuals are more likely to demonstrate maladaptive social media behaviors
or if maladaptive social media behaviors cause depression (Lin et al., 2016). Similarly,
lonely individuals may be more likely to engage with social media to develop a sense of
connectedness, or the isolation caused by maladaptive social media use may lead to
loneliness. Lonely individuals may seek out online relationships to compensate for
challenges in their in-person relationships (Andersson et al., 2016).
Researchers developed a seven-block model of social media to illustrate the ways
individuals engage with social media and how they relate to one another (Kietzmann et
al., 2011). The seven blocks include sharing, presence, conversations, identity,
relationships, groups, and reputation. Sharing describes the degree to which individuals
exchange and disseminate content on social media. Presence describes the degree to
which individuals can determine the accessibility of others. Conversations describes the
degree to which individuals communicate with other social media users. Identity
describes the degree to which individuals reveal information about themselves.
Relationships describe the degree to which individuals connect with one another through
social media. Groups describe the degree to which social media users form communities.
Finally, reputation describes the degree to which users know the standing of other social
media users. Using this seven-block model of social media, researchers have described
the dark side of social media as follows:
• Sharing: Users may share inappropriate content with one another.
26
• Presence: Users may monitor one another’s whereabouts through location
tracking technologies.
• Conversations: The conversations between users may include misinformation
or aggressive engagement.
• Identity: Users may exploit their online selves.
• Relationships: Users may threaten, abuse, or intimidate others with whom
they have online relationships.
• Groups: Users may establish group biases.
• Reputation: Users may shame and defame one another. (Bacccarella et al.,
2018)
Addressing these darker facets of social media requires an awareness of the potential
harm of social platforms. In addition, more research is needed to examine the negative
outcomes that can result from social media engagement and develop strategies to
overcome those harms. Further, it is important to address the negative effects of social
media multitasking and related attentional deficits, especially in terms to academic
outcomes.
Media Multitasking
Media multitasking can result from environmental disruptions or self-
interruptions when individuals prioritize interaction with media over other tasks (le Roux
et al., 2017). Typically, multitasking requires individuals to rapidly switch between tasks,
creating continuous disruptions and shifts in attention (Chen & Yan, 2016). Students are
constantly bombarded by a stream of information and communication through their
27
phones, computers, and other smart devices; as a result, media multitasking has become
the norm for members of the net generation (le Roux et al., 2017).
As Demirilek and Talan (2017) explained, the constant and pervasive information
stream created through social media and smart devices has resulted in a “multitasking
generation” (p. 1). Today’s students overwhelmingly engage in media multitasking. In a
study on media multitasking, Hwang et al. (2014) found that 90% of college students
multitasked with media. While few students may develop the ability to efficiently
multitask, long-term rapid switching behaviors from media multitasking typically leads to
reduced learning and poor performance on academic tasks (Kirschner & De Bruyckere,
2017). Kirschner and De Bruyckere (2017) explained that media multitasking may create
the illusion of effective multitasking and technical savvy, but for most users, media
multitasking undermines performance across many domains. Findings from the existing
research largely indicate that non-academic media multitasking with technology and
social media has a negative effect on academic performance. Such findings support the
scattered attention hypothesis, as attempting to pay attention to class materials while
engaging with technologies can undermine learning by drawing learners’ attention away
from class materials (May & Elder, 2018).
In-class media multitasking is increasingly problematic on modern school
campuses. Smart devices and widespread access to wireless networks allow students to
engage in multitasking activities, such as texting and browsing social media, during class
(Demirilek & Talan, 2017). As May and Elder (2018) explained, “The ubiquity of media
multitasking among today’s students raises concerns about its consequences and
28
outcomes in relation to student learning and cognition” (p. 2). Researchers have found
multitasking activities can create distractions, memory reductions, lack of attention and
engagement, and declines in performance and productivity (Demirilek & Talan, 2017).
According to Harrison and Risler (2015), the most common in-class multitasking
activities that students engage in are texting and browsing social media.
Social media multitasking can negatively affect academic performance (Lau,
2017), as frequent media multitasking can create habitual, scattered levels of attention
(van der Schuur et al., 2019). The negative school-related outcomes of social media
multitasking occur in the areas of academic performance, study behaviors, and perceived
learning (van der Schuur et al., 2015). Researchers have examined the effects of social
media multitasking on various facets of academic performance. For example, Demirilek
and Talan (2017) studied the relationship between off-task social media multitasking
during class and grade performance among post-secondary students. Results confirmed
that when students engaged in social media multitasking during lectures, their grade
performance declined.
Lau (2017) also found that social media multitasking had a negative effect on the
academic performance of postsecondary students. The researcher surveyed 348
undergraduate students to assess nonacademic social media use, academic social media
use, social media multitasking, and academic performance. Analysis revealed social
media multitasking significantly and negatively predicted academic performance. More
recently, Uzun and Kilis (2019) examined the relationships between media use,
multitasking, self-regulation, and academic performance. Analysis of a survey completed
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by 631 college students revealed multitasking and media use were both negatively
associated with academic outcomes; self-regulation appeared to have no effect on
academic outcomes.
Findings reported by Lau (2017) Demirilek and Talan (2017), and Uzun and Kilis
(2019) echo those from previous researchers, who also reported in-class multitasking was
correlated with lower GPA (Al-Menayes, 2015; Bellur et al., 2015; Walsh et al., 2013).
Al-Menayes (2015) studied the ways social media use, engagement, and addiction
affected academic performance and found time spent using social media was negatively
related to academic performance. Accordingly, addictive social media behaviors were
also associated with poor academic outcomes (Al-Menayes, 2015). Similarly, Bellur et al.
(2015) conducted a survey of college students and found in-class multitasking was
negatively predictive of GPA. The most common form of in-class multitasking reported
by Bellur et al. was texting. Walsh et al.’s (2013) study on media use and academic
outcomes among female, first-year college students revealed high rates of media use
(nearly 12 hours per day, on average), which was negatively associated with GPA.
Kuznekoff and Titsworth (2015) examined the effects of in-class multitasking on
academic performance among three groups of students: one that did not multitask, a
second group that performed low-distraction multitasking, and a third group that
performed high distraction multitasking. The scholars found participants in the non-
multitasking groups took more detailed notes and earned higher grades than students in
the low- and high-distraction multitasking groups. An interesting finding from this study
was that students in the low-distraction group who sent text messages related to the
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lecture demonstrated 70% better information recall than students in the high-distraction
group, which did not sent texts related to the lecture. This finding suggests that the effects
of multitasking may depend on the purpose of multitasking activities (Kuznekoff &
Titworth, 2015).
Downs et al. (2015) also studied the effects of academic multitasking using a
variety of conditions. In this study, the researchers assigned students to one of six
classroom settings: (a) one that was distracted by Facebook, (b) one that took notes with
pen and paper, (c) a control group that used no media, (d) a group with mixed
distractions, (e) a group that took notes using a laptop, and (f) one that experienced
distracted combination. The researchers found that participants in the distracted
conditions (a, d, and f) performed worse on an academic assessment than those in the
control, pen and paper note-taking, and laptop note-taking groups.
While computers and other smart devices such as tablets have the potential to
prompt non-academic multitasking in classroom settings, smartphones are the most
common devices carried by students, and may create the most significant problems.
Brooks (2015) studied nonacademic multitasking with smart phones and found students
who used social media on these devices had lower academic performance. Indeed, media,
technology type, and a number of other factors, including academic subject matter, may
influence the ways in-class social media multitasking affects academic performance. Le
Roux and Parry (2017) examined differences in the effects of in-class media use on
academic performance, by subject matter. The researchers found that arts and social
science students were negatively affected by in-class media use, while students in
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engineering, economics, medical, and health sciences classes did not experience these
negative academic outcomes from in-class media engagement. In contrast, Gaudreau et
al. (2014) found no significant subject area differences in the effect of media multitasking
on students’ academic performance.
Importantly, not all researchers report that multitasking with smart devices and
social media results in declines to academic performance. For example, Hartnell-Young
and Vetere (2008) found that the use of smart phones during class was associated with
increased creativity. Junco (2012) reported that social media multitasking was associated
with increased rates of homework completion, while Corbeil and Valdes-Corbeil (2007)
found such multitasking was associated with increases in collaborative learning.
Importantly, in the aforementioned studies, smart devices were leveraged as tools to
support learning. When explicitly used as support tools, smart devices are associated with
improved communication and social acceptance (Valkenburg et al., 2006), self-esteem
(Yu et al., 2010), and student involvement (Heiberger & Harper, 2008).
Finally, Van der Schuur et al. (2015) conducted a systematic review of 43 studies
on the way media multitasking affected academic performance. Of the studies examined,
17 reported a negative effect and four studies demonstrated no significant effect of media
multitasking. In the remaining studies, the direction of the relationship between media
multitasking and academic outcomes could not be determined (Van der Schuur et al.,
2015). Other researchers have also reported media multitasking had no effects on
academic outcomes (Elder, 2013; Lee et al., 2012). These contrasting findings indicate
future research is needed to better understand the relationships between media
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multitasking and academic outcomes, including factors that may serve as moderators or
mediators, as well as the effects of characteristics of individual users (le Roux et al.,
2017).
Fear of Missing Out
In-class social media multitasking may be prompted by several needs, such as the
desire for information or boredom with class materials. However, a growing body of
literature suggests social media multitasking may be primarily associated with the fear of
missing out. Although the fear or missing out is not a new concept, it has received
growing attention from scholars in light of the rise of social media (Abel et al., 2016).
Fear of missing out describes an apprehension that others are having rewarding
experiences that one is missing out on, when absent (Rozgonjuk et al., 2019). Fear of
missing out can have a number of negative effects such as anxiety, depression (Krasnoa
et al., 2015; Oberst et al., 2017), and smartphone addiction (Chotpitayasunondh &
Douglas, 2016). Other negative consequences of the fear of missing out include poor
motivation in school (Alt, 2018), declines in life satisfaction (Dossey, 2014), and alcohol
consumption (Riordan et al., 2018).
Fear of missing out is also associated with problematic use of social networking
sites (Alt, 2015; Beyens et al., 2016). As Whelan et al. (2017) explained, “In a vicious
cycle, the possibility to be constantly connected with others may further fuel FoMO,
driving people towards greater use of social media” (p. 3). Problematic social networking
site use is likely the result of a deficit in need satisfaction; accordingly, fear of missing
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out mediates the relationship between poor needs satisfaction and problematic social
networking site behaviors (Przybylski et al., 2013).
A number of researchers have reported on the correlation between fear of missing
out and social media use. For example, Beyens et al. (2016) studied the relationship
between fear of missing out, the need for popularity, and Facebook use among a sample
of 402 adolescents. Analysis revealed the need for popularity was positively associated
with Facebook use, and fear of missing out moderated this association. Duman and
Ozkara (2019) examined the mediating role of fear of missing out in the relationship
between social identity and online game addiction, as well as the moderating role of the
need to belong in the relationship between social identity and online game addiction.
Analysis revealed that the relationship between social identity and addictive gaming
behavior was fully mediated by players’ fear of missing out (Duman & Ozkara, 2019).
Social Media Use and Academic Outcomes
Several researchers have examined the links between social media use and
academic outcomes and found that excessive or problematic use is associated with poorer
academic outcomes (Felisoni & Godoi, 2018; Kates et al., 2018; Rozgonjuk & That,
2017; Wammes et al., 2019). As Rozgonjuk et al. (2019) explained, factors that link the
use of technology and social media to poor academic outcomes need to be understood to
identify students at-risk for negative effects of use, and to develop ways to mitigate such
risks. Some of these factors may be social in nature, as students face increasing pressures
to fit in and stay abreast of events and information within their social circles. Because
social information proliferates through social media, fears of missing out on information,
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when not constantly engaging with social media, may foster excessive and distracting
social media use. Distracting social media use, in turn, may undermine academic
performance.
Fear of Missing Out and Academic Outcomes
Fear of missing out has also been examined in relation to academic outcomes, and
most recent investigations examine academic outcomes associated with technology use
that is prompted by the fear of missing out. A number of researchers have investigated
the relationships between fear of missing out and academic performance (Lemay et al.,
2019; Qutishat & Sharour, 2019; Rozgonjuk et al., 2019). For example, Rozgonjuk et al.
(2019) examined the relationships between instant notifications on users’ smart phones,
fear of missing out, and surface learning. Instant notifications are also known as push
notifications or pop-ups, are designed to alert users about updated content or information.
Surface learning describes an instrumental approach to learning that is often incentivized
by external factors and associated with minimal effort made studying or trying to
synthesize new information (Dolmans et al., 2016). Rozgonjuk et al. surveyed 316
undergraduate students in the United States and found students with greater fear of
missing out were more likely to interact with instant notifications and become distracted
from current tasks by the notifications they received on their phones. Further, a positive
relationship was noted between fear of missing out and surface learning, suggesting
people with greater fear of missing out may place more of their attention on social
interactions and experiences than on their studies. The researchers concluded that
35
notifications can disrupt studying, especially for those with higher levels of fear of
missing out (p. 6).
Not all investigations reveal fear of missing out negatively influences academic
performance. Lemay et al. (2019) employed self-determination theory to examine the
relationship between fear of missing out, loneliness, self-determination, and academic
performance. Survey data from 102 university students revealed fear of missing out was
positively related to academic performance, while autonomy was negatively associated
with academic performance. In a similar investigation, Qutishat and Sharour (2019)
investigated associations between fear of missing out and academic outcomes. The
researchers surveyed university students from Oman and found no statistically significant
relationship between the two variables. The positive effect of fear of missing out on
academic performance reported by Lemay et al., and the lack of relationship reported by
Qutishat and Sharour (2019) contradict findings from Rozgonjuk et al.’s (2019) study.
The inconsistency in findings regarding the effect of fear of missing out on academic
performance suggests additional research is needed to better understand the relationships
between these constructs.
Nonacademic Social Media Multitasking
The negative effects of social media use on academic outcomes is likely the result
of multitasking behaviors that distract students’ attention from their academic endeavors.
Factors that distract students’ attention can interrupt learning and academic achievement
because the ability to focus one’s attention on academic tasks is essential to processing
information and excelling in academic settings (van der Schuur et al., 2019).
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Nonacademic social media multitasking describes social media use that is not directly
associated with classroom activities and can distract from learning (Ravizza et al., 2014).
These types of multitasking behaviors are also described in the cognitive science
literature as task switching (Junco, 2015). Today’s students of all levels, especially those
in college, multitask more than any previous generation (Junco, 2015). Social media
multitasking is particularly prevalent among youth and young adults between the ages of
13 and 24 (Voorveld & van der Goot, 2013).
The relationship between social media multitasking and academic outcomes is
important to understand in light of school initiatives that require students to own laptop
computers, as well as the pervasive access and influence of technology (Junco, 2015).
When students have laptops in class, they may be more likely to engage in off-task or
multitasking behaviors. With connected laptops and other smart devices in hand, students
can easily connect to social media, exchange instant messages, surf the net, and engage in
other off-task activities during lectures (Demirbilek & Talan, 2017).
Nonacademic social media use is associated with poor academic performance on
standardized tests (Ravizza et al., 2014) and low self-reported GPA (Lau, 2017). In most
of the studies on the effects of nonacademic social media multitasking, academic
outcomes are assessed as GPA or test scores (le Roux et al., 2017). Nonacademic social
media use may occur as social media multitasking. According to Konova and Chiang
(2015), social media multitasking involves simultaneously engaging in several activities,
at least one of which is social media. In a classroom setting, an example of nonacademic
social media multitasking could involve simultaneously taking lecture notes while
37
checking social media feeds. The reasons for the negative effects of multitasking
behaviors on academic outcomes have been examined. Early cognitive science
researchers found that cognitive bottlenecking can occur when the simultaneous
performance of two tasks increases the time needed to perform the second task (Welford,
1967). As Junco (2015) explained, “trying to attend to more than one task at a time
‘clogs’ up the bottleneck by overloading the capacity of the human information
processing system” (p. 20).
A number of researchers have examined the effects of social media multitasking
behaviors on various academic outcomes. For example, Junco (2015) studied the
relationships between time spent using Facebook, time spent multitasking on Facebook,
and GPA. Data were collected via 1,774 surveys completed by U.S. undergraduate
students. The researcher found that students significantly multitasked Facebook use and
academic work. Sophomores demonstrated the most social media multitasking with
Facebook, at 70 minutes per day. Seniors performed the least amount of social media
multitasking, but still spent 49 minutes per day switching between Facebook use and
scholarly work. An important finding from Junco’s study was that the effects of
Facebook use and multitasking activities varied by class rank. For example, time spent on
Facebook was negatively associated with GPA among freshmen, but not students from
other ranks. Among freshmen, sophomores, and juniors, multitasking with Facebook was
a negative predictor of GPA; however, the negative association was not present for
seniors.
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Demirbilek and Talan (2017) also explored the ways nonacademic multitasking
with Facebook affected academic outcomes of college students. In this study,
nonacademic multitasking included the following non-lecture-related uses: multitasking
with phones, sending text messages, reading Facebook profiles, browsing news, and
looking at shared multimedia. Academic outcomes from the Facebook multitasking group
were compared to those of a control group that only took pen-and-paper notes during
lectures. The researchers found that grade performance declined when students engaged
in nonacademic multitasking via Facebook or texting. The researchers suggested that the
reason for the negative effect of multitasking behaviors may be due to limited capacity
for cognitive processing.
Cognitive Overload
Despite research recognizing the benefits of social media (Chiang et al., 2019;
Domahidi, 2018; Edwards et al., 2015; Roberts & David, 2020; Seo et al., 2016; Utz,
2016), such benefits can disappear when social media use exceeds an optimal level;
Whelan et al. (2020) referred to this phenomenon as social media overload. Two
dimensions of social media overload exist, including information overload and
communication overload (Whelan et al., 2017). Information overload occurs when the
available information to be processed exceeds the cognitive processing abilities of an
individual, while communication overload occurs when interruptions from
communication demands, such as responding to text messages or emails, exceed an
individuals’ communication capacity (Whelan et al., 2017).
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Other types of overload can also occur from social media use, including social
overload, connection overload, information overload, and communication overload
(Whelan et al., 2020). Social overload occurs when a social media user perceives they are
receiving too much support through social platforms, while connection overload
describes the demands placed on an individual to receive, maintain, and update
information on social media. Information overload happens when the information
received on a social platform exceeds an individuals’ processing capabilities. Finally,
communication overload happens when communicative demands of social media exceed
the processing capabilities of users. Together, these types of social media-induced
overload can contribute to the phenomenon known as cognitive overload.
The negative academic effects of social media use and multitasking are often
understood through cognitive load theory (Lau, 2017). As Byyny (2016) explained,
“Social norms have introduced a presumed requirement to participate, and collaborate, in
every message we receive” (p. 2). The growing prevalence and use of social media have
exposed users to more information than ever before, often requiring cognitive processing
that exceeds their mental capacities (Whelan et al., 2017). The development of new
information and communication technologies has resulted in an enormous amount of
online content in competition for users’ attention (Kononova & Chiang, 2015). While the
potential for technology continues to expand, the cognitive capabilities of humans are
limited (Byyny, 2016). The increase in available information and communication does
not result in increased processing speed of the human brain; rather, efforts to maintain
pace with social expectations of participation, communication, and digesting information
40
requires an increasing proportion of individuals’ time, attention, and cognitive
capabilities (Byyny, 2016). As Byyny explained,
We are continuously distracted from important priorities – thinking, learning,
reflecting, decision-making. The presentation of information has become an
insidious influence in the loss of our intellectual independence. We are
bombarded with jibber-jabber, rumor, and opinions that are often biased with
inaccurate or false information. (p. 3)
Increased demands on individuals’ attention and cognitive abilities, often inherent
to social media use, can result in cognitive overload (Whelan et al., 2017). Cognitive load
describes the cognitive demands placed on a learner’s information processing system,
based on the type, design, and nature of the material (Edwards et al., 2015). As Byyny
(2016) explained, cognitive overload describes the amount of mental effort utilized by
human’s working memory at any time. Limitations to cognitive processing and working
memory undermine individuals’ abilities to process new information (Demirbilek &
Talan, 2017). Switching between tasks, as required for nonacademic social media
multitasking, requires a change of attention and redirection of processing energy
(Demirbilek & Talan, 2017).
Cognitive load theory (Sweller, 1994) posits that the performance of one task can
be undermined when another task is simultaneously performed because of limitations to
cognitive processing abilities. That is, task performance declines when multiple tasks are
performed at once. Sweller’s (1994) cognitive load theory proposes that an individual’s
total cognitive processing abilities is the sum of intrinsic, extraneous, and germane loads.
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In cognitive load theory, intrinsic load represents the mental demand required to process
learning materials (Edwards et al., 2015). Germane load is the demand placed by
transferring processed learning materials into schema for storage in long-term memory
(Paas et al., 2004). Extraneous load represents “unnecessary materials or activities that
are unrelated to or distracting from the learning tasks; this includes multitasks and
distractions” (Edwards et al., 2015, p. 3028). Extraneous loads can include instructional
aspects that do not directly contribute to learning.
According to Paas et al. (2004), learning is negatively affected when the demand
imposed by extraneous load interferes or distracts from the construction of schemas; thus,
for learning to be effective, extraneous loads must be kept low. Extraneous loads, such as
nonacademic social media multitasking, have the potential to override the processing of
learning materials when competition for cognitive resources occurs. This potential effect
of extraneous loads becomes even more evident when extraneous demands are more
engaging, motivating, and require more attention (Edwards et al., 2015).
Academic Effects
Cognitive overload has been associated with negative learning outcomes in a
variety of contexts. For example, Jiang et al. (2016) found that individuals’ ability to
comprehend information was negatively affected by browsing micro-blogging posts
through the Chinese social media platform, Weibo; mediation analysis revealed that
cognitive overload mediated this relationship. While the posts on microblogging sites like
Twitter are limited to 140 characters, participants face a virtually unlimited number of
42
messages. The quantity of messages can contribute to cognitive overload, as can the
complexity and vagueness inherent to microblogging posts (Jiang et al., 2016).
Whelan et al. (2020) also studied the mechanics of overload caused by social
media. The researchers surveyed a sample of 186 college students to understand how
cognitive overload caused by social media impacted academic performance. Data were
collected via survey, which measured fear of missing out, information overload,
communication overload, deficient self-regulation, and academic performance. Results
indicated fear of missing out created overload, which undermined users’ abilities to self-
regulate. Faulty self-regulation, in turn, resulted in reduced academic performance.
Findings from Whelan et al.’s study contributed valuably to the existing body of research
because they provide a more detailed explanation of the causal pathway between social
media overload and academic performance.
Nonacademic Effects
The detrimental effects of cognitive overload, which encompasses social
overload, information overload, and communication overload, extend far beyond the
classroom. According to Whelan et al. (2017), almost 30% of workers’ time is spent
dealing with interruptions, including those caused by social media. The associated losses
in productivity equate to annual losses of $650 billion. Cognitive overload is also
associated with increased stress (Matthews et al., 2019), poor decision-making (Sos et al.,
2019), mental health problems (Collins, 2020), depression (Arcand et al., 2020), and
declines in performance (Biondi et al., 2020). Other negative effects of cognitive
overload on workers include deterioration in communication skills, increased rate of
43
errors, and difficulties completing tasks that were previously achievable (Iskander, 2019).
Among athletes, cognitive burnout is associated with reduced physical performance
(Kalenscher, 2019). Acculturative stress can contribute to cognitive overload among U.S.
immigrants (Pena, 2019). Difficult courses can create cognitive among students,
especially those just beginning their postsecondary education (Benko et al., 2019).
Cognitive Overload and Fear of Missing Out
Cognitive overload may be exacerbated by the fear of missing out. Whelan et al.
(2017) examined the relationship between three aspects of cognitive control,
communication overload, and information overload. The three aspects of cognitive
control examined by the researchers included the fear of missing out, internet cognitive
failure, and poor self-regulation. Internet cognitive failure occurs when an individual
loses focus, becomes absent-minded, and makes errors while completing internet-related
tasks that they would normally be able to perform, error-free (Whelan et al., 2017).
Deficient self-regulation occurs when individuals lose self-control required to moderate
their consumption of media (Whelan et al., 2017).
Whelan et al. (2017) collected survey data from 129 U.S. and Irish college
students and found that all three aspects of cognitive control were associated with
communication overload. Specifically, the researchers reported fear of missing out
caused individuals to actively use many channels of communication, which provided
them relief in believing that if something happened, they would know about it. However,
limitations to the amount of communication an individual can effectively process can
result in communication overload. While internet cognitive failure and poor self-
44
regulation were positively associated with information overload, the fear of missing out
was not. The researchers posited that the reason for this lack of association may be that
individuals experiencing fear of missing out are more likely to turn to communicative
channels such as email, texting, and social media to ease their discomfort, than they are to
seek out information.
Social Media and the Fear of Missing Out
Research indicates that the fear of missing out can prompt social media use.
Modern smart devices provide users with constant access to social media, allowing them
to constantly observe updates from those in their networks. In this way, social media can
drive the fear of missing out and prompt users to engage with their smart devices so they
do not miss any updates from individuals in their networks (Rozgonjuk et al., 2019). For
example, Wolniewicz et al. (2018) found that fear of missing out was associated with
both social and problematic smartphone use, illustrating how individuals high in fear of
missing out may use their connected devices to access social networks. Several other
researchers have reported the strong, positive association between problematic
smartphone use and fear of missing out (Alt & Boniel-Nissim, 2018; Wolniewicz et al.,
2018). Makki et al. (2018) found that use of the social media platform, Snapchat, was
associated with users’ desire to experience a sense of connection, affiliation, and
acceptance.
Social media use that is prompted by the fear of missing out can also include
unhealthy, addictive behaviors (Alt, 2017). Negative affect, such as isolation, depression,
and loneliness, can prompt social media engagement; this relationship can be partially
45
explained by the fear of missing out. Reer et al. (2019) surveyed a representative sample
of German internet users to explore how loneliness, anxiety, and depression related to
social media engagement, and how social comparison behaviors and fear of missing out
moderated those relationships. The researchers found individuals who experienced
loneliness, depression, and anxiety were more likely to engage in social media. The
relationship between these markers of well-being and social media engagement were
moderated by the fear of missing out and social comparison behaviors. That is, decreases
in well-being were associated with increases in fear of missing out and social comparison
behaviors, which then prompted increases in social media engagement.
Reer et al.’s (2019) findings reveal that fear of missing out can prompt individuals
to use social media, and those with lower levels of well-being are more likely to use
social media to soothe the fear of missing out. Oberst et al. (2017) explained that fear of
missing out may explain tendencies for those with chronic deficits in need satisfaction to
perpetually seek out opportunities to engage with social media and keep abreast of
updates, even when that engagement occurs during inappropriate times. Other researchers
have reported the association between fear of missing out and social media engagement is
influenced by users’ affective states. Among a sample of 386 undergraduate students,
Baker et al. (2016) found that fear of missing out was positively associated with social
media use, and higher levels of fear of missing out were associated with depression, a
lack of mindfulness, and physical symptoms. When the researchers included fear of
missing out in the model, time spent on social media was longer associated with
46
depression and lack of mindfulness, suggesting that fear of missing out is a stronger
predictor of social media engagement than individuals’ mental states.
In a similar investigation on fear of missing out, wellbeing, and social media
engagement, Alt (2018) found the relationship between maladjustment to college and
social media engagement was mediated by fear of missing out, leading the researchers to
conclude that fear of missing out could help explain social media use during class,
especially among students struggling to adjust to college. Oberst et al. (2017) also
explored the mediation of fear of missing out in a study on the negative effects of
problematic social media use. The researchers found that fear of missing out mediated the
relationship between psychopathological symptoms and negative consequences of heavy
social media use. Interestingly, the mechanisms of these mediations varied by gender;
depression triggered problematic social media use in girls, while anxiety was more likely
to prompt boys to engage in heavy social media use. Roberts and David (2018) also
examined fear of missing out, social media engagement, and well-being. Among a
sample of 458 college students, the researchers found that fear of missing out influenced
subjective well-being in direct and indirect ways, through social media intensity and need
for social connection. Dhir et al. (2018) reported compulsive social media use mediated
the association between social media fatigue and fear of missing out.
While low levels of well-being can prompt fear of missing out and social media
engagement, well-being can also be undermined by social media engagement and fear of
missing out. That is, poor states can contribute to fear of missing out and social media
use, and fear of missing out and social media use can contribute to poor states. This is an
47
important point to note because it reveals the downward spiral that individuals can
experience when they use social media to quell fear of missing out. For example, Scott
and Woods (2018) explored fear of missing out, social media use, and sleep habits and
outcomes among adolescent internet users. The researchers found that nighttime
engagement with social media was associated with later bedtimes and shorter sleep
duration. Fear of missing out predicted shorter sleep duration in two ways. First, late
night social media use resulted in later bedtimes, and increased levels of cognitive arousal
at night delayed the onset of sleep, resulting in fewer hours of rest.
Conclusion
Findings from this literature review confirm the ways social media use can
undermine academic achievement. A number of researchers have reported negative
relationships between social media use and academic outcomes, and the negative effects
of multitasking on academic outcomes have also been explored. Extensive findings on
the relationship between social media use and fear of missing out, in a variety of contexts
and among a wide variation in samples, underscore the importance of examining fear of
missing out in studies on social media use (Alt, 2017). Missing from the existing body of
literature is an examination of the way fear of missing out may moderate the relationship
between nonacademic social media multitasking and academic outcomes.
Researchers have examined the effects of social media use, as well as
multitasking, but less is known about social media multitasking. Further, conflicting
findings regarding the effects of fear of missing out, social media use, and multitasking
on academic outcomes indicate additional investigation is needed to better understand
48
relationships between these constructs. The current study aimed to answer that call for
investigation by examining the potential relationships between nonacademic social media
multitasking, academic performance (measured as self-reported, overall GPA), and fear
of missing out. Findings shed light on the ways nonacademic social media multitasking
and fear of missing out can affect academic performance. This chapter provided essential
background and context for the current investigation. The following chapter contains
details of the methodology, including the method, design, sampling strategy, data
collection procedures, and data analysis plan.
49
Chapter 3: Research Method
Though some research indicates that social media use is associated with poor
academic outcomes (Rozgonjuk et al., 2019), the mechanism of this relationship is less
understood. The purpose of this quantitative investigation was to examine the potential
relationships between nonacademic social media multitasking, academic performance
(measured as self-reported, overall GPA), and fear of missing out. This chapter includes
details of the study’s method and design. It begins with a discussion of the design and
rationale, followed by a discussion of my positionality, as the researcher. The population,
sample, and sampling strategy are discussed next. Procedures for recruitment,
participation, and data collection are then detailed. Instrumentation is outline and
variables are operationalized. The data analysis plan is described, followed by threats to
validity and how such threats will be mitigated. The chapter closes with the study’s
ethical procedures and a summary.
Research Design and Rationale
The nature of this research was a quantitative, nonexperimental design. A
quantitative method was selected because the aim was to explore relationships between
variables (Queirós et al., 2017), and the design was nonexperimental because the sample
was not randomized, nor were there control or intervention groups (Seeram, 2019). For
research question 1, the independent variable was nonacademic social media
multitasking, measured using Lau’s (2017) instrument on social media multitasking; the
dependent variable, academic performance, was measured using self-reported GPA. For
Research Question 2, the independent variable corresponded to fear of missing out, which
50
was measured using the Fear of Missing Out scale; the dependent variable corresponded
to nonacademic social media multitasking, which was measured using Lau’s (2017)
instrument on social media multitasking. For Research Question 3, the independent
variable corresponded to nonacademic social media multitasking; the dependent variable
corresponded to academic performance; and the moderator corresponded to fear of
missing out. Although significant time restraints were not associated with this design or
data collection strategy, it is important to mention financial constraints associated with
the use of SurveyMonkey. Because each completed survey was paid for, I only gathered
the number of completed surveys determined by the power analysis.
Methodology
Population
A target population describes the population for which inferences will be made;
the study population is a subset of the target population that is obtained through sampling
(Howe & Robinson, 2018). Information gathered from the study sample is used to make
inferences about the target population (Howe & Robinson, 2018). In the current study,
the target population included all U.S. undergraduate students who were currently
attending public, 4-year institutions in the United States. According to the National
Center for Education Statistics (2020), 9% of undergraduate students enrolled at public,
4-year universities were exclusively enrolled in distance courses; the other 91% took
classes exclusively on campus or participated in both online and in-person courses. The
original intention of this project was to sample only students attending in-person classes
in public, brick-and-mortar schools; however, due to the Covid-19 pandemic, many in-
51
person classes switched to online formats. For this reason, the target population of the
research included all undergraduate students attending 4-year institutions, regardless of
class format. The most recent data from the National Center for Education Statistics
revealed the total undergraduate enrollment at public 4-year institutions was
approximately 13 million students.
Sample and Sampling Strategy
The sample for this study consisted of U.S. undergraduate college students
enrolled at public, 4-year universities. To be eligible for this study, individuals had to be
currently enrolled in a 4-year, postsecondary U.S. institute, be at least a sophomore, and
be at least 18 years old. Participants were recruited via SurveyMonkey based on these
criteria. SurveyMonkey has a database of registered users who can be recruited based on
criteria provided by researchers. These criteria were sent to SurveyMonkey, and the
company distributed the study invitation to eligible individuals. A convenience sampling
strategy was used, which is a nonprobability sampling strategy in which participants are
selected based on practical criteria such as accessibility or their willingness to participate
(Etikan et al., 2016). In this way, the sample was not random and may not be
representative of the population of U.S. students attending 4-year schools. This is a
limitation associated with convenience sampling (Etikan et al., 2016).
Sample Size
The sample size was determined based on a power analysis performed using
G*Power 3.1.9 software (Faul et al., 2014). The primary inferential analysis consisted of
regression analyses. The following criteria were entered: a conventional power of 0.80, a
52
significance level of α = 0.05, and a medium effect size, f
2
= 0.15. The hierarchical
regression for Research Question 3 included three total predictors. For a regression
analysis with these parameters, the minimum sample for this research was 77
participants.
Recruitment, Participation, and Data Collection
Study participants were recruited through SurveyMonkey, which is a web-based
survey company that assists researchers with recruitment and data collection.
SurveyMonkey was used for this study because it provided timely data collection using a
sample of individuals from all over the country. In addition, I selected the inclusion
criteria so only eligible individuals received a study invitation, helping to ensure findings
were relatively reflective of the study population, despite the lack of representativeness.
The sample was limited to individuals ages 18 years and older to avoid the ethical
challenges associated with sampling minors. In addition, only students who had been in
college for at least 1 full year were included to ensure all participants possessed
postsecondary experience needed to gauge fear of missing out and nonacademic social
media multitasking behaviors among college students. Students under the age of 18 and
those who were not currently enrolled in a public, 4-year postsecondary institution were
not eligible to participate.
Data were collected via online survey, which was distributed through
SurveyMonkey. The online survey consisted of the Fear of Missing Out Scale
(Przybylski et al., 2013), items from Lau’s (2017) study on social networking and
academic performance, and a demographic questionnaire that gathered descriptive
53
statistics, including respondents’ GPA. I selected online surveys over traditional, paper-
and-pencil surveys for two main reasons. First, in light of the Covid-19 pandemic, online
surveys were safer and preferred. Online surveys do not possess risks of disease transfer,
which could occur with traditional paper surveys that respondents are required to
physically handle and fill out. Second, online surveys help ensure anonymity and are
often more reliable than traditional surveys (Tuten, 2010). Traditional surveys tend to be
more costly to administer than online surveys (King et al., 2014), and obtaining the
mailing addresses of prospective participants would be more difficult than distributing
the survey online (Saleh & Bista, 2017).
I uploaded the online survey to SurveyMonkey and provide the company with the
list of inclusion criteria as well as the required sample size. SurveyMonkey then sent
study invitations to eligible individuals. The study invitation outlined the study’s purpose,
inclusion criteria, and participation requirements. Interested individuals were prompted to
click a link embedded within the email invitation, which took them to the study survey
via a new window. The first page of the survey was the informed consent form, which
once again detailed the study’s purpose, inclusion criteria, and participation requirements.
In addition, the informed consent form assured respondents of anonymity, explained
participation was completely voluntary, and provided my contact information for any
questions they may have had. To access the study survey, respondents were required to
click a button at the bottom of the consent form, indicating their understanding of the
consent and agreement to participate. Individuals who did not provide consent were
redirected to a screen thanking them for their consideration and then exited from the
54
survey. Individuals who provided consent were taken to the first page of the survey.
Upon completion of the survey, participants were sent to a screen thanking them for their
time and participation, and then they were exited from the survey. The data collection
period lasted less than 1 week. After the required sample of 77 completed surveys were
gathered, the survey was closed and downloaded to analyze the raw data.
Instrumentation and Operationalization of Constructs
Data for this study were collected via online survey, which was distributed via
SurveyMonkey. The survey was used to measure the three study variables, including fear
of missing out, nonacademic social media multitasking, and academic performance. The
measurement of each of these variables is described in the following sections.
Fear of Missing Out
Fear of missing out describes an apprehension that others are having rewarding
experiences that one is missing out on, when absent (Rozgonjuk et al., 2019). Fear of
missing was assessed using the Fear of Missing Out Scale (Przybylski et al., 2013;
Appendix A). This scale consists of 10 items designed to measure apprehension
associated with missing out on potentially rewarding experiences. Each item is a
statement that is responded to along a five-point scale ranging from not at all true of me
to extremely true of me. The instrument is scored by averaging responses to all 10 items,
producing a score ranging from 1 to 5. This variable was treated as interval. A sample
item is: “I fear my friends have more rewarding experiences than me.” The Fear of
Missing Out Scale has strong internal consistency. Rozgonjuk et al. (2019) reported a
Cronbach’s alpha of .89. Other researchers have reported Cronbach’s alphas of .80
55
(Wang et al., 2018) and .84 (Beyens et al., 2016; Duman & Ozkara, 2019). This scale is
open access and is free to use for academic purposes.
The Fear of Missing Out Scale (Przybylski et al., 2013) has been extensively used
in research on the fear of missing out construct. For example, Beyens et al. (2016) used
the instrument to examine the relationships between fear of missing out, Facebook use,
stress, and social needs among adolescents. Rozgonjuk et al. (2019) employed the
measure to assess associations between fear of missing out, smartphone notifications, and
learning among college students. Among a sample of adolescents, Oberst et al. (2017)
studied the way fear of missing out mediated the relationship between social networking
behaviors and negative affect, such as anxiety and depression. The Fear of Missing Out
Scale was selected for the current study because of its strength and widespread use by
previous researchers.
Social Media Multitasking
Media multitasking describes simultaneously participating in multiple activities –
at least one of which involves media use (Konova & Chiang, 2015). Nonacademic social
media multitasking was assessed using three items used in Lau’s (2017) study on social
media multitasking and academic performance (Appendix B). Permission was obtained
from the author to use these three items in the current study. Those three items were “I
multitask with my social media account while studying,” “I do not check my social media
account if I am doing my work for school” (reverse coded), and “I remain online with my
social media site(s) while doing homework.” The items were rated along a five-point
Likert-like scale ranging from strongly disagree to strongly agree. The instrument is
56
scored by averaging responses to all 3 items, producing a score ranging from 1 to 5. This
variable was treated as interval. Lau (2017) adapted these items from the instrument in
Ozer’s (2014) study on Facebook addiction, social media multitasking, and academic
performance. Although the effects of social media multitasking on academic performance
have been examined by other researchers, little was known about the specific effects of
nonacademic social media multitasking. This scale was selected because it had been
previously used in the little available research on nonacademic social media multitasking
(Lau, 2017; Ozer, 2014), and has demonstrated reliability, as reported by Lau and Ozer
(2014). Lau reported the scaled to be unidimensional, with acceptable reliability of .719.
Academic Performance
Academic performance describes outcomes in educational settings, such as test
scores, course grades, or GPA (Womack & McNamara, 2017). Academic performance
was measured as students’ self-reported GPA. GPA data were collected in the
demographic questionnaire (Appendix C). Self-reported GPA is one of the most
commonly used measures of academic performance (York et al., 2015). This variable was
treated as interval.
Data Analysis Plan
Study data were downloaded from SurveyMonkey and then uploaded into SPSS
version 27.0 for Windows. Data were screened for missing cases and incomplete surveys
were removed. Frequencies and percentages were used to explore the trends in the
demographic variables. Means and standard deviations were used to explore the trends in
the three variables of interest: fear of missing out, nonacademic social media
57
multitasking, and academic performance. Cronbach’s alpha test of internal consistency
and reliability was examined for the fear of missing out and nonacademic social media
multitasking scales. The strength of the alpha values will be interpreted through
suggestions by George and Mallery (2016), in which α > .9 Excellent, α > .8 Good, α > .7
Acceptable, α > .6 Questionable, α > .5 Poor, and α < .5 Unacceptable.
To address Research Question 1—To what degree is nonacademic social media
multitasking (as assessed by Ozer’s items for measuring social media multitasking)
related to academic performance (as assessed by self-reported GPA) among U.S.
undergraduate students?—an ordinal logistic regression was conducted to examine the
predictive relationship between nonacademic social media multitasking and academic
performance among U.S. undergraduate students. An ordinal logistic regression is
appropriate when testing the predictive relationship between an independent variable and
an ordinal dependent variable (Tabachnick & Fidell, 2013). The independent variable
corresponded to nonacademic social media multitasking, which was measured using
Lau’s (2017) instrument on social media multitasking. The dependent variable, academic
performance, was measured using self-reported GPA.
There are not strict parametric assumptions to verify for an ordinal logistic
regression. The primary assumption is that the dependent variable is an ordinal
measurement, which was supported due to academic achievement being a multiple-
choice response. The second assumption is that there are one or more predictor variables
that are continuous, ordinal, or categorical. There was one continuous predictor variable
in the regression model. The χ
2
test and Wald test were used to assess the predictive
58
ability between nonacademic social media multitasking on academic performance. The χ
2
test assessed the predictive effect of the collective regression model. The Wald test
assessed the predictive effect of the independent variable specifically. Due to one
predictor being examined, the p-values matched between the χ
2
test and Wald test. The
coefficient of determination, Nagelkerke R
2
, explained the amount of variance in
academic performance that could be explained by nonacademic social media
multitasking.
To address Research Question 2—To what degree is fear of missing out (as
assessed by the Fear of Missing Out Scale) related to nonacademic social media
multitasking (as assessed by Ozer’s items for measuring social media multitasking),
among U.S. undergraduate students?—a simple linear regression was conducted to
examine the predictive relationship between fear of missing out and nonacademic social
media multitasking among U.S. undergraduate students. The independent variable
corresponded to fear of missing out, which was measured using the Fear of Missing Out
scale. The dependent variable corresponded to nonacademic social media multitasking,
which was measured using Lau’s (2017) instrument on social media multitasking
Prior to analysis, the assumptions of a simple linear regression were tested,
including linearity, normality, and homoscedasticity. All three assumptions were assessed
with scatterplots. Linearity was assessed with a scatterplot between nonacademic social
media multitasking and fear of missing out. The assumption of linearity would be met if
the data followed a distinct trend (Howell, 2013). Normality as tested with a P-P
scatterplot. The assumption of normality would be met if the data in the scatterplot
59
followed the normality trend line (Field, 2013). Homoscedasticity was tested with a
residuals scatterplot. The assumption of homoscedasticity was met if the data did not
follow a clear pattern (Pallant, 2013).
Following an examination of the assumptions, the findings of the linear regression
were examined. The F test and t-test were used to assess the predictive ability between
fear of missing out and nonacademic social media multitasking. The coefficient of
determination, R
2
, explained the amount of variance in nonacademic social media
multitasking scores that could be explained by fear of missing out scores. If the F test and
t-test were significant, the unstandardized beta coefficient (β) would explain how
nonacademic social media multitasking changed based on a one-unit increase in fear of
missing out scores.
To address Research Question 3—To what degree does fear of missing out
moderate the relationship between nonacademic social media multitasking and academic
performance?—Baron and Kenny’s (1986) method for moderation was used to test
whether fear of missing out moderated the relationship between nonacademic social
media multitasking and academic performance. Moderators alter the direction or strength
of a relationship between an independent variable and a dependent variable (Baron &
Kenny, 1986). A stepwise ordinal logistic regression model was used to test the
moderating effect. The independent variable corresponded to nonacademic social media
multitasking. The dependent variable corresponded to academic performance. The
moderator corresponded to fear of missing out. An interaction term was created between
60
nonacademic social media multitasking and fear of missing out. The same assumptions
were verified in research question one.
Using a hierarchical regression, the independent variables were entered in two
steps. In the first step of the regression model, the independent variable (nonacademic
social media multitasking) and the moderator (fear of missing out) were entered into the
model. In the second step, the interaction term nonacademic social media
multitasking*fear of missing out was added to the model. A significant interaction term
suggested moderation was supported. Results of the analysis are presented in Chapter 4
of this dissertation. Findings were also posted on social media, to make available to the
public.
Threats to Validity
Threats to external validity are factors that affect an outcome and can hinder the
generalization of the findings. Through use of a convenience sampling approach,
selection bias can potentially cause limit the generalizability of the findings. In addition,
statistical conclusions can be limited based on the findings of the parametric assumptions
of linearity, normality, homoscedasticity, and absence of multicollinearity. I was cautious
in the interpretation of the findings and did not automatically generalize the findings to
the overarching population.
Threats to internal validity correspond to limitations that exist within the scope of
the current research. The selection of a quantitative methodology limited my ability to
explore underlying thoughts and perceptions of the participants. Qualitative methods
allow for a more in-depth examination of the experiences of participants. I traded the
61
richness of qualitative data for a level of statistical significance that relationships exist
between the variables of interest. There was also the potential threat that confounding
variables altered the statistical relationships established in the analysis. It was not
possible to control or account for every potential confounding variable in the research.
Ethical Procedures
To ensure the ethical treatment and protection of all study participants, a number
of ethical procedures were implemented. First, prior to participant recruitment and data
collection, permission was obtained from Walden University’s Institutional Review
Board (approval number 03-05-21-0641743). Because the study survey was online and
anonymous, there was no risk to the identities of any respondents. Deidentification of
study data was not necessary because no identifying information was collected. In order
to ensure autonomy and respect for all participants, informed consent procedures were
followed. Prior to accessing the online survey, respondents were required to provide
informed consent by reading the first page of the survey form and checking a box that
says “I agree to participate in this study.” After indicating consent, individuals were
forwarded to the first question of the online survey. Individuals who did not agree to the
informed consent from were exited from the survey and forwarded to a screen expressing
gratitude for their consideration.
There were no significant risks associated with participation, beyond minimal
stress that may result from personal reflection. No incentives were offered for
participation and no individuals with whom I had personal or professional relationships
were knowingly recruited. Although study data were completely anonymous, raw data
62
were carefully protected. After an adequate sample was obtained, I closed the survey and
download the raw data from SurveyMonkey. Data were stored on my personal, password-
protected computer. Analysis was also conducted on my personal computer. I will
securely store data for a period of 5 years, post study, as required by Walden University.
After the 5-year period has passed, I will destroy all raw data.
Summary
The purpose of this quantitative investigation was to examine the potential
relationships between nonacademic social media multitasking, academic performance
(measured as self-reported, overall GPA), and fear of missing out. Data were collected
via online survey, distributed via SurveyMonkey. The survey was used to measure the
three study variables, including fear of missing out, nonacademic social media
multitasking, and academic performance. A minimum sample of 77 participants was
recruited. To be eligible, individuals had to be currently enrolled in a 4-year, post-
secondary U.S. institution, be at least sophomores, and be at least 18 years old. The first
two research questions were assessed via simple linear regressions. To address research
question three, Baron and Kenny’s (1986) method for moderation was used to test
whether fear of missing out moderated the relationship between nonacademic social
media multitasking and academic performance. This chapter provided details of the
current study’s research methods. Results from the analysis are presented in Chapter 4. A
discussion of findings appears in Chapter 5.
63
Chapter 4: Results
The purpose of this quantitative investigation was to examine the potential
relationships between nonacademic social media multitasking, academic performance
(measured as self-reported, overall GPA), and fear of missing out. The study was guided
by three research questions related to the degree that nonacademic social media
multitasking affects academic performance and the degree that fear of missing out is
related to nonacademic social media multitasking and moderates this relationship. In this
chapter, the findings of the data analysis are presented. The chapter begins with a
description of the data collection process and sample. Results are presented by research
question. Frequencies and percentages were used for the nominal-level variables. Means
and standard deviations were examined for the continuous-level data. To address the
research questions, a series of regression analyses were conducted. Statistical significance
was evaluated at the generally accepted level, α = .05.
Data Collection
Data were collected via online survey, which was distributed through
SurveyMonkey. The online survey consisted of the Fear of Missing Out Scale
(Przybylski et al., 2013), items from Lau’s (2017) study on social networking and
academic performance, and a demographic questionnaire that gathered descriptive
statistics, including respondents’ GPA. I partnered with SurveyMonkey for participant
recruitment and data collection, employing the company to distribute the survey
invitation to individuals who met the following inclusion criteria: (a) were enrolled in a
4-year, post-secondary U.S. institute, (b) were at least a sophomore, and (c) were at least
64
18 years old. A power analysis using G*Power 3.1.9 software revealed a minimum
sample of 77 participants was needed. After the survey was launched, data collection was
complete in less than 24 hours. The final sample consisted of 99 individuals. Because this
was a nonprobability convenience sample, findings cannot be generalized to the entire
U.S. population of undergraduate students.
Results
A total of 115 participants were invited to complete the survey questionnaire. Six
participants did not consent to respond to the questionnaire. In addition, 10 participants
consented but did not respond to a majority of the questionnaire. These participants were
subsequently removed. Potential outliers in the data were examined by converting the
fear of missing out and nonacademic social media multitasking data to z scores. No
outliers were found in the dataset. The final sample consisted of 99 participants and a
response rate of 86%.
The sample included 52 males, 46 females, and one individual who identified
their gender as “Other.” In terms of racial identity, the majority (n = 54, 54.55%) of the
sample consisted of Caucasian students, followed by African American students (n = 29,
29.29%). In terms of academic performance, the GPA of most participants ranged
between 3.00–3.99 (n = 61, 61.62%). Approximately one-fifth (n = 20, 20.20%) of the
participants reported GPAs of 4.00 or above. Collectively, over 80% of the respondents
had GPAs of at least 3.00, suggesting an overall high level of academic performance
among respondents. Frequencies and percentages are presented in Table 1.
65
Table 1
Frequency Table for Nominal Variables
Variable n %
Sex
Male 52 52.53
Female 46 46.46
Other 1 1.01
Race
White or Caucasian 54 54.55
Black or African
American 29 29.29
Hispanic or Latino 6 6.06
Asian or Asian American 8 8.08
Native Hawaiian or Other
Pacific Islander 1 1.01
Biracial 1 1.01
GPA
1.00–1.99 3 3.03
2.00–2.99 15 15.15
3.00–3.99 61 61.62
4.00 and over 20 20.20
Note. Due to rounding errors, percentages may not equal 100%.
Composite scores were developed for fear of missing out and nonacademic social
media multitasking by computing an average of the respective items comprising the
scales. Cronbach’s alpha test of internal consistency and reliability were examined for the
two scales. The strength of the alpha values was assessed through use of the guidelines
suggested by George and Mallery (2016), in which α > .9 excellent, α > .8 good, α > .7
acceptable, α > .6 questionable, α > .5 poor, and α < .5 unacceptable. Fear of missing out
(α = .91) reached the acceptable threshold for internal consistency. Nonacademic social
media multitasking (α = .36) had low reliability, which may be attributed to the low
number of items comprising the scale. The scoring instructions were followed, and one
item was reverse scored, which did not improve the reliability coefficient. Therefore,
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findings of the descriptive and inferential analysis for nonacademic social media
multitasking will be interpreted with a level of caution.
Fear of missing out scores ranged from 1.00 to 5.00, with M = 2.77 and SD =
0.99. Nonacademic social media multitasking scores ranged from 1.00 to 5.00, with M =
3.18 and SD = 0.85. Figures 2 and 3 present histograms of the data. Both fear of missing
out and nonacademic social media multitasking appeared to follow an approximate bell-
shaped distribution.
Figure 2
Histogram for Fear of Missing Out Scores
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Figure 3
Histogram for Nonacademic Social Media Multitasking Scores
Research Question 1
Research Question 1: To what degree is nonacademic social media multitasking
(as assessed by Ozer’s items for measuring social media multitasking) related to
academic performance (as assessed by self-reported GPA) among U.S. undergraduate
students? To address Research Question 1, an ordinal logistic regression was conducted
to examine the relationship between multitasking and academic performance. The
predictor variable corresponded to social media multitasking and the criterion variable
corresponded to academic performance (GPA). An ordinal logistic regression does not
have strict parametric assumptions in comparison to a linear regression. The primary
assumption is that the outcome variable is an ordinal measurement, which is supported
due to academic achievement being a multiple-choice response. The second assumption
is that there are one or more predictor variables that are continuous, ordinal, or
categorical. There was one continuous predictor variable in the regression model. The χ
2
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test and Wald test were used to assess the predictive ability between nonacademic social
media multitasking on academic performance. The χ
2
test assessed the predictive effect of
the collective regression model. The Wald test assessed the predictive effect of the
independent variable specifically. Due to one predictor being examined, the p-values
matched between the χ
2
test and Wald test. The coefficient of determination, Nagelkerke
R
2
, explained the amount of variance in academic performance that could be explained by
nonacademic social media multitasking.
The results of the model were not statistically significant, χ
2
(1) = 0.47, p = .493,
indicating there was not a significant predictive relationship between nonacademic social
media multitasking and academic performance. The Nagelkerke R-squared was .005,
indicating that approximately 0.5% of the variance in academic performance could be
explained by nonacademic social media multitasking. Due to non-significance of the
overall model, the individual predictor of nonacademic social media multitasking was not
examined further. The null hypothesis (H
01
) was not rejected for research question one.
Table 2 summarizes the results of the ordinal regression model.
Table 2
Ordinal Logistic Regression Results between Nonacademic Social Media Multitasking
and Academic Performance (GPA)
Predictor B SE Wald p
Nonacademic social media
multitasking 0.16 0.24 0.47 .496
Note. Overall model fit: χ
2
(1) = 0.47, p = .493
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Research Question 2
To what degree is fear of missing out (as assessed by the Fear of Missing Out
Scale) related to nonacademic social media multitasking (as assessed by Ozer’s items for
measuring social media multitasking), among U.S. undergraduate students?
To address research question two, a linear regression was conducted to examine the
relationship between fear of missing out and nonacademic social media multitasking. The
predictor variable corresponded to fear of missing out and the criterion variable
corresponded to nonacademic social media multitasking. Prior to analysis, the
assumptions of a linear regression were tested. Linearity was visually examined with a
scatterplot between fear of missing out scores and nonacademic social media
multitasking scores. The scatterplot depicted a slight positive trend, indicating the
assumption was supported (see Figure 4). Normality was visually assessed with a normal
P-P scatterplot. The data in the scatterplot closely followed the normality trend line,
suggesting the assumption of normality was verified (see Figure 5). The assumption of
homoscedasticity was visually tested with a residuals scatterplot. There was not a
recurring pattern in the residuals scatterplot, indicating the assumption for
homoscedasticity was supported (see Figure 6).
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Figure 4
Scatterplot Between Fear of Missing out and Nonacademic Social Media Multitasking
Figure 5
Normal P-P Scatterplot for Regression Model with Fear of Missing Out Predicting
Nonacademic Social Media Multitasking
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Figure 6
Residuals Scatterplot for Regression Model with Fear of Missing Out Predicting
Nonacademic Social Media Multitasking
The results of the regression model were statistically significant, F(1, 97) = 8.96,
p = .004, indicating a significant predictive relationship between fear of missing out and
nonacademic social media multitasking. The coefficient of determination, R-squared,
indicated approximately 8.5% of the variance in nonacademic social media multitasking
could be explained by fear of missing out scores. With every one-unit increase in fear of
missing out scores (B = 0.25, t = 2.99, p = .004), nonacademic social media multitasking
scores increased by approximately 0.25 units. The null hypothesis (H
02
) was rejected for
research question two. Table 3 summarizes the results of the ordinal regression model.
Table 3
Linear Regression Results for Fear of Missing Out and Nonacademic Social Media
Multitasking
Predictor B SE β t p
Fear of missing out 0.25 0.08 .29 2.99 .004
Note. Overall model fit: F(1, 97) = 8.96, p = .004
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Research Question 3
To what degree does fear of missing out moderate the relationship between
nonacademic social media multitasking and academic performance?
To address research question three, an ordinal logistic regression was conducted
in two steps to identify whether fear of missing out moderated the relationship between
nonacademic social media multitasking and academic performance. In step one, the
predictor (nonacademic social media multitasking) and moderator (fear of missing out)
were entered into the model. In step two, the interaction term was added to the model –
multitasking*fear of missing out. The criterion variable corresponded to academic
performance (GPA). The same assumptions were supported as research question one.
The results of the first step of the model were not statistically significant, χ
2
(2) =
1.57, p = .457, indicating that collectively, there was not a significant predictive
relationship between nonacademic social media multitasking, fear of missing out, and
academic performance. The Nagelkerke R-squared was .018, indicating that
approximately 1.8% of the variance in academic performance could be explained by
nonacademic social media multitasking and fear of missing out.
The results of the second step of the model were not statistically significant, χ
2
(3)
= 2.80, p = .423, indicating that collectively, there was not a significant predictive
relationship between nonacademic social media multitasking, fear of missing out,
multitasking*fear of missing out, and academic performance. The Nagelkerke R-squared
was .032, indicating that approximately 3.2% of the variance in academic performance
could be explained by nonacademic social media multitasking and fear of missing out.
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Due to the interaction term not indicating significance, there was not sufficient evidence
for moderation. The null hypothesis (H
03
) was not rejected for research question three.
Table 4 summarizes the results of the ordinal regression model.
Table 4
Two-Step Ordinal Logistic Regression Results between Nonacademic Social Media
Multitasking, Fear of Missing Out, and Academic Performance (GPA)
Predictor B SE Wald p
Step 1:
Nonacademic Social Media Multitasking 0.24 0.25 0.93 .336
Fear of Missing Out -0.23 0.21 1.14 .285
Step 2:
Nonacademic Social Media Multitasking -0.44 0.67 0.44 .505
Fear of Missing Out -1.08 0.80 1.82 .178
Multitasking*Fear of Missing Out 0.26 0.24 1.21 .271
Note. Overall model fit: Step 1 - χ
2
(2) = 1.57, p = .457; Step 2 - χ
2
(3) = 2.80, p = .423
Summary
The purpose of this quantitative investigation was to examine the potential
relationships between nonacademic social media multitasking, academic performance
(measured as self-reported, overall GPA), and fear of missing out. In this chapter, the
findings of the data analysis were presented. Frequencies and percentages were used to
summarize the nominal-level variables. Cronbach alpha was examined for the scales.
Means and standard deviations were examined for the continuous-level data. To address
the research questions, a series of regression analyses were conducted. Analysis revealed
there was not a significant relationship between nonacademic social media multitasking
and academic performance. There was, however, a significant predictive relationship
between fear of missing out and nonacademic social media multitasking. These findings
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revealed nonacademic social media multitasking may not be related to students’
academic performance; however, multitasking was significantly associated with fear of
missing out. As respondents’ fear of missing out increased, so too did their social media
multitasking behaviors. Analysis for research question three revealed there was not
sufficient evidence that fear of missing out moderated the relationship between
nonacademic social media multitasking and academic performance. The null hypothesis
for research question two was rejected. The null hypotheses for research questions one
and three were not rejected. Statistical significance was evaluated at the generally
accepted level, α = .05. A discussion of study results, implications, and associated
recommendations is provided in the following chapter.
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Chapter 5: Discussion, Conclusions, and Recommendations
In educational settings, nonacademic social media use is associated with poor
academic performance on standardized tests (Ravizza et al., 2014) and low self-reported
GPA (Lau, 2017). Fear of missing out is an important factor to consider in the potential
relationship between nonacademic social media multitasking and poor academic
performance, as it can perpetuate high levels of social media use (Bright & Logan, 2018).
The purpose of this quantitative investigation was to examine the potential relationships
between nonacademic social media multitasking, academic performance (measured as
self-reported GPA), and fear of missing out. The study followed a nonexperimental
design and included a sample of 99 U.S. undergraduate students. A series of regression
analyses were conducted to address the research questions. Results revealed no
significant relationship between nonacademic social media multitasking and academic
performance. There was, however, a significant predictive relationship between fear of
missing out and nonacademic social media multitasking. As respondents’ fear of missing
out increased, so too did their social media multitasking behaviors. Analysis for Research
Question 3 revealed insufficient evidence that fear of missing out moderated the
relationship between nonacademic social media multitasking and academic performance.
This chapter includes a discussion of study findings, beginning with an
interpretation of results in context of previous research. Next, study limitations and
recommendations for future research are presented. Practical and theoretical implications
are also discussed. The chapter closes with my concluding thoughts.
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Interpretation of Findings
Research Question 1
An ordinal logistic regression was conducted to assess the first research question.
Results were not statistically significant, indicating no significant predictive relationship
between nonacademic social media multitasking and academic performance. This finding
was challenges previous research that indicated significant relationships between
nonacademic social media use and poor academic outcomes (Felisoni & Godoi, 2018;
Kates et al., 2018; Rozgonjuk & That, 2017; Wammes et al., 2019). As van der Schuur et
al. (2019) explained, factors that distract students’ attention (such as nonacademic social
media multitasking) can interrupt learning and academic achievement because the ability
to focus one’s attention on academic tasks is essential to processing information and
excelling in academic settings. Previous researchers have linked nonacademic social
media multitasking with poor academic performance on standardized tests (Ravizza et al.,
2014) and low self-reported GPA (Lau, 2017). Academic performance can decline when
students engaged in nonacademic multitasking via Facebook or texting (Demirbilek &
Talan, 2017).
It is important to explore possible explanations for the surprising results from the
current study. First, it is possible that participants did not respond truthfully, whether
intentionally or unintentionally. Inaccurate self-reports of GPA or nonacademic social
media use could have undermined the reliability of findings. It is also possible that the
sample was disproportionately comprised of high-achieving students with more cognitive
processing abilities than average students. Based on self-report GPA data, 81% of the
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sample had GPAs of 3.0 or higher, with over 20% reporting GPAs over 4.0. Assuming
self-reported GPAs were accurate, the study sample consisted mostly of students who
performed above average. It could be that more studious individuals find ways to balance
social media multitasking behaviors with studying and academic responsibilities so
academic outcomes do not suffer from social media use. It could also be that high-
achieving students are capable of handling greater cognitive loads, so social media
multitasking does not have a negative impact on their performance. Finally, it is possible
that students who grew up with technology have become increasingly adept at social
media multitasking, so it does not influence their academic outcomes.
Research Question 2
A liner regression was conducted to examine the relationship between fear of
missing out and nonacademic social media multitasking. Results indicated a significant
predictive relationship between fear of missing out and nonacademic social media
multitasking. That is, the greater participants’ fear of missing out, the greater their social
media multitasking behaviors. This finding aligns with those reported in a number of
previous investigations, which indicated fear of missing out was associated with
increased or problematic social media use. For example, Makki et al. (2018) found
increased Snapchat use was associated with fear of missing out. Reer et al. (2019) found
increased fear of missing out and social comparison behaviors predicted increased social
media engagement. Oberst et al. (2017) explained individuals who experienced deficits in
needs satisfaction often engaged with social media to soothe fear of missing out.
Although the current study did not examine well-being, previous research revealed that
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poor well-being can prompt fear of missing out and subsequent social media engagement,
sparking an ongoing cycle that is detrimental to users’ well-being (Alt, 2018; Baker et al.,
2016; Dhir et al., 2018).
Research Question 3
An ordinal regression was conducted to determine if fear of missing out
moderated the relationship between nonacademic social media multitasking and
academic performance. Results revealed variances in academic performance could not be
significantly predicted by nonacademic social media multitasking of fear of missing out.
Because of the lack of significance in the interaction terms, evidence for moderation was
not sufficient. These findings revealed that social media multitasking and fear of missing
out did not have expected negative association with academic performance, though many
previous researchers have reported that social media multitasking often undermines
academic outcomes (Felisoni & Godoi, 2018; Kates et al., 2018; Rozgonjuk & That,
2017; Wammes et al., 2019). Similarly, previous researchers have reported on the
negative relationship between fear of missing out and academic outcomes. For example,
Rozgonjuk et al. (2019) found fear of missing out prompted nonacademic social media
multitasking, which could then undermine academic outcomes.
Though findings for Research Question 3 were unexpected, they are not wholly
contradicted by results from previous studies. Other researchers have similarly reported
fear of missing out was not associated with declines in academic performance. For
example, Lemay et al. (2019) found that fear of missing out was positively related to
academic performance. Qutishat and Sharour (2019) also found no statistically significant
79
relationship between fear of missing out and academic outcomes. From previous
research, it appears fear of missing out is more likely to have a negative effect on
academic outcomes if those fears prompt behaviors that distract from studies, such as
social media or smartphone use. The positive association between fear of missing out and
academic outcomes reported by Lemay et al. could relate to academic socialization or
expectations. For example, if a student is surrounded by other students who excel in
academics and enjoy the rewards of strong academic outcomes (such as scholarships,
recognition, etc.), a fear of missing out on similar rewards and experiences could prompt
students to study harder and perform better in school. Alternatively, among students with
a strong sense of self or orientation toward individualism, fear of missing out may simply
be a less influential factor in their lives.
Limitations of the Study
This study was subject to a few important limitations. First, the study was limited
by the use of self-report data, particularly for the measure of academic performance. GPA
is often used to assess academic performance in research; it is a standardized metric for
all college students which is reported along the same four-point scale (Guo et al., 2015;
Yeager et al., 2016). Though self-report data, such as GPA, are widely used and accepted
in the academic literature, self-report data are prone to bias (Rosen et al., 2017). A large
meta-analysis of discrepancies between students’ self-reported GPA and their official
GPA records revealed significant discrepancies, with students’ tending to self-report
GPAs that were higher than their official records indicated (Kuncel et al., 2005). Low-
performing students are more likely to self-report inaccurate academic performance data
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(Teye & Peaslee, 2015). As Schwartz and Beaver (2015) explained, the inaccuracy of
self-reported GPA and the prevalent use of this metric “poses a serious threat for
researchers examining factors related to academic achievement” (p. 1127).
While many researchers have reported a discrepancy between self-reported and
actual GPA, others have found self-reports to be a valid proxy for official GPA (Marley
& Platau, 2017; Ratelle & Duchesne, 2014). Further, it is even possible that students
underreport GPA. For example, Caskie et al. (2014) found low-performing male students
were likely to underreport GPA. Given the conflicting research reports of the accuracy of
self-report GPA data, the use of this metric in the current study must be acknowledged as
a limitation.
The instrumentation used to assess study variables may present another limitation.
Specifically, the instrument used to assess nonacademic social media multitasking
demonstrated low reliability, despite acceptable reliability reported by a previous
researcher (Lau, 2017). Cronbach’s alpha is a measure of an instrument’s internal
consistency (Tavakol & Dennick, 2011). The social media multitasking measure was
very short, consisting of just three items. Shorter instruments are more likely to have
weak reliability, as Cronbach alphas are affected by the length and dimensionality of an
instrument (Tavakol & Dennick, 2011).
Upon recognizing the weak reliability of this instrument in the current research (α
= .36), each of the three items were examined for low correlation values. Removal of the
second item would have resulted in significantly higher reliability (α = .738); however,
that would have shortened an instrument that was already very brief. I chose to retain all
81
three items of the instrument, as designed, rather than rely on two items to assess an
important study variable. It seems likely that the low alpha was due to inconsistencies in
responses that may have occurred from quick responses or the failure to carefully read
and respond to each item. Unfortunately, this is an unavoidable limitation of any data
collected through online anonymous surveys. I had no way of knowing if responses were
thought out or rushed through.
Another limitation was that factors other than nonacademic social media
multitasking could have influenced academic performance. For example, socioeconomic
status may have affected academic performance, but this factor was not accounted for.
Finally, I had no control over who actually completed the study survey. I used screening
questions and did not offer incentives for participation, so it was unlikely that ineligible
individuals would desire to complete the survey. My lack of control over the sample is
another inevitable limitation of anonymous online surveys conducted in partnership with
SurveyMonkey.
Recommendations
Findings from the current study offer a number of opportunities for future
research. Although the sample for this research was adequate in size, it was not random
or representative of the larger population of U.S. college students. Accordingly, future
researchers could replicate the study with a larger sample that was randomly chosen in
order to provide findings that could be generalized to the larger population. As previously
mentioned, the low reliability of the nonacademic social media multitasking instrument
was a significant limitation of the current study. It is likely that the short length of the
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instrument contributed to the low Cronbach’s alpha for the measure. A thorough review
of existing validated instruments revealed no other available measure, which is why this
short instrument was selected for the current study. However, a longer inventory is likely
to produce more reliable findings. Thus, future researchers could develop and pilot a
more thorough measure of nonacademic social media multitasking and then replicate the
study with the new instrument.
The current study employed SurveyMonkey to assist with participant recruitment.
This recruitment strategy was a limitation because the pool of potential participants
consisted only of individuals who were registered users with SurveyMonkey. Future
researchers may replicate this study with a different recruitment strategy, such as social
media posts or flyers at college campuses. Such strategies may create a more varied
sample of college students. Although self-reported GPA is a commonly accepted measure
of academic achievement, it has the potential to be both subjective and inaccurate. Self-
reported GPA is often used because it is difficult to obtain official GPA data for
participants. However, it is possible that different results could emerge if official GPA
were used, rather than self-reported grades. Researchers with access to student GPA
records could consider assessing the relationships between nonacademic social media use
and academic achievement using more objective data, such as data from official
transcripts.
The current investigation was limited in that it did not account for a number of
factors that could influence the relationships between nonacademic social media
multitasking, fear of missing out, and academic achievement. Accordingly, future
83
researchers could assess for the presence of moderators in the relationships between these
variables. Possible moderators include race, gender, socioeconomic status, age,
employment status, and academic institution type (i.e., community college, private
college, public university). Similarly, it is possible that the effects of nonacademic social
media multitasking may differ by generation. Students who grew up using social media
may be affected by nonacademic use in different ways from older students who are less
adapted to social media.
Because multitasking behaviors are limited by individuals’ cognitive processing
abilities, researchers may consider the role intellect plays in the influences of distractive
behaviors such as nonacademic social media multitasking. More intelligent individuals
may have higher cognitive processing abilities that help circumvent the negative effects
of distractive behaviors on academic outcomes. Essentially, researchers could examine
the degree to which intelligence moderates the effects of nonacademic social media
multitasking on academic outcomes. Finally, future researchers could study the impact of
specific forms of nonacademic social media multitasking on academic outcomes. For
example, researchers may consider how effects differ according to the social media
platform used, or the electronic devices through which social media are accessed (i.e.,
smartphone, laptop, tablet, etc.). Such investigation could reveal other factors that
influence the effects of social media multitasking on academic outcomes.
Implications
Findings from this study have a number of practical, theoretical, and social
change implications. From a practical perspective, findings provide insights that may be
84
of value to educators. While nonacademic social media multitasking should not be
encouraged in class, educators who are leery of technology use in classrooms may be
encouraged by findings from the current study. Specifically, the use of social media has
potential educational benefits, when properly harnessed. For example, Edwards et al.
(2015) reported social media fostered increased collaboration and classroom engagement.
Dumpit and Fernandez (2017) found that collaboration through social media helped
improve learning. Social media has also redefined the scope of formal and informal
education, creating opportunities for distance learning that was not previously attainable
for many (Greenhow & Lewin, 2016).
This study also has theoretical implications. The framework for this study was
based on Baumeister and Leary’s (1995) belonging hypothesis and van dur Schuur et al.’s
(2015) scattered attention hypothesis. The belonging hypothesis is useful for
understanding how the need to belong prompts a fear of missing out and subsequent
behaviors (Beyens et al., 2016). Results from the current study revealed a significant
predictive relationship between fear of missing out and nonacademic social media
multitasking; participants with greater fear of missing out were more likely to engage in
nonacademic social media multitasking. Because the need to belong correlates with fear
of missing out (Wang et al., 2018), this finding contributes to the belonging hypothesis
by revealing how the inherent human need of belonging may prompt social behaviors in
modern society.
The scattered attention hypothesis states that the brain utilizes and allocates
cognitive resources to complete tasks, as necessary (van dur Schuur et al., 2015). Because
85
attention is a limited resource, factors that distract one’s focus could undermine the
performance of primary tasks (May & Elder, 2018). Through the lens of the scattered
attention hypothesis, it was assumed that nonacademic social media multitasking would
undermine academic performance. However, results from the current investigation
indicated this was not the case. Thus, nonacademic social media multitasking may not be
a factor that significantly detracts attention from primary tasks among the current
generation of college students. Students may be less affected by social media because
they have adapted to the constant distractions of social media through electronic devices.
In terms of social change, implications relate to the importance of a well-informed
and educated public (Mackey, 2019). Policymakers and educational leaders have become
increasingly concerned with declines in the academic performance of U.S. students (Jain,
2019). Because findings from the current study did not reveal social media multitasking
was detrimental to academic performance, educational leaders and researchers may work
to identify other factors that impede academic outcomes, and focus efforts on reducing
those barriers for students. For example, attention may be better placed on addressing
racial inequities and access to technology, in efforts to improve the academic
performance of U.S. students.
Conclusion
The constant and pervasive information stream created through social media and
smart devices has resulted in a generation of multitaskers (Demirilek & Talan, 2017).
Today’s students overwhelmingly engage in social media multitasking. One study
revealed 90% of college students multitasked with media (Hwang et al., 2014). Although
86
technology and social media have a number of potential benefits, in-class social media
multitasking is increasingly problematic on modern school campuses. Smart devices and
widespread access to wireless networks allow students to engage in nonacademic social
media multitasking behaviors during class (Demirilek & Talan, 2017). Although previous
researchers studied the effects of social media use and multitasking behaviors (Chen &
Yan, 2016; Hwang et al., 2014; Kirschner & De Bruyckere, 2017; Kononova & Chiang,
2015), little was known about the effects of nonacademic social media multitasking. In
addition, it was unknown whether fear of missing out was correlated with nonacademic
social media multitasking, or whether it moderated the relationship between social media
multitasking and academic performance.
Accordingly, the current study involved an examination of the potential
relationships between nonacademic social media multitasking, academic performance
(measured as self-reported, overall GPA), and fear of missing out. Three key findings
emerged from this investigation. First, analysis revealed no significant relationship
between nonacademic social media multitasking and academic performance. Second, a
significant predictive relationship between fear of missing out and nonacademic social
media multitasking was evident. Finally, there was insufficient evidence that fear of
missing out moderated the relationship between nonacademic social media multitasking
and academic performance.
Results for research questions 1 and 3 were somewhat unexpected, and could
have been the result of some inherent subjectivity and poor instrument reliability, as
previously discussed. The relationship between fear of missing out and nonacademic
87
social media multitasking aligned with findings reported by previous investigators
(Makki et al., 2018; Oberst et al., 2017; Reer et al., 2019). Educators should consider an
important practical implication of these results. While nonacademic social media
multitasking should not be encouraged in class, educators who are leery of technology
use in classrooms may be encouraged by study findings. Specifically, the use of social
media has potential educational benefits, when properly harnessed. It is also possible that
some students may be more distracted by social media, creating negative outcomes that
others do not experience.
Love it or hate it, social media is here to stay. Rather than recoil from its potential
drawbacks, educators should consider ways to integrate this pervasive new social element
into education. At the same time, it is critical to create boundaries that limit in-class
distractions caused by social media.
