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

and Tahir Hameed

Concordia University of Edmonton, Edmonton, Canada

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

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

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

Interve

ntion

Self-Help

Interventi

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

, 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

=0.499)

Self-help

=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.

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