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A test of the pathway model of problematic
smartphone use
NATALE CANALE
1
p, TANIA MORETTA
2
,
LUCA PANCANI
3
, GIULIA BUODO
2
, ALESSIO VIENO
1
,
MARIO DALMASO
1
and JO€
EL BILLIEUX
4
1
Department of Developmental and Social Psychology, University of Padova, Padova, Italy
2
Department of General Psychology, University of Padova, Padova, Italy
3
Department of Psychology, University of Milano-Bicocca, Milan, Italy
4
Institute of Psychology, University of Lausanne, Lausanne, Switzerland
Received: September 18, 2020 •Revised manuscript received: December 05, 2020 •Accepted: December 06, 2020
ABSTRACT
Background and aims: Problematic smartphone use (PSU) has been described as a growing public
health issue. In the current study, we aimed to provide a unique and comprehensive test of the pathway
model of PSU. This model posits three distinct developmental pathways leading to PSU: (1) the
excessive reassurance pathway, (2) the impulsive pathway and (3) the extraversion pathway. Methods:
Undergraduate students (n5795, 69.8% female, mean age 523.80 years, sd 53.02) completed online
self-report measures of PSU (addictive use, antisocial use and dangerous use) and the psychological
features (personality traits and psychopathological symptoms) underlying the three pathways. Results:
Bayesian analyses revealed that addictive use is mainly driven by the excessive reassurance pathway and
the impulsive pathway, for which candidate etiopathological factors include heightened negative ur-
gency, a hyperactive behavioural inhibition system and symptoms of social anxiety. Dangerous and
antisocial use are mainly driven by the impulsive pathway and the extraversion pathway, for which
candidate etiopathological factors include specific impulsivity components (lack of premeditation and
sensation seeking) and primary psychopathy (inclination to lie, lack of remorse, callousness and
manipulativeness). Discussion and conclusions: The present study constitutes the first comprehensive
test of the pathway model of PSU. We provide robust and original results regarding the psychological
dimensions associated with each of the postulated pathways of PSU, which should be taken into account
when considering regulation of smartphone use or tailoring prevention protocols to reduce problematic
usage patterns.
KEYWORDS
problematic smartphone use, pathway model, undergraduate students, personality traits, psychopathological
symptoms, Bayesian analytical approach
INTRODUCTION
In the last two decades, a growing number of studies have found that excessive smartphone
use is related to a wide range of negative consequences, such as addiction-like symptoms (e.g.
loss of control, apparent withdrawal and tolerance, and interference with daily life),
emotional symptoms (e.g. depression and anxiety), road accidents and interference with sleep
(Billieux, 2012; De-Sola Guti
errez, Rodr
ıguez de Fonseca, & Rubio, 2016; Elhai, Dvorak,
Levine, & Hall, 2017). From a developmental perspective, the ongoing digital technology
revolution naturally increases and expands the ordinary adolescent proclivities to explore,
seek and learn from socially and affectively salient experiences (Giovanelli, Ozer, & Dahl,
2020). Previous research suggests that children, adolescents and young adults (often
described as ‘digital natives’) might be a particularly vulnerable population, as they frequently
Journal of Behavioral
Addictions
DOI:
10.1556/2006.2020.00103
© 2020 The Author(s)
FULL-LENGTH REPORT
pCorresponding author.
E-mail: natale.canale@unipd.it
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display heavy mobile phone usage patterns (Cheever, Rosen,
Carrier, & Chavez, 2014; Giovanelli, Ozer, & Dahl, 2020;
Lepp, Barkley, & Karpinski, 2014; Lepp, Li, Barkley, &
Salehi-Esfahani, 2015; Smetaniuk, 2014; Walsh et al., 2020).
In view of these issues, it is of paramount importance to
focus research on the public health implications and risk
associated with excessive problematic mobile phone use.
Problematic smartphone use (PSU) has been defined as an
uncontrolled pattern of smartphone use linked with
impairment in academic, occupational and/or social func-
tioning (Billieux, Maurage, Lopez-Fernandez, Kuss, & Grif-
fiths, 2015). Although PSU presents overlapping symptoms
with addictive behaviours (e.g., loss of control, tolerance-like
and withdrawal-like phenomena) (see De-Sola Guti
errez
et al., 2016 for a review), its recognition as a potential
addictive disorder is debated (Billieux et al., 2017; Montag,
Wegmann, Sariyska, Demetrovics, & Brand, 2020; Panova &
Carbonell, 2018).
Despite the blossoming of PSU research, uncertainty still
abounds regarding the psychological factors that may be
implicated in PSU, as most research in this field has been
conducted in the absence of theoretical models. Although
PSU has been generally viewed either as a genuine addictive
disorder (a behavioural addiction) (Choliz, 2010; Montag
et al., 2020)oras‘compensatory’behaviour displayed by
individuals who present emotional symptoms (e.g. anxiety,
depression, stress) and maladaptive emotion regulation
strategies (Elhai, Yang, & Montag, 2019a; Elhai, Levine, &
Hall, 2019b), few theoretical models of PSU have been
formulated. One notable exception is the framework pro-
posed by Billieux and colleagues (Billieux, 2012; Billieux
et al., 2015) to account for the multiple forms and etiologies
of PSU. This model proposed three distinct developmental
pathways to PSU, each being associated with distinct psy-
chosocial and psychopathological variables: (1) the excessive
reassurance pathway, (2) the impulsive pathway and (3) the
extraversion pathway. The excessive reassurance pathway
comprises individuals whose PSU is mainly driven by the
necessity to maintain relationships and obtain reassurance
from others (Ha, Chin, Park, Ryu, & Yu, 2008; Lu, Katoh,
Chen, Nagata, & Kitamura, 2014). This pathway is postu-
lated to typically result in perceived dependence on the
mobile phone and/or addiction-like symptoms (e.g., toler-
ance). Such a pathway was formulated on the basis of
numerous studies that linked PSU to childhood maltreat-
ment and a poor self-model of adult attachment, low self-
esteem, and heightened levels of general and social anxiety,
depression or neuroticism (Emirtekin, 2019; Igarashi,
Motoyoshi, Takai, & Yoshida, 2008; Lu et al., 2014; Lepp
et al., 2015). This excessive reassurance pathway is consis-
tent with the hypothesis proposed by Elhai and colleagues
(2019a, b) that PSU is mainly driven by negative rein-
forcement (e.g. a way for anxious or depressed people to
cope with negative emotions and distract themselves from
these emotions) and can be viewed as a maladaptive coping
strategy. The impulsive pathway corresponds to PSU pat-
terns promoted by poor impulse control, ultimately resulting
in uncontrolled urges and dysregulated use. The existence of
this pathway is supported by research that linked PSU with
specific impulsivity traits, such as lack of planning/pre-
meditation and urgency (the tendency to act rashly in
intense emotional contexts) (Billieux, Van der Linden, &
Rochat, 2008; Fjeldsoe, Marshall, & Miller, 2009; Khang,
Kim, & Kim, 2013). It was also suggested that this pathway
characterises individuals with aggressive and antisocial
personality traits. Notably, this impulsive pathway might
lead to various forms of PSU, including addictive use,
antisocial use (e.g. using the mobile phone in banned places
or in an aggressive way) or dangerous use (e.g. using the
mobile phone while driving and in other hazardous situa-
tions) (Billieux, 2012; Billieux et al., 2008). Finally, the ex-
traversion pathway is defined as the consequence of reward
and excitement-driven smartphone usage patterns poten-
tially linked to a wide range of risky behaviours (e.g.
phoning while driving, sexting). The existence of this pattern
is supported by studies that showed that PSU is linked with
high extraversion and increased levels of sensation seeking
(Augner & Hacker, 2012; Ehrenberg, Juckes, White, &
Walsh, 2008), need for stimulation and excitement, and a
high sensitivity to rewards (Igarashi et al., 2008). A funda-
mental aspect of this model is that it assumes not only that
various pathways can lead to different manifestations of PSU
(addictive, dangerous and antisocial use), but also that
various pathways can lead to similar manifestations or
‘symptoms’through the interplay of different etiological
mechanisms. In particular, within this model, addictive use
of the mobile phone can be viewed as the result of either
emotional and attachment-related variables (excessive reas-
surance pathway) or impulsivity and self-control-related
variables (impulsive pathway), which might resolve the
recurrent debate that opposes the addictive behaviour
(Choliz, 2010; Montag et al., 2020) versus maladaptive
coping hypotheses of PSU (Elhai et al., 2019a,b).
Although the pathway model developed by Billieux and
colleagues (2015) is frequently used to theoretically anchor
PSU research and/or interpret related research findings, this
model has received little direct empirical support to date. In
a recent study conducted in a representative sample of Swiss
men, Dey et al. (2019) tested how psychological factors
underlying the three postulated pathways predicted addic-
tive use of smartphones. These authors found that psycho-
logical factors underlying the excessive reassurance pathway
(depression, social anxiety) and the impulsive pathway
(attention deficit hyperactivity disorder, aggression-hostil-
ity), but not the extraversion pathway, predicted addictive
smartphone use. In another study, Pivetta et al. (2019)
provided partial support for the pathway model of PSU
through a path analysis in which psychological factors
included in the pathway model were used to predict different
types of PSU. The results showed that trait neuroticism
specifically predicted addictive smartphone use, whereas
impulsivity traits (assessed by the Barratt Impulsivity Scale;
Patton, Stanford, & Barratt, 1995) predicted all types of PSU,
namely addictive, dangerous and antisocial use. Contrary to
what was formulated in the model, extraversion was not
found to predict PSU. The major limitation of these
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preliminary attempts to validate the pathway model of PSU
is the absence of some key model variables, including
emotion-related impulsivity traits (which have been sys-
tematically linked to PSU and which are not measured by
the Barratt Impulsivity Scale) or psychopathy-related per-
sonality traits.
To address this gap in the literature, we aimed in the
present study to provide the first comprehensive test of the
pathway model of PSU in young adults (e.g. undergraduate
students) by considering the specific personality traits that
were not included in the preliminary validation attempts of
the model (Dey et al., 2019; Pivetta, Harkin, Billieux, Kanjo,
& Kuss, 2019). Notably, Billieux et al. (2015) stated that the
three PSU pathways should not be considered as mutually
exclusive, and it is possible that interactions among different
factors may occur in some cases of PSU (e.g. a smartphone
user characterised simultaneously by poor self-esteem and
high impulsivity). In order to directly take into account this
model premise, in the present study we used a Bayesian
approach that allowed us to (statistically) consider the three
pathways as being not mutually exclusive.
Capitalising on a Bayesian approach, we were able to test
all possible combinations among personality traits and
psychopathological features characterising the assessed
developmental pathways leading to PSU (i.e. addictive,
dangerous and antisocial use). In other words, starting from
the ‘prior assumption’that all the personality traits and
psychopathological features have the same probability of
being included in the three pathways that are postulated in
the model, we tested whether their posterior probabilities in
describing addictive, antisocial and dangerous patterns of
smartphone use matched the relationships hypothesised by
the pathway model of PSU. In line with the initial formu-
lations of the pathway model of PSU (Billieux et al., 2015),
we predicted that (1) psychological factors associated with
the three pathways (e.g. impulsivity traits, emotional
symptoms) would be associated with addictive use of the
smartphone and (2) psychological factors underlying the
impulsive pathway (e.g. impulsivity trait, psychopathic trait,
aggressiveness) and the extraversion pathway (e.g. sensation
seeking, extraversion, sensitivity to reward) would be asso-
ciated with dangerous and antisocial smartphone use.
METHODS
In this study, we used data from a cross-sectional online
survey available from June 13 to October 15, 2019. Partici-
pants were recruited through online advertisements shared
in local online messaging boards and social network groups.
Interested participants were directed to an online informed
consent statement. Inclusion criteria included (1) being a
college student (2) being young adults aged 18–35 years and
(3) being fluent in Italian. Participants had the opportunity
to receive compensation (25 Euros) by taking part in a lot-
tery of 10 prizes. Anonymity of the participants was guar-
anteed (no personal data or Internet Protocol addresses were
collected) unless participants wanted to take part in the
lottery by providing their e-mail address and personal details
(name and surname) at the end of the survey (these data
were dissociated from the data set before the analyses to
ensure anonymity). The entire online survey (173 items)
took approximately 30 min to complete. In total, 1,397
participants started the survey, of whom 187 were excluded
because they did not meet the inclusion criteria. Another
413 potential participants were removed because either (1)
they did not respond correctly on a set of four items that
aimed to identify careless answering (e.g. ‘click now on
number 3’) or (2) they did not complete all of the items in
the survey. Participants who reported non-binary gender
self-identification were also excluded because of the small
number in our sample (n52). The final sample comprised
795 participants whose demographic characteristics are
presented in Table 2. This study was part of a larger research
project on smartphone use in Italian college students; other
data not related to this study will be presented elsewhere.
Measures
After completing a series of questions on demographics (i.e.
gender, age, employment, relationship status and place of
residence), participants were invited to fill in a series of
validated questionnaires.
Questionnaires included in the online survey were
selected to prioritise instruments that have been validated
and for which published versions exist in Italian. PSU was
assessed by using the short version of the Problematic Mo-
bile Phone Use Questionnaire (Lopez-Fernandez et al.,
2018), which measures different types of PSU, namely
addictive use (e.g. difficulty controlling smartphone use),
antisocial use (e.g. using the mobile phone in banned places)
and dangerous use (e.g. using the mobile phone while
driving).
Other questionnaires were selected to assess the psy-
chological factors (personality traits and psychopathological
symptoms) underlying the three pathways (i.e. reassurance
seeking, impulsive, extraversion) postulated by Billieux et al.
(2015). Impulsivity traits (i.e. lack of premeditation, lack of
perseverance, negative urgency, positive urgency and
sensation seeking) were assessed by using the Italian version
of the Short UPPS-P Impulsive Behaviour Scale (D'Orta
et al., 2015). Aggressive traits were measured with the Italian
validation of the Aggression Questionnaire (Fossati, Maffei,
Acquarini, & Di Ceglie, 2003). Extraversion and neuroticism
were measured by selecting specific subscales of the Big Five
Questionnaire –Short Version (Italian version: Caprara,
Barbaranelli, Borgogna, & Perugini, 1993). Psychopathic
traits were measured with the Italian version of the Levenson
Self-Report Psychopathy Scale (Somma, Fossati, Patrick,
Maffei, & Borroni, 2014). The Behavioural Activation Sys-
tem (i.e., reward responsiveness, reward drive and fun
seeking) and the Behavioural Inhibition System were
assessed by using the Italian version of the BIS/BAS scales
(Leone, Pierro, & Mannetti, 2002). A series of additional
questionnaires were used to measure psychopathological
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symptoms. Social anxiety was measured with the Italian
version of the Social Interaction Anxiety Scale (Sica et al.,
2007). Emotional symptoms (stress, anxiety and depression)
were measured with the short-form version of the Depres-
sion Anxiety Stress Scales (Italian version: Bottesi et al.,
2015). Finally, self-esteem was measured by using the Italian
version of the Rosenberg Self-Esteem Scale (Prezza, Trom-
baccia, & Armento, 1997). Table 1 comprehensively de-
scribes all the questionnaires and variables included in the
present study.
Analysis
Analyses were performed with R software (R Core Team,
2018). The existence of three developmental pathways
leading to PSU was tested by assessing the possible contri-
butions of personality measures and psychopathological
symptoms (i.e. statistical predictors) to addictive, antisocial
and dangerous mobile phone use (i.e. dependent variables)
through a Bayesian approach, which is considered a
powerful procedure for testing hypotheses in psychological
research (Wagenmakers et al., 2018; West, 2016). More
specifically, Bayesian inference permits quantification and
assessment of evidence in favour of both the null hypothesis
and of alternative hypotheses (Wagenmakers et al., 2018),
and the Bayesian parameter estimation is not affected by the
sampling plan (Sch€
onbrodt, Wagenmakers, Zehetleitner, &
Perugini, 2017). Here, Bayesian adaptive sampling for vari-
able selection and model averaging (R package: BAS; the R
script is available at https://github.com/TaniaMoretta/A-
test-of-the-pathway-model-of-problematic-smartphone-use-
Bayesian-model-averaging;Clyde, Ghosh, & Littman, 2011)
was used to assess what combination of statistical predictors
provided an adequate description of the distributions that
generated the observed addictive, antisocial and dangerous
patterns of mobile phone use. Specifically, 19,792 models (all
possible combinations between predictors when taking
gender into account) were estimated by a Markov Chain
Monte Carlo sampling method by using the Zellner–Siow
Cauchy prior on the coefficients (i.e. all Bayes factors are
compared with the null model) and a uniform prior distri-
bution over the models (i.e. by assigning equal probabilities
to all models). The null hypothesis was rejected when the
95% Bayesian credibility intervals (BCIs) did not include the
null value (Kruschke, 2014). As an extension of Bayesian
inference, this approach considers parameter uncertainty
through prior distribution and model uncertainty and ob-
tains posterior distributions for the model parameters and
the model by using Bayes’theorem, allowing for model se-
lection and combined estimation (Clyde, 2011; Fragoso,
Bertoli, & Louzada, 2018).
Multicollinearity was monitored by examining the vari-
ance inflation factor (VIF). In this study, the VIF indicated
that multicollinearity was not a concern (see Supplement,
section 1).
Residual plots were used to evaluate the normality and
homogeneity of variance. The scatterplot of the standardised
residuals showed that the data met the assumptions of
homogeneity of variance and linearity (see Supplement,
section 2).
Ethics
Ethical clearance was obtained from the ethical committee of
the University of Padova (protocol number: 3,104). This
study did not involve human and/or animal experimenta-
tion.
RESULTS
Descriptive statistics and correlations between variables are
reported in Table 2. In the present study, skewness and
kurtosis values were all less than 0.80 and 0.56, respectively,
indicating that the distributions of variables closely mirrored
a normal distribution.
The associations between potential predictors and
addictive, antisocial and dangerous patterns of smartphone
use were modelled through a Bayesian approach. Consid-
ering the addictive pattern of smartphone use as the
dependent variable, social anxiety, behavioural inhibition,
negative urgency, primary psychopathy and BAS-RR
showed marginal posterior inclusion probabilities (PIPs) of
>0.5. However, across the 19,792 models, the combination of
social anxiety, behavioural inhibition, negative urgency and
primary psychopathy was the more plausible in describing
the addictive pattern of use; i.e. it received the highest pos-
terior probability (Bayes factor (BF) 51; R
2
50.13).
Therefore, we focused on these predictors in subsequent
analyses. The 95% BCIs showed that higher levels of an
addictive pattern of mobile use were associated with higher
negative urgency (b50.22, 95% BCI 5[0.14; 0.30]), higher
behavioural inhibition (b50.11, 95% BCI 5[0.06; 0.16]),
higher primary psychopathy (b50.06, 95% BCI 5[0.03;
0.10]) and higher social anxiety (b50.02, 95% BCI 5[0.01;
0.04]).
Considering the antisocial pattern of smartphone use as
the dependent variable, lack of premeditation, sensation
seeking, aggressive traits and primary psychopathy showed
marginal PIPs of >0.5, and their combination was the more
plausible in describing the antisocial pattern of use (BF 51;
R
2
50.14). The 95% BCIs showed that higher levels of an
antisocial pattern of smartphone use were associated with
higher lack of premeditation (b50.17, 95% BCI 5[0.09;
0.24]), higher sensation seeking (b50.08, 95% BCI 5[0.02;
0.13]), higher levels of aggressive traits (b50.02, 95% BCI
5[0.01; 0.03]) and higher primary psychopathy (b50.07,
95% BCI 5[0.04; 0.10]).
Finally, considering the dangerous pattern of smart-
phone use as the dependent variable, lack of premeditation,
sensation seeking and primary psychopathy showed mar-
ginal PIPs of >0.5, and their combination was the more
plausible in describing the antisocial pattern of use (BF 51;
R
2
50.16). The 95% BCIs showed that higher levels of an
antisocial pattern of mobile use were associated with higher
lack of premeditation (b50.30, 95% BCI 5[0.22; 0.39]),
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Table 1. Characteristics and reliability for the scales
Tools
Authors (Italian
version) Definition Items Likert response format
Reliability values
(Cronbach’s alpha)
Postulated
pathway
DASS –
21
Henry & Crawford,
2005 (Bottesi et al.,
2015)
Emotional distress in
three subcategories of
depression (e.g. loss of
self-esteem/incentives
and depressed mood),
anxiety (e.g. fear and
anticipation of negative
events) and stress (e.g.
persistent state of over
arousal and low
frustration tolerance)
21 4-point ranging from
0 (never or almost
never) to 3 (almost
always or always)
Total score: 0.93 Reassurance
seeking
SIAS Mattick & Clarke, 1998
(Sica et al., 2007)
Anxiety over social
interactions (e.g. ‘I have
difficulty talking with
other people’)
19 5-point ranging from
0 (Not at all) to 4
(Extremely)
Total score: 0.92 Reassurance
seeking
BIS/BAS
scale
Carver & White 1994
(Leone, Pierro &
Mannetti, 2002)
Behavioural inhibition
system (BIS): sensitivity
to aversive stimuli (e.g.
worrying about making
mistakes)
7 5-point ranging from 1
(not describe me at all)
to 5 (describes me
completely) for both
the BIS and BAS scales
BIS: 0.83 Reassurance
seeking
Behavioural activation
system scales (BASs) –
Reward Responsiveness
(RRs): sensitivity to
anticipated/acquired
rewards;
13 BAS-RR: 0.78 Extraversion
BAS –Drive: motivation
to achieve desired goals;
BAS-Drive: 0.70
BAS –Fun Seeking:
willingness to approach
new appetitive stimuli
BAS-Fun: 0.78
BFQ –
SV
Caprara, Barbaranelli,
Borgogna & Perugini,
(1993)
Emotional Stability/
Neuroticism: Capability
of controlling one’s affect
and emotional reactions
8 5-point ranging from 1
(completely true) to 5
(completely false)
Emotional Stability:
0.75
Reassurance
seeking
Extraversion/
Introversion: activity,
enthusiasm, and self-
confidence (e.g. ‘I like to
joke’)
Extraversion: 0.75 Extraversion
RSES Rosenberg, 1965
(Prezza, Trombaccia &
Armento, 1997)
Self-Esteem: a person’s
overall evaluation of his
or her worthiness as a
human being
10 4-point ranging from 1
(strongly disagree) to 4
(strongly agree)
0.90 Reassurance
seeking
S-UPPS-
P
Billieux et al., 2012
(D'Orta et al., 2015)
Negative urgency:
tendency to experience
strong reactions under
conditions of intense
negative affect
20 4-point ranging from 1
(strongly agree) to 4
(strongly disagree)
Negative urgency:
0.82
Impulsive
Positive urgency:
tendency to experience
strong reactions under
conditions of intense
positive affect
Positive urgency: 0.79
Lack of premeditation:
tendency to fail to think
and reflect on the
consequences of an act
Lack of
Premeditation: 0.82
(continued)
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Table 1. Continued
Tools
Authors (Italian
version) Definition Items Likert response format
Reliability values
(Cronbach’s alpha)
Postulated
pathway
before engaging in that
act
Lack of perseverance:
difficulties remaining
focused on a task that
may be long, boring, or
difficult
Lack of Perseverance:
0.90
Sensation-seeking: the
tendency to enjoy and
pursue exciting activities
and an openness to
trying new experiences
that may or may not be
dangerous
Sensation Seeking:
0.87
AQ Buss & Perry, 1992
(Fossati, Maffei,
Acquarini & Di Ceglie,
2003)
Trait aggressiveness
which measures four
components of
aggression: physical
aggression; verbal
aggression; anger; and
hostility.
29 5-point ranging from 1
(uncharacteristic of
me) to 5 (very
characteristic of me)
0.89 Impulsive
LSRP Levenson, Kiehl &
Fitzpatrick, 1995
(Somma, Fossati,
Patrick, Maffei &
Borroni, 2014)
Primary psychopathy:
inclination to lie, lack of
remorse, callousness, and
manipulativeness (e.g. ‘I
enjoy manipulating other
people’s feelings’)
26 4-point ranging from 1
(strongly disagree) to 4
(strongly agree)
Primary psychopathy:
0.78
Impulsive
Secondary psychopathy:
frustration tolerance,
quick-temperedness, and
lack of long-term goals
(e.g. ‘Ifind myself in the
same kinds of trouble,
time after time’)
Secondary
psychopathy: 0.70
PMPUQ
–SV
Lopez-Fernandez et al.,
(2018)
Addictive use: perceived
dependence on the
smartphone (e.g. ‘It is
hard for me to turn my
mobile phone off’);
15 4-point ranging from 1
(strongly agree) to 4
(strongly disagree)
Addictive use: 0.81
Antisocial use: the
tendency to use mobile
phones in contexts where
they are banned (e.g. ‘I
don't use my mobile
phone in a library,
cinema or hospital’)
Antisocial use: 0.60
Dangerous use:
unequivocally risky
behaviours (e.g. ‘I use my
mobile phone while
driving’)
Dangerous use:0.84
List of tools: Depression, Anxiety and Stress Scales –21 (DASS –21); Social Interaction Anxiety Scale (SIAS); Carver and White
Questionnaire (Behavioural Inhibition System: BIS; Behavioural Activation System: BAS); Italian Version of the Big Five Questionnaire –
Short Version (BFQ –SV); Rosenberg Self-Esteem Scale (RSES); Short UPPS-P Impulsive Behaviour Scale (S-UPPS-P); Aggression
Questionnaire (AQ); Levenson Self-Report Psychopathy Scale (LSRP); Problematic Mobile Phone Use Questionnaire –Short Version
(PMPUQ –SV).
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Table 2. (a) Descriptive statistics
N of cases/Mean (SD) Range
Sex Male: 240; Female: 555
Age 23.8 (3.02) 18–35
Employment a:484; b:132; c:43; d:66; e:70
1
Relationship a:264; b:39; c:468; d:6; e:2; f:16
2
Living place a:26; b:9; c:411; d:223; e:61; f:65
3
Addictive mobile phone use 12.88 (3.03) 5–20
Antisocial mobile phone use 9.78 (2.35) 5–16
Dangerous mobile phone use 7.94 (2.95) 5–19
Negative urgency 9.16 (2.60) 4–16
Positive urgency 9.24 (2.41) 4–16
Lack of premeditation 7.56 (2.21) 4–16
Sensation seeking 9.39 (2.83) 4–16
Self-esteem 28.50 (5.65) 11–40
Aggression 70.92 (15.95) 36–128
Neuroticism 11.94 (3.26) 4–20
Extraversion/introversion 8.88 (2.73) 4–18
Social anxiety 28.48 (13.10) 1–71
Stress, depression and anxiety 20.32 (11.06) 0–62
Primary psychopathy 30.13 (6.15) 19–52
Secondary psychopathy 20.97 (4.57) 10–38
BAS-reward responsiveness 19.55 (3.09) 11–25
BAS-drive 12.33 (2.74) 5–20
BAS-fun seeking 11.63 (3.07) 4–20
Behavioural inhibition 25.17 (4.69) 13–35
1
a5Nothing; b 5fixed-term, part-time; c 5permanent, part-time; d 5fixed-term, full-time; e 5permanent, full-time.
2
a5Single; b 5casually date; c 5in a committed relationship; d 5married; e 5divorced; f 5widow/widower; g 5I prefer not to answer.
3
a5student residence; b 5college; c 5at your parents’house; d 5house for rent with other students; e 5alone in a house for rent; f 5other. ppp P< .001; pp P< .01; pP< .05.
Journal of Behavioral Addictions 7
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higher sensation seeking (b50.11, 95% BCI 5[0.04; 0.18])
and higher primary psychopathy (b50.11, 95% BCI 5
[0.07; 0.14]). See Table 3 for a summary of these results.
DISCUSSION
The aim of the current study was to provide a first
comprehensive test of the pathway model of PSU (Billieux
et al., 2015) by capitalising on a Bayesian analytical
approach. This analysis revealed robust and original results
regarding the psychological dimensions associated with each
of the postulated pathways of PSU. Consistent with the
hypotheses formulated in the pathway model of PSU (Bil-
lieux et al., 2015), it appeared that some psychological di-
mensions played a unique role in explaining PSU (e.g. social
anxiety is only associated with addictive smartphone use),
whereas other psychological dimensions contribute to more
than one type of PSU (e.g. sensation seeking predicts both
antisocial and dangerous patterns of PSU). The results for
each type of PSU are elaborated below in relation to each of
the distinct pathways theorised in the model.
First, our results suggest that addictive smartphone use is
mainly driven by the excessive reassurance pathway and the
impulsive pathway, for which candidate etiopathological
factors include heightened negative urgency (i.e. emotion-
related impulsive behaviours), the hyperactive behavioural
inhibition system (i.e. a greater sensitivity to punishment)
and symptoms of social anxiety. This pattern of results is
consistent with the view that addictive smartphone use is
mostly fuelled by negative reinforcement and reflects a
compensatory mechanism to regulate aversive emotions
(Chen et al., 2017; Elhai et al., 2017) or to alleviate social
anxiety symptoms by preferring online social interactions
over face-to-face interactions (Enez Darcin et al., 2016).
Furthermore, our results are consistent with previous
research that linked addictive mobile phone use with nega-
tive urgency traits (see Billieux, 2012 for a review) and the
hyperactive behavioural inhibition system (Kim et al., 2016).
In line with previous research (Elhai et al., 2019a,b), the
present study revealed that an addictive pattern of smart-
phone use is more likely to occur in the context of intense
distressing emotions. This view is consistent with previous
evidence suggesting that intense negative emotional states
impair decision making (Bechara, 2004; Dreisbach, 2006)by
causing less discretionary use of information (Forgas &
Bower, 1987) or greater distractibility (Dreisbach &
Goschke, 2004) or, alternatively, by interfering in one’s
ability to control the urge to use a smartphone (Contractor,
Weiss, Tull, & Elhai, 2017). Thus, individuals with higher
negative urgency may be motivated by immediately rein-
forcing experiences that help to reduce or distract from
negative affect in the short term (Inzlicht & Schmeichel,
2012). The current research also revealed a positive associ-
ation between social anxiety and addictive smartphone use,
confirming previous evidence (Darcin, Noyan, Nurmedov,
Yilmaz, & Dilbaz, 2015; Elhai, Tiamiyu, & Weeks, 2018).
Table 2. (b) Pearson’s correlations
(b) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
1. Addictive mobile
phone use
1.00
2. Antisocial mobile
phone use
0.30ppp 1
3. Dangerous mobile
phone use
0.18ppp 0.56ppp 1
4. Negative urgency 0.26ppp 0.15ppp 0.17ppp 1
5. Positive urgency 0.18ppp 0.20ppp 0.21ppp 0.54ppp 1
6. Lack of premeditation 0.08p0.25ppp 0.31ppp 0.41ppp 0.43ppp 1
7. Sensation seeking 0.03 0.19ppp 0.21ppp 0.11pp 0.28ppp 0.22ppp 1
8. Self-esteem 0.16ppp
0.07 0.03 0.23ppp
0.14ppp
0.07 0.03 1
9. Aggression 0.19ppp 0.23ppp 0.22ppp 0.49ppp 0.34ppp 0.27ppp 0.17ppp
0.34ppp 1
10. Neuroticism 0.24ppp 0.04 0.01 0.32ppp 0.20ppp 0.05 0.12ppp
0.51ppp 0.35ppp 1
11. Extraversion/
introversion
0.15ppp 0.11pp 0.10pp 0.13ppp 0.04 0.17ppp
0.14ppp
0.43ppp 0.08p0.21ppp 1
12. Social anxiety 0.22ppp 0.04 0.01 0.18ppp 0.08p
0.04 0.10pp
0.55ppp 0.23ppp 0.37ppp 0.52ppp 1
13. Stress, depression and
anxiety
0.19ppp 0.07p0.11pp 0.27ppp 0.16ppp 0.08p0.10pp
0.57ppp 0.46ppp 0.54ppp 0.24ppp 0.39ppp 1
14. Primary psychopathy 0.13ppp 0.29ppp 0.31ppp 0.22ppp 0.29ppp 0.25ppp 0.28ppp
0.07p0.38ppp
0.01 0.16ppp 0.07 0.17ppp 1
15. Secondary
psychopathy
0.23ppp 0.25ppp 0.24ppp 0.47ppp 0.37ppp 0.40ppp 0.20ppp
0.47ppp 0.58ppp 0.38ppp 0.38ppp 0.36ppp 0.49ppp 0.39ppp 1
16. BAS-reward
responsiveness
0.11pp 0.01 0.10pp 0.10pp 0.18ppp
0.15ppp 0.09p0.12ppp 0.09p0.05 0.30ppp
0.06 0.05 0.02 0.12ppp 1
17. BAS-drive 0.04 0.08p0.12ppp 0.14ppp 0.21ppp 0.06 0.26ppp 0.12pp 0.21ppp
0.03 0.30ppp
0.15ppp 0.07p0.30ppp 0.00 0.38ppp 1
18. BAS-fun seeking 0.05 0.21ppp 0.18ppp 0.21ppp 0.43ppp 0.31ppp 0.60ppp
0.01 0.26ppp
0.03 0.10pp
0.06 0.11pp 0.33ppp 0.30ppp 0.24ppp 0.35ppp 1
19. Behavioural inhibition 0.23ppp
0.06 0.14ppp 0.19ppp 0.09p
0.17ppp
0.27ppp
0.45ppp 0.15ppp 0.57ppp 0.13ppp 0.45ppp 0.32ppp
0.22ppp 0.13ppp 0.25ppp
0.07p
0.16ppp 1
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According to previous studies (Darcin et al., 2015; Enez
Darcin et al., 2016), individuals with social anxiety symp-
toms can be more at risk of displaying addictive smartphone
use because social anxiety causes avoidance of face-to-face
relationships. For these individuals, virtual socialisation (and
communication) via smartphone use can alleviate their fear
or concern of showing signs of physiological arousal, and
may be guided either by the need to feel free or by the need
to behave without the perception of social pressure.
Our results are important in relation to the current de-
bates about the conceptualisation of addictive smartphone
use and suggest that this problematic behaviour should
preferentially be understood as a maladaptive form of coping
rather than a genuine addictive disorder (Harris, Regan,
Schueler, & Fields, 2020; Panova & Carbonell, 2018). It is
worth noting that we cannot exclude the possibility that
specific activities performed via the smartphone (such as
video gaming or online gambling) constitute discrete
addictive behaviours, yet the present study focused on
general smartphone use and cannot answer this question.
We also found that primary psychopathy –characterised by
callousness, shallow affect, manipulation and superficial
charm (Levenson, Kiehl, & Fitzpatrick, 1995; Somma et al.,
2014)–is an important predictor of addictive smartphone
use. A tenable explanation for this finding is that individuals
who are prone to primary psychopathy might perceive
themselves as being dependent on the smartphone, as this
tool has much functionality that allow one to efficiently
manipulate or deceive others.
The present study also revealed that dangerous and
antisocial smartphone use are mainly driven by the impul-
sive pathway and the extraversion pathway, for which
candidate etiopathological factors include specific impul-
sivity components (lack of premeditation and sensation
seeking) and primary psychopathy. More specifically, lack of
premeditation was reported to have the strongest association
with both antisocial and dangerous smartphone use. This
specific impulsivity component has been linked to short-
term-based choices and risky decision-making (Zermatten,
Van der Linden, d'Acremont, Jermann, & Bechara, 2005),
and it is thus likely that short-term-based choices and a
myopia towards long-term consequences favour both anti-
social smartphone use (e.g. fines or public disapproval
resulting from using the smartphone in a prohibited area)
and dangerous use (e.g. lethal risks associated with using the
smartphone while driving). In line with the results reported
by Billieux et al. (2008), our findings confirm that lack of
premeditation is related to prohibited use of the mobile
phone and demonstrate (for the first time) its additional
association with the dangerous pattern of use. Primary
psychopathy also showed positive associations with both
dangerous and antisocial smartphone use, while aggressive
traits were uniquely linked to an antisocial pattern of
smartphone use. These results suggest that individuals with
primary psychopathy traits who are aggressive prone
possibly use their smartphone as a medium to behave in an
antisocial manner (e.g. cyberbullying via social networking
sites, Orue & Calvete, 2019). Lastly, sensation seeking was
identified as explaining both the antisocial and the
dangerous use of a smartphone. This finding is consistent
with previous research showing that heightened sensation
seeking is linked to mobile phone-based aggressive behav-
iours (Kokkinos, Antoniadou, & Markos, 2014), phoning
while driving (Billieux et al., 2008) and sexting (e.g. ex-
change of sexual pictures via the smartphone) (Dir & Cy-
ders, 2015), implying that various risky and/or antisocial
smartphone-mediated behaviours might be displayed in
order to fuel a need for pleasure, excitement or stimulation.
We found dangerous and antisocial smartphone use to be
specifically related to sensation seeking, as previously found
Table 3. Hypotheses based on the pathway model of Billieux et al. (2015) and results
Reassurance Impulsive Extraversion
Psychological factors
Social anxiety ●
Neuroticism ○
Self-esteem ○
Psychological distress# ○
Behavioural inhibition ●
Negative urgency ●○ ○
Positive urgency ○○ ○
Lack of premeditation ○● ●
Aggression ○● ○
Primary psychopathy ●● ●
Secondary psychopathy ○○ ○
Sensation seeking ○● ●
Extraversion ○○ ○
Reward responsiveness ○○ ○
Drive ○○ ○
Fun seeking ○○ ○
Type of problematic use Addictive Addictive Antisocial Dangerous Addictive Antisocial Dangerous
○5hypothesised association; ●5hypothesised and credible association; #Stress, Depression, and Anxiety.
Journal of Behavioral Addictions 9
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by Billieux et al. (2008) and Dey et al. (2019), but not
necessarily to the more overarching construct of extraver-
sion (in accordance with Pivetta et al., 2019; Dey et al.,
2019). In this regard, Zuckerman and Glicksohn (2016)
suggested that the link between sensation seeking and psy-
choticism is probably more pronounced than that between
sensation seeking and extraversion, mainly because both
sensation seeking and psychoticism (but not extraversion)
are characterised by recklessness and proneness to taking
risks (e.g. Jonason, Lyons, Bethell, & Ross, 2013; Zeigler-Hill
& Vonk, 2015). Thus, we believe that the extraversion
pathway should be further investigated and potentially
renamed ‘the sensation seeking pathway’. Although the
antisocial and the dangerous patterns of smartphone use
largely overlapped in terms of psychological antecedents, our
results showed a key difference between them, namely trait
aggression. Aggression is defined as any behaviour directed
towards another person aimed at causing harm (Anderson &
Bushman, 2002; Bushman & Huesmann, 2010), and trait
aggression is the individual disposition to behave aggres-
sively. Trait aggression was specifically and solely associated
with the antisocial pattern (and not with the dangerous
pattern). This difference substantiates the typical behaviours
theorised by Billieux et al. (2015) for the two types of PSU.
Indeed, our results suggest that the antisocial pattern con-
sists in behaviours not intentionally (and solely) aimed at
causing harm to others (e.g. cyberbullying), which can be
traced back to trait aggression. Conversely, the dangerous
pattern consists of behaviours that, though potentially
dangerous for both the smartphone user and other in-
dividuals (e.g. phoning while driving might cause a car ac-
cident involving the user and other drivers), are not
primarily driven by the intention to harm.
Our study has several limitations. Data were collected via
self-report measures and were cross-sectional, which pre-
cludes any causality statement. Recent studies have used a
longitudinal design (Herrero, Torres, Vivas, & Urue~
na, 2019;
Lapierre, Zhao, & Custer, 2019; Lee et al., 2020) or a qual-
itative approach (Yang, Asbury, & Griffiths, 2019) for the
study of smartphone use/dependency or PSU, and further
studies that use these alternative methodologies should be
conducted to complement our approach. The present study
focused on a specific demographic group (i.e., college stu-
dents with regular access to the Internet, predominantly
females), thus limiting the generalisability of our findings.
Our results revealed, among the established risk factors for
each developmental pathway of PSU, the robust presence of
personality traits instead of psychopathological symptoms.
Although this result might suggest that stable personality
dispositions are better predictors of PSU than potentially
transient psychopathological symptoms, we cannot exclude
the possibility that it can be explained by the nature of our
sample (community sample instead of clinical sample).
Future research should focus on more representative,
gender-balanced samples of adults and also on clinical (or
subclinical) samples. Furthermore, the present study did not
consider some psychological factors (e.g. attentional bias and
cue reactivity) included in other theoretical frameworks such
as the I-PACE model of technology-mediated addictive
disorders (Brand et al., 2016, 2019). Finally, the present study
did not assess the objective use of smartphones (e.g. daily time
spent on social media, tracking data on the number of mi-
nutes of screen time and the number of phone screen unlocks;
Ellis et al., 2019)orspecific smartphone functionalities (e.g.
Lowe-Calverley & Pontes, 2020), and these aspects should be
considered and measured in future studies.
Overall, and despite these limitations, our study is the
first comprehensive test of the frequently cited pathway
model of PSU (Billieux et al., 2015), which was initially
formulated to guide research efforts in a research field in
which most studies are conducted with an atheoretical and/
or symptom-focused approach. The present study, in addi-
tion to providing empirical support to the model, further
showed that PSU is a multi-determined and multi-faceted
construct, which has to be taken into account when regu-
lating smartphone use or tailoring preventive actions to
reduce problematic usage patterns.
Funding sources: This work was partially supported by an
intramural grant of the University of Padova (Year: 2018 –
prot. BIRD183124).
Authors’contribution: NC, TM, GB, AV, MD and JB are
responsible for the study concept and design. TM performed
analysis. NC and LP supervised the statistical analysis and
contribute to the interpretation of data. NC wrote the first
draft of the manuscript and all authors critically reviewed
and approved the final version of the manuscript.
Conflict of interest: The authors declare no conflict of in-
terest.
APPENDIX ASUPPLEMENTARY MATERIAL
The online version of this article offers supplementary ma-
terial https://doi.org/10.1556/2006.2020.00103.
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