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Behaviour & Information Technology
ISSN: 0144-929X (Print) 1362-3001 (Online) Journal homepage: http://www.tandfonline.com/loi/tbit20
Identifying commonalities and differences in
personality characteristics of Internet and social
media addiction profiles: traits, self-esteem, and
self-construal
Nazir Hawi & Maya Samaha
To cite this article: Nazir Hawi & Maya Samaha (2018): Identifying commonalities and differences
in personality characteristics of Internet and social media addiction profiles: traits, self-esteem, and
self-construal, Behaviour & Information Technology, DOI: 10.1080/0144929X.2018.1515984
To link to this article: https://doi.org/10.1080/0144929X.2018.1515984
Published online: 30 Aug 2018.
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Identifying commonalities and differences in personality characteristics of
Internet and social media addiction profiles: traits, self-esteem, and self-construal
Nazir Hawi and Maya Samaha
Computer Science Department, Notre Dame University-Louaize, Zouk Mosbeh, P.O. Box: 72, Zouk Mikael, Lebanon
ABSTRACT
Excessive use of the Internet and social media has been associated with behavioural addiction,
which sparked the researchers’interest in gaining a better understanding of this global
phenomenon. The aim of this study was to fill a gap in knowledge by using just one sample to
identify similarities and differences in relationships between technology addictions and
personality characteristics, especially traits, self-esteem, and self-construal. The sample consisted
of 512 undergraduate students. The results showed that Internet addiction and social media
addiction shared many more similarities than differences. Agreeableness, conscientiousness,
openness to experiences, emotional stability, self-esteem, the frequency of checking account,
and Internet usage were predictors of both Internet addiction and social media addiction. Age,
satisfaction with life, and interdependent self-construal did not predict Internet addiction or
social media addiction, whereas real self and extraversion predicted Internet addiction only, and
gender, posting updates, a number of friends, and independent self-construal predicted social
media addiction only. These results provide some basis for an understanding of Internet and
social media addiction profiles.
ARTICLE HISTORY
Received 28 February 2017
Accepted 17 August 2018
KEYWORDS
Internet addiction; social
media addiction; personality
traits; self-esteem;
satisfaction with life
1. Introduction
Internet use is becoming an integral part of our lives with
approximately 9 out of 10 American adults using the
Internet in 2018 (Pew Research Center 2018). For
instance, Facebook use is a common daily activity with
over 2.13 billion monthly active users as of December
2017. Facebook, which is a social networking site, is
only one of many social media applications that are
used excessively, especially with mobile devices. Kuss
and Griffiths (2017) stated that ‘Social media refers to
the web 2.0 capabilities of producing, sharing, and colla-
borating on content online (i.e. user-generated content,
implying a social element)’. Accordingly, social media
includes Facebook, Instagram, Twitter, Snapchat, You-
Tube, Blogging sites, NewsFeed, Wikis, social gaming
platforms and many others. However, excessive use of
technology has become problematic among some users
who exhibit signs of behavioural addictions, such as sal-
ience, mood modification, withdrawal, tolerance and
conflict (Griffiths 1995; Kuss et al. 2014). In addition,
studies have reported that different technology addic-
tions are positively associated with each other as they
have common underlying risk factors (Salehan and
Negahban 2013; Andreassen et al. 2016), but they also
have their differences (Király et al. 2014; Wang et al.
2015). To start, studies on the use of personality traits
in profiling different technology addiction behaviours
have yielded interesting results (Armstrong, Phillips,
and Saling 2000; Cole and Hooley 2013; Randler, Hor-
zum, and Vollmer 2013; Wang et al. 2015). While
some studies showed that extraversion was negatively
associated with Internet use (Van Der Aa et al. 2009;
Wilson, Fornasier, and White 2010), other studies
showed that extraversion was positively correlated with
Facebook use (Wilson, Fornasier, and White 2010;
Moore and Mcelroy 2012; Seidman 2013).
In addition, agreeableness, conscientiousness, open-
ness to experience and emotional stability were nega-
tively correlated with problematic Internet use (Wilson,
Fornasier, and White 2010; Ko et al. 2012; Dong et al.
2013; Kuss, Griffiths, and Binder 2013; Müller et al.
2013) and with Social Media Addiction (SMA) (Błachnio
and Przepiorka 2016;Tang et al. 2016). In a study of 218
students from the University of Bergen, openness to
experience did not correlate with Internet Addiction
(IA) but did correlate with Facebook addiction
(Andreassen et al. 2013).
Several studies showed a negative relationship
between self-esteem on the one hand and IA (Bozo-
glan, Demirer, and Sahin 2013; Krasnova et al. 2013;
© 2018 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Maya Samaha msamaha@ndu.edu.lb Computer Science Department, Notre Dame University-Louaize, Zouk Mosbeh, P.O. Box: 72, Zouk Mikael,
Lebanon
BEHAVIOUR & INFORMATION TECHNOLOGY
https://doi.org/10.1080/0144929X.2018.1515984
Sariyska et al. 2014) and SMA (Steinfield, Ellison, and
Lampe 2008; Gonzales and Hancock 2011; Denti et al.
2012) on the other hand. The term self-construal was
coined by Markus and Kitayama (1991). It refers to
how individuals perceive themselves and how they
see themselves in relation to others. A distinction was
drawn between people from the Western world
(USA, Europe) who construe the self as independent
from others (independent self-construal), meaning
they evaluate the self, not in the context of others,
such as family members, co-workers or colleagues,
and people from Asia (in particular, Japan), Africa
and Central and South America who construe the self
in connection with others (interdependent self-con-
strual) (Triandis 1989; Markus and Kitayama 1991;
Singelis 1994). Studies that investigate self-construal
with Internet and social media addictions are rare.
One study showed that independent self-construal
was associated with social media use (Kim, Kim, and
Nam 2010).
Additional studies have examined associations
between technology addictions and stress, anxiety
(Hawi and Samaha 2017a), depression, and psychologi-
cal well-being, which all had negative impacts on satis-
faction with life (Lepp, Barkley, and Karpinski 2014;
Kabasakal 2015; Hawi and Samaha 2016; Samaha and
Hawi 2016). Problematic Internet use among Chinese
adolescents was negatively related to satisfaction with
life (Cao et al. 2011) and it negatively predicted subjec-
tive happiness (Akın2012), while IA negatively predicted
life satisfaction (Bozoglan, Demirer, and Sahin 2013).
Facebook addiction was negatively associated with life
satisfaction (Satici and Uysal 2015;Błachnio, Przepiorka,
and Rudnicka 2016b).
Studies have shown gender to be a predictor of social
media use. Specifically, women were more likely to be
addicted to social media use and texting, and men
were more likely to be addicted to video gaming (Van
Deursen et al. 2015; Wang et al. 2015; Wittek et al.
2015; Andreassen, Pallesen, and Griffiths 2017). The
gender differences reported in these studies support
the suggestion to replace the concept, Internet addic-
tion, with descriptions of specific online activities (Star-
cevic and Aboujaoude 2016; Van Rooij et al. 2017). The
number of weekday and weekend hours spent online
was significantly higher for Internet addicts compared
to other technology addicts (Kuss, Griffiths, and Binder
2013). Most of the studies that examined personality
characteristics in relation to Internet addiction did
not examine social media. That is, Internet addiction
and social media addiction were not investigated within
the same sample of participants. The aim of our study
is to investigate the relationships between personality
characteristics and both Internet addiction and social
media addiction using the same sample of participants
to identify similarities and differences between the two
addictions. Achieving this aim sheds light on the con-
troversial issue of whether IA and SMA are separate
concepts or IA is a unifying construct for technology
addictions like SMA. Thus far, some researchers cate-
gorised technology addictions such as social media
addiction and video games addiction as subtypes of
Internet addiction (Young et al. 1999). In 1999,
Young et al. subtyped the Internet into cyber-sexual
addiction (including pornography), cyber-relationship
addiction (virtual relationships), net compulsions (gam-
bling, shopping, trading), information overload
(surfing), and computer addiction (gaming). Since
then, a stream of studies emerged reminding the
research community of these classifications (Chou,
Condron, and Belland 2005; Shaw and Black 2008;
Kuss and Griffiths 2014), suggesting other classifi-
cations (Weinstein and Lejoyeux 2010), and consider-
ing Internet addiction as overarching other
technology addictions (Griffiths, Kuss, and Demetrovics
2014;Błachnio, Przepiórka, and Pantic 2015). Regard-
ing classifications, researchers have suggested that
Internet addiction has a minimum of three subtypes
but yet only listed excessive gaming, sexual preoccupa-
tions, and e-mail/text messaging (Block 2008; Wein-
stein and Lejoyeux 2010). Other researchers have
suggested exactly four subtypes, which were infor-
mation burden, compulsions, cybersex habit, and
cyber-relationship compulsion (Nandhini and Krishna-
veni 2016). Other subtypes of Internet addiction found
in the literature are instant messaging addiction (Cao
et al. 2007), social networking addiction (Błachnio,
Przepiórka, and Pantic 2015), and mobile phone depen-
dence (Billieux 2012; Wang et al. 2016). Moreover,
Griffiths, Kuss, and Demetrovics (2014) stated that
social networking addiction arguably falls into the
cyber-relationship addiction category of Young’s typol-
ogy of Internet addiction. Additionally, Petry and
O’brien (2013)pointed out that Internet Gaming Dis-
order is a subtype of Internet addiction. However,
Király et al. (2014) showed clear distinctions between
Internet Addiction and Video game addiction. More-
over, Andreassen et al. (2016) compared video games
addiction to social media addiction and showed that
addictive online behaviours had low interconnectivity
suggesting that the concept of Internet addiction as a
unified construct is not ‘warranted’. It is extremely
important to bring about a better understanding of
the aforementioned relationships because it has a
huge impact on building appropriate assessments, diag-
noses, and treatments.
2N. HAWI AND M. SAMAHA
2. Method
2.1. Sample
This study was carried out at a private university in Leba-
non and was conducted in English because it is the
language of instruction at this university, which adopts
the American system of education. It was cross-sectional
based on voluntary participation of university students.
To give an equal chance of participation to each student,
systematic random sampling was implemented by ran-
domly picking the first student from the student popu-
lation, ordered by student identification number, and
then selecting every 5th student from the list. With sys-
tematic random sampling, it is highly likely that the
sample is representative of the population. Approval
for the study was obtained from the university research
committee. Before completing the survey, a form
explained the purpose of the study and assured volun-
teers that data collection, storage, and reporting tech-
niques would protect confidentiality and anonymity.
The overall response rate was 41.86%. A total of 586 par-
ticipants filled out the online survey in English through
the university’s Student Information System. All cases
with one or more missing responses were removed, leav-
ing 512 participants (M
age
= 21.23 years, SD = 2.47 years,
age range: 18–38 years, 55.8% male).
2.2. Data collection instruments
Basic demographic information included sex and age. In
addition, participants were asked to report the amount of
time they spent on the Internet on weekdays ‘How many
hours a day do you log into the Internet on a typical
weekday?’, as well as on weekends ‘How many hours a
day do you log into the Internet on a typical weekend?’.
Moreover, there were two items about the daily fre-
quency of checking and posting on social media sites,
‘How often do you check your social media account?’,
and ‘How often do you post updates or other infor-
mation to your social media accounts?’, respectively.
Furthermore, one item asked about the number of
friends on social media ‘Approximately how many
friends do you have on your social media account?’,
and another about real self ‘Where do you feel better
able to be your real self?’The survey also comprised
five research instruments to assess scores of IA, SMA,
self-esteem, satisfaction with life, personality traits, and
self-construal. The survey items were approved by the
university research committee.
The Internet Addiction Test (IAT) is one of the most
commonly used instruments in measuring addiction to
the Internet (Young 1998). It consists of 20 items rated
on a six-point Likert-type scale, ranging from ‘not appli-
cable’, coded 0, to ‘always’, coded 5. The higher the score,
the more severe the problems related to Internet use.
Summing responses to determine levels of IA among
participants in the current study yielded scores that ran-
ged from 2 to 100, with mean 37.78 and SD = 15.90. As
with the findings of other research that used IAT, Cron-
bach’sα(.917) fell in the excellent range. Additionally,
most studies have shown IAT’s good psychometric prop-
erties (Hawi 2013).
The Social Media Addiction Questionnaire (SMAQ)
measures addiction to social media (Hawi and Samaha
2017b) and it is based on the Facebook Intrusion ques-
tionnaire (Elphinston and Noller 2011). It consists of 8
items rated on a seven-point Likert-type scale, ranging
from ‘strongly disagree’, coded 1, to ‘strongly agree’,
coded 7. The higher the score, the more severe the pro-
blems related to online social media use. Summing
responses to determine levels of addiction to social
media among participants in the current study yielded
scores that ranged from 8 to 50, with mean 24.26 and
SD = 9.76. In addition, Cronbach’s alpha coefficient was
0.85.
Rosenberg’s Self-Esteem Scale (RSES) assesses a per-
son’s overall evaluation of worthiness as a human
being (Rosenberg 1965). It consists of 10 items rated
on a five-point Likert-type scale, ranging from ‘strongly
disagree’, coded 1, to ‘strongly agree’, and coded
5. Higher scores indicate higher self-esteem. The scores
ranged from 17 to 50, with mean 38.87 and SD = 6.2.
In addition, Cronbach’s alpha coefficient was 0.83.
The Satisfaction with Life Scale (SwLS) concerns sub-
jective well-being, assessed by measuring cognitive self-
judgement about satisfaction with one’s life (Diener
and Diener 2009). It consists of 5 items rated on a 7-
point Likert-type scale, ranging from 1 (strongly dis-
agree) to 7 (strongly agree). The scores ranged from 5
to 35, with mean 23.38 and SD = 6.04. In addition, Cron-
bach’s alpha coefficient was 0.83.
The Ten Item Personality Inventory (TIPI) (Gosling,
Rentfrow, and Swann 2003) was used to measure the Big
Five personality dimensions, which are extraversion,
neuroticism, conscientiousness, agreeableness, and
openness to experience. Each dimension is measured
by two 7-point Likert-type questions, ranging from 1
(strongly disagree) to 7 (strongly agree). For instance,
summing responses to determine levels of extraversion
among participants in the current study yielded scores
that ranged from 2 to 14, with mean 8.35 and SD =
2.28. The higher the score, the more extraverted the par-
ticipant. The mean for agreeableness was 8.72 and SD =
1.95, conscientiousness was 10.18 and SD = 2.44,
emotional stability was 8.73 and SD = 2.49, and openness
BEHAVIOUR & INFORMATION TECHNOLOGY 3
to experience was 10.66 and SD = 2.15. As each dimen-
sion of the scale is a two-item test, reliability was deter-
mined using Spearman-Brown coefficient rather than
Cronbach’s alpha (Eisinga, Te Grotenhuis, and Pelzer
2013). The Spearman-Brown coefficients for the extra-
version, agreeableness, conscientiousness, emotional
stability, and openness to experiences were .470, .473,
.503, .352, and .503, respectively. Gosling stated that it
is highly unlikely to get high alpha coefficients and
good fit indices on TIPI with only two items per dimen-
sion and both items are on opposite poles (Gosling, Ren-
tfrow, and Swann 2003). The scale was used for its good
psychometric properties and high levels of convergence
with the Big Five Inventory.
The Self-Construal Scale (SCS) (Singelis 1994)isa
measure of self-construal with two subscales that
measure independence and interdependence. Each item
is rated on a 4-point Likert scale, ranging from 1
(strongly disagree) to 4 (strongly agree). The interdepen-
dence self-construal scores ranged from 7 to 28, with
mean 20.07, SD = 3.13, and Cronbach’s alpha coefficient
of 0.70. The independence self-construal scores ranged
from 6 to 24, with mean 18.55, SD = 2.79, and Cron-
bach’s alpha coefficient of 0.67.
2.3. Data analysis
Descriptive data for all variables were analysed, and
means and standard deviations are presented by gender
(see Table 1). A confirmatory factor analysis (CFA)
was executed in a structural equation modelling (SEM)
with IBM SPSS Amos Graphics 24 to test the structure
underlying all the scales used to collect data in this
study. The reported goodness of fit measures were Chi-
square / Degrees of freedom (χ
2
/df)(Carmines and Mci-
ver 1981), Root-mean-square error of approximation
(RMSEA) (Browne et al. 1993), PCLOSE, Normed fit
index (NFI) (Bentler and Bonett 1980), Comparative fit
index (CFI) (Bentler 1990), Tucker –Lewis index (TLI)
(Tucker and Lewis 1973), Goodness of fit index (GFI)
(Bentler and Bonett 1980), and Standardized RMR
(SRMR) (Hu and Bentler 1999). The latent variables
were addiction to the Internet, measured with IAT;
addiction to social media, measured with SMAQ; the
person’s overall evaluation of worthiness as a human
being, measured with RSES; satisfaction with one’s life,
measured with SwLS; self-construal, measured with
SCS; and the five personality dimensions, measured
with TIPI. Each of the latent variables was measured
with observed variables described in the previous section.
Table 2 reports the CFA results separately for IAT,
SMAQ, and Self-construal. It provides an overview of
indices for different scales on CFA, except for TIPI.
The CFA on TIPI dimensions performed extremely
poorly, which is in line with Gosling’s stated conviction
that ‘the TIPI was designed using criteria that almost
guarantee it will perform poorly in terms of alpha and
Confirmatory Factor Analysis (CFA) or Exploratory Fac-
tor Analysis (EFA) indices’(Gosling, Rentfrow, and
Swann 2003).
Correlations were investigated using Pearson pro-
duct-moment correlation coefficients. Regression analy-
sis was performed to assess the ability of research
variables to predict levels of addiction to the Internet
and social media. In all the simple linear regression ana-
lyses, a preliminary analysis was first conducted to
ensure that all assumptions in regression analysis were
met. For the same dataset that we collected, that is, for
the same participants, we used linear regression to exam-
ine two things: (1) whether each independent variable is
a significant predictor of Internet Addiction on the one
hand and/or social media on the other hand and (2)
the magnitude and sign of the beta estimate to determine
the relationship between the predictor variable and the
dependent variable. Consequently, a linear regression
analysis was conducted using a single independent vari-
able and IA as a dependent variable, followed by a second
linear regression analysis using the same independent
variable and SMA as a dependent variable.
3. Results
An important finding of this research is the significant
high correlation between IA and SMA (r= .588, p
< .001). Tables 3 and 4show the correlations and
regression analyses, respectively, between IA and SMA
on the one hand, and the independent variables on the
other hand. The first type of association (Type I) includes
independent variables that are positively and significantly
correlated with both dependent variables IA and SMA.
Table 1. Descriptive statistics of research variables by gender.
Males Females Total
Scale MSD MSD MSD
IAT 37.04 17.00 38.69 14.41 37.78 15.90
SMAQ 22.56 9.60 26.27 9.59 24.26 9.76
RSES 38.52 6.63 39.29 5.70 38.87 6.20
SwLS 22.71 6.19 24.19 5.77 23.38 6.04
Extraversion 8.19 2.29 8.54 2.26 8.35 2.28
Agreeableness 8.61 2.00 8.87 1.89 8.72 1.95
Conscientiousness 9.95 2.39 10.46 2.48 10.18 2.44
Emotional stability 8.94 2.57 8.48 2.37 8.73 1.95
Openness to experience 10.63 2.26 10.69 2.02 10.66 2.15
Interdependent Self-
Construal
20.30 2.98 19.93 2.91 20.07 3.13
Interdependent Self-
Construal
18.43 2.64 19.01 2.47 18.55 2.79
Notes: IAT = Internet Addiction Text, SMAQ = Social Media Addiction Ques-
tionnaire, RSES = Rosenberg’s Self-Esteem Scale, SwLS = Satisfaction with
Life Scale.
4N. HAWI AND M. SAMAHA
Type I includes the duration of Internet usage and fre-
quency of social media usage (see Table 5). For instance,
the independent variable, ‘How often do you check your
social media account?’had a stronger relationship with
SMA compared to IA (see Table 3). It can be concluded
that SMA changes much more than IA with changes in
this independent variable (see Table 4). The second type
of association (Type II) includes independent variables
that are negatively and significantly correlated with both
IA and SMA (see Table 5). Type II includes self-esteem,
agreeableness, conscientiousness, openness to experi-
ences, and emotional stability. The third type of associ-
ation (Type III) includes variables, such as age, that did
not correlate with IA or SMA. For a complete list in this
category, see Table 5. The fourth type of association
(Type IV) includes variables that proved to be dis-
tinguishing features, such as extraversion. Extraversion
correlated only with IA (r=−.120, p< .05) (see Table
3). In addition, simple linear regression showed that
while extraversion explained 1.4% of the variance in IA,
F(1, 511) = 4.5, p< .05, it did not explain any of the var-
iance in SMA (see Table 4). It is noteworthy that indepen-
dent self-construal, and sex associated only with SMA (see
Table 3).
Table 2. Model fit indices of study scales.
Goodness-of-fit measure Perfect fit Good fit IAT SMAQ RSES SwLS ISE IDSE
Chi-square/Degrees of freedom (χ
2
/df)
a
1 <2 or 3 2.051 1.955 2.660 2.545 2.269 2.420
Root-mean-square error of approximation (RMSEA)
b
0 <0.08 0.049 0.048 0.063 0.060 0.055 0.058
PCLOSE >0.05 0.502 0.539 0.110 0.286 0.360 0.281
Normed fit index (NFI)
c
1 >0.95 0.909 0.971 0.953 0.984 0.950 0.944
Comparative fit index (CFI)
d
1 >0.95 0.950 0.986 0.970 0.990 0.971 0.966
Tucker–Lewis index (TLI)
e
1 >0.90 0.942 0.979 0.950 0.980 0.945 0.940
Goodness of fit index (GFI)
c
1 >0.90 0.925 0.979 0.966 0.988 0.986 0.982
Standardized RMR (SRMR)
f
0 <0.08 0.040 0.027 0.043 0.022 0.040 0.035
a
Carmines and Mciver (1981).
b
Browne et al. (1993).
c
Bentler and Bonett (1980).
d
Bentler (1990).
e
Tucker and Lewis (1973).
f
Hu and Bentler (1999).
Notes: IAT = Internet Addiction Text, SMAQ = Social Media Addiction Questionnaire, RSES = Rosenberg’s Self-Esteem Scale, SwLS = Satisfaction with Life Scale,
ISE = Independent Self-Construal, ISDE = Interdependent Self-Construal.
Table 3. Correlations among technology addictions, their usage,
sex, personality traits, self-construal, satisfaction with life, and
self-esteem.
Variable
Internet
Addiction Test
Social Media
Addiction
Questionnaire
How often do you check your social
media account?
.213** .470**
How often do you post updates or
other information to your social
media accounts?
.081 .380**
Approximately how many friends
do you have on your social media
account?
.092 .233**
How many hours a day do you log
into the Internet on a typical
weekday?
.503** .388**
How many hours a day do you log
into the Internet on a typical
weekend?
.475** .387**
Where do you feel better able to be
your real self?
.337** .113
Sex .096 .203**
Age .001 −.110
Extraversion −.120* −.048
Agreeableness −.202** −.119*
Conscientiousness −.114* −.112*
Emotional stability −−.264**
Openness to experiences −.123* −.107*
Interdependent self-construal .014 .049
Independent self-construal −.010 −.132*
Satisfaction with life .012 −.036
Self-esteem −.308** −.198**
*P< .05, **P< .01.
Table 4. Regression analysis of research variables on Internet
addiction and social media addiction.
Internet Addiction
Test
Social Media
Addiction
Questionnaire
Variable B
SE
BβB
SE
Bβ
How often do you check
your social media
account?
.06 .02 .21*** .23 .03 .47***
How often do you post
updates or other
information to your
social media accounts?
.03 .03 .08 .30 .04 .38***
Approximately how many
friends do you have on
your social media
account?
.00 .01 .092 .00 .00 .23***
How many hours a day do
you log into the Internet
on a typical weekday?
.28 .03 .50*** .35 .05 .39***
How many hours a day do
you log into the Internet
on a typical weekend?
.24 .03 .48*** .32 .04 .39***
Where do you feel better
able to be your real self?
.16 .03 .34*** .06 .02 .11
Sex .14 .09 .10 .50 .14 .20***
Age .00 .02 .00 −.05 .03 −.11
Extraversion −.04 .02 −.12 −.02 .03 −.05
Agreeableness −.08 .02 −.20*** −.08 .04 −.12*
Conscientiousness −.04 .02 −.11* −.06 .03 −.11*
Emotional Stability −.09 .02 −.30*** −.17 .03 −.26***
Openness to experiences −.05 .02 −.12* −.06 .03 −.11
Interdependent self-
construal
.00 .01 .01 .02 .02 .05
Independent self-
construal
−.00 .02 −.01 −.06 .03 −.13*
Satisfaction with life −.00 .01 −.01 −.04 .06 −.04
Self-esteem −.38 .07 −.31*** −.39 .11 −.20***
*P< .05. **P< .01. ***P< .001.
BEHAVIOUR & INFORMATION TECHNOLOGY 5
Overall, there were independent variables that had
predictive power for both IA and SMA (see Table 4).
Within this set of variables, some variables positively
and significantly correlated with IA and SMA (Type I)
and other variables negatively and significantly corre-
lated with them (Type II). It is noteworthy that not
one variable correlated with IA and SMA in opposite
directions. Three variables correlated neither with IA
nor SMA (Type III): age, satisfaction with life, and inter-
dependent self-construal (see Table 5). Several variables
were distinguishing features (Type IV). While real self
and extraversion were positively associated with IA,
sex, the frequency of posting updates, number of social
media friends, and independent self-construal were posi-
tively associated with SMA.
The scores of the IAT were used to classify partici-
pants into four profiles: nonaddicted (8.0%), mildly
addicted (49.3%), partially addicted (37.7%), and
addicted (5%). In comparison with a study conducted
six years ago in the same country (Hawi 2012), the dis-
tribution of participants by their levels of Internet addic-
tion showed that the cohort of nonaddicted decreased by
13%, from 21.8% to 8.0%, the cohort of mildly addicted
increased by 10.1%, from 39.2% to 49.3%, the cohort of
partially addicted increased by 2.8%, from 34.9% to
37.7%, and the cohort of addicted increased by 0.8%,
from 4.2% to 5.0%. It is noteworthy that all cohorts
identified with addiction increased, while the cohort of
nonaddicted drastically decreased. The highest shift
was from the nonaddicted to the mildly addicted cohort
that is encompassing almost half of the sample. Another
important observation is that the addicted cohort only
slightly increased, despite the considerable lapse of
time between the two studies.
4. Discussion
The aim of this study was to determine the nature of the
relationship between IA and SMA using the same sample
of participants by examining commonalities and differ-
ences in the ability of a repertoire of variables to predict
these addictions. The results of this profiling showed that
commonalities outnumbered differences, which is in line
with the significant high correlation between IA and
SMA. For instance, self-esteem, agreeableness, conscien-
tiousness, openness to experiences, emotional stability,
Internet usage, and social media usage predicted both
types of addiction, whereas age, satisfaction with life,
and interdependent self-construal predicted neither.
Our results in relation to personality traits, and self-
esteem are congruent with those of other studies that
examined IA and SMA. Personality traits, including con-
scientiousness, agreeableness, openness to experiences,
and emotional stability, were all negative predictors of
both IA (Ko et al. 2012; Kuss, Griffiths, and Binder
2013; Müller et al. 2013) and SMA (Andreassen et al.
2013;Błachnio and Przepiorka 2016; Tang et al. 2016).
Additionally, self-esteem was negatively associated with
both IA and SMA, similar to previous findings (Bozo-
glan, Demirer, and Sahin 2013; Andreassen, Pallesen,
and Griffiths 2017).
Equally important were the distinguishing predictors,
where extraversion and real-self predicted IA, gender,
independent self-construal, the frequency of posting
updates, and a number of social media friends predicted
SMA. Low scores on extraversion predicted IA but not
SMA. This result is in line with the findings of other
studies that showed negative correlations with extraver-
sion and excessive Internet use (Van Der Aa et al. 2009;
Kuss, Griffiths, and Binder 2013; Müller et al. 2013; Hawi
and Samaha 2017b), but no relationship between extra-
version and SMA (Tang et al. 2016).
In this study, gender predicted SMA but not IA. Pre-
vious studies in the same country showed that gender is
not associated with IA (Hawi 2012) nor with technology
use among children (Hawi and Rupert 2015; Samaha and
Table 5. Profiling Internet and social media addictions by
categorising potential risk factors showing similarities and
dissimilarities.
Variables with a predictive power on both addictions
Positive Predictive Power
(Type I)
Negative Predictive Power (Type II)
How many hours a day do you
log into the Internet on a
typical weekday?
Self-esteem
How many hours a day do you
log into the Internet on a
typical weekend?
Agreeableness
How often do you check your
social media accounts?
Conscientiousness
Openness to experiences
Emotional stability
Variables with no predictive powers on either addiction (Type III)
Age
Satisfaction with life
Interdependent self-construal
Variables with a predictive power on one of the addictions (Type IV)
Positive
Predictive
Power on
Internet
Addiction
Negative
Predictive
Power on
Internet
Addiction
Positive Predictive
Power on Social
Media Addiction
Negative
Predictive
Power on
Social Media
Addiction
Where do you
feel better
able to be
your real
self?
Extraversion How often do you
post updates or
other information
to your social
media accounts?
Independent
self-construal
Approximately how
many friends do
you have on your
social media
accounts?
Sex
6N. HAWI AND M. SAMAHA
Hawi 2017). It is possible that because male students had
Internet gaming in mind while completing the IAT and
female students had social media use in mind, the effects
of the two genders cancelled each other out for IA and
SMA.
An interesting finding of this study is the negative
association of independent self-construal with SMA.
People with higher independent self-construal per-
ceive themselves as separate from others. They are
more characterised by individualism with a weaker
sense of connectedness with others (friends, family,
colleagues, society, etc.) and a weaker sense of belong-
ing to groups and their roles in these groups. This
view of the self may explain why this category of
people would be less concerned with active social
media engagement and therefore they have less risk
of SMA. On a related note, ‘Feeling better being
able to be your real self’was not linked to addiction
to social media in this study, a finding that can be
attributed to the social pressure of self-presentation
and self-image on social media. On the other hand,
being your real self, which may be interpreted as com-
plete freedom to be on the Internet without limits or
social constraints was a predictor of IA.
These findings lead to the question of whether SMA
is a subtype of IA. , a notion that became controversial
recently. For instance, the main difference between the
Internet addiction and gaming addiction is the strong
association of problematic online gaming with being
male (Király et al. 2014). The view that states that Inter-
net addiction overarches other technology addictions is
contradicted by the existence of addictions to technol-
ogy that does not require Internet connectivity, such
as obsessive engagement in apps on consoles, laptops
or smartphones. Indeed, our results support the view
that SMA is not a subtype of IA. Both are behavioural
addictions and their commonalities stem from (1)
being mediated by the Internet and (2) personality
traits common to humans. In addition, like Király
et al. (2014), SMA in our study was associated with
being female and with functions that can only be associ-
ated with social media. For instance, it does not make
sense to consider the number of friends that users
have on the Internet. IA had its own distinguishing fea-
tures too. It is important to mention that it is totally
acceptable to say addiction on the Internet and addic-
tion to social media but not addiction to the Internet
and addiction on social media. After all, the Internet
is the network that is overarching all networks. Thus,
SMA is not a subtype of IA or nested in it. Had it
been nested in IA, it would have shared just some of
the characteristics of IA, without having its own dis-
tinguishing features.
Our study is one of the rare studies that have used the
same sample to examine the predictor variables of both
IA and SMA. Most studies have either examined the
relationships with only one technology addiction or
used two different samples to study the two addictions
(Błachnio and Przepiorka 2016). Another strong aspect
of this study is the large number of variables used,
which allowed for a better analysis of the similarities
and differences between IA and SMA. Additionally, in
this study a generic SMA scale was used, similar to
other recent studies (Andreassen et al. 2016; Olufadi
2016; Van Den Eijnden, Lemmens, and Valkenburg
2016), instead of using one of the many scales specific
to measuring Facebook addiction. Future studies should
address diversity by drawing samples from different age
groups and different cultures. The current study used
simple regression analysis and it turned out that some
variables explained a very small amount of variance.
Future studies should use multiple regression analysis,
which may result in fewer variables identified as signifi-
cant predictors.
Another limitation of this study is that it adopted a
cross-sectional design that does not ensure cause and
effect relationships. The sample included undergraduate
students from one university, which does not make it
representative of the whole population of undergradu-
ates. Considering our results, future studies should inves-
tigate a very important concern, which is whether
Internet addiction is a condition of social media addic-
tion or the other way around.
5. Conclusion
Based on our findings, the similarities in profiling IA and
SMA outweighed the differences. For this reason, Inter-
net addiction and social media addiction were found to
be strongly and significantly correlated.
It is noteworthy that the dissimilarities between the
two technology addictions identified in our study sup-
port earlier suggestions that not all addictive online
behaviours are the same (Király et al. 2014; Andreassen,
Pallesen, and Griffiths 2017). The major contribution of
this study is that it provides evidence that SMA and IA
are not separate concepts. The correlation between
SMA and IA, as well as the overlap between most of
the investigated variables, either as predictors of both
concepts or not predictors of both concepts, indicate
that the two concepts are very similar. In addition, the
research findings do not support the argument that
one concept is nested within the other because each con-
cept had one or more predictors of its own. It is very
important for future studies to investigate addiction to
more than one technology with a focus on the strengths
BEHAVIOUR & INFORMATION TECHNOLOGY 7
of the correlations between pairs of addictions when con-
sidered as dependent variables. In the case of moderately
correlated addictions, multivariate analysis of variance
analysis should be implemented.
Even with the current research on the topic and our
important findings, the search for scientific evidence
for this puzzling phenomenon should continue, and an
explanation may not be discovered soon.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This project has been jointly funded with the support of the
National Council for Scientific Research (CNRS) –Lebanon
and Notre Dame University-Louaize.
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