Fear of Missing out, Social Media Engagement, Smartphone
Addiction and Distraction: Moderating Role of Self-Help Mobile
Apps-based Interventions in the Youth
Bobby Swar
1
and Tahir Hameed
2
1
Concordia University of Edmonton, Edmonton, Canada
2
SolBridge International School of Business, Daejeon, Republic of Korea
Keywords: Basic Psychological Need Satisfaction, Fear of Missing out, Social Media Engagement, Smartphone
Addiction, Smartphone Distraction, Self-Help Intervention.
Abstract: Smartphones offer high mobility and internet connectivity at the same time which has led to a substantial
increase in the number of active social media users on the move, especially the ‘Millennials’. The excessive
use of smartphone has been linked with several issues including mental well-being. Recently, different
mobile applications have emerged to help users track their excessive use of smartphones and protect them
from potential risks to mental health. This paper uses self-determination theory to examine the moderating
role of such mobile applications (or self-help interventions) on inter-relationships between social media
engagement, smartphone addiction and smartphone distractions. Survey responses from 284 college
students reveal that mobile applications could prove to be quite effective self-help interventions that can
help the young people in self-regulating their smartphone use. These results have substantial implications
for designing effective mobile app-based interventions to save young people from potential risks to their
mental health, productivity, and safety in performing their daily tasks. Future research directions have also
been pointed out.
1 INTRODUCTION
Social media users have grown exponentially in the
past decade. There are now 2.34 billion social media
users around the globe (Number of social media
users worldwide 2010-2020 | Statistic, no date).
Compared to the general population students
attending colleges these days are the heaviest users
of the social media (Alt, 2015). The high rate of
smartphone penetration is believed to be one of the
dominant driving force behind such increase in
active social media users.
The advanced functionalities of the smartphone
provide smartphone users with ubiquitous
accessibility to the Internet transcending the limits of
time and place, therefore enabling them to check
social media updates in real time (Kim, Chun and
Lee, 2014). The smartphone has become the first
thing that people look at when they wake up in the
morning and the last thing that they look at before
going to sleep (Lee et al., 2014). Smartphones have
now become central to people’s everyday lives (Gill,
Kamath and Gill, 2012).
While there are several benefits of smartphones,
they do not come without their issues. It has both
positive and negative effects in people’s daily
routine, habits, social behaviors, emancipative
values, family relations and social interactions
(Samaha and Hawi, 2016). The excessive use of
smartphones has also been linked to lot of negative
outcomes like health and well-being (Lee et al.
2014; Park and Lee 2012; Samaha and Hawi 2016;
Wang et al. 2015), student’s academic performances
(Duncan et al. 2012; Hawi and Samaha 2016; Li et
al. 2015), distracted driving (Rocco and Sampaio
2016; Terry and Terry 2016) and smartphone
addictions (Aljomaa et al. 2016; Chiu 2014;
Gökçearslan et al. 2016; Haug et al. 2015). Among
all the applications that smartphones provide, the use
of social media is found to be a stronger predictor of
smartphone addiction (Jeong et al., 2016).
In order to prevent excessive smartphone usage
its potential negative outcomes, a variety of mobile
applications are available nowadays. These
Swar B. and Hameed T.
Fear of Missing out, Social Media Engagement, Smartphone Addiction and Distraction: Moderating Role of Self-Help Mobile Apps-based Interventions in the Youth.
DOI: 10.5220/0006166501390146
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 139-146
ISBN: 978-989-758-213-4
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
139
applications come with features like tracking the
time spent on each of the mobile applications,
turning off or restricting the (push-up) distracting
notifications from certain applications and also
restricting smartphone usage by locking or even
turning it off after a specified time period. Most of
the applications also provide reports and charts on
the smartphone usage behavior. Using such
applications is referred to as self-help intervention
which helps the smartphone users control their
excessive smartphone use and therefore protects
them from its potential negative effects.
Research so far have identified and examined
some negative effects of excessive social media and
smartphone use. However, there are no studies, to
the best of our knowledge, that examine whether
mobile applications-based self-help interventions
actually help in preventing excessive smartphone
use, smartphone addiction and smartphone
distractions. In this context, the objective of this
study is to (1) examine the moderating role of self-
help mobile applications-based interventions in
preventing excessive smartphone use, smartphone
addiction and smartphone distraction, and (2)
examine the relationship of social media
engagement on smartphone with smartphone
addiction and smartphone distraction. To investigate
these research objectives, a research model has been
proposed and tested using the data collected from a
large sample of university students from several
countries.
2 THEORETICAL
BACKGROUND AND
HYPOTHESIS DEVELOPMENT
2.1 Basic Psychological Need
Satisfaction
Self-determination theory (SDT), a macro-theory of
human motivation, development, and wellness (Deci
and Ryan, 2008) helps to explain the basic
psychological need satisfaction of human beings.
According to SDT, effective self-regulation and
psychological health of human beings are based on
satisfaction of their basic psychological needs for
competence, autonomy and relatedness (Przybylski
et al., 2013). Competence refers to the individuals
desire to feel effective in their interactions with the
environment (Roca and Gagné, 2008). Autonomy
refers to the individual desire to self-initiate and
self-regulate own behavior (Sørebø et al., 2009).
Relatedness refers to the individual desire to feel
connected and supported by others (Sørebø et al.,
2009).
Przybylski et al. (2013) suggested that
individuals with low basic need satisfaction for
competence, autonomy and relatedness have higher
levels of Fear of Missing Out (FoMO). On this
pretext, the following hypothesis is proposed.
H1: Basic psychological need satisfaction is
positively associated with fear of missing out.
2.2 Fear of Missing out (FoMO)
FoMO phenomenon has been defined as a
“pervasive apprehension that others might be having
rewarding experiences from which one is absent,
FoMO is characterized by the desire to stay
continually connected with what others are doing”
(Przybylski et al. 2013, p. 1841).
Research has explored the prevalence of FoMO
and its relation to social media (JWT, 2012; Abel,
Buff and Burr, 2016). Przybylski et al. (2013) is the
first study that operationalizes the FoMO construct
by collecting a diverse international sample of
participants. A recent study by Alt (2015) in
academic arena shows that there is a positive link
between social media engagement and two
motivational factors: Extrinsic and amotivation for
learning are more likely to be mediated by FoMO.
FoMO plays an important role in individuals
engaging in social media. Yin et al. (2015) indicate
that FoMO and enjoyment are positively related to
continuance intention of using social media.
According to Przybylski et al., (2013) individual
with high levels of FoMO relate to higher levels of
social media engagement.
The use of social media has been associated with
greater levels of emotional support from close
friends (Putnam 2000 as cited in Alt 2015). People
with low basic need satisfaction generally perceive
social media as a platform to connect with others in
order to develop social competence, and an
opportunity to deepen social ties (Przybylski et al.,
2013). This context leads to the proposal of the
following hypothesis.
H2: Fear of missing out is positively associated
with social media engagement.
2.3 Social Media Engagement
Social media refers to the websites and online tools
that facilitate interactions between users by
providing them opportunities to share information,
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opinions, and interests (Khan, Swar and Lee, 2014).
Social media engagement simply refers to the extent
of an individual immersing into social media
activities. People basically engage into social media
by sharing personal or social information with close
actors in social networks, such as family and friends
(Alt, 2015).
Jeong et al. (2016) suggested that people who
use smartphones for social media, games, and
entertainment were more likely to be addicted to
smartphones. The use of social networking mobile
applications is a significant predictor of mobile
addiction (Salehan and Negahban, 2013). These
days, it has been much easier for individuals to
engage in social media activities due to ubiquitous
accessibility to the Internet through smartphones.
This phenomenon leads to the formulation of the
following hypothesis.
H3: Social media engagement is positively
associated with smartphone addiction.
Social media engagement is also believed to play
an important role in smartphone distraction. A user
preoccupied with social media activities tends to be
distracted from other primary tasks. This
phenomenon of distraction can be explained with the
concept of multitasking. Research in cognitive
science shows that individuals performance
decreases while multitasking (Junco, 2012).
Studies in an academic setting have also shown
the negative relationship between the use of social
media and academic performance. For example,
according to Rosen, Mark Carrier and Cheever,
(2013) those who use Facebook and text applications
while studying had lower GPAs compared with the
students who did not. This is clear evidence that
excessive engagement in social media is associated
with smartphone distraction and it leads to the
proposal of the following hypothesis.
H4: Social media engagement is positively
associated with smartphone distraction.
2.4 Smartphone Addiction
Smartphone addiction can be defined as the
excessive use of smartphones in a way that is
difficult to control and its influence extends to other
areas of life in a negative way (Park and Lee, 2012).
Smartphones are a significant source of
distraction for decision-based activities such as
driving, classroom learning, and work-related tasks
(Gill, Kamath and Gill, 2012). In academic settings,
studies have linked the excessive smartphone use
with the poor academic performance of the
smartphone users by distracting them from their
primary tasks. Duncan, Hoekstra and Wilcox (2012)
have found a significant negative correlation
between in-class phone use and final grades of the
students. Similarly, Hawi and Samaha (2016) have
reported that students who were at a high risk of
smartphone addiction were less likely to achieve
cumulative GPAs suitable for distinction or higher.
According to Gill, Kamath and Gill, (2012)
smartphones are known to be detrimental to
cognitive performance and the use of smartphones
increases reaction time, reduces focus (attention),
and lower performance of task needing mental
concentration and decision making. Based on the
observation the following hypothesis is proposed.
H5: Smartphone addiction is positively
associated with smartphone distraction
.
2.5 Self-help Intervention
Installation and use of mobile applications that could
help the users in regulating their excessive
smartphone use and therefore help in preventing
smartphone addictions and smartphone distractions
is referred to as self-help intervention in this study.
There are a variety of such mobile applications
available these days.
The mobile applications come with a variety of
features such as blocking the websites one would
want to avoid and periodic, customize setting on
social media sites to give time-fixed updates
(Andreassen, 2015), tracking the time spent on
specific mobile applications, turning off or
restricting the distracting (push-up) notifications
from certain applications, and limiting the overall
smartphone usage time by locking or even turning
off the smartphone device.
Generally, self-motivated people use such mobile
applications and with a belief in their ability and
intention to prevent excessive smartphone use. This
argument leads to the formulation of the following
hypothesis.
H6: Self-help intervention has a moderating
effect on the relationship between social media
engagement, smartphone addiction and smartphone
distraction.
3 RESEARCH MODEL
To examine the relationship between the use of
social media through smartphone and its impact on
smartphone addiction and distraction, and to
Fear of Missing out, Social Media Engagement, Smartphone Addiction and Distraction: Moderating Role of Self-Help Mobile Apps-based
Interventions in the Youth
141
examine the moderating role of self-help
intervention, a research model is proposed in Figure
1. The model shows that basic psychological need is
a direct antecedent of fear of missing out which
affects social media engagement, smartphone
addiction, and smartphone distraction. Smartphone
distraction is also directly influenced by social
media engagement and smartphone addiction.
Figure 1: Research Model.
4 RESEARCH METHOD
The proposed research model was tested using the
data collected through an online survey. The survey
instruments were adapted from the existing
measures to this research context. Each of the items
was measured on a seven-point Likert-type scale.
The data was collected from university students
from several countries. As shown in Table 1
majority of the respondents are 18 to 27 years old.
According to Pew Research Center, ages 18 to 29
have always been the most likely users of social
media by a considerable margin. Total of 284 useful
responses were identified for the analysis in this
study. The sample size of 284 should be adequate to
test the research model against the required 50
samples for five paths in the research model
according to Hair et al. (2011).
The respondents were categorized into two
different groups based on whether or not they use
self-help mobile applications in their smartphone to
monitor and control their smartphone usage
behavior. The respondents that use such kind of
mobile applications are identified as “Self-help”
group and the respondents that do not use such kind
of self-help mobile application are categorized as
“No-help intervention” group in this study.
Table 1: Sample demographics.
Category Frequency Percent
(%)
Gender Male 130 45.77
Female 154 54.23
Age <17 0 0
18 - 22 205 72.18
23 - 27 72 25.35
28 - 32 5 1.76
>33 2 0.70
Nationality Bangladesh 10 3.52
China 71 25
Kazakhstan 25 8.80
Russia 17 5.98
South Korea 113 39.79
Uzbekistan 23 8.10
Others 25 8.80
Groups Self-Help
Intervention
182 64.08
No
Intervention
102 35.92
5 RESULTS
5.1 Assessment of Measurement Model
This study utilizes structural equation modeling
(SEM) supported by partial least squares (PLS)
method to examine the research model and its
hypotheses. The study in particular uses SmartPLS 3
software package for data analysis (Ringle, Wende
and Becker, 2015).
Table 2 shows the assessment results of the
measurement model for both the groups. Internal
consistency reliability is investigated by using
composite reliability. The constructs in the proposed
model are above the 0.7 thresholds indicating a high
reliability of items used for each construct.
Convergent validity is assessed by evaluating the
average variance extracted (AVE) from the
measures. The AVE is above the threshold value of
0.5, meeting the criteria of convergent validity.
Discriminant validity is assessed by examining
the square root of AVE as recommended by (Fornell
and Bookstein, 1982). Table 3 and 4 shows Fornell-
Larcker tests of discriminant validity for no-help and
self-help group respectively. Table 3 and 4 shows
that the square root of AVE of each construct is
greater than the correlations between it and all other
constructs. Moreover, all the constructs are found to
have a stronger correlation with their own measures
than to those of others. This shows the proper
assessment of discriminant validity.
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Table 2: Assessment of the measurement model.
Variables
Average Variance
Extracted (AVE)
Composite
Reliability
No
Interve
ntion
Self-Help
Interventi
on
No
Inter
vent
ion
Self-
Help
Interve
ntion
Basic
Psychological
Needs
0.65 0.68 0.96 0.96
Fear of
Missing Out
0.55 0.55 0.90 0.89
Social Media
Engagement
0.61 0.71 0.89 0.92
Smartphone
Addiction
0.69 0.69 0.90 0.90
Smartphone
Distraction
0.58 0.63 0.88 0.89
Table 3: Fornell-Larcker tests of discriminant validity for
no-help group.
Table 4: Fornell-Larcker test of discriminant validity for
self-help group.
Note: In Table 3 and table 4 the diagonal elements (in
bold) represent the square root of AVE.
5.2 Testing the Model
Table 5 shows the results obtained from the PLS
analysis. The coefficient of determination, R
2
, is
0.499 for “No-help group” and 0.355 for “Self-help
intervention” group respectively. The results show
that the model explains a substantial amount of
variance for smartphone distraction in both groups.
As shown in Table 5 all the hypotheses are for both
the groups are statistically significant.
Table 5: Results of the structural model with path
coefficients.
Path
Groups
No-help
(R
2
=0.499)
Self-help
(R
2
=0.355)
Basic
Psychological
Needs - Fear of
Missing Out (β1)
0.4
(4.66)**
0.523
(8.823)**
Fear of Missing
Out - Social
Media
Engagement (β2)
0.544
(8.237)**
0.594
(11.843)**
Social Media
Engagement -
Smartphone
Addictions (β3)
0.423
(4.811)**
0.541
(9.057)**
Social Media
Engagement -
Smartphone
Distractions (β4)
0.278
(3.255)**
0.231
(2.765)*
Smartphone
Addictions -
Smartphone
Distractions (β5)
0.542
(7.488)**
0.438
(5.178)**
Note: Associated t-statistics are in parentheses and
*p<0.05, **p<0.01
Table 6 summarize the results of the multi-group
analysis with their path coefficients. All the
relationships (path coefficients) differ significantly
across the two groups of smartphone users. These
findings make intuitive sense considering that self-
motivated people use self-help mobile applications
to control their levels of social media engagement,
smartphone addictions and smartphone distractions.
(1) (2) (3) (4) (5)
(1) Basic
Psychologic
al Needs
0.81
(2) Fear of
Missing Out
0.40
0.74
(3) Social
Media
Engagement
0.51 0.54
0.78
(4) Smartphone
Addiction
0.14 0.48 0.42
0.83
(5) Smartphone
Distraction
0.34 0.50 0.51 0.66
0.76
(1) (2) (3) (4) (5)
(1) Basic
Psychologica
l Needs
0.83
(2) Fear of
Missing Out
0.52
0.74
(3) Social Media
Engagement
0.56 0.59
0.84
(4) Smartphone
Addiction
0.37 0.52 0.54
0.83
(5) Smartphone
Distraction
0.46 0.48 0.47 0.56
0.79
Fear of Missing out, Social Media Engagement, Smartphone Addiction and Distraction: Moderating Role of Self-Help Mobile Apps-based
Interventions in the Youth
143
Table 6: Results of multi-group analysis with path
coefficients.
Paths Path
coefficients
differences
P Value
Basic Psychological Needs -
Fear of Missing Out (β1)
0.119 0.873**
Fear of Missing Out - Social
Media Engagement (β2)
0.050 0.726**
Social Media Engagement -
Smartphone Addictions (β3)
0.119 0.873**
Social Media Engagement -
Smartphone Distractions
(β4)
0.047 0.346**
Smartphone Addictions -
Smartphone Distractions
(β5)
0.104 0.178*
Note: *p<0.05, **p<0.01
6 DISCUSSION AND
CONCLUSION
The primary objective of this study was to examine
the moderating role of self-help mobile applications
in regulating social media engagement, smartphone
addictions and distractions. Additionally, it would
examine the relationship between social media
engagement on smartphone and smartphone
addictions or smartphone distractions. To achieve
the objective, this study established a path model
and tested six hypotheses.
Table 7: Summary of results.
Paths Hypotheses Results
Basic Psychological
Needs - Fear of
Missing Out
H1 Supported
Fear of Missing Out -
Social Media
Engagement
H2 Supported
Social Media
Engagement -
Smartphone
Addictions
H3 Supported
Social Media
Engagement -
Smartphone
Distractions
H4 Supported
Smartphone
Addictions -
Smartphone
Distractions
H5 Supported
Moderating role of
self-help mobile
applications
H6 Supported
The summary of the result is shown in Table 7.
Empirical analysis of the research model provides
several key findings, which are discussed below.
The findings clearly show that self-help mobile
applications can actually help in regulating social
media engagement, smartphone addictions and
distractions. This finding is very crucial as
smartphone addictions and distractions have become
somewhat a new illness in today’s society. People
needing help for their smartphone usage behavior
can use self-help mobile applications to protect
themselves from the negative effects of the
smartphones. In academic settings, educators can
recommend students needing help to use such self-
help mobile applications.
This study also shows that people with low
psychological needs have a high fear of missing out
which leads to higher levels of social media
engagement. The high levels of social media
engagement then lead to smartphone addictions and
smartphone distractions. The findings are in line
with the emerging recent research (e.g., Przybylski
et al., 2013; Alt, 2015). The findings contribute to
the existing literature by illustrating the mediating
role of social media engagement in explaining
smartphone addiction and smartphone distractions.
Nevertheless, there are some limitations of this
study and the others that can provide opportunities
for future research direction. This study does not
take into considerations the types of (and underlying
techniques) of mobile applications used as self-help
interventions. These days such mobile applications
come with a variety of features and capabilities that
may have several different types of impact. Future
research may take into consideration the types of
self-help mobile apps-based interventions and their
impact on smartphone use.
This study only deals with the smartphone users
that are already using self-help mobile applications.
It can be assumed that this group of users are self-
motivated and are already less likely to get affected
by the negative effects of the smartphones. Future
research could ask smartphone users to install such
self-help mobile applications and then examine the
same effect after some period of time in a regulated
environment.
This study has used the data from university
students with the majority of respondents ranging
from 17 to 27 years old. This group of people are
among the heaviest users of social media and
smartphones. Therefore, caution is needed while
generalizing the results of this study. Different age
groups can show different results. Future research is
also recommended for other age group and settings.
HEALTHINF 2017 - 10th International Conference on Health Informatics
144
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