Investigation of nomophobia and smartphone addiction predictors among adolescents in Turkey: Demographic variables and academic performance

Abstract

Most individuals spend a great amount of time on their smartphones. The intense usage of smartphones leads to some physical symptoms, good and bad feelings, pathological addiction, depression, symptoms such as fear–anxiety, productivity and low academic achievement. For this reason, prevention activities must be prioritized when dealing with the intense and uncontrolled usage of smartphones. The aim of this study is to determine nomophobia levels and smartphone addiction among 12–18 age group secondary and high school students and to investigate the demographic and academic variables predicting these levels. Designed with a relational model, the population of this research consists of 612 students studying at all levels of secondary school and high school. Personal information form and two different scales were used in the research. Descriptive analyses and hierarchical linear multiple regression analysis were used in the analysis of the data obtained by means of data collection in the research. As a result of the research, there is a significant relationship between smartphone addiction and nomophobia. In this study, Model 4 has been identified to be the most important predictor of smartphone addiction and nomophobia. In Model 4, variables related to smartphone usage are included in the analysis. Recommendations have been made according to the results of the study.

Full text

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
Contents lists available at ScienceDirect
The
Social
Science
Journal
journal homepage: www.elsevier.com/locate/soscij
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance
Hatice
Yildiz
Durak
Bartin
University,
Faculty
of
Education,
Department
of
Computer
Education
and
Instructional
Technology,
Turkey
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
25
May
2018
Received
in
revised
form
15
September
2018
Accepted
16
September
2018
Available
online
xxx
Keywords:
Smartphone
usage
Nomophobia
Smartphone
addiction
Demographic
variables
ICT
usage
Secondary
school
students
High
school
students
a
b
s
t
r
a
c
t
Most
individuals
spend
a
great
amount
of
time
on
their
smartphones.
The
intense
usage
of
smartphones
leads
to
some
physical
symptoms,
good
and
bad
feelings,
pathological
addiction,
depression,
symptoms
such
as
fear–anxiety,
productivity
and
low
academic
achievement.
For
this
reason,
prevention
activities
must
be
prioritized
when
dealing
with
the
intense
and
uncontrolled
usage
of
smartphones.
The
aim
of
this
study
is
to
determine
nomophobia
levels
and
smartphone
addiction
among
12–18
age
group
secondary
and
high
school
students
and
to
investigate
the
demographic
and
academic
variables
predicting
these
levels.
Designed
with
a
relational
model,
the
population
of
this
research
consists
of
612
stu-
dents
studying
at
all
levels
of
secondary
school
and
high
school.
Personal
information
form
and
two
different
scales
were
used
in
the
research.
Descriptive
analyses
and
hierarchical
lin-
ear
multiple
regression
analysis
were
used
in
the
analysis
of
the
data
obtained
by
means
of
data
collection
in
the
research.
As
a
result
of
the
research,
there
is
a
significant
relationship
between
smartphone
addiction
and
nomophobia.
In
this
study,
Model
4
has
been
identified
to
be
the
most
important
predictor
of
smartphone
addiction
and
nomophobia.
In
Model
4,
variables
related
to
smartphone
usage
are
included
in
the
analysis.
Recommendations
have
been
made
according
to
the
results
of
the
study.
©
2018
Western
Social
Science
Association.
Published
by
Elsevier
Inc.
All
rights
reserved.
1.
Introduction
Nowadays,
smartphone
usage
is
quite
prevalent,
espe-
cially
among
young
people
(Aljomaa,
Qudah,
Albursan,
Bakhiet,
&
Abduljabbar,
2016;
Yildiz-Durak,
2018b).
The
functions
of
smartphones,
including
providing
users
with
the
means
of
communicating
in
different
environments
anytime
and
fulfilling
the
tasks
typically
performed
through
computers,
have
increased
the
utilisation
potential
of
these
technologies
(Forgays,
Hyman,
&
Schreiber,
2014;
Kwon,
Kim,
Cho,
&
Yang,
2013a).
When
Statista
(2017a)
data
is
checked,
it
is
seen
that
the
number
of
smartphone
ownership
is
expected
to
be
2.48
billion
in
2018
and
can
be
considered
that
it
seems
E-mail
address:
hatyil05@gmail.com
to
increase
at
the
same
speed.
In
Turkey,
on
the
other
hand,
it
is
reported
that
more
than
half
of
the
population
(44.6
million)
have
smartphones
in
2018.
As
indicated
by
Montag,
Blaskiewicz,
Lachmann
et
al.
(2015)
and
Montag,
Błaszkiewicz,
Sariyska
et
al.
(2015),
almost
40%
of
indi-
viduals
having
smartphones
use
their
mobiles
before
the
last
five
minutes
and
after
the
first
five
minutes.
In
this
technological
era,
in
which
the
smartphone
ownership
and
its
usage
are
gradually
increasing,
a
continuous
develop-
ment
draw
the
attention
in
mobile
technologies
(Yildirim
&
Correia,
2015).
Mobile
broadband
subscriptions
have
grown
more
than
20%
in
five
years
period
and
reached
4.3
billion
at
the
end
of
2017
(International
Telecommunications
Union
[ITU],
2017).
According
to
Choi
et
al.
(2015),
internet
access
is
a
preliminary
condition
for
a
good
many
functions
of
smart-
https://doi.org/10.1016/j.soscij.2018.09.003
0362-3319/©
2018
Western
Social
Science
Association.
Published
by
Elsevier
Inc.
All
rights
reserved.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
2
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
phones
and
there
is
a
strong
relationship
between
internet
and
smartphone
usage.
Mobile
internet
access
has
made
possible
the
usage
of
web-based
social
networks,
such
as
Facebook,
Twitter
and
Instagram
on
smartphones
(Duke
&
Montag,
2017a;
Przybylski,
Murayama,
DeHaan,
&
Gladwell,
2013).
Fur-
thermore,
some
message
applications,
such
as
WhatsApp,
which
has
1.3
billion
users
in
a
day
(Statista,
2017b),
have
been
designed
to
make
the
communication
more
conve-
nient
and
active
at
the
present
time.
The
real
usage
rates
of
“WhatsApp
and
Social
Networks”
which
can
also
be
used
on
smartphones
show
the
importance
of
social
media
in
understanding
of
smartphone
usage.
Therefore,
it
is
thought
that
internet
and
social
media
plays
an
important
role
in
developing
excessive
smartphone
usage
since
inter-
net
and
social
media
were
involved
with
each
other
(Kuss
et
al.,
2014;
Lachmann
et
al.,
2018).
Shan,
Deng,
Zhang
and
Zhao
(2013)
mentions
that
excessive
smartphone
usage
is
closely
linked
to
physical
health
problems.
This
problematic
usage
of
smartphones
leads
to
time
management
issues
(Lin
et
al.,
2015),
insom-
nia
(Yogesh,
Abha,
&
Priyanka,
2014),
low
productivity
and
academic
performance
(Duke
&
Montag,
2017b;
Lanaj
et
al.
2014;
Montag
&
Walla,
2016;
Samaha
&
Hawi,
2016)
as
well
as
physical
health
symptoms.
Although
the
excessive
usage
of
smartphones
brings
lots
of
benefits
to
the
users,
such
as
high
productivity,
searching
for
information,
social
inter-
action,
relaxation
and
entertainment
(Cho,
2015;
Elhai,
Dvorak,
Levine,
&
Hall,
2017;
Van
Deursen,
Bolle,
Hegner,
&
Kommers,
2015),
they
cause
problems
which
affect
daily
lives
of
most
individuals
in
a
negative
way
(Cheever,
Rosen,
Carrier,
&
Chavez,
2014;
Clayton
et
al.,
2015;
Kwon
et
al.,
2013b).
The
problematic
smartphone
usage
is
connected
with
mental
health
problems,
including
depression
and
anx-
iety
(Elhai,
Levine,
Dvorak,
&
Hall,
2016;
Elhai,
et
al.,
2017;
Thomée,
Harenstam,
&
Hagberg,
2011;
Wolniewicz,
Tiamiyu,
Weeks,
&
Elhai,
2018).
In
the
existing
literature,
it
has
been
mentioned
that
psychopathological
prob-
lems
such
as
anxiety
has
a
two-way
relationship
with
smartphone
use
disorder,
thereby
leading
to
reciprocal
cause-effect
relationship
between
excessive
smartphone
usage
and
anxiety
(van
den
Eijnden,
Meerkerk,
Vermulst,
Spijkerman,
&
Engels,
2008;
Thomée
et
al.,
2011).
Social
media
usage
via
smartphones
may
lead
to
the
necessity
of
being
online
continuously,
while
lack
of
digital
environment
has
a
possibility
of
causing
anxiety
(Przybylski
et
al.,
2013).
Fears
of
not
being
able
to
know
about
new
information
considered
as
important
by
individ-
uals
are
explained
by
the
concept
of
“Fear
of
Missing
Out
(FoMO)¨
.
FoMO
is
a
fear
and
apprehension
state
in
which
individuals
spend
a
great
deal
of
time
on
social
media
tools
since
they
fear
the
possibility
of
missing
the
latest
flows
in
social
networks
(Hato,
2013).
Turkle
(2012)
has
stated
that
this
excessive
online
communication
has
brought
about
nomophobia
which
means
the
fear
of
lacking
communica-
tion,
not
being
able
to
develop
social
skills
needed
for
face
to
face
communicatin
and
a
negative
psychological
effect.
Individuals
who
are
highly
active
on
social
networks
have
a
more
tendency
to
develop
some
psychopatho-
logical
problems
such
as
depression
and
anxiety
than
others
(Piwek
&
Ellis,
2016).
For
this
reason,
FoMO
can
be
regarded
as
a
part
of
nomophobia
concepts
which
mean
the
anxiety
experienced
in
the
event
of
lacking
internet
con-
nection.
The
increase
in
nomophobic
behaviours
displayed
by
individuals
affects
the
academic
performance,
motiva-
tion
levels
in
learning
processes
as
well
as
family
and
friend
relations
adversely
as
it
may
cause
mental
tiredness,
how-
ever
(Dixit
et
al.,
2010).
The
focal
point
of
this
study
is
to
investigate
whether
demographic
variables,
Information
Technology
(ICT)
usages
and
academic
performance
predict
the
smartphone
addiction
and
nomophobia
behaviours.
No
research
on
the
relation
nomophobia
and
smartphone
usage
has
been
reached
even
though
there
are
some
studies
founding
a
relation
between
smartphone
addiction
and
depression,
anxiety
and
FoMO
(Elhai
et
al.,
2016,
2017).
In
analysing
the
risk
factors
for
smartphone
use
disorder,
related
vari-
ables
such
as
demographic,
psychopathological
problems,
academic
performance
and
technology
usage
(Elhai
et
al.,
2017)
have
also
been
considered
related
factors
for
nomo-
phobia.
Even
though
it
can
be
thought
that
smartphone
addic-
tion
and
nomophobia
may
be
related
to
each
other
in
existing
literature
(Elhai
et
al.,
2016),
the
relation-
ship
of
these
two
variables
haven’t
been
examined
yet.
There
are
a
great
many
studies
examining
the
relation-
ship
between
demographic,
psychopathological,
academic
performance
and
technology
usage
and
internet
addic-
tion
(Kuss,
Griffiths,
Karila,
&
Billieux,
2014;
Yildiz-Durak,
2018a).
Smartphone
addiction
is
pertinent
to
structure
of
problematic
internet
usage
(Kuss
et
al.,
2014)
and
both
include
similar
symptoms.
However,
these
concepts
are
different
(Kiraly
et
al.,
2014).
For
this
reason,
the
rela-
tionship
between
basic
variables
considered
as
related
to
the
investigation
of
problematic
internet
usage,
smart-
phone
addiction
and
nomophobia
has
been
examined
in
this
study.
1.1.
The
Importance
of
the
Study
Smartphones
are
frequently
being
employed
for
a
wide
range
of
uses
which
influence
all
dimensions
of
life,
includ-
ing
social
communication,
entertainment
and
education
(Yildiz-Durak,
2018a).
This
situation,
on
the
other
hand,
poses
some
adverse
effects
along
with
the
positive
ones.
Adolescence
can
be
defined
as
a
sensitive
period
in
which
individuals
may
be
addicted
to
ICT
(Wang,
Tao,
Fan,
Gao,
&
Wei,
2017).
For
this
reason,
this
study
is
important
in
that
it
dwells
on
smartphone
addiction
and
nomophobia
levels
of
the
young.
The
relationship
between
academic
achieve-
ment
and
smartphone
addiction
and
nomophobia
levels
of
adolescence
haven’t
been
able
to
understand
yet.
Smartphone
addiction
and
nomophobia
are
considered
as
a
psyhological
problems
affecting
academic
achieve-
ment
negatively
in
adoloscence
(Hsiao,
Shu,
&
Huang,
2017).
However,
the
results
of
previous
studies
have
not
exam-
ined
whether
academic
achievement
has
a
predictive
role
on
smartphone
addiction
and
nomophobia.
According
to
self-determination
theory
(Ryan
&
Deci,
2000),
students
will
have
low
levels
of
motivation
when
they
cannot
sat-

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
3
isfy
their
pshyological
needs
such
as
efficacy
(when
their
academic
achievements
are
lower)
and
they
head
towards
the
resources
out
of
classroom.
In
this
situation,
it
can
be
argued
that
ICT
use
behaviours
may
increase.
For
this
rea-
son,
academic
achievement
are
investigated
as
students’
smartphone
addiction
and
nomophobia
levels
in
this
study.
Additionally,
the
investigation
of
smartphone
addic-
tion
and
nomophobia
along
with
demographic
variables
and
parents
can
develop/broaden
the
researches
on
smart-
phone
addiction
and
nomophobia.
It
can
present
some
suggestions
as
to
prevention
implementations
of
ICT
addiction
towards
adolescents
at
K12
level.
The
current
literature
generally
focuses
on
the
investigation
of
prob-
lematic
use
of
smartphone
and
its
adverse
effects
(Billieux,
Maurage
et
al.,
2015).
Only
a
few
ones
address
the
pre-
dictors
of
smartphone
addiction
(Aljomaa
et
al.,
2016;
Van
Deursen
et
al.,
2015).
This
study
is
believed
to
contribute
to
the
existing
literature,
by
addressing
predictors
of
smart-
phone
addiction
and
nomophobia.
The
investigation
of
predictor
factors
relating
o
smart-
phone
use
disorder
and
nomophobia
in
adolescence
was
aimed
to
perform
through
social
cognitive
theory
in
terms
of
conceptual
framework
of
the
study.
This
theory
is
one
of
the
theoretical
models
employed
to
examine
media
con-
sumption
behaviours
(Bandura,
2001a,
2001b;
LaRose,
Lin,
&
Eastin,
2003).
This
present
study
has
the
potential
to
expand
the
social
cognitive
theory
in
the
context
of
smart-
phone
use
disorder
and
nomophobia.
The
first
and
most
important
theoretical
contribution
is
to
address
many
“per-
sonal
factors”
as
an
explanation
of
smartphone
use
disorder
and
nomophobia.
While
the
effects
of
factors
such
as
inter-
net,
social
dependency
and
frequent
factors
such
as
gender,
age,
ethnicity,
belief
and
self-efficacy
are
examined
in
liter-
ature,
explanations
on
these
addictions
are
not
addressed
holistically.
Secondly,
the
original
theory
dwells
on
personal
structures
and
ignored
the
role
and
contribution
of
envi-
ronmental
factors
as
well
as
technological
addiction
and
nomophobic
behaviors.
On
the
other
hand,
the
effect
formed
when
both
personal
and
external
variables
are
dealt
with
together
hasn’t
been
addressed
within
the
framework
of
social
cognitive
theory.
Such
variables
as
parents
ICT
use
level,
education
level,
sibling
number,
the
living
area
(rural/urban)
have
been
investigated
in
terms
of
predicting
the
smartphone
use
disorder
and
nomophobia
so
that
envi-
ronmental
factors,
one
of
the
important
patterns
of
social
cognitive
theory,
can
be
able
to
enrich.
In
a
nutshell,
we
develop
a
social
cognitive
model
by
including
environmen-
tal
factors
in
order
to
explain
smartphone
use
disorder
and
nomophobia
bevahiors
of
adolescence
in
Turkey.
1.2.
Aim
of
the
study
This
study
aims
to
determine
the
nomophobia
and
smartphone
addiction
levels
among
12–18
age
group
sec-
ondary
and
high
school
students
and
to
investigate
the
demographic
and
academic
variables
predicting
these
lev-
els.
In
line
with
this
aim,
the
research
questions
and
hyptheses
are
as
follows:
Q1:
What
are
the
students’
nomophobia
and
smart-
phone
addiction
scores,
ICT
and
smartphone
usage
status,
academic
achievement
scores
related
to
basic
courses?
Q2:
Is
there
a
significant
difference
between
nomopho-
bia
and
smartphone
addiction
scores
of
students
in
terms
of
demographic
variables
(age,
educational
level,
income
level,
urban
or
rural
area
and
number
of
siblings),
variables
related
to
parents
(mother
education
level,
father
educa-
tion
level,
mother
ICT
usage
level,
father
ICT
usage
level),
ICT
and
smartphone
usage
status
(Internet
access
status,
Internet
usage
experience,
Internet
usage
skill
level,
daily
internet
usage
time,
smartphone
control
frequency,
daily
smartphone
usage
time,
smartphone
usage
experience,
smartphone
usage
purpose)
and
academic
achievement
scores
related
to
basic
courses?
Q3:
Ö˘
grencilerin
nomofobi
puanları
ile
akıllı
telefon
ba˘
gımlılı˘
gı
puanları
arasında
anlamlı
bir
ilis¸
ki
var
mıdır?”
Q3:
Is
there
a
significant
relation
between
nomophobia
and
smartphone
addiction
scores
of
students?
Q4:
Do
variables
related
to
the
students,
including
demographic
variables,
variables
related
to
parents,
ICT
and
smartphone
usage
status
and
academic
achievement
scores
related
to
basic
courses
predict
nomophobia
and
smartphone
addiction
scores
significantly?
2.
Conceptual
framework
2.1.
Theoretical
background
There
are
several
theories
explaining
the
technolog-
ical
and
smartphone
use
disorders.
Behaviourism,
for
example,
considers
behavioral
disorders
based
on
tech-
nology
regard
the
issue
as
a
learned
behavior
relied
on
stimulant-reaction-reinforcement
principle.
For
this
rea-
son,
it
mentions
that
addictive
behavior,
like
other
learned
behaviors,
is
changeable.
Psychodynamic
theory
defines
smartphone
use
disorder
as
a
way
to
achieve
escape
and
pleasure
from
negative
emotions.
The
socio-cultural
ten-
dency
regards
smartphone
use
disorder
as
a
result
of
a
culture
of
a
society.
Cognitive
theory
bases
smartphone
use
disorder
on
distorted
ideas
and
schematics.
In
conclusion,
there
is
a
complementary
view
proposed
by
Davis
(2001),
which
puts
forward
that
smartphone
use
disorder
is
caused
by
a
com-
bination
of
personal,
cultural,
social,
environmental,
and
emotional
factors.
The
conceptual
framework
of
this
study
is
based
on
“the
social
cognitive
theory”
(Bandura,
1986,
1989).
The
purpose
of
this
study
is
to
dwell
on
whether
the
personal
and
environmental
factors
are
predictive
of
behavioral
addiction
tendencies
such
as
smartphone
use
disorder
and
nomophobia
among
Turkish
adolescents.
“The
Social
Cognitive
Theory”:
The
social
cognitive
theory
asserts
that
human
behaviors
can
be
explained
by
three-
some
and
mutual
causality
of
personal
factors,
behaviors
and
environment.
According
to
Bandura
(1982),
individual
factors,
the
behavior
of
the
individual
and
the
environ-
ment
interact
with
each
other,
and
these
interactions
have
some
influences
on
the
individual’s
future
behaviors.
In
this
present
study,
personal
and
environmental
factors
are
regarded
as
explanations
of
smartphone
use
disorder
and
nomophobia.
According
to
social
cognitive
theory,
individ-

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
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4
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
uals
opt
for
realizing
the
behaviurs
which
they
expect
to
receive
prizes
or
to
have
positive
outcomes
(Bandura,
1982;
Compeau
&
Higgins,
1995).
In
existing
literature,
there
is
some
evidence
on
pos-
itive
outcome
expectancies
are
associated
with
higher
dependence
behaviors
such
as
gambling
among
adoles-
cents
(Shell,
Newman,
&
Xiaoyi,
2010).
Likewise,
the
use
of
the
internet,
social
media
and
smartphones
is
often
reinforced
by
positive
outcomes,
including
relieving
loneli-
ness,
having
social
anxiety,
and
avoiding
negative
emotions
(Chakraborty,
Basu,
&
Kumar,
2010).
At
this
precise
point,
Tsai
and
Lin
(2001)
suggest
that
there
is
a
positive
rela-
tion
between
internet
addiction
symptoms
and
perceived
benefit
in
technology
use.
Smartphones,
on
the
other
hand,
host
several
appli-
cations
(games/social
media
apps,
etc.)
and
individuals
often
perceive/expect
several
benefits
such
as
interaction,
socializing,
entertainment,
getting/sharing
information
(Yildiz-Durak
&
Sefero˘
glu,
2018).
Social
cognitive
theory
suggests
an
optimistic
scenario,
stating
that
individuals
can
control
their
actions.
But
at
present,
it
seems
that
individ-
uals,
especially
the
adolescents,
are
inadequate
to
restrict
themselves
in
using
smartphones
or
to
resist
willingness
to
media
consumption
(Grant,
Potenza,
Weinstein,
&
Gorelick,
2010;
Yildiz-Durak,
2018a,
2018b).
LaRose
et
al.
(2003)
developed
social
cognitive
theory
in
order
to
explain
how
overuse
of
media
can
be
reduced
by
employing
media
use
habits
and
insufficient
self-regulation
mechanisms
of
indi-
viduals.
Individuals
are
willing
to
employ
smartphones
primar-
ily
for
getting
information
and
building
relationships.
The
role
of
feedback
in
this
process
should
be
stressed
in
par-
ticular
(Davis
2001).
The
function
of
instant
feedback
on
smartphones
enhances
interaction
and
creates
impulsive-
ness
for
immediate
participation.
This,
in
turn,
causes
fear
of
staying
away
from
smartphones,
increasing
the
fre-
quency
of
control
of
smartphones
(Park
&
Lee,
2011).
In
this
present
study,
the
predicting
effects
of
several
vari-
ables,
including
experiences
of
smartphone/ICT
uses,
the
frequency
of
use,
usage
level
on
smartphone
use
disorder
and
nomophobic
behaviours
were
investigated.
Additionally,
self-regulation
is
a
significant
determinant
of
the
actual
behavior
of
the
theoretical
framework
of
the
social
cognitive
model,
and
is
influenced
by
the
inhibi-
tion
of
nomophobic
behavior
and
the
maintenance
of
one’s
self-control.
In
this
study,
smartphone
use
disorders
and
nomophobia
behaviours
in
the
context
of
social
cognitive
theory
academic
achievement
were
investigated,
by
mov-
ing
from
the
evidence
(LaRose,
2010)
which
notes
that
individuals
experiencing
negative
emotions
are
more
likely
to
use
media
in
a
problematic
way
in
order
to
relieve
these
negative
situations.
Adolescents
with
low
academic
achievements
are
sup-
posed
to
use
their
smartphone
such
levels
that
they
are
considered
as
disorders
in
order
to
get
rid
of
this
negative
situation.
Additionally,
it
can
be
noted
that
individuals
with
high
level
of
nomophobia
are
trying
to
get
rid
of
negative
situations
by
using
more
of
their
smartphones
and
appli-
cations,
assuming
that
individuals
use
their
technology
in
an
extreme
way
as
a
means
of
alleviating
their
life
stresses
and
their
negative
situations/emotions
(Long
et
al.,
2016;
Wang,
Ho,
Chan,
&
Tse,
2015;
Zhitomirsky-Geffet
&
Blau,
2016).
2.2.
Nomophobia
Nomophobia
is
defined
as
“the
fear
of
being
out
of
mobile
phone
contact”
and
this
term
is
the
abbreviation
of
“no-mobile-phone
phobia”
expression
(Yildirim,
2014).
King,
Valenc¸
a
and
Nardi
(2010)
considers
nomophobia
as
the
discomfort
or
anxiety
caused
by
leaving
smartphone
or
computer
connection.
In
this
definition,
it
can
be
sug-
gested
that
nomophobia
includes
computers
as
well
as
smartphones.
Nomophobia
is
an
apprehension
that
individuals
feel
when
they
feel
they
can
not
get
a
signal
from
a
mobile
hand-
set,
when
the
phone
is
out
of
charge,
when
it
forgets
to
pick
up
the
phone,
or
when
it
only
receives
phone
calls,
emails
and
notifications
for
a
certain
period
of
time
(King,
Valenc¸
a,
Silva,
Sancassiani,
Machado,
&
Nardi,2014).
Ina
nutshell,
it
is
a
psychological
situation
which
occurs
when
the
individ-
ual
cannot
access
to
smartphone
or
internet
connection.
The
social
cognitive
theory
presents
important
struc-
tures
such
as
self-regulation
and
the
importance
of
feedback
to
explain
nomophobia.
In
environments
such
as
social
media,
individuals
who
fear
from
missing
devel-
opments
in
instant
messaging
applications
and
seek
for
immediate
feedback
may
display
uncontrolled/anxious
smartphone
usage
behaviors.
Self-regulation,
however,
is
the
determinant
of
individual’s
smartphone
use
behavior
and
can
lead
to
nomophobic
behavior
that
an
individual
cannot
achieve
self-regulation
competence.
They,
however,
feel
discomfort
without
these
technolo-
gies.
Nomophobia,
on
the
other
hand,
is
the
degree
of
an
individual’s
anxiety
about
not
being
able
to
communicate
with
the
group
(Yildirim
&
Correia,
2015).
The
nomological
network
displayed
in
Fig.
1
is
formed
by
summarizing
the
results
of
the
literature
search
related
to
nomophobia.
There
are
several
studies
dwelling
on
the
relation
between
nomophobia
and
smartphone
addiction
in
the
existing
literature
(Dixit
et
al.,
2010;
Forgays
et
al.,
2014).
According
to
Yildirim
(2014),
the
use
of
addiction
concept
suppressed
the
meaning
of
nomophobia,
and
therefore
it
is
not
the
addiction
of
smartphone,
however.
Nomophobia
is
a
situational
phobia
(King
et
al.,
2010,
2014).
Situational
phobia
is
caused
by
an
intense
reaction
that
can
be
both
physical
and
emotional,
and
is
caused
by
an
irrational
fear
(Choy,
Fyer,
&
Lipsitz,
2007).
Therefore,
in
the
event
of
nomophobia,
people
will
have
an
irrational
fear
and
anxiety
about
breaking
their
communication
with
the
smartphone
or
leaving
the
Internet
untapped.
Fomo,
on
the
other
hand,
is
a
fear,
anxiety
and
appre-
hension
situation
experienced
by
individuals
involved
in
following
the
events,
news
and
conversations
(Przybylski
et
al.,
2013)
and
is
considered
as
an
important
factor
in
order
to
explain
nomophobia
described
as
fear
of
lacking
internet
connection.
FoMO
has
been
found
to
be
associated
with
excessive
use
of
individuals’
smartphones
due
to
the
anxiety
in
which
individuals
fear
from
not
being
able
to
fol-
low
the
latest
developments
(Carbonell,
Oberst,
&
Beranuy,
2013).

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
5
Fig.
1.
Nomological
network
related
to
nomophobia.
Referenced
resources
(Ainsworth
&
Bell,
1970;
Bandura,
1991;
Bivin,
Mathew,
Thulasi,
&
Philip,
2013;
Blumler
&
Katz,
1974;
Bragazzi
&
Del
Puente,
2014;
Cheever
et
al.,
2014;
Gezgin
&
C¸
akır,
2016;
Gezgin,
S¸
ahin,
&
Yıldırım,
2017;
Iskender
&
Akin,
2010;
Jafarkarimi,
Sim,
Saadatdoost,
&
Hee,
2016;
Kardefelt-
Winther,
2014;
King
et
al.,
2014;
Sharma
et
al.,
2017;
Yildirim
&
Correia,
2015;
Yildirim,
2014;
Yildirim,
Sumuer,
Adnan,
&
Yildirim,
2015;
Yildiz-Durak,2018a)
In
order
to
grasp
nomophobia
better,
dimensions
related
to
it
were
investigated
and
it
was
found
that
“not
being
able
to
communicate,
losing
connectedness,
not
being
able
to
access
information
and
giving
up
conve-
nience”
are
common
dimensions
employed
to
explain
the
term
“nomophobia”
(See
Fig.
1).
“Not
being
able
to
commu-
nicate”
dimention
stands
for
anxiety
which
occurs
when
individuals
cannot
instantly
communicate.
“Losing
con-
nectedness”
is
associated
with
the
fear
that
individuals
are
suffering
from
the
loss
of
connections
to
smartphones
and
the
interruption
of
a
person’s
online
identity
(especially
in
the
social
media).
“Not
being
able
to
access
informa-
tion”
reflects
the
disturbance
of
the
loss
of
information
via
smartphones,
the
inability
to
receive
information
via
smartphones,
and
the
blocking
of
information
calls
with
smartphones.
The
“giving
up
convenience”
dimension
con-
cerns
individuals’
feelings
of
giving
up
on
smartphones
and
reflects
the
comfort
of
having
a
smartphone
and
the
desire
to
use
smartphones.
2.3.
Smartphone
addiction
Smartphone
addiction
is
defined
as
“exces-
sive/obsessive
use
of
smartphones,
which
will
interfere
with
the
daily
lives
of
users
and
cause
negative
conse-
quences,”
although
it
is
not
officially
considered
a
disorder
(Lee,
Ahn,
Choi,
&
Choi,
2014a).
When
people
are
addicted
to
their
smartphones,
they
are
actually
dependent
on
the
functionality
they
allow
their
phones
to
do
(Kuss
&
Griffiths,
2017).
The
primary
obsessive-compulsive
symptoms
of
using
smartphone
devices
for
users
with
smartphone
addiction
can
be
listed
as:
(1)
Conflict:
It
is
the
use
of
the
smart
phone
to
prevent
other
important
tasks
such
as
work
from
being
performed.;
(2)
reinstatement:
Smartphone
users
are
not
able
to
diminish
their
smartphone
usage
vol-
untarily;
(3)
behavioral
salience:
The
behavior
of
users
is
managed
by
their
smartphones.;
and
(4)
withdrawal:
Users
are
beginning
to
feel
negative
emotions
when
they
can
not
use
their
smartphones.
(Charlton
&
Danforth,
2007;
Turel,
Serenko,
&
Giles,
2011;
Xu,
Ryan,
Prybutok,
&
Wen,
2012).
Kwon
et
al.
(2013a,
2013b)
suggested
“daily-life
disturbance,
positive
anticipation,
withdrawal,
cyberspace-oriented
relationship,
overuse,
and
tolerance”
dimensions
in
order
to
explain
smartphone
use
disorder,
on
the
other
hand.
In
this
study,
the
addictive
symptoms
of
smartphone
users
were
evaluated
in
terms
of
their
discomfort
when
they
could
not
use
their
smartphone
in
daily
life,
the
symptoms
of
physical
health,
happiness/expectation
when
using
smartphone,
unhappiness
when
they
could
not
use
smartphone,
developing
addiction
on
social
media
environments,
living
negative
emotions.
The
nomological
network
shown
in
Fig.
1
is
formed
by
summarizing
the
results
of
the
survey
of
the
literature
on
smartphone
use
disorder.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
6
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
Fig.
2.
Nomological
network
related
to
smartphone
addiction.
Referenced
resources
(Ainsworth
&
Bell,
1970;
Bae,
2015;
Baxter
&
Simon,
1993;
Billieux,
Van
der
Linden,
&
Rochat,
2008;
Blumler
&
Katz,
1974;
Brambilla
et
al.,
2017;
Buxton
et
al.,
2015;
Casey,
2012;
Chahal
et
al.,
2013;
Cho,
Kim,
&
Lee,
2016;
Cho
&
Lee,
2017;
Choliz,
2012;
Connell,
Lauricella,
&
Wartella,
2015;
Demirci
et
al.,
2014;
Dube
et
al.,
2017;
Eutsler,
2018;
Ezoe
et
al.,
2009;
Falbe
et
al.,
2015;
Gallimberti
et
al.,
2016;
Guxens
et
al.,
2016;
James
&
Drennan,
2005;
Jeong
et
al.,
2016;
Joo,
2013;
Kabali
et
al.,
2015;
Kardefelt-Winther,
2014;
Kesten
et
al.,
2015;
King
et
al.,
2014;
Kwon
et
al.,
2013a;
Lauricella
et
al.,
2015;
Lee
&
Cho,
2015;
Lee
et
al.,
2016;
McCloskey
et
al.,
2018;
Merlo,
Stone,
&
Bibbey,
2013;
Pavia,
Cavani,
Di
Blasi,
&
Giordano,
2016;
Park,
2005;
Pea
et
al.,
2012;
Perry
&
Lee,
2007;
Raman
et
al.,
2017;
Seo
&
Choi,
2018;
Toda,
Monden,
Kubo,
&
Morimoto,
2006;
Walsh
et
al.,
2008;
Walsh,
White,
&
McD
Young,
2010;
Yen
et
al.,
2012;
Young,
1998)
Smartphone
addiction
can
be
seen
to
be
similar
to
other
technology-based
dependencies
such
as
Internet,
gam-
ing,
computer
addiction
(Kim,
2013).
However,
it
can
be
argued
that
it
is
more
dangerous
because
of
the
mobile
nature
of
smartphones
and
the
scope
of
other
technology-
based
dependencies
of
smartphone
dependency
(See
Fig.
2)
(Demirci,
Orhan,
Demirdas,
Akpınar,
&
Sert,
2014).Current
studies
focus
on
the
effects
of
excessive
smartphone
usage
on
mental
and
physical
health
(Jenaro,
Flores,
Gómez-
Vela,
González-Gil,
&
Caballo,
2007).
The
findings
show
that
smartphone
users
who
tend
to
be
dependent
on
smartphones
have
a
tendency
towards
internet
addic-
tion
(Beranuy,
Oberst,
Carbonell,
&
Chamarro,
2009),
game
addiction
(Lee,
Ko,
&
Chou,
2015)
and
have
health
problems
(Beranuy
et
al.,
2009;
Lee
et
al.,
2015).
In
addition,
smartphone
addiction
has
been
associated
with
anxiety
and
depression
(Thomée
et
al.,
2011;
Beranuy
et
al.,
2009;
Cheever
et
al.,
2014;
Chotpitayasunondh
&
Douglas,
2016).
It
has
also
been
determined
that
there
is
a
positive
relationship
between
the
level
of
tendency
of
smartphone
addiction
in
children
and
aggression
and
attention
deficit
(Davey
&
Davey,
2014).
For
this
reason,
there
are
concerns
about
the
consequences
of
overuse

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
7
of
smartphones
especially
in
children
and
young
people
(Chotpitayasunondh
&
Douglas,
2016).
2.4.
Smartphone
addiction
and
nomophobia
Current
studies
has
shown
that
FoMO
is
associated
with
smartphone
addiction
(Cheever
et
al.,
2014;
Lepp,
Barkley,
&
Karpinski,
2014).
Therefore,
nomophobia
is
thought
to
be
associated
with
smartphone
addiction.
In
addition,
nomophobic
behaviours
of
individuals
are
associated
with
self-control.
They
are
closely
related
to
self-control
depen-
dency
behaviours,
on
the
other
hand
(Gökc¸
earslan,
Mumcu,
Has¸
laman,
&
C¸
evik,
2016).
Similar
to
symptoms
associ-
ated
with
substance
abuse,
individuals
who
have
not
been
able
to
control
their
concerns
about
staying
away
from
smartphone
use
seem
to
have
used
their
phones
problem-
atically
(Yildiz-Durak,
2018b).
For
this
reason,
it
is
logical
to
argue
that
smartphone
addiction
is
related
to
nomopho-
bia.
However,
both
nomophobia
and
smartphone
addiction
may
have
the
same
characteristics
since
they
are
associated
with
problematic
smartphone
usages
and
behaviours.
2.5.
Demographic
variables
as
predictor
of
smartphone
addiction
The
findings
of
the
studies
vary
in
terms
of
whether
there
is
a
relationship
between
demographic
variables
and
problematic
technology
usage
behaviours.
Some
researchers
suggest
that
there
is
no
relationship
between
gender
and
the
problematic
use
of
smartphones
(Walsh
&
White,
2007),
but
it
can
be
said
that
smartphone
usage
pat-
terns
differ
according
to
gender
in
general
(Wei
&
Lo,
2006).
As
the
reason
of
this
situation,
it
is
argued
that
women
spend
more
time
talking
over
the
phone,
whereas
men
use
the
phone
to
get
information.
On
the
other
hand,
there
are
limitations
in
literature
on
the
generalizability
of
the
relation
with
smartphone
usage
patterns
and
demographic
variables
such
as
gender,
age,
education
level.
Within
this
context,
there
is
a
need
for
more
studies
with
different
age
groups
in
regions
with
different
cultural
characteristics
(Cameron,
2009).
Differences
in
the
developmental
status
of
individuals
according
to
age
groups
lead
to
the
problematic
use
of
tech-
nology
and
the
differentiation
of
its
effects,
as
well.
For
this
reason,
there
is
a
need
for
more
information
on
problem-
atic
usages
of
ICT
and
smartphone
according
to
age
and
education
level
(Gilly,
Celsi,
&
Schau,
2012).
It
is
therefore
important
to
examine
the
role
that
smartphones
play
in
the
lives
of
students
and
to
shed
light
on
problematic
usage
behaviors
they
exhibit.
2.6.
Demographic
variables
as
predictor
of
nomophobia
Smartphone
usages
of
individuals,
the
amount
of
time
in
which
individuals
spend
on
WhatsApp,
Facebook,
Twit-
ter
and
other
social
networking
sites,
the
activities
they
perform,
the
sharing
frequencies
as
well
as
motivations
vary
according
to
demographic
variables
(Anshari,
Alas,
Hardaker,
Jaidin,
Smith,
&
Ahad,
2016;
Baron
&
Campbell,
2012;
Ha
&
Hwang,
2014;
Jang
&
Ji,
2012).
Smartphone
usage,
therefore,
may
show
differences
according
to
indi-
vidual
variables.
As
indicated
by
Van
Deursen
et
al.
(2015),
The
type
of
smartphones
used,
their
distinguishing
fea-
tures,
and
the
way
smartphones
are
used
differ
between
men
and
women.
According
to
Nakhaie,
Silverman
and
LaGrange
(2000),
demographic
variables
in
online
events
affect
individ-
ual
self-control,
social
norm
perceptions,
consumption
behaviors,
communication
desires,
ethical
behavior
in
communication
and
sharing,
and
anxiety/fear
situations.
It
is
therefore
logical
to
suggest
that
demographic
variables
play
an
important
role
in
predicting
nomophobia.
In
addi-
tion,
there
are
studies
showing
that
demographic
variables
are
related
to
nomophobia,
which
is
an
example
of
inappro-
priate
use
of
smartphone
as
smartphone
addiction
(Arpaci,
Balo˘
glu,
Kozan
&
Kesici,
2017;
Elhai
et
al.,
2016).
2.7.
Variables
related
to
parents
as
predictor
of
smartphone
addiction
In
the
research
conducted,
parental
variables
such
as
education
level
of
parents
of
adolescents,
level
of
tech-
nology
knowledge
and
financial
status
of
the
family
were
found
to
be
related
to
their
internet
dependence
(Mei,
Yau,
Chai,
Guo,
&
Potenza,
2016;
Zhang,
Wang,
Yuan,
Zhang,
&
Li,
2014).
However,
it
can
be
argued
that
studies
exam-
ining
the
relationship
between
smartphone
addiction
and
parental
variables
are
very
limited
(Lim
&
Kim,
2014;
Na,
2013;
Yim
et
al.,
2014).
As
indicated
by
Cho
and
Lee
(2017),
parents’
smartphone
usage
intensities
are
very
high
in
many
countries
and
parents
encourage
their
children
to
use
their
smartphones
to
reduce
emotional
and
physi-
cal
fatigue.
Since
children
have
less
physical
and
mental
maturity,
this
excessive
smartphone
usage
may
affect
them
adversely.
In
addition,
the
attitudes
and
approaches
of
parents
have
an
important
place
in
the
emergence
or
dis-
appearance
of
negative
behaviors.
Livingstone,
Haddon,
Görzig
and
Ólafsson
(2011)
have
emphasized
that
it
is
important
to
take
support
of
social
environments
in
negative
situations
in
which
children
involved
in
ICT
usages.
Hence,
parents
whom
we
can
call
children’s
social
environment
are
the
most
important
stakeholders
in
terms
of
technology-dependent
addiction
(Mascheroni
&
Ólafsson,
2014).
Children,
on
the
other
hand,
learn
most
of
the
negative
behaviours
in
their
surround-
ings,
by
observing
them.
Bandura’s
Social
Learning
Theory
(1977)
explains
this
situation
and
emphasizes
that
obser-
vations
mean
experiencing
indirectly.
From
moving
on
this
point,
it
can
be
said
that
the
interaction
of
parents
with
technology
will
be
modeled
by
children
and
this
will
be
reflected
in
the
behavior
of
technology
usage.
It
can
be
said
that
parents
will
direct
their
children
in
the
use
of
conscious
technology
in
the
context
of
technology
com-
petencies
(C¸
etinkaya
&
Sütc¸
ü,
2016).
2.8.
Variables
related
to
parents
as
predictor
of
nomophobia
Oulasvirta
et
al.
(2012)
mentions
that
nomophobic
behaviors
and
problematic
smartphone
use
are
developed
through
habit.
The
continuous
control
behavior
of
the
phone
and
the
anxiety
state
experienced
when
away
from

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
8
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
the
phone
include
learned
behaviors
(Elhai
et
al.,
2017).
For
this
reason,
parents’
attitudes
and
behaviours
on
this
issue
are
significant.
The
article
by
Cho
and
Lee
(2017)
shows
that
parental
variables
are
associated
with
the
tendency
of
stu-
dents
to
use
problematic
and
uncontrolled
smartphones.
From
moving
on
this
point,
it
has
been
assumed
that
vari-
ables
related
to
parents’
education
levels
and
ICT
usage
are
associated
with
nomophobia.
2.9.
ICT
usage
as
predictor
of
smartphone
addiction
In
existing
literature,
it
has
been
argued
that
ICT
use
intensity
is
associated
with
smart
phone
dependency.
Psy-
chological
dependence
models
suggest
that
what
can
be
called
“addiction”
is
the
result
of
a
positive
and/or
neg-
ative
reinforcement
process
(Robinson
&
Berridge,
2003).
Negative
reinforcement
models
show
that
the
dependency
originates
as
a
means
of
dealing
with
negative
feeling
(Baker
et
al.,
2004).
Therefore,
the
excessive
usage
of
the
smartphone
and
the
applications
it
contains
to
abstain
from
the
negative
situations
that
individuals
experience
causes
problematic
usage.
Negative
reinforcement
models
present
a
system
for
ICT
usage,
but
don’t
explain
how
this
usage
may
evolve
into
a
pathological
situation.
In
explaining
how
the
usage
may
evolve
into
a
pathological
situation,
negative
posi-
tive
reinforcement
models
are
put
into
use
(Robinson
&
Berridge,
2001).
This
indicates
that
dependency
is
initially
in
the
form
of
using
the
basic
features
of
technology
(eg,
notification
control)
and
then
is
transformed
into
a
strong
desire
for
the
use
of
technology.
For
this
reason,
as
in
the
case
of
smartphone
use,
pathological
use
can
begin
as
a
positive
reinforcement
process
(Wise
&
Koob,
2014).
As
a
result,
the
intensity
of
smartphone
usage
increases
so
as
to
ease
negative
symptoms
originating
when
using
smart-
phone
(Billieux,
Maurage
et
al.,
2015;
Billieux,
Schimmenti,
Khazaal,
Maurage,
&
Heeren,
2015).
For
this
reason,
it
is
believed
that
smartphone
and
ICT
usages
are
related
to
each
other.
2.10.
ICT
usage
as
predictor
of
nomophobia
In
the
present
day,
smartphones
can
be
used
both
as
a
mobile
phone
and
as
a
personal
computer
with
features
they
have
(Samaha
&
Hawi,
2016).
The
ever-
increasing
features
of
smartphones
include
many
things
to
do
in
everyday
life
such
as
making
calls,
instant
messag-
ing/chatting,
surfing
the
web,
catching
up
on
news,
sending
email,
watching/sharing
photos/videos,
playing
games
and
especially
social
networks
resulting
in
overuse
of
these
technologies
(Zheng
&
Lionel,
2010).
Social
networks
support
the
individuals’
self-esteem
perceptions
and
make
them
feel
well
psychologically
(Andreassen
&
Pallesen,
2014;
Anshari
et
al.,
2016;
Stead
&
Bibby,
2017).
Individuals
experience
FoMO
which
means
not
being
able
to
follow
the
latest
developments,
conver-
sations
with
their
friends
and
fear
of
missing
the
likes
and
nomophobia
which
means
fear
of
keeping
away
from
smartphone
or
losing
the
internet
access
(Yildirim,
Sumuer,
Adnan,
&
Yildirim,
2015).
Individuals
who
display
nomophobic
behaviors
expe-
rience
some
psychosocial
problems,
behavior
and
anxiety
disorders
that
affect
their
lives
when
they
are
away
from
mobile
devices
(Dixit
et
al.,
2010;
Yildiz-Durak,
2018a).
For
this
reason,
the
ICT
and
smartphone
usage
durations
and
the
control
frequency
of
these
technologies
are
asso-
ciated
with
nomophobia
(Yildiz-Durak,
2018a).
There
is
also
a
relationship
between
the
competence
and
experi-
ence
of
individuals
using
technology
such
as
smartphones
and
their
problematic
behavior,
on
the
other
hand
(Anshari
et
al.,
2016).
2.11.
Academic
achievement
as
predictor
of
smartphone
addiction
One
of
the
factors
that
is
considered
as
related
to
smart-
phone
use
disorder
is
academic
performance
(Dixit
et
al.,
2010).
However,
when
the
related
literature
is
examined,
it
can
be
argued
that
the
two-sided
relationship
between
academic
performance
and
smartphone
addiction
has
been
ignored.
In
existing
literature,
it
has
been
reported
that
smartphone
addiction
causes
the
symptoms
of
depression,
anxiety,
physical
and
mental
fatigue
(Beranuy
et
al.,
2009;
Cheever
et
al.,
2014;
Chotpitayasunondh
&
Douglas,
2016;
Davey
&
Davey,
2014;
Rosen,
Carrier,
&
Cheever,
2013;
Thomée
et
al.,
2011).
As
indicated
by
Samaha
and
Hawi
(2016),
it
is
inevitable
that
these
sypmtoms
affect
the
aca-
demic
performances
of
students
adversely.
However,
students
may
exhibit
problematic
smart-
phone
use
behaviors
to
reduce
emotional
level
of
motivation
in
their
learning
process
due
to
the
nega-
tive
situation
related
to
their
academic
performance,
and
to
be
emotionally
satisfied
due
to
their
sense
of
fail-
ure.
Kardefelt-Winther
(2014)
emphasizes
that
individuals
use
smartphones
to
tackle
real-world
problems
and
avoid
duties
and/or
avoid
negative
feelings
and
effects.
As
a
result,
it
can
be
argued
that
smartphone
use
behavior
is
related
to
academic
achievement
because
it
affects
children
physically
and
mentally.
When
the
studies
conducted
are
analyzed,
it
can
be
seen
that
there
is
a
negative
relationship
between
smartphone
usage
and
academic
performance
(Judd,
2014;
Karpinski,
Kirschner,
Ozer,
Mellott,
&
Ochwo,
2013;
Samaha
&
Hawi,
2016).
2.12.
Academic
achievement
as
predictor
of
nomophobia
Nomophobia
is
more
likely
to
appear
in
younger
gen-
eration
students
who
think
they
are
socially
addicted.
Nomophobia
may
lead
to
anxiety
and
trouble
when
the
smartphones
aren’t
controlled
for
a
while
(Cheever
et
al.
2014;
Hong,
Chiu,
&
Huang,
2012;
Lee,
Chang,
Lin,
&
Chen,
2014b;
Lepp
et
al.,2014;
Nazri
&
Latiff,
2013).
Cheever
et
al.
(2014)
states
that
students
who
exhibit
nomopho-
bic
behaviors
are
faced
with
problems
such
as
not
being
able
to
collect
concentrate
or
deriving
cognitive
skills.
The
works
of
Froese
et
al.
(2012)
tell
us
that
being
occupied
with
using
smartphone
during
instructional
time
reduces
the
academic
achievement.
Thus,
learning
activities
may
be
confined
when
the
students’
achievements,
interests
and
satisfaction
levels
are
low
(Baddeley,
Lewis,
Eldridge,
&
Thomson,
1984).

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
9
It
is
possible
that
students
who
have
low
attitudes
towards
the
course
and
who
are
not
able
to
perform
satisfactorily
at
academic
level
tend
to
concentrate
on
using
smartphone
during
and
after
the
instructional
time.
Mendoza,
Pody,
Lee,
Kim,
and
McDonough
(2018)
men-
tion
that
mobile
phone
usage
and
learning
performance
must
be
carefully
thought
to
have
a
more
balanced
per-
spective
of
the
effects
of
mobile
phones
on
attention
and
learning.
Therefore,
this
belief
differs
from
the
view
that
smartphones
are
always
harming
learning.
Some
researchers
even
argue
that
smartphones
con-
tribute
to
learning
environments
positively
when
used
as
a
supplementary
(Eyyam
&
Yaratan,
2014;
Jan,
Ullah,
Ali,
&
Khan,
2016).
As
indicated
by
Rashid
and
Asghar
(2016),
smartphones
are
key
to
self-directed
learning
in
relation
to
learning
performance
of
problem-based
use,
and
nomo-
phobic
behaviors
are
less
common
in
learners
who
can
take
responsibility
for
learning
and
manage
their
learning
performance.
According
to
the
social
cognitive
theory,
it
has
been
assumed
that
individuals
can
overuse
technol-
ogy
as
a
means
of
alleviating
their
life
stresses,
self-esteem
and
their
negative
situations/emotions
related
to
the
low
level
of
academic
performance.
Therefore,
it
is
believed
that
low
academic
performance
of
students
may
increase
FoMO
levels
and
nomophobia
behaviours.
In
this
study,
the
pre-
dictive
power
of
the
academic
achivements
levels
of
the
students
on
nomophobia
has
been
dealt
with.
3.
Method
Since
it
examines
the
relationships
between
students’
smartphone
addiction
and
nomophobia
levels,
this
study
is
a
relational
study.
The
relational
model
aims
to
determine
the
state
and/or
degree
of
covariance
between
two
or
more
variables
(Karasar,
2013).
3.1.
Population
and
its
characteristics
The
population
of
the
study
consists
of
612
students
at
all
levels
of
secondary
and
high
school.
The
population
was
formed
voluntarily
from
schools
where
the
researcher
was
able
to
reach
participants
face-to-face
using
appropriate
sampling
method.
The
schools
in
which
the
participants
attend
have
been
defined
as
moderate
socio–economic
level
by
District
Directorate
of
National
Education.
There-
fore,
it
is
thought
that
participants
represent
the
general
population.
Distribution
of
demographic
characteristics
of
the
students
participating
in
the
research
are
given
in
Table
1.
Of
the
students
who
participated
in
the
study,
48.0%
are
female
and
52.0%
are
male.
The
study
group
consists
mostly
of
secondary
school
students
(69.1%).
The
average
age
of
participants
is
12.79.
The
average
number
of
siblings
is
2.35.
Participants’
average
monthly
income
is
$
537.5.
Most
of
them
live
in
urban
areas,
participants’
parents
mainly
have
educational
levels
of
high
school,
and
they
stated
that
their
parents’
ICT
use
level
was
“having
very
little
information”
(See
Table
1).
3.2.
Data
collection
instruments
“Personal
Information
Form”
and
two
different
data
collection
tools
were
used
in
the
research.
These
are
the
Smart
Phone
Addiction
Scale
and
the
Nomophobia
Scale.
Some
information
on
data
collection
instruments
and
items
were
given
in
Table
A1
in
Appendix
A.
Data
collection
instruments
used
in
this
study
were
developed
by
different
researchers
and
were
suitable,
reliable
and
valid
instru-
ments
according
to
the
aims
of
the
study.
Personal
information
form
was
developed
by
researcher
to
collect
data
on
participants’
personal
infor-
mation,
access
to
technology,
and
smartphone
usage
parameters.
Data
on
participants’
academic
performance
were
also
collected
with
this
questionnaire.
This
question-
naire
consists
of
48
items.
Most
of
the
questions
are
likert
type.
While
developing
the
data
collection
instruments,
opinions
of
five
field
experts
were
asked
and
it
was
designed
according
to
their
opinions.
Smartphone
addiction
questionnaire
was
developed
by
Kwon
et
al.
(2013a),
and
adapted
to
Turkish
by
Demirci
et
al.
(2014).
The
Likert-type
scale’s
rating
options
range
between
“1-Definitely
No”
and
“5-Definitely
Yes”.
An
increase
in
the
scale
score
indicates
that
risk
of
smart-
phone
addiction
also
increases.
There
are
7
sub-dimensions
of
the
scale,
and
the
items
related
to
these
dimensions
are
as
follows:
“Factor
1
(daily
life
disturbance
and
tolerance)
8
items;
Factor
2
(deprivation
indications)
7
items;
Factor
3
(positive
expectations)
5
items;
Factor
4
(cyber-focused
associations)
4
items;
Factor
5
(overuse)
4
items;
Factor
6
(social
network
addiction)
2
items;
Factor
7
(physical
symptoms)
3
items
”.
Cronbach’s
alpha
reliability
coeffi-
cient
for
this
scale
is
.925.
The
reliability
coefficients
of
the
scale
are
as
follow:
.789,
.815,
.799,
.814,
.901,
.713,
.901.
Nomophobia
questionnaire
(NMP-Q)
was
developed
by
Yildirim
and
Correia
(2015)
and
adapted
to
Turkish
by
Yildirim
et
al.
(2015).
The
scale
is
composed
of
5
items
with
likert
type
and
a
total
of
20
items.
There
are
four
sub-
dimensions
of
the
scale.
These
include:
Losing
Access
to
Information
(4
items),
Losing
Connection
(5
items),
Com-
munication
Failure
(6
items)
and
Feeling
Uncomfortable
(5
items).
The
reliability
coefficient
of
the
study
was
found
as
.95.
The
reliability
coefficients
of
the
scale
are
as
follow:
.741;
.819;
.856,
.906.
3.3.
Collection
of
the
data
The
practice
of
this
present
study
was
conducted
in
the
last
week
of
the
fall
semester
(January)
and
spring
(June)
of
the
academic
year
2016–2017.
In
the
study,
data
were
col-
lected
from
the
research
population
by
means
of
on-line
data
collection
tools
in
the
information
technology
class
in
public
schools
affiliated
to
Ministry
of
National
Education
(MoNE)
schools
by
getting
permission
from
school
admin-
istration
and
teachers.
As
the
practice
was
carried
out
in
the
schools
affiliated
to
MoNE,
the
relevant
ministry
ethics
committee
decided
that
the
study
had
no
ethical
problems
in
terms
of
purpose
and
content
and
sent
a
written
decision
to
implement
the
practice
to
the
district
national
education
directorates.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
10
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
Table
1
Distribution
of
demographic
characteristics
of
the
students
participating
in
the
research.
Variables
Options
f
%
Gender Female
294
48.0
Male
318
52.0
Educational
level Middle
school
level
423
69.1
High
school
level 189
30.9
Age
Min
=
10,
Max
=
18,
Mean
=
12.79,
SD
=
1.671
Monthly
income
Min
=
500TL/about
125$,
Max
=
8000TL/about
2000$,
Mean
=
2150TL/about
537.5
$,
SD
=
1000.721
Number
of
siblings
Min
=
0,
Max
=
7,
Mean
=
2.35,
SD
=
1.211
Urban
or
rural
area Rural
209
34.2
Urban
403
65.8
Mother
ICT
level
of
use
Having
very
little
information
262
42.8
Novice
54
8.8
Moderate
72
11.8
Advanced
80
13.1
Expert
144
23.5
Father
ICT
usage
level
Having
very
little
information 257
42.0
Novice
53
8.7
Moderate
68
11.1
Advanced
60
9.8
Expert
174
28.4
Educational
background
of
your
mother
Primary
school
graduate 148
24.2
Middle
school
graduate
157
25.7
High
school
graduate
216
35.3
Bachelor’s,
Master’s
or
PhD
degree
91
14.9
Educational
background
of
your
father
Primary
school
graduate
90
14.7
Middle
school
graduate
113
18.5
High
school
graduate
276
45.1
Bachelor’s,
Master’s
or
PhD
degree 133
21.7
In
addition,
the
permissions
of
school
administrators
and
teachers
working
in
the
schools
in
which
the
practice
was
conducted
were
taken.
The
students
were
voluntar-
ily
included
in
this
present
study.
Prior
to
the
application,
the
students
were
given
the
necessary
guidelines
about
the
study
and
process.
During
the
implementation
pro-
cess,
grades
of
students
on
data
collection
instrument
were
transferred
from
a
web-based
software,
called
as
“e-okul”,
which
is
a
school
administration
system
including
detailed
information
on
students
such
as
personal
information
and
school
grades.
The
teacher
who
conducted
the
practice
helped
students
to
be
able
to
provide
reliable
data
about
their
grades.
At
the
end
of
the
practice,
the
average
score
of
the
grades
that
the
students
stated
and
the
grades
of
the
grades
were
checked
with
the
grading
average
and
grade
distribution
from
the
system.
The
students
were
provided
enough
time
to
read
the
data
collection
instruments
and
to
provide
reliable
data.
3.4.
Analysis
of
the
data
Descriptive
analyses
and
hierarchical
linear
multi-
ple
regression
analysis
were
used
for
the
analysis
of
data
obtained
by
means
of
data
collection
tools
in
the
research.
For
the
analyses,
the
suitability
of
the
data
before
regression
was
first
examined.
It
has
been
specified
after
examining
whether
the
relationship
between
the
depen-
dent
variables
is
linear
and
whether
the
scores
are
normally
distributed,
that
there
is
a
linear
relationship
and
a
normal
distribution.
In
addition,
the
Mahalanobis’
distance
values
were
assessed
to
determine
whether
there
were
extreme
values
related
to
the
variables
included
in
the
analysis
to
assess
the
multivariate
normality
test.
The
data
of
5
par-
ticipants
with
extreme
values
were
not
included
in
the
analysis.
Since
some
of
the
predictors
in
the
study
are
categori-
cal
variables,
these
variables
are
transformed
into
dummy
variables
(Field,
2005).
Since
some
of
the
predictors
in
the
study
are
categorical
variables,
these
variables
are
trans-
formed
into
dummy
variables
(Field,
2005).
At
this
point,
in
regression
analysis,
dependent
and
independent
vari-
ables
must
be
continuous
variable
at
least
equal
interval
scale
and
show
normal
distribution.
However,
in
some
studies,
independent
variables
which
are
considered
as
classifting
scale
is
needed
to
examine
whether
they
are
effective
on
dependent
variables
or
not.
A
new
artifical
variable
which
is
called
“dummy”
variable
and
is
formed
as
one-minus
of
level
numbers
(G-1),
by
exclud-
ing
one
of
the
categorical
variables
in
the
analysis.
The
fact
that
one
of
these
new
variables
has
a
significant
effect
on
dependent
variable
means
that
this
variable
has
a
signifi-
cant
effect
on
dependent
variable
(Buyukozturk,
2009).
Discrete
variables
included
in
this
present
study
were
included
in
the
regression
analysis
as
“dummy
variable”,
while
continuous
variables
were
employed
as
their
origi-
nal
values.
Discrete
variables
coded
as
dummy
variable
are
listed
below:
•Gender
variable
is
two
categories
as
“female”
and
“male”.
“Male”
category
was
coded
as
“0”
and
was
turned
into
a
dummy
variable.
Similarly,
in
educational
level
with
two
categories,
“high
school
level”
variables
was
coded
as
“0”.
In
urban
or
rural
area
category,
“rural”
was
coded
as
“0”
and
was
turned
into
a
dummy
variable.
•ICT
level
of
use
variable
related
to
mother
and
father
were
examined
in
five
categories.
“Having
very
little
informa-
tion”
category
was
coded
as
“0”
and
was
turned
into
a

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
11
dummy
variable.
Education
level
of
mother
and
father
variable
was
examined
in
four
categories.
“Bachelor’s,
Master’s
or
PhD”
category
was
coded
as
“0”
and
was
turned
into
a
dummy
variable.
•Level
of
internet
use
variable
was
examined
in
five
cat-
egories.
“Having
very
little
information”
category
was
coded
as
“0”
and
was
turned
into
a
dummy
variable.
Smartphone
purpose
of
use
variable
was
examined
in
four
categories.
“educational”
category
was
coded
as
“0”
and
was
turned
into
a
dummy
variable.
In
addition,
the
assumption
of
“multicollinearity”
has
been
tested
among
predictor
variables.
Once
the
assump-
tions
were
performed,
the
hierarchical
regression
analysis
has
been
made.
In
hierarchical
linear
multiple
regression
analysis,
the
independent
variables
are
taken
to
analysis
as
analytic
blocks,
and
each
block
becomes
the
control
vari-
able
for
the
variables
that
enter
the
analysis
after
itself
(See
Fig.
2).
In
this
analysis,
the
order
of
the
entry
of
equations
into
independent
variables
has
been
determined
according
to
the
literature
search
of
the
researcher.
In
this
present
study,
demographic
variables
(age,
edu-
cational
level,
income
level,
urban
or
rural
area
and
number
of
siblings)
were
placed
on
the
first
block.
Variables
related
to
parents
(mother
education
level,
father
education
level,
mother
ICT
usage
level,
father
ICT
usage
level)
were
placed
on
the
second
block.
For
the
third
and
forth
blocks,
ICT
and
smartphone
usage
status
variables
(Internet
access
sta-
tus,
Internet
usage
experience,
Internet
usage
skill
level,
daily
internet
usage
time,
smartphone
control
frequency,
daily
smartphone
usage
time,
smartphone
usage
experi-
ence,
smartphone
usage
purpose)
were
included.
Academic
achivements
variables
were
at
the
last
block
(Fig.
3).
4.
Findings
4.1.
Descriptives
Descriptive
analysis
results
of
variables
are
shown
in
Table
2.
Roughly
49%
(n
=
300)
of
the
participants
got
50
or
above
scores
and
were
classified
as
probable
problematic
users
according
to
the
nomophobia
scale.
Roughly
53%
(n
=
327)
of
the
participants
got
75
or
above
scores
and
were
classified
as
probable
problematic
users
according
to
the
smartphone
use
disorder
scale.
The
classification
recom-
mended
by
Young
(1998)
was
taken
as
a
reference
in
the
literature
while
defining
the
scores
as
low/high.
Table
2
shows
that,
the
highest
total
score
average
taken
from
the
nomophobia
subscales
is
the
item
of
“not
being
able
to
communicate”
(M
=
15.84,
Sd
=
4.736).
The
lowest
average
among
these
subscales
is
the
item
of
“not
being
able
to
access
information”
(M
=
10.00,
Sd
=
4.674).
The
highest
total
score
average
taken
from
the
smartphone
addiction
subscales
is
the
item
of
“positive
expectation”
(M
=
13.10,
Sd
=
5.310).
The
lowest
average
among
these
subscales
is
the
item
of
“overuse”
(M
=
9.48,
Sd
=
4.332).
Table
3
demonstrates
that
about
half
of
the
participants
have
continuous
internet
access.
Average
internet
usage
experience
of
students
is
5
years,
while
that
of
smartphones
usage
is
4.9
years.
31.2%
of
participants
rated
their
internet
usage
skill
levels
as
“expert”.
Students
spend
an
average
of
4.6
h
a
day
on
the
internet
and
of
2.99
h
on
smartphones
on
a
daily
basis.
The
control
variables
are
related
to
how
many
times
the
participants
looked
or
controlled
their
smart-
phones
for
any
reason
(writing
messages
and
controlling
mailing,
social
media,
instant
messaging
environments,
etc.).
Students
were
asked
to
write
a
number,
by
taking
into
account
the
daily
smartphone
uses.
The
students
were
provided
some
explanations
on
the
time
interval
(as
soon
as
wake
up
in
the
morning-until
sleepig
at
night)
for
the
control
variable
duing
data
collection
process.
21.2%
of
par-
ticipants
control
their
phone
every
5
min.
Participants
use
smartphones
for
entertainment,
social
media,
communica-
tion
and
educational
purposes,
respectively.
The
data
on
academic
achivements
included
in
Table
3
was
taken
from
the
online
school
administration
system
used
by
MoNE
in
Turkey,
which
is
called
“e-school”.
During
the
practice,
the
students
were
asked
to
write
down
their
grades
displayed
on
“e-school”
on
the
form.
In
order
to
this
happens,
the
teachers
conducting
the
study
supported
the
students
when
needed.
At
the
end
of
each
application
for
each
class,
the
average
and
grades
of
grades
indicated
by
the
students
in
that
class
and
the
grade
average
and
distribution
obtained
from
the
system
were
examined.
The
results
show
that
the
grades
reported
by
the
students
reflect
the
truth.
For
example,
the
e-school
grade
average
of
class
5C
mathematics
is
2.44;
grade
distributions
are
as
follows:
“5
students
with
a
rating
of
1;
15
students
with
grade
2;
9
students
with
grade
3;
4
students
with
grade
4;
1
student
with
grade
5”.
When
the
data
of
the
students
in
this
class
was
checked,
it
was
seen
that
the
e-school
grade
average
of
the
mathematics
course
is
2.45.
When
the
distributions
of
grades
were
checked,
“4
students
with
a
notation
of
1;
14
students
with
grade
2;
9
students
with
grade
3;
3
students
with
grade
4;
1
student
with
a
rating
of
5”.
It
is
thought
that
the
difference
between
the
e-school
student
declarations
was
caused
by
the
stu-
dents
who
were
not
eager
to
participate
in
the
research
or
who
are
absent.
According
to
Table
3,
the
most
successful
course
of
the
students
is
information
technologies,
and
the
most
unsuc-
cessful
one
is
mathematics.
The
grades
included
in
Table
4
were
obtained
by
converting
the
scores
of
the
relevant
course
into
a
5-point
system
during
a
semester
(is
calcu-
lated
by
taking
2
or
3
exams
scores-
change
according
to
the
course
hours-
performance
scores
and
teacher
average
scores
for
activities
in
the
classroom).
The
grades
and
grade
points
according
to
5
grading
system
for
students
in
Turkey
are
as
follows:
0
and
1
“fail”;
2
“passes”;
3
“medium”;
4
“good”;
5
“very”.
4.2.
T-test
and
one-way
ANOVA
result
T-tests
and
one-way
analysis
of
variance
(ANOVA)
were
performed
on
demographic
variables,
parental
variables,
children’s
ICT
and
smartphone
use,
and
achievement
lev-
els
to
determine
the
mean
differences
between
the
groups
in
relation
to
nomophobia
and
smartphone
use
disorder.
Before
the
analysis,
the
assumptions
of
the
ANOVA
test
were
examined
and
it
was
seen
that
these
assumptions

Please
cite
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as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
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Fig.
3.
Model
examined
with
hierarchical
regression
analysis.
were
met.
Detailed
results
were
presented
in
Table
A2
in
Appendix
A.
Nomophobia
scores
show
significant
differences
according
to
demographic
variables;
gender,
age,
monthly
income
and
urban
or
rural
area
(p
<
.05).
Smartphone
addiction
scores
show
significant
differences
according
to
demographic
variables;
gender,
age
and
urban
or
rural
area
(p
<
.05).
Male
students
had
higher
scores
on
nomophobia

Please
cite
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article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
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SOCSCI-1518;
No.
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Pages
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H.
Yildiz
Durak
/
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Social
Science
Journal
xxx
(2017)
xxx–xxx
13
Table
2
Descriptive
Statistics.
Scales
Number
of
items
Min
score
Max
score
Mean
Mean/k
SD
Nomophobia
20
20.00
100.00
51.29
2.56
26.26
Not
being
able
to
access
information
4
4.00
20.00
10.00
2.50
4.674
Losing
connectedness
5
5.00
25.00
12.95
2.59
4.657
Not
being
able
to
communicate 6
6.00
30.00
15.84
2.64
4.736
Giving
up
convenience 5
5.00
25.00
12.55
2.51
4.586
Smartphone
Addiction
33
33.00
165.00
81.84
2.58
41.044
Daily
life
disturbance
and
tolerance
8
8.00
40.00
19.36
2.44
6.270
Deprivation
indications
7
7.00
35.00
17.50
2.49
6.324
Positive
expectations
5
5.00
25.00
13.10
2.62
5.310
Cyber-focused
associations
4
4.00
20.00
9.64
2.41
4.341
Overuse
4
4.00
20.00
9.48
2.37
4.332
Social
network
addiction
2
2.00
10.00
4.98
2.54
3.404
Physical
symptoms 3
3.00
15.00
7.53
2.50
3.323
k:
number
of
items.
Table
3
Distributions
in
terms
of
ICT,
smartphone
usage
and
academic
achievement
of
students.
Variables
f
%
Internet
access
status
1-
Limited
access
98
16.0
2-Rare
access
possibility
48
7.8
3-Sometimes
they
have
access
66
10.8
4-Accessibility
most
of
the
time
133
21.7
5-
Always
accessible 267
43.6
Internet
use
experience
(years)
Min
=
1,
Max
=
10,
Mean
=
5.03,
SD
=
2.958
Level
of
internet
using
skill
Having
very
little
information
184
30.1
Novice
43
7.0
Moderate
94
15.4
Advanced
100
16.3
Expert
191
31.2
Daily
internet
usage
time Min
=
1,
Max
=
14,
Mean
=
4.60,
SD
=
2.087
Smartphone
control
frequency
(number
of
times
per
day)
Min
=
1,
Max
=
144(Once
every
5
min),
Mean
=
4.60,
SD
=
2.087
Every
5
min
130
21.2
Every
10
min
69
11.3
Every
30
min
46
7.5
One
per
hour
96
15.7
Every
two
to
three
hours
88
14.4
Several
times
a
day 183
29.9
Daily
smartphone
usage
time
(hours)
Min
=
1,
Max
=
12,
Mean
=
2.99,
SD
=
1.649
Smartphone
usage
experience
(years)
Min
=
1,
Max
=
10,
Mean
=
4.925,
SD
=
2.350
Smartphone
purpose
of
use
Educational
Min
=
1,
Max
=
5,
Mean
=
3.06,
SD
=
1.585
Communication
(video
and
non-video
call,
SMS)
Min
=
1,
Max
=
5,
Mean
=
3.48,
SD
=
1.600
Social
media
Min
=
1,
Max
=
5,
Mean
=
3.12,
SD
=
1.543
Entertainment
(game,
video,
etc.)
Min
=
1,
Max
=
5,
Mean
=
3.57,
SD
=
1.505
Academic
achievement
Math
Mean
=
2.64,
SD
=
1.113
Science
Mean
=
3.68,
SD
=
.799
Language
course
(English)
Mean
=
3.66,
SD
=
.804
Social
studies
Mean
=
4.30,
SD
=
.817
Information
technologies
Mean
=
4.36,
SD
=
.822
and
smartphone
addiction
scores
(M
=
51.37,
M
=
85.06)
than
female
students
(M
=
51.21,
M
=
78.23).
In
terms
of
age,
the
scores
of
nomophobia
and
smart-
phone
addiction
scores
(M
=
53.86,
M
=
94.79)
for
students
aged
16–18
years
were
between
10
and
12
years
(M
=
50.90,
M
=
80.27).
Another
remarkable
finding
is
that
the
scores
of
nomophobia
and
smartphone
addiction
scores
(M
=
53.19,
M
=
83.11)
were
higher
in
urban
settlement
areas
than
those
living
in
rural
areas
(M
=
47.61,
M
=
79.22).
Variables
related
to
parents
differ
only
in
nomophobia
and
smartphone
addiction
scores
according
to
the
vari-
able
“educational
background
of
your
mother”.
Higher
education
graduates
(M
=
52.27,
M
=
84.52)
have
higher
scores
than
other
groups.
There
are
significant
differences
between
nomophobia
and
smartphone
scores
according
to
the
variables
related
to
ICT
and
smartphone
usages,
includ-
ing
internet
use
experience
(years),
daily
internet
usage
time,
smartphone
control
frequency
(number
of
times
per
day),
daily
smartphone
usage
time
(hours),
smartphone
usage
experience
(years),
smartphone
purpose
of
use.
According
to
academic
achievement
variables,
nomo-
phobia
and
smartphone
addiction
scores
showed
signifi-
cant
differences
according
to
academic
achievement
levels
(p
<
.05).
As
the
level
of
academic
achievement
increases,
nomophobia
and
smartphone
addiction
scores
decrease.
4.3.
Correlation
There
is
a
positive
correlation
at
a
high
level
between
nomophobia
scores
and
smartphone
addiction
(r
=
.819,

Please
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as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
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14
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
Table
4
Hierarchical
regression
analysis
results.
Smartphone
addiction
Nomophobia
Predictor
ˇ
t
ˇ
t
Block
1:
demographic
variables
Gender
.069
1.986*.009
.261
Age
.424
3.908*.318
2.901*
Educational
level .415
3.803*.320
2.946*
Income
level
.068
1.679
.100
2.474*
Urban
or
rural
area
.019
.463
.089
2.151*
Number
of
siblings
−.004
−.101
−.013
−.350
R
=
.193,
R2=
.037,
F(6,605)
=
3.884
Sig.
=
.001
R
=
.190,
R2=
.036,
F(6,605)
=
3.795,
Sig.
=
.001
Block
2:
related
variables
with
parents
Mother
education
level
.088
1.970*.050
1.110
Father
education
level
−.005
−.120
−.022
−.489
Mother
ICT
usage
level
.072
1.739
.068
1.636
Father
ICT
usage
level
−.043
−1.037
−.002
−.039
R
=
.222,
R2=
.049,
F(6,605)
=
3.129
Sig.
=
.001 R
=
.208,
R2=
.043,
F(6,605)
=
2.721,
Sig.
=
.003
Block
3:
ICT
usage
status
Internet
access
status
.038
1.002
.055
1.217
Internet
usage
experience
−.195
-4.545*−.255
−3.050*
Internet
usage
skill
level
−.056
−1.241
−.085
−2.520*
Daily
internet
usage
time .150
2.359*.559
16.191*
R
=
.456,
R2=
.208,
F(6,605)
=
11.222,
Sig.
=
.000
R
=
.497,
R2=
.247,
F(6,605)
=
13.570
Sig.
=
.000
Block
4:
smartphone
usage
status
Smartphone
control
frequency
−.205
−6.180*−.151
−4.454*
Daily
smartphone
usage
time
.207
5.828*.157
4.649*
Smartphone
usage
experience
−.120
−3.001*−.105
−2.150*
Smartphone
usage
purpose
.269
7.473*.214
5.796*
R
=
.692,
R2=.479,
F(6,605)
=
16.998,
Sig.
=
.000 R
=
.675,
R2=
.456,
F(6,605)
=
27.619
Sig.
=
.000
Block
5:
achievement
Mathematics
achievement
−.005
−.107
−.015
−.381
Science
achievement
−.033
−.321
−.197
−1.988*
Language
lesson
(English)
achievement
−.010
−.100
−.186
−1.799
Social
sciences
achievement −.071 −1.173 −.066
−1.068
Information
technologies
achievement
.122
2.039*.093
1.523
R
=
.696,
R2=
.484,
F(6,605)
=
13.361,
Sig.
=
.000
R
=
.680,
R2=
.462,
F(6,605)
=
21.982,
Sig.
=
.000
*p
<
.05.
p
<
.001).
Related
to
the
absolute
value
of
the
correla-
tion
coefficients,
the
ranges
can
be
listed
as
.07
and
1.00
high;
.70–.30
moderate
and
.30–.00
low
level
relation-
ships
(Buyukozturk,
2009).
Therefore,
as
scores
from
the
nomophobia
scale
increase,
it
can
be
said
that
smartphone
addiction
scores
will
also
increase.
4.4.
Hierarchical
regression
of
smartphone
addiction
and
nomophobia
factors
Hierarchical
linear
multiple
regression
analysis
results
are
given
in
Table
4
When
the
values
in
Table
4
are
examined,
it
was
found
that
demographic
variables
which
are
predictors
of
smart-
phone
addiction
employed
in
the
analysis
(all
together)
explained
the
variance
3.7%
[F(6,605)
=
3.884,
Sig.
=
.001]
in
the
first
step;
this
ratio
increased
4.9%
[F(6,605)
=
3.129,
Sig.
=
.001];
when
the
variables
related
to
the
parents
(all
together)
were
included
in
the
analysis
in
the
second
step;
this
ratio
increased
20.8%
[F(6,605)
=
11.222,
Sig.
=
.000]
when
the
variables
related
to
ICT
usage
(all
together)
were
included
in
the
analysis
in
the
third
step;
this
ratio
increased
47.9%
[F(6,605)
=
16.998,
Sig.
=
.000]
when
the
variables
related
to
smartphone
use
situation
(all
together)
were
included
in
the
analysis
in
the
fourth
step;
this
ratio
increased
48.4%
when
the
variables
related
to
academic
achievement
(all
together)
were
included
in
the
analysis
in
the
last
step.
It
was
found
that
the
increase
of
R2in
all
last
steps
was
significant
(p
<
.01)
Models
1–5
are
significant.
Model
4
(R
=
.692,
R2=
.479,
p
<
.05)
are
the
most
important
pre-
dictors
of
students’
smartphone
addiction.
From
moving
the
current
findings,
it
can
be
argued
that
the
smartphone
use
situations
must
be
investigated
before
the
explanation
smartphone
addiction
and
taking
precautions.
Even
though
demographic
variables
all
together
seems
as
significant
predictors
of
smartphone
addiction
in
regres-
sion
analysis,
only
age,
educational
level
and
gender
variables
predict
smartphone
addiction
significantly.
Simi-
larly,
variables
related
to
parents
all
together
are
predictors
of
smartphone
use
disorder,
but
all
variables
except
mother
education
are
not
statistically
significant
predictors
of
smart
phone
use
disorder.
ICT
use
situations
are
significant
predictors
of
smartphone
use
disorder,
as
well.
However,
only
internet
usage
experience
and
daily
internet
usage
time
variables
are
significant
predictors
of
smartphone
use
disorder.
The
striking
point
is
here
that
the
standardized
regression
coefficient
between
internet
usage
experience
and
smartphone
addiction
is
negative.
Therefore,
it
can
be
argued
that
there
is
a
significant
negative
correlation
between
internet
usage
experience
and
smartphone
use
disorder.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
15
All
of
the
smartphone
usage
variables
(both
individually
and
collectively)
are
significant
predictors
of
smartphone
use
disorder.
While
academic
achievement
variables
are
all
significant
predictors
of
smartphone
dependency,
variables
outside
the
information
technologies
achievement
variable
do
not
significantly
predict
smartphone
dependency.
When
the
values
in
Table
4
are
examined,
it
was
found
that
variables
which
are
predictors
of
nomphobia
employed
in
the
analysis
(all
together)
explained
the
vari-
ance
3.6%
[F(6,605)
=
3.795,
Sig.
=
.002];
in
the
first
step;
this
ratio
increased
4.3%
[F(6,605)
=
2.721,
Sig.
=
.003]
when
the
variables
related
to
the
parents
(all
together)
were
included
in
the
analysis
in
the
second
step;
this
ratio
increased
24.7%
[F(6,605)
=
13.570
Sig.
=
.000];
when
the
variables
related
to
ICT
usage
(all
together)
were
included
in
the
analysis
in
the
third
step;
this
ratio
increased
45.6%
[F(6,605)
=
27.619
Sig.
=
.000];
when
the
variables
related
to
smartphone
use
situation
(all
together)
were
included
in
the
analysis
in
the
fourth
step;
this
ratio
increased
46.2%
when
the
vari-
ables
related
to
academic
achievement
(all
together)
were
included
in
the
analysis
in
the
last
step.
It
was
found
that
the
increase
of
R2in
all
last
steps
was
significant
(p
<
.01)
Models
1–5
are
significant.
Model
4
(R
=
.675,
R2=
.456,
p
<
.05)
are
the
most
important
pre-
dictors
of
students’
smartphone
addiction.
Even
though
demographic
variables
all
together
seems
as
significant
predictors
of
nomophobia
in
regression
anal-
ysis,
gender
and
the
number
of
sibling
variables
don’t
predict
smartphone
addiction
significantly.
Similarly,
vari-
ables
related
to
parents
all
together
are
predictors
of
nomophobia,
but
none
of
the
variables
are
not
statisti-
cally
significant
predictors
of
nomophobia
when
evaluated
separately.
ICT
use
situations
are
significant
predictors
of
nomophobia,
as
well.
Internet
access
status
isn’t
signifi-
cant
predictors
of
nomophobia.
However,
the
standardized
regression
coefficient
between
internet
usage
experience
and
Internet
usage
skill
level
and
nomophobia
is
negative.
Therefore,
it
can
be
argued
that
there
is
a
significant
neg-
ative
correlation
between
Internet
usage
experience
and
Internet
usage
skill
level
and
nomophobia
scores.
All
of
the
smartphone
usage
variables
(both
individually
and
collec-
tively)
are
significant
predictors
of
nomophobia
scores.
While
academic
achievement
variables
are
all
signifi-
cant
predictors
of
nomophobia
scores,
variables
except
the
science
achievement
variable
do
not
predict
nomophobia
scores
significantly.
In
addition,
the
standardized
regres-
sion
coefficient
between
the
nomophobia
scores
and
the
academic
achievement
variable
is
negative.
5.
Discussion,
conclusion,
limitations
and
suggestions
In
this
present
study,
10
different
models
(5
+
5)
which
were
related
to
both
smartphone
addiction
and
nomo-
phobia
were
tested
in
order
to
determine
the
smartphone
addiction
and
nomophobia
levels
among
12–18
age
group
secondary
and
high
school
students
and
to
investigate
demographic
and
academic
variables
predicting
them.
This
study
is
the
first
one
which
dwells
on
the
investiga-
tion
of
demographic
variables
and
academic
achievement
in
a
holistic
manner
as
predictors
of
smartphone
addiction
and
nomophobia
among
adolescents
in
Turkey.
In
gen-
eral,
there
are
four
main
findings
that
are
significant
in
the
present
study.
The
first
one
is
that
demographic
variables
have
the
predicting
power
as
to
gender-related
symptoms
of
smartphone
addiction
and
nomophobia.
Second,
parents’
education
and
ICT
use
levels
are
indi-
cators
of
the
symptoms
of
smartphone
addiction
and
the
nomophobia
levels
of
adolescents.
Third,
academic
achievement
is
predictive
of
smartphone
addiction
symp-
toms
and
nomophobia
levels.
Finally,
it
was
found
that
the
most
important
variables
that
need
to
be
examined
in
pre-
diction
of
smartphone
addiction
symptoms
and
increases
in
nomophobia
levels
are
the
smartphone
usage
frequen-
cies/durations,
purposes
and
smartphone
experience.
5.1.
Smartphone
addiction
and
nomophobia
According
to
findings
of
the
study,
it
is
seen
that
there
is
a
significant
relationship
between
smartphone
addic-
tion
and
nomophobia.
Social
cognitive
theory
stresses
that
personal
self-regulation
and
self-control
form
the
basis
of
personal
behavior
(Bandura
1991).
Regarding
smartphone
use
literature,
It
is
emphasized
that
individuals
with
low
self-regulation
and
self-control
skills
can
use
their
smart-
phones
uncontrollably
and
create
a
higher
dependency
risk.
(Van
Deursen
et
al.,
2015).
From
this
point
of
view,
students
who
show
intense
nomophobic
behavior
do
not
exhibit
rigorous
self-regulation
and
appear
to
be
prone
to
smartphone
use
disorder.
Thus,
in
accordance
with
previ-
ous
findings
(Beranuyet
al.,
2009;
Lee
et
al.,
2014b;
Lepp,
Li,
Barkley,
&
Salehi-Esfahani,
2015),
the
likelihood
of
having
symptoms
associated
with
smartphone
addiction
is
higher
in
adolescents
with
high
levels
of
nomophobic
behavior.
Przybylski
et
al.
(2013)
pointed
out
the
concept
of
FoMO
and
stated
that
individuals
have
been
experienc-
ing
fear,
anxiety
and
anxiety
over
their
inability
to
follow
events,
news,
exchanges
and
discussions,
especially
in
social
networks.
Nomophobia
concept
is
associated
with
smartphone
addiction
and
FoMO
(Cheever
et
al.,
2014;
Lepp
et
al.,
2014).
From
moving
on
this
point,
it
can
be
argued
that
as
the
anxiety
about
the
breakdown
of
the
com-
munication
caused
by
the
applications
that
can
be
used
in
smartphones
such
as
social
media
increases,
the
symptoms
of
smartphone
addiction
will
increase.
The
works
of
Gökc¸
earslan
et
al.
(2016)
tell
us
that
self-control
variable
is
moderator
in
this
relationship.
As
indicated
by
Yildiz-Durak
(2018b),
individuals
who
can-
not
keep
their
smartphone
access
away
from
control
their
problems
are
using
their
phones
in
a
problematic
way.
For
this
reason,
it
is
important
to
develop
students’
self-control
skills
to
prevent
problematic
smartphone
usage
behaviors.
5.2.
Discussion
of
models
related
to
smartphone
addiction
5.2.1.
Demographic
variables
and
smartphone
addiction
According
to
the
findings
of
the
study,
it
was
deter-
mined
that
the
demographic
variables
predict
smartphone
addiction
levels
significantly.
When
the
relative
effect
of
demographic
variables
on
smartphone
addiction
is
exam-
ined,
it
is
seen
that
age
is
the
most
effective
predictor

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
16
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
variable.
It
has
been
determined
that
the
level
of
smart-
phone
addiction
increases
with
age.
Consistent
with
this
finding,
some
studies
in
the
literature
have
shown
that
lev-
els
of
smartphone
addiction
increase
with
age
(Gilly
et
al.,
2012).
Differences
in
the
developmental
duties
of
individuals
according
to
age
groups,
change
of
interests,
more
attrac-
tive
social
networks,
increased
communication
need
can
be
considered
as
the
main
reasons
for
this
situation.
When
the
findings
of
the
study
are
analyzed,
it
has
been
found
that
the
level
of
education
is
the
most
effective
predictor
of
smart-
phone
addiction
after
age
within
demographic
variables.
As
education
level
increased
with
age,
it
was
observed
that
the
level
of
smartphone
dependency
increased.
For
this
reason,
it
may
be
important
for
schools
to
arrange
awareness
train-
ings
on
smartphone
use
disorder,
especially
at
high
school
level,
to
prevent
these
symptoms.
In
research
findings,
gender
was
found
to
be
the
third
most
effective
predictor
of
smartphone
addiction
among
demographic
variables.
In
addition,
students’
lev-
els
of
smartphone
addiction
were
higher
in
males
than
in
females.
In
the
literature,
the
patterns
of
phone
use
differ
according
to
gender
(Wei
&
Lo,
2006).
It
can
be
said
that
the
reason
for
this
is
the
more
intense
use
of
ICT
by
males
than
females
in
social
networks.
Turkstat
(2017)
conducted
a
study
on
ICT
Usage
in
Household
in
Turkey
and
found
that
males
had
more
use
of
smartphone,
ICT
access
and
social
networking
than
females.
According
to
the
findings
of
the
study,
variables
such
as
income
level,
area
and
number
of
siblings
have
less
impact
on
smartphone
addiction
and
are
not
significant
predictors
of
smartphone
addiction.
Even
though
there
is
no
significant
relationship,
the
level
of
smartphone
addiction
increases
as
the
level
of
income
increases.
In
addition,
students
living
in
rural
areas
have
lower
levels
of
smartphone
addiction
than
those
liv-
ing
in
cities.
Restrictions
on
access
to
technology
can
be
considered
as
a
reason
for
this
situation.
Another
notable
finding
is
that
the
sibling
number
and
smartphone
addic-
tion
predictivity
coefficient
are
negative.
Therefore,
it
can
be
said
that
as
the
number
of
siblings
increases,
the
level
of
smartphone
addiction
decreases.
It
can
be
thought
that
the
reason
for
this
situation
is
that
the
students
communicate
with
their
siblings
to
satisfy
their
communication
needs.
5.2.2.
Variables
related
to
parents
and
smartphone
addiction
The
variables
which
are
related
to
parents
and
predict-
ing
smartphone
addiction
were
investigated
in
this
study.
According
to
the
findings
of
the
study,
it
was
determined
that
variables
related
to
parents
predict
students’
level
of
smartphone
addiction
significantly.
This
finding
may
give
an
idea
of
the
smartphone
addiction
of
adolescents
in
Turkey.
These
results
have
broadened
the
scope
of
work
on
parent-related
situations
and
smartphone
use
disorder.
On
the
other
hand,
according
to
Sharabi,
Levi
and
Margalit
(2012),
the
family
environment
with
better
edu-
cated
parents
affects
cognition
about
individual
awareness
and
self-efficacy
positively
and
this
can
diminish
loneli-
ness
by
affecting
social
relations
of
adolescents.
According
to
Yan,
Li
and
Sui
(2014),
this
also
reduces
the
likeli-
hood
of
adolescents
seeking
online
support
and
ultimately
becoming
a
smartphone
addict.
According
to
social
cog-
nitive
development,
environmental
factors
(in
the
context
of
family-related
variables)
are
predictors
of
individual
behavior,
as
well.
However,
we
need
to
know
more
about
the
internal
mechanism
of
parents’
ICT
usage
levels
and
how
their
edu-
cation
levels
affect
their
smartphone
usage
habits,
and
there
is
a
need
for
qualitative
work
in
the
future
as
we
can-
not
make
some
practical
implications
for
taking
measures.
When
the
predictive
effects
of
the
variables
in
this
model
formed
through
variables
related
to
parents
are
examined
separately,
it
is
seen
that
none
of
them
(except
mother
and
father
education
level)
have
a
significant
effect
on
smartphone
addiction
but
they
all
have
influence
on
smartphone
use
disorder.
Whang,
Lee,
and
Chang
(2003)
observed
that
internet-addicted
teenagers
tend
to
estab-
lish
new
online
social
relationships
and
show
a
desire
to
communicate
with
strangers
rather
than
their
family
and
friends.
However,
adolescents
in
Turkey
tend
to
create
a
digital
identity
of
the
person
they
dreamed
of
(Durak,
2016).
While
creating
this
digital
identity,
the
applications
they
employ
on
smartphones
offer
a
wide
range
of
oppor-
tunities.
The
perception
of
family
and
acquaintances
as
an
obstacle
to
creating
the
desired
digital
identity
is
regarded
as
a
limitation
of
social
cognitive
theory.
The
environmen-
tal
factor
ranges
which
were
suggested
by
social
cognitive
theory
have
negative
effects
in
explaining
smrtphone
use
disorders
of
adolescents.
Even
though
these
variables
don’t
predict
smartphone
addiction
significantly,
it
is
seen
that
as
mother
ICT
usage
skill
level
and
mother
education
level
increase,
smart-
phone
addiction
level
also
increases
when
the
arithmetic
average
is
evaluated.
It
has
been
found
that
there
are
no
similar
patterns
in
father-related
variables.
As
indicated
by
Livingstone,
Haddon,
Görzig
and
Ólafsson
(2011),
par-
ents
are
the
most
important
stakeholders
in
ICT
use
and
technology-oriented
addictions.
Parents
are
both
a
model
for
providing
information
about
getting
rid
of
adverse
sit-
uations
that
children
are
exposed
to,
raising
awareness
about
children’s
conscious
usage,
and
using
ICT
as
an
exam-
ple.
In
this
respect,
it
can
be
said
that
the
interaction
of
parents
with
technology
will
be
modeled
by
children
and
this
will
be
reflected
in
the
behavior
of
technology
usage.
It
is
important
for
parents
to
guide
their
children
on
the
use
of
conscious
technology
in
the
context
of
technology
competencies
(C¸
etinkaya
&
Sütc¸
ü,
2016).
5.2.3.
Variables
related
to
ICT
usage
status
and
smartphone
addiction
According
to
the
findings
of
the
study,
it
was
deter-
mined
that
the
variables
related
to
ICT
use
predict
students’
levels
of
smartphone
addiction
significantly.
The
Internet
usage
experience
variable
in
this
model
seems
to
have
a
significant
and
negative
effect
on
smartphone
use
disorder.
Similarly,
there
is
a
negative
relationship
between
Internet
usage
skill
level
and
smartphone
use
disorder.
The
most
important
contribution
in
explaining
smartphone
addic-
tion
has
been
provided
by
the
variable
“Smartphone
Usage
Status”.
According
to
the
findings
of
the
study,
it
was
deter-
mined
that
variables
related
to
smartphone
usage
predict
students’
smartphone
addiction
levels
significantly.
Kim

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
17
et
al.
(2016)
found
that
smartphone
usage
was
one
of
the
predictors
of
smartphone
use
disorder.
Haug
et
al.
(2015)
also
coincides
with
the
findings
of
this
study.
As
a
result,
smartphone
addiction
can
be
regarded
as
the
most
impor-
tant
risk
factor
for
smartphone
use
purpose,
duration
of
use,
smartphone
usage
experience
in
predicting
participants’
smartphone
use
disorder.
5.2.4.
Variables
related
to
academic
achievement
and
smartphone
addiction
According
to
the
findings
of
the
study,
it
was
determined
that
variables
related
to
academic
achievement
predict
students’
levels
of
smartphone
addiction
significantly.
In
the
research
findings,
it
was
determined
that
the
relation-
ship
between
smartphone
addiction
and
other
variables
other
than
achievement
towards
Information
Technolo-
gies
course
was
negative.
In
other
words,
it
can
be
argued
that
the
level
of
smartphone
addiction
will
decrease
as
the
achievement
scores
in
these
courses
increase.
This
research
confirms
various
studies
showing
that
there
is
a
relationship
between
academic
achievement
and
prob-
lematic
smartphone
usage
(Jackson,
Von
Eye,
Witt,
Zhao,
&
Fitzgerald,
2011;
Jacobsen
&
Forste,
2011;
Lepp
et
al.,
2014;
Wentworth
&
Middleton,
2014).
However,
it
has
been
reported
that
the
relationship
between
smartphone
addiction
and
academic
achievement
is
two-way
(Mendoza
et
al.,
2018),
but
it
is
reported
that
smartphone
addiction
generally
affects
academic
achieve-
ment.
In
this
respect,
it
is
thought
that
this
study
has
brought
a
different
perspective
to
the
existing
literature.
For
example,
the
theory
of
Internet
addiction
(Young,
1998)
reports
that
people
who
are
addicted
to
Internet
are
increasingly
losing
their
control
and
motivations
towards
learning
over
the
course
of
their
online
life,
losing
their
internationals
for
entertainment
(games,
conversations,
videolars,
etc.)
and
consequently
academic
achievement.
From
this
point
of
view,
this
work
has
brought
a
different
perspective.
In
this
study,
we
found
that
the
reduction
in
academic
achievement
was
linked
to
an
increase
in
the
symptoms
of
smartphone
use
disorder.
Low
academic
achievement
points
to
low
levels
of
participation
and
low
learning
moti-
vation
for
both
academic
and
behavioral,
cognitive,
and
emotional
tasks
(Ryan
&
Deci,
2000;
Skinner
&
Pitzer,
2012).
In
addition,
low
academic
achievement
in
the
field
has
led
to
an
increase
in
school
burnout
with
extreme
internet
addiction
(Salmela-Aro
et
al.,
2017).
Stage-environment
fit
theorists
(Eccles
et
al.,
1993)
emphasize
that
the
academic
commitment
of
the
adoles-
cents
will
disappear
if
the
academic
environment
does
not
satisfy
the
needs
of
the
students,
and
the
partic-
ipation
in
learning
activities
will
diminish
and
so
will
academic
achievement.
In
this
case,
it
is
obvious
that
the
students
will
go
on-line
activities.
(Zhang,
Qin,
&
Ren,
2018).
However,
in
secondary
and
high
school
levels
in
Turkey,
a
strong
academic
achievement
is
needed
in
order
to
ensure
achievement
in
the
exams
at
the
national
level.
Academic
achievement
puts
pressures
on
adolescents
and
their
parents
in
Turkey.
It
is
possible
for
students
to
use
smartphones
problematically
because
these
pressures
cre-
ate
school
burnout
(Durak
&
Sefero˘
glu,
2017).
This
may
make
sense
in
predicting
the
smartphone
addiction
of
aca-
demic
achievement
in
this
study.
In
conclusion,
this
study
lighlights
that
smartphone
addiction
level
may
increase
due
to
the
pressure
caused
by
national
exams
at
secondary
and
high
school
levels
in
Turkey.
5.3.
Discussion
of
models
related
to
nomophobia
5.3.1.
Demographic
variables
and
nomophobia
According
to
the
findings
of
the
study,
it
was
determined
that
the
demographic
variables
predict
students’
nomo-
phobia
significantly.
This
finding
can
provide
important
clues
to
the
diagnosis
of
nomophobia
and
to
the
inter-
vention
of
nomophobia.
For
example,
we
found
that
the
students
at
high
school
level
had
higher
nomophobia
lev-
els
and
that
the
adolescents
in
this
period
were
at
a
more
vulnerable
time
to
show
nomophobic
behavior,
which
pro-
vided
a
clue
that
we
should
pay
more
attention
to
high
school
students
during
adolescence.
When
the
relative
effect
of
demographic
variables
on
nomophobia
is
examined,
it
is
seen
that
age
is
the
most
effective
predictor
variable
as
it
is
in
smartphone
use
dis-
order.
It
was
determined
that
the
nomophobia
level
of
the
students
increases
along
with
age.
According
to
Van
Deursen
et
al.
(2015),
smartphone
usage
patterns
may
vary
depending
on
various
personal
characteristics.
It
is
obvious
that
personal
factors
based
on
social
cognitive
development
are
fundamental
to
explain
nomophobia.
For
instance,
people
with
high
impulsivity
tend
to
initiate
an
urgent
action
to
instantly
meet
their
needs
(Patton
et
al.,
1995).
The
impulsivity
level,
influenced
by
demo-
graphic
characteristics,
can
lead
to
nomophobic
behaviors
due
to
activities
such
as
acquiring
information
and
satisfy-
ing
relational
needs,
especially
through
smartphones,
and
these
individuals
are
prone
to
problematic
smartphone
use
symptoms
(Wu,
Cheung,
Ku,
&
Hung,
2013).
Education
level
was
found
to
be
the
most
effective
predictor
of
nomophobia
after
age
among
demographic
variables.
At
this
point,
it
may
be
useful
to
include
train-
ing
of
problematic
technology
use
in
curriculum.
Income
level
was
found
to
be
the
third
most
effective
predictor
of
nomophobia
among
demographic
variables.
According
to
Van
Deursen
et
al.
(2015),
the
type
of
smartphones
used
affects
the
nomophobia
levels
of
the
smartphone
features
used.
From
this,
it
can
be
said
that
depending
on
the
level
of
income,
the
characteristics
of
the
device
which
is
owned
will
be
different
and
the
nomophobic
behaviors
differenti-
ate
depending
on
the
device
characteristics.
The
urban
or
rural
area
was
the
fourth
most
effective
variable
on
nomophobia.
Students
living
in
rural
areas
have
lower
levels
of
nomophobia
than
those
living
in
the
city.
It
is
thought
that
this
difference
is
caused
by
smartphone
and
internet
access
opportunities.
As
indicated
in
a
study
conducted
at
K-12
level
in
all
regions
of
Turkey
by
Yildiz
and
Seferoglu
(2014),
ICT
access,
usage
and
literacy
were
found
to
be
digital
divide
according
to
the
urban
or
rural
area.
It
has
been
found
that
gender
and
sibling
number
variables
have
less
influence
on
nomophobia
than
other
variables
and
are
not
significant
predictors
of
nomophobia.
Even
though
there
is
no
significant
relationship,
it
is
found
that
males
have
higher
levels
of
nomophobia
than

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
18
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
females.
In
addition,
it
is
observed
that
the
levels
of
the
nomphobia
decrease
as
the
number
of
siblings
increases.
Based
on
the
number
of
siblings,
it
can
be
argued
that
the
levels
of
empathy
and
life
satisfaction
of
the
individuals
can
vary
and
this
can
differentiate
the
levels
of
nomophobia
(Lachmann
et
al.,
2018).
5.3.2.
Variables
related
to
parents
and
nomophobia
According
to
the
findings
of
the
study,
it
was
deter-
mined
that
the
variables
related
to
the
parents
predicted
the
nomophobia
levels
of
the
students
significantly.
When
the
predictive
effects
of
the
variables
included
in
this
model
are
examined
separately,
it
is
seen
that
none
of
them
has
a
significant
effect
on
nomophobia
but
all
of
them
are
effec-
tive
on
it.
Even
though
these
variables
are
not
statistically
significant
predictors
on
the
nomophobia,
it
is
seen
that
as
the
mother
ICT
usage
skill
level
and
mother
education
level
increase,
the
nomophobia
level
also
increases
when
the
arithmetic
average
is
evaluated.
There
are
no
similar
patterns
in
the
variables
related
to
the
father.
As
indicated
by
Hwang,
Choi,
Yum
and
Jeong
(2017),
given
that
parents
‘smartphone
usage
behaviors
and
par-
enting
styles
may
affect
children’s
problematic
use
of
smartphones,
parents’
approach
will
play
a
role
in
prevent-
ing
adverse
effects
of
problematic
smartphone
use.
In
the
study
conducted
by
parents
of
secondary
school
students,
it
has
been
mentioned
that
the
level
of
smartphone
usage
and
nomophobia
symptoms
may
increase
in
students
as
the
tendency
of
parents
to
show
smartphone
addiction
increases.
In
addition,
according
to
Elhai
et
al.
(2017),
the
behavior
of
constantly
controlling
the
smartphone
and
the
anxiety
state
experienced
when
keeping
away
from
the
smart-
phone
include
learned
behaviors.
From
this
point
of
view,
it
can
be
said
that
students
are
influenced
by
parents’
ICT
literacy
situations
as
well
as
the
frequency
of
smart
phone
use
and
their
problematic
use.
It
is
thought
that
the
severity
of
this
effect
may
be
related
to
parental
children’s
associa-
tions,
attachment
and
parenting
styles.
For
this
reason,
one
of
the
precautionary
measures
that
can
be
taken
to
reduce
the
tendency
of
students
to
show
nomophobic
behavior
is
to
increase
the
level
of
knowledge
about
parents’
usage
of
conscious
technology.
5.3.3.
Variables
related
to
ICT
usage
and
nomophobia
According
to
the
findings
of
the
study,
it
was
deter-
mined
that
the
variables
related
to
the
use
of
ICT
predict
students’
nomophobia
significantly.
The
Internet
usage
experience
variable
included
in
this
model
seems
to
be
significantly
effective
in
a
negative
way
on
nomophobia.
It
can
be
argued
that
nomophobia
levels
decrease
as
the
experience
of
internet
use
of
students
increases.
This
may
be
due
to
the
development
of
conscious
habits
as
students
gain
both
positive
and
negative
experiences.
On
the
other
hand,
according
to
Avcı,
Usluel,
Kurto˘
glu
and
Uslu
(2013),
“innovation
effect”
has
an
effect
on
ICT
usage
behaviors.
For
this
reason,
it
can
be
said
that
experience
has
formed
a
differentiation
on
nomophobia.
According
to
the
findings
of
the
study,
it
was
determined
that
the
variables
related
to
the
use
of
smartphone
pre-
dict
students’
nomophobia
levels
significantly.
In
addition,
the
relationship
between
smartphone
control
frequency
and
smartphone
usage
experience
and
nomophobia
is
in
a
negative
way.
Smartphone
control
frequency
(1
–
Every
5–10
min,
.
.
.
–
6
–
Several
times
a
day)
have
been
ordered
by
time
intervals.
For
this
reason,
this
finding
suggests
that
the
level
of
control
increases
along
with
the
frequency
of
control.
Similarly,
as
the
duration
of
smartphone
use
increases
over
the
course
of
the
day,
the
level
of
nomopho-
bia
seems
to
increase.
On
the
other
hand,
it
has
been
found
that
as
the
experience
of
smartphone
usage
increases,
the
level
of
nomophobia
decreases.
The
nomophobia
level
of
students
who
use
the
smart-
phone
for
instructional
purposes
significantly
differs
from
those
who
use
communication,
social
media
and
enter-
tainment,
and
the
nomophobia
level
of
students
who
use
educational
purposes
is
lower.
Moving
from
this
finding,
as
indicated
in
the
study
conducted
by
Yildiz-Durak
(2018a),
it
can
be
said
that
it
is
important
to
take
advantage
of
the
educational
use
potential
of
smartphones
to
prevent
the
symptoms
of
nomophobia.
When
the
existing
literature
is
examined,
the
studies
that
focus
on
the
nomophobia
reveal
the
features
of
smartphones,
the
inability
to
access
social
media
and
various
internet
based
gaming
applications
lead
to
the
addiction
on
individuals
and
cause
them
to
have
fear
of
staying
away
from
these
applications
(Anshari
et
al.,
2016;
Yildirim
et
al.,
2015).
At
this
point,
it
seems
natu-
ral
that
ICT
usage
frequency
and
nomophobia
are
related
terms.
5.3.4.
Variables
related
to
academic
achievement
and
nomophobia
In
the
present
study,
it
was
determined
that
variables
related
to
academic
achievement
significantly
predicted
students’
nomophobia
levels.
The
relationship
between
nomofobia
and
other
variables
other
than
achievement
towards
Information
Technologies
course
is
negative.
In
other
words,
it
can
be
said
that
as
the
scores
of
achieve-
ment
in
these
courses
increase,
the
level
of
nomophobia
will
decrease.
There
are
studies
that
report
that
academic
performance
is
related
to
nomophobia
in
literature
(Rashid
&
Asghar,
2016).
In
conclusion,
it
is
thought
that
stu-
dents
who
have
negative
academic
life
performance
tend
to
exhibit
nomophobic
behavior.
5.4.
Practical
implication
This
present
study
broadened
the
nomological
network
of
the
variables
including
demographic,
academic,
and
ICT
variables
that
totally
predicted
nomophobia
and
smart-
phone
addiction
as
it
dealt
with
these
variables
in
a
holistic
manner.
Additionally,
it
is
important
that
parents’
ICT
usage
and
educational
levels
are
predictors
of
smartphone
addic-
tion
and
nomophobia
levels
of
adolescents.
In
this
context,
it
should
be
noted
that
during
intervention
programs
for
problematic
ICT
use,
we
should
pay
more
attention
to
the
level
of
education
and
ICT
use
of
the
parents,
and
to
the
personal
characteristics
of
the
individual.
Differences
in
gender,
class
level,
and
region-specific
differences
indicate
that
different
“ICT
addiction
interven-
tion
practices”
are
needed
for
adolescents.
Despite
studies
that
prove
the
relationship
between
parent
factors
and

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
19
ICT
dependence,
there
was
no
research
that
examined
this
relationship
in
the
context
of
ICT
usage
level
variance.
This
study
attempted
to
reveal
this
mechanism
by
tak-
ing
into
account
both
demographic
and
parental
factors
as
well
as
academic
achievement
variables
in
hopes
of
helping
teenagers
establish
a
more
comprehensive
theory
for
understanding
smartphone
addiction
and
nomophobic
behavior.
Moreover,
when
choosing
variables
for
this
study,
the
results
are
expected
to
provide
some
practical
advice
for
schools
to
intervene
in
ICT
dependencies.
For
example,
the
finding
which
puts
forward
that
smartphone
addic-
tion
and
nomophobia
levels
decrease
as
ICT
use
knowledge
increases
in
the
present
study
suggests
that
the
devel-
opment
of
ICT
use
skills
in
other
lessons
as
well
as
the
informatics
courses
to
prevent
ICT
dependence
in
schools
is
a
solution.
Similarly,
this
finding
can
help
parents
and
teachers
design
interventions
to
reduce
anxiety
levels
in
nomophobic
individuals
or
to
prevent
the
symptoms
of
smartphone
use
disorder.
This
present
study
suggests
that
academic
performance
may
also
be
a
predictor
of
nomophobia
and
smartphone
use
disorder,
as
well
as
the
association
between
academic
achievement
and
smart-
phone
addiction
and
nomofobia
in
previous
research.
5.5.
Theoretical
implication
The
findings
obtained
from
this
study
is
believed
to
have
contributed
to
the
existing
literature
in
terms
of
the-
oretical
bases.
To
begin
with,
one
of
the
most
important
contributions
of
this
study
is
to
provide
an
expanded
model
of
adolescents’
smartphone
use
disorder
and
nomophobic
behavior
in
the
theoretical
context.
In
this
model,
in
addi-
tion
to
the
original
social
cognitive
model,
the
time
spent
with
smartphones,
the
frequency
of
control,
smartphone
experience
and
skill
are
more
predictive
on
smartphone
use
disorder
and
nomophobia
than
personal
factors.
External
factors
(variables
related
to
parents)
are
less
predictive
than
the
personal
ones
on
smartphone
use
dis-
order
and
nomophobia.
In
this
present
study,
it
was
found
that
there
was
a
negative
relationship
between
smart-
phone
use
disorder
and
nomophobia.
This
finding
suggests
that
low
academic
achievement
may
lead
to
problematic
smartphone
use
disorders
in
adolescents.
In
a
nutshell,
we
present
compelling
evidence
showing
that
smartphone
use
disorder
and
nomophobia
are
related
to
academic
achieve-
ment
and
academic
achievement
issues
have
some
driven
forces
in
smartphone
use
disorders.
5.6.
Limitations
and
recommendations
This
research
has
some
limitations.
As
the
results
of
this
research
reveal
the
relationship
between
problem-
atic
use
of
smartphones
and
demographic
variables,
it
is
important
to
conduct
further
research
with
similar
groups
and
related
variables,
both
within
the
same
culture
and
between
cultures.
Also,
in
this
study,
the
current
rela-
tionship
between
smartphone
use
disorder,
nomophobia
and
academic
performance
is
defined
by
using
the
cross-
sectional
research
design.
However,
the
research
design
does
not
reveal
cause-effect
relationships.
Students’
smartphone
habits
have
been
evaluated
based
on
the
information
they
have
reported.
In
studies
con-
ducted
in
the
existing
literature,
it
was
stated
that
obtained
data
based
on
self-report
constitute
a
limitation
in
terms
of
“reliability
of
results”
for
research
(Lin
et
al.,
2015;
Montag,
Blaskiewicz,
Lachmann
et
al.,
2015;
Montag,
Błaszkiewicz,
Sariyska
et
al.,
2015).
In
order
to
prevent
this
limitation
from
adversely
affecting
the
results
of
the
research,
con-
sistency
was
checked
between
the
responses
given
to
the
items
in
the
scales
that
were
in
the
scales.
It
was
seen
that
the
responses
to
the
items
in
the
opposite
direction
were
almost
the
same
as
the
arithmetic
average.
When
the
literature
is
examined,
it
has
been
suggested
that
the
results
of
the
research
are
evaluated
in
this
context
and
the
variables
in
the
opposite
direction
are
evaluated
as
control
elements
(Guo,
Li,
&
Yu,
2017).
Also
in
literature,
in
future
years,
It
is
also
emphasized
that
the
organiza-
tion
of
psychological
data
with
computational
techniques
will
gain
importance
in
the
coming
years
(Montag,
Duke,
&
Markowetz,
2016;
Montag
et
al.,
2016;
Yarkoni,
2012).
This
study
only
includes
secondary
and
high
school
students
since
the
most
important
group
to
suffer
from
smartphone
addiction
is
adolescents.
Future
research
may
explore
the
same
hypothesis
model
with
larger
research
kits.
Declaration
of
conflicting
interests
The
authors
declared
no
potential
conflicts
of
interest
with
respect
to
the
research,
authorship,
and/or
publication
of
this
article.
Funding
The
authors
received
no
financial
support
for
the
research,
authorship,
and/or
publication
of
this
article.
Appendix
A.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
20
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
Table
A1
Outline
on
data
collection
instruments
and
sample
Items.
Questions
Reference
Sample
items
Demographic
items
(Q1–Q6)
Parent
variables
(Q7–Q10)
ICT
usage
status
(Q11–Q39).
Developed
by
researchers
Gender
a.
Male
b.
Female
Age
.
.
..
.
.
How
many
hours
do
you
use
your
smartphone
in
a
day?
Lees
than
one
hour
1–3
h
4–6
h
7–9
h
10
and
over
Educational
Background
of
Your
Mother
Primary
school
graduate
Middle
school
graduate
High
school
graduate
Bachelor’s
degree
Master’s
PhD
degree
Your
internet
access
status:
1-
Limited
access
2-Rare
access
possibility
3-Sometimes
they
have
access
4-Accessibility
most
of
the
time
5-
Always
accessible
Academic
achievement
(Q40–Q48)
Developed
by
researchers
40.
Academic
achievement
Select
your
grades
in
your
school
report
in
the
last
semester
for
the
following
courses.
Please
pay
attention
that
your
answers
match
up
with
the
grades
in
the
e-school
system.
Smartphone
addiction
Scale
(SAS)
(Q49–Q81)
Demirci,
Orhan,
Demirdas¸
,
Akpınar
and
Sert
(2014)
2.
I
have
difficulty
in
concentrating
due
to
smartphone
usage
while
studying
in
classroom
or
doing
homework.
9.
It
is
possible
to
get
rid
of
stress
through
smartphone.
13.
Using
a
smartphone
is
the
most
enjoyable
thing
in
my
life.
25.
I
check
social
networks
such
as
Twitter
and
Facebook
as
soon
as
I
wake
up.
Nomophobia
scale
(Q82–Q101)
Yildirim,
S¸
umuer,
Adnan
and
Yildirim
(2015)
1.
I
feel
disturbed
when
I
cannot
access
to
information
on
my
smartphone.
11.
If
my
smartphone
is
not
with
me,
I
worry
that
my
family
and/or
my
friends
will
not
be
able
to
reach
me.
16.
If
my
smartphone
is
not
with
me,
I
get
nervous
since
I
am
offline.
20.
If
my
smartphone
is
not
with
me,
I
feel
strange
since
I
am
not
sure
what
I
will
do.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
21
Table
A2
T-test
and
one-way
ANOVA
result.
Variables
Nomophobia
Smartphone
addiction
Demographic
variables
Options
M
(SD)
F
p
M
(SD)
F
p
Gender Female
51.21
(26.34) 2.16 .040*78.23
(38.32) 2.10 .039*
Male
51.37
(26.22)
85.06
(43.21)
**Age
10–12
years
50.90
(26.05)
2.96 .031*
80.27
(40.52)
3.10 .045*
13–15
years
51.48
(26.12)
81.81
(40.97)
16–18
years
53.86
(30.38)
94.79
(45.36)
Educational
level Middle
School
Level
50.62
(26.45) 0.581
.561 79.86
(41.03) 1.07
.285
High
School
Level 51.86
(26.11) 83.43
(41.04)
**Monthly
Income
Low
45.82
(26.09)
5.045 .007*
71.60
(39.83)
5.163
.006
Moderate
51.69
(26.56) 83.14
(38.87)
High
56.68
(24.22)
84.86
(41.56)
***Number
of
siblings
I
have
not
got
brother
or
sister
52.99
(25.59)
1.90 .102
82.88
(41.70)
1.502 .223
1–3
siblings
51.78
(26.74)
81.93
(40.70)
4
and
up
siblings 44.41
(22.75)
72.97
(35.72)
Urban
or
rural
area Rural
47.61
(25.35) 2.504 .013*79.22
(40.05) 2.541 .011*
Urban
53.19
(26.54) 83.11
(41.54)
Related
Variables
with
Parents
Options
M
(SD)
F
p
M
(SD)
F
p
Mother
ICT
Level
of
Use
Having
very
little
information
48.62
(24.66)
1.243 .291
77.67(37.48)
1.216 .303
Novice
53.64
(28.57)
86.74(45.50)
Moderate
53.20
(28.29)
83.65(43.57)
Advanced
52.01
(26.89)
83.66
(42.98)
Expert
53.87
(26.69)
85.41
(42.95)
Father
ICT
Usage
Level
Having
very
little
information
51.11
(26.23)
.191
.943
82.78
(41.26)
.376
.826
Novice
49.85(27.36)
81.69
(43.28)
Moderate
52.56
(25.57)
85.37
(39.96)
Advanced
49.52
(23.95)
77.57
(38.45)
Expert
52.09
(27.19)
80.37
(41.59)
Educational
Background
of
Your
Mother
Primary
school
graduate
49.09
(29.85)
2.635 .049*
71.33
(44.41)
2.798 .039*
Middle
school
graduate
48.40
(25.28)
75.51
(39.13)
High
School
Graduate
51.99
(25.97)
82.89
(41.01)
Bachelor’s,
Master’s
or
PhD
52.27
(26.57)
84.52
(41.41)
Educational
Background
of
Your
Father
Primary
school
graduate
51.50
(17.16)
.740
.528
64.00
(15.55)
1.944
.121
Middle
school
graduate
55.55
(28.15) 86.39
(45.66)
High
school
graduate
50.75
(26.81)
81.54
(39.72)
Bachelor’s,
Master’s
or
PhD
50.60
(25.57)
78.32
(38.05)
ICT
Usage
Status
Options
M(SD)
F
p
M(SD)
F
p

Please
cite
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article
in
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as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
22
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
Table
A2
(Continued)
Variables
Nomophobia
Smartphone
addiction
Internet
Access
Status
Limited
access
43.93
(16.05)
2.030
.089
76.00
(29.65)
1.275
.278
Rare
access
possibility
50.12
(26.77)
80.27
(39.95)
Sometimes
they
have
access
51.19
(26.32)
81.21
(42.25)
Accessibility
most
of
the
time
51.20
(27.05)
81.22
(42.43)
Always
accessible 54.15
(26.89) 85.67
(41.80)
***Internet
use
experience
(years)
Less
than
1
year
63.82
(30.08)
30.979 .000*
104.73
(43.44)
43.960 .000*
1–3
years 57.17
(21.04) 92.44
(36.14)
4
years
and
over
45.00
(24.25)
70.31
(37.21)
Level
of
internet
using
skill
Having
very
little
information
55.44
(28.11)
1.657 .158
88.69
(45.54)
.707 .587
Novice
55.27
(26.49)
84.15
(41.71)
Moderate
51.02
(24.93) 81.77
(41.04)
Advanced
50.03
(23.14)
80.86
(40.39)
Expert
50.00
(23.11) 80.11
(39.90)
***Daily
internet
usage
time
Less
than
1
h
44.89
(22.11)
9.729 .000*
71.54
(34.49)
10.730 .000*
1–3
h
54.41
(26.76)
86.15
(41.85)
4
h
and
over
55.56
(31.76)
91.77
(49.21)
Smartphone
Usage
Status
Options
M
(SD)
F
p
M
(SD)
F
p
Smartphone
control
frequency
(number
of
times
per
day)
Every
5–10
min
61.17
(27.13)
43.499 .000*
100.08
(41.57)
51.042 .000
Every
10–30
min
58.67
(23.08)
94.11
(35.92)
Every
one
to
three
hours
55.15
(23.46)
85.49
(34.16)
Several
times
a
day 34.79(30.49)
55.04
(33.83)
***Daily
smartphone
usage
time
(hours)
Less
than
1
h
49.80
(25.85)
2.63 0.033*
79.53
(40.11)
2.53 0.044*
1–3
h
50.04
(25.72)
80.04
(40.24)
4
h
and
over 53.17
(26.85) 84.42
(42.18)
***Smartphone
usage
experience
(years)
Less
than
1
year
66.03
(31.90)
10.867 .000*
89.25
(41.68)
15.073 .000*
1–3
years
50.57
(25.71)
79.35
(38.37)
4
years
and
over
50.47
(24.22)
78.22
(39.29)
Smartphone
purpose
of
use
Educational
39.32
(20.90)
90.733 .000*
61.77
(30.44)
106.323 .000*
Communication
(video
and
non-video
call,
SMS)
56.89
(21.81)
90.60
(34.70)
Social
media
63.46
(23.01)
108.69
(27.43)
Entertainment
(game,
video,
etc.)
76.85
(24.22) 121.28
(42.62)
Achievement
Options
M
(SD)
F
p
M
(SD)
F
p
Academic
average
0–1
Failed
62.44
(27.37)
2.955 .020*
106.04
(42.56)
3.708 .005*
2
Pass 53.72
(25.73) 86.46
(45.49)
3
Medium
53.91
(27.51)
79.37
(39.81)
4
Good
49.48
(25.96)
82.65
(40.14)
5
Well 32.71
(15.09) 53.00
(27.07)
*p
<
.05.
** Turkstat
(2017)
in
Turkey
are
classified
according
to
their
income
level
limits
set
by
the
http://www.tuik.gov.tr/pretablo.do?alt
id=1013.
*** Variables
belonging
to
continuous
data
are
given
to
experts
and
categorized.

Please
cite
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article
in
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as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
H.
Yildiz
Durak
/
The
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Science
Journal
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(2017)
xxx–xxx
23
References
Ainsworth,
M.
D.
S.,
&
Bell,
S.
M.
(1970).
Attachment,
exploration,
and
separation:
Illustrated
by
the
behavior
of
one-year-olds
in
a
strange
situation.
Child
Development,
49–67.
Aljomaa,
S.
S.,
Qudah,
M.
F.
A.,
Albursan,
I.
S.,
Bakhiet,
S.
F.,
&
Abduljabbar,
A.
S.
(2016).
Smartphone
addiction
among
university
students
in
the
light
of
some
variables.
Computers
in
Human
Behavior,
61,
155–164.
Andreassen,
C.
S.,
&
Pallesen,
S.
(2014).
Social
network
site
addiction-an
overview.
Current
Pharmaceutical
Design,
20(25),
4053–4061.
Anshari,
M.,
Alas,
Y.,
Hardaker,
G.,
Jaidin,
J.
H.,
Smith,
M.,
&
Ahad,
A.
D.
(2016).
Smartphone
habit
and
behavior
in
Brunei:
Personaliza-
tion,
gender,
and
generation
gap.
Computers
in
Human
Behavior,
64,
719–727.
Arpaci,
I.,
Balo˘
glu,
M.,
Kozan,
H. ˙
I.
Ö.,
&
Kesici,
S¸
.
(2017).
Individual
dif-
ferences
in
the
relationship
between
attachment
and
nomophobia
among
college
students:
The
mediating
role
of
mindfulness.
Journal
of
Medical
Internet
Research,
19(12)
Avcı,
Ü.,
Usluel,
Y.
K.,
Kurto˘
glu,
M.,
&
Uslu,
N.
(2013).
The
examination
of
the
variables
playing
a
role
on
the
process
of
adoption
of
innovations
by
descriptive
review
method.
Pamukkale
University
Faculty
of
Education
Journal,
33(33),
53–71.
Baddeley,
A.,
Lewis,
V.,
Eldridge,
M.,
&
Thomson,
N.
(1984).
Attention
and
retrieval
from
long-term
memory.
Journal
of
Experimental
Psychology:
General,
113(4),
518–540.
Bae,
S.
M.
(2015).
The
relationships
between
perceived
parenting
style,
learning
motivation,
friendship
satisfaction,
and
the
addictive
use
of
smartphones
with
elementary
school
students
of
South
Korea:
Using
multivariate
latent
growth
modeling.
School
Psychology
International,
36(5),
513–531.
Baker,
T.
B.,
Piper,
M.
E.,
McCarthy,
D.
E.,
Majeskie,
M.
R.,
&
Fiore,
M.
C.
(2004).
Addiction
motivation
reformulated:
An
affective
processing
model
of
negative
reinforcement.
Psychological
Review,
111(1),
33.
Bandura,
A.
(1977).
Self-efficacy:
Toward
a
unifying
theory
of
behavioral
change.
Psychological
Review,
84(2),
191–215.
Bandura,
A.
(1982).
The
assessment
and
predictive
generality
of
self-
percepts
of
efficacy.
Journal
of
Behavior
Therapy
and
Experimental
Psychiatry,
13(3),
195–199.
Bandura,
A.
(1986).
Social
foundations
of
thought
and
action.
pp.
1986.
NJ:
Englewood
Cliffs.
Bandura,
A.
(1989).
Regulation
of
cognitive
processes
through
perceived
self-efficacy.
Developmental
Psychology,
25(5),
729.
Bandura,
A.
(1991).
Social
cognitive
theory
of
self-regulation.
Organiza-
tional
Behavior
and
Human
Decision
Processes,
50(2),
248–287.
Bandura,
A.
(2001a).
Social
cognitive
theory:
An
agentic
perspective.
Annual
Review
of
Psychology,
52(1),
1–26.
Bandura,
A.
(2001b).
Social
cognitive
theory
of
mass
communication.
Media
Psychology,
3(3),
265–299.
Baron,
N.
S.,
&
Campbell,
E.
M.
(2012).
Gender
and
mobile
phones
in
cross-
national
context.
Language
Sciences,
34(1),
13–27.
Baxter,
L.
A.,
&
Simon,
E.
P.
(1993).
Relationship
maintenance
strategies
and
dialectical
contradictions
in
personal
relationships.
Journal
of
Social
and
Personal
Relationships,
10(2),
225–242.
Beranuy,
M.,
Oberst,
U.,
Carbonell,
X.,
&
Chamarro,
A.
(2009).
Problem-
atic
Internet
and
mobile
phone
use
and
clinical
symptoms
in
college
students:
The
role
of
emotional
intelligence.
Computers
in
Human
Behavior,
25(5),
1182–1187.
Billieux,
J.,
Maurage,
P.,
Lopez-Fernandez,
O.,
Kuss,
D.
J.,
&
Griffiths,
M.
D.
(2015).
Can
disordered
mobile
phone
use
be
considered
a
behavioral
addiction?
An
update
on
current
evidence
and
a
comprehensive
model
for
future
research.
Current
Addiction
Reports,
2(2),
156–162.
Billieux,
J.,
Schimmenti,
A.,
Khazaal,
Y.,
Maurage,
P.,
&
Heeren,
A.
(2015).
Are
we
overpathologizing
everyday
life?
A
tenable
blueprint
for
behavioral
addiction
research.
Journal
of
Behavioral
Addictions,
4(3),
119–123.
Billieux,
J.,
Van
der
Linden,
M.,
&
Rochat,
L.
(2008).
The
role
of
impulsivity
in
actual
and
problematic
use
of
the
mobile
phone.
Applied
Cognitive
Psychology:
The
Official
Journal
of
the
Society
for
Applied
Research
in
Memory
and
Cognition,
22(9),
1195–1210.
Bivin,
J.
B.,
Mathew,
P.,
Thulasi,
P.
C.,
&
Philip,
P.
C.
(2013).
Nomophobia-do
we
really
need
to
worry
about?
Reviews
of
Progress,
1(1).
Retrieved
from.
https://pdfs.semanticscholar.org/db25/
ffde1b3ef0e267e217b6eb1022f9cd274706.pdf
Bragazzi,
N.
L.,
&
Del
Puente,
G.
(2014).
A
proposal
for
including
nomopho-
bia
in
the
new
DSM-V.
Psychology
Research
and
Behavior
Management,
7,
155–160.
Brambilla,
P.,
Giussani,
M.,
Pasinato,
A.,
Venturelli,
L.,
Privitera,
F.,
del
Giudice,
E.
M.,
&
Chiappini,
E.
(2017).
Sleep
habits
and
pattern
in
1–14
years
old
children
and
relationship
with
video
devices
use
and
evening
and
night
child
activities.
Italian
Journal
of
Pediatrics,
43(7)
http://dx.doi.org/10.1186/s13052-016-0324-x
Buxton,
O.
M.,
Chang,
A.
M.,
Spilsbury,
J.
C.,
Bos,
T.,
Emsellem,
H.,
&
Knutson,
K.
L.
(2015).
Sleep
in
the
modern
family:
Protective
family
routines
for
child
and
adolescent
sleep.
Sleep
Health,
1(1),
15–27.
Buyukozturk,
S¸
.
(2009).
Data
analysis
handbook
for
social
sciences:
Statis-
tics,
research
design,
SPSS
applications
and
interpretation
(9th
edition).
Pegem
Publications:
Ankara.
Cameron,
D.
(2009).
A
language
in
common.
The
Psychologist,
22,
578–580.
Carbonell,
X.,
Oberst,
U.,
&
Beranuy,
M.
(2013).
The
cell
phone
in
the
twenty-first
century:
A
risk
for
addiction
or
a
necessary
tool?
In
Prin-
ciples
of
addiction.
pp.
901–909.
Casey,
B.
M.
(2012).
Linking
psychological
attributes
to
smart
phone
addic-
tion,
face-to-face
communication,
present
absence
and
social
capital
Graduation
Project.
Graduate
School
of
the
Chinese
University
of
Hong
Kong.
C¸
etinkaya,
L.,
&
Sütc¸
ü,
S.
S.
(2016).
Parents’
restrictions
on
their
children’s
use
of
information
technologies
and
their
reasons.
Turkish
Online
Jour-
nal
of
Qualitative
Inquiry,
7(1),
18–36.
Chahal,
H.,
Fung,
C.,
Kuhle,
S.,
&
Veugelers,
P.
J.
(2013).
Availability
and
night-time
use
of
electronic
entertainment
and
communication
devices
are
associated
with
short
sleep
duration
and
obesity
among
C
anadian
children.
Pediatric
obesity,
8(1),
42–51.
Chakraborty,
K.,
Basu,
D.,
&
Kumar,
K.
G.
(2010).
Internet
addiction:
Consensus,
controversies,
and
the
way
ahead.
East
Asian
Archives
of
Psychiatry,
20(3),
123–132.
Cheever,
N.
A.,
Rosen,
L.
D.,
Carrier,
L.
M.,
&
Chavez,
A.
(2014).
Out
of
sight
is
not
out
of
mind:
The
impact
of
restricting
wireless
mobile
device
use
on
anxiety
levels
among
low,
moderate
and
high
users.
Computers
in
Human
Behavior,
37,
290–297.
Cho,
J.
(2015).
Roles
of
smartphone
app
use
in
improving
social
capital
and
reducing
social
isolation.
Cyberpsychology,
Behavior,
and
Social
Networking,
18(6),
350–355.
Cho,
J.
Y.,
Kim,
T.,
&
Lee,
B.
G.
(2016).
Effective
parental
mediation
for
the
right
use
of
smartphones
in
early
childhood.
International
Information
Institute
(Tokyo).
Information,
19(2),
365–372.
Cho,
K.
S.,
&
Lee,
J.
M.
(2017).
Influence
of
smartphone
addiction
proneness
of
young
children
on
problematic
behaviors
and
emo-
tional
intelligence:
Mediating
self-assessment
effects
of
parents
using
smartphones.
Computers
in
Human
Behavior,
66,
303–311.
Choi,
S.
W.,
Kim,
D.
J.,
Choi,
J.
S.,
Ahn,
H.,
Choi,
E.
J.,
Song,
W.
Y.,
&
Youn,
H.
(2015).
Comparison
of
risk
and
protective
factors
associated
with
smartphone
addiction
and
Internet
addiction.
Journal
of
Behavioral
Addictions,
4(4),
308–314.
Choliz,
M.
(2012).
Mobile-phone
addiction
in
adolescence:
The
test
of
mobile
phone
dependence
(TMD).
Progress
in
Health
Sciences,
2(1),
33–44.
Chotpitayasunondh,
V.,
&
Douglas,
K.
M.
(2016).
How
phubbing
becomes
the
norm:
The
antecedents
and
consequences
of
snubbing
via
smart-
phone.
Computers
in
Human
Behavior,
63,
9–18.
Choy,
Y.,
Fyer,
A.
J.,
&
Lipsitz,
J.
D.
(2007).
Treatment
of
specific
phobia
in
adults.
Clinical
Psychology
Review,
27(3),
266–286.
Clayton,
R.
B.,
Leshner,
G.,
&
Almond,
A.
(2015).
The
extended
iSelf:
The
impact
of
iPhone
separation
on
cognition,
emotion,
and
physiology.
Journal
of
Computer-Mediated
Communication,
20(2),
119–135.
Compeau,
D.
R.,
&
Higgins,
C.
A.
(1995).
Application
of
social
cognitive
theory
to
training
for
computer
skills.
Information
Systems
Research,
6(2),
118–143.
Connell,
S.
L.,
Lauricella,
A.
R.,
&
Wartella,
E.
(2015).
Parental
co-use
of
media
technology
with
their
young
children
in
the
USA.
Journal
of
Children
and
Media,
9(1),
5–21.
Davey,
S.,
&
Davey,
A.
(2014).
Assessment
of
smartphone
addiction
in
Indian
adolescents:
A
mixed
method
study
by
systematic-review
and
meta-analysis
approach.
International
Journal
of
Preventive
Medicine,
5(12),
1500–1511.
Davis,
R.
A.
(2001).
A
cognitive-behavioral
model
of
pathological
Internet
use.
Computers
in
Human
Behavior,
17(2),
187–195.
Demirci,
K.,
Orhan,
H.,
Demirdas,
A.,
Akpinar,
A.,
&
Sert,
H.
(2014).
Valid-
ity
and
reliability
of
the
Turkish
version
of
the
smartphone
addiction
scale
in
a
younger
population.
Klinik
Psikofarmakoloji
Bülteni-Bulletin
of
Clinical
Psychopharmacology,
24(3),
226–234.
Dixit,
S.,
Shukla,
H.,
Bhagwat,
A.,
Bindal,
A.,
Goyal,
A.,
Zaidi,
A.,
&
Shrivas-
tava,
A.
(2010).
A
study
to
evaluate
mobile
phone
dependence
among
students
of
a
medical
college
and
associated
hospital
of
central
India.
Indian
Journal
of
Community
Medicine,
35(2),
339–341.
Dube,
N.,
Khan,
K.,
Loehr,
S.,
Chu,
Y.,
&
Veugelers,
P.
(2017).
The
use
of
entertainment
and
communication
technologies
before
sleep
could
affect
sleep
and
weight
status:
A
population-based
study
among
chil-

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
24
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
dren.
International
Journal
of
Behavioral
Nutrition
and
Physical
Activity,
14(1),
97.
Duke,
É.,
&
Montag,
C.
(2017a).
Smartphone
addiction
and
beyond:
Initial
insights
on
an
emerging
research
topic
and
its
relationship
to
Internet
addiction.
In
Internet
addiction.
pp.
359–372.
Springer,
Cham.
Duke,
É.,
&
Montag,
C.
(2017b).
Smartphone
addiction:
Daily
interruptions
and
self-reported
productivity.
Addictive
Behaviors
Reports,
6,
90–95.
Durak,
H.
(2016).
Investigation
of
how
secondary
and
high
school
students
construct
their
digital
profiles
on
facebook.
International
Social
Science
Journal,
9(44),
1–16.
Durak,
H.
Y.,
&
Sefero˘
glu,
S.
S.
(2017).
Examination
of
teachers’
sense
of
burnout
in
terms
of
various
variables.
Gazi
University
Journal
of
Gazi
Educational
Faculty,
37(2),
759–788.
Eccles,
J.
S.,
Midgley,
C.,
Wigfield,
A.,
Buchanan,
C.
M.,
Reuman,
D.,
Flanagan,
C.,
&
Mac
Iver,
D.
(1993).
Development
during
adolescence:
The
impact
of
stage-environment
fit
on
young
adolescents’
experiences
in
schools
and
in
families.
American
Psychologist,
48(2),
90.
Elhai,
J.
D.,
Dvorak,
R.
D.,
Levine,
J.
C.,
&
Hall,
B.
J.
(2017).
Problematic
smart-
phone
use:
A
conceptual
overview
and
systematic
review
of
relations
with
anxiety
and
depression
psychopathology.
Journal
of
Affective
Dis-
orders,
207,
251–259.
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.
Eutsler,
L.
(2018).
Parents’
mobile
technology
adoption
influences
on
ele-
mentary
children’s
use.
The
International
Journal
of
Information
and
Learning
Technology,
35(1),
29–42.
Eyyam,
R.,
&
Yaratan,
H.
S.
(2014).
Impact
of
use
of
technology
in
mathe-
matics
lessons
on
student
achievement
and
attitudes.
Social
Behavior
and
Personality:
An
international
Journal,
42(1),
31S–42S.
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
Per-
sonality:
an
international
journal,
37(2),
231–238.
Falbe,
J.,
Davison,
K.
K.,
Franckle,
R.
L.,
Ganter,
C.,
Gortmaker,
S.
L.,
Smith,
L.,
Land,
T.,
&
Taveras,
E.
M.
(2015).
Sleep
duration,
restfulness,
and
screens
in
the
sleep
environment.
Pediatrics,
135(2),
e367–e375.
Field,
A.
(2005).
Discovering
statistics
using
SPSS
(2th
edition).
London:
Sage.
Forgays,
D.
K.,
Hyman,
I.,
&
Schreiber,
J.
(2014).
Texting
everywhere
for
everything:
Gender
and
age
differences
in
cell
phone
etiquette
and
use.
Computers
in
Human
Behavior,
31,
314–321.
Froese,
A.
D.,
Carpenter,
C.
N.,
Inman,
D.
A.,
Schooley,
J.
R.,
Barnes,
R.
B.,
Brecht,
P.
W.,
&
Chacon,
J.
D.
(2012).
Effects
of
classroom
cell
phone
use
on
expected
and
actual
learning.
College
Student
Journal,
46(2),
323–332.
Gökc¸
earslan,
S¸
.,
Mumcu,
F.
K.,
Has¸
laman,
T.,
&
C¸
evik,
Y.
D.
(2016).
Modelling
smartphone
addiction:
The
role
of
smartphone
usage,
self-regulation,
general
self-efficacy
and
cyberloafing
in
university
students.
Comput-
ers
in
Human
Behavior,
63,
639–649.
Gallimberti,
L.,
Buja,
A.,
Chindamo,
S.,
Terraneo,
A.,
Marini,
E.,
Rabensteiner,
A.,
&
Baldo,
V.
(2016).
Problematic
cell
phone
use
for
text
messag-
ing
and
substance
abuse
in
early
adolescence
(11-
to
13-year-olds).
European
journal
of
pediatrics,
175(3),
355–364.
Gezgin,
D.
M.,
&
C¸
akır,
Ö.
(2016).
Analysis
of
nomofobic
behaviors
of
ado-
lescents
regarding
various
factors.
Journal
of
Human
Sciences,
13(2),
2504–2519.
Gezgin,
D.
M.,
S¸
ahin,
Y.
L.,
&
Yildirim,
S.
(2017).
The
investigation
of
social
network
users’
nomophobia
levels
regarding
to
various
factors.
Journal
of
Educational
Technology
Theory
and
Practice,
7(1),
1–17.
Gilly,
M.
C.,
Celsi,
M.
W.,
&
Schau,
H.
J.
(2012).
It
don’t
come
easy:
Overcom-
ing
obstacles
to
technology
use
within
a
resistant
consumer
group.
Journal
of
Consumer
Affairs,
46(1),
62–89.
Grant,
J.
E.,
Potenza,
M.
N.,
Weinstein,
A.,
&
Gorelick,
D.
A.
(2010).
Introduc-
tion
to
behavioral
addictions.
The
American
Journal
of
Drug
and
Alcohol
Abuse,
36(5),
233–241.
Guo,
Q.,
Li,
Y.,
&
Yu,
S.
(2017).
In-law
and
mate
preferences
in
Chinese
soci-
ety
and
the
role
of
traditional
cultural
values.
Evolutionary
Psychology,
15(3),
1474704917730518.
Guxens,
M.,
Vermeulen,
R.,
van
Eijsden,
M.,
Beekhuizen,
J.,
Vrijkotte,
T.
G.,
van
Strien,
R.
T.,
&
Huss,
A.
(2016).
Outdoor
and
indoor
sources
of
residential
radiofrequency
electromagnetic
fields,
personal
cell
phone
and
cordless
phone
use,
and
cognitive
function
in
5–6
years
old
chil-
dren.
Environmental
Research,
150,
364–374.
Ha,
Y.
M.,
&
Hwang,
W.
J.
(2014).
Gender
differences
in
internet
addiction
associated
with
psychological
health
indicators
among
adolescents
using
a
national
web-based
survey.
International
Journal
of
Mental
Health
and
Addiction,
12(5),
660–669.
Hato,
B.
(2013).
(Compulsive)
mobile
phone
checking
behavior
out
of
a
fear
of
missing
out:
Development,
psychometric
properties
and
testretest
reli-
ability
of
a
C-FoMO-Scale
Master’s
Thesis.
Tilburg,
Netherlands:
Tilburg
University.
Haug,
S.,
Castro,
R.
P.,
Kwon,
M.,
Filler,
A.,
Kowatsch,
T.,
&
Schaub,
M.
P.
(2015).
Smartphone
use
and
smartphone
addiction
among
young
people
in
Switzerland.
Journal
of
behavioral
addictions,
4(4),
299–307.
Hong,
F.
Y.,
Chiu,
S.
I.,
&
Huang,
D.
H.
(2012).
A
model
of
the
relation-
ship
between
psychological
characteristics,
mobile
phone
addiction
and
use
of
mobile
phones
by
Taiwanese
university
female
students.
Computers
in
Human
Behavior,
28(6),
2152–2159.
Hsiao,
K.
L.,
Shu,
Y.,
&
Huang,
T.
C.
(2017).
Exploring
the
effect
of
com-
pulsive
social
app
usage
on
technostress
and
academic
performance:
Perspectives
from
personality
traits.
Telematics
and
Informatics,
34(2),
679–690.
Hwang,
Y.,
Choi,
I.,
Yum,
J.
Y.,
&
Jeong,
S.
H.
(2017).
Parental
mediation
regarding
children’s
smartphone
use:
Role
of
protection
motivation
and
parenting
style.
Cyberpsychology,
Behavior,
and
Social
Networking,
20(6),
362–368.
International
Telecommunications
Union
(2017).
ICT
Facts
and
Figures-
2017.
Retrieved
from
https://www.itu.int/en/ITU-D/Statistics/
Documents/facts/ICTFactsFigures2017.pdf.
Iskender,
M.,
&
Akin,
A.
(2010).
Social
self-efficacy,
academic
locus
of
con-
trol,
and
internet
addiction.
Computers
&
Education,
54(4),
1101–1106.
Jackson,
L.
A.,
Von
Eye,
A.,
Witt,
E.
A.,
Zhao,
Y.,
&
Fitzgerald,
H.
E.
(2011).
A
longitudinal
study
of
the
effects
of
Internet
use
and
videogame
playing
on
academic
performance
and
the
roles
of
gender,
race
and
income
in
these
relationships.
Computers
in
Human
Behavior,
27(1),
228–239.
Jacobsen,
W.
C.,
&
Forste,
R.
(2011).
The
wired
generation:
Academic
and
social
outcomes
of
electronic
media
use
among
university
students.
Cyberpsychology,
Behavior,
and
Social
Networking,
14(5),
275–280.
Jafarkarimi,
H.,
Sim,
A.
T.
H.,
Saadatdoost,
R.,
&
Hee,
J.
M.
(2016).
Facebook
addiction
among
Malaysian
students.
International
Journal
of
Informa-
tion
and
Education
Technology,
6(6),
465–469.
James,
D.,
&
Drennan,
J.
(2005).
Exploring
addictive
consumption
of
mobile
phone
technology.
In
Australian
and
New
Zealand
Marketing
Academy
conference.
December
Jan,
S.
R.,
Ullah,
F.,
Ali,
H.,
&
Khan,
F.
(2016).
Enhanced
and
effective
learning
through
mobile
learning
an
insight
into
students
perception
of
mobile
learning
at
university
level.
International
Journal
of
Scientific
Research
in
Science,
Engineering
and
Technology
(IJSRSET),
Print
ISSN,
2395–1990.
Jang,
M.
H.,
&
Ji,
E.
S.
(2012).
Gender
differences
in
associations
between
parental
problem
drinking
and
early
adolescents’
Internet
addiction.
Journal
for
Specialists
in
Pediatric
Nursing,
17(4),
288–300.
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.
Jeong,
S.
H.,
Kim,
H.,
Yum,
J.
Y.,
&
Hwang,
Y.
(2016).
What
type
of
content
are
smartphone
users
addicted
to?
SNS
vs.
games.
Computers
in
Human
Behavior,
54,
10–17.
Joo,
J.
(2013).
Exploring
relationships
among
Korean
Childrens
depression,
smartphone
addiction,
and
school
life
satisfaction:
Focusing
on
Par-
tial
Least
Square
(PLS)
path
modeling.
Journal
of
Digital
Convergence,
11(12),
49–60.
Judd,
T.
(2014).
Making
sense
of
multitasking:
The
role
of
Facebook.
Com-
puters
&
Education,
70,
194–202.
Kabali,
H.
K.,
Irigoyen,
M.
M.,
Nunez-Davis,
R.,
Budacki,
J.
G.,
Mohanty,
S.
H.,
Leister,
K.
P.,
&
Bonner,
R.
L.
(2015).
Exposure
and
use
of
mobile
media
devices
by
young
children.
Pediatrics,
136(6),
1044–1050.
Karasar,
N.
(2013).
Scientific
research
methodology.
Ankara:
Nobel
Publish-
ing.
Kardefelt-Winther,
D.
(2014).
A
conceptual
and
methodological
critique
of
internet
addiction
research:
Towards
a
model
of
compensatory
internet
use.
Computers
in
Human
Behavior,
31,
351–354.
Karpinski,
A.
C.,
Kirschner,
P.
A.,
Ozer,
I.,
Mellott,
J.
A.,
&
Ochwo,
P.
(2013).
An
exploration
of
social
networking
site
use,
multitasking,
and
academic
performance
among
United
States
and
European
university
students.
Computers
in
Human
Behavior,
29(3),
1182–1192.
Kesten,
J.
M.,
Sebire,
S.
J.,
Turner,
K.
M.,
Stewart-Brown,
S.,
Bentley,
G.,
&
Jago,
R.
(2015).
Associations
between
rule-based
parenting
practices
and
child
screen
viewing:
A
cross-sectional
study.
Preventive
medicine
reports,
2,
84–89.
Kim,
H.
(2013).
Exercise
rehabilitation
for
smartphone
addiction.
Journal
of
Exercise
Rehabilitation,
9(6),
500–505.
Kim,
Y.,
Jeong,
J.
E.,
Cho,
H.,
Jung,
D.
J.,
Kwak,
M.,
Rho,
M.
J.,
&
Choi,
I.
Y.
(2016).
Personality
factors
predicting
smartphone
addiction
predis-
position:
Behavioral
inhibition
and
activation
systems,
impulsivity,
and
self-control.
PloS
One,
11(8),
e0159788.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
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H.
Yildiz
Durak
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Science
Journal
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(2017)
xxx–xxx
25
King,
A.
L.
S.,
Valenc¸
a,
A.
M.,
&
Nardi,
A.
E.
(2010).
Nomophobia:
The
mobile
phone
in
panic
disorder
with
agoraphobia:
Reducing
pho-
bias
or
worsening
of
dependence?
Cognitive
and
Behavioral
neurology,
23(1),
52–54.
King,
A.
L.
S.,
Valenc¸
a,
A.
M.,
Silva,
A.
C.,
Sancassiani,
F.,
Machado,
S.,
&
Nardi,
A.
E.
(2014).
Nomophobia:
Impact
of
cell
phone
use
interfer-
ing
with
symptoms
and
emotions
of
individuals
with
panic
disorder
compared
with
a
control
group.
Clinical
practice
and
epidemiology
in
mental
health:
CP
&
EMH,
10,
28–35.
Kiraly,
O.,
Griffiths,
M.
D.,
Urbán,
R.,
Farkas,
J.,
Kökönyei,
G.,
Elekes,
Z.,
&
Demetrovics,
Z.
(2014).
Problematic
internet
use
and
problematic
online
gaming
are
not
the
same:
Findings
from
a
large
nationally
rep-
resentative
adolescent
sample.
Cyberpsychology,
Behavior,
and
Social
Networking,
17(12),
749–754.
Kuss,
D.
J.,
&
Griffiths,
M.
D.
(2017).
Social
networking
sites
and
addiction:
Ten
lessons
learned.
International
Journal
of
Environmental
Research
and
Public
Health,
14(3)
http://dx.doi.org/10.3390/ijerph14030311
Kuss,
D.
J.,
Griffiths,
M.
D.,
Karila,
L.,
&
Billieux,
J.
(2014).
Internet
addiction:
A
systematic
review
of
epidemiological
research
for
the
last
decade.
Current
Pharmaceutical
Design,
20(25),
4026–4052.
Kwon,
M.,
Kim,
D.
J.,
Cho,
H.,
&
Yang,
S.
(2013).
The
smartphone
addiction
scale:
Development
and
validation
of
a
short
version
for
adolescents.
PloS
One,
8(12)
http://dx.doi.org/10.1371/journal.pone.0083558
Kwon,
M.,
Lee,
J.-Y.,
Won,
W.-Y.,
Park,
J.-W.,
Min,
J.-A.,
Hahn,
C.,
et
al.
(2013).
Development
and
validation
of
a
smartphone
addiction
scale
(SAS).
PLoS
One,
8(2),
e56936.
http://dx.doi.org/
10.1371/journal.pone.0056936
LaRose,
R.
(2010).
The
problem
of
media
habits.
Communication
Theory,
20(2),
194–222.
LaRose,
R.,
Lin,
C.
A.,
&
Eastin,
M.
S.
(2003).
Unregulated
internet
usage:
Addiction,
habit,
or
deficient
self-regulation?
Media
Psychology,
5(3),
225–253.
Lachmann,
B.,
Sindermann,
C.,
Sariyska,
R.
Y.,
Luo,
R.,
Melchers,
M.
C.,
Becker,
B.,
&
Montag,
C.
(2018).
The
role
of
empathy
and
life
satisfac-
tion
in
internet
and
smartphone
use
disorder.
Frontiers
in
Psychology,
9,
398.
Lanaj,
K.,
Johnson,
R.
E.,
&
Barnes,
C.
M.
(2014).
Beginning
the
workday
yet
already
depleted?
Consequences
of
late-night
smartphone
use
and
sleep.
Organizational
Behavior
and
Human
Decision
Processes,
124(1),
11–23.
Lauricella,
A.
R.,
Wartella,
E.,
&
Rideout,
V.
J.
(2015).
Young
children’s
screen
time:
The
complex
role
of
parent
and
child
factors.
Journal
of
Applied
Developmental
Psychology,
36,
11–17.
Lee,
H.,
Ahn,
H.,
Choi,
S.,
&
Choi,
W.
(2014).
The
SAMS:
Smartphone
addic-
tion
management
system
and
verification.
Journal
of
Medical
Systems,
38(1)
http://dx.doi.org/10.1007/s10916-013-0001-1
Lee,
Y.
K.,
Chang,
C.
T.,
Lin,
Y.,
&
Cheng,
Z.
H.
(2014).
The
dark
side
of
smartphone
usage:
Psychological
traits,
compulsive
behavior
and
technostress.
Computers
in
Human
Behavior,
31,
373–383.
Lee,
J.,
&
Cho,
B.
(2015).
Effects
of
self-control
and
school
aAdjustment
on
smartphone
addiction
among
elementary
school
students.
Inter-
national
Journal
of
Contents,
11(3),
1–6.
Lee,
Y.
H.,
Ko,
C.
H.,
&
Chou,
C.
(2015).
Re-visiting
internet
addiction
among
Taiwanese
students:
A
cross-sectional
comparison
of
students’
expectations,
online
gaming,
and
online
social
interaction.
Journal
of
Abnormal
Child
Psychology,
43(3),
589–599.
Lee,
S.,
Lee,
K.,
Yi,
S.
H.,
Park,
H.
J.,
Hong,
Y.
J.,
&
Cho,
H.
(2016).
Effects
of
parental
psychological
control
on
child’s
school
life:
Mobile
phone
dependency
as
mediator.
Journal
of
Child
and
Family
Studies,
25(2),
407–418.
Lepp,
A.,
Barkley,
J.
E.,
&
Karpinski,
A.
C.
(2014).
The
relationship
between
cell
phone
use,
academic
performance,
anxiety,
and
satisfaction
with
life
in
college
students.
Computers
in
Human
Behavior,
31,
343–350.
Lepp,
A.,
Li,
J.,
Barkley,
J.
E.,
&
Salehi-Esfahani,
S.
(2015).
Exploring
the
relationships
between
college
students’
cell
phone
use,
personality
and
leisure.
Computers
in
Human
Behavior,
43,
210–219.
Lim,
M.
H.,
&
Kim,
S.
H.
(2014).
A
study
on
the
smart
phone
and
tablet
PC
utilization,
parental
perception,
and
addictive
tendencies
of
young
children.
Korean
Journal
of
Child
Education
and
Care,
14(4),
237–260.
Lin,
Y.
H.,
Lin,
Y.
C.,
Lee,
Y.
H.,
Lin,
P.
H.,
Lin,
S.
H.,
Chang,
L.
R.,
et
al.
(2015).
Time
distortion
associated
with
smartphone
addiction:
Iden-
tifying
smartphone
addiction
via
a
mobile
application
(App).
Journal
of
Psychiatric
Research,
65,
139–145.
Livingstone,
S.,
Haddon,
L.,
Görzig,
A.,
&
Ólafsson,
K.
(2011).
Risks
and
safety
on
the
internet:
The
perspective
of
European
children
Full
Findings.
LSE,
London:
EU
Kids
Online.
Long,
E.
C.,
Verhulst,
B.,
Neale,
M.
C.,
Lind,
P.
A.,
Hickie,
I.
B.,
Martin,
N.
G.,
&
Gillespie,
N.
A.
(2016).
The
genetic
and
environmental
contributions
to
internet
use
and
associations
with
psychopathology:
A
twin
study.
Twin
Research
and
Human
Genetics,
19(1),
1–9.
McCloskey,
M.,
Johnson,
S.
L.,
Benz,
C.,
Thompson,
D.
A.,
Chamberlin,
B.,
Clark,
L.,
&
Bellows,
L.
L.
(2018).
Parent
perceptions
of
mobile
device
use
among
preschool-aged
children
in
rural
head
start
centers.
Journal
of
Nutrition
Education
and
Behavior,
50(1),
83–89.
Mei,
S.,
Yau,
Y.
H.,
Chai,
J.,
Guo,
J.,
&
Potenza,
M.
N.
(2016).
Problematic
Internet
use,
well-being,
self-esteem
and
self-control:
Data
from
a
high-school
survey
in
China.
Addictive
Behaviors,
61,
74–79.
Mendoza,
J.
S.,
Pody,
B.
C.,
Lee,
S.,
Kim,
M.,
&
McDonough,
I.
M.
(2018).
The
effect
of
cellphones
on
attention
and
learning:
The
influences
of
time,
distraction,
and
nomophobia.
Computers
in
Human
Behavior,
86,
52–60.
Merlo,
L.
J.,
Stone,
A.
M.,
&
Bibbey,
A.
(2013).
Measuring
problematic
mobile
phone
use:
Development
and
preliminary
psychometric
properties
of
the
PUMP
scale.
Journal
of
Addiction,
144,
1184–1188.
Montag,
C.,
Blaskiewicz,
K.,
Lachmann,
B.,
Sariyska,
R.,
Andone,
I.,
Trendafilov,
B.,
&
Markowetz,
A.
(2015).
Recorded
behavior
as
a
valuable
resource
for
psychodiagnostics
in
mobile
phone
addiction:
evidence
from
psychoinformatics.
Behavioral
Sciences,
5,
434–442.
Montag,
C.,
Błaszkiewicz,
K.,
Sariyska,
R.,
Lachmann,
B.,
Andone,
I.,
Trendafilov,
B.,
.
.
.&
Markowetz,
A.
(2015).
Smartphone
usage
in
the
21st
century:
Who
is
active
on
WhatsApp?
BMC
Research
Notes,
8(1),
331.
Montag,
C.,
&
Walla,
P.
(2016).
Carpe
diem
instead
of
losing
your
social
mind:
Beyond
digital
addiction
and
why
we
all
suffer
from
digital
overuse.
Cogent
Psychology,
3(1),
1157281.
Montag,
C.,
Duke,
É
&
Markowetz,
A.
(2016).
Towards
Psychoinformatics:
Computer
Science
meets
Psychology.
Computational
and
Mathemat-
ical
Methods
in
Medicine,
Volume
2016,
Article
ID
2983685.
Na,
Y.
S.
(2013).
The
effects
of
the
parents’
child-rearing
attitude
on
the
smartphone
addiction
of
small
children.
Journal
of
Early
Childhood
Education
&
Educare
Welfare,
17(3),
32–53.
Nakhaie,
M.
R.,
Silverman,
R.
A.,
&
LaGrange,
T.
C.
(2000).
Self-control
and
social
control:
An
examination
of
gender,
ethnicity,
class
and
delin-
quency.
Canadian
Journal
of
Sociology/Cahiers
Canadiens
de
Sociologie,
35–59.
Nazri,
S.
M.,
&
Latiff,
Z.
A.
(2013).
Social
evolution
in
human
relations
behavior
of
utilizing
Facebook.
ISICO,
2013,
2013.
Oulasvirta,
A.,
Rattenbury,
T.,
Ma,
L.,
&
Raita,
E.
(2012).
Habits
make
smart-
phone
use
more
pervasive.
Personal
and
Ubiquitous
Computing,
16(1),
105–114.
Park,
B.
W.,
&
Lee,
K.
C.
(2011).
The
effect
of
users’
characteristics
and
experiential
factors
on
the
compulsive
usage
of
the
smartphone.
In
International
Conference
on
Ubiquitous
Computing
and
Multimedia
Applications
(pp.
438–446).
April.
Park,
W.
K.
(2005).
Mobile
phone
addiction.
In
Mobile
communications.
pp.
253–272.
London:
Springer.
Patton,
J.
H.,
Stanford,
M.
S.,
&
Barratt,
E.
S.
(1995).
Factor
structure
of
the
Barratt
Impulsiveness
Scale.
Journal
of
Clinical
Psychology,
51(6),
768–774.
Pavia,
L.,
Cavani,
P.,
Di
Blasi,
M.,
&
Giordano,
C.
(2016).
Smartphone
Addiction
Inventory
(SPAI):
Psychometric
properties
and
confirma-
tory
factor
analysis.
Computers
in
Human
Behavior,
63,
170–178.
Pea,
R.,
Nass,
C.,
Meheula,
L.,
Rance,
M.,
Kumar,
A.,
Bamford,
H.,
&
Zhou,
M.
(2012).
Media
use,
face-to-face
communication,
media
multitasking,
and
social
well-being
among
8-
to
12-year-old
girls.
Developmental
Psychology,
48(2),
327–336.
Perry,
S.
D.,
&
Lee,
K.
C.
(2007).
Mobile
phone
text
messaging
overuse
among
developing
world
university
students.
Communicatio,
33(2),
63–79.
Piwek,
L.,
&
Ellis,
D.
A.
(2016).
Can
programming
frameworks
bring
smart-
phones
into
the
mainstream
of
psychological
science?
Frontiers
in
Psychology,
7,
1252.
http://dx.doi.org/10.3389/fpsyg.2016.01252
Przybylski,
A.
K.,
Murayama,
K.,
DeHaan,
C.
R.,
&
Gladwell,
V.
(2013).
Moti-
vational,
emotional,
and
behavioral
correlates
of
fear
of
missing
out.
Computers
in
Human
Behavior,
29(4),
1841–1848.
Raman,
S.,
Guerrero-Duby,
S.,
McCullough,
J.
L.,
Brown,
M.,
Ostrowski-
Delahanty,
S.,
Langkamp,
D.,
&
Duby,
J.
C.
(2017).
Screen
exposure
during
daily
routines
and
a
young
child’s
risk
for
having
social-
emotional
delay.
Clinical
Pediatrics,
56(13),
1244–1253.
Rashid,
T.,
&
Asghar,
H.
M.
(2016).
Technology
use,
self-directed
learn-
ing:
student
engagement
and
academic
performance:
Examining
the
interrelations.
Computers
in
Human
Behavior,
63,
604–612.
Robinson,
T.
E.,
&
Berridge,
K.
C.
(2001).
Incentive-sensitization
and
addic-
tion.
Addiction,
96(1),
103–114.
Robinson,
T.
E.,
&
Berridge,
K.
C.
(2003).
Addiction.
Annual
Review
of
Psy-
chology,
54,
25–53.

Please
cite
this
article
in
press
as:
Yildiz
Durak,
H.
Investigation
of
nomophobia
and
smartphone
addiction
predictors
among
adolescents
in
Turkey:
Demographic
variables
and
academic
performance.
The
Social
Science
Journal
(2017),
https://doi.org/10.1016/j.soscij.2018.09.003
ARTICLE IN PRESS
G Model
SOCSCI-1518;
No.
of
Pages
26
26
H.
Yildiz
Durak
/
The
Social
Science
Journal
xxx
(2017)
xxx–xxx
Rosen,
L.
D.,
Carrier,
L.
M.,
&
Cheever,
N.
A.
(2013).
Facebook
and
tex-
ting
made
me
do
it:
Media-induced
task-switching
while
studying.
Computers
in
Human
Behavior,
29(3),
948–958.
Ryan,
R.
M.,
&
Deci,
E.
L.
(2000).
Intrinsic
and
extrinsic
motivations:
Classic
definitions
and
new
directions.
Contemporary
Educational
Psychology,
25(1),
54–67.
Salmela-Aro,
K.,
Upadyaya,
K.,
Hakkarainen,
K.,
Lonka,
K.,
&
Alho,
K.
(2017).
The
dark
side
of
internet
use:
Two
longitudinal
studies
of
excessive
internet
use,
depressive
symptoms,
school
burnout
and
engagement
among
Finnish
early
and
late
adolescents.
Journal
of
Youth
and
Adoles-
cence,
46(2),
343–357.
Samaha,
M.,
&
Hawi,
N.
S.
(2016).
Relationships
among
smartphone
addiction,
stress,
academic
performance,
and
satisfaction
with
life.
Computers
in
Human
Behavior,
57,
321–325.
Seo,
M.,
&
Choi,
E.
(2018).
Classes
of
trajectory
in
mobile
phone
depen-
dency
and
the
effects
of
negative
parenting
on
them
during
early
adolescence.
School
Psychology
International,
39(2),
156–169.
Shan,
Z.,
Deng,
G.,
Li,
J.,
Li,
Y.,
Zhang,
Y.,
&
Zhao,
Q.
(2013).
Correlational
anal-
ysis
of
neck/shoulder
pain
and
low
back
pain
with
the
use
of
digital
products,
physical
activity
and
psychological
status
among
adoles-
cents
in
Shanghai.
Plos
One,
8(10).
Sharabi,
A.,
Levi,
U.,
&
Margalit,
M.
(2012).
Children’s
loneliness,
sense
of
coherence,
family
climate,
and
hope:
Developmental
risk
and
protec-
tive
factors.
The
Journal
of
Psychology,
146(1-2),
61–83.
Sharma,
N.,
Sharma,
P.,
Sharma,
N.,
&
Wavare,
R.
R.
(2017).
Rising
concern
of
nomophobia
amongst
Indian
medical
students.
International
Journal
of
Research
in
Medical
Sciences,
3(3),
705–707.
Shell,
D.
F.,
Newman,
I.
M.,
&
Xiaoyi,
F.
(2010).
The
influence
of
cul-
tural
orientation,
alcohol
expectancies
and
self-efficacy
on
adolescent
drinking
behavior
in
Beijing.
Addiction,
105(9),
1608–1615.
Skinner,
E.
A.,
&
Pitzer,
J.
R.
(2012).
Developmental
dynamics
of
student
engagement,
coping,
and
everyday
resilience.
In
Handbook
of
research
on
student
engagement.
pp.
21–44.
Boston,
MA:
Springer.
Statista
(2017a).
Share
of
internet
users
in
the
United
States
who
use
WhatsApp
as
of
August
2017,
by
gender.
Retrieved
from
https://www.statista.com/statistics/740654/whatsapp-app-chat-
usage-usa/.
Statista
(2017b).
Statistics
and
market
data
on
mobile
internet
&
apps.
Retrieved
from
https://www.statista.com/markets/424/topic/
538/mobile-internet-apps/.
Stead,
H.,
&
Bibby,
P.
A.
(2017).
Personality:
Fear
of
missing
out
and
prob-
lematic
internet
use
and
their
relationship
to
subjective
well-being.
Computers
in
Human
Behavior,
76,
534–540.
Thomée,
S.,
Härenstam,
A.,
&
Hagberg,
M.
(2011).
Mobile
phone
use
and
stress,
sleep
disturbances,
and
symptoms
of
depression
among
young
adults-a
prospective
cohort
study.
BMC
public
health,
11(1),
66.
http://dx.doi.org/10.1186/1471-2458-11-66
Toda,
M.,
Monden,
K.,
Kubo,
K.,
&
Morimoto,
K.
(2006).
Mobile
phone
dependence
and
health-related
lifestyle
of
university
students.
Social
Behavior
and
Personality:
An
International
Journal,
34(10),
1277–1284.
Tsai,
C.,
&
Lin,
S.
J.
(2001).
Analysis
of
attitudes
toward
computer
networks
and
Internet
addiction
of
Taiwanese
adolescents.
CyberPsychology
&
Behavior,
4(3),
373–376.
Turkle,
S.
(2012),
The
flight
from
conversation,
New
York
Times,
SR1,
Retrieved
from
www.nytimes.com/2012/04/22/opinion/sunday/
the-flight-from-conversation.html?pagewanted=all&
r=0.
TURKSTAT
(Turkish
Statistical
Institute)
(2017).
Information
and
com-
munication
technology
(ICT)
usage
in
households
and
by
individuals.
Retrieved
from
http://www.tuik.gov.tr/PreTablo.do?alt
id=1028.
Turel,
O.,
Serenko,
A.,
&
Giles,
P.
(2011).
Integrating
technology
addic-
tion
and
use:
An
empirical
investigation
of
online
auction
users.
Mis
Quarterly,
35(4),
1043–1062.
Van
Deursen,
A.
J.,
Bolle,
C.
L.,
Hegner,
S.
M.,
&
Kommers,
P.
A.
(2015).
Modeling
habitual
and
addictive
smartphone
behavior:
The
role
of
smartphone
usage
types,
emotional
intelligence,
social
stress,
self-
regulation,
age
and
gender.
Computers
in
human
behavior,
45,
411–420.
Van
den
Eijnden,
R.
J.,
Meerkerk,
G.
J.,
Vermulst,
A.
A.,
Spijkerman,
R.,
&
Engels,
R.
C.
(2008).
Online
communication,
compulsive
Internet
use,
and
psychosocial
well-being
among
adolescents:
A
longitudinal
study.
Developmental
Psychology,
44(3),
655–665.
Walsh,
S.
P.,
&
White,
K.
M.
(2007).
Me,
my
mobile,
and
I:
The
role
of
self-
and
prototypical
identity
influences
in
the
prediction
of
mobile
phone
behavior.
Journal
of
Applied
Social
Psychology,
37(10),
2405–2434.
Walsh,
S.
P.,
White,
K.
M.,
&
Young,
R.
M.
(2008).
Over-connected?
A
qual-
itative
exploration
of
the
relationship
between
Australian
youth
and
their
mobile
phones.
Journal
of
Adolescence,
31(1),
77–92.
Walsh,
S.
P.,
White,
K.
M.,
&
Young,
R.
M.
(2010).
Needing
to
connect:
The
effect
of
self
and
others
on
young
people’s
involvement
with
their
mobile
phones.
Australian
Journal
of
Psychology,
62(4),
194–203.
Wang,
C.
W.,
Ho,
R.
T.,
Chan,
C.
L.,
&
Tse,
S.
(2015).
Exploring
personality
characteristics
of
Chinese
adolescents
with
internet-related
addictive
behaviors:
Trait
differences
for
gaming
addiction
and
social
network-
ing
addiction.
Addictive
Behaviors,
42,
32–35.
Wang,
L.,
Tao,
T.,
Fan,
C.,
Gao,
W.,
&
Wei,
C.
(2017).
The
association
between
Internet
addiction
and
both
impulsivity
and
effortful
control
and
its
variation
with
age.
Addiction
Research
&
Theory,
25(1),
83–90.
Wei,
R.,
&
Lo,
V.
H.
(2006).
Staying
connected
while
on
the
move:
Cell
phone
use
and
social
connectedness.
New
Media
&
Society,
8(1),
53–72.
Wentworth,
D.
K.,
&
Middleton,
J.
H.
(2014).
Technology
use
and
academic
performance.
Computers
&
Education,
78,
306–311.
Whang,
L.
S.,
Lee,
S.,
&
Chang,
G.
(2003).
Internet
over-users’
psycho-
logical
profiles:
A
behavior
sampling
analysis
on
Internet
addiction.
CyberPsychology
&
Behavior,
6,
143–150.
Wise,
R.
A.,
&
Koob,
G.
F.
(2014).
The
development
and
maintenance
of
drug
addiction.
Neuropsychopharmacology,
39(2),
254–262.
Wolniewicz,
C.
A.,
Tiamiyu,
M.
F.,
Weeks,
J.
W.,
&
Elhai,
J.
D.
(2018).
Prob-
lematic
smartphone
use
and
relations
with
negative
affect,
fear
of
missing
out,
and
fear
of
negative
and
positive
evaluation.
Psychiatry
Research,
262,
618–623.
Wu,
A.
M.,
Cheung,
V.
I.,
Ku,
L.,
&
Hung,
E.
P.
(2013).
Psychological
risk
factors
of
addiction
to
social
networking
sites
among
Chinese
smart-
phone
users.
Journal
of
Behavioral
Addictions,
2(3),
160–166.
Xu,
C.,
Ryan,
S.,
Prybutok,
V.,
&
Wen,
C.
(2012).
It
is
not
for
fun:
An
exami-
nation
of
social
network
site
usage.
Information
&
Management,
49(5),
210–217.
Yan,
W.,
Li,
Y.,
&
Sui,
N.
(2014).
The
relationship
between
recent
stress-
ful
life
events,
personality
traits,
perceived
family
functioning
and
internet
addiction
among
college
students.
Stress
and
Health,
30(1),
3–11.
Yarkoni,
T.
(2012).
Psychoinformatics:
New
horizons
at
the
interface
of
the
psychological
and
computing
sciences.
Current
Directions
in
Psy-
chological
Science,
21(6),
391–397.
Yen,
J.
Y.,
Yen,
C.
F.,
Chen,
C.
S.,
Wang,
P.
W.,
Chang,
Y.
H.,
&
Ko,
C.
H.
(2012).
Social
anxiety
in
online
and
real-life
interaction
and
their
associated
factors.
Cyberpsychology,
Behavior,
and
Social
Networking,
15(1),
7–12.
Yildirim,
C.
(2014).
Exploring
the
dimensions
of
nomophobia:
Developing
and
validating
a
questionnaire
using
mixed
methods
research.
Published
Theses.
IOWA
university.
Yildirim,
C.,
&
Correia,
A.
P.
(2015).
Exploring
the
dimensions
of
nomo-
phobia:
Development
and
validation
of
a
self-reported
questionnaire.
Computers
in
Human
Behavior,
49,
130–137.
Yildirim,
C.,
Sumuer,
E.,
Adnan,
M.,
&
Yildirim,
S.
(2015).
A
growing
fear:
Prevalence
of
nomophobia
among
Turkish
college
students.
Informa-
tion
Development,
32(5),
1322–1331.
Yildiz,
H.,
&
Seferoglu,
S.
S.
(2014).
An
examination
of
primary
school
stu-
dents’
opinions
about
their
digital
divide
levels
in
terms
of
different
variables.
Hacettepe
Unıversity
Journal
of
Education,
29(3),
220–235.
Yildiz-Durak,
H.
(2018a).
What
would
you
do
without
your
smartphone?
adolescents’
social
media
usage,
locus
of
control,
and
loneliness
as
a
predictor
of
nomophobia.
Addicta:
The
Turkish
Journal
on
Addictions,
5(2),
151–163.
Yildiz-Durak,
H.
(2018b).
Modeling
of
variables
related
to
problematic
internet
usage
and
problematic
social
media
usage
in
adolescents.
Current
Psychology,
http://dx.doi.org/10.1007/s12144-018-9840-8
Yildiz-Durak,
H.,
&
Sefero˘
glu,
S.
S.
(2018).
Factors
associated
wıth
smart-
phone
use
and
addiction
among
middle
school
students.
Journal
of
Educational
Technology
Theory
and
Practice,
8(1),
1–18.
Yim,
K.
S.,
Kim,
S.
H.,
&
Hong,
H.
K.
(2014).
A
study
of
correlation
among
young
children’s
smart-phone
addiction,
self-respect,
and
self-
regulation.
Journal
of
Future
Early
Childhood
Education,
21(4),
203–221.
Yogesh,
S.,
Abha,
S.,
&
Priyanka,
S.
(2014).
Short
Communication
Mobile
usage
and
sleep
patterns
among
medical
students.
Indian
Journal
of
Physiology
and
Pharmacology,
58(1),
100–103.
Young,
K.
S.
(1998).
Internet
addiction:
The
emergence
of
a
new
clinical
disorder.
Cyberpsychology
&
behavior,
1(3),
237–244.
Zhang,
Q.,
Wang,
Y.,
Yuan,
C.,
Zhang,
X.,
&
Li,
Y.
(2014).
The
gender
effect
on
the
relationship
between
internet
addiction
and
emotional
and
behavioral
problems
in
adolescents.
Chinese
Journal
of
Clinical
Psychol-
ogy,
22(6),
1004–1009.
Zhang,
Y.,
Qin,
X.,
&
Ren,
P.
(2018).
Adolescents’
academic
engagement
mediates
the
association
between
Internet
addiction
and
academic
achievement:
The
moderating
effect
of
classroom
achievement
norm.
Computers
in
Human
Behavior,
89,
299–307.
Zheng,
P.,
&
Lionel,
N.
(2010).
Smart
phone
and
next
generation
mobile
computing.
San
Francisco,
CA:
Morgan
Kaufmann.
Zhitomirsky-Geffet,
M.,
&
Blau,
M.
(2016).
Cross-generational
analysis
of
predictive
factors
of
addictive
behavior
in
smartphone
usage.
Com-
puters
in
Human
Behavior,
64,
682–693.