Associations Between Fear of Missing Out, Problematic Smartphone Use, and Social Networking Services Fatigue Among Young Adults

Abstract

In this study, we aimed to adapt the Information and communication technology (ICT) Overload and social networking service (SNS) Fatigue Scale to measure the overload and SNSs fatigue experienced by individuals while using ICTs in the Turkish language and analyze the adapted scale based on various variables. The scale adaptation procedure was conducted by surveying 225 undergraduate-level university students. In addition to discriminant and convergent reliability, the general fitness index parameters were compared with confirmatory factor analysis (CFA), and the model results were found in accordance with the acceptable fitness index criteria, with clarification as a complete model in all sub-dimensions. The relationships between fear of missing out (FoMO), problematic smartphone use (PSU), and SNSs Fatigue levels of the participants were also investigated. The adapted scale was then applied to 469 participants. The findings demonstrated that there was a significant difference between PSU and SNS Fatigue levels of participants based on gender, favoring females. It was also revealed that the variables of interest FoMO and SNS Fatigue together predicted the PSU.

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Article
Introduction
Information and communication technologies (ICTs) and
social networks have led to significant changes in the social-
ization and communications among individuals (Apaolaza
et al., 2013; Desjarlais & Willoughby, 2010; Lee & Ma,
2012; Salehan & Negahban, 2013; Zhong et al., 2011).
Widespread usage of social networks has enabled individuals
to communicate with more people and learn more about
these people when compared to individuals they meet in real
life. Although the introduction of the Internet has provided
significant advantages, the availability of excessive informa-
tion on the Internet has reportedly led to several different
problems. One of the most significant factors for the intro-
duction and prevalence of these problems is the frequent use
or overuse/overdose of social networks.
Social networks can be defined as Internet-based appli-
cations that enable individuals to freely express and share
their emotions and ideas, and to facilitate interpersonal
communication (Mahajan, 2009). SNSs/sites are Internet-
based services where individuals can share information of
their choosing as users, and communicate with each other
by allowing others to send images, videos, or messages
(Boyd & Ellison, 2007; Pempek et al., 2009). With the
introduction of Web 2.0 technologies, social networks have
enabled users to communicate online within various social
communities. In providing these facilities, social network
operators made it possible for individuals to utilize several
social networks frequently only by creating a simple user
963760SMSXXX10.1177/2056305120963760Social Media <span class="symbol" cstyle="Mathematical">+</span> SocietyTugtekin et al.
research-article20202020
1Mersin University, Turkey
2Anadolu University, Turkey
3Dokuz Eylül University, Turkey
Corresponding Author:
Ufuk Tugtekin, Department of Computer Education and Instructional
Technology, Faculty of Education, Mersin University, 33160 Mersin,
Turkey.
Email: ufuktugtekin@gmail.com
Associations Between Fear of Missing
Out, Problematic Smartphone Use,
and Social Networking Services Fatigue
Among Young Adults
Ufuk Tugtekin1, Esra Barut Tugtekin2, Adile Aşkım Kurt2,
and Kadir Demir3
Abstract
In this study, we aimed to adapt the Information and communication technology (ICT) Overload and social networking
service (SNS) Fatigue Scale to measure the overload and SNSs fatigue experienced by individuals while using ICTs in the
Turkish language and analyze the adapted scale based on various variables. The scale adaptation procedure was conducted
by surveying 225 undergraduate-level university students. In addition to discriminant and convergent reliability, the general
fitness index parameters were compared with confirmatory factor analysis (CFA), and the model results were found in
accordance with the acceptable fitness index criteria, with clarification as a complete model in all sub-dimensions. The
relationships between fear of missing out (FoMO), problematic smartphone use (PSU), and SNSs Fatigue levels of the
participants were also investigated. The adapted scale was then applied to 469 participants. The findings demonstrated that
there was a significant difference between PSU and SNS Fatigue levels of participants based on gender, favoring females. It
was also revealed that the variables of interest FoMO and SNS Fatigue together predicted the PSU.
Keywords
social networks, SNS fatigue, FoMO, problematic smartphone use

2 Social Media + Society
profile. One of the most important reasons for the prefer-
ence of the Internet and social networks is the fact that indi-
viduals can communicate with people from different
cultures, different education levels, and different ages
through social networks. They can even create special
interest-groups and share information with group members
based on their common interests (Boyd & Ellison, 2007).
Thus, facilitated usage of Internet-based social networks
increases communications and ensures a continuous flow
of information. Continuous information sharing on social
networks can prevent access to the desired information in
the complex and continuously updated information stream.
It also makes the analysis, tracking, and assessment of the
available information more difficult. Thus, extensive usage
of social networking sites increases the significance of ICT
overload and social networking services (SNSs) fatigue.
The overload could be associated with different concepts
depending on the study content. In a study on the ICT para-
dox by Karr-Wisniewski and Lu (2010), technological over-
load was explained in the framework of a three-dimensional
concept as information, communication, and system fea-
tures. It was observed in previous studies that the use of ICT
had a negative impact on overload (Ahuja et al., 2007;
Moore, 2000). Three types of information overload were
defined after SNSs and the Internet became popular. These
were rapid changes in the technological characteristics of
social networks, involuntary social network extensions, and
excessive information availability in social networks. When
individuals are exposed to a volume of information which
is higher than their processing capacity, they experience
information overload (Eppler & Mengis, 2004; Farhoomand
& Drury, 2002). In the research model developed by Lee
et al. (2016), the three factors that result in ICT overload
were determined as Information Overload, Communications
Overload, and System Feature Overload. In Figure 1, the
research model and results of the structural model testing by
Lee et al. (2016) presented. Correspondingly, theoretical
framework, stressors, and variables of interest are discussed
in the next-section, briefly.
Theoretical Framework
Currently, the excitement of SNSs is waning following a
period of flourishing development, and passive actions,
including intentionally ignoring some messages or prevent-
ing some friends by technological means, is increasingly
common because of lack of time, resources, expertise, and
personal interests (Guo et al., 2020). As a consequence, many
common SNSs, like Facebook and Twitter, have practiced a
dramatically decline within the number of active users.
Studies have seen established experiences from the problem-
atic usage of SNS in recent years and mentioned the “SNS
Fatigue” phenomenon (Ravindran et al., 2014).
Because the experience of fatigue has circulated quickly
among SNS users, in recent years, scholars have begun to
investigate the antecedents which will cause the phenome-
non. In general, we reviewed the literature and determined
that during the last years, studies have drawn scholars’ atten-
tion and that the researchers have to this point presented an
initial insight into the SNS fatigue phenomenon. This
Figure 1. The research model and results of the structural model testing by Lee etal. (2016).

Tugtekin et al. 3
phenomenon recently stimulated researchers from all around
the world to carry out empirical researches to investigate the
context, motives, and reasons of SNS fatigue (Luqman et al.,
2017; Sasaki et al., 2016). However, SNS fatigue is charac-
terized as a condition within which users of social media
encounter mental exhaustion after witnessing numerous
technical, informational, and communicative overloads
through their involvement and engagement within the varied
social media online platforms (Bright et al., 2015; Lee et al.,
2016; Ravindran et al., 2014). Since fatigue experience is
rapidly growing among SNS users, researchers recently
began to explore the factors that might contribute to SNS
fatigue. Although many studies demonstrate the role of
fatigue in social media and provide significant perspectives
into its antecedents, the aspects of SNS which might cause
fatigue have not been established.
The main focus of several analyses on the impact of activ-
ity in social media (e.g., overuse) on SNS fatigue was on the
person-environment fit model and cognitive behavior theory
(Lee et al., 2016; Zheng & Lee, 2016). The implications and
etiology of the SNS fatigue among young adults are investi-
gated in this study, and the situation in which pervasive SNS
usage exposes individuals to a vast amount, cognitive
resources, and capacity demand beyond their cognitive
capacities (reviewed in Lee et al., 2016). As a basis for
understanding SNS’s users’ information avoidance behavior,
the stressor-strain-outcome (SSO) model was used as a theo-
retical framework to handle the gap in the literature. The
SSO framework is widely used to research stress-related cir-
cumstances and outcomes in the context of technology use
(Ayyagari et al., 2011; Dhir et al., 2018; Ragu-Nathan et al.,
2008), which exactly suits the main aim of this study, namely,
to research how stress-related factors lead users’ behavior.
Stressors (Information Overload, Communication
Overload, and System Feature Overload)
The SSO framework is composed of three main components,
namely strain, stressor, and result. Stressors are stress-induc-
ing causes. In this study, we consider namely, information
overload, communication overload, and system feature over-
load as stressors for users. The concept of overload is associ-
ated with the difficulty experienced by an individual in
understanding the information or making a decision in the
presence of excessive information (Gomez-Rodriguez et al.,
2014). When social networks solicit users with too much
information and exceed the user’s capacity to process that
information, users may experience an excessive bombard-
ment of information (Cherubini et al., 2010; Edmunds &
Morris, 2000; Pennington & Tuttle, 2007). If the user is fre-
quently exposed to unrelated information, the individual
could experience incompatibility either between personal
goals and environmental material or between environmental
demands and personal skills. The user is not supposed to deal
with unrelated content. When this is experienced, information
overload occurs. However, the user is unlikely to experience
information overload when the provided information is
deemed relevant (Ayyagari et al., 2011). Bontcheva et al.
(2013) noted that microblog users extensively complained
about information overload.
Communication overload takes place when individuals
are exposed to excessive information that they cannot cope
with, either from different communication sources or from a
single source (Chen & Lee, 2013). Communication overload
occurs when communications that exceed the capacity of an
individual arrive from ICT tools such as social networks
(Cho et al., 2011). A similar concept to communication over-
load, connection overload, occurs when individuals read and
respond to wanted or unwanted messages on online social
media and their ability to maintain their relations on social
media exceeds their cognitive capacity (LaRose et al., 2014).
It is known that connection overload adversely affects the
productivity of individuals by over-disrupting their attention
(Karr-Wisniewski & Lu, 2010).
System feature overload occurs when the usage is complex
and new features are added to technical resources or the given
technology is too complex to perform a specific task within a
social network. The fact that it is necessary to use ICTs in the
digital world leads to the continuous use of these technologies
by individuals. The software, which is altered and updated
almost daily, require the end-users to learn every innovation.
The condition that occurs due to system feature overload is
considered to be one of the triggers of technostress. The con-
cept of technostress describes the inadequacy of individuals
to use ICTs effectively (Ayyagari, 2012; Çoklar & Şahin,
2011). When the energy required to cope with this condition
is high, overload could lead to fatigue (Ravindran et al.,
2014). Thus, social network usage overload could lead to
negative consequences such as SNS fatigue.
SNS Fatigue, Fear of Missing Out, and
Problematic Smartphone Use
The number of active social network users in all countries
has increased exponentially, since the proliferation of smart-
phones, new mobile devices, and apps over the past years.
Some individuals consider social networks as a novel form
of occupation, allowing social networks to wholly penetrate
their lives. The ability and styles of ICT to present their vari-
ous functions and complex features to users change rapidly
over time. These features and changes could lead to an
incompatibility between ICT and users, and thus, cause
stress in individuals (Ragu-Nathan et al., 2008). Furthermore,
information overload in social networks compels users to
utilize social networks faster than their abilities can cope
with (Moore, 2000). There may be a difference between the
abilities of social network users and the demands of social
network media.
Social networking is a worldwide influential communi-
cation network and educational appliance. Despite the

4 Social Media + Society
various positive effects that benefit people in all aspects of
life, adverse outcomes are often likely due to excessive
usage of social media. SNS fatigue is a significant factor
that adversely affects the behavioral and mental characteris-
tics of users. Therefore, information overload, overuse of
social networks, and inability to meet the demands of social
networks could lead to stress in individuals. Ayyagari et al.
(2011) stated that the gap between ICT features and the
capabilities of individuals is an important factor in the for-
mation of stress among individuals. SNS fatigue, at the
user’s level, leads to the degradation of both mental and
physiological capacities, which can lead to unsafe behavior
for users (reviewed in Dhir et al., 2018). Thus, social net-
work use could lead to feelings of exhaustion, restlessness,
or dissatisfaction among individuals. Over recent years,
some scholars have discovered fatigue over conjunction
with the problematic use of SNSs and proposed the idea of
“SNS fatigue” (Ravindran et al., 2014). SNS fatigue has
been caused by the increased dedication to social media
platforms, as well as a spike in different content kinds on
these websites. The latest researches indicate that SNS
fatigue has a detrimental effect on the psychosocial well-
being of social media users (Dhir et al., 2018). SNS fatigue
was commonly seen as an outcome of overload, and several
scholars discussed the relation between fatigue and overload
(Ravindran et al., 2014; Zhang et al., 2016).
There are several reasons for SNS fatigue such as social
dynamics, content, and social network platform-based fac-
tors (Ravindran et al., 2014). It is also known that SNS
fatigue is associated with mental burnout and physical symp-
toms (Çoklar & Şahin, 2011). Friends constantly sending
requests, game requests, or sharing photos and messages
through social networks can lead to fatigue. Furthermore,
individuals may experience compatibility issues when using
social networks and may need to spend more time to reach
the desired content due to system updates on social networks.
Similarly, in social networks such as Twitter, frequent
updates make it harder to follow these networks and could,
therefore, be seen as disturbing (Gross, 2011). Furthermore,
sudden and frequent advertisements related to price dis-
counts or product promotions on social media could lead to
frustration, fatigue, and exhaustion among social media users
(Bright et al., 2015; Shashank, 2011).
Users who experience difficulties in using and managing
social networks and feel stress, as a result, may go on to feel
fatigued that could adversely affect their social network
usage, or even cause them to abandon the use of certain
social networks (Ravindran et al., 2014; Walker, 1986). Thus,
it is important to determine the effect of SNS fatigue and
information overload on social networks on individuals. The
use of social networking has led to Fear of Missing Out
(FoMO), due to the incessant need for increased popularity
among young people and the need to improve relations. As a
result, the perceived amount of stress caused by social net-
working is increasing (Beyens et al., 2016). This stress can
also be caused by communication with friends, but also by
the use of social networking. FoMO in social networks,
socialization, and the need to increase popularity can cause
social networking dependency of users (Müller et al., 2016).
Several empirical experiments reveal that in social and psy-
chological demands people who experiencing FoMO are
vulnerable to being closely linked to other individuals
(Beyens et al., 2016). In addition, individuals with low levels
of emotional satisfaction or life satisfaction are likely to be
exposed by FoMO (Przybylski et al., 2013). FoMO has been
identified as an essential risk factor in the growth of PSU and
represents a desire to remain continuously online and con-
nected through SNSs. Concurrently, social networks can
cause negative psychological influences such as jealousy and
shame (Lim & Yang, 2015). Certain previous FoMO studies
specifically show the psychological and physiological
adverse consequences of elevated FoMO rates. Nevertheless,
it is not yet clear whether FoMO is substantially related to
SNS fatigue, although the use of social networking by people
with high FoMO rates has been reported to indicate greater
engagement and social media usage (Wolniewicz et al.,
2018). Excessive and compulsive engagement in social net-
working is potentially leading to SNS fatigue (Karapanos
et al., 2016; Zheng & Lee, 2016). Therefore, high rates of
FoMO may induce SNS fatigue.
Another variable mentioned with SNS fatigue is
Problematic Smartphone Use (PSU). PSU is a critical prob-
lem, especially among young adults (Smetaniuk, 2014).
Several scholars have investigated the constructs of PSU
with respect to theories of psychopathology. Anxiety, stress,
depression, and low self-esteem symptoms are the most
commonly examined psychopathology constructs through-
out tandem with PSU in the literature (reviewed in Elhai
et al., 2017). PSU might not have formal diagnosis criteria;
however, it has similar characteristic features of other addic-
tion habits such as functional impairment, overuse, and with-
drawal cessation of use (Ezoe et al., 2009). In today’s culture,
a number of leisure and social practices may be accessed
through the new generation of smartphones. PSU is a remark-
able phenomenon, because a mobile device is lightweight,
highly compact, and available in individuals’ pockets or bags
generally continuously. Accessibility and portability pro-
mote mobile device usage or overuse and are also likely to
develop the habit of related to it (Oulasvirta et al., 2012). It
may also be argued that smartphones easily replace personal
computers and become personal assistants for individuals
(Elgan, 2017). Despite the benefits of constant connectivity
to a multi-functional mobile device, excessive usage of
smartphones is a growing potential public health issue
(Billieux et al., 2015). Nevertheless, according to Elhai et al.
(2017), PSU can be evaluated with various situations such as
Internet addiction (reviewed in Kuss et al., 2014) and Internet
gaming addiction (reviewed in Kuss & Griffiths, 2012), but
there are structural differences between these concepts.
Although the common signs of these structures are similar;

Tugtekin et al. 5
PSU is evaluated in a different context for reasons such as
smartphone platforms and the interface of a smartphone.
In the literature, there are several empirical studies (i.e.,
Błachnio & Przepiórka, 2018; Blackwell et al., 2017; Dhir
et al., 2018; Elhai et al., 2016; Fuster et al., 2017; James
et al., 2017; Oberst et al., 2017; Wolniewicz et al., 2018) that
reveal a moderate and large relationship between FoMO and
PSU variables and problematic SNS usage (reviewed in
Elhai et al., 2018a). Thus, it is clear that there is a relation-
ship between FoMO, PSU, and SNS fatigue. The antecedents
of PSU and term, however, are fairly recent, and, as such,
concepts tend to be defined as well. Analyzing PSU predic-
tors is therefore essential to further prevention and interven-
tion. Characteristics of personality and adult attachment
under the PSU usage model (Billieux et al., 2015) are key
psychological factors that can be useful for understanding
the problem and addiction of individuals to cell phones. It
was decided to examine these variables together in this study.
Consequently, it is thought that the results of the current
research will contribute to the literature in the context of
using social networks and SNS fatigue.
Method
The research consists of two sections. In the first section, it
was aimed to adapt the ICT Overload and SNS fatigue scale
developed by Lee et al. (2016) to the Turkish language con-
text with 225 participants. In the second section, in addition
to using the current adapted scale, FoMO scale developed by
Przybylski et al. (2013) and adapted into the Turkish lan-
guage by Gokler et al. (2016) was used to collect data about
FoMO. Concurrently, NMP-Q scale developed by Yildirim
and Correia (2015) and adapted into the Turkish language by
Adnan and Gezgin (2016) was also used to collect data
related to PSU. It was aimed to analyze the scale based on
certain variables to measure the overload and SNS fatigue
experienced by individuals while using ICTs with 469 par-
ticipants. The study was conducted as paper-based research.
The research questions that guided the study are as follows:
RQ1. How should the ICT Overload and SNS fatigue
scale be adapted to the Turkish language context?
RQ2. What are the PSU, FoMO, and SNS fatigue levels of
participants?
RQ3. Do the PSU, FoMO, and SNS fatigue levels of par-
ticipants differ based on the variables of gender, social
network usage duration, smartphone usage duration, fre-
quency of checking the media, frequency of checking
email, and frequency of checking for calls?
RQ4. Is there a correlation between SNS fatigue level and
PSU and/or FoMO levels?
RQ5. Do SNS fatigue levels and FoMO explain PSU?
Scale Adaptation Procedure and Statistical
Results
There is a limited number of studies that have investigated
SNS fatigue in Turkish. In current studies, social networks
were discussed in terms of social (social dynamics, power
struggles, etc.) and technological (platform features, techno-
logical environment, etc.) perspectives. Unlike these studies,
Lee et al. (2016) focused on internal psychological processes
that lead to fatigue and developed the ICT Overload and SNS
fatigue scale in a study conducted with data obtained from
201 online and offline participants.
The exploratory factor analysis (EFA) conducted on the
ICT Overload and SNS fatigue scale resulted in an eight-factor
scale. The eight factors identified in the EFA were “Information
Relevance,” “Information Equivocality,” “System Pace of
Change,” “System Complexity,” “Information Overload,”
“Communication Overload,” “System Feature Overload,” and
“SNS Fatigue.” Based on the findings of the confirmatory fac-
tor analysis (CFA), items with a factor weight of .60 and above
were included in the scale. The scale includes 32-items and the
Cronbach alpha (α) internal consistency reliability coeffi-
cients for all the eight factors are greater than (.70) level.
In this study, overload was accepted as the main determi-
nant of SNS fatigue based on the literature (Cherubini et al.,
2010; Lee et al., 2016; Ravindran et al., 2014). Overload was
discussed under “Information Overload,” “Communication
Overload,” and “System Feature Overload” sub-dimensions.
Furthermore, social networking characteristics were included
as the precursor of overload on the scale. The scale is config-
ured as a seven-point, Likert-type that included the option of
“I completely disagree” (1), “I disagree” (2), “I somehow
disagree” (3), “Neither agree nor disagree” (4), “I somehow
agree” (5), “I agree” (6), and “I completely agree” (7).
The statistical analysis was conducted using IBM SPSS
23 software. CFA was conducted using LISREL 9.1 software
to test the measurement models that describe the correlations
between the factors and the indicators. A number of different
compliance criteria are used to determine the fitness of the
tested model in CFA. Kline (2011) suggested that the fitness
indices have weaker and stronger aspects when compared,
thus proposed the use of more than one fitness criterion. The
fitness indices presented in the Table 1 were tested to exam-
ine the factor structure in the scale.
When the fit indices of the model tested with CFA were
examined, it was found that the chi-square value (x2 = 816.75,
df = 434, p < .001) was significant. It was found that the cal-
culated model x2/df = 1.88 ratio indicated a perfect fit. It was
observed that other model fitness values (RMSEA = .063,
SRMR = .086, CFI = .91, GFI = .81, NNFI = .90, IFI = .92)
were all within an acceptable fitness interval. It was deter-
mined that the factor loads for all tested scale items were also
statistically significant in the model. When the general fit-
ness index criteria were compared, it was observed that the
values for the model were within the acceptable fitness index

6 Social Media + Society
interval, and that the model and all model sub-dimensions
were verified. In addition to the structural validity results, it
is important to examine the discriminant and convergent
validity. To test convergent validity, all construct reliability/
composite reliability (CR) values for the scale are expected
to be greater than the average variance extracted (AVE)
value, and the AVE value is expected to be greater than a
value of 0.5. The CR value could be calculated using the for-
mula of Fornell and Larcker (1981). This value is expected to
be greater than 0.7. To test discriminant validity (DV), maxi-
mum squared variance (MSV) < AVE and average shared
square variance (ASV) < MSV requirements must be met. In
addition, it is necessary to ensure that the square root of the
AVE is greater than the correlation between the scale’s fac-
tors. Also, the DV value was calculated. The AVE, CR, and
DV values were calculated as .82, .97, and .90, respectively.
Thus, it is understood that the scale also verifies discriminant
and convergent validity, so it is possible to answer RQ1.
Scale Translation Procedure
The original scale’s authors’ permission was obtained on the
condition that the scale would be used for the purposes of
academic study. A professional translator converted the orig-
inals to Turkish with revisions suggested by experts and
English language teachers at the host institution. Then, the
data collection tool procedure was initiated.
Study Group
According to the literature, SNS fatigue and FoMO are
mostly seen within younger groups of adolescents and under-
graduate-level university students (Alt, 2015; Blackwell
et al., 2017; Elhai et al., 2016; Krishnamurthy & Chetlapalli,
2015; Oberst et al., 2017). SNSs have also achieved consid-
erable popularity in recent years, in Turkey as in the entire
world. According to the We Are Social (2020) report, 92% of
the population in Turkey own a smartphone (about 77
million) while 64% of the population are active users of
social media (about 54 million). The most popular SNSs are
YouTube (90%), Instagram (83%), WhatsApp (81%),
Facebook (76%), and Twitter (61%), respectively, as propor-
tioned to the population. There was a 3.4% rise in smart-
phone usage compared to 2019 and a 4.2% growth in the
number of social media users. In comparison, according to
Internet usage statistics, smartphone usage has risen over the
last 3 years (about +31%), contrary laptop (about −55%) and
personal computer (PC; about −40%) use rates have dropped
dramatically. Another conspicuous case is that male social
media user rates are higher for any age range than females.
There is growing concern about how individual differences
in general, and particularly gender, predispose other individ-
uals to participate (Sween et al., 2017; Toda et al., 2016).
Gender is therefore a critical factor in this research and social
networking dynamics are becoming increasingly important
for individuals in particular Turkey, one of the top five coun-
tries with higher rates of SNS usage than the United States
and Europe. Therefore, in this study, undergraduate-level
university students whose social network usage is considered
their focus were examined as a study group for SNS fatigue
within the framework of different variables. In the first, the
adaption of the scale was conducted with 225 undergraduate-
level university students (n = 225; 137 females [60.9%]). The
sample was a convenience sample of undergraduate-level
university students. The students voluntarily and anony-
mously participated in the study.
Results
The scale was applied to 469 undergraduate-level university
students attending Anadolu University Faculty of Education
(n = 469, 274 females [58.4%]; grade levels: second grade
n = 267 [56.9%]; third grade n = 159 [33.9%]; fourth grade
n = 43 [9.2%]). In terms of the participants’ department of
study, 24.5% (n = 115) were attending English Language
Education, 19.0% (n = 89) were attending Computer and
Table 1. Model Fit Indices for Measurement Model Results.
Fit criteria Perfect fitaAccepted valuesbModel results Rationale
Sample size Item*5
100
225 Kass & Tinsley (1979)
Tanaka, Panter, Winborne, & Huba (1990)
x2/df x2/df ≤ 3 3 ≤ x2/df ≤ 5 816.75/434 = 1.88aKline (2011)
RMSEA 0 ≤ RMSEA ≤ .05 .05 ≤ RMSEA ≤ .08 .063bHooper, Coughlan, & Mullen (2008)
SRMR SRMR ≤ .10 SRMR ≤ .10 .086aWorthington & Whittaker (2006)
IFI .95 ≤ IFI ≤ 1.00 .90 ≤ IFI ≤ .95 .92bSchermelleh–Engel & Moosbrugger (2003)
NNFI .97 ≤ NNFI ≤ 1.00 .90 ≤ NNFI ≤ 1.00 .90bSchumacker & Lomax (1996)
CFI .97 ≤ CFI ≤ 1.00 .90 ≤ CFI ≤ .95 .91bHu & Bentler (1999)
GFI .95 ≤ GFI ≤ 1.00 .80 ≤ GFI ≤ .95 .81bCheng (2011)
Note. CFI: comparative fit index; GFI: goodness of fit index; IFI: incremental fit index; NNFI: non-normed fit index; RMSEA: root mean square error of
approximation; SRMR: standardized root mean square residual.
aPerfect fit values.
bAcceptable values.

Tugtekin et al. 7
Instructional Technologies Education (CEIT), 13.9% (n = 65)
were attending Special Education, 13.6% (n = 64) were
attending Primary Education, 8.1% (n = 38) were attending
German Language Education, 7.5% (n = 35) were attending
Guidance and Psychological Counseling Education, 7.0%
(n = 33) were attending Art Education, and 6.2% (n = 29)
were attending French Language Education departments. In
addition, 0.002% of the participants (n = 1) selected “other”
as their department of study. Demographics for the partici-
pants are presented in Table 2.
Table 2. Demographics.
Female Male Total
f%f%f%
Department
German language education 28 10.2 10 5.1 38 8.1
CEIT 27 9.9 62 31.8 89 19.0
French language education 22 8.0 7 3.6 29 6.2
English language education 70 25.5 45 23.1 115 24.5
Guidance and psychological counseling program 28 10.2 7 3.6 35 7.5
Art education 19 6.9 14 7.2 33 7.0
Primary education 52 19.0 12 6.2 64 13.6
Special education 28 10.2 37 19.0 65 13.9
Other – – 1 0.5 1 0.2
Total 274 100.0 195 100 469 100
Grade
Second grade 169 61.7 98 50.3 267 56.9
Third grade 87 31.8 72 36.9 159 33.9
Fourth grade 18 6.6 25 12.8 43 9.2
Total 274 100.0 195 100 469 100
Spending time on social networks
Less than 1 hr 26 9.5 30 15.4 56 11.9
More than 1 hr–Less than 3 hr 107 39 73 37.4 180 38.6
More than 3 hr–Less than 5 hr 91 33.2 60 30.8 151 32.2
More than 5 hr 50 18.2 32 16.4 82 17.5
Total 274 100.0 195 100 469 100
Continuous Internet access
Yes 265 96.7 183 93.8 448 95.5
No 9 3.3 12 6.2 21 4.5
Total 274 100.0 195 100 469 100
Spending time with a smartphone
Less than 1 hr 12 4.4 19 9.7 31 6.7
More than 1 hr–Less than 3 hr 79 28.8 74 37.9 153 32.6
More than 3 hr–Less than 5 hr 103 37.6 58 29.7 161 34.3
More than 5 hr 80 29.2 44 22.6 124 26.4
Total 274 100.0 195 100 469 100
Frequency of social media checking
Every other day 31 11.3 19 9.7 50 10.7
Every day 153 55.8 110 56.4 263 56.0
Every hour 90 32.8 66 33.8 156 33.3
Total 274 100.0 195 100 469 100
Frequency of email checking
Every other day 144 52.6 97 49.7 241 51.4
Every day 126 46.0 82 42.1 208 44.3
Every hour 4 1.5 16 8.2 20 4.3
Total 274 100.0 195 100 469 100
Checks for call-to-call frequencies
Every other day 30 10.9 18 9.2 48 10.2
Every day 133 48.6 93 47.7 226 48.2
(Continued)

8 Social Media + Society
It was determined that 95.5% of the participants (n = 448)
had continuous Internet access, demonstrating that a very
high percentage of participants had Internet access. In order
to eliminate such potential confounding factors, participants
who specified that had no any continuous Internet access
(n = 21; 9 females [42.9%]) were excluded from all the ongo-
ing analysis. When the time the participants spent with a
smartphone was examined, it was found that the rate of those
who spent between 1 and 5 hr on their smartphones was
66.7% (n = 299). Those who spent less than 1 hr on their
smartphones amounted to only 6.5% (n = 29). It was deter-
mined that 26.8% (n = 120) spent more than 5 hr on their
smartphones. The fact that about one-quarter of the partici-
pants spent more than 5 hr on their smartphones was a strik-
ing finding. When the time they spent on social networks
was examined, it was found that the rate of those who spent
between 1 and 5 hr was about 69.8% (n = 313). The rate of
those who spent less than 1 hr on social networks was 12.1%
(n = 54), while the rate of those who spent 5 hr or more on
social networks was 18.1% (n = 81).
Analysis of the frequency of checking social media
accounts demonstrated that 55.4% (n = 248) of the partici-
pants checked their social media accounts every day and that
33.5% (n = 150) checked their social media accounts every
hour. The rate of those who checked their social media
accounts every other day was as low as 11.2% (n = 50).
Analysis of the frequency of checking email accounts demon-
strated that 50.2% (n = 225) of the participants checked their
email every other day, while 45.8% (n = 205) checked their
email every day. The rate of those who checked their email
every hour was as low as 4.0% (n = 18). It was found that
48.2% (n = 216) of the participants checked their smartphones
for calls every day, 41.3% (n = 185) checked every hour, and
10.5% (n = 47) checked their smartphones for calls every
other day. Participants most frequently used social networks
such as WhatsApp, Facebook, Instagram, Snapchat, and
Twitter, respectively. It was determined that the least pre-
ferred social network was Flickr. Considering that partici-
pants used more than one device to connect to social networks,
it was determined that 96.65% (n = 433) preferred smart-
phones, 49.78% (n = 223) preferred personal computers, and
5.58% (n = 25) preferred using tablet computers. While the
most frequently preferred device type was the smartphone,
the preference for tablet computers was the lowest.
The standard deviations of the arithmetic mean and stan-
dard deviation of the scale scores were calculated in order to
answer the RQ2 and the results are as follows (n = 448, MSNS
Fatigue = 3.823, SDSNS Fatigue = 0.73; MPSU = 4.067, SDPSU = 1.26;
MFoMO = 2.659, SDFoMO = 0.75). According to this results, the
mean PSU score was the highest and the mean FoMO score
was the lowest among participants.
The MANOVA and Multilinear Regression prerequisites
for research questions were examined. Mahalanobis distance
was calculated for multivariate normality. Since the high
Mahalanobis value for all three variables was below 16.27,
multivariate normality was confirmed. The Box-M Test was
conducted for the equality of the variance–covariance matrix
and was confirmed (p > .05). For multilinear correlation, the
Female Male Total
f%f%f%
Every hour 111 40.5 84 43.1 195 41.6
Total 274 100.0 195 100 469 100
Preferred social media
Facebook 224 81.8 177 90.8 401 85.5
Twitter 131 47.8 99 50.8 230 49.0
Google Plus 50 18.2 51 26.2 101 21.5
WhatsApp 267 97.4 187 95.9 454 96.8
Swarm 107 39.1 96 49.2 203 43.3
Instagram 232 84.7 163 83.6 395 84.2
Snapchat 154 56.2 97 49.7 251 53.5
Pinterest 61 22.3 31 15.9 92 19.6
Tumblr 20 7.3 18 9.2 38 8.1
Flickr 2 0.7 6 3.1 8 1.7
LinkedIn 8 2.9 11 5.6 19 4.1
Other 13 4.7 24 12.3 37 7.9
Preferred device for social media connection
Smartphone 272 99.3 182 93.3 454 96.8
Tablet PC 19 6.9 6 3.1 25 5.3
PC 127 46.4 117 60.0 244 52.0
Note. CEIT: Computer and Instructional Technologies Education; PC: personal computer.
Table 1. (Continued)

Tugtekin et al. 9
correlation between the scales was examined and a linear
relationship was found among the variables. It was also evi-
dent that the dependent variable fulfilled the multilinear cor-
relation requirement since the dependent variable variance
inflation factor (VIF) value was less than 10 and the toler-
ance values were greater than 0.1. Since there was no high
correlation among the variables, it was determined that the
singularity requirement was achieved (r < .90). Since the
multiple regression analysis is sensitive to outliers, the outli-
ers were examined with a scatter graph, and it was deter-
mined that there were no outliers since they were within the
range of ±3.3 (Tabachnick & Fidell, 1996). Furthermore,
since Cook’s distance value was lower than 1 and centered
leverage was below .02, it was concluded that there was no
outlier problem. Finally, the Durbin–Watson value was
examined in order to determine that the residual terms were
not correlated. Since this value was within the range of 1–3
(Field, 2005), it was determined that the residual terms were
not correlated. After the confirmation of all the prerequisites,
MANOVA, followed by regression analysis was conducted
to research the sub-objectives of the study.
MANOVA was conducted in order to obtain findings for
the sub-research question (RQ3). The analysis findings are
presented in Table 3.
MANOVA findings demonstrated that Wilks’ Lambda value
for gender was significant (p < .05). Also, Levene-Test result
demonstrated variance equality (p > .05). Analysis results dem-
onstrated that there were significant differences between SNS
fatigue levels, λ = .019; F(1, 446) = 5.543, p < .05,
η
p
2
= .012,
Power = .651, and PSU levels, λ = .000; F(1, 446) = 21.650, p < .05,
ηp
2 = .046, Power = .996, of the students based on gender.
Analysis of the mean SNS fatigue levels demonstrated that the
mean score for females (M = 3.89) was higher than for males
(M = 3.72). Accordingly, it was determined that the SNS fatigue
significantly differed favoring the females. Analysis of the
mean PSU levels demonstrated that the mean score for females
(M = 4.29) was higher than for males (M = 3.73). Therefore, it
can be argued that PSU levels differed significantly favoring
females as well. It was determined that there was no statisti-
cally significant difference between the FoMO levels of par-
ticipants based on gender.
MANOVA was conducted to obtain findings on the sub-
research question (RQ3). The findings are presented in
Table 4.
MANOVA results demonstrated that Wilks’ Lambda Test
value for time spent on social networks was significant
(p < .05). Also, Levene-Test result demonstrated variance
equality (p > .05). Analysis results demonstrated that there
Table 3. Analysis of Differences Between FoMO, PSU, and SNS Fatigue Levels Based on Gender.
Source of variance Dependent variable Sum of squares df MS f p
η
p
2Power
Gender SNS fatigue 2.935 1 2.935 5.543 .019* .012 .651
PSU 33.282 1 33.282 21.650 .000* .046 .996
FoMO .086 1 .086 .151 .697 .000 .067
Error SNS fatigue 236.201 446 .530
PSU 685.620 446 1.537
FoMO 253.928 446 .569
Total SNS fatigue 6787.616 448
PSU 8129.163 448
FoMO 3423.164 448
Note. FoMO: fear of missing out; PSU: problematic smartphone use; SNS: social networking service.
*p < .05.
Table 4. Analysis of Differences Between FoMO, PSU, and SNS Fatigue Levels Based on Time Spent on Social Networks.
Source of variance Dependent variable Sum of squares df MS f P
η
p
2Power
Time spent on social networks SNS fatigue 3.342 3 1.114 2.098 .100 .014 .536
PSU 37.294 3 12.431 8.098 .000* .052 .991
FoMO 14.207 3 4.736 8.768 .000* .056 .995
Error SNS fatigue 235.794 444 .531
PSU 681,608 444 1.535
FoMO 239.807 444 .540
Total SNS fatigue 6787.616 448
PSU 8129.163 448
FoMO 3423.164 448
Note. FoMO: fear of missing out; SNS: social networking service; PSU: problematic smartphone use.
*p < .05.

10 Social Media + Society
were significant differences between PSU levels, λ = .000;
F(3, 444) = 8.098, p < .05, ηp
2 = .052, Power = .991, and
FoMO levels, λ = .000; F(3, 444) = 8.768, p < .05, ηp
2 = .056,
Power = .995, of participants based on the social network
usage duration. The post hoc Scheffe Test was conducted to
determine the dependent variables significantly differed on
which dimensions of social network usage duration. Based
on the test results, it was observed that the PSU and FoMO
levels of those who spent more than one and less than 3 hr,
more than 3 hr and less than 5 hr, and more than 5 hr on social
networks significantly differed when compared to those who
spent less than 1 hr on social networks (p < .05). There was no
significant difference found between SNS fatigue levels of
participants based on social network usage duration.
MANOVA was conducted to obtain findings on the sub-
research question (RQ3). Analysis findings are presented in
Table 5.
MANOVA results demonstrated that Wilks’ Lambda for
smartphone usage duration was significant (p < .05). Also,
Levene-Test result demonstrated variance equality (p > .05).
Analysis results demonstrated that there were significant dif-
ferences between SNS fatigue levels, λ = .031; F(3, 444) = 2.975,
p < .05, ηp
2 = .020, Power = .703, PSU levels, λ = .000;
F(3, 444) = 12.981, p < .05, ηp
2 = .081, Power = 1.00, and FoMO
levels, λ = .000; F(3, 444) = 9.208, p < .05, ηp
2 = .059, Power = .997,
Table 5. Analysis of Differences Between FoMO, PSU, and SNS Fatigue Levels Based on Time Spent on Smartphones.
Source of variance Dependent variable Sum of squares df MS f p
η
p
2Power
Time spent on smartphones SNS fatigue 4.713 3 1.571 2.975 .031* .020 .703
PSU 57.971 3 19.324 12.981 .000* .081 1.000
FoMO 14.878 3 4.959 9.208 .000* .059 .997
Error SNS fatigue 234.423 444 .528
PSU 660.931 444 1.489
FoMO 239.136 444 .539
Total SNS fatigue 6787.616 448
PSU 8129.163 448
FoMO 3423.164 448
Note. FoMO: fear of missing out; SNS: social networking service; PSU: problematic smartphone use.
*p < .05.
Table 6. Analysis of Differences Between FoMO, PSU, and SNS Fatigue Levels Based on Checking the Media.
Source of variance Dependent variable Sum of squares df MS f p ηp
2Power
Frequency of social media checking SNS fatigue .835 2 .418 .780 .459 .003 .183
PSU 39.573 2 19.786 12.961 .000* .055 .997
FoMO 13.489 2 6.744 12.478 .000* .053 .996
Error SNS fatigue 238.301 445 .536
PSU 679.329 445 1.527
FoMO 240.526 445 .541
Total SNS fatigue 6787.616 448
PSU 8129.163 448
FoMO 3423.164 448
Note. FoMO: fear of missing out; SNS: social networking service; PSU: problematic smartphone use.
*p < .05.
of participants based on smartphone usage duration. The post
hoc Scheffe Test was conducted to determine on which
dimensions of smartphone usage duration the dependent
variables significantly differed. It was found that the PSU
levels of those who spent more than 3 hr and less than 5 hr on
their smartphones differed significantly when compared to
those who spent less than 1 hr on their smartphones (p < .05).
Furthermore, there were significant differences found
between the FoMO levels of participants who spent more
than 1 hr and less than 3 hr, more than 3 hr and less than 5 hr,
and more than 5 hr on their smartphones when compared to
those who spent less than 1 hr. Besides, the FoMO levels of
those using their smartphones more between 3 hr and less
than 5 hr, and more than 5 hr were significantly different
when compared to those using their smartphones for less
than 1 hr (p < .05) and of those using their smartphones more
than 5 hr were significantly different when compared to those
who used their smartphones for less than 1 hr (p < .05).
MANOVA was conducted on the mean scale scores to
obtain findings on the sub-research question (RQ3), and the
findings are presented in Table 6.
MANOVA results demonstrated that Wilks’ Lambda Test
for the frequency of checking the media was significant
(p < .05). Also, Levene-Test result demonstrated variance
equality (p > .05). Analysis results demonstrated that there

Tugtekin et al. 11
were significant differences between PSU levels, λ = .000;
F(2, 445) = 12.961, p < .05, ηp
2 = .055, Power = .997, and
FoMO levels, λ = .000; F(2, 445) = 12.478, p < .05, ηp
2 = .053,
Power = .996, of participants based on frequency of checking
the media. The post hoc Scheffe Test was conducted to deter-
mine which dimensions of the frequency of checking the
media the dependent variables significantly differed. It was
found that the PSU levels and FoMO levels of those who
checked the media every hour differed significantly when
compared to those who checked the media every day and
every other day (p < .05).
MANOVA was conducted on the mean scale scores to
obtain findings on the sub-research question (RQ3). The
findings are presented in Table 7.
MANOVA results demonstrated that Wilks’ Lambda Test
for the frequency of checking their email was significant
(p < .05). Also, Levene-Test result demonstrated variance
equality (p > .05). However, results demonstrated that there
were no significant differences between participants’ SNS
fatigue, PSU levels, and FoMO, based on the frequency of
checking their emails.
MANOVA was conducted on the mean scale scores in
order to obtain findings on the sub-research question (RQ3).
The findings are presented in Table 8.
MANOVA results demonstrated that Wilks’ Lambda
Test for the frequency of checking for calls was not signifi-
cant (p > .05). Also, Levene-Test result demonstrated vari-
ance equality (p > .05). Analysis results demonstrated that
there were significant differences between PSU levels,
λ = .002; F(2, 445) = 6.335, p < .05, ηp
2 = .028, Power = .898,
and FoMO levels, λ = .004; F(2, 445) = 4.757, p < .05,
ηp
2 = .021, Power = .792, of participants based on frequency
of checking for calls. The post hoc Scheffe Test was con-
ducted to determine the dependent variables significantly
differed based on which dimensions of the frequency of
checking for calls. It was found that the PSU levels of par-
ticipants who checked for calls every hour differed signifi-
cantly when compared to those who checked for calls every
other day and every day (p < .05).
Significant differences between the dimensions were not
determined in the post hoc test conducted to identify which
levels of frequency of checking for calls led to a difference
between the SNS fatigue levels of the students since the
Scheffe is a rigid test. Thus, the least significant difference
(LSD) method was conducted as the post hoc test, and to
determine between which dimensions there were significant
differences. As a result, it was determined that there were
differences found between the SNS fatigue levels of those
Table 7. Analysis of Differences Between FoMO, PSU, and SNS Fatigue Levels Based on Checking Their Email.
Source of variance Dependent variable Sum of squares df MS f p
η
p
2Power
Frequency of email checking SNS fatigue 1.004 2 .502 .938 .392 .004 .213
PSU 7.943 2 3.971 2.486 .084 .011 .498
FoMO 1.058 2 .529 .931 .395 .004 .211
Error SNS fatigue 238.132 445 .535
PSU 710.959 445 1.598
FoMO 252.956 445 .568
Total SNS fatigue 6787.616 448
PSU 8129.163 448
FoMO 3423.164 448
Note. FoMO: fear of missing out; SNS: social networking service; PSU: problematic smartphone use.
Table 8. Analysis of Differences Between FoMO, PSU, and SNS Fatigue Levels Based on Checking the Calls.
Source of variance Dependent variable Sum of squares df MS f p
η
p
2Power
Checks for call-to-call
frequencies
SNS fatigue 2.853 2 1.427 2.687 .069 .012 .532
PSU 19.900 2 9.950 6.335 .002* .028 .898
FoMO 5.317 2 2.659 4.757 .009* .021 .792
Error SNS fatigue 236.283 445 .531
PSU 699.002 445 1.571
FoMO 248.697 445 .559
Total SNS fatigue 6787.616 448
PSU 8129.163 448
FoMO 3423.164 448
Note. FoMO: fear of missing out; SNS: social networking service; PSU: problematic smartphone use.
*p < .05.

12 Social Media + Society
who checked for calls hourly when compared to those who
checked for calls every day and every other day (p < .05).
Correlation analysis was conducted on the mean scale
scores in order to obtain findings on the RQ4. The relation-
ships across the variables of interest are as follows: The find-
ings revealed that positive and significant relationships
between SNS fatigue and PSU (r = .362, p < .05), SNS fatigue
and FoMO (r = .227, p < .05) and, PSU and FoMO (r = .520,
p < .05). The correlations between all the variables of interest
are summarized in the Table 9. Analysis of the correlation
coefficients among the variables demonstrated that the cor-
relation coefficient between PSU and FoMO was high, since
it was between .50 and 1.00, according to Cohen (1988) and
Huck (2008). The correlation coefficient between SNS
fatigue and PSU was between .30 and .49, according to
Cohen (1988) and Huck (2008), and the correlation was
moderate. There was also a low-level correlation found
between SNS fatigue and FoMO, which was between .10 and
.29, according to Cohen (1988) and Huck (2008).
Multiple regression analysis was conducted on the mean
scale scores to obtain findings on the Q5. The regression
analysis results on the prediction of PSU by the variables of
FoMO and SNS Fatigue are presented in Table 10.
There was a significant correlation found between FoMO
and SNS fatigue variables and the PSU variable (R = .577,
R2 = .333, p < .05). The two variables explained approximately
33% of the total variance in PSU. Based on the standardized
regression coefficient (β), the relative significance of the pre-
dicting variables on the PSU can be ranked as FoMO fol-
lowed by SNS fatigue. When the t-test results conducted to
determine the significance of the regression coefficients were
examined, it was observed that both variables were signifi-
cant predictors. The regression equation mathematical model
for the prediction of PSU based on the regression analysis
results was determined as follows
PSU = .296 + .777 FoMO + .446 SNS fatigue
Discussion and Recommendations
The study findings determined that the significant differ-
ence between SNS fatigue and PSU levels of students
favored female students. Other studies in the literature also
reported that females had a higher tendency toward PSU
(Jenaro et al., 2007; Takao et al., 2009). However, other
studies reported that SNS fatigue increased the risk of
depression among females significantly higher compared to
males (Oberst et al., 2017). However, in a study by Lee et al.
(2016), it was reported that no significant difference was
found between SNS fatigue levels based on gender. It was
considered that this was due to differences in the sample,
economic, social, and cultural environment of the
Table 9. Relationships Between Variables of Interest.
PSU FoMO Information
relevance
Information
equivocality
System
pace of
change
System
complexity
Information
overload
Communication
overload
System
feature
overload
SNS
fatigue
PSU –
FoMO .520** –
Information relevance −.237** −.188** –
Information equivocality .150** .011 −.297** –
System pace of change .305** .229** −.042 .103* –
System complexity .361** .258** −.216** .294** .555** –
Information overload .292** .213** −.220** .427** .261** .467** –
Communication overload .274** .194** .038 .031 .656** .489** .313** –
System feature overload .208** .106* .086 .090* .498** .380** .350** .619** –
SNS fatigue .362** .227** .088 .258** .751** .673** .520** .830** .790** –
Note. SNS: social networking service; PSU: problematic smartphone use.
*p < .05. **p < .01.
Table 10. The Level that FoMO and SNS Fatigue Explained the PSU.
Predictor variables BStandard error BβT p Paired rPartial r
Constant .296 .287 1.031 .303
FoMO .777 .067 .462 11.614 .000* .520 .482
SNS fatigue .446 .069 .257 6.476 .000* .362 .293
Note. FoMO: fear of missing out; SNS: social networking service; PSU: problematic smartphone use.
R = .577, R2 = .333, Adjusted R2 = .330, F(2,445) = 111.151, p = .000.
*p < .05.

Tugtekin et al. 13
corresponding studies. The rapid development and diffusion
of technology have contributed to technology becoming an
industry and a global characteristic. Access to information
and communication through technology has reached a
global level, and nowadays, Internet users anywhere in the
world have little difference in their access to information
(Callan, 2000), except in Iran, China, Democratic People’s
Republic of Korea (DPNK), and so on. Therefore, there are
similarities in the global culture in terms of access to
resources such as technology and the Internet in SNS fatigue
and PSU. However, differences can be seen in the context of
economics, perception, and attitudes. Consequently, the
individual possibilities and perspectives of the users can
create diversity in the context of using technology (Yılmaz
& Horzum, 2005).
Accordingly, it would be beneficial to investigate the dif-
ferentiation of SNS fatigue based on gender in different sam-
ple groups. It is also determined that future studies could be
conducted to raise awareness among female participants
about PSU. However, the FoMO levels among participants
did not significantly differ by gender. Similarly, it was deter-
mined that there was no significant difference between
FoMO levels based on gender in a study conducted by
Hoşgör et al. (2017). In other respects, it was observed that
the PSU and FoMO levels of undergraduate-level university
students significantly differed based on social network usage
duration. Based on the findings, PSU and FoMO levels of
participants who spend between 1 and 3 hr, between 3 and
5 hr, and more than 5 hr on social networks differed signifi-
cantly when compared to those who spend less than 1 hr per
day on social networks. The SNS fatigue levels of partici-
pants did not differ based on social network usage duration.
This was similar to the finding determined in a study con-
ducted by Elhai and Contractor (2018), in that the PSU levels
of undergraduates who spent more time on social networks
were significantly higher.
The SNS fatigue, PSU, and FoMO levels of participants
differed significantly based on smartphone usage duration.
Based on the findings, as the amount of time spent using
social networks increased, SNS fatigue, and PSU also
increased. Also, there was a significant difference found
between the FoMO levels of those who spent between 3 and
5 hr, and more than 5 hr on smartphones when compared to
those who spent less than 1 hr on smartphones; and of those
who spent more than 5 hr on smartphones when compared to
those who spent less than 1 hr per day on smartphones. Thus,
it can be argued that SNS fatigue, PSU, and FoMO levels led
to significant differences for those who spent more than 3 hr
on smartphones over a short period of time, causing negative
consequences. This was consistent with the findings of Wang
et al. (2015), in that PSU may lead to negative consequences
such as an increase in the perceived stress level. Thus, it was
observed that as students spend more time on social net-
works, the level of PSU and FoMO increases. The awareness
of undergraduate-level university students on the filtering
features of social networking application could be raised to
enable them to be safe from negative consequences. It was
observed that the levels of SNS fatigue, PSU, and FoMO
increased as the time spent on smartphones increased.
Furthermore, it was also observed that PSU and FoMO
levels of undergraduate-level university students differed
significantly based on the frequency of checking the media.
Based on the findings, PSU and FoMO levels of those who
checked their social media accounts every hour differed sig-
nificantly when compared to those who checked their social
media accounts every day or every other day. Furthermore,
PSU levels of those who checked their social media accounts
every hour differed significantly when compared to those
who checked every other day. It was also found that the PSU
level and FoMO levels of participants significantly differed
based on the frequency of checking for calls. The PSU levels
of those who checked for calls every hour differed signifi-
cantly when compared to those who checked for calls every
day or every other day. Furthermore, the FoMO levels of
those who checked for calls every hour differed significantly
when compared to those who checked for calls every other
day. These results were consistent with the findings reported
by Przybylski et al. (2013), when the impacts of the fre-
quency of checking on PSU, FoMO, and SNS fatigue are
examined. In other words, this finding was consistent with
the finding reported by Przybylski et al. (2013) that FoMO
was one of the key factors that determine social media
overuse.
There was a significant and high positive correlation
between SNS fatigue and PSU, and when the other variable
was controlled, it was found that the correlation between the
two variables had a moderate positive correlation. However,
a previous study by Dhir et al. (2018) suggests that the use of
social media is a mediating variable between FoMO and
SNS fatigue. This means that FoMO contributes to SNS
fatigue indirectly by the excessive use of social media. While
there was a moderate positive correlation between FoMO
and PSU in this study, when the other variable was con-
trolled, it was observed that there was a low positive correla-
tion between the two variables. When the multiple regression
between the three variables was examined, it was observed
that FoMO and SNS fatigue together predicted the PSU vari-
able. The FoMO variable had the most significant impact on
the SNS fatigue variable, followed by PSU.
These findings provide a significant contribution to the
literature and support the finding that there was a significant
positive correlation between problematic Internet use and
FoMO reported in a study conducted by Stead and Bibby
(2017) on bilateral relations. Since FoMO leads to spending
more time on social networks, this can also lead to SNS
fatigue in individuals, which then triggers PSU. According to
Elhai et al. (2018b), excessive PSU has potential adverse
outcomes in society. In this study, we examined possible pre-
dictors of PSU. The main research contribution is to deter-
mine significant predictors of PSU. However, we are aware

14 Social Media + Society
that some limitations are possessed, nevertheless, the psy-
chometrics of the Turkish version of the SNS fatigue scale on
the basis of the findings in this study are satisfactory to mea-
sure the Turkish context. However, PSU is a cultural and
technological phenomenon that is changing over time and
differentiating between social groups and countries. Further
studies may also aim to duplicate and expand this study by
exploring characteristics of personality with the PSU in vari-
ous target populations. The results of this study have signifi-
cant contributions to PSU research and risk factors, but these
early implications should be investigated more broadly
before any potential preventive approaches are established.
In addressing the issue of PSU, it is important to investigate
the etiological causes and adverse effects on the lives of peo-
ple. This research provides a further appreciation of gender
gaps in the characteristics of young adults and PSU, in
Turkey. In addition, this study is thought to have the potential
to contribute in the context of Turkish culture to SNS fatigue
and other variables of interest. The present findings are
advantageous not only for relevant literature, theory, or prac-
titioners, but also for precautions and confounding harmful
aspects about SNS fatigue. Thus, it would also pave the door
for potential work to investigate the SNS fatigue, in further
studies.
Limitations
These results are limited to participants using social net-
works, as well as an adapted scale, the data collection tools
used, and hypotheses created within the scope of the
research. Moreover, the fact that the distribution of the study
group by gender is not equal and the participants’ level of
education is not equal may create a limitation on the results
of the study in terms of generalizability. SNS fatigue, PSU,
and FoMO should be investigated within different sample
groups and based on different variables, and further studies
are recommended on the impact and more beneficial usage
of Internet technologies. The subject of this study is only
Turkish university students, and the findings may not be
generalized for the whole Turkish population (or others),
and the current analysis should be repeated using represen-
tative samples (both Turkey and others). Our previous litera-
ture review found that the emphasis was on young adults
from either the Western or East Asian countries, who attend
universities. In comparison, young social media users by
developing countries like Turkey, are uncommonly investi-
gated. Correlatively, several of the preceding analyses are
cross-sectional, wherein data are gathered concurrently.
Repeated cross-sectional experiments should be carried out
to overcome these limitations, with the same interventions
being tested over time with specific focus groups in further
studies. A literature review demonstrated that FoMO and
PSU contributed negatively to the overall well-being levels
of individuals (Stead & Bibby, 2017). This study’s findings
suggested that individuals generally experience FoMO and
that their Internet usage is at problematic levels in the cur-
rent environment where the Internet and technology usage
have become increasingly popular. In consequence, further
studies focusing on the determining of underlying factors
for PSU and applications that would reduce PSU among stu-
dents should be conducted.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
ORCID iD
Ufuk Tugtekin https://orcid.org/0000-0003-0129-3477
References
Adnan, M., & Gezgin, D. M. (2016). A modern phobia: Prevalence
of nomophobia among college students. Ankara University
Journal of Faculty of Educational Sciences, 49(1), 141–158.
https://doi.org/10.1501/Egifak_0000001378
Ahuja, M. K., Chudoba, K. M., Kacmar, C. J., McKnight, D. H.,
& George, J. F. (2007). IT road warriors: Balancing work-
family conflict, job autonomy, and work overload to mitigate
turnover intentions. MIS Quarterly, 31(1), 1–17. https://doi.
org/10.2307/25148778
Alt, D. (2015). College students’ academic motivation, media engage-
ment and fear of missing out. Computers in Human Behavior,
49, 111–119. https://doi.org/10.1016/j.chb.2015.02.057
Apaolaza, V., Hartmann, P., Medina, E., Barrutia, J. M., &
Echebarria, C. (2013). The relationship between socializing
on the Spanish online networking site Tuenti and teenagers’
subjective wellbeing: The roles of self-esteem and loneliness.
Computers in Human Behavior, 29(4), 1282–1289. https://doi.
org/10.1016/j.chb.2013.01.002
Ayyagari, R. (2012). Impact of information overload and task-
technology fit on technostress. In Proceedings of the southern
association for information systems conference (pp. 18–22).
Association for Information Systems. https://aisel.aisnet.org/
sais2012/4
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress:
Technological antecedents and implications. MIS Quarterly,
35(4), e831–e858. https://doi.org/10.2307/41409963
Błachnio, A., & Przepiórka, A. (2018). Facebook intrusion, fear
of missing out, narcissism, and life satisfaction: A cross-sec-
tional study. Psychiatry Research, 259, 514–519. https://doi.
org/10.1016/j.psychres.2017.11.012
Beyens, I., Frison, E., & Eggermont, S. (2016). “I don’t want to
miss a thing”: Adolescents’ fear of missing out and its relation-
ship to adolescents’ social needs, Facebook use, and Facebook
related stress. Computers in Human Behavior, 64, 1–8. https://
doi.org/10.1016/j.chb.2016.05.083
Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D. J., &
Griffiths, M. D. (2015). Can disordered mobile phone use be

Tugtekin et al. 15
considered a behavioral addiction? An update on current evi-
dence and a comprehensive model for future research. Current
Addiction Reports, 2(2), 156–162. https://doi.org/10.1007/
s40429-015-0054-y
Blackwell, D., Leaman, C., Tramposch, R., Osborne, C., & Liss, M.
(2017). Extraversion, neuroticism, attachment style and fear of
missing out as predictors of social media use and addiction.
Personality and Individual Differences, 116, 69–72. https://
doi.org/10.1016/j.paid.2017.04.039
Bontcheva, K., Gorrell, G., & Wessels, B. (2013). Social media
and information overload: Survey results. https://arxiv.org/
pdf/1306.0813v1.pdf
Boyd, D. M., & Ellison, N. B. (2007). Social network sites:
Definition, history and scholarship. Journal of Computer
Mediated Communication, 13(1), 210–230. https://doi.
org/10.1111/j.1083-6101.2007.00393.x
Bright, L. F., Kleiser, S. B., & Grau, S. L. (2015). Too much
Facebook? An exploratory examination of social media
fatigue. Computers in Human Behavior, 44, 148–155. https://
doi.org/10.1016/j.chb.2014.11.048
Callan, H. (2000). Higher education internationalization strategies:
Of marginal significance or all-pervasive? The international
vision in practice: A decade of evolution. Higher Education in
Europe, 25(1), 15–23.
Chen, W., & Lee, K. H. (2013). Sharing, liking, commenting, and
distressed? The pathway between Facebook interaction and
psychological distress. Cyberpsychology, Behavior, and Social
Networking, 16(10), 728–734.
Cheng, S. (2011). Comparisons of competing models between atti-
tudinal loyalty and behavioral loyalty. International Journal of
Business and Social Science, 2(10), 149–166.
Cherubini, M., Gutierrez, A., De Oliveira, R., & Oliver, N. (2010).
Social tagging revamped: Supporting the users’ need of self-
promotion through persuasive techniques. In Proceedings of
the SIGCHI conference on human factors in computing systems
(pp. 985–994). Association for Computing Machinery. https://
doi.org/10.1145/1753326.1753473
Cho, J., Ramgolam, D. I., Schaefer, K. M., & Sandlin, A. N. (2011).
The rate and delay in overload: An investigation of communi-
cation overload and channel synchronicity on identification and
job satisfaction. Journal of Applied Communication Research,
39(1), 38–54. https://doi.org/10.1080/00909882.2010.536847
Cohen, J. (1988). Statistical power analysis for the behavioral sci-
ences (2nd ed.). Lawrence Erlbaum.
Çoklar, A. N., & Şahin, Y. L. (2011). Technostress levels of
social network users based on ICTs in Turkey. European
Journal of Social Sciences, 23(2), 171–182. http://www.
tecnostress.it/wp-content/uploads/2011/09/Technostress-in-
Social-Turkey.pdf
Desjarlais, M., & Willoughby, T. (2010). A longitudinal study
of the relation between adolescent boys’ and girls’ computer
use with friends and friendship quality: Support for the social
compensation or the rich-get-richer hypothesis? Computers in
Human Behavior, 26(5), 896–905. https://doi.org/10.1016/j.
chb.2010.02.004
Dhir, A., Yossatorn, Y., Kaur, P., & Chen, S. (2018). Online social
media fatigue and psychological wellbeing: A study of com-
pulsive use, fear of missing out, fatigue, anxiety and depres-
sion. International Journal of Information Management, 40,
141–152. https://doi.org/10.1016/j.ijinfomgt.2018.01.012
Edmunds, A., & Morris, A. (2000). The problem of information
overload in business organisations: A review of the literature.
International Journal of Information Management, 20(1), 17–
28. https://doi.org/10.1016/S0268-4012(99)00051-1
Elgan, M. (2017). With smartphones like these, why do we need
laptops? https://www.computerworld.com/article/3241233/
Elhai, J. D., & Contractor, A. A. (2018). Examining latent classes of
smartphone users: Relations with psychopathology and prob-
lematic smartphone use. Computers in Human Behavior, 82,
159–166.
Elhai, J. D., Dvorak, R. D., Levine, J. C., & Hall, B. J. (2017).
Problematic smartphone use: A conceptual overview and sys-
tematic review of relations with anxiety and depression psy-
chopathology. Journal of Affective Disorders, 207, 251–259.
https://doi.org/10.1016/j.jad.2016.08.030
Elhai, J. D., Levine, J. C., Alghraibeh, A. M., Alafnan, A. A.,
Aldraiweesh, A. A., & Hall, B. J. (2018a). Fear of missing out:
Testing relationships with negative affectivity, online social
engagement, and problematic smartphone use. Computers
in Human Behavior, 89, 289–298. https://doi.org/10.1016/j.
chb.2018.08.020
Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2016).
Fear of missing out, need for touch, anxiety and depression are
related to problematic smartphone use. Computers in Human
Behavior, 63, 509–516.
Elhai, J. D., Levine, J. C., O’Brien, K. D., & Armour, C. (2018b).
Distress tolerance and mindfulness mediate relations between
depression and anxiety sensitivity with problematic smartphone
use. Computers in Human Behavior, 84, 477–484. https://doi.
org/10.1016/j.chb.2018.03.026
Eppler, M. J., & Mengis, J. (2004). The concept of infor-
mation overload: A review of literature from organiza-
tion science, accounting, marketing, MIS, and related
disciplines. The Information Society, 20(5), 325–344. https://
doi.org/10.1080/01972240490507974
Ezoe, S., Toda, M., Yoshimura, K., Naritomi, A., Den, R., &
Morimoto, K. (2009). Relationships of personality and lifestyle
with mobile phone dependence among female nursing students.
Social Behavior and Personality: An International Journal,
37(2), 231–238. https://doi.org/10.2224/sbp.2009.37.2.231
Farhoomand, A. F., & Drury, D. H. (2002). Managerial information
overload. Communications of the Association for Computing
Machinery, 45(10), 127–131. https://doi.org/10.1145/570907.
570909
Field, A. (2005). Discovering statistics using SPSS (2nd ed.).
SAGE.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equa-
tion models with unobservable variables and measurement
error. Journal of Marketing Research, 18(1), 39–50. https://
doi.org/10.1177/002224378101800104
Fuster, H., Chamarro, A., & Oberst, U. (2017). Fear of missing out,
online social networking and mobile phone addiction: A latent
profile approach. Revista de Psicologia, Ciències de l’educació
I de l’esport, 35(1), 23–30.
Gokler, M. E., Aydin, R., Unal, E., & Metintas, S. (2016).
Determining validity and reliability of Turkish version of Fear
of Missing out Scale. Anatolian Journal of Psychiatry, 17(S1),
53–60. https://doi.org/10.5455/apd.195843
Gomez-Rodriguez, M., Gummadi, K. P., & Schoelkopf, B. (2014).
Quantifying information overload in social media and its

16 Social Media + Society
impact on social contagions. In Proceedings of the eighth inter-
national conference on weblogs and social media (ICWSM)
(pp. 170–179). Association for the Advancement of Artificial
Intelligence Press.
Gross, D. (2011, September 23). Users not happy with new Facebook
changes. CNN Business. https://edition.cnn.com/2011/09/21/
tech/social-media/facebook-changes-react/index.html
Guo, Y., Lu, Z., Kuang, H., & Wang, C. (2020). Information
avoidance behavior on social network sites: Information irrel-
evance, overload, and the moderating role of time pressure.
International Journal of Information Management, 52, Article
102067. https://doi.org/10.1016/j.ijinfomgt.2020.102067
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equa-
tion modelling: Guidelines for determining model fit. The
Electronic Journal of Business Research Methods, 6(1), 53–60.
Hoşgör, H., Koç Tütüncü, S., Gündüz Hoşgör, D., & Tandoğan,
Ö. (2017). Examination in terms of different variables of
prevalence of fear of missing out among college students.
International Journal of Academic Value Studies, 3(17), 213–
223. https://doi.org/10.23929/javs.668
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in
covariance structure analysis: Conventional criteria versus new
alternatives. Structural Equation Modeling: A Multidisciplinary
Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huck, S. W. (2008). Reading statistics and research (5th ed.).
Pearson.
James, T. L., Lowry, P. B., Wallace, L., & Warkentin, M. (2017).
The effect of belongingness on obsessive-compulsive disorder
in the use of online social networks. Journal of Management
Information Systems, 34(2), 560–596. https://doi.org/10.1080/
07421222.2017.1334496
Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., &
Caballo, C. (2007). Problematic internet and cell-phone
use: Psychological, behavioral, and health correlates.
Addiction Research & Theory, 15(3), 309–320. https://doi.
org/10.1080/16066350701350247
Karapanos, E., Teixeira, P., & Gouveia, R. (2016). Need fulfill-
ment and experiences on social media: A case on Facebook
and WhatsApp. Computers in Human Behavior, 55, 888–897.
https://doi.org/10.1016/j.chb.2015.10.015
Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much:
Operationalizing technology overload and exploring its
impact on knowledge worker productivity. Computers in
Human Behavior, 26(5), 1061–1072. https://doi.org/10.1016/j.
chb.2010.03.008
Kass, R. A., & Tinsley, H. E. A. (1979). Factor analysis. Journal of
Leisure Research, 11(2), 120–138. https://doi.org/10.1080/002
22216.1979.11969385
Kline, R. B. (2011). Principles and practice of structural equation
modeling (3rd ed.). Guilford Press.
Krishnamurthy, S., & Chetlapalli, S. K. (2015). Internet addiction:
Prevalence and risk factors: A cross-sectional study among
college students in Bengaluru, the Silicon Valley of India.
Indian Journal of Public Health, 59(2), 115–121. https://doi.
org/10.4103/0019-557X.157531
Kuss, D. D., Griffiths, M., Karila, L., & Billieux, J. (2014). Internet
addiction: A systematic review of epidemiological research
for the last decade. Current Pharmaceutical Design, 20(25),
4026–4052. https://doi.org/10.2174/13816128113199990617
Kuss, D. J., & Griffiths, M. D. (2012). Internet gaming addiction: A
systematic review of empirical research. International Journal
of Mental Health and Addiction, 10(2), 278–296. https://doi.
org/10.1007/s11469-011-9318-5
LaRose, R., Connolly, R., Lee, H., Li, K., & Hales, K. D. (2014).
Connection overload? A cross cultural study of the conse-
quences of social media connection. Information Systems
Management, 31(1), 59–73.
Lee, A. R., Son, S. M., & Kim, K. K. (2016). Information and com-
munication technology overload and social networking service
fatigue: A stress perspective. Computers in Human Behavior,
55, 51–61. https://doi.org/10.1016/j.chb.2015.08.011
Lee, C. S., & Ma, L. (2012). News sharing in social media: The
effect of gratifications and prior experience. Computers in
Human Behavior, 28(2), 331–339. https://doi.org/10.1016/j.
chb.2011.10.002
Lim, M., & Yang, Y. (2015). Effects of users’ envy and shame
on social comparison that occurs on social network services.
Computers in Human Behavior, 51, 300–311. https://doi.
org/10.1016/j.chb.2015.05.013
Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017).
Empirical investigation of Facebook discontinues usage inten-
tions based on SOR paradigm. Computers in Human Behavior,
70, 544–555. https://doi.org/10.1016/j.chb.2017.01.020
Mahajan, P. (2009). Use of social networking in s linguistically
and culturally rich India. The International Information &
Library Review, 41(3), 129–136. https://doi.org/10.1016/j.
iilr.2009.07.004
Moore, J. (2000). One road to turnover: An examination of work
exhaustion in technology professionals. MIS Quarterly, 24(1),
141–168. https://doi.org/10.2307/3250982
Müller, K. W., Dreier, M., Beutel, M. E., Duven, E., Giralt, S.,
& Wölfling, K. (2016). A hidden type of internet addiction?
Intense and addictive use of social networking sites in adoles-
cents. Computers in Human Behavior, 55, 172–177. https://doi.
org/10.1016/j.chb.2015.09.007
Oberst, U., Wegmann, E., Stodt, B., Brand, M., & Chamarro, A.
(2017). Negative consequences from heavy social network-
ing in adolescents: The mediating role of fear of missing out.
Journal of Adolescence, 55, 51–60. https://doi.org/10.1016/j.
adolescence.2016.12.008
Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2012). Habits
make smartphone use more pervasive. Personal and Ubiquitous
Computing, 16(1), 105–114. https://doi.org/10.1007/s00779-
011-0412-2
Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009).
College students’ social networking experiences on Facebook.
Journal of Applied Developmental Psychology, 30(3), 227–
238. https://doi.org/10.1016/j.appdev.2008.12.010
Pennington, R., & Tuttle, B. (2007). The effects of information over-
load on software project risk assessment. Decision Sciences, 38(3),
489–526. https://doi.org/10.1111/j.1540-5915.2007.00167.x
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V.
(2013). Motivational, emotional, and behavioral correlates of
fear of missing out. Computers in Human Behavior, 29(4),
1841–1848. https://doi.org/10.1016/j.chb.2013.02.014
Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q.
(2008). The consequences of technostress for end users in orga-
nizations: Conceptual development and empirical validation.

Tugtekin et al. 17
Information Systems Research, 19(4), 417–433. https://doi.
org/10.1287/isre.1070.0165
Ravindran, T., Yeow Kuan, A. C., & Hoe Lian, D. G. (2014).
Antecedents and effects of social network fatigue. Journal
of the Association for Information Science and Technology,
65(11), 2306–2320. https://doi.org/10.1002/asi.23122
Salehan, M., & Negahban, A. (2013). Social networking on smart-
phones: When mobile phones become addictive. Computers in
Human Behavior, 29(6), 2632–2639. https://doi.org/10.1016/j.
chb.2013.07.003
Sasaki, Y., Kawai, D., & Kitamura, S. (2016). Unfriend or ignore
tweets? A time series analysis on Japanese Twitter users suffer-
ing from information overload. Computers in Human Behavior,
64, 914–922. https://doi.org/10.1016/j.chb.2016.07.059
Schermelleh-Engel, K., & Moosbrugger, H. (2003). Evaluating
the fit of structural equation models: Tests of significance
and descriptive goodness-of-fit measures. Methods of
Psychological Research Online, 8(2), 23–74.
Schumacker, R. E., & Lomax, R. G. (1996). A beginner’s guide to
structural equation modeling. Lawrence Erlbaum Associates.
Shashank. (2011). Brands start to show signs of social media
fatigue. http://fastgush.com/marketing/brands-start-to-show-
signs-of-social-media-fatigue.html
Smetaniuk, P. (2014). A preliminary investigation into the
prevalence and prediction of problematic cell phone use.
Journal of Behavioral Addictions, 3(1), 41–53. https://doi.
org/10.1556%2FJBA.3.2014.004
Stead, H., & Bibby, P. A. (2017). Personality, fear of missing out
and problematic internet use and their relationship to subjec-
tive well-being. Computers in Human Behavior, 76, 534–540.
https://doi.org/10.1016/j.chb.2017.08.016
Sween, M., Ceschi, A., Tommasi, F., Sartori, R., & Weller, J.
(2017). Who is a distracted driver? Associations between
mobile phone use while driving, domain-specific risk taking,
and personality. Risk Analysis, 37(11), 2119–2131. https://doi.
org/10.1111/risa.12773
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate sta-
tistics. HarperCollins.
Takao, M., Takahashi, S., & Kitamura, M. (2009). Addictive per-
sonality and problematic mobile phone use. Cyber Psychology
& Behavior, 12(5), 501–507. https://doi.org/10.1089/
cpb.2009.0022
Tanaka, J. S., Panter, A. T., Winborne, W. C., & Huba, G. J. (1990).
Theory testing in personality and social psychology with struc-
tural equation models. In C. Hendrick & M. S. Clark (Eds.),
Research methods in personality and social psychology (pp.
217–243). SAGE.
Toda, M., Ezoe, S., Mure, K., & Takeshita, T. (2016). Relationship
of smartphone dependence to general health status and per-
sonality traits among university students. Open Journal of
Preventive Medicine, 6(10), 215–221. https://doi.org/10.4236/
ojpm.2016.610020
Walker, G. A. (1986). Burnout: From metaphor to ideology.
Canadian Journal of Sociology, 11(1), 35–55. https://doi.
org/10.2307/3340447
Wang, J. L., Wang, H. Z., Gaskin, J., & Wang, L. H. (2015). The
role of stress and motivation in problematic smartphone use
among college students. Computers in Human Behavior, 53,
181–188. https://doi.org/10.1016/j.chb.2015.07.005
We are Social. (2020). Digital in 2020: Global digital overview.
https://wearesocial.com/digital-2020
Wolniewicz, C. A., Tiamiyu, M. F., Weeks, J. W., & Elhai, J. D.
(2018). Problematic smartphone use and relations with nega-
tive affect, fear of missing out, and fear of negative and posi-
tive evaluation. Psychiatry Research, 262, 618–623. https://
doi.org/10.1016/j.psychres.2017.09.058
Worthington, R. L., & Whittaker, T. A. (2006). Scale develop-
ment research: A content analysis and recommendations for
best practices. The Counseling Psychologist, 34(6), 806–838.
https://doi.org/10.1177/0011000006288127
Yılmaz, K., & Horzum, M. B. (2005). Globalization, information
technologies and university. Inonu University Journal of the
Faculty of Education, 6(10), 103–121.
Yildirim, C., & Correia, A. P. (2015). Exploring the dimensions
of nomophobia: Development and validation of a self-reported
questionnaire. Computers in Human Behavior, 49, 130–137.
https://doi.org/10.1016/j.chb.2015.02.059
Zhang, S., Zhao, L., Lu, Y., & Yang, J. (2016). Do you get tired of
socializing? An empirical explanation of discontinuous usage
behaviour in social network services. Information & Management,
53(7), 904–914. https://doi.org/10.1016/j.im.2016.03.006
Zheng, X., & Lee, M. K. (2016). Excessive use of mobile social net-
working sites: Negative consequences on individuals. Computers
in Human Behavior, 65, 65–76. https://doi.org/10.1016/j.
chb.2016.08.011
Zhong, B., Hardin, M., & Sun, T. (2011). Less effortful thinking
leads to more social networking? The associations between the
use of social network sites and personality traits. Computers in
Human Behavior, 27(3), 1265–1271. https://doi.org/10.1016/j.
chb.2011.01.008
Author Biographies
Ufuk Tugtekin is a researcher in the Department of Computer
Education and Instructional Technology at Mersin University,
Turkey. He has a PhD degree in the instructional design and tech-
nology. His research interests are ICT integration, cyber-psychol-
ogy, technology use in special education, social media, multivariate
statistics, and multimedia learning.
Esra Barut Tugtekin is a researcher in the Department of Computer
Education and Instructional Technology at Anadolu University,
Turkey. She has a PhD in Computer Education and Instructional
Technology. Her studies are on instructional technology, media lit-
eracy, social networking, social media in education, multimedia
learning, and virtual identity.
Adile Aşkım Kurt is an associate professor in the Department of
Computer Education and Instructional Technology at Anadolu
University, Turkey. She has a PhD in Computer Education and
Instructional Technology. She lists her primary research interests as
ICT integration, digital literacy, technology use in special educa-
tion, and data analysis.
Kadir Demir is a research assistant in the Department of Computer
Education and Instructional Technology at Dokuz Eylul University,
Turkey. He has an MA in instructional design and technology. His
research interests are mobile learning, digital fluency, and technol-
ogy integration.